diff --git a/asyncroscopy/DigitalTwin.py b/asyncroscopy/DigitalTwin.py
index 8618136..5b616c6 100644
--- a/asyncroscopy/DigitalTwin.py
+++ b/asyncroscopy/DigitalTwin.py
@@ -5,21 +5,18 @@
 """
 
 import json
-from datetime import datetime
-from pathlib import Path
 
 import numpy as np
 import pyTEMlib.image_tools as it
 import pyTEMlib.probe_tools as pt
 import tango
-from ase.io import read
 from ase import Atoms
 from ase.build import bulk
-from PIL import Image, TiffImagePlugin
 from tango import AttrWriteType, DevState
 from tango.server import Device, attribute, device_property
 
 from asyncroscopy.Microscope import Microscope
+from asyncroscopy.software.DataWriter import save_acquisition
 
 DEFAULT_ACQUISITION_DIR = "outputs/tiled_acquisitions"
 
@@ -65,8 +62,8 @@ class DigitalTwin(Microscope):
     )
     acquisition_file_format = device_property(
         dtype=str,
-        default_value="tiff",
-        doc="Acquisition file format. TIFF stores simulated image data and metadata.",
+        default_value="h5",
+        doc="Acquisition file format. HDF5 stores simulated acquisition data and metadata.",
     )
     data_device_address = device_property(
         dtype=str,
@@ -445,26 +442,6 @@ def write_beam_pos(self, value):
         self._beam_pos_x = float(x)
         self._beam_pos_y = float(y)
 
-    def _make_filename(self, acquisition_type: str, detector: str, data_server, extension: str = "tiff") -> Path:
-        save_directory = self.acquisition_save_directory
-        if data_server is not None:
-            try:
-                save_directory = data_server.save_path
-            except tango.DevFailed as exc:
-                self.warn_stream(f"DATA device not ready: {exc}")
-
-        directory = Path(save_directory).expanduser()
-        directory.mkdir(parents=True, exist_ok=True)
-        stamp = datetime.now().strftime("%Y%m%dT%H%M%S%f")
-        name = f"{acquisition_type}_{detector}_{stamp}.{extension.lower().lstrip('.')}"
-        return directory / name
-
-    def _register_path(self, path: Path) -> str:
-        data_server = self._detector_proxies.get("data")
-        if data_server is None:
-            return str(path)
-        return data_server.register_path(str(path))
-
     def _viewport_metadata(self) -> dict:
         fov_ang = self._fov * 1e10
         stage_xyz_ang = self._stage_position[:3] * 1e10
@@ -488,11 +465,6 @@ def _viewport_metadata(self) -> dict:
             "particle_count": len(self._particle_records_base),
         }
 
-    def _save_tiff(self, path: Path, image: np.ndarray, metadata: dict) -> None:
-        tiff_info = TiffImagePlugin.ImageFileDirectory_v2()
-        tiff_info[270] = json.dumps(metadata)
-        Image.fromarray(np.asarray(image)).save(str(path), format="TIFF", tiffinfo=tiff_info)
-
     def _render_stem_image(self, imsize: int, dwell_time: float, detector_list: list) -> np.ndarray:
         """Simulate STEM image acquisition using convolutions of the pseudo-potential and electron probe."""
         self._sync_stage_from_proxy()
@@ -537,53 +509,21 @@ def _render_stem_image(self, imsize: int, dwell_time: float, detector_list: list
         noisy_image += self._lowfreq_noise(noisy_image, noise_level=0.1, freq_scale=0.1, rng=rng) * blur_noise_level
         return np.clip(noisy_image, 0.0, 1.0).astype(np.float32)
 
-    def _acquire_stem_image(self, imsize: int, dwell_time: float, detector_list: list) -> str:
-        """Simulate STEM acquisition, save a TIFF with metadata, and return its DATA/Tiled key."""
-        detector = detector_list[0].upper() if detector_list else "HAADF"
-        image = self._render_stem_image(int(imsize), float(dwell_time), detector_list)
-        data_server = self._detector_proxies.get("data")
-        extension = str(self.acquisition_file_format or "tiff")
-        path = self._make_filename("stem_image", detector, data_server, extension)
-        metadata = {
-            "acquisition_type": "stem_image",
-            "detector": detector,
-            "dwell_time": float(dwell_time),
-            "shape": list(image.shape),
-            "dtype": str(image.dtype),
-            "simulation_backend": self.__class__.__name__,
-            **self._viewport_metadata(),
-        }
-        self._save_tiff(path, image, metadata)
-        return self._register_path(path)
-
-    def _acquire_stem_image_advanced(
+    def _acquire_scanned_image(
         self,
         imsize: int,
         dwell_time: float,
-        detector_list: list[str],
-        scan_region: list[float],
-    ) -> list[str]:
-        """Perform advanced simulated STEM acquisition and return DATA/Tiled keys."""
-        saved_paths = []
+        detector_list: list[str] = ["haadf"],
+        scan_region: list[float] = [0.0, 0.0, 1.0, 1.0],
+    ) -> str:
+        """Simulate STEM acquisition, save HDF5 data with metadata, and return its DATA/Tiled key."""
+        detector_list = [detector.upper() for detector in detector_list]
+        data_server = self._detector_proxies.get("data")
+        images = []
         for detector in detector_list:
             image = self._render_stem_image(int(imsize), float(dwell_time), [detector])
-            detector_name = detector.upper()
-            data_server = self._detector_proxies.get("data")
-            extension = str(self.acquisition_file_format or "tiff")
-            path = self._make_filename("stem_image", detector_name, data_server, extension)
-            metadata = {
-                "acquisition_type": "stem_image_advanced",
-                "detector": detector_name,
-                "dwell_time": float(dwell_time),
-                "scan_region": [float(v) for v in scan_region],
-                "shape": list(image.shape),
-                "dtype": str(image.dtype),
-                "simulation_backend": self.__class__.__name__,
-                **self._viewport_metadata(),
-            }
-            self._save_tiff(path, image, metadata)
-            saved_paths.append(self._register_path(path))
-        return saved_paths
+            images.append(image)
+        return save_acquisition(self, data_server, "stem_image", detector_list, images)
 
     def _simulate_spectrum(self, detector_name: str, exposure_time: float) -> dict[str, float]:
         """Simulate EDS spectrum acquisition at the current beam position weighted by surrounding particles."""
@@ -639,27 +579,11 @@ def _simulate_spectrum(self, detector_name: str, exposure_time: float) -> dict[s
         return {el: val / total for el, val in noisy.items()}
 
     def _acquire_spectrum(self, detector_name: str, exposure_time: float) -> str:
-        """Simulate spectrum acquisition, save a NumPy file, and return its DATA/Tiled key."""
+        """Simulate spectrum acquisition, save HDF5 data, and return its DATA/Tiled key."""
         spectrum = self._simulate_spectrum(detector_name, exposure_time)
         data_server = self._detector_proxies.get("data")
-        path = self._make_filename("spectrum", detector_name, data_server, "npy")
-        spectrum_array = np.array(
-            list(spectrum.items()),
-            dtype=[("element", "U8"), ("intensity", "f8")],
-        )
-        # TODO: migrate simulated spectra to .emd once the EDS data model is settled.
-        np.save(path, spectrum_array)
-        metadata = {
-            "acquisition_type": "spectrum",
-            "detector": detector_name,
-            "exposure_time": float(exposure_time),
-            "format_note": "Temporary .npy spectrum; migrate to .emd later.",
-            "spectrum": spectrum,
-            "simulation_backend": self.__class__.__name__,
-            **self._viewport_metadata(),
-        }
-        path.with_suffix(".json").write_text(json.dumps(metadata, indent=2), encoding="utf-8")
-        return self._register_path(path)
+        spectrum_array = np.array(list(spectrum.values()), dtype=np.float64)
+        return save_acquisition(self, data_server, "spectrum", detector_name, spectrum_array, dataset_name="spectrum")
 
     def _place_beam(self, position) -> None:
         """Place the electron beam at the specified [x, y] coordinates."""
diff --git a/asyncroscopy/DigitalTwinBeta.py b/asyncroscopy/DigitalTwinBeta.py
index 7d3652c..3e41d5c 100644
--- a/asyncroscopy/DigitalTwinBeta.py
+++ b/asyncroscopy/DigitalTwinBeta.py
@@ -129,7 +129,13 @@ def write_beam_pos(self, value):
     # ------------------------------------------------------------------
     # Internal acquisition helpers
     # ------------------------------------------------------------------
-    def _acquire_stem_image(self, imsize: int, dwell_time: float, detector_list: list) -> np.ndarray:
+    def _acquire_scanned_image(
+        self,
+        imsize: int,
+        dwell_time: float,
+        detector_list: list[str] = ["haadf"],
+        scan_region: list[float] = [0.0, 0.0, 1.0, 1.0],
+    ) -> np.ndarray:
         """
         Acquire a simulated STEM image using the pre-cooked sample state.
         Requires _cook_sample_recipe() to have been called immediately before this.
@@ -216,7 +222,7 @@ def lowfreq_noise(image, noise_level=0.1, freq_scale=0.1):
         particle_lookup  = self._particle_lookup     # {label -> metadata}
 
         # ── Rebuild _particle_records from cooked projection ──────────────────────
-        # This keeps the contract that _acquire_stem_image always refreshes
+        # This keeps the contract that _acquire_scanned_image always refreshes
         # _particle_records to match whatever the current image will show,
         # accounting for stage shift/tilt applied in _cook_sample_recipe.
         #
@@ -263,7 +269,7 @@ def lowfreq_noise(image, noise_level=0.1, freq_scale=0.1):
             })
 
         self._particle_records = particle_records
-        print(f"_acquire_stem_image: {len(particle_records)} particles in FOV")
+        print(f"_acquire_scanned_image: {len(particle_records)} particles in FOV")
 
         # ── Pseudo-potential + PSF convolution (unchanged logic) ──────────────────
         edge     = 2 * edge_crop * pixel_size
@@ -294,18 +300,6 @@ def lowfreq_noise(image, noise_level=0.1, freq_scale=0.1):
 
         return np.array(noisy_image, dtype=np.float32)
 
-    def _acquire_stem_image_advanced(
-        self,
-        imsize: int,
-        dwell_time: float,
-        detector_list: list[str],
-        scan_region: list[float],
-    ) -> list[np.ndarray]:
-        return [
-            self._acquire_stem_image(imsize, dwell_time, [detector])
-            for detector in detector_list
-        ]
-    
     def _make_sample_recipe(self):
         """
         Build three persistent data structures for the sample:
@@ -319,13 +313,13 @@ def _make_sample_recipe(self):
 
         # ── World geometry ──────────────────────────────────────────────────────────
         fov_ang     = self._fov * 1e10          # Angstroms, lateral
-        world_z_ang = fov_ang * 0.5             # thin slab, same as _acquire_stem_image
+        world_z_ang = fov_ang * 0.5             # thin slab, same as _acquire_scanned_image
         vox_size    = fov_ang / self._imsize    # Angstroms per voxel (isotropic)
 
         nx = ny = self._imsize
         nz = max(1, int(round(world_z_ang / vox_size)))
 
-        # ── Particle parameters (mirror _acquire_stem_image) ────────────────────────
+        # ── Particle parameters (mirror _acquire_scanned_image) ────────────────────────
         particle_radius  = 16.0
         radius_std       = 2.0
         aspect_ratio     = 0.4
@@ -354,7 +348,7 @@ def rotation_matrix(alpha, beta, gamma):
                         [0, np.sin(g),  np.cos(g)]])
             return Rz @ Ry @ Rx
 
-        # ── 1. Place particle centres (same exclusion logic as _acquire_stem_image) ─
+        # ── 1. Place particle centres (same exclusion logic as _acquire_scanned_image) ─
         placed_centers   = []
         placed_particles = []
         particle_lookup  = {}   # label (1-based int) -> metadata dict
@@ -406,7 +400,7 @@ def rotation_matrix(alpha, beta, gamma):
 
         # ── 2. Build 3-D label map ─────────────────────────────────────────────────
         # Each voxel gets the integer label of whichever particle owns it (0 = none).
-        # We use an ellipsoidal test identical to _acquire_stem_image's r_scaled mask.
+        # We use an ellipsoidal test identical to _acquire_scanned_image's r_scaled mask.
         label_map = np.zeros((nx, ny, nz), dtype=np.uint8)
 
         # Pre-build voxel coordinate arrays once (Angstrom positions of voxel centres)
@@ -484,7 +478,7 @@ def _cook_sample_recipe(self):
         to both the atom positions and the label map, then project both to 2-D.
 
         After this call, self._cooked_atoms and self._cooked_projection are ready
-        for consumption by _acquire_stem_image.
+        for consumption by _acquire_scanned_image.
 
         Projection strategy
         -------------------
diff --git a/asyncroscopy/Microscope.py b/asyncroscopy/Microscope.py
index b8566f8..6379a71 100644
--- a/asyncroscopy/Microscope.py
+++ b/asyncroscopy/Microscope.py
@@ -14,10 +14,10 @@
 from typing import Optional
 
 
-from abc import abstractmethod, ABC, ABCMeta
+from abc import abstractmethod, ABCMeta
 
 import tango
-from tango import AttrWriteType, DevEncoded, DevState, DevVarFloatArray, DevFloat
+from tango import AttrWriteType, DevEncoded, DevState, DevVarFloatArray, DevFloat, DevVarStringArray
 from tango.server import Device, DeviceMeta, attribute, command, device_property
 
 class CombinedMeta(DeviceMeta, ABCMeta):
@@ -157,29 +157,17 @@ def acquire_spectrum(self, detector_name: str) -> str:
         proxy = self._detector_proxies.get(detector_name)
         return self._acquire_spectrum(detector_name, proxy.exposure_time)
 
-    @command(dtype_out=str)
-    def acquire_scanned_image(self) -> str:
-        """Acquire a STEM image and return a key pointing to that data. You can get the data with the get_image_from_key command"""
+    @command(dtype_in=DevVarStringArray, dtype_out=str)
+    def acquire_scanned_image(self, detector_list: list[str] = ["haadf"]) -> str:
+        """Acquire an image with scanning detectors and return a key pointing to that data. You can get the data with the get_image_from_key command"""
         scan = self._detector_proxies.get("scan")
-        return self._acquire_stem_image(scan.imsize, scan.dwell_time, ["haadf"])
-
-    @command(dtype_out=str)
-    def acquire_scanned_image_advanced(self) -> str:
-        """Acquire a STEM image using advanced STEM settings from the scan device."""
-        scan = self._detector_proxies.get("scan")
-        detector_names = [detector for detector in ("haadf", "bf") if bool(getattr(scan, detector))] or ["haadf"]
-        unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, list(scan.scan_region))
-        if isinstance(unique_ids, str):
-            return unique_ids
-
-        unique_ids = list(unique_ids)
-        return unique_ids[0] if len(unique_ids) == 1 else json.dumps(unique_ids)
+        return self._acquire_scanned_image(scan.imsize, scan.dwell_time, detector_list, list(scan.scan_region))
 
     @command(dtype_out=str)
     def acquire_scanned_data_advanced(self) -> str:
-        """Trigger an advanced 4D STEM data acquisition with the Ceta camera."""
+        """Trigger an advanced 4D scanned data acquisition with the Ceta camera."""
         scan = self._detector_proxies.get("scan")
-        return self._acquire_stem_data_advanced(scan.imsize, scan.dwell_time, "BM-Ceta", list(scan.scan_region))
+        return self._acquire_scanned_data_advanced(scan.imsize, scan.dwell_time, "BM-Ceta", list(scan.scan_region))
 
     @command(dtype_out=str)
     def acquire_camera_image(self) -> str:
@@ -193,33 +181,11 @@ def acquire_flucam_image(self) -> str:
         flucam = self._detector_proxies.get("flucam")
         return self._acquire_camera_image(flucam.imsize, flucam.exposure_time, "Flucam", flucam.readout_area)
 
-    @command(dtype_in=('str',), dtype_out=str)
-    def acquire_images(self, detector_names: list[str]) -> str:
-        """
-        Acquire multiple STEM images simultaneously.
-
-        Parameters
-        ----------
-        detector_names: list of detector names, e.g. ["HAADF", "BF"]
-
-        Returns
-        -------
-        JSON string containing unique ids returned by the vendor-specific
-        implementation.
-        """
-        detector_names = [name.strip() for name in detector_names]
-        scan = self._detector_proxies.get("scan")
-        unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, [0.0, 0.0, 1.0, 1.0])
-        if isinstance(unique_ids, str):
-            return unique_ids
-        unique_ids = list(unique_ids)
-        return unique_ids[0] if len(unique_ids) == 1 else json.dumps(unique_ids)
-
     @command(dtype_in=int, dtype_out=DevEncoded)
     def get_image_data_cached(self, index: int) -> tuple[str, bytes]:
         """Retrieve cached image by index."""
         if not hasattr(self, '_cached_images'):
-            tango.Except.throw_exception("NoCache", "Call acquire_images() first", "get_image_data()")
+            tango.Except.throw_exception("NoCache", "Call acquire_scanned_image() first", "get_image_data()")
         if index >= len(self._cached_images):
             tango.Except.throw_exception("InvalidIndex", f"Index {index} out of range", "get_image_data()")
         
@@ -344,8 +310,14 @@ def set_image_shift(self, shift):
     # Internal acquisition helpers
     # ------------------------------------------------------------------
     @abstractmethod
-    def _acquire_stem_image(self, imsize: int, dwell_time: float, detector_list: list[str]):
-        """Vendor-specific STEM acquisition implementation."""
+    def _acquire_scanned_image(
+        self,
+        imsize: int,
+        dwell_time: float,
+        detector_list: list[str] = ["haadf"],
+        scan_region: list[float] = [0.0, 0.0, 1.0, 1.0],
+    ) -> str:
+        """Vendor-specific scanned image acquisition implementation."""
         pass
 
     def _acquire_camera_image(self, imsize: int, exposure_time: float, detector: str, readout_area: str) -> str:
@@ -356,31 +328,20 @@ def _acquire_camera_image(self, imsize: int, exposure_time: float, detector: str
             "_acquire_camera_image()",
         )
 
-    def _acquire_stem_data_advanced(
+    def _acquire_scanned_data_advanced(
         self,
         imsize: int,
         dwell_time: float,
         detector: str,
         scan_region: list[float],
     ) -> str:
-        """Vendor-specific advanced 4D STEM data acquisition trigger."""
+        """Vendor-specific advanced 4D scanned data acquisition trigger."""
         tango.Except.throw_exception(
             "UnsupportedCommand",
-            "This microscope does not support advanced STEM data acquisition.",
-            "_acquire_stem_data_advanced()",
+            "This microscope does not support advanced scanned data acquisition.",
+            "_acquire_scanned_data_advanced()",
         )
 
-    @abstractmethod
-    def _acquire_stem_image_advanced(
-        self,
-        imsize: int,
-        dwell_time: float,
-        detector_list: list[str],
-        scan_region: list[float],
-    ) -> list:
-        """Vendor-specific multi-image acquisition implementation."""
-        pass
-
     def _place_beam(self, position):
         # define in the inherit class
         pass
diff --git a/asyncroscopy/ThermoMicroscope.py b/asyncroscopy/ThermoMicroscope.py
index 22c8cdf..4cace0e 100644
--- a/asyncroscopy/ThermoMicroscope.py
+++ b/asyncroscopy/ThermoMicroscope.py
@@ -18,18 +18,14 @@
 
 import math
 import time
-from datetime import datetime
-from pathlib import Path
 
-import h5py
 import numpy as np
 import tango
 from tango import AttrWriteType, DevState
 from tango.server import attribute, device_property
 
 from asyncroscopy.Microscope import Microscope
-
-DEFAULT_ACQUISITION_DIR = "outputs/tiled_acquisitions"
+from asyncroscopy.software.DataWriter import DEFAULT_ACQUISITION_DIR, save_acquisition
 
 # AutoScript imports — only available on the microscope PC.
 # Wrapped in try/except so the device can still be imported and tested
@@ -73,8 +69,8 @@ class ThermoMicroscope(Microscope):
     )
     acquisition_file_format = device_property(
         dtype=str,
-        default_value="tiff",
-        doc="Acquisition file format. TIFF preserves AutoScript image metadata.",
+        default_value="h5",
+        doc="Acquisition file format. HDF5 stores acquisition data and parsed metadata attributes.",
     )
     data_device_address = device_property(
         dtype=str,
@@ -152,16 +148,23 @@ def read_manufacturer(self) -> bool:
     # ------------------------------------------------------------------
     # Internal acquisition helpers
     # ------------------------------------------------------------------
-    def _acquire_stem_image(self, imsize: int, dwell_time: float, detector_list: list) -> str:
+    def _acquire_scanned_image(
+        self,
+        imsize: int,
+        dwell_time: float,
+        detector_list: list[str] = ["haadf"],
+        scan_region: list[float] = [0.0, 0.0, 1.0, 1.0],
+    ) -> str:
         """
-        Call AutoScript acquisition, save the adorned image, and return its path.
+        Call AutoScript scanned image acquisition, save one HDF5 file, and return its DATA/Tiled key.
         """
-        detector_type = detector_list[0].upper() if detector_list else "HAADF"
-        adorned = self._microscope.acquisition.acquire_stem_image(detector_type, imsize, dwell_time)
+        detector_list = [d.upper() for d in detector_list]
+        settings = StemAcquisitionSettings(dwell_time=dwell_time, detector_types=detector_list, size=imsize, region=Region(RegionCoordinateSystem.RELATIVE, Rectangle(*scan_region)))
+        adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)
+        if not isinstance(adorned, list):
+            adorned = [adorned]
         data_server = self._detector_proxies.get("data")
-        path = self._make_filename("stem_image", detector_type, data_server)
-        adorned.save(str(path))
-        return data_server.register_path(str(path))
+        return save_acquisition(self, data_server, "stem_image", detector_list, adorned)
 
     def _acquire_camera_image(self, imsize: int, exposure_time: float, detector: str, readout_area: str) -> str:
         """
@@ -171,45 +174,13 @@ def _acquire_camera_image(self, imsize: int, exposure_time: float, detector: str
         settings = CameraAcquisitionSettings(camera_detector=detector, size=imsize, exposure_time=exposure_time, fixed_readout_area=readout_area, frame_combining=1)
         adorned = self._microscope.acquisition.acquire_camera_image_advanced(settings)
         data_server = self._detector_proxies.get("data")
-        path = self._make_filename("camera_image", detector, data_server)
-        adorned.save(str(path))
-        return data_server.register_path(str(path))
-
-    def _acquire_stem_image_advanced(
-        self,
-        imsize: int,
-        dwell_time: float,
-        detector_list: list,
-        scan_region: list[float],
-    ) -> list[str]:
-        """
-        Call AutoScript acquisition, save adorned images, and return their paths.
-        """
-        detector_list = [d.upper() for d in detector_list]
-
-        settings = StemAcquisitionSettings(dwell_time=dwell_time, detector_types=detector_list, size=imsize, region=Region(RegionCoordinateSystem.RELATIVE, Rectangle(*scan_region)))
-
-        adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)
-        adorned_images = adorned if isinstance(adorned, list) else [adorned]
-        saved_paths = []
-        data_server = self._detector_proxies.get("data")
-        for image, detector in zip(adorned_images, detector_list):
-            path = self._make_filename("stem_image", detector, data_server)
-            image.save(str(path))
-            saved_paths.append(data_server.register_path(str(path)))
-        return saved_paths
+        return save_acquisition(self, data_server, "camera_image", str(detector), adorned)
 
-    def _acquire_stem_data_advanced(
-        self,
-        imsize: int,
-        dwell_time: float,
-        detector: str,
-        scan_region: list[float],
-    ) -> str:
+    def _acquire_scanned_data_advanced(self, imsize: int, dwell_time: float, detector: str, scan_region: list[float]) -> str:
         """
-        Trigger AutoScript advanced STEM data acquisition with a camera detector.
+        Trigger AutoScript advanced scanned data acquisition with a camera detector.
 
-        AutoScript offloads the 4D STEM data storage for Ceta acquisitions, so
+        AutoScript offloads the 4D scanned data storage for Ceta acquisitions, so
         this command returns an acknowledgement and the settings used rather
         than a local saved file path.
         """
@@ -217,23 +188,7 @@ def _acquire_stem_data_advanced(
         settings = StemDataSettings(dwell_time=dwell_time, detector_types=[camera_detector], size=imsize, region=Region(RegionCoordinateSystem.RELATIVE, Rectangle(*scan_region)))
         adorned = self._microscope.acquisition.acquire_stem_data_advanced(settings)
         data_server = self._detector_proxies.get("data")
-        path = self._make_filename("stem_data", detector, data_server)
-        adorned.save(str(path))
-        return data_server.register_path(str(path))
-
-    def _make_filename(self, acquisition_type: str, detector: str, data_server, extension: str = "tiff") -> Path:
-        save_directory = self.acquisition_save_directory
-        if data_server is not None:
-            try:
-                save_directory = data_server.save_path
-            except tango.DevFailed as exc:
-                self.warn_stream(f"DATA device not ready: {exc}")
-
-        directory = Path(save_directory).expanduser()
-        directory.mkdir(parents=True, exist_ok=True)
-        stamp = datetime.now().strftime("%Y%m%dT%H%M%S%f")
-        name = f"{acquisition_type}_{detector}_{stamp}.{extension.lower().lstrip('.')}"
-        return directory / name
+        return save_acquisition(self, data_server, "stem_data", str(detector), adorned, dataset_name="stem_data")
 
     # test: not sure this is how we want to save
     def _acquire_spectrum(self, detector_name: str, exposure_time: float) -> str:
@@ -245,10 +200,7 @@ def _acquire_spectrum(self, detector_name: str, exposure_time: float) -> str:
         settings.exposure_time_type = ExposureTimeType.LIVE_TIME
         spectrum = self._microscope.analysis.eds.acquire_spectrum(settings)
         data_server = self._detector_proxies.get("data")
-        path = self._make_filename("spectrum", detector_name, data_server, "emd")
-        with h5py.File(path, "w") as emd:
-            emd.create_dataset("spectrum", data=spectrum.data)
-        return data_server.register_path(str(path))
+        return save_acquisition(self, data_server, "spectrum", detector_name, spectrum, dataset_name="spectrum")
 
     def _place_beam(self, position) -> None:
         """
diff --git a/asyncroscopy/hardware/SCAN.py b/asyncroscopy/hardware/SCAN.py
index f753039..beea3dd 100644
--- a/asyncroscopy/hardware/SCAN.py
+++ b/asyncroscopy/hardware/SCAN.py
@@ -1,12 +1,11 @@
 """
-ALL SCANNING DETECTORS
-This device holds acquisition settings for the HAADF and BF detectors.
+SCAN hardware settings.
+This device holds scan acquisition settings.
 It does NOT talk to AutoScript directly — the Microscope device
 reads these attributes via DeviceProxy before acquiring.
 """
 from tango import AttrWriteType, DevState
-from tango.server import Device, attribute, command
-from tango import DevVarStringArray
+from tango.server import Device, attribute
 
 
 class SCAN(Device):
@@ -35,20 +34,6 @@ class SCAN(Device):
         doc="Acquisition width in pixels (should match an AutoScript ImageSize preset)",
     )
 
-    haadf = attribute(
-        label="HAADF Detector",
-        dtype=bool,
-        access=AttrWriteType.READ_WRITE,
-        doc="HAADF detector state: True = active, False = inactive",
-    )
-
-    bf = attribute(
-        label="BF Detector",
-        dtype=bool,
-        access=AttrWriteType.READ_WRITE,
-        doc="Bright Field detector state: True = active, False = inactive",
-    )
-
     scan_region = attribute(
         label="Scan Region",
         dtype=(float,),
@@ -69,8 +54,6 @@ def init_device(self) -> None:
         self.set_state(DevState.ON)
         self._dwell_time: float = 1e-6
         self._imsize: int = 512
-        self._haadf: bool = False
-        self._bf: bool = False
         self._scan_region: list[float] = [0.0, 0.0, 1.0, 1.0]
         self.info_stream("SCAN device initialised")
 
@@ -90,20 +73,6 @@ def read_imsize(self) -> int:
     def write_imsize(self, value: int) -> None:
         self._imsize = value
 
-    def read_haadf(self) -> bool:
-        return self._haadf
-
-    def write_haadf(self, value: bool) -> None:
-        self._haadf = value
-        self.info_stream(f"HAADF detector set to {'active' if value else 'inactive'}")
-
-    def read_bf(self) -> bool:
-        return self._bf
-
-    def write_bf(self, value: bool) -> None:
-        self._bf = value
-        self.info_stream(f"BF detector set to {'active' if value else 'inactive'}")
-
     def read_scan_region(self) -> list[float]:
         return self._scan_region
 
@@ -127,26 +96,6 @@ def _validate_scan_region(value) -> list[float]:
             raise ValueError("scan_region must fit within the relative [0, 1] scan area")
         return region
 
-    # ------------------------------------------------------------------
-    # Commands
-    # ------------------------------------------------------------------
-
-    VALID_DETECTORS = {"haadf", "bf"}
-
-    @command(dtype_in=DevVarStringArray, doc_in="List of detectors to activate, e.g. ['haadf', 'bf']. All others are deactivated.")
-    def Activate(self, detectors) -> None:
-        """Activate the named detectors and deactivate all others."""
-        requested = {d.lower() for d in detectors}
-        unknown = requested - self.VALID_DETECTORS
-        if unknown:
-            raise ValueError(f"Unknown detector(s): {unknown}. Valid options: {self.VALID_DETECTORS}")
-
-        self._haadf = "haadf" in requested
-        self._bf    = "bf"    in requested
-
-        self.info_stream(f"Active detectors: {requested or 'none'}")
-
-
 # ----------------------------------------------------------------------
 # Server entry point
 # ----------------------------------------------------------------------
diff --git a/asyncroscopy/software/DATA.py b/asyncroscopy/software/DATA.py
index 2fe39d0..26abb42 100644
--- a/asyncroscopy/software/DATA.py
+++ b/asyncroscopy/software/DATA.py
@@ -123,7 +123,7 @@ def register_path(self, path: str) -> str:
         async def register_with_tiled_client() -> None:
             client = from_uri(self._uri(), api_key=self._api_key)
             await register(client, path, walkers=[ONE_NODE_PER_FILE_WALKER], key_from_filename=identity)
-            # here is where we insert a new reader type
+            # TODO: here is where we insert a new reader type
 
         asyncio.run(asyncio.wait_for(register_with_tiled_client(), timeout))
         self._tiled_server_status = "running; registered path"
diff --git a/asyncroscopy/software/DataWriter.py b/asyncroscopy/software/DataWriter.py
new file mode 100644
index 0000000..629a56a
--- /dev/null
+++ b/asyncroscopy/software/DataWriter.py
@@ -0,0 +1,84 @@
+"""Simple HDF5 acquisition writer."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from xml.etree import ElementTree as ET
+
+import h5py
+import numpy as np
+
+DEFAULT_ACQUISITION_DIR = "outputs/tiled_acquisitions"
+
+
+def acquisition_filename(
+    device,
+    acquisition_type: str,
+    detector: str,
+    data_server=None,
+    extension: str = "h5",
+) -> Path:
+    """Create a timestamped acquisition filename."""
+    save_directory = DEFAULT_ACQUISITION_DIR
+    try:
+        save_directory = device.acquisition_save_directory
+    except AttributeError:
+        pass
+    if data_server is not None:
+        save_directory = data_server.save_path
+
+    directory = Path(save_directory).expanduser()
+    directory.mkdir(parents=True, exist_ok=True)
+    stamp = datetime.now().strftime("%Y%m%dT%H%M%S%f")
+    return directory / f"{acquisition_type}_{detector}_{stamp}.{extension.lower().lstrip('.')}"
+
+
+def save_acquisition(device, data_server, acquisition_type: str, detectors, data, dataset_name: str = "image") -> str:
+    """Save one acquisition to one HDF5 file and return its DATA/Tiled key."""
+    detector_list = list(detectors) if isinstance(detectors, (list, tuple)) else [detectors]
+    data_list = list(data) if isinstance(data, (list, tuple)) else [data]
+    has_labeled_datasets = len(detector_list) > 1 or len(data_list) > 1
+    detector_label = "_".join([str(detector) for detector in detector_list])
+    path = acquisition_filename(device, acquisition_type, detector_label, data_server)
+
+    datasets = []
+    for index, source in enumerate(data_list):
+        detector = str(detector_list[index]) if index < len(detector_list) else f"item_{index}"
+        name = f"{dataset_name}/{detector}" if has_labeled_datasets else dataset_name
+        datasets.append({"name": name, "source": source, "attrs": {"acquisition_type": acquisition_type, "detector": detector}})
+
+    save_acquisition_hdf5(path, datasets)
+    return data_server.register_path(str(path)) if data_server is not None else str(path)
+
+
+def save_acquisition_hdf5(path: str | Path, datasets: list[dict], file_attrs: dict | None = None) -> None:
+    """Save one acquisition event to one HDF5 file."""
+    with h5py.File(path, "w") as h5:
+        for key, value in (file_attrs or {}).items():
+            h5.attrs[key] = value if isinstance(value, (str, int, float, bool, np.number)) else json.dumps(value)
+
+        for item in datasets:
+            source = item.get("source", item.get("data"))
+            data = source.data if hasattr(source, "data") and not isinstance(source, np.ndarray) else source
+            name = item["name"]
+            if "/" in name:
+                group_name, dataset_name = name.rsplit("/", 1)
+                dset = h5.require_group(group_name).create_dataset(dataset_name, data=data, compression=None)
+            else:
+                dset = h5.create_dataset(name, data=data, compression=None)
+
+            for key, value in item.get("attrs", {}).items():
+                dset.attrs[key] = value if isinstance(value, (str, int, float, bool, np.number)) else json.dumps(value)
+
+            metadata = getattr(source, "metadata", None)
+            metadata_xml = getattr(metadata, "metadata_as_xml", None)
+            if metadata_xml:
+                root = ET.fromstring(metadata_xml)
+                for elem in root.iter():
+                    if elem.text and elem.text.strip():
+                        key = elem.tag
+                        if key in dset.attrs:
+                            key = f"{key}_{len(dset.attrs)}"
+                        dset.attrs[key] = elem.text.strip()
diff --git a/data_integration.md b/data_integration.md
index 1baeff3..e2be40e 100644
--- a/data_integration.md
+++ b/data_integration.md
@@ -2,20 +2,20 @@
 
 See more at https://github.com/bluesky/tiled.
 
-`ThermoMicroscope` now saves real AutoScript acquisitions on the microscope
-side and returns a JSON descriptor string through Tango. `asyncroscopy/software/DATA.py`
-is the Tango data device for reading those descriptors back through the Tiled
-HTTP server.
+`ThermoMicroscope` saves real AutoScript acquisitions on the microscope side
+and returns the registered Tiled key through Tango. `asyncroscopy/software/DATA.py`
+is the Tango data device for registering those files with the Tiled HTTP server.
 
-The preferred save format is `.emd`:
+The save format is one HDF5 file per acquisition event. Each correlated output
+is stored as a dataset in that file, with parsed AutoScript XML leaf metadata
+written as HDF5 dataset attributes.
 
-```python
-EmdFile.create("stem_image.emd", EmdStemFeature([adorned_image])).close()
-```
+Typical dataset names are:
 
-AutoScript requires complete image metadata for EMD export. If EMD creation
-fails, asyncroscopy falls back to `adorned.save("...tiff")`, because TIFF is
-the format supported by `AdornedImage.save()` that preserves metadata.
+- `image` for single image acquisitions
+- `images/HAADF`, `images/BF`, etc. for multi-detector STEM acquisitions
+- `spectrum` for spectra
+- `stem_data` for STEM data acquisitions
 
 ## Notebook setup
 
@@ -33,21 +33,12 @@ data.port = 9091
 data.save_path = "/path/served/by/tiled"
 ```
 
-Acquire as usual, but treat the return value as a descriptor:
+Acquire as usual, and treat the return value as the Tiled key:
 
 ```python
-descriptor = mic.acquire_scanned_image()
+tiled_key = mic.acquire_scanned_image()
 ```
 
-Use the DATA device to resolve data through Tiled:
-
-```python
-array = json.loads(data.get_data(descriptor))
-```
-
-The descriptor includes both extension-stripped and extension-preserving Tiled
-path candidates because Tiled directory adapters can be configured either way.
-
 ## Server Roles
 
 There are two data-related servers:
diff --git a/docs/MCP/asyncroscopy_mcp.md b/docs/MCP/asyncroscopy_mcp.md
index 939f494..5679a3e 100644
--- a/docs/MCP/asyncroscopy_mcp.md
+++ b/docs/MCP/asyncroscopy_mcp.md
@@ -31,7 +31,7 @@ Asyncroscopy defines Tango Device subclasses for microscopy hardware:
 ### Base: `Microscope` (asyncroscopy/Microscope.py)
 
 Core microscope control:
-- `acquire_scanned_image()` - Acquire STEM image
+- `acquire_scanned_image(["haadf"])` - Acquire STEM image
 - `acquire_spectrum()` - Acquire spectrum
 - Attributes: voltage, magnification, probe_current
 
@@ -128,50 +128,39 @@ How an LLM acquires a microscope image through MCP:
 ```python
 # asyncroscopy/Microscope.py
 class Microscope(Device):
-    @command(dtype_in=int, dtype_out=str)
-    def acquire_scanned_image(self, exposure_ms: int) -> str:
-        """Acquire a STEM image with specified exposure time."""
-        # Interact with AutoScript microscope API
-        img = self._acquire_stem_image(exposure_ms)
-        # Return as DevEncoded (binary image + metadata)
-        return (json.dumps(metadata), img.tobytes())
+    @command(dtype_in=DevVarStringArray, dtype_out=str)
+    def acquire_scanned_image(self, detector_list: list[str] = ["haadf"]) -> str:
+        """Acquire a STEM image and return a key pointing to the HDF5 data."""
+        scan = self._detector_proxies.get("scan")
+        return self._acquire_scanned_image(scan.imsize, scan.dwell_time, detector_list, list(scan.scan_region))
 ```
 
 ### MCP Tool Registration
 
 1. Server queries Tango: `Microscope.acquire_scanned_image` exists
-2. Extracts parameter name from source: `exposure_ms`
-3. Maps Tango type to Python: `int` → `int`
+2. Extracts parameter name from source: `detector_list`
+3. Maps Tango type to Python: `DevVarStringArray` → `list[str]`
 4. Builds function signature:
    ```python
-   def Microscope_acquire_scanned_image(exposure_ms: int) -> dict:
-       """Acquire a STEM image with specified exposure time.
+   def Microscope_acquire_scanned_image(detector_list: list[str]) -> str:
+       """Acquire a STEM image.
        
        Tango Device Class: Microscope
        Tango Command: acquire_scanned_image
        """
-       result = dev.acquire_scanned_image(exposure_ms)
-       return {
-           "encoding": "base64",
-           "metadata": metadata,
-           "payload": base64.b64encode(img).decode()
-       }
+       return dev.acquire_scanned_image(detector_list)
    ```
 
 ### LLM Usage
 
 ```
-Agent: "Acquire an image with 5ms exposure."
+Agent: "Acquire a HAADF image."
 
-MCP Server invokes: Microscope_acquire_scanned_image(exposure_ms=5)
+MCP Server invokes: Microscope_acquire_scanned_image(detector_list=["haadf"])
 
-Result: {
-    "encoding": "base64",
-    "metadata": {"shape": [1024, 1024], "dtype": "uint8"},
-    "payload": "iVBORw0KGgo..."
-}
+Result: "stem_image_HAADF_20260528T170000000000.h5"
 
-Agent: "Image acquired. Dimensions: 1024×1024."
+Agent: "Image acquired and saved."
 ```
 
 ## Multi-Device Coordination
diff --git a/docs/Microscopy/modify_base_microscope.md b/docs/Microscopy/modify_base_microscope.md
index 0a5fb0f..0e75359 100644
--- a/docs/Microscopy/modify_base_microscope.md
+++ b/docs/Microscopy/modify_base_microscope.md
@@ -12,8 +12,7 @@ If you’re editing this class - `Microscope` located at asyncroscopy/Microscope
 
 3. **ADD a new functionality pertaining to a microscope api - Adding Internal acquisition helpers in the base Microscope**
 - examples - already existing
-   - `_acquire_stem_image`
-   - `_acquire_stem_image_advanced`
+   - `_acquire_scanned_image`
    - `_acquire_spectrum`
 
 5. **Changing the transport format**
@@ -21,4 +20,3 @@ If you’re editing this class - `Microscope` located at asyncroscopy/Microscope
 
 6. **Improving robustness**
    (Handle connection failures, missing proxies, AutoScript errors, simulation fallback logic, or state transitions like `FAULT`, `ON`, `OFF`.)
-
diff --git a/docs/Microscopy/modify_thermo_microscope.md b/docs/Microscopy/modify_thermo_microscope.md
index 9f07df9..b3705be 100644
--- a/docs/Microscopy/modify_thermo_microscope.md
+++ b/docs/Microscopy/modify_thermo_microscope.md
@@ -18,9 +18,7 @@ If you’re editing this class, you’re usually doing one of these:
 
 5. **ADD a new functionality pertaining to a microscope api - Adding Internal acquisition helpers**
 - examples - already existing
-   - `_acquire_stem_image`
-   - `_acquire_stem_image_advanced`
+   - `_acquire_scanned_image`
    - `_acquire_spectrum`
 -  **Adding or changing acquisition settings**
    (Extend what is read from detector devices—e.g., dwell time, resolution, scan region—and ensure they propagate correctly into acquisition helpers.)
-
diff --git a/docs/digital_twin.md b/docs/digital_twin.md
index 3de6034..190732e 100644
--- a/docs/digital_twin.md
+++ b/docs/digital_twin.md
@@ -7,9 +7,9 @@ It provides realistic-enough image and spectrum behavior for development, testin
 
 1. On startup, the twin generates a **persistent synthetic sample** (deterministic from seed).
 2. Stage pose (`x, y, z, alpha, beta`) defines the current viewport into that sample.
-3. `acquire_scanned_image()` calls the inherited microscope command, which delegates to `_acquire_stem_image()`.
-4. `_acquire_stem_image()` renders the current pose/FoV, writes a TIFF with metadata, and returns the DATA/Tiled key when a DATA device is configured.
-5. `acquire_spectrum("eds")` delegates to `_acquire_spectrum()`, which estimates composition at the current beam position and saves a `.npy` spectrum for now.
+3. `acquire_scanned_image(["haadf"])` calls the inherited microscope command, which delegates to `_acquire_scanned_image()`.
+4. `_acquire_scanned_image()` renders the current pose/FoV, writes HDF5 data with metadata attributes, and returns the DATA/Tiled key when a DATA device is configured.
+5. `acquire_spectrum("eds")` delegates to `_acquire_spectrum()`, which estimates composition at the current beam position and saves a HDF5 spectrum.
 
 This means moving the stage navigates the sample, and revisiting the same pose can reproduce the same view when stage noise is disabled.
 
@@ -24,7 +24,7 @@ This means moving the stage navigates the sample, and revisiting the same pose c
 - Viewport metadata reporting
 - Manual sample regeneration with a new seed
 
-Spectrum files are currently saved as `.npy` with a JSON metadata sidecar. This is a temporary format; simulated spectra should migrate to `.emd` to match microscope EDS output.
+Simulated image and spectrum acquisitions use the same HDF5 writer as the hardware-backed microscope path.
 
 ## Key properties
 
@@ -38,7 +38,7 @@ Spectrum files are currently saved as `.npy` with a JSON metadata sidecar. This
 - `move_stage([x, y, z, alpha, beta])`
 - `get_stage()`
 - `set_fov(fov)`
-- `acquire_scanned_image()`
+- `acquire_scanned_image(["haadf"])`
 - `place_beam([x, y])`
 - `acquire_spectrum("eds")`
 - `get_viewport_metadata()`
diff --git a/notebooks/00_Testing.ipynb b/notebooks/00_Testing.ipynb
new file mode 100644
index 0000000..f30dd2d
--- /dev/null
+++ b/notebooks/00_Testing.ipynb
@@ -0,0 +1,653 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6b91a65c",
+   "metadata": {},
+   "source": [
+    "### Run the servers\n",
+    "\n",
+    "Make sure you are on the VPN and the AutoScript server is running. Then start the asyncroscopy Tango servers from the repository root:\n",
+    "\n",
+    "```bash\n",
+    "uv run scripts/run_servers.py\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import time\n",
+    "import h5py\n",
+    "import xml.etree.ElementTree as ET\n",
+    "\n",
+    "import tango\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tiled.client import from_uri\n",
+    "\n",
+    "from autoscript_tem_microscope_client import TemMicroscopeClient\n",
+    "from autoscript_tem_microscope_client.enumerations import EdsDetectorType\n",
+    "from autoscript_tem_microscope_client.enumerations import CameraType, RegionCoordinateSystem, ExposureTimeType\n",
+    "from autoscript_tem_microscope_client.structures import Region, Rectangle\n",
+    "from autoscript_tem_microscope_client.structures import StemAcquisitionSettings, EdsAcquisitionSettings, RunOptiStemSettings, CameraAcquisitionSettings, StemDataSettings\n",
+    "\n",
+    "%matplotlib ipympl\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d28060dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Client connecting to [10.46.217.242:9094]...\n",
+      "Client connected to [10.46.217.242:9094]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mic = TemMicroscopeClient()\n",
+    "\n",
+    "gatan_ip = '10.46.217.242'\n",
+    "gatan_port = 9094\n",
+    "\n",
+    "mic.connect(gatan_ip, gatan_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ce44cb7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector_type = 'HAADF'\n",
+    "dwell_time = 1e-6\n",
+    "imsize = 2048\n",
+    "im = mic.acquisition.acquire_stem_image(detector_type, imsize, dwell_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "68161779",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AdornedImage(width=1024, height=1024, bit_depth=16),\n",
+       " AdornedImage(width=1024, height=1024, bit_depth=16)]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detector_list = ['HAADF', 'BF']\n",
+    "scan_region = (0,0,.5,.5)\n",
+    "detector_list = [d.upper() for d in detector_list]\n",
+    "settings = StemAcquisitionSettings(dwell_time=dwell_time, detector_types=detector_list, size=imsize, region=Region(RegionCoordinateSystem.RELATIVE, Rectangle(*scan_region)))\n",
+    "adorned_list = mic.acquisition.acquire_stem_images_advanced(settings)\n",
+    "adorned_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f7ce1d2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TIFF save time: 0.0062 s\n",
+      "HDF5 raw XML save time: 0.0024 s\n",
+      "HDF5 parsed attrs save time: 0.0043 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = '/Users/austin/Desktop/testing/'\n",
+    "\n",
+    "# autoscript data\n",
+    "image = im.data\n",
+    "metadata_xml = im.metadata.metadata_as_xml\n",
+    "\n",
+    "# ------------------------------------------------\n",
+    "# TIFF timing\n",
+    "# ------------------------------------------------\n",
+    "t0 = time.perf_counter()\n",
+    "im.save(path + 'im0.tiff')\n",
+    "print(f'TIFF save time: {time.perf_counter() - t0:.4f} s')\n",
+    "\n",
+    "# ------------------------------------------------\n",
+    "# HDF5 timing (raw XML blob)\n",
+    "# ------------------------------------------------\n",
+    "t0 = time.perf_counter()\n",
+    "\n",
+    "with h5py.File(path + 'im0_rawxml.h5', 'w') as f:\n",
+    "    f.create_dataset('image', data=image, compression=None)\n",
+    "    f.create_dataset('metadata_xml', data=metadata_xml.encode('utf-8'))\n",
+    "\n",
+    "print(f'HDF5 raw XML save time: {time.perf_counter() - t0:.4f} s')\n",
+    "\n",
+    "# ------------------------------------------------\n",
+    "# HDF5 timing (parsed XML attributes)\n",
+    "# ------------------------------------------------\n",
+    "t0 = time.perf_counter()\n",
+    "\n",
+    "root = ET.fromstring(metadata_xml)\n",
+    "\n",
+    "with h5py.File(path + 'im0_attrs.h5', 'w') as f:\n",
+    "\n",
+    "    dset = f.create_dataset('image', data=image, compression=None)\n",
+    "\n",
+    "    for elem in root.iter():\n",
+    "\n",
+    "        # leaf nodes only\n",
+    "        if elem.text and elem.text.strip():\n",
+    "\n",
+    "            key = elem.tag\n",
+    "            value = elem.text.strip()\n",
+    "\n",
+    "            # avoid duplicate names\n",
+    "            if key in dset.attrs:\n",
+    "                key = f'{key}_{len(dset.attrs)}'\n",
+    "\n",
+    "            dset.attrs[key] = value\n",
+    "\n",
+    "print(f'HDF5 parsed attrs save time: {time.perf_counter() - t0:.4f} s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da5266b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = mic.acquisition.acquire_stem_image(detector_type, imsize, dwell_time)\n",
+    "\n",
+    "image = im.data\n",
+    "metadata_xml = im.metadata.metadata_as_xml\n",
+    "\n",
+    "root = ET.fromstring(metadata_xml)\n",
+    "\n",
+    "with h5py.File(path + 'im0_attrs.h5', 'w') as f:\n",
+    "    dset = f.create_dataset('image', data=image, compression=None)\n",
+    "    for elem in root.iter():\n",
+    "        # leaf nodes only\n",
+    "        if elem.text and elem.text.strip():\n",
+    "            key = elem.tag\n",
+    "            value = elem.text.strip()\n",
+    "            if key in dset.attrs:\n",
+    "                key = f'{key}_{len(dset.attrs)}'\n",
+    "            dset.attrs[key] = value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Connect to devices\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "asyncroscopy/stage/default ON\n",
+      "asyncroscopy/scan/default ON\n",
+      "asyncroscopy/eds/default ON\n",
+      "asyncroscopy/camera/default ON\n",
+      "asyncroscopy/data/default ON\n",
+      "asyncroscopy/microscope/default ON\n"
+     ]
+    }
+   ],
+   "source": [
+    "DB_HOST = \"127.0.0.1\"\n",
+    "DB_PORT = 9094\n",
+    "\n",
+    "os.environ[\"TANGO_HOST\"] = f\"{DB_HOST}:{DB_PORT}\"\n",
+    "\n",
+    "server_names = ['stage', 'scan', 'eds', 'camera', 'data', 'microscope']\n",
+    "\n",
+    "for name in server_names:\n",
+    "    device_name = f\"asyncroscopy/{name}/default\"\n",
+    "    proxy = tango.DeviceProxy(device_name)\n",
+    "    proxy.set_timeout_millis(120_000)\n",
+    "    proxy.ping()\n",
+    "    print(device_name, proxy.state())\n",
+    "\n",
+    "\n",
+    "scan = tango.DeviceProxy(\"asyncroscopy/scan/default\")\n",
+    "microscope = tango.DeviceProxy(\"asyncroscopy/microscope/default\")\n",
+    "data = tango.DeviceProxy(\"asyncroscopy/data/default\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start Tiled data server\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f8b4b66d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled server is already running.\n",
+      "{\n",
+      "  \"host\": \"127.0.0.1\",\n",
+      "  \"port\": 9091,\n",
+      "  \"uri\": \"http://127.0.0.1:9091\",\n",
+      "  \"save_path\": \"/Users/austin/Desktop/testing\",\n",
+      "  \"tiled_server\": \"yes\",\n",
+      "  \"tiled_server_status\": \"running; watcher started\"\n",
+      "}\n",
+      "Tiled keys: ['im0.h5', 'im0.tiff']\n"
+     ]
+    }
+   ],
+   "source": [
+    "TILED_HOST = '127.0.0.1'\n",
+    "TILED_PORT = 9091\n",
+    "save_path = '/Users/austin/Desktop/testing'\n",
+    "\n",
+    "data.host = TILED_HOST\n",
+    "data.port = TILED_PORT\n",
+    "data.save_path = save_path\n",
+    "\n",
+    "if str(data.tiled_server).lower() != \"yes\":\n",
+    "    print(\"Tiled server is not responding; starting it from the DATA device...\")\n",
+    "    config = json.loads(data.start_tiled_server())\n",
+    "else:\n",
+    "    print(\"Tiled server is already running.\")\n",
+    "    config = json.loads(data.get_config())\n",
+    "\n",
+    "print(json.dumps(config, indent=2))\n",
+    "\n",
+    "client = from_uri(config.get(\"uri\", f\"http://{TILED_HOST}:{TILED_PORT}\"))\n",
+    "print(\"Tiled keys:\", list(client))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9511e1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c5d8789",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ac82f72",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b52e288",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e3ef90",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec79c73e",
+   "metadata": {},
+   "source": [
+    "### Configure scan\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "478b95f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dwell_time : 1e-06\n",
+      "image size : 512\n",
+      "scan region: [np.float64(0.0), np.float64(0.0), np.float64(1.0), np.float64(1.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "scan.dwell_time = 1e-6\n",
+    "scan.imsize = 512\n",
+    "scan.scan_region = [0, 0, 1, 1]\n",
+    "\n",
+    "print(\"dwell_time :\", scan.dwell_time)\n",
+    "print(\"image size :\", scan.imsize)\n",
+    "print(\"scan region:\", list(scan.scan_region))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac5997de",
+   "metadata": {},
+   "source": [
+    "### Acquire a HAADF image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4442aab2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled key  : stem_image_HAADF_20260528T092407483993.tiff\n",
+      "Metadata   : {'ImageWidth': 512, 'ImageLength': 512, 'BitsPerSample': 32, 'Compression': 1, 'PhotometricInterpretation': 1, 'ImageDescription': '{\"acquisition_type\": \"stem_image\", \"detector\": \"HAADF\", \"dwell_time\": 1e-06, \"shape\": [512, 512], \"dtype\": \"float32\", \"simulation_backend\": \"DigitalTwin\", \"stage_position\": [0.0, 0.0, 0.0, 0.0, 0.0], \"beam_position\": [0.5, 0.5], \"fov_m\": 2e-08, \"fov_angstrom\": 200.0, \"imsize\": 512, \"sample_seed\": 12345, \"sample_size_xy\": 6e-09, \"sample_size_z\": 6e-09, \"viewport_world_angstrom\": {\"x_min\": -100.0, \"x_max\": 100.0, \"y_min\": -100.0, \"y_max\": 100.0, \"z_center\": 0.0}, \"world_bounds_angstrom\": {\"x_min\": -30.0, \"x_max\": 30.0, \"y_min\": -30.0, \"y_max\": 30.0, \"z_min\": -30.0, \"z_max\": 30.0}, \"particle_count\": 3}', 'StripOffsets': [754], 'RowsPerStrip': 512, 'StripByteCounts': [1048576], 'PlanarConfiguration': 1, 'SampleFormat': 3}\n",
+      "Image shape: (512, 512)\n",
+      "Image dtype: float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "key = microscope.acquire_scanned_image()\n",
+    "node = client[key]\n",
+    "image = np.asarray(node.read())\n",
+    "metadata = dict(node.metadata)\n",
+    "\n",
+    "print(\"Tiled key  :\", key)\n",
+    "print(\"Metadata   :\", metadata)\n",
+    "print(\"Image shape:\", image.shape)\n",
+    "print(\"Image dtype:\", image.dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0005c0f",
+   "metadata": {},
+   "source": [
+    "### Display the image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6ea573ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad1f166a93764a4787e86840acec17d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "ax.imshow(image, cmap=\"gray\", interpolation=\"none\")\n",
+    "ax.set_title(f\"HAADF - dwell {scan.dwell_time * 1e6:.1f} us\")\n",
+    "ax.axis(\"off\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca1b9a8c",
+   "metadata": {},
+   "source": [
+    "### Acquire multiple detectors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "24ffc973",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "DevFailed",
+     "evalue": "DevFailed[\n    DevError[\n        desc = autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        origin = Traceback (most recent call last):\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\server.py\", line 1790, in wrapped_command_method\n                return get_worker().execute(cmd_method, *args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\green.py\", line 110, in execute\n                return fn(*args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\Microscope.py\", line 212, in acquire_images\n                unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, [0.0, 0.0, 1.0, 1.0])\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\ThermoMicroscope.py\", line 192, in _acquire_stem_image_advanced\n                adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope\\_acquisition.py\", line 107, in acquire_stem_images_advanced\n                call_response = self.__application_client._perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope_client.py\", line 242, in _perform_call\n                call_response = self.__endpoint.perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_core\\orc\\engines.py\", line 206, in perform_call\n                raise api_exception\n            autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        reason = PyDs_PythonError\n        severity = ERR\n    ],\n    DevError[\n        desc = Cannot execute command\n        origin = class CORBA::Any *__cdecl PyCmd::execute(class Tango::DeviceImpl *,const class CORBA::Any &) at (C:\\gitlab-runner\\builds\\ehTiiTbyF\\4\\tango-controls\\pytango\\ext\\server\\command.cpp:87)\n        reason = PyDs_UnexpectedFailure\n        severity = ERR\n    ],\n    DevError[\n        desc = Failed to execute command_inout on device asyncroscopy/microscope/default, command acquire_images\n        origin = virtual DeviceData Tango::Connection::command_inout(const std::string &, const DeviceData &) at (/Users/runner/miniforge3/conda-bld/cpptango_1758200193404/work/src/client/devapi_base.cpp:2029)\n        reason = API_CommandFailed\n        severity = ERR\n    ]\n]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mDevFailed\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m scan.dwell_time = \u001b[32m1e-6\u001b[39m\n\u001b[32m      2\u001b[39m scan.imsize = \u001b[32m512\u001b[39m\n\u001b[32m      3\u001b[39m scan.scan_region = [\u001b[32m0\u001b[39m, \u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m]\n\u001b[32m      4\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m keys = json.loads(microscope.acquire_images([\u001b[33m\"HAADF\"\u001b[39m, \u001b[33m\"BF\"\u001b[39m]))\n\u001b[32m      6\u001b[39m images = []\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;28;01min\u001b[39;00m keys:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/device_proxy.py:358\u001b[39m, in \u001b[36m__get_command_func.<locals>.f\u001b[39m\u001b[34m(*args, **kwds)\u001b[39m\n\u001b[32m    357\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mf\u001b[39m(*args, **kwds):\n\u001b[32m--> \u001b[39m\u001b[32m358\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcommand_inout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/green.py:231\u001b[39m, in \u001b[36mgreen.<locals>.decorator.<locals>.greener\u001b[39m\u001b[34m(obj, *args, **kwargs)\u001b[39m\n\u001b[32m    229\u001b[39m green_mode = access(\u001b[33m\"\u001b[39m\u001b[33mgreen_mode\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    230\u001b[39m executor = get_object_executor(obj, green_mode)\n\u001b[32m--> \u001b[39m\u001b[32m231\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/green.py:121\u001b[39m, in \u001b[36mAbstractExecutor.run\u001b[39m\u001b[34m(self, fn, args, kwargs, wait, timeout)\u001b[39m\n\u001b[32m    119\u001b[39m \u001b[38;5;66;03m# Synchronous (no delegation)\u001b[39;00m\n\u001b[32m    120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.asynchronous \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.in_executor_context():\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    122\u001b[39m \u001b[38;5;66;03m# Asynchronous delegation\u001b[39;00m\n\u001b[32m    123\u001b[39m accessor = \u001b[38;5;28mself\u001b[39m.delegate(fn, *args, **kwargs)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/connection.py:72\u001b[39m, in \u001b[36m__Connection__command_inout\u001b[39m\u001b[34m(self, name, cmd_param)\u001b[39m\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__Connection__command_inout\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, cmd_param=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m     39\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     40\u001b[39m \u001b[33;03m    command_inout( self, cmd_name, cmd_param=None, __GREEN_KWARGS__) -> any\u001b[39;00m\n\u001b[32m     41\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m     70\u001b[39m \u001b[33;03m        For commands with a DEV_STRING input argument, invalid data will now raise TypeError instead of SystemError.\u001b[39;00m\n\u001b[32m     71\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m     r = \u001b[43mConnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcommand_inout_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmd_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     73\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(r, DeviceData):\n\u001b[32m     74\u001b[39m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/connection.py:112\u001b[39m, in \u001b[36m__Connection__command_inout_raw\u001b[39m\u001b[34m(self, cmd_name, cmd_param)\u001b[39m\n\u001b[32m     86\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     87\u001b[39m \u001b[33;03mcommand_inout_raw( self, cmd_name, cmd_param=None) -> DeviceData\u001b[39;00m\n\u001b[32m     88\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    109\u001b[39m \u001b[33;03m    For commands with a DEV_STRING input argument, invalid data will now raise TypeError instead of SystemError.\u001b[39;00m\n\u001b[32m    110\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    111\u001b[39m param = _get_command_inout_param(\u001b[38;5;28mself\u001b[39m, cmd_name, cmd_param)\n\u001b[32m--> \u001b[39m\u001b[32m112\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__command_inout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[31mDevFailed\u001b[39m: DevFailed[\n    DevError[\n        desc = autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        origin = Traceback (most recent call last):\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\server.py\", line 1790, in wrapped_command_method\n                return get_worker().execute(cmd_method, *args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\green.py\", line 110, in execute\n                return fn(*args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\Microscope.py\", line 212, in acquire_images\n                unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, [0.0, 0.0, 1.0, 1.0])\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\ThermoMicroscope.py\", line 192, in _acquire_stem_image_advanced\n                adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope\\_acquisition.py\", line 107, in acquire_stem_images_advanced\n                call_response = self.__application_client._perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope_client.py\", line 242, in _perform_call\n                call_response = self.__endpoint.perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_core\\orc\\engines.py\", line 206, in perform_call\n                raise api_exception\n            autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        reason = PyDs_PythonError\n        severity = ERR\n    ],\n    DevError[\n        desc = Cannot execute command\n        origin = class CORBA::Any *__cdecl PyCmd::execute(class Tango::DeviceImpl *,const class CORBA::Any &) at (C:\\gitlab-runner\\builds\\ehTiiTbyF\\4\\tango-controls\\pytango\\ext\\server\\command.cpp:87)\n        reason = PyDs_UnexpectedFailure\n        severity = ERR\n    ],\n    DevError[\n        desc = Failed to execute command_inout on device asyncroscopy/microscope/default, command acquire_images\n        origin = virtual DeviceData Tango::Connection::command_inout(const std::string &, const DeviceData &) at (/Users/runner/miniforge3/conda-bld/cpptango_1758200193404/work/src/client/devapi_base.cpp:2029)\n        reason = API_CommandFailed\n        severity = ERR\n    ]\n]"
+     ]
+    }
+   ],
+   "source": [
+    "scan.dwell_time = 1e-6\n",
+    "scan.imsize = 512\n",
+    "scan.scan_region = [0, 0, 1, 1]\n",
+    "\n",
+    "# think we can delete this from the \n",
+    "scan.haadf = True\n",
+    "scan.bf = True\n",
+    "\n",
+    "keys = json.loads(microscope.acquire_images(detector_list = [\"HAADF\", \"BF\"]))\n",
+    "images = []\n",
+    "\n",
+    "for key in keys:\n",
+    "    node = client[key]\n",
+    "    img = np.asarray(node.read())\n",
+    "    metadata = dict(node.metadata)\n",
+    "    detector_name = metadata.get(\"detector\", key)\n",
+    "    images.append((detector_name, key, img, metadata))\n",
+    "    print(detector_name, key, img.shape, img.dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc3c1849",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, len(images), figsize=(6 * len(images), 5))\n",
+    "if len(images) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for ax, (detector_name, key, img, img_metadata) in zip(axes, images):\n",
+    "    im = ax.imshow(img, cmap=\"gray\")\n",
+    "    ax.set_title(str(detector_name).upper())\n",
+    "    ax.axis(\"off\")\n",
+    "    plt.colorbar(im, ax=ax, fraction=0.046)\n",
+    "\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01ce295e",
+   "metadata": {},
+   "source": [
+    "### Acquire a cropped rectangle scan\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e8cc27a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled key  : stem_image_HAADF_20260528T074825597771.tiff\n",
+      "Metadata   : {'ImageWidth': 512, 'ImageLength': 102, 'BitsPerSample': 16, 'Compression': 1, 'PhotometricInterpretation': 1, 'StripOffsets': [15750], 'RowsPerStrip': 102, 'StripByteCounts': [104448], 'PlanarConfiguration': 1, 'ResolutionUnit': 3, 'FEI_TITAN': '<?xml version=\\'1.0\\' encoding=\\'utf-8\\'?>\\n<Metadata xmlns:nil=\"http://schemas.fei.com/Metadata/v1/2013/07\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\">\\n  <Core>\\n    <Guid>fbb5dabc-1c93-4d89-a747-087cca7234de</Guid>\\n    <ApplicationSoftware>AutoScript TEM</ApplicationSoftware>\\n    <ApplicationSoftwareVersion>1.15.0.484</ApplicationSoftwareVersion>\\n  </Core>\\n  <Instrument>\\n    <ControlSoftwareVersion>3.21.1</ControlSoftwareVersion>\\n    <Manufacturer>FEI Company</Manufacturer>\\n    <InstrumentClass>Titan</InstrumentClass>\\n    <InstrumentModel>Spectra</InstrumentModel>\\n    <InstrumentID>4018</InstrumentID>\\n    <ComputerName>TITAN52340180</ComputerName>\\n  </Instrument>\\n  <Acquisition>\\n    <AcquisitionStartDatetime>2026-05-28T11:48:25Z</AcquisitionStartDatetime>\\n    <AcquisitionDatetime>2026-05-28T11:48:25.5877447Z</AcquisitionDatetime>\\n    <SourceType>XFEG</SourceType>\\n  </Acquisition>\\n  <Optics>\\n    <Apertures>\\n      <Aperture>\\n        <Number>1</Number>\\n        <Name>C1</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>0.002</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>2</Number>\\n        <Name>C2</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>7e-05</Diameter>\\n        <Index>1</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>3</Number>\\n        <Name>C3</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>0.002</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>4</Number>\\n        <Name>OBJ</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>None</Type>\\n        <Diameter>0</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>5</Number>\\n        <Name>SA</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>None</Type>\\n        <Diameter>0</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n    </Apertures>\\n    <GunLensSetting>785.380554</GunLensSetting>\\n    <ExtractorVoltage>3600.03662</ExtractorVoltage>\\n    <AccelerationVoltage>200000</AccelerationVoltage>\\n    <SpotIndex>7</SpotIndex>\\n    <C2LensIntensity>0.194291203</C2LensIntensity>\\n    <C3LensIntensity>0.35150091</C3LensIntensity>\\n    <C1LensIntensity>-0.451997455</C1LensIntensity>\\n    <ObjectiveLensIntensity>0.823973011</ObjectiveLensIntensity>\\n    <IntermediateLensIntensity>0.0603362652</IntermediateLensIntensity>\\n    <DiffractionLensIntensity>0.191390786</DiffractionLensIntensity>\\n    <Projector1LensIntensity>0.2809157</Projector1LensIntensity>\\n    <Projector2LensIntensity>0.910340794</Projector2LensIntensity>\\n    <LorentzLensIntensity>0</LorentzLensIntensity>\\n    <MiniCondenserLensIntensity>0.343435914</MiniCondenserLensIntensity>\\n    <BeamDiameter>0</BeamDiameter>\\n    <BeamConvergence>0.0300123314</BeamConvergence>\\n    <ScreenCurrent>0</ScreenCurrent>\\n    <LastMeasuredScreenCurrent>2.42726296e-10</LastMeasuredScreenCurrent>\\n    <IlluminationIntensityNormalized>0.194291203</IlluminationIntensityNormalized>\\n    <FullScanFieldOfView>\\n      <X>3.27940413e-08</X>\\n      <Y>3.27940413e-08</Y>\\n    </FullScanFieldOfView>\\n    <BeamTilt>\\n      <X>4.05897982e-18</X>\\n      <Y>-6.81691992e-18</Y>\\n    </BeamTilt>\\n    <ImageShift>\\n      <X>0</X>\\n      <Y>0</Y>\\n    </ImageShift>\\n    <BeamShift>\\n      <X>0</X>\\n      <Y>0</Y>\\n    </BeamShift>\\n    <SampleTiltCorrectionOn>false</SampleTiltCorrectionOn>\\n    <Focus>2.60934069e-05</Focus>\\n    <STEMFocus>2.60934069e-08</STEMFocus>\\n    <Defocus>2.60934069e-08</Defocus>\\n    <NominalMagnification>2560000</NominalMagnification>\\n    <HighMagnificationMode>None</HighMagnificationMode>\\n    <OperatingMode>STEM</OperatingMode>\\n    <TEMOperatingSubMode>None</TEMOperatingSubMode>\\n    <ProjectorMode>Diffraction</ProjectorMode>\\n    <EFTEMOn>false</EFTEMOn>\\n    <ObjectiveLensMode>HM</ObjectiveLensMode>\\n    <IlluminationMode>Probe</IlluminationMode>\\n    <ProbeMode>Nanoprobe</ProbeMode>\\n    <CameraLength>0.091</CameraLength>\\n  </Optics>\\n  <StageSettings>\\n    <StagePosition>\\n      <X>-4.0947825e-05</X>\\n      <Y>-2.652768e-05</Y>\\n      <Z>-8.998404e-05</Z>\\n      <Tilt>\\n        <Alpha>-3.136e-06</Alpha>\\n      </Tilt>\\n    </StagePosition>\\n    <HolderType>SingleTilt</HolderType>\\n  </StageSettings>\\n  <ScanSettings>\\n    <DwellTime>1e-06</DwellTime>\\n    <ScanSize>\\n      <Width>512</Width>\\n      <Height>512</Height>\\n    </ScanSize>\\n    <ScanArea>\\n      <X>0</X>\\n      <Y>204</Y>\\n      <Width>512</Width>\\n      <Height>102</Height>\\n    </ScanArea>\\n    <MainsLockOn>false</MainsLockOn>\\n    <LineTime>0.000779</LineTime>\\n    <LineIntegrationCount>1</LineIntegrationCount>\\n    <LineInterlacing>1</LineInterlacing>\\n    <FrameTime>0.081047</FrameTime>\\n    <ScanRotation>0</ScanRotation>\\n  </ScanSettings>\\n  <VacuumProperties>\\n    <VacuumMode>Ready</VacuumMode>\\n  </VacuumProperties>\\n  <Detectors>\\n    <ScanningDetector>\\n      <DetectorName>BF-S</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>25.935</Gain>\\n      <Offset>0</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0</Begin>\\n        <End>0.029320892</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <ScanningDetector>\\n      <DetectorName>DF-S</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>false</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>21.75261</Gain>\\n      <Offset>0</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0</Begin>\\n        <End>0</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <ScanningDetector>\\n      <DetectorName>HAADF</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>31.1406715</Gain>\\n      <Offset>-1.752</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0.0728785469</Begin>\\n        <End>0.2</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG21</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>0.785398163</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.36</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG22</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>2.35619449</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.17</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG23</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>3.92699082</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>24.35</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG24</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>5.49778714</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.46</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <ImagingDetector>\\n      <DetectorName>BM-Ceta</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Binning>\\n        <X>2</X>\\n        <Y>2</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>0</X>\\n        <Y>0</Y>\\n        <Width>2048</Width>\\n        <Height>2048</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.5</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"PostSpecimen\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n    <ImagingDetector>\\n      <DetectorName>EF-CCD</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Binning>\\n        <X>2</X>\\n        <Y>2</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>0</X>\\n        <Y>0</Y>\\n        <Width>2048</Width>\\n        <Height>2048</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.5</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"PostSpecimen\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n    <ImagingDetector>\\n      <DetectorName>Flucam</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Gain>0.7</Gain>\\n      <Binning>\\n        <X>1</X>\\n        <Y>1</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>256</X>\\n        <Y>256</Y>\\n        <Width>512</Width>\\n        <Height>512</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.0064</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"None\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n  </Detectors>\\n  <BinaryResult>\\n    <Detector>HAADF</Detector>\\n    <PixelSize>\\n      <X unit=\"m\" unitPrefixPower=\"1\">6.40508648e-11</X>\\n      <Y unit=\"m\" unitPrefixPower=\"1\">6.40508648e-11</Y>\\n    </PixelSize>\\n    <Offset>\\n      <X>-1.63970214e-08</X>\\n      <Y>-3.27940408e-09</Y>\\n    </Offset>\\n    <DetectorIndex>5</DetectorIndex>\\n  </BinaryResult>\\n  <CustomPropertyGroup>\\n    <CustomProperties scope=\"\">\\n      <CustomProperty name=\"Detectors[SuperX1].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C1].Name\" value=\"2000\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C2].Name\" value=\"70\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C3].Name\" value=\"2000\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[OBJ].Name\" value=\"None\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[SA].Name\" value=\"None\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"StemMagnification\" value=\"2550000\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Optics.MonoSpotSize\" value=\"&lt;=11\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[EF-CCD].CommercialName\" value=\"Continuum 1065\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[EF-CCD].ElectronCounted\" value=\"False\" rawdatatype=\"Boolean\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A11\" value=\"1\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A12\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A13\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A21\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A22\" value=\"1\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A23\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"IlluminationIntensity\" value=\"0\" type=\"double\" rawdatatype=\"Double\"/>\\n    </CustomProperties>\\n  </CustomPropertyGroup>\\n</Metadata>'}\n",
+      "Image shape: (102, 512)\n",
+      "Image dtype: uint16\n"
+     ]
+    }
+   ],
+   "source": [
+    "scan.Activate([\"haadf\"])\n",
+    "scan.dwell_time = 1e-6\n",
+    "scan.imsize = 512\n",
+    "scan.scan_region = [0, 0.4, 1, 0.2]\n",
+    "\n",
+    "key = microscope.acquire_scanned_image_advanced()\n",
+    "node = client[key]\n",
+    "cropped_image = np.asarray(node.read())\n",
+    "cropped_metadata = dict(node.metadata)\n",
+    "\n",
+    "print(\"Tiled key  :\", key)\n",
+    "print(\"Metadata   :\", cropped_metadata)\n",
+    "print(\"Image shape:\", cropped_image.shape)\n",
+    "print(\"Image dtype:\", cropped_image.dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6418befe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f72552e843f2413e9f92507fce6f4937",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 5))\n",
+    "im = ax.imshow(cropped_image, cmap=\"gray\")\n",
+    "ax.set_title(f\"Cropped scan: {key}\")\n",
+    "ax.axis(\"off\")\n",
+    "plt.colorbar(im, ax=ax, fraction=0.046)\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52350cf1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "description": "Acquire a full-frame image, a cropped rectangle scan, and images from multiple detectors.",
+  "kernelspec": {
+   "display_name": "pytango-tutorial",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  },
+  "title": "Image Acquisition"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/01_Aberrations.ipynb b/notebooks/01_Aberrations.ipynb
index 81ac43e..0472bdd 100644
--- a/notebooks/01_Aberrations.ipynb
+++ b/notebooks/01_Aberrations.ipynb
@@ -3,72 +3,221 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Aberrations\n\nAcquire corrector tableau data and explore aberration parameters with the existing GUI workflow.\n"
+   "source": [
+    "# Aberrations\n",
+    "\n",
+    "Acquire corrector tableau data and explore aberration parameters with the existing GUI workflow.\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Run the servers\n\nMake sure you are on the VPN and the AutoScript server is running. Then start the asyncroscopy Tango servers from the repository root:\n\n```bash\nuv run scripts/run_servers.py\n```\n"
+   "source": [
+    "### Run the servers\n",
+    "\n",
+    "Make sure you are on the VPN and the AutoScript server is running. Then start the asyncroscopy Tango servers from the repository root:\n",
+    "\n",
+    "```bash\n",
+    "uv run scripts/run_servers.py\n",
+    "```\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Imports\n"
+   "source": [
+    "### Imports\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": "import os\nimport json\nimport time\n\nimport tango\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tiled.client import from_uri\n\nimport pyTEMlib\nimport pyTEMlib.probe_tools as pt\nimport pyTEMlib.image_tools as it\nimport ipywidgets as widgets\n\n%matplotlib ipympl\n"
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import time\n",
+    "\n",
+    "import tango\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tiled.client import from_uri\n",
+    "\n",
+    "import pyTEMlib\n",
+    "import pyTEMlib.probe_tools as pt\n",
+    "import pyTEMlib.image_tools as it\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "%matplotlib ipympl\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Ping servers\n"
+   "source": [
+    "### Ping servers\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": "DB_HOST = \"10.46.217.241\"\nDB_PORT = 9094\n\nos.environ[\"TANGO_HOST\"] = f\"{DB_HOST}:{DB_PORT}\"\n\nserver_names = ['stage', 'scan', 'eds', 'camera', 'data', 'microscope', 'corrector']\n\nfor name in server_names:\n    device_name = f\"asyncroscopy/{name}/default\"\n    proxy = tango.DeviceProxy(device_name)\n    proxy.ping()\n    print(device_name, proxy.state())\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "asyncroscopy/stage/default ON\n",
+      "asyncroscopy/scan/default ON\n",
+      "asyncroscopy/eds/default ON\n",
+      "asyncroscopy/camera/default ON\n",
+      "asyncroscopy/data/default ON\n",
+      "asyncroscopy/microscope/default ON\n",
+      "asyncroscopy/corrector/default ON\n"
+     ]
+    }
+   ],
+   "source": [
+    "DB_HOST = \"10.46.217.241\"\n",
+    "DB_PORT = 9094\n",
+    "\n",
+    "os.environ[\"TANGO_HOST\"] = f\"{DB_HOST}:{DB_PORT}\"\n",
+    "\n",
+    "server_names = ['stage', 'scan', 'eds', 'camera', 'data', 'microscope', 'corrector']\n",
+    "\n",
+    "for name in server_names:\n",
+    "    device_name = f\"asyncroscopy/{name}/default\"\n",
+    "    proxy = tango.DeviceProxy(device_name)\n",
+    "    proxy.ping()\n",
+    "    print(device_name, proxy.state())\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Connect to devices\n"
+   "source": [
+    "### Connect to devices\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": "scan = tango.DeviceProxy(\"asyncroscopy/scan/default\")\nmicroscope = tango.DeviceProxy(\"asyncroscopy/microscope/default\")\ndata = tango.DeviceProxy(\"asyncroscopy/data/default\")\ncorrector_proxy = tango.DeviceProxy(\"asyncroscopy/corrector/default\")\n\n# Backward-compatible aliases used by the workflow cells below.\nmic_proxy = microscope\nmicroscope_proxy = microscope\n\nfor proxy in (scan, microscope, data, corrector_proxy):\n    proxy.set_timeout_millis(120_000)\n\nprint(\"scan      :\", scan.state())\nprint(\"microscope:\", microscope.state())\nprint(\"data      :\", data.state())\nprint(\"corrector :\", corrector_proxy.state())\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scan      : ON\n",
+      "microscope: ON\n",
+      "data      : ON\n",
+      "corrector : ON\n"
+     ]
+    }
+   ],
+   "source": [
+    "scan = tango.DeviceProxy(\"asyncroscopy/scan/default\")\n",
+    "microscope = tango.DeviceProxy(\"asyncroscopy/microscope/default\")\n",
+    "data = tango.DeviceProxy(\"asyncroscopy/data/default\")\n",
+    "corrector_proxy = tango.DeviceProxy(\"asyncroscopy/corrector/default\")\n",
+    "\n",
+    "# Backward-compatible aliases used by the workflow cells below.\n",
+    "mic_proxy = microscope\n",
+    "microscope_proxy = microscope\n",
+    "\n",
+    "for proxy in (scan, microscope, data, corrector_proxy):\n",
+    "    proxy.set_timeout_millis(120_000)\n",
+    "\n",
+    "print(\"scan      :\", scan.state())\n",
+    "print(\"microscope:\", microscope.state())\n",
+    "print(\"data      :\", data.state())\n",
+    "print(\"corrector :\", corrector_proxy.state())\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Start Tiled data server\n"
+   "source": [
+    "### Start Tiled data server\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": "TILED_HOST = \"10.46.217.241\"\nTILED_PORT = 9091\nsave_path = \"D:/microscopedata/tiled/ahoust17/2026_05_22_AtomFab/\"\n\ndata.host = TILED_HOST\ndata.port = TILED_PORT\ndata.save_path = save_path\n\nif str(data.tiled_server).lower() != \"yes\":\n    print(\"Tiled server is not responding; starting it from the DATA device...\")\n    config = json.loads(data.start_tiled_server())\nelse:\n    print(\"Tiled server is already running.\")\n    config = json.loads(data.get_config())\n\nprint(json.dumps(config, indent=2))\n\nclient = from_uri(config.get(\"uri\", f\"http://{TILED_HOST}:{TILED_PORT}\"))\nprint(\"Tiled keys:\", list(client))\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled server is not responding; starting it from the DATA device...\n",
+      "{\n",
+      "  \"host\": \"10.46.217.241\",\n",
+      "  \"port\": 9091,\n",
+      "  \"uri\": \"http://10.46.217.241:9091\",\n",
+      "  \"save_path\": \"D:/microscopedata/tiled/ahoust17/2026_05_22_AtomFab/\",\n",
+      "  \"tiled_server\": \"yes\",\n",
+      "  \"tiled_server_status\": \"running; watcher started\"\n",
+      "}\n",
+      "Tiled keys: ['stem_image_haadf_20260523T111209062547.tiff', 'stem_image_haadf_20260523T111220409985.tiff', 'stem_image_haadf_20260523T111229255509.tiff', 'stem_image_haadf_20260523T111239549217.tiff', 'stem_image_haadf_20260523T111244131107.tiff', 'stem_image_haadf_20260523T111249273402.tiff', 'stem_image_haadf_20260523T111251830895.tiff', 'stem_image_haadf_20260523T111306042795.tiff', 'stem_image_haadf_20260523T111308605451.tiff', 'stem_image_haadf_20260523T111313521046.tiff', 'stem_image_haadf_20260523T111316186473.tiff', 'stem_image_haadf_20260523T111318714622.tiff', 'stem_image_haadf_20260523T111322542492.tiff', 'stem_image_haadf_20260523T111328412921.tiff', 'stem_image_haadf_20260523T111333610334.tiff', 'stem_image_haadf_20260523T111349715191.tiff', 'stem_image_haadf_20260523T111354702397.tiff', 'stem_image_haadf_20260523T111356956051.tiff', 'stem_image_haadf_20260523T111401557673.tiff', 'stem_image_haadf_20260523T111403917517.tiff', 'stem_image_haadf_20260523T111415959027.tiff', 'stem_image_haadf_20260523T111418371372.tiff', 'stem_image_haadf_20260523T111423282634.tiff', 'stem_image_HAADF_20260525T185456760289.tiff']\n"
+     ]
+    }
+   ],
+   "source": [
+    "TILED_HOST = \"10.46.217.241\"\n",
+    "TILED_PORT = 9091\n",
+    "save_path = \"D:/microscopedata/tiled/ahoust17/2026_05_22_AtomFab/\"\n",
+    "\n",
+    "data.host = TILED_HOST\n",
+    "data.port = TILED_PORT\n",
+    "data.save_path = save_path\n",
+    "\n",
+    "if str(data.tiled_server).lower() != \"yes\":\n",
+    "    print(\"Tiled server is not responding; starting it from the DATA device...\")\n",
+    "    config = json.loads(data.start_tiled_server())\n",
+    "else:\n",
+    "    print(\"Tiled server is already running.\")\n",
+    "    config = json.loads(data.get_config())\n",
+    "\n",
+    "print(json.dumps(config, indent=2))\n",
+    "\n",
+    "client = from_uri(config.get(\"uri\", f\"http://{TILED_HOST}:{TILED_PORT}\"))\n",
+    "print(\"Tiled keys:\", list(client))\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Inspect the corrector device\n"
+   "source": [
+    "### Inspect the corrector device\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "6971fcba",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- corrector commands ---\n",
+      "  Init\n",
+      "  State\n",
+      "  Status\n",
+      "  acquire_tableau\n",
+      "  correct_aberration\n",
+      "  get_aberrations_coeff_sim\n",
+      "  get_info\n",
+      "  measure_c1a1\n",
+      "  reconnect\n",
+      "  set_aberrations_coeff_sim\n"
+     ]
+    }
+   ],
    "source": [
     "print('\\n--- corrector commands ---')\n",
     "for cmd in corrector_proxy.get_command_list():\n",
@@ -78,7 +227,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Acquire a corrector tableau\n"
+   "source": [
+    "### Acquire a corrector tableau\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -105,14 +256,56 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Aberration correction GUI\n"
+   "source": [
+    "### Aberration correction GUI\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "a8e3fd71",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bef1b1e5c3b940328e882bfb7becc9e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(BoundedIntText(value=5, description='Max outer:', layout=Layout(width='160px'), …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a926ac2721eb4c4a87cf4238f8bfc3cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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qeAOASiUA2Y0UYJSHDIu6F52d3S4PHd6+0j797mj7l89klQ47fdzzXxOl406N2lc8KaJUjLYv/jyKL39L1PzwK+3LOfaUqH3d0yK3ZVMUz39KlC59Tvu8mzdG2ye+2bmc8qSpWThS+7YXRG7zxihPPixavntV1D3rpPb7Dzs6av79PZH/xNv3b+sPGRa5h2ZG3Tc+FcXnvDJa//PXUfvPV0TNFz8Yba98exSvfF/k3/ea9uc69Kio+eibI/+Zf4m2N38oWr/5++w15OfPjdb3fT6KL3tz1Hzl/+1fO4A+V3ztyVH4W/uP1U5D66Pueb+O8oiGaLnxFVH41cNZeNH2pUuj8NMHo/DLh6J09Mho/cOLI343d4dllo4aGW0fPC/qnvWr9se97bRo/dzFUffqP2b3l08ZFzXP+FXkH12XBRut33tW1L7oN5GfuzaKVxwXMXJAZ3VI29eeEjXv+lvkH1kX5YaaaP3tCyJ3z/LI37fiiRdQirrL/3f/V8AuXmv2/JMGR22qjBlcFy23vDLyw2ZH7Qv/L2LcoKwLscKvHorchpb9f26g1333fVviolPau5x6zge39oOfFEsR5791cHb9p/9vc9z5cCGe/f5BMXF0KWZ8c1PMWVC3w/JSF1M//FBjPOXdg+LBxwrx0ie3xM8/uiWmv7Z9OcdMKcU7vzIg3vQfNVFbU46Hfrwxrvz8wLj27pq49LTWeNXTtlZSfOd9jfG5n9XHTffVZFUe//epLfH8C1vjNzfWZvePGlqOC96a2rx/VSQp3EjtrK2JmPm9jXHWcbUxY1b7T4LjDy/FU989KFJB3szvbYrhQ5rjGe8dFHW1EQ/9ZGP84M91WeUHUFmOG1EXzz10cBYg7K1UrZHCj2TGiqY4YSfjaaSKibS/SOFHcuvypljRWIyTR9XH4s1t0VIsx8/mtQccQ2rz2fQfzd2Q3X5wbUvcumxrl4LPnjo4zho7IN5x4ojs9oBCPorbVAf//JENPRJ+JKn9KfxI7lnVHM2lcmxqbX+yVDly5ND2/S8AUJkEILuRn3N/tB12VLS+/z8if9ctO1Rn7ErpzCdF4Y8/i1xr+wGx/G9/FMUXvT7iiQAk//ffZuFHUj7josj/7bdZwJEUfv2DbByNzgDmkMOj9dt/3mbhpSiPn9x+ffH8yN99S+y3psYoXP+n7Gpu1j0RWzZn1SVZGx+8O9qe/uLOWXOLHo38w/d2zptb+GgWfnTMW7r4WfvfDqBPtb31tCgfOixqrri+y/T87+Zkf3NrmyK3cEOUDxkasbklyseNjvz/PNw+z+w1kbtz59Vf5XMnRf6GxzuDhMKPHojiO87IAo1suXcty8KPpDR9XOQeXpWFH9lyf/lQxL9d2L6cI4ZHOYUpX3vq1mUProvytBERTwQgKYQ4EDt7rR3tzv/5kfbuuTY0Z/cVrmnvNizS/Wsaozx5aORmtZ/ZCFSG1322vXLiFU9piU+9oSmeu00I8sOrtgYcF08vxgf+uz2MXbIqH3+6bedfnc84thgPPprPwo/kF9fUZVUfk0a3H0B7bGk+CzSSo6eUoq0YWfiR/P2u2nh0SfuwfAMbynHxqW0xdsTWbmYGD4g46pDUzWn7Abgf/zW1b/+70Prf62uzrrOKLRH3PlLIApEZs9rv++MtNdHc2r7sBx7Lx9/+URNtxVzW3ocfz8eRk0sCEKhA540fEFOH1MaDLz4su50GPP+v88fF+IGr49sPrd/pY1JXUx3S1W16q9qt8jadRW1pK+2266ht502Lf+nfl8S8DTvvWm9T6gvrAOS2aX9t9l20vIvXWo7m7V/7E99dAYDKJADZjdzix6PuhWdH6YwLo3zWk6LlnR+Puiv2Y3DbbftWSLbsrnx223lzkZ9xXdR+6A07zFUaOzFyu13OXngioGlfYDGipbnr7cI2Z/g1b3fftreL280LVIy2K0+J0tMOj9qX/y5yTdsNwNu8zbhCu/vlu/0+bpe6zpfbvJu+47ssMxexrinqnvHLXc+/5QD7od/da93+vua2rre7a1RPoNf95Oq6+Nq7GmPk0FKs2dAeQmxq3PVneq93d9vZ1HW84V0ut+OZL3zb4M4gYsdlHdg+p6kl16XaJXXDtav7tm1D+rq37bxA5Ughx7ZBRxpT42sPrI0/PN7+e3JDaykbl2NfzVnfEikbSGN8XLN4S5w9tiEbTD1VTYxuKOwwMPnMVc3xiiOHZlUgxw6vi3PHDYifP1EhktrynpNHxltvXp59vRpel4+RDYV4dBeByL46c2wKs9dFQyEXF04YGH9e0H5CIgBQ/fb9W85BpDx2YvaLtHDjVVH40oezX6XlcZOzrqrKg4fu8nH5O66P4jNfGuWa2mxw8tJzXxn526/d6by5O67PusAqDxycHRosPu81W5dz2zVZNUnpyOM7p5WOP7WbXyVwsGp7/clRunxa1L7893vdhVNuU2vkHlwVpRccnd0uTRsZ5dMn7HzeWxdH6aKpUR7bfqZ18eUnRP7WRTsd7DyfxiA5ZnQ2vki23JccG1Hf/sM59+jayG1qieKLjumcvzx1WJSHGWwc2DfDBpVjwqitZxGnQbxXb8jFmg07DxVSlcarn96+fxw/shTPPGe7oPgJd8wqZN1HHXdoe2D6ootbsoqRxat2XO7sBfksSOjoguuSU9viiEntbdrclMvG3vjXK7aeaJLaO2n0gZ35DLA7X39wbXz9gnEx43lT4qSddHW1K6ko4yV/X5INJP6P50+Nz589Nl52zZJsgPGded0NS+O1xwyLu54/NT52+ui4ednWdPhfb1+Rjblxx/OmZsu66hmT49DB3df1VOqC66bLp8Tfn3VI/HXR5njFtKFxyijfJQHgYNDvK0COPfbIGDZsSKxfvyEeeuiRHe4fPHhQHHXUYZHP52LFitWxYMGSfX6O1g9+qX0g8FHjsnEwYsumqH/uqVE68rgovu2j7fWyhUIU/vTLyM97MAs18o8+FC2/vDWrEtl+EPT8b36QjdfR+tMbstu5u26Ows++sdPnLtzytygff1r7vNsNgp5b9FjUfuj10fahL0U0DIyorY3c7Psiv5OKEKC69PS+rzx+UBQ/cn7E4+uj9RfPbZ/YUoy65+55HI2ad/892v7jyVF8wymRe2x95O7Y+XPn56yJmk/fEq0/evbWQdDfd91O582taYqaf7022r719IiWJwZBX9P+ozhXLEftP/0p2j56fhRfd3JEIZ/dV/uOv0Ws7zqQJ1DZenrflwKQn350Sza4eMpiV63LxfM/tOvxNN7zXw3ZIOj3fG9jFmhcP3PnX51Xrc/Haz49IL77/i2dg6C/7OMDd7rc1rZcvOITA7NB0NPuLA2Cfu+8reckpeV87s1Ncdd3NmaVISkUeduXBsRiPe1B1eqN37zbesqfFnW5/f3ZG7JLh47B0Dv896z2Lkt35u5VzXHxH3YcW+TxTW0x/sddX8uc9a3xpJ3Mm6TQ5J9vW7FX7d0ff1+0Of7zwa2v4yNPDMw+c7tB4T9wR9edbRrgHQCobLn6+mE9NJRY90hfBAuFfIwbN3qnXwZPOeW4mDPnsdiypTFOPvnYmDt3fnadrlo/9vXIzb4/an6+8yCmuzXftS7qLpoauU0771MW2D37vp5RfOExUXrKYVF75VU9svy2d52RDahe8/Gbe2T5UO3s+7rPhSe3xeff0hhnvXFI9IRXPrUlq6B58f/rOog8sO/s+7rP5EE1cePlU2JlU1u84YblWZjz7QvHZd1ybRuA7I+3nzA8Xnv0sFi6pS2ecdXibmszAHCQd4G1fv3GKKZOiHeirq42K87oCDxWrlwTI0e2d59CV7lNG6L0otdF6we+2KOrJg3g3vKzmyJWLY8o664B9pd9Xw9paovScaOj5Q8v6vZFt37lsig+7+iITXvXnRiwI/u+7tPSGjFqaDlmfHNjjBnevd/JPn1lY9ZNV6p0AQ6cfV/3WbS5LQ7/+aNx1v8t6KxkecONyw84/Ei+9sC6mP7rx4UfAFBh+n0XWLuTApDm5q2DorW0tGRnz+xKLpfLyoa7ykepVP0H6mu/8MGIdElyPZd7Fe6dEYWXX7R1Qg8+F9UnfUZLpW0GnGan7Pv2X82fHs0uPbF/qnvXNVtv2PexD+z79vIz5nvfPpnxUF1Me1ndNu+z7vtYfujbg+JD3+7+5XJwse/bO/Z9AECly/Xx8b6KDkD21SGHTIipUyd13m5tLcVjj23s0zYBXa1c+YgQpJvZ90H/Z9/X/ez7oP+z7+t+9n0AQH+0sg+P91V0ANLS0hr19bWdt+vq6rJpu7Jw4dJYvHhZl/Rp1KgjYs2a1H9nvx4KBQ4CuRg5clKU04iv7JZ9H1QT+769Zd8H1cS+b2/Z9wEAlS3X58f7Kj4ASetu4MAB2TggY8aMzAZB35W0oovFrSs719lFSZrmoCtQGez7gIORfR9wMLLvAwCo8gDkhBOOisGDB0Y+n48zzzw5HnpoXkyZMjELOtKXwUceeTyOOeaIbGyPFStWdw6IDlDJ7PuAg5F9H3Awsu8DAOg5ufr6YQdt6UOqABk79shYs2aRChAqRC7y+UJUuvY+/8o7KYmbHCtWzItyudRHLTs42PdR+SpzX2jf17fs+6h89n3sx7vGb14AqDCV+Z2vP//m7fcVIEC7QqEuBg4cno1dU+lSd3RbtqyLYrGlr5sCVJhK3hfa9wH7y74PAKD6VfJ3vv78m1cAAhUhl+0A0w6kuXlzRQ8Unnbi9fWDstezceNK1VfAQbEvtO8DDmAPYt8HAFD1Kvc7X3//zSsAgQqQSt/SjiTtAIvF1qh06XUMGlSfva5Sqa2vmwNUiErfF9r3AfvDvg8AoPpV+ne+/vybN9/XDQD2XqWlv9X+OoC+Uan7kEptN9A/VOo+pFLbDQDQFyr5u1O5n7ZdBQiwR83v/LfIL3gkav/vR9ntLT+9Lmr+8uuo+/F/tt/+5u+i5uaro/WKN0Vu1bIoDxgY+flzo+6bn43CvIesYaBqbHr4NZGfs7b9Rlsp6j90SxQeWhNt502MlveeHlGTj9jcGvUfvjUKHfMBVIGJo4vx+TdviBMOa4t1m3Lx+PJCfPP3g7Jp6bduWzHivd8YGjMequvrpgIAcABedsTA+No5I+OIXy2OdS1bQ42nTW6I/3nymDjzd8vioXWVU6WiAgTY847igTujeMKp2fXywMERrS1RPOG09tuFmigdOi1yG9ZFzVX/EwNfdVkMetF5UfvHX0bTV38VpZGjrWGgemxoiYGX/y671H3zvmh52ynZ5Nyapmh43d9i4DN/G3VfvSeaP3ZOX7cUoBuV4xcfXRt/vaM+Tn7tmLjoHaPjv387KNZvysX5bxsV5711dLz5i8PiS2/fYK0DAFS45x86MO5e3RLPnjKwc1p9PuKtxw2Jf6xsjkojAIEqlEKHxi/+NDb99cHs74GGEIX77+oMPFIQUrj1miiPGNX+XEcdn1V7RFvX5LfmhquicNu10XbZ8w7ouQEORGlUQzR+97LY9I+XZX/T7e5SHlwXuQ0t2fVUBZJf1dh+/b5VUR6/9YsiQG8bM7wYv/7Empj/q+XZ33T7QDxpektsbszFj/66dd9264N18cBjtVEq5bLbA+vLWSUIAAC9Y0xDPn795NEx/yUTs7/p9oEaUZePI4fWxEfuXBfPP3RA5/R3nTA0vjN7UzQVK+8Lny6woAJtumZuRG3trmeoqYnI5SNyuSiec3Fs+ePMiLbdDD7U2hqDnzxtl3fnVyyJaBgQ5aEjonT8aVF44K4oT5oapYlTsmCkcP8/dv642fdH6dAj9+m1AeytTTNfGVG3hy94qUuqdGwu7Q8vnBxbbrsi67pqp1pKMfiUH+9+eUPrYsvvnxPlATVRHl4fA1/8xx1maX3BtCjctNiGBHrEkt8sj7qa3f/wTF8F87ls1xeXnd4Sc3+2crdfBVvacjHx+eN2ef8xU9pi5rydf/d80vTm+I83b4gxI0rxwo+M2PsXAgDALi152aSoS1/o9vBzN/0iToOnXzapIea+eOIuf+4mLaVyTPzZ7n+rXj51QPx5YVPcvrIljhhaG6Pq8zGoJhdnjKmLz963Id54zOCoNCpAoBo9EX60X0+/fg/8o55/4O6s+qN4/PQoPHB35B+8O4onnh6lLAC5axft2P2OGqDHPRF+bN0fdk8XWIMu+3U0fOTWaP7YuV3uLp48JlpfenTUf+nuA3wigP3XEX4k6e8efjsfkOvvqY/TrxyThR8feuWmnnsiAAC66Ag/4om/+W7q/uo387dk1/+4oDELRD51+vD46N3rK3btqwCBCrS7ao0kdXtVPPPC9tP/2tqicMeNMeDdLz+g50xVHynwKA8bEbkNa6Pw4N3R9rQXRPG46VH3lY9G8ZxLdnhM1j3W7AcO6HkBdmWP1Rppf/jdy6J43qT2U2PaSlG4ZXEMeN3fumWlFq5dGE2fv7Dzdmny4Gj6jwuj4S3XRG5d5fWLClSG3VVqdEjdXl1yakvUFNoHJ7/27rp4wUdG7vdzzl5QE5ef17Tbef7xcF1MGl2MUUNLsXqD8+wAAA7Enio1ktTt1SUTG6Imn4u2UjmuXdIUL7hm1X4/5+iGfJwzti5+cFF7t/epAmXO+taYMrgmfnlJe/f64wYU4reXjY7Lr14Zs9fvpsS4H/HNFKpQ/SffmYUesW519jfdPlCF+++Mtqc8L/KL5me3U7DRdsZF2WmF+TU77lzbLnhqFM++OGr+9n8H/NwA+6v+vTdloUesacr+ptvdpXjq2Mgv3JhdLw+ti6b/vjTqP3ZbFOaus8GAPvWmLwzLQo/V63PZ33T7QFx3T10MGViOl1/WfjZgcs7xLXH8Ya2Rz7d3x3Xs1NYYPLAcqzeoAAYA6A1vumVNFnqsbipmf9PtA/GcqQPiu3M2xwm/XppdjvqfJVn4cemfl3dOS4OgP/dvqyom/EhUgEAVSoHEgVZ87LDM2fdHecz4yD/Q3t1Vrq01cpvWdwYiSdvTXxTFMy6IcsOAyD8+Lxre8ZLIr13dre0A2Bf51U3dVvGx7RggWZ8yxVLUf+iWbHLrK46N0uQh0fK+MyIbFr2lFANf+AcbC+gTK9cVDqjiY0e5uOLfRsTn37wh3nvF5mhuzcWs+TVx77ya+P7710drW0RzSy5e9+/Dn+h7EACAnrayqXRAFR/be8GhA+NTMzd0mXbVwsZ47qED41sPV25Xp7n6+mGVN3R7N8nl8jF27JGxZs2idO5mXzcHdimfr4nBg0fFpk2ro1SqnIR1315PLkaOnBwrVsyLcnk3IzZxwOz7qFSVvi+07+tb9n1UKvs+DoR9HwBUhkr/zteff/PqAgsAAAAAAKg6AhAAAAAAAKDqCEAAAAAAAICqIwABAAAAAACqjgAEAAAAAACoOgIQAAAAAACg6tT0dQOAylAaMjian3pxlMaNiVxjU+TWbYi62++M5ssuitKEcVH39xuj7s6Zfd1MgB530lMb45nvWR9fet7YaNqUjxMua4xzr9gc5XLElnX5+P2/D4uNKwu2BFBV3nj6pnjqtKYolSNaihH/+pfh8YlL18fgunLU5Mvx17kD4pt3Du7rZgIAcACuqZkYj0Zr1EYumqMcvy9ujj+Vt3SZ55xcQ3ymZlT8U+vyeCza+v36FoAAe1SOiKYXPydq77o3av/3D9m04iGTImoKUf+3G6LtqCOsReCgcdzFjbHk4do4+oKmuPeqgbF2cSF++I6R0bwpH9OftSUufv3G+P1nhvd1MwG6zcnjW+KMSS3x4l+OirZSLsYOKkZjWy7e/scRsbk1H4VcOX7w/DVxw/z6eHhVrTUPAFChNkUp3tC2Mrs+NgrxyZqRkStG/PGJEKQuIl6YHxSzSi1RKXSBBVWoNGhgNL70ebHp3W/O/qbbB6J42JSIltaonflA57TCwsVRWLA4CkuWRZRK3dBqgO43aEQxXvrZNfHu3y3P/qbbB6JhSClGHlKMa745JI67uCmbtnhWXRZ+JCkYGTLaPhHoWyMHFOO/nrUmbnjt8uxvun0gRg8sxdqmXBZ+JCs2F2Jjcz4LP5KafLqUs5NmAADoHSMiH58tjIrf1YzP/qbb3WlFFOPrxfXx3MKgzmkvzQ+J35c2R0sFffNTAQIVaNO/vi2isJudWj4fkctll+IRh8aWd165+5CiWIrBn//PXd5dGj0qCsuWH2CrAbrXv/55eRRqdv+lK1/TuTuMI85siXf+emWUdlGhW2zLxeefMW63yzvmwqaYc3N9LHqgNkZOLsaAYaVoXL91f3zy0xrj0TvTOTEAPePWNyyP2sLu930pkEhRRdr3nTelJa75p5XRtpuvgq3FXJz77V3v/25bWBdvPnNT/OaKlXH7wvr44+wBMWtle6XHD5+/OqaNaotfPTAwZqv+AADoFn+umRA12Te63R/Yb//Ol4szIx+/rhm/2w6p2qIcz2hbuk/tmFtujUOi/Xvf+CjEcbm6+FFpYzwvKqfrUxUgUI06jvZtfx3gINPdu8NU9THr+obsa+bsm+rj2Avbq0CSo85viknHt8Yd/7v17BiAvtARfnTu+w5weVta8/GSX46Kz900NJrbcvHNy9fE2ZObs/te/ZtRcekPxsTRo1vjyJGtB954AAD2Skf4kV3P5Q74O9/ObLvMNxeGxbeK66PSqACBCrS7ao0kdXtVPHxqeyVIqRSFRx+PAb/4v/1+vvyq1dFyzJH7/XiAnrCnao0kdXt1+GktWSVIqvx49K66+MX7Ru7X8w0cXoxDTmyJ5390XXa7UBOxekFN3P2HgTHh6Na45MqN8ZN/HhnFVqEz0HN2V6nRIXV7dfYhLVklSKr8uH1hXbz1j/u37+tQLOfi9kX12WVtUz4uPqw5u94RkMxYVBfnTmmJeWuMAQIAcKD2plIjdXt1WtRHTS4XbeVy3FVujvcVV3fryj8yVxsLov0kl2m52vh0zajs+sgoxOdqRsd72lbF4/18IHQVIFCF6v/w1yz0iC2N2d90+0AUHlsQUVcXrScd1zmteMjEKI5p3+kB9Fd/+PdhWeixZX0u+5tu769jLmyOu38/MP7zpWOzy1deODaGjS/GpONa4rkfXhe/+djw2LS60K3tB9gfH7lmWBZ6rG3MZX/T7QMxdXhbTB7a8cO2nFV6rGnKx4iG9n61avPlLPyYv9Y+EACgt/x7cW0WeqwvF7O/6XZ3GhOFeHN+WPxfcXN2+2Vty+OlT1xmlVvivRUQfiQqQKAK5TdvOaCKj+2lc5kb/uf30fzUi6PlgrMj19YW+RWro+7q62LzO94Q5fq6iFI5Ws89IwZ99dvd9rwAB2rz2sJ+V3xs7/hLGuOG7w/pMm3urfXx6q+tiaZNubj8g+2lwOuWFuJ/PzKiW54TYH+saSwccMXHtgbWluMDF26IwXXtY4/MWlETv394QPzXs9dEbT4in4u4el5D3Ph46iIQAIDesDZK3V7xMTjy8Z2aMdn4I81Rjt8XN8efy1uikglAgL2S37AxBvzP73eYLvAADhY/fteOVW9X/+fQ7AJQzR5aWRuv+vWO+8CX/c/oPmkPAAA948ltS/ZqvncVV1XMJtAFFgAAAAAAUHUEIAAAAAAAQNURgAAAAAAAAFVHAAIAVJRcLheVqFLbDQAAAJXKIOgAQEUolYpRLpejvn5QNDdvzq5XUviR2p3anF4HAAAA0PMEIABAhSjHli3rYuDA4TFoUH1UmhR+pPan1wEAAAD0PAEIsNcaJl0SQ054e6y65hVRbtscNcOOiiHHvzlqhhwW6+/+VLSs/Ie1CfSoYrElNm5cGfl8oeLWdHvlh/ADqEwve+Hw+Nq/T4wjTns41q0vxQuePSze87YxUSqVY/OWUrztvYtjziMtfd1MAAAOwNrPfSlmLVuaXW8tFuPt//OLuH/J4s77T5gwMW7653+NK77/nfjLQw9WxLoWgAB7rX78hdG6fm7Ujz83mhb9LUrNq2Pj/V+NAYc9z1oEelHqRqrNGgfoRc9/1rC4+77GePZTh8aPf7Uu/nrtxvj1H9Zn9z390iHx8Q+Mj5e+foFtAgBQwdY3NcZ5X/xcdv3yE0+O91/2tHj5D7/bef/HnvnsuHbO7KgkBkGHKpSrGx7DTvtYjH7yz7K/6fYBL7N2cBQGTYzND38/6sdfkE0rNa2Oto2PRZRL3dBqAAC6w5jRhfj1D6fG/HuPyf6m2wdixLBCHHl4XXzk08vj+c8elk3btHnr978BA3JRQcMyAQBUhTGDh8SvX/+mmP/xz2R/0+3uNLShIQtEOlxx2hlx47y5sXLTxqgkKkCgAo2+7FeRy9fueoZc+pGbywbdrRtzWoy+5EcR5V0Pulsutcaqv714t89ZP+7caFkxI1rXPRQ1gyZGrnZolFs3HMjLAABgHy2ZdWzU1eZ2O09NTS7y+ci+C172pMEx9x/HRFvbrhOKltZyTDzuoV3ef/nTh8afr94Yt9+5JY44rD5GjSjE6rXFuOIFw+O97xgTAxry8YwXP2ZbAgB0kyWf+lzU1ez+0H1NPh/5XPvxv8uOOTbmfvQT0Vba9UnKLW1tMfFD793tMoc1DIhb3v3eGFhXFyMHDopLv/alzjDk1WedE8/67/+M/3zxFVFJVIBAVWrf+WXXsr+7/5G8NxomXBBNS2/Krjcvvy3rBgsAgP6nI/xI0t90+0Ckqo/f/LG9u6s//mVDXP6Modn1n/96XUy/aG6896NL433vHHPgDQcAYK91hB+d3/meuN4dXWBN//dPxjv+95fxhee/KJv+gac8Pb503d93G7D0VypAoALtqVojdXtVN3p65PKFKJeK0bLqnlh/18f2+/lydcOidsRxMeyU97VPyNdEzeZF0bTwL/u9TAAA9t3uKjU6pG6vLrlgcFYJkio/rr1pU7zg1Y/v1+oePaoQ55wxMH7wX4dkt+vqcjFnXnN8/6drO+f5/V82xFc+M3G/lg8AwI72VKmRpG6vLjnq6KgpFKKtWMzG5njBd/6721bnX2Y9EN+64hXZ9emTD4lnn3hSfCEiRg0anFWcvOFnP4lr5zzc7zefAASq0Ib7vxxDT3xX1A6fFq3r5ma3D0TD+HOjccFVsenh73ROG3XRdyJfNzxKLeu6ocUAAHSXN/3LovjvL0yO004eEHfd25jd3l/Pefqw+O5P1sQHPr6sc9p9Nx8V5545MG69Y0t2++ILBsWCxa3d0nYAAPbOm37x0/jvl748TpsyJe5asCC73Z3OOvSwmL96VXb9aV//auf09Jy/vXdmRYQfiQAEqlC5Zd0BVXxsr378hbF5XtedaPOKO6J+wgUx8PAXRL5mcNSPPSOKm5fG2tvf023PCwDAvlu5qrjfFR/be8Gzh8Wnvri8y7Sr/r4hfv7tKbF8ZVu0tpZj3fpivPHd+x+yAACw79Jg5N1Z8bHtGCCpS622YinrBqvSCUCAPVp3xwd2mLbpoW9lfxsf/4M1CABQpZ7xkh0HN3/fx5ZlFwAAqsuI9/7zHufp7kqTnmYQdAAAAAAAoOoIQAAAAAAAgKojAAEAAAAAAKqOAAQqSBqAqBpUy+sA4P+3d+9fUV153oC/VSIlN1EUL4moqElMXHaP06vn//9xeqYn6W7baTNekggqooLKRblU1bv2yQsBQxINBVW19/OsxQqHOhabA/Wpk/rUPhs4LP16/tSv4wYA6IZ+Pneq9ejYLYIOfaDVaka73Y5GYyTW1laqz/s5DNPPkX6G9HMBAJDneaDzPgCA/M/5ev28TwECfaEdq6uvYnj4RIyMNKLfpTBMP0/6uQAAyPc80HkfAED+53y9fN6nAIE+0Wyux9LS86jXj0S/+7EJ7q0wBADoVf18Hui8DwAg/3O+Xj7v64sCZGJiPKanp6qpNDMzT+PZsxe7bp+cnIipqfPV56urb+Pbb7/ru2lC8GHSNLJNB6sQsg8okeyDX+I8MGeyDwD4kXO+IhdBn56+GLdvfxtff30nLlw4FwMDu1uwVI784x93q9uT06dPdmmkAJ0j+4ASyT6gRLIPAKDQAmRsbLSa1bG+vhGtVisWF1/HyZPju/ZJC8xvTQ1K/037AvQz2QeUSPYBJZJ9AAAFXwKr0Tga6+vr29traxsxODi4a5/79x/Fn/50I1qtdrx69SZev17a877SJbTq9dqubYBeJPuAEsk+oESyDwCg4ALkt6QS4/z5yeryV2tr6/HFF1dicvJUPH/+8mf7pnVCLl36dHu72WzHgwdvDnnEAPsn+4ASyT6gRLIPACDjAuT9GR/p3TFLSyvb2yMjw9WC56n8SF6+XIzx8eN7FiBpAfXHj+d2nUieOnX1wH8GgI8l+4ASyT6gRLIPAKDgNUCWlpZjZGQoBgePRr1er9b/SOuAbEmXx0olyNbC6CdOHI+3b9/teV+pKGk2Wzs+2of2cwB8DNkHlEj2ASWSfQAABc8ASR4+nImbN69Xi53Pzs7F5mYzbtz4LO7d+75a8DzN7PjjH7+sCo6Vlbfx9Ol8t4cMsG+yDyiR7ANKJPsAAA5GrdEYL3YaRK1WjzNnrsXCwmyaH9Lt4UDhajExcSHm5+9Hu93q9mCyJvugl8i+QzvSzvugh8i+QzvSsg8AKPy8r+cvgQUAAAAAAPCxFCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2FCAAAAAAAEB2BqIPTEyMx/T0VNRqtZiZeRrPnr3YdfvAwJH4/PPpGBo6Vm3fuXMv3r1b69JoATpD9gElkn1AiWQfAEDBBcj09MW4fftubG4249atr+Lly8Xq8y1Xr16K588Xqo963aQWIA+yDyiR7ANKJPsAAA5Gz7cFY2Ojsbr6NtbXN6LVasXi4us4eXJ8+/YjR47E6OhwVX4kaZ/0AdDPZB9QItkHlEj2AQAUPAOk0Tga6+vr29traxsxODi4vX3s2GBsbGzGF19cieHhY/H69VI8fDiz532lS2jV67Vd2wC9SPYBJZJ9QIlkHwBAwQXIb0klxtjYSDx48EOsrLytipBz507H3NzudUKSqanzcenSp9vbzWY7Hjx4c8gjBtg/2QeUSPYBJZJ9AAAZFyDvz/hI745ZWlrZdXta8DyVH0laH2R8fGzP+0oLqD9+PLfrRPLUqasHOn6A30P2ASWSfUCJZB8AQMFrgCwtLcfIyFAMDh6tFjhP63+kdUC2bGxsVJfAajR+LElS+bG6+m7P+2q329FstnZ8tA/t5wD4GLIPKJHsA0ok+wAACp4BkqQ1PW7evB5pyY7Z2bnY3GzGjRufxb1731eLoz98+Ci++upaNaNjeXk15uaed3vIAPsm+4ASyT6gRLIPAOBg1BqN8WKnQdRq9Thz5losLMym+SHdHg4UrhYTExdifv5+tNutbg8ma7IPeonsO7Qj7bwPeojsO7QjLfsAgMLP+3r+ElgAAAAAAAAfSwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkRwECAAAAAABkZyD6wMTEeExPT0WtVouZmafx7NmLPff74x+/jPX19fjXvx4c+hgBOk32ASWSfUCJZB8AQMEFyPT0xbh9+25sbjbj1q2v4uXLxerznc6dOx1ra+tRq3VtmAAdJfuAEsk+oESyDwCg0EtgjY2Nxurq21hf34hWqxWLi6/j5MnxXfsMDByJyclTMTc337VxAnSS7ANKJPuAEsk+AICCZ4A0Gkery1ptWVvbiMHBwV37XL58IR49evKb95UuoVWv13ZtA/Qi2QeUSPYBJZJ9AAAFFyC/ZWRkuJoB8vr1UoyPj/3qvlNT5+PSpU+3t5vNdjx48OYQRgnQWbIPKJHsA0ok+wAAMi5A3p/xkd4ds7S0sr19/PhIHD8+Fn/+8x+iXq/HkSP1uHbtUty//8PP7istoP748dyuGSCnTl09hJ8C4OPIPqBEsg8okewDACi4AFlaWo6RkaEYHDxaLXye1v/Yebmrp0+fVx9JmgHyySdn9iw/kna7Xc362FKr9fwSKEChZB9QItkHlEj2AQAUXIAkDx/OxM2b1yMt2TE7O1cVITdufBb37n1fLY4OkCPZB5RI9gElkn0AAAej1miM/zQlojBpBsiZM9diYWE2zQ/p9nCgcLWYmLgQ8/P3o91udXswWZN90Etk36Edaed90ENk36EdadkHABR+3ucaUAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYUIAAAAAAAQHYGog9MTIzH9PRU1Gq1mJl5Gs+evdi+rV6vx5dfXo1jxxrRbkfMzc3HkyfzXR0vQCfIPqBEsg8okewDACi4AJmevhi3b9+Nzc1m3Lr1Vbx8uVh9vmV2di5ev16qypB0+8LC63j3bq2rYwbYL9kHlEj2ASWSfQAAhV4Ca2xsNFZX38b6+ka0Wq1YXHwdJ0+Ob9+evpbKj63P3759F4ODR7s4YoD9k31AiWQfUCLZBwBQ8AyQRuNorK+vb2+vrW3E4ODgnvumr4+MDMfy8uqet6dLaNXrtV3bAL1I9gElkn1AiWQfAEDBBciHSmVGWgvku+9mqpkge5maOh+XLn26vd1stuPBgzeHOEqAzpJ9QIlkH1Ai2QcAkGEB8v6Mj/TumKWllZ/t98UXV2Jh4VW8eLH4i/eVFlB//Hhu1wnkqVNXD2DUAPsj+4ASyT6gRLIPAKDgNUCWlpZjZGSoWtcjLXKe1v9I64DsdPnyhWg2m1XB8Wva7XY0m60dH+0DHj3A7yP7gBLJPqBEsg8AoOAZIMnDhzNx8+b1SEt2zM7OxeZmM27c+Czu3ft++9JWKytv49atG9V2ugzWq1cubQX0N9kHlEj2ASWSfQAAB6PWaIwXOw2iVqvHmTPXYmFhNs0P6fZwoHC1mJi4EPPz96Pd3nsdHzp0pGUf9BDZd2hHWvZBD5F9h3akZR8AUPh5X89fAgsAAAAAAOBjKUAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsKEAAAAAAAIDsDEQfmJgYj+npqajVajEz8zSePXux6/bR0ZH4/PPpqNdrMT//Mh49etK1sQJ0iuwDSiT7gBLJPgCAgmeATE9fjNu3v42vv74TFy6ci4GBI7tuv3btUty9+yD++tfbcfLkeAwPD3VtrACdIvuAEsk+oESyDwCg0AJkbGw0Vlffxvr6RrRarVhcfF2VHFsGB49GrRbVPsnz5wsxMXGiiyMG2D/ZB5RI9gElkn0AAAVfAqvROBrr6+vb22trGzE4OLirAElf25L2HR8f2/O+0iW00mWydm7//88OZOzAx/A43En2QSlk306yD0oh+3aSfQBAvmrdHkDvFyCdNDV1Pi5d+nR7e2OjFd99txQTEz99DeiuVEy2234LnST7oPfJvs6TfdD7ZF/nyT4AoBfVuvh6X88XIO/P+EjvjllaWtneTpfGSl/bkvZNX9tLWkD98eO5975ary6tddiOHKnFf/zHrfiv//omms3efbW3H8ZpjPkcyxSGrVbz0L9vL5J9ZT8WPoQx5nMsZd9PZF/Zj4UPYYz5HEvZl3/29ZtuPybwu+hVHhu9w++id/hd9Nd5X88XIEtLyzEyMlRd6mpzs1mt//Ho0ZPt29OJX2qP0sLnaR2QycmJuHfv+z3vq91u73Ei050TwXa7Xj1Y0pja7d49Ge2HcRpjPsfSzI+fyL6yHwsfwhjzOZay7yeyr+zHwocwxnyOpezLP/v6TbcfE/hd9CqPjd7hd9E7/C7667yv5wuQ5OHDmbh583q12Pns7Fx1UnjjxmfVSV86GXzw4Ie4fv1qtb7H/PzL7QXRAfqZ7ANKJPuAEsk+AICCC5CFhVfVx0537tzb/jxND/766392YWQAB0f2ASWSfUCJZB8AwMGoH9D98htarXb88MPj6r+9rB/GaYxlHUv6W7/8jfXDOI2xrGNJf+uXv7F+GKcxlnUs4TB5TPQOv4ve4vfRO/wueoffRX+pNRrjzngBAAAAAICsmAECAAAAAABkRwECAAAAAABkRwECAAAAAABkZ6DbA8jd6OhIfP75dNTrtZiffxmPHj352T7HjjXi+vWrMTBwJF69ehP37/+w6/bp6ak4e/ZU/Od//q0nx5nGNzExHu12O5aWVuLeve87OrZ03+l71Gq1mJl5Gs+evfigsf/Wce2FMX7xxZUYHR2ujt3Cwuv4/vvZAxvjfsa55csvr0aj0Yi//e1/D3Sc9D/Zt3+yr3NkH4dF9u2f7Osc2Qf9ndUl6OXXIUrRD8+7JemX15ZK4DwqL2aAHLBr1y7F3bsP4q9/vR0nT47H8PDQz/aZnr4Qjx49rvY5enSgepBtGR4+FoODR3t6nIuLr+N//uef8fXXd6Jer8fZs6c7Orbp6Ytx+/a31f1fuHCueoL9kLH/2nHttN87xvn5F9Wx++ab/43jx0difHzswMa4n3EmJ04cj3b7QIdHRmTf/sm+zpF9HBbZt3+yr3NkH/R3Vpegl1+HKEU/PO+WpF9eWyqB86i8KEAOUDopqtUiVlffVtvPny/ExMSJn+03NjZaNbRJanB37nP58tSBN7f7HWdq+7ekd1508mQwfc80rvX1jWi1WtVJTgr5Dxn7rx3XTtrPGBcXfzx2qaVfXn4bjcbggYxxv+NM7z6Ymjq/5zty4H2yr7uPV9nXuWMp+/gYsm//ZF/nyD7o76wuQS+/DlGKfnjeLUm/vLZUAudR+VGAHKAUTmtrG9vb6+vr0WjsflIeGBiIzc3N7e20/+Dgj0F1+vRELC+vxNraek+Pc6czZ07tOhHZrzSONJ5f+r6/NPYPHW83x7jTkSP16h0Tr14tHcgY9zvOTz89W029bDabBzY+8iH79k/2dY7s47DIvv2TfZ0j+6C/s7oEvfw6RCn64Xm3JP3y2lIJnEflxxogHXDr1o2qhX3ffq6BmKZwfvLJmfjnP7+NXh7nTleuTMXS0nL17gs+TrqG49On87ue7HpFepJN7zpI0zC9i4CdZN+PZN/vJ/voR7LvR7Lv95N9cPD6JatL4HUIODy9fI5RAq+f9S4FSAd8882dX/zD39nGpuY2TWXbKbXmqT1/v2VMC0oNDR2LP/3p5o+/qIGB+Pd/v1FdB7CXxrnl/PnJGBoaijt3/i866f3GO33fnQVLGudeY/+t8fbCGLdcvnwhNjeb8fjxswMZ337HOTIyXF3n9s9//kN1OZh0fc8bNz6LO3fuHeh46X2yT/bJPkok+2Sf7IPe1y9ZXYJ+fB2iFP3wektJ+uW1pRJ4/Sw/LoF1gFIQpUWjtxYlmpyciJcvX/1svxRoWwtGTU6eioWFV9U1/f7yl7/Ff//3P6qP9ARzUCdW+xlnkmYHnDs3GXfv3u/42NKMkpGRoerkKL3jJn2vdB3EDxn7L423l8aYjtvo6HDHZuEcxDjTPn/5y9+rv8O///1fsbLyVvnBr5J93Xu8/vhvZZ/soxtk3/7Jvs5x3gf9ndUl6OXXIUrRD8+7JemX15ZK4DwqP2aAHLAHD36I69evRr1eqxaG2lqs6LPPLlfT0paXV+O772aqfa5cuVhdt3JrIal+GefVqxermQF/+MOX1faLFwsxM/O0Y2N7+HAmbt68Xk1hnp2dqxrtNAPh3r3vqyeAXxr7YR7X3zvGa9cuxbt3a/Fv//ZVtf3kybNqrY1eGyd8LNnXvcer7OvcsYSPJfv2T/Z1juyD/s7qEvTy6xCl6Ifn3ZL0y2tLJXAelZdaozHe7vYgAAAAAAAAOsklsAAAAAAAgOwoQAAAAAAAgOwoQAAAAAAAgOwoQAAAAAAAgOwoQAAAAAAAgOwoQAAAAAAAgOwoQOiKgYEjceHC2Th2bDBGR4c6dr8jI0MxNNTY3j5zZiKGh4917P4B9kP2ASWSfQDguRegW2qNxni7a9+d4p04MVaVIHNzLztyLFL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+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=1600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Aberration Correction GUI\n",
@@ -656,9 +849,18 @@
     "display(widgets.VBox([controls, log_panel], layout=widgets.Layout(gap='6px')))\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c831a9d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "description": "Acquire corrector tableau data and explore aberration parameters with the existing GUI workflow.",
   "kernelspec": {
    "display_name": "asyncroscopy",
    "language": "python",
@@ -676,8 +878,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.12.12"
   },
-  "title": "Aberrations",
-  "description": "Acquire corrector tableau data and explore aberration parameters with the existing GUI workflow."
+  "title": "Aberrations"
  },
  "nbformat": 4,
  "nbformat_minor": 5
diff --git a/notebooks/02_Image_Acquisition.ipynb b/notebooks/02_Image_Acquisition.ipynb
index 31e45fc..17f7163 100644
--- a/notebooks/02_Image_Acquisition.ipynb
+++ b/notebooks/02_Image_Acquisition.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,9 +59,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "asyncroscopy/stage/default ON\n",
+      "asyncroscopy/scan/default ON\n",
+      "asyncroscopy/eds/default ON\n",
+      "asyncroscopy/camera/default ON\n",
+      "asyncroscopy/data/default ON\n",
+      "asyncroscopy/microscope/default ON\n"
+     ]
+    }
+   ],
    "source": [
     "DB_HOST = \"10.46.217.241\"\n",
     "DB_PORT = 9094\n",
@@ -73,37 +86,14 @@
     "for name in server_names:\n",
     "    device_name = f\"asyncroscopy/{name}/default\"\n",
     "    proxy = tango.DeviceProxy(device_name)\n",
+    "    proxy.set_timeout_millis(120_000)\n",
     "    proxy.ping()\n",
-    "    print(device_name, proxy.state())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Connect to devices\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scan = tango.DeviceProxy(\"asyncroscopy/scan/default\")\n",
-    "microscope = tango.DeviceProxy(\"asyncroscopy/microscope/default\")\n",
-    "data = tango.DeviceProxy(\"asyncroscopy/data/default\")\n",
-    "\n",
-    "# Backward-compatible aliases used by the workflow cells below.\n",
-    "mic_proxy = microscope\n",
-    "microscope_proxy = microscope\n",
+    "    print(device_name, proxy.state())\n",
     "\n",
-    "for proxy in (scan, microscope, data):\n",
-    "    proxy.set_timeout_millis(120_000)\n",
     "\n",
-    "print(\"scan      :\", scan.state())\n",
-    "print(\"microscope:\", microscope.state())\n",
-    "print(\"data      :\", data.state())\n"
+    "scan = tango.DeviceProxy(\"asyncroscopy/scan/default\")\n",
+    "microscope = tango.DeviceProxy(\"asyncroscopy/microscope/default\")\n",
+    "data = tango.DeviceProxy(\"asyncroscopy/data/default\")"
    ]
   },
   {
@@ -115,10 +105,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f8b4b66d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled server is not responding; starting it from the DATA device...\n",
+      "{\n",
+      "  \"host\": \"127.0.0.1\",\n",
+      "  \"port\": 9091,\n",
+      "  \"uri\": \"http://127.0.0.1:9091\",\n",
+      "  \"save_path\": \"/Users/austin/Desktop/new_data\",\n",
+      "  \"tiled_server\": \"yes\",\n",
+      "  \"tiled_server_status\": \"running; watcher started\"\n",
+      "}\n",
+      "Tiled keys: ['stem_image_haadf_20260522T133345386818.tiff', 'stem_image_haadf_20260522T105051975754.tiff', 'stem_image_haadf_20260522T121318386154.tiff', 'stem_image_haadf_20260522T121744296246.tiff', 'stem_image_haadf_20260522T133103139329.tiff', 'stem_image_haadf_20260522T133056564985.tiff', 'stem_image_haadf_20260522T133341430628.tiff', 'stem_image_haadf_20260522T133342823544.tiff', 'stem_image_haadf_20260522T133340068138.tiff', 'stem_image_haadf_20260522T104941655770.tiff', 'stem_image_haadf_20260522T121544526198.tiff', 'stem_image_haadf_20260522T105103408463.tiff', 'stem_image_haadf_20260522T121448707630.tiff', 'stem_image_haadf_20260522T133344106682.tiff', 'stem_image_haadf_20260522T104922343816.tiff', 'stem_image_haadf_20260522T133342193219.tiff', 'stem_image_haadf_20260522T133343525123.tiff', 'stem_image_haadf_20260522T121735109675.tiff', 'stem_image_haadf_20260522T121309985914.tiff', 'stem_image_haadf_20260522T105025195581.tiff', 'stem_image_haadf_20260522T133219164122.tiff', 'stem_image_haadf_20260522T104911280047.tiff', 'stem_image_haadf_20260522T133339208324.tiff', 'stem_image_haadf_20260522T121606892035.tiff', 'stem_image_haadf_20260522T133344786945.tiff', 'stem_image_haadf_20260522T133340693322.tiff', 'stem_image_haadf_20260522T121713644725.tiff']\n"
+     ]
+    }
+   ],
    "source": [
     "TILED_HOST = \"10.46.217.241\"\n",
     "TILED_PORT = 9091\n",
@@ -151,14 +158,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "478b95f1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dwell_time : 1e-06\n",
+      "image size : 512\n",
+      "scan region: [np.float64(0.0), np.float64(0.0), np.float64(1.0), np.float64(1.0)]\n"
+     ]
+    }
+   ],
    "source": [
-    "scan.Activate([\"haadf\"])\n",
     "scan.dwell_time = 1e-6\n",
-    "scan.imsize = 1024\n",
+    "scan.imsize = 512\n",
     "scan.scan_region = [0, 0, 1, 1]\n",
     "\n",
     "print(\"dwell_time :\", scan.dwell_time)\n",
@@ -176,10 +192,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "4442aab2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled key  : stem_image_HAADF_20260528T092407483993.tiff\n",
+      "Metadata   : {'ImageWidth': 512, 'ImageLength': 512, 'BitsPerSample': 32, 'Compression': 1, 'PhotometricInterpretation': 1, 'ImageDescription': '{\"acquisition_type\": \"stem_image\", \"detector\": \"HAADF\", \"dwell_time\": 1e-06, \"shape\": [512, 512], \"dtype\": \"float32\", \"simulation_backend\": \"DigitalTwin\", \"stage_position\": [0.0, 0.0, 0.0, 0.0, 0.0], \"beam_position\": [0.5, 0.5], \"fov_m\": 2e-08, \"fov_angstrom\": 200.0, \"imsize\": 512, \"sample_seed\": 12345, \"sample_size_xy\": 6e-09, \"sample_size_z\": 6e-09, \"viewport_world_angstrom\": {\"x_min\": -100.0, \"x_max\": 100.0, \"y_min\": -100.0, \"y_max\": 100.0, \"z_center\": 0.0}, \"world_bounds_angstrom\": {\"x_min\": -30.0, \"x_max\": 30.0, \"y_min\": -30.0, \"y_max\": 30.0, \"z_min\": -30.0, \"z_max\": 30.0}, \"particle_count\": 3}', 'StripOffsets': [754], 'RowsPerStrip': 512, 'StripByteCounts': [1048576], 'PlanarConfiguration': 1, 'SampleFormat': 3}\n",
+      "Image shape: (512, 512)\n",
+      "Image dtype: float32\n"
+     ]
+    }
+   ],
    "source": [
     "key = microscope.acquire_scanned_image()\n",
     "node = client[key]\n",
@@ -202,10 +229,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "6ea573ed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad1f166a93764a4787e86840acec17d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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+SmhDbdd4CLOnF9pv7ZL502ya19pj5o/F2PjrmTOAiC2J7TmZN1CgZJDupmlSiVN72fkvwEMrSdFExILjCmpJQCFtvaHXeng66j494Bl8lT69En6f9ODk3yPa4E/klGXmdccLaWwJn77vpo6nhEkJr6IU8pfmi/BAsJfKnvpbhKHhQ4uISoZZkPUn+UpT/1odEl3RTPU/5I/ufGyoEgp0Ot73IO2+N18p/GKumgY8uUt5w/ubjr+nftTzntZnioo/xTDmUsTWOqtriKV/c0wKIBq+Asp8Rg7IzwXNI2+IQ4JZLEs2ufxtptT3Xd0yp1RrDZ37WMC1QJCGgd/nM87+h+Vd7pfkrwySmdHK2XVWCa2p0AWFGaXXXXnA5oz13XzVA0wOhEmJ7uuqwLkrSbFTDTuk5PLlg52558hX6deHGB3iU3Bmk345IS3Dm2pvpn/2k8qMtpBt7ZRX+m2Ven23OuO/41Qet3jZFppjmBo2QPKcGSL2e3Cuj6wdzFBeXLxRw+eHCc3Uyf49H528q+VA7na0opLlEW+injY/KbJIOTm7QTF24MmDs3n3PMnFxplvI2QdatASwddOcc/b2JA6JPdW6AWG+lRPa7MnoGCNe+SwQONGjwV/n0PttyT2FcNqLB0qhc8HS/E8hJdV095WP6rddcIEgJ3s0gNQjZDQbkge25flXOpCM325KCvNqXztymNsgL23MJiIDRteMSgAMGqmvyNyUV1x7vuTiQ+NN/K7DY+kzJg7QCAPo7gVD3W5fkG6i5unzIcgAj8Znkd/xP+pra4qfzV9d/6tPFrfqnsusDBuu4MWCfDrysbF79U+2dM22UzeWxxJ8DCGxziuPK107cSn24uJfirxs/l2+UdoCZfiZ7n+6TUu69SIClT+UEKgoumHBWRqP3XMBNCV0NTfvU9pNRJGCXgi1ldsG0prodQHMJZAe0ELAlw0pB0e1DconZ7c3YCdCrJEfZXJBx/TGRrsg58E4iSHymPtLY9v1pb82KDya7aXl9F5C4T8pDWRJgcMlC72mSIXHcCa70HvlhO23TvJ901Z9uVtvphbNwTgepP0dqFyTGrOR7vJ5mXtUWJzulLE4Prn83m6B9ENSo/JMvOl0SvGi/nJWinOQj7TPleKbc4WL3avaPfUxA0OWVlY+Th7QHbxU/UxzQ3K6U7GQHnOmYNGZYxRPuNUt8XcwM1pSwd5StdVMQ7X/OuVb8peCjZab6LLOT4LUcnZLtO4Ni3y5QsQ1Xl8FFr1Evbq/ElLyTs+0ae0qG030XlFwAoyyknUSSwOJOgHFjR3LLRVqfQM0BrBQwJ5sjj84f02ZsBx5bRs7Q9GcK7hNwUL5on07OJFfSC/mh3dJfHH8T6EnLTnrfJeIdHbEv+nPTNHueAMEkm+qHsUMfyJy9pIfdabm/73fcZ57hzCH1SN2qXadyDlzw+yq2uZjLuMSxmMq4PXv0E8eDG3Mnl7bZ3zXXTJv2rxczUhPP7EPHW213wheOl4vbg8WB7Gv6XadkCRLU0bSstu36dP27IKCzCC54rRK9k1V5cABE9zitAr3rV42KgcEZvEtCO0E7fXfyUl/O0OkYeu8cILA6AUbiqTINVC7AOJru07aULyUXBLhvbBV8XJtqy45XZ09uDByvTLAuCXFPoNo3H3r0U33O7St0CZPy9H0FmMmvKPfKzyaApoBB+U36dIBFeVW5HcByS2W7e7N2yhBc0fcmcOHuN3WCTHvYJqA68a2242xfgSVl4+ESJ4+W7++cQZlsie2zXZWjP50NsWyKBxqD3H3qS/lPccq1l8Y15ahEK/tpPXM7jANoXT61f5vg6lYBlgbPBLJowEVaVpNHK9k9faW+u3+SJtoUvHeSbgIn2u8qYCQ5UvJ3SSeV69kNlmcgTc7myifgs0pYrswkP+sk0LpyJgIDyrsaZ/LBeikhOXIBceUXaj/Tplv3m36VbHEKeC75Op27eyzj2i9S/3YyU0cELG62LNlq4m+V9F3/tEWnV/JB212NEWVXmUn0o6ktNy5JL05/k82r3FxyIzB0fThgoO1qPW1vms1JS3+US687W3BJftIj7YPx241jim9T+6mcGz/q0R1iSL7jwFUCdkWMCS5nrnJPiuvue2rjtuh40ehBBx100EEHHXTQvTqDlZY/+L1InzL0mnvym540XNt6jU/NulTknkzczM5qNoezGytyTynsI73XxD0965OdPt25WYTEZ/fJuu4JcjU7QHncPY71pIv0lOR4ol3s8Mq+U1lnN7yf2kizGq5MegJ29sq66YmevKfZGrbHcUhPkW6Gx8nu+qEOk9xp9mjyZ/3u6iZ/W43laimV8uh3tX/Ky+vO/lMccmPi7MT9TnrVcpOvrWYU2AeXkh25mEs9ub1+SX7WVV6cXhgvJ13xut7X2Vo3O+Tq0ufSjM8qP9AuVe96X9tiHnLbUHSrTRHbcqR1kx1pueQbK0qHHC4CYBUlY+h7q6P2KbHSWdyAOsMhpWSUNvAxCDNQ0NlXRpZ4cksdLpDS0NtxXGJzfbvExSDFtpJj6jg5sDAF9OQ8blNjOo3jgpwLmpMeU4BxunNj4GiV2Fz5tgHX9mpsHRhydZN9chzdPpqi9t3+rnp1tuvAhPLP02QqP3mnHPzubI6JtL9TVupA+2N7DPTJVpye6T/TuNIOXL0pwTtaxaemtOHcgQT2qzbi5KfdpGSrMXEVN8mLyprio9Mr702+Sd2rziY71nbTd+pn2u7B9pMNtww8dJJenZQ21LuT0DeIA05e1qUd8qBCb3fhS6GTj/P7Re7BSobFQKeAhuW4NyslhCkYEUDRCfXN2m7D4fSb15KsyqvW4fcpQSRK/br3CyXn1n8MiNxg6QIrv9PJ2N8U+PVeB2gHHhMYSonGBV0GrVWgI9+OryTzyk+UZ27wdME1BbDJdhQUOL/R+x1U+edNdPxVbw44sR8HbJQf3bOpdscNy04u7TPpw/Hf390+UOrN9TvFOlc2zaArpRPBCRi4PicfU57Sb+UvJUnXHvWXYqGLqSw7nUCc4g75YG5pSoAs6ar9weksxR2X95zO3HXKOfGYYh/bc21PecuNm+6Pdv5zbfxrytNaRmNEf29wRUBGQPhWA7DSJm8XBPpfCoz9zihHqtQpANHZCLi4pOkAVJInAawEQly7rpwzyCSz6lDrTDOJWnZKXukUC+u5um55cwdcOZCV6rlAOrWdytHRmVxcMk3jPfGq/adZKj2N1+V3NqwnYt1d8NV+QgBBeVyAdgDL+aoum1BWd+ReedU22ZfK6p6+nT6c/e0EbeWBm+8VrDoAQ7tzcpAn6s7xkuRIvqr31A7oXwngrHzQjdcKEDjZqKvULpPvubpIutkBSimXOdtLKwXMS6ucsgJfSZ/JT92JbaUJPE25iX7oNtkzl/G0dN+fZkjvBtC69dc0qGHzkycpVPEMSs44OVjTqwhoPFOAZj9apq+5xJqc1hk+p1bJp6ujbfL9IKoT6tLVd3qhPDo+aa08OZYzapdMeJ3617rTUsMOeJoo2ai26cbUgR2Wc0GZSV/vcdaOddyDQJJZA2UHJOdH1F0KhtofkyPvp1dzJMDA5OR8wbVTpEBQbWhlV1NccDqhflO51L/qxIFWLTuBCzd27rprK8Wm1C9lY7kp7iXfnPpwbblY5fpN+nH+T/tcJWICIsbvCWyQb5dDXMxbjQfL0Z5XelI/VZ2kh4H0agbydh0mKJKOk1+7tgmy9B5ldGNxMS8aJbnBJTrvey6ZuuDM4KlPvP2ZAiMdMBn4jpH2Z0qobDMFVu1j0qFLWs4hXbLXe043muj7c0pO+i8BFPKZgnwKyhPAce1Q9pRgeZ+6ckHa6SPJm/h1ezhWgENncabXdKSg03V7H8OkL/Kh5d04uKfw5l15dQdf+lN9l76Qlu7ZFgGW6zfpiDKtbG5KBEmPbNsBZupU+eDM3gQumrgfdIqH3bbj1emNn9xq4cY7zSwRcE4gS/cMpRmgKbZNiTcBIJVLv08gbkr2DthxDLk07sYixbUdG+/+00qD6sLFbkfXC79gPJvylV5LPrPq+xw/vScB1pR4V0mZRqTOxAGiUzBpupkyNXz97ZLeTrA5Rz+rOikgsgwDgQNhqW3H1yQzr6nR93W3KZY8uoCzSwyUvDeBW3ddZWL5tLHTJcpEKQHWPwU7jl8GF5dkHe96PQV2jgGBmPLH9pRHysZyDkg6nrqsTvszqSbSNgleHSB2eqU83VZKZEmv2s7Kd8kvl4dTvOEDpBsHl5h4XXVOGRX4UD+pD/JCG+39Szt+v9Jdf9JWXN1VzFj17fLBFEd2++BpbT2Nl/hxNuvquHyg19Pm9CLOntMv3NhchxigMuqBGbe8txo36svtzUvY4+KWCItccHLOweDr9iwopYCdDDsZmguubJ8866BN/E36mBIyk/fKWV2Qc/JMMicApXWoDweyuiyncVdGTp5dAuSYJ76TPqkHJbfHywVr7V9t1AEkJxs3bjuZHd+uTU3MTu50jWOVnl4JJFd7Hchb66Y3pyZ5d8dMx4g25QI1l1nTfiVnX9ofwRr73PHPZN/sN/lY0vME8qY2dsFhiiVO5iST62Pl0wlEaPx1McjpVvtJ8hI4cLaYet2NZ9QH+SSoYZ6abCDpcPIvjVWOJ6cTEnl0se/G6LF/p9UpbZt5Q/Xg7JvtuDG4yFOENIK+158cUAeW3GC5hN+nnZwBKDljYLsJFKwcywWQSWZX1zmZK7Paw6X6cX2lBEX9p3Y0WeseGC4JTQ6ZnFj7SJu7pzFOck6BckpCKei17CsdtS5UJ1zmmfTg5EizhU3cn9f1+tAIgyvbODfZN7VO3CZZN04piCceXFnqmLpYPfWSR2enbknItUPZuJfUUdLPlFj4ydiV9J3qq55cDKBOlW8Xj6l7BSqpvalfZ68OhExyrw7t6KnZFHeTfyf+qK++7k6KMuaoH01gQ+2T5aYYm/za5WPHq/LVRPndDCPLJXtQcjbu2nD1LhJgMTnqp5ZzCcop0CU83lslW9Zx36drU5sOTKWnXU2wbC8FvVUgdmVW+xO45MpxS+07OXeSL2VN11MCnACF62sKhikhsKxeY3JWUO/2+UyJXMs4nbvy2pbaO/1LfYjLSVNQdfYy8ehOJ7oESd7Z7yrYTr6oOujj3C7ZTnHFJbdV8KasyYYcwKKu3GlS6pwJNfGTwMCkc8bN5Bcck53EOO0pSvImu9a+tU7ya+3bxVGdWZ32BjlizlnlEAdYnJ/oPX2Ac3pQvaYYxLLKu/ZTlA7QTL7AsVJy8UbHiOOlOmI72p4b58neL3IPliorGYkDRm6QqXh1BpcszjEA/dR+lacp8DKAM0iQ38kQXD8JYLmAmAyOx/6V9MlS33lEOafE2cmt29sxdKfH5DSrZJfanRIg+5kAkbbl9ge5oLMKSv3plsNd0HHkgBXr6F4HnrabNr8TADj7cUCG1zjTmuRwepxkJg/T5mz2PflfspeJ7wTOVqCW5TVJss0EbhJfq6ScQIj6G8el76f9XGyH8rBf1neJWPlYlUn6W5G2036QYt1EjBH6AJIeqFMf0/iTZ20jjUkCT27Zzi2L9u/kU00uFrgxS7OI2s7kL4wtWm/6fREAK23AdU7DYE9HdMFCDXlnH4zy4RJI/54MlnXI3yoJsuxOkE/3XULVoJ74SOCD+1NcMmKQoyzJsZwunLO6cWZ7vQSpiSAFX9WFXnPHq5Mjc+zSpsrk1NQT9/BM4IX63LFtx0NKcnwI0D77GsFf0hV5SO/uod6UphNTzs6SzCrrpFuVZwKPqjetq/zq5m13CnIiyun6d3KmpJzaT4k3fU4Aw/kcwQRfRbPDT5Hbp9jlXLzXPlJsp15p9+6ka5J/4t3RChiqTlle7zHfMOexHm0v5VHHg5Pbtc34VqTjnuJryhdNLhep3C5nr2S9uBms3txKh3CGNSk0DbSul09tu+9si3VSUujvU/B1CZXOOg2+C+bsN5XRPhkEtY2UWPs6AVffm2YXKSt5dkmNsiSHpDNrv2k50zmfWwLbOQFIPWj/7rvTDwOKWzbieLigQv1TZteGu+ZO8Wi7DngQ3DgedoNbKq/9cuZbE2rX1ydwZ9vUixsbB8S1rZQY9X7HIre3Jvmq058uUaVYuEpQes2BPsfLNEYrQOH41LFquciPk8ud+tSHEbfthECOm6ud7zHOsD2ngwReErnxLdqZ1SXfbM/ZeGrL6bnjucb16TQp5ecey/vM60Z2bHSlR2f3Gh8mHpyeLuoUYQmX9kQUTcrV4OWMaXJSB0BSvy6hazkaFtt0TqAoXgO4mzXQdhKI0brUD9uZ5Ng9CeZ4cw7HMtqXfndj4colh+vrbup64pvfGYxdEuN1tkWQtkqezuZXQZX2wyRJ/VFXyqvjhQFV+ddE74Ct6yfJkeTlGLLvBMCnwE1Apn25DfcTrw74Olvu722X+me3phjhrieQnHTL/UMEmk1uCbp5dmO+0h+XdrQuifFKD0DUb/3zJ25sHShLoFdlSQmf/Ggf02tAUh5IYzaBH/qgk0nbYrtaLtVzfKR45A4fpFdFuH8cq6mvc/Kryzsuh+v48ZDVLhC+U5Qt6KCDDjrooIMOOuige2MGa/Ukq2Xc00Ca+eITWHplgVufZ/30bo7pidIhbC3jZiH6Hje+utkhbaf757H79OTrnmjcE6CbVUmzDaovPjU7fZ3z1KBPYWkWQu87nbkZB35XvVF3pC7jlntcO6TpVQzTUyrbnt6NMxFnJChveolo19G+WM/xkJ40J5928tOmqA9XR7chdP32FbeHjDy4WQGdaebY8eQk+Umy8bvaCGPXdIrX6UXH2dkKx8sdCJjkcrMFOoPW2zU4DpyNcjM56idJD9o29en8wZ025oyL6pazLy4GunFh/9NsTd9fbczenc1RWRgDHWl5p8dUVstNcUypx4qz8CT10f5dpPmTvKx0zFhzsa9pmAKylmMg18TWv90xVNfedCLLGawO3rSXh20lgKXJ352S0H6n5M6+HB+OP/5OgSLJqYEkARb+dmNIWSZikCcoTmCGwcW1S/2loOFkS3w6nlyQdUsw1BX7Tzqj3XCMXd20HK28af/udKS2PwXXNEZd3+2JYf0dmVw/7WsT2Ndk7xIsy3KZT8s6sOZijkuk6TUjOhaqKxdH9fpEyQ9pvzr+jLfaN9tNPuAAi95nPE9jkpY8WZZj5dp0vLlEPIGB1ZI52yQpaEj9JL9T+TRv0b+TTInYH7+rTqkLynYdYlBaetXx0EMirSf6LnlMuVXL3Sa4ulWARaNPCdBRUkwaxJR0VsF9eo1BmrmYAmhKChNAozM5XTh+NRgnHbprLtFPDubkZ5B3+k+ghr+pg9S320A/yce6UyBKgCYdL59s0MlFHem/pFedgUmzsynpsZyTzdlv4jnp2flEksclpam+Sx6pH7bnfCsRfd6dOqSuGCNc0OdJKmf33QYPlDjenL6cPbk2nM7SDJZu/u79UV2edZ3OVV6dxVB+q0y1zQSZ5O72nb+4QxD0BR1bNxOm7Tngk4Am+3A27mI/Z6Wd/Mp3iqWOaPduVlL7WcV7BcL9EJP0eD0AHacrtRHKPtk1bXqFIyb/vxiA1ZQC4yoZTIqcSJ3QBcEJtJE/ypAcZAVGugw3F06Gou1OeqBRTaCNetmt6/paAZbuMyVblk0nhdgO+0vOpAB6h1/26co5XqgXlWdnhpBt8EQo604BP+lkkpd1kj6dzU7yuLZS8HeJ7JwETJ+e2pvGpMdfN2K7caU+GFtW4EdPQmu7PMTj5O0nf6dfltfvBETUh/KdZpTZp95TXTodkSf3u0iXIN0siuo2bczusvfff/9D3vPnAFmKKwo4Up5K+ln9/cWUAygfv1PGc304tTfxtuKxiL7iYtnkx00TOEo4Quuu2rjn92BNQVCvFSk67usc0NUyA+spHzTIFHCmZUqtT2dOsrJ/GmLiWcumz0SrgM623HcHSF177mnQBexEGuSSg0yA0pVxcjLwU1YX7FMCmMAUyxFcUbbEf0qok525ui4Z8rdrc/KtRE4HE9iinpzPO/tMS/muTwU8js/JFvTN8AToTt6UZBlXHO+dhCm38jWd7JquO73sxECnE86+9J9d0jbV1zg+tKEdkNX9OjtPOaGBjfI55ZcptrpylG2SUZev236dPye7UZnT/lT3e4rbK4Dj7MHpZPrdpDpLscjtVXV5oSnJRd2lV9FcxHuw3HcSl+aco6Zk7jaRJ6Iha7B2x+7dOn2Sh0Hf8TNt3J2M2SWiJJe77sCG40t10uVWINIF8RSAKPsu/6oDXlsB7pRkVjrRPh2Rf6c3x+tKTnfPAQzlYwqgKaGuAFaXccfc3dNp6jPpfCUv21HS5OTsyCXLNOvA8g4kcfbKzR66pJr64hhwz2hKZK4Pt2mceuzfbgy4vMZleNcv++DeNv5zYNgl8XMTuiZh5b/lbMCqm+SZV7hHlgCMD8AqPw9WOLnIu+qM4+T263XdKeZPvjXp08XANM46biqDq1eUcp+bUKBOHE1+tQuIL/JN7qukQmNto3bB0w1Efed7VFJf2h+/85o6onN0PT1D/roMjZuG6vSgfbiEwfoJUJAfJ2fSbyozgQoX+MmX49Xxxe9JluQ8LmG6xOnqpn4caZtMdExcyhf7d0uFKVC7dmiL5CfZ0Upebb9JT461L7on+ck+k5wJRLr6rh2XSGgHK59gW7RBzoJ0mZ2HPJegVXaXqMlPohRHprHXMuRplXB17BUg6J9acknOJWvnmyt5u6zqUB+2my9nnwRVbuZk0mXSIWVzPsAZHDf77/KKk32H6FP1L204n/IneVD9uvo30i8fHtTWUm5O13mNdqX3zrGle/pvEfb3ncBJR+97KfiuAMEKYDCB6H22qZs93abjSZ6dp3DyskLeDGAJMKQ6NGjqLj1R7YAeOtsEtFPSdWVd0E66obxOBw74kTcHkPT3buJyNqE6SkFZ29N6XcfZ4wRyUyBz5Ti77BJnSiQJQLjy9GmXGJwO2JbzCfazshuXWDtRJ3DFGfBJPvLqTkjR1qd2UgJOOk3y8jPFNIJB9uNip+s38TbFZdeOO43YenXJPcVARy7vTAk/+TDjqbNpFyMdgGFsJQ+8riDHxWfyr/VczGGbSSc3w5+pcm0yFlKW1Vgpz6v8cDGvadgxYK5HuyTKhNJ1tR3SjiNMQIMJpPvVkzXKH5MRN7nqRkeVW4FbXXfLcsnRNZiQHxdAGISmwD9tGGV53mcQUB53El4aO8d7SsauHR1L54gpUDgbmfZCTPy7+w5kJvt2AcnZiAMaUx2l1YzMlNSZ0NTeXZ3UngKbFGRdEqNvumSzItqZPrEzRiRdUTba284nZ/N3QRP7n3yDsYJxz4GoVWKdVjKcrgiEnH+6AxEEWKulYLdZn2VUbu7Bdf5JG3Q5Re+RP5ZNfpJiemqH32mHTZO/aNvT/amvlZ3s+A1xAesl+7wbIOuuvGg0kVNGKU6nmvtzSmYpUDvjT/cTOHDJLyUOOn47tG4EpbEScDE5FnHNnuAnOa7bQ+OMlPpLTuzkVP05HTYYcbM4dIydILKaQp/Al7bt9v5RthTUnbyUbyf5tK0ngMHE6OxZE7/Tl/MTt4TYfabk4XTOhM266icM7OzbPeE7UOP0qXJ1W07f9Cfqip/a7rSUm9qiLErt72mZ1clDP3eASX8rv04XLqax7cR/0pXW7Wt8oS1tQ7+zzwSw6AvUB3UyxRUto3FB7USXQJ1OaS9OF1P+0fIT305fmjfY1kreFShxtuN41/5vEBdTfy4nrWa/dsaQ9nybQOtWlwgTgNpJkCk5sCwHdUqYWjbVUyebnKKNgDMzOsCcnWLfzilplHpvFfRcwFVA4Qyc19WwJ2OmITPJM0hQbtXL5GxO9zsBVXXnHG5KPq4/twznwEQi9unshMmEQZ56cm0nol3vBh/K7PhRmbReAwhtR/lNiXhld9rOxGuSLwEOlYnxxxH1nuwsgUyXKNkuQRZldXFH5aGv8V6yJS3rDpOsYvN0z41hAlhuD5XW1f2ASXbXZ4o53Lw+tUMZitIM6U6uWumP4MrZcMq56cS66tnF6MSTO8R0Y3xhd6/yCiyu7JTt7cTki9jk7oLLZOyOpnvdxnQvJWQ6dBpUZ0QOYLmg7AKd8qXt61Pt5JAaeFZBxfVLnjmb40Cj04u7zrF0jsOTaJpEeAxc9U5w4JJVGlft3zkmx9eBoC6XQKjK7GzczUiwXUfONlPfep8nRF1QXAUyx4ur74Kb40V/Ozvjfqadh52p3wTCkh4Zr1y/TKj64ky24Wyy+XDJoWd79YFF7caNObcjpPFxcdLJlpKmK+9sZVWG+kmgjteo39ZXA60dXpJcWp5xj/Es2VjKIaTVsneKu4lvxm43Zmor3Ni/M1vkfGFnuf0+cwKX+U7lSrwTNCV+NV5POrvnAVY/japiizhIU5B04EwpIdg0AFqOyVb3RLlTYNqnfibSpEKj59NEJ5v+ruVS0nBPHI5nJirqNAU5J7O2l4LwlKC5IVX1vtKlc0oS5SewmQJwcti0b80lTLap7U5BL1HyFQb0CVg4va38kbJqu10mJRNns+70VHo4Sbbr9NrkTlI6+0z+QRvQl3cSoHAsde+P6totbaYEpmU407TysxQz2T7H031Xe0pjv6IJHCS/SPEl3dP7jHsc2wRkKNMK6Li4x7acXyS/pK6SD5BXNy56zeUaZyNsa4pPClroi9MS/vUQV9RXnDyMTwTQtNlzbfEilgh1g+MUKFhvCgirJKOJewWyHHHTvfLk2lgl7SSvcyp1UpXN8Zxmflh+Sjx6bQpmjlwST/WUr/SkqbJOiYBBdZKryQGKyWkpC4P+yraSHqgL6j3t50u08iPqdArwjjeXaF0d8sSA7sD9arYuJTiVy/kSxyf5cbLBNOuufTOhab99Lc2srJK205kjneXm7HQClMm/XB87yT7RzglARw4IrMCKi3Fsk21TTyy7OkFNXhKQcPX4XcdQZ0G1HPOB/na26nKAiwO7sUDbmR6grjdjlbafgCPLujoTOOuZ4FUeu9P08Cz/oIMOOuiggw466KC3jL9F6J4cufyWniKUdqYy3ZOie32B1tHy+hQ0LRFq++51DWkGxz21kI9eVt1Z0tIlFte308l0EsfpVMk9YaSN7enprtvRpxYe0d55auWTK2cpWLf7cbMeq9OELafq3T0BJnIzrZR/qpdmiFhuOt6tfDv53Cm13ZkN5VN5cTY+9e+eNlf2OfmoGyM3++lmdXQJU/8osdMR5dX62ifHg/2qHrkHa1XezVROPjzZ78qmydPEI69P5HTkNvZrXOc9dxAlzS51Ozuzm9RXinnnxAPHu9t/Sn9nOxrbnBzOX51tUef02f6t/pBmh5W0jC4nujacPulvGrfZvutP27wogFV/WLNIkyEN1Rk/KTkrB8oFznYiOo6r45YyJ1qBiRRYU/tqOM7wtJxLgq4vJad7996gHXldknE6WPHQ5Kb46Whcuk3JI/Gi491y8704EyX7cLaXgjHbcwFnolQ2AcQpobPcjqzcQ+fK1mfaGkAb16Ce+N3RAe1RdZvq79hct8U3lGtb7rvjfScBTTGCyUZtp3We+nIAYWWfE78EAQmQ8N5kg65fl+CL+L6rBC4SaTt86EzlVyBrZQuuzCpmp/Y0r7Gue9cXY3aSUf1C66jPaPt6AEVlLaKeHBhLubP7nXhOOtNrO1s57mmAxWC6c1qhyZWlwym5wVs5jRoRjYXtuvrkSa+rYTrjYbtTQCLP+l0dn/syuCGcfPKJaXqaaJo2HK+CRI+pBjX+rTfKOcmuuk7OuErkK13vtOvqTg8TOl4p4Sc7dDaq91Mb5DUlhGQLei35SV8nuFKw40CtO/FD3U97i/q7xgwmTpekXJ885eve4+OenunrDgRQZzsn5dS2V8DI2aT6XKrLWHVOYnZ9u2TKcjs5QK87f9frzq8TUN2RxV2jb7h/jtJrd5zO3BhP47Mj1wrQKi9O3+63Xku2eGP0RftM+dPFEerG5SvlqerrH/y+KIBVgvWT34R0p6TpDNElKg1CLJs2QCo/Ors2DWqTm4rWdrVeSuisk5KxktORAxcTSOr60wnFqW9tK822TYlZk0pPN7OdSVcq6wRwVrxPcvangsnp5B2J+l/1NfGg7U26cXK5wErQouVc23pvJ5lovWTXtGN9l5Hjl2062Z3fddtsbyWne2dQ0nvScRFfTZESYvJ9B4jIJ/3YAZHdBKPxKs3uJhtIPshx7k/GaibP5NvUVVpe7vs7cYmyO5nUdzSBuzyR8hv7oZ5XJ+rIi5MhURorN06sw/7IWwJw10Zm9r/Kn2xzsj2W4Wnli36T+yoZ7gSx6X4KJg5gTQnLBWutU6QzRc5JCfwSyGOyY2Ahfy4pUTdMnu4JnDyQn8lR3Xi52cZVAnHjnRIf23bT4K6+C5oOxKWk7XTj7jsbme71fc5ypP5T0E/tOln5T/900w65wOp0MvGVAJbandsrx+WcXXJL4E5/016c6aTpjt2o7aYEymsEJCmpEFxMiS7ZEGOWlk/x1vE+6U+BVeuBe14nGR05m558I+l6ik20QYIs/c1yaamt9eBsa0fPOumg/SY/TvHcgZJE3T4fGHQpnnZ7PYBM8pZsSfWs19yrjNju3VgevGt/7HkFkpKROcNJTuSUqYbMJ/aEvle8McGkROoSEtvtQDPx4ECgtsHZwSlha5v87oKUayttYE51kr70fjpy7vjXYJ0cd5KbvHBJaAfkMbj2WDp9aUDQoMrAmwCJjq0LirSPCdhpOfWJVVmnC5Yjr9qeS1ZOR9MyVvOsPsxkQ76oF+f3KclO7w+a9mu2DtSvV7GGfLJf7tdjOfZB33BbAcizs6XEK+Wib6pvrUBEksPFM/ZD3U224+51X9NrXByPjneNw9Q/+2YcoP05mR3AIIjYjV3kXe06xRr27ez5ZphxTWNF4uEQ2gLzhcs/SeaLA1hNuxvId8AOlcWgS0dZvdPEORxloBHu1HMJR2XsQOgMP/XjgmUCBUlmlxA1MJBHrZNABvlJjuf229S/mk2p6zyh0mUIRAmGVomrAZBOF7sTO6yrY84EtxtYCAS0b6dX6saNg5N5FVzdsowLsNRb6o82k3iiXWgCdbbg9EeeJ8CSgrte45O49rOaYdgBSe7aajmWgI4yk7isnmKQ81Enu/PtiaYxSOCKwCDx6njs7wmQ0V+Srya5d20pjbMDLtSVk3WlRzfGK77Zb7KNtJcw6dvpdIqHHAutv4otO36V/JP6vtg/lbOjLAbdpHiHrBkstSzbUqfWJMuN4K7f1BYdlTJODp4Mh4nZGV/arD0FcTVyp9Nd/qfj55PTODk5fg4E6PUpoEwBqu9PszW8rrYyHZl3fDm7cCBmsrs0likxsx0HJOhnBKi7NsC2pnKcoXVA1dlh10/tuv1aTt/nAKK+ruAq6Wgi6tHZiQNA7HfSLX3N2WiagVnpJI0HdZTkUL52x8QlfCdjemB2D9nT8qKTpevsgr3UlouDSU8JAJE3HlZYvcZj8vvVvjXq1cmlDyHTOD8Af0+nd3f1SRt1YG7HBi4SYCVi4Eknqjj4OgDpyHsPss5erACUcwrKpXxPMq4SpNMD9UGDbT7c7EECfvrbBY+JLxq449GdLmR9BpU+5dHkpqhX+qVsSoknJjRtQ3lxQIXjsZPIE0ggL04eHTMNjEyeWm8HELrf2kZKtlMd6mFVl224YOr8mcE12dxOP6uArnpKDzzdl8rp4olbikob6pPN7pD2Nx3ySfpb9aX2WN8dKJn2VVH/jCOTnbm9pavZSe3b/d451ODiqGs3gUeCpimuOz51MmAVD6eYzzjiHlTYDj/V3rXODWzbxZmUfxIPvMb6qjvXT+v0opcIVdGT8U7lXODa6V+DozshOAEV7m/qdp0BJdl3AmYCVjRcFxBXAcmBATq78uT6cMbNPhnMXUJ0dZVP3Xg92cgkB9vUYOD2STBgpaDC++RFibxzKVj75/0kL+s74MH+HYhwACeBgdRX6pdtJl2lAJt8xNmiW153lMCVs0nXzgSu2EeScYqFKovGOL0/+XXSXZObodf6q3ia/EPruvF0J7jod9THLnDReLPye8rsfIM8rfpe6ZAyrNpWuamvnfYm0rKd6zTnOX9o4rYMfqqenW7vM/XT/qmkn6QH1Z8uPbPczkGCexZgpYCkZdITTwoKivRdstV23bVuxwVF7UfvOZDVBlqgICUA9qvtJ4Dl9OMSIv85HpPu6AAMNhPI0LINiqaEzTq8zlkmtw9oCr4qi3vCU12vAEIKZNP47BJ1zOSgfHe/KTmyTQeapoeD1dIM7V7HiG82d7qbfk+2kOq6NtLhENqzsxsmGFd31f/kn/1dE3fzyyTgNuozca3AB8eDfEzxTQGGtkebXPk4yyeA5frp+mm7B+u6766+sw9nd6qDKdZNG7ynvYiub+ezCYw4mVIMW8nq+E9xYRqfnZic2iZNeYuUfKDvpRx58a9pmAZ8dXLMBTD3REmn213ac8aqv93SlSYcGtpqkyyTd9JTMgxnPNxP5hyk/zHYr4KVBn0HPBpkpfV6J4sm7qkc+2D7TLqrIJuCUhqHFLwdDxNY0zrTuOrntCelaAr4bMslYXetZeV+NeV9tTm2+0sAiDzq92mmyMnn+k4+pvwTYDEmOCCxE6xdX3qvfyc75Vi68XC6IBhgm64PtSnVB8ES+5pkT/Vc3ck+6CvUPYGRxp7kL64fHRPKQb41xjq70r5TWxMfkx+77y5/pXIOKFGHO/2zLReTm/oe9eZieIrLKcam+Ov0vfLZexpgOcCi91R4XdPn0p3bWO7aUYOZBk0DCg1sFQicgazQMxOLG/TkJC5AJhCTjCsFaGf4XS4tqTre6bQM3E6f074t5X9yrl1KyWbVFoGK1nFAo387MJN+pwBJ++I19+nknvwh+V/yN8qR+k/6XQVxyuT82dnzBAaYpJm4Xb9TW4w16eCHWwbWP7uTdMaZCybFpDftW/XPB9h+WEm+sBv/HFBjGedbLQ/5ZF23lKWk+p30wmu0L8cPgZezIf5OsWryzen6lDsdX0lPTQrq6ztPbbuDYDv24K7fIGbxpdJqP0lfrl+CscTLShcXAbCSQSSnUOCgIIuBwZEGMg4AnwC1f/1TLWmJKZHySgNdJZXUvkuuLJtAJuunJK5B1/FCwJgMnnVTUu2kog7i/iVdpUSS+GfZSZdszwUJJlK9z0CR+NWyRdxDMtVRvtjOJJe2OyVFgqyinplV32EiWgU49u+AJXlzMqiuUsJKeuT9RJQnJWn6UfOm4+LsukjH281KrRKrs7HJdvs+NzQnP0wxQdvlAxbHlDwSpKRx1jiqPKcNygQ1DuRQ/y6mkQfVTd9LG8/Jh+uTumSc24l37v7K71zf5KP75kSGfrrlY/LodHpjZra4NSLJl2Tl+K18dic+3Wm63TOLBx100EEHHXTQQW8FdGszWA6du6l+La+ks1h8cu7ybMc9XelmSz7ZOH5WqFeRebelPE+zS+63Q+CrGR22tWqjdaN8uu86S+HeGZWeEPhEpE9F02wJx8E9qajOeRycT/huliTNGrj2nWzkVWeg3MwYy7v2OYvldEte+JnsjnVWtr0zo9S86ifbcXsc2R43puuTrZN15WuOONOYZlfc5nK3DEfambFzs1fNG2MQXy+zmkWbyOnOzZLSl+p7L+FwZoBjqTY7xYdpJmdHb9OMmptNcvlmN8/wPmPYNGvrbDIdFNL4NM3ATLNELsa5fpqPRGmcNTY1sR0Xs1O+uF5spdidYUr6Ypxxet/t454CWPpCtKJpQ/OOUlzQSobOweBpFpbX5JH6pmGoTA4MadAkb9reuYGIdVZO6mR2AUr5mwwy9ct7U90igiXy52wh6SkBGSaYvu/2zDgeHTmZk71MoM0BNWfb1IPaXuKVSdGR07+zcY4J5U/A3dHK1t2YO9CwIvLExOFsLr07jDw4np1NJL06fbgYofI6XtISzhRX9J7bg+r6d/EgnW5rftODtBvX1bg5/t1954P6Od3XvV4NjiYbnHhM4M/pYWU3Tm/NYwITqkcHPrQdt+VhN567OHYTAD77X/lFopX9tO4YJ9zp1IuZweJvBzbSk8Z0SqrJPV24gXAOqX2wzyYOmONFBzY5A+trHe1f9aLXtD23UTDpMgUH5Z062QFijhi0VDck5dfJmpI+wanTEXlygM21OwXHtg3ayA5gcMHeJcVUf6KUBNzvJOMKqCVeHFBR/azshzyuQI6bdUqUbI5yUf4EaFJiSkmpfWA1LsnXHN/ue7KxIgUOUyKfwBXvrwCAiwFOhw54aP00ttP4TO3VJ2dl+p6L0VVW38s3+ejuJIIbO9XbdJiDtkI9TXmp/ukLncmHm5V2sdnFXdf/DWKm1unvri/97u4T/DqfmXLgxQEsd805mQNBBECpvf6cjufvJBDXjjrgqj75a+Cnm2BXlAI/jS8ZvpN/FYAUtNCB0pFy1x4dSJ8IHT8uUDrHqfo89cR2tC5nhFKCc064k5RTwHOUEu8ENLSs9qHBhfpPwdvp1QVmx7ezL/rGtCRf39VvVrO9bnxoU248lKYENJGzCde28/Fz2k/8T0mM39lussP63st+zatLctR7enhIunX2tQOCXCJ18jEWELgl+3Z89MENlWnyy9UJ6Z0ctQIj2qYDekmmyVYdafzQNshTsrtVvEjl3f3pfWdanjpyMZBjtpMbL+pN7j2Izjicg6UksKMsBpuUTFNdJk8HchKvyi/7534dtrmSb0ocK9AwJaQkN9vS0z3k1zmlCwpORoJPbS8F0eRU1Imrq/tdWNf9ntqbxindo72n9vq3OzlH/bH+lAhJkz6T7nR8UrJxM1ErubUPykFKT9yU240B69Gu9doqwLMt9uFmQJKtstzEP2mKof3JvViqb9qRytrk9gpS/5NNFKV3yaX4pXEgta86ZhtOD278duyz++eezi7HF05rvdU4JeBEGZx8/enkp2+7E52u3KQvl7/1s4k8TafWHXW/rOdOc5N28v3F/S1CNSC3IZbJfUdJbvPdLjmwssMvy3dZ8u/AROLV1XcOt0r47ji7S0JddwJ4yZGn8m78pqTQwXd33Ag03HcnZ+vGBRLydC5IYn3lMwV9Levqa7nkC0yOLJ/a1/5doFodj9c+XPB3CdYlNTd+CVwlW0rJdOJbeeB3jUNT0nFjo21wz6fzB+f/OiaTrORfedf+3DW2pYeAitIsCsn5PgFIGudd0KDldLnTJdwq52bfXOxK+kp8q3z9Wh+NK9PqCfum70w5YwW4lbfUN3+78XW8uv7TODkgNG1pmfrtT41vtD8+lKcxZtsX+7cIk9O42RE15imorBK43ktBQgedm/Q0YKyW+BJfKcGoHtL1VWLlPb6PJ+lmlZQ0cExB0Bk4ZUh9Mxk556MjuqS22qOXHI7U9d04Jzl27eHh0gq80L64STctX2ngSk+C+jLMCVRQ1rrO02hdhvbEMSMv6fRbAkBT2ytSm9f+WYbgoGME++TJQMdjAjvKz+SflD/pU/kgrxNId326a85Gku5TrJ70wq0GTgf6j3K7+OHALEFHmm1j7J4enB0lkOXyRJcn8Nc2nJ0lEDPx6OzJfXdjnOz0PsmfU07SvnW8CHL5mXJP8pGLncEiYKHQPPXglOkCtKP0VOLqTIldgxLbUEd2QGQV7B3QcAk0ARLnVJRd23V9U7/sx7WX6q4cMP3eSSRJP01ueYN80mkdH86hnYyuDfLs9EV5kq6pQ9oD9zV1Ww2K+HTN9lL/5DclHCYFUl0vXnRcpgSddLAKrOxTeWWfzpccP5rUd5cDp9kg5dvx5GJksnu2kZLoNMbuQE/aX+l4JhEc0N4Ikti/a899Op6cb5DcLGKKz64N5w9pPFlm0lvSX7K3KWYpT3yoSj7Og2Gpjx1bTnFZaaXbZGcpt7hyvL97EOki9mCtFD29J2NyIhql1nXoOc14aPLqe+mocUqa2keSh5T4mBwt1dEEuwIBTOCTc5MceGHAc0vA1BEBSwpcdGRt240ngVHSAWVcAZ1ziHbLey4Qap1pZjIlac5g9f2UpBN/q0C5Iu3T8aztTDaaANa072zieRWs1XZbXw4QuDZow5M8TdNS2mQ7O3aa/Hn6nQCEA00JqPR+KPVVLec2Kfcn26JMLnk6fTF+Kv+cSXHxi7mAMk86VtqxSbal8lMXKa4x5rrXJTj/cz64sg8ng+r1JoD7yc7YN+PjZG+UxeU1x/vF/bHnKWCwjDP4ibQ86yWD0Vkgt4/LDSgHz7U/HePu+9OpOuWNfbJd5XV6JYILyFqfOtF+E7lgyzHQ/t1MijpBCrrkm2v5KSg7+Xev614UXb5U2ZP8Tk9pTCY5UwBOp29ckEnvytEyyS/ZJ5P/BDKcDWj70wOPyjgBS7fnxfnqJC9tR+MB/aLrpNnsnSSrfVAGPRjgxsLZu7arMlGP08Ortu94XcUO8kE9rsbF9eX8a+qPiVQ3QTPmazv0G7aRbKjLuiX45MPJ35KOXa5xD/wOhOj1xN+UV1lnihG7+fmB/+fLKoPL9+73Dp/TWN0muLorr2lgcEigR+8xGLEe67vA7o7ra/BW59TA5PqYBswlngQe2CbvpeWuFMCnwJ905uRje8lxErhIiavbn4J5l0kbRZ29uL71/mRjysMEctp+HHid5E0608TKpDAlVurHBdUpITg5VwBLE4ibMeoylL++T3u7yKvqpPsiv1OMIL8uxjh9uGTk9M3N3y7oKz/TskTyd5Vf+SM1cOADGvtg3GEcJD8rW56StYuNzqdc/GROcGOs5Sf+eK915WyEbfJBSv82bYqfyWZ24u0UH1dxd3U4zIGVFBdWMdndm2R0MfZmsFHyq3nb2dbUl1Lae3qxAItOrPfcUpFTOo2Q/SRjYWJyAIt9Oz6dXCQG11Ww1X6VPzWu6SQXKQG/VXkNtO4f22Ow3OEzBUlNAmyTRLuYnC8Bp5QQ0jj3dQKYFAQTkHMyJXDDesknWu+9NOzIjZ/rw8ng9hNN5IBRkpN7cmiDTIw7wDABAZZf9dG/dVYptetiVPIl1aurm+TSMXIJ1oEaJjDOfkxJVvueDpGkuDvJzrHqeu7o/RR7nSx63c08c3zc6bYUz1bgz40Jy6xi2lSuy+qrIJy9pZjv4q+zVVdP77mVGdXdzWIigHanMqwOLGm/3A9KntzkykW/aLSdnE/hTpkJhNFpGFgmYDABNhdAJydy11dGsTJgNQwGkakfpzvneHovBSc6eeJ5JduUNFxASk+Vq4DDNlUXCRSQJ20nBfRpEzD7T4mR9fR+AjoMrJxJ0tOCrh8XiJON8v04u6CKfU52TPlTG10nxYWUWHd9kGVbt+SVMqQlGgc02J/7PZ0+YzxTQOo2jFN3jANdbxoH7Ys8E2iufNLJ4/rVsv0SUAccJ56VT0fatxtn57OUcZUPUtkVOdvhSWY+dDuQ5ORVeVyccVtCXBxSnejMoIs5Nyb2O//nuK58d9JXyo87oO9iZrCapkSgA9m/U+BiG5p0Eqhwzp3eCZKupfdwEDiSX5fQ9X5aGqROSCmIMVC5frWeOptulp4AQiI3bikAalkGkBQAWZ8nvpLDTQHYlSFIIZBxcqUElsbEJTW9Ttuib2gCnZLeTmBS/nfsL9mSS8T0FYIMN62/SmTu3pSEyWdanqM+1MYIbrQd9rdKynov6ZKviUm+kGzQbXh2/bf9uf76n+prl9Q+VSf0b8brlS8r79RzSthTLJjiqMo8jSPrp/bVp1bxxPkN9zGt/Ibl075G1mds1k+XsyeA1UR/0bpKU+58S6db3eSuyuvpTedckyJdMmqqweo/o9L9ubZpUAycTGYTpRNyfDqaAmfSFeWedJIckkFN+WZ7ugyin730lBLPuUFrksO9u0vlcu+/oX56mSwF18QHA4KWSYk5lVkF6R0+dL8XP6dgnA4/JMCg11X3GozpZ853lEcHkFNwdXqf9Op4dnpMlMZm0g2TkgIN9XnV4eTrjldt221G1/tsgzMd9CHK4HTg4uDK9lRe7glzSZZglraV7Ma153TteEv7Biff3rERR5pXHDjka1WmQxTaRhorZ6dubJS/VId1XTybQPrk39cDcGZ9p9+0r/jh0JtS91za39Rz0EEHHXTQQQcddNBb9h4sJUW27ji5PoHwac5tQOS/6UlYv6clxYln8q97w9juLrGsexpWHRRNJwf1O/fXONn0yS/162ZD+nN6skll2Ab54/c0G6QzKZzRZNvpaTzJ5WZO+p97jQNtPj1Nall9qtcTa2qLTmckd1DAHT5wMwScGZmelrlcRnlbN+S/7+meJ/LE8eEsQNso9a424GRSvjhOST9O/+onrXPampuJmOKR6rT7dPsSVT7XV/pczQI4O2d5lcVtJ9Dr6q99Pc1ckr9uU2dP3MZqxvgpXiqv1B3HZ9KT8pt0mnyKvujsmm3odZWx/1JCiuXObtMYJzunLI7HrrfKc9fDSgDHlnVcDkgyOXnS+F7cn8pZGS3/OYDl2k3O5UCWGnkCYY7XDszqqJNxTwG6y7jAywCgjuoAyxRYnG5W4+GSgyb81DaDcF9zAUXLuXvKF5dctQ6BiHu3kradAK87zcY62i4TzWR/KRhoWbdxXdtOPsCx0M9pE2p/TuOhfLCfBgLuz+FowHWAwCXrCXjwuvLb46VLw2xDbXGyN7ffSIEOxyDtF6MdOnlVtzzplPSmbSQ7WOlZr7v+prIu0ap9Tvvo2B5tMMU1Lj93+65djp22pZ9TzNd7Kec4UOdAB2XmMir1u9ITdcM8tkMOXGmfu3FmspFUZ8eeUw5zfp38Ta9zvC52BssFy5RkpiPRq6CRAtUEbFyASQPvHCmVXQEZUtrf4WiVfLSc+85A6WbFVEbKkcYw6WfiU0EFwZFLPtqWAkAHnij/BIoJJjS4r+woJYyH69g7SVX7JDjovlfJLgVTTZjalxuLlHhd32oTaVw5TglcJV0keZQ02e34h8YlN76qf52Foi6dP7l+JzDj5NF2GJOmBNj8crP+CkBofef/yecTkErASONhj1eD6Z6hdjHHPXA5G5ni2A7QcLFh2jzOfMcyKWdMeYcyTfXOtfNJztS+05FSGo9VzGTOnijt171IgOWe8N2gpcCcBlYTgE4hq2NNicC16T71O5PqdGpQ6+o/55DKZ/9egazEf5IhHefu75ocyLcjTYauffbBelxK6Hv92XU5q+Q2VXc5XksA0/EzOTn1kMqlRLETiJzeprJpWcolx/QKBwYtBr3J/tT++VDkxjbxyjGlbpPeOdOb9JvuTScuGV/ow/RZ9qc+3NdUP9NYUGfJflLCmeKdi0HOf5wt6bhqXy6OO6BE3ljGLTXvHChydtrtu7FgzH64m6hVdtrhtCSvOlAw42bKkz5SrE3gyf1WUj0SYPGhW4kHLFTWJsrM6y5m9XdHtBUXKznuKtvFAaz+u1S6Z8MZW5O7ptcTwFKn4j8mDm1z5VwMeOmERMuY2kiB0QWpSQ9sw/XjAk4yRCVd9lGwmwLYDpFX50DJ8VNi0bq6d2lKYgxctBctx0BHgDyNl0tMHBfXlpY9J+ArgEoAy8lLHp2fJL3ofU12ys/EvwKqtjnqztkseXJLSMrLpDPy5vpNNq5JUXVISj4+2buLK7y34i3FAeeDrh2nn9WyqLvOPWVOrykGtx2pbbhE72xN+3Ez2zzJnvZAJb1M/pkAwASwOna5fcgTWCIodveVLxeD9N8068w+nF5cmeugK+dD7lP5IwilnTu76hla7uO7uE3ufJpzA7ETVJzxaf0JdLhkz6A39cXvGkAcwlfZ3DXndCtHSm2qg+wALOqgE920NJYCjDpzkkX11ffZTpppcbLQnlLgd/W071XdKfG6MaR+KENqYwruLoDp0k5K3I6vlFTZ3yrZJ74cD6q7abaKy8T0XeppCvzOvlQejoNLZlo+BfVJhyqT82unO42PKTalvlKSTICG9svZa9e2Auk0263tOYDFsXB7k9x+SMezjmfKLU4HzrYpw2qcnR9oH3zo46fGpC7rwA7zIH870Kn1p1zh2tM61I/TWfLLopTfHKCaYqi7Tl3x4Ncqzl0EwKKRuUTiFD8FiZRIXMJmnRVwaIPRBJaWAptvN72dBngKktO/aV+KK7/SWd/XmcX+R6S/w7MLiM5pNSg4nh8Okb+kd+57oI0420u8aXKYHHlHPhew9Dd5YmJhWW1zB0ytyJVZBdSVvI6/vq77Md1yI8uv7LyvTzGAoGsVL+qfzs6zHY5RsgFeSwAh8aptMKmuElY6laZJa/LRNC7aXxrrJJPra9pfSVBFm0j+Q34mH3V1E3BKdVVXlLVtnrwkH9N2J1+b5HTtu1zWxPjOOO50cL14MHk4AMjFZpeD2E9aXboogDWhShpg2mOh5fsen5TSJtupHS1DR3DleC2BmwkUab3+rmCOAED/3txuYmFZjkUCCpMsidcU9Fx/ygufppwNtPOznaSHFPSnvR6UoXmjDpW/lf53bHEnkFKPWnYncU88TjxoMljxRXKJZCpPP6BPOf4nm+0xIogm+GFbKWGrT2o9XXJy+nP8uT53yYGrFH/Yj7vuYtXqQcvxPQEpd01nbigf5UnLwY4vbm7ftZuUC/R3ijm0UdadeHCx3sUiZ98TmOR4rij5pMvN5+SfmzCzOtFKd7QRLdN6ZL7Y0cE9C7B0gFbI1U1V9+8mGo/uHXJJM/WdkvOK3xVQKOJTIR2RjpDuOcdhgmA/SivDXvU/JX3254L1pGenR8qj1+jcUyKh3HrCp/tL+w4mnbPfdDAjBYC+t0q+KxBDWZOvUJ7WI3nQdtOyzcp3JyBFP0y2lcCe8kH59Z76bhGXpdOynfbJBNf60lnQFGcc8Oby/ZSctB3a4QQCJoDVepgACg+KON9Tnp3OSAlYEFQw5jtQnPimzhKPWm7yl6kPnhJVUttL47Xy6XTd5RDGThc/XbkVOb0whzs/1zJNLgY6nnexwcQ/eV3l/ot5D5YL9umYNPfpcHnOGR6dS53UGZmSOoQz/t6XlBKl8uYMbLXuPclUlJ7kaTx6PclCXjkmK74cuWTqQEgCcY7fKaCuknICQa599wSdApHa5Wq/Qwow2gf1n04G0aZcUk/tuvLu+6RnZ7cs6/RHfl2gc+PItlnOLdtPMUHjivoM2+u+06ZbpyPnM+7UFXXo/IJ9OltzelZeVsmWsru44fpz9ZNdT6ffXCxOS/RdTw9wUK9sjzNXq31I5I+U4pjTY9o/6mKBa3tlb9q3iyUJVLTOUix01xLAokxTTm1K8jDn0OZpa1rOjd3Uz6rMPf+3CBOA0SP+zondpu00oGpc07qwXiO40qepdGLB7cdS3mh8bD+BMVeveem3XjuwmYKKtkfHdiCnwQbHzMm6AjQTOOr6LVMKDMl505g4Xbpx1zGf+HbJ07WlAGuyTTcWbt+FC8hO/xOAYbmdoJ78bArM1CPLp7Y4BpN8vD5dI0ChbaqN6dh1O+5UIMcqxSImudV1rauzY0mvK51qLE26SnWdnH2fdqWf7qHA6Y513QtqqZsJEDmdutccUKfatgM71I/77mav1K7IY7LjpFfVIe1omqXTODht8k7xxJVL17j30PnwFDOU+DaAdMiK+2eV3GTNbYKpt4g3ua+WJFywZ/JSw6HD9YvnOAhToNH7bnrX7e1SB3b8u/YpC3VDXbg9BG6fUnK0Jrcx3v2JEtXBlNhSMEzHi7VfF3wmcLVzcIAyp4Stfac9VWk/lbbNJRT3hOySK3mZZoQmnel91mfgn5KGyu/6cDIlP3LATuV3shRxdtnxqO2Qr7RpVU/1MlZoe9V/+UInC9c/bZNJWWOBA3dODtqxK+cedii/I46nXiPAYD3nR5ONuL4dr2xPeUgzvzq+LiFrTHQyKq8JKEz8TcBLbeGcrQEJSO3EW+ZABahJJkdTfF7VpV6cXI6n6+HAiN5P8bDvK5/axnTq/Rzd3NMzWLrE1qQOspMkU2DVdgiAVop1wME5QF9zMzzqnOnYctrkPAXiLkNe2L724QKWe5pxjqDyT0GVgc0lfpdIJvmm5DQBEe2XOmJ/Tj8p2bmErPqhXlxgX1GVce/jcTJMy5jOrigD91MlIE99aSLRek5nkw4ScFE+nAw77aU4QR5T0pqSVIpJzp9og5Ovsm2Wa+J+n7RROOlgRUxulEeXxdNeRZLbHO/4cYBG20j+1aTj132mNl1btKdz4vDuEqQD3rShFE9c/mBbSbaJnNzJDpIephiQwFcR5VVbcflM+3GYgODK6ZL56KL/VI4qicsjRS6xTImwv6+cPwXbLpcMiAPVPDXt7r1xsyYMnuqwLhC7IMBEODmb9uucKTmX61Pl0/Gh3AkUJB07h2dydzM1STfUU3J8JlDtd9IV7ZK2MumR5VwQcpTAxc740ZfoX+SN49g2O53oWtmhlnHyJHuaSO9zKWECfomvxBP1nUCD20Tf92kjDoxoeW2rE5KLS/w++W3yVdqgm5mebJsxJsXsnTF1Pp7uN02b9Ff5wX2njp3eU4xLekp9TTQBIBf/VjEk1WveV/GHMUDHvfPxhAFc3HV27eK1y+uJXweybotu77ziQQcddNBBBx100FsJ3eoMVj99NU3vxEjTiRPpLJZ7apyQbiqvvKZ7+mSx8zSvsy+cweq9IO7UoO4rWT15sn8+0Wv70+yFyshj7pxhSv1P+7tcWT4B9r66ptW+G63bffKaI86UpRkC/T3NZqQnqjSLuGPrnA2Ynq7Zn5thc2PveEizNDuzbE4u2h9p5wmaT8lOB72PKS3bq11r22n2aeprmt3ivb7OU5CTD6c+Jh2xjakdnWHn7CRjzU4sVj1OszrOzzS+OJlcLHM6mXxS7d/xrm2xzV7W19jM8V7pR9t2p0XJC8dFf3O2SGeYnE2lmaSJX+U7+b7GtOuwVEdZ+nfaKkTssMrVkwy3OXt163uwUkCdArle14S+A7J4PfVBY5iMzxkjTyG5Nhg4nKOQD2232+BeDCcHDSklnp0j4xNwY98pibBcuqfAg21SD2l8aV8OaE3jOyUvtkt+OM4T6Zg7vaQgkoIl5dOX0ZKcTpycLpklPUwypr7pEyzj6rO82+ujsrnxYfulK7681+mDxD0x2m4Du8lO3Th0UlR++YDhaAISTjf8roeDzvEN8uWWjdwWCFc3kdohbZNJl2N/DmBtffPEISk9JLpEv0rqysdK59Rrb3LXawRjzv9YfsoXSg7gqC7cgQxn60lPzfNq4oU6czGfdfRzWo6/5wEWg96b0pZ+TgmB5XbAVxow1m2gwhmcyfCds1MfDqzQYdxeDAI29yTQfDv9p0BEw951mDTOTEysl/QwATnO0qVA4YIL5dmZHXPAKtV3CTDtbUm2vQJtrg23CVbLtt4S35QpjRt1p+AojbNrm7yukrGzdbahcUftvvkjKKCPpPHU8Xd6dbpJySDZUPehAIwxReufG9taTj3azzLJ3yY9cOYk2cDEL8ukWEKQ4Pxmirn8R7Drxl+TtAMy7NuNA3lZ5QUCVpWb/Ti+kw85/U65mTHTxRvV0QM4iJDsZxWDHa/dJvfdOf0nX76oJcIipwgtxzIuKK0C7sRHqpcC4ATY3GsXlGcmUpf8td9kHHSs7tsFLs5yTVOvSQ/kMc0SpCCi7XHsnU5dkHDJJ9nOBBYcOHB2kpIIy7ugwOS1SnQ6pgwuK15S+/QZLTeBj5TE+JnGekrszq/0MwGM1PYEGFzw1evtp9OBCZe0VrrXtlhv4r+INsXXSqRxcr7i7IjlHF+MYTqb52x8GqPJ7pPP7pKO3QRYVG53iEh55qtS9JUdVe+Rj3zk8pR34oPJXcs4/3Gb81c64lhzJYQy67Uppp1DjL197YHFy8HdNpgUC9UGeY+zlUoEo7c5e3XrAGvn2G4TZyO6bHJOBh4uv/B+4sMZF/dJuHI01l4ioJMlg3KOOC1bOFlS8mECJTkHIL+uDpOTykNQyUShfSgPrqwLBIlcMuvPackmkZvl0IBISjKprlZBlnoipX5Tu9q+Xp+SjvbDtp0fOvDkfI2/V4l5AigEM1qHyYrBXu3SLXOw7cTjJGOKP66MzpY539G+JnDBbQXqOyT2PSX6JGOyuwT+nB7TNXefcdzlCPLgyrk+GZ+ntru804OzHfaRcln67vh2S5UOgExtOr84Jz46naRYdj08EOzYh8ub05hyqb15WtnaPbtEWHSucBp4XLBwjqefTqnJ+FObq6SeeFKQNZEz6rQpN71TZXLqJPcqeCZ9qNzq1DpW9ZlmZ6YERJ6cTJoEmJBYbqWTJPOOHlyy199avvlz9pmcf/quPGhidnyxDolBzoG8CUyo7lPATWPu+EpB2I0x61BXfa+XEXTPFu3Iydw2PIHO1C/lcwmf4+oAViJnE8qryp3Gj7pTvWgfyQ5UR05G8naOPO7hjXy7+DXlCM7mMy5pzJr6T77MT47J7kOei5cJlJEmP3FyuPYme6HMLu5xZvA6PJDxuxtPbdfJMdUhHysbvKf/2PN0yo80JeYV8p8McTIcBvN0je1pP+QjBUEn1yoBdlmdVWGCo4FzSr1nxZgwyMukywkoMJAwUbD9dJqMJy27vrOfrkteViCaY3ZueVdG9eJ4dHUZCJqoY+VDZXQB2Nmvtsvrk226f+RzFRRTP06OJLN+Z1lNqrR76sTZYLI7bbs3gpPnxJvTk/Lr/LPb3Nk752LLipQ/9xCXeHY86vep/5bTPei6sn3P2UWTW/ZLwE550DhCHTKe6vJiihdpA3/3wWscBydD0qOzN953v50+tP7UzgQwUxzetc/rjQdfJ8s5+km+c5EAq6hB1rRcyCClQWdSYP92T23qPOlkkzOavq+fyqsrx3uu7amtKRmluhogXcBPcmt550x1rUCZc8yVnNw87PglYGKwZD8MoqvkPs0guj6nZKl8On1yTFwidydtVjIn/aY+CTaYXJL8To4J+JDn1JbjYerbJZ7+7k4GajLW/hw5EKFJkInPncbkeDm90kd3gEp/cjl62myv17jZX8eP/ezaIfvgNTcjQz21DDwMpHK606DKO+XfmeXr+2kPan8mHek17mUt0lN8KiPlZpvUkfNl2rjTrbN7lzdTvtBPJ/e0wX8VixzfD/y/sU+nDV29ZMsaB1ycov6nMbioJUI38ClA6ROkC+R0mP7uEpkGril4uwEhrYAW5XV9JXIyUVY+cbtgTudukOQc0P12MrROk9Em552MP81oTgEiOSh1lnSdxkKDmnN0bdeBGAYLl/BdkKG8ztbTadGUFLS+m61M4+xAZOrL1U3XOdZTMtPvanNubKfgyrYmWadxdWOkv3lyiv1N48pyqxjVZQgEVZeUh37kxtEt85EmMEy7STGMdfWTPkEA5uIXbeGcmKt1OXPJPrQvzS8uRjk/pczUyxTvEhFkufgz2VvKB1Mu0Fm5XX+8xjgxBibfV55Xfu6uOZu7bbo1gNUvZmtyyWdlALzH624Q1VFdsNG6PE6qBuMCrRKdmtPLyeAc745S0k/TzypPMk4auZshdOSWKLXfBJbIn3PQHreyF57u0k+dVUs8rJKEsyG2ofphuxpwJz1rm4l3F2AYKKbTb1PyYpvJZ8inJnzXtutr0jd/U+4028kDK+TTtU0ZEv9sh/pJG4ldezWuOvOk5ScbS/pwfpH8Ic16ubKJVslcP1nPybg6jMF+eZ0zKK59F0ccyE2yTjmG/uUe2nf0kHjvdtkebdbxt8pHrk/WJZjaOYTTtuX2i048XePh1YGrie8UJ9Uu+/Sn2oHSbZ8gvFWAdf/995+94ZuDmoJul2USZDDm9akfUkpYzghdktSyqX+Vo8u7+k5fE4BJG+5p5H0vbbZ2wV2ds/XnANgqOBC4uASq5dM1NzYJKCo/znFXttq6IsDSvqd9QY5n1tG6CRBqeZZxe0S0f9qIkgZdUuIh2b1ra8endCxcm8l+qJeka7c0RR4cuNDv6XBBAr2MVexDx9stOzke+3cDrQQaky/QbqYYxfJq+0ykTn6n2xTzUnkFVzpD68DCSnfJ//Qet0l0H65dp3+n6wSumtwDh7OZKebwM/llitXalssriTdHN8OMMHl0cSLx4mTt78wptw2ybg1g8WRLf3fKTM7NIOk2trpAoH07w3cBITkjn+hTUE2GOPWdylDWVXJz/LtgrG0z+Ls6DpCxHh3VtTUlx5WMK7CU5O267GsVdBMfRdMS5y7Prh8XPFwbTNSafAiwGIRdcOu6q6TneJkSeF1zs1DUwUp3lIP2t7MEynq6l8P1oXy4ZRHOsrqEvtKNfvIkrotLBDeUjxvoz7XPKaGROFvkyq4AQOrX6cgBLPattp/60j6TD2oZN5Obyupnsnn27R5kdmLQJF/Ko9RR0oHjtz/THr6k5xvRgwNz6l+uLwK4BK6czE7+iwNYLsC3sllmAgduAJp0c3uXddPI3bc++Tgg5MBfMkRts/vRPV0EMTqdqf2kAM0grvenhKfBISValZfJw40Dv7vxSuVZxvFAmSawpDI62VdOr/0l3ov45OPGaDXbc25Qo86m/slLKrdjW84mWNfZwAqE6RP/VN7J4XhUfavfuxNi2o7bXO2SPWMF95BRXxOInOTkbwKsVEZ1qjqaAJnKtgtw6OOujrOTvu+SI+s73im3xmyOg15LtuXi+SQ329rVX/JVfnczumw75b/VPfLkaMoBqe50uKCur2aIrsNDjv5jfwrKyGO3uQJNK7ku5jUNUxKjkzlDnZzFDfiUnKdgRXLAJvXRgaBAlAaE1oE7kUJ5V6ClyS3raCDShOL2NGk7LqAk/WlfLqg53pt0f5s7ZeMcyBHB6dQn76e+WJ+yqr76kzpO4MIF15RAHT9JB/05ASue4pqAJdtRG9Pk33JPPkVgRP4mnllH21rtcXT9cJy0vOpJ7Yr2zfZcgk+b+bWNXUDh7js9s00uLTq/X9nUVDaNm9ORXt851eV8wtlXslf9rjxM/TiQVuTAN2We/JPlNd7pkubKDtzvtucdgOHI2a6LhUr0d64OTTm0KPlM6l9fjUJwN9lZE31uZ6zuJM3aOOiggw466KCDDjroLXcGK/3ZF85k8WmDT2Wr2Yu+52bH0pO5Q8KurRXpkyP3CKRZGfZF0qcUJ4O2454O6nufbkpPe+npnTqZeHVPzN2GzhjszIgl/lin9a02Mz2pU3Y3S9F6Sk+GbnNtl289T0+Dk3x80nL2kp7WnQ6op50lYL2m/sFZL/LS11ev8FCeyLt7yuc+mtWsS7rHGae00d3J6caRfqZyubF3NpPGOLWVfNHxML3WI81GOR93MxJuKVx5dZvy3Z6pSY86bl1/GoukbyejI40jU7yZ2tay7juv0Y8Y52kzqgdnz6lfx3fKAy7+q5zkTft1vuOIM8/6jwcHqAN3Ilh5dbPtLHubM1i3CrCmxON+6yfBRFNyLAcSWIb0pgArx4srR9kS8HAOlIJjt8slKhqwk1E/3XQ497T1J51qCupM6lNw4linwLgKYjwtx7ZSgnc6U1m6PnVF/a+OMbt+J3LBlEHfHX12dpzu0SYnvSffYHtu78SOn+xuop7am5IsX3swkYsjzpb4fQIOK9mbR930vkouTn/k3dVhYkxJOCVebSPpQj/T6WWtR1+kPCxfRJ/Tk9Muh3B8+HDg4hflWcWWNB4pbzlKY5OWuhX4pFybYnoa51WOnuS+2ZCND7TugIbzZ7WP6Q0A+uB8m3SrLxp1AdY5zFSPhu4CSzIqXqNx6AA7HlJbiW8XwF07zkgnvUzyaFBg25QvOZeWV6NcBc/UBuVIICoFn5Q8kn4S/1Mw2Em0zr400CUgwd8cI3fP2aJLSCrrJMeki5TApiTqxsQBUiapNMuZbMqBCurf6cnJ1f33b46ZflIe6ngFsCZ7Yn1HbtOv2rQDS8mmnGzu9CP352m/OzGcgI3XaXvpUIjaSbJbx1viN8XFc8bO1Z1oFV9S/zsnP2m7O/13PY6JOwSm9/XTvTZi6vvGxD+d4WdbqhPu9VKdORtQm6FtMkavxu6eBFiTISen0WS8SqytYB5zdkExBQQ3eFMy0/61vBrLakCZMJMBpf7cU9eUDBIlJ9NTHImnnbbTazoSYJiCncqXNpWvHD0l0gSSpuXTFcDSdxq55KjkAgH5TUl05U/p/TxcdlBedhKYkuNhOkBBObVdJjXtw43ZTsJhH22LemihZdfxTP27cWZ9fjobdDJPiSgBrO6bCcclKqc/jok7tZfGyoEG8pns2PGVZGe/bqxd25OtqXzJvpTcmE5xQuvpfcq+WqqkbvibwNT136RxycWYlPtcDlzl6AfCdoOVfvndyZ1k6LpTvL3IP5XTxMRUROd0wUHJJU+tz3KuzRToSGrEkwO64NfyOsBFnfQ1N3PkPrWeS2LkX9unHpKDur0wzrFcn0m//OeCetLvxIOr0+26YKptqh6SznT8+9rKebVfB1xcknNtuPamYJUCDv2ml1PYxhTopv7d+NAuyYvjMwXIyX+1/gpkTD6V4oezR5KONf2nTxgn3ikD+0kJKYEcty9OifZM0OB4TNc0hjg5ekzIW1r+pD0lP3MAZWpv8m/nz6yb7JG6X726RWVLtuj6cbFLbd3JkcZ2AkZN6QT6TgwipXgx+XGX4djunpi9W3Tr78HS7zsO4wDTjuJc22oMbvqdZd31VN4FN5c0J4CVwBHvE4joE+vKoZLuk04UQNDQOTOj7Tt+eb9/a2J3rxEg39SJ8t/6SP053SSeUyJVgESdsH/q0rU/JbekR3cv9buyqaQXN3bOp1w7icepLbeHTMu7WdQV0KPOSQ7oKAigT6f2m9IDivN9gp/WgZbXV5pMlJLTVDftWWHcncAl++d4KP+TrrT91svqHV9NfBBNdpryAceMvK4AUsvigOWkf/YzgR3q1j0ka/zuMu5wmfbL75SNdTizm8D7FD934of2y8kDl9Mcz+lw2MUCLAcuVoGXCWkyVjqao7rX76dyzkCnWm2G5YA7JyBISSBsShLdvguKKcE4YJT0n54CHK9NOpvjDNlNd6/Ai0tCznkcOeCzCo4pwDn+kl05G01LqgTB3fY0Tg6QJWDheCPw1P6ZAJVX9u02DTs9sI9kd04H+l3rnbvHzX122ckfmrjVgO2kZJTKJ99WGZj09HRjojQGTr963Z30m3yGfE7EhO3GSr87n1HgkGxT+9LfjnfGTnfaO8lPPaUY5ijF7BSDWMbZkMZcx4fyyZnpBKgmn3G8sD9t043RffJiX9adMMAqjqt96EOR9ptyzcUBLBpDCowpOa0GQhNBMtguw9N2el+vKV8rI+tPN9BTXfajstIpihLIIt8Tz81nffbL3Bwfygv7JbAi/ytyyV/bnoLHdG1KGEyALknvJJpkE8l+djeIFu0csZ/0m5J+ssM0bgRxaWkz8TL5UgIBRaor6iDxtrI354tTjEiAiLrlLIsDRUlWjidtkDJrWcf/DrhSv3fxz/1zNjHZsXvQTQDL6dTFePYzxUDOeHCmrO+5WafUppZxszZOH8n+U3x1bbrxJ4DgfW2T+p74W+Wpc/Skfd1nDta4+lpHc910elb7SrZD3dwm3fomd/3tAgKfkDnlSfAyKVMNpweJ7yoiLyuHmwL/ThDV/tjelEQmPWo7bD8BFBdoGVg1UE37zlbgMSUJfjIRrIIV9eeCybQ/KiV4JxPbdrODnLFL9sC+yBPtsMqW3TKIuLFj2zqGE7hKCVDbVxmTLTj9ciwT7+rjuzbDzbPs2yUNd3qQvHFmw8mYdOkOEzh+HNhge2kWK8UBlzQdL9wOsErkbHuKBZ0UE9BkbEo26Mo5WZI9qqwuFk8x0tmH65t7k5wdTnHG5QL6MvNYy6OvaXAxM5HO3vXDtcuZ1EnfW62McFzvMxMmKx/XB+/VKpLqbSc/XvQfe1aiUWhiV0fVgKfKdpv4JpDjkmbzxvXrKbB2u/zO/p0RukCqdV1/mrjdOnsiTUDKA+/RcRzv0/4ubZ/fnd77u/bvnnrTWDlQ4fhIgS0FNVfWtT3ZkgZ1trUCDSn5kWcHwFg2Tcmn9ly7qZ7qbAUYViAr9ZGSL3WVbGEa344druxkG1MiPoc08XCvl/bJcm68HaAij6QUh7TftKmZ7bpx0qSY+nHfOa7dRprFoO+oHWq8nOL5Sl9JBurRjZ3GgZ08ovWdbvmw1QCLAKxl5QEL9VEuj05jQfn4Xku1TbeP8j7jaxxP2rjGe5e/XdykXadYtcqb9yTA6s2kjlxS199uxmlyXnWy6R8Hg3UdpaSt5bkxNSUEbWOSaUq+zvjYLo2Rcrg2HBhZARunn7Q0Rmcg2NPyTgYGkRQoXTJhGxqEVgDDjZtrf6rH345XZ5/OjhKA0n44o0Jdq22Rf+WTdufGNtmvC4zaFvVBQO/qqfwJXDk5yFN6HYXed/Kynf7tgj37djrg50pnLn5p3cRHihWatPnwmg4EJRmd3U3k4iJlT7bF391GmqGf6jIuJBkdANA+km25cm7v6Eq2JAvHMcUr6iXNyDPnun5VZ9rXtckRq7wx5d8EoFL81eurWeuLmMFyju6mWN1eHxphChJ6Pw3Gak03Da6jbm+qMwEs7gVzwYCOQTDHdXjn0C7YOodIgUX15kCN9utkpM7dE66TLwW35NRJ/5STfK6SrbbvgozjI42Dk8vxuAocfDJNwcvJzNlal1iUb8cnN9C6sUw8a/tTMk86c/6RdKkPaJRzkk/rO3BFUj6nzekuHiS/1PKkKuceTNR+3GGe/ucAMwHWlOyK0uxSOnDkVgo0ttAX2V/6rdfSzIqL0xwHBfYupiUenD1NeYh1Emhx9qjtupibAJZrqz+TbpIdq2040JjsdRULk7x9z+lmesh2dro6OHLP7sFKA7xTxoGsrtOfDpgVuadhN3COJ/K3Ku9kmACW8uMSgNtEq20kx3CONQXxFCCcvsgXy2o/zvgT8OgxVNkT2FuBK0cpALqklHhl8FXdOid3vFFHLjmyb+0zgQMno+MhJQ0GQLbFxNjX1CZdGbZLXp09rRKq2kePWQIMk/5T/05vKXFou26ZQ9vhSbadhKL11Z8SECXPjCEETgQVzoaS7PTZqW/XjsY+giyWc2CRbatedXlOgWO31/uPqGf6tQM7zoZSDJpiU4qNTt4US5M974CdqU0lpw+2leKWEvMc/+3kMvV1tR2NB03JHy/2PVhMEDvG4oIcleaCeTJWOht5dO0queSYktVkkOl3apeGnJ5Q+LQ9nap0fe4kbucMKi9/T22r06gMqawLDi6QTGDDATRt1wUZLav8aP1dgJVmFhyoXAUzlxBc/5OdURfUyWqfxNS3A26TztImcQ3C54A36pJt9X4SrUsf0oStMSzNsqhNpoTleHZLNQQejG38TBuItT5BFSklVGfLKbY4HkqWfjdTAg8phqf2nR5dLNGYr6BwsrUum/KAy2W0L5e/nO6czSTdTuAk+ZWWI+jWMonUtqYcTjmLqBv1pdVsb9pH3O262Nn3+pVM/fu2Qdbt/uXDgw466KCDDjrooLcCuvU/lcMnFaJKnvrgTNP0pO6ePhUxE/lOTwfsS++7px7XjuvHPaXxyYWI2z1hst/VE557mlE+3XX3ZKe/pyeX9OSZng45RpPs0xOUlnGyu6fdpBMnB+VL7fKa+86nXH0KS0/MjufWWz+5s8y0lyU9gU6y6di4p/Yu42aJd/vVdimryuWe4Nl+epJWUr6TXE6+5CMrf0u8cQlwmpVwsz2TbbrY5cqpLNwoPsnm9D6NQZ+Gc/KlmZ7VLI3jT22RvlD/1I6SfVDe3Zmllc56iT2Nq5N56muK9UpuhnaKDc4P3WxrkvnG3NMY4mKv1lO53X5s1yf3N2u/F7tEqEGEGyGdQyVjbuLA6wk+FxynQMT22L8zdAck6ATO6J1DMag43lJi0O9qhAmITEFWy3HPiOM90cqQOc5TkCGPRQmMpCTn2lgl3DQeLiHvBOckH5NraoPjrX+VgAdGitzeJvrDpCfqgO3TprTffucc+3XtrwIzy/Q7fJxeUpDue84vnE1PgCUlnkl/rr677nSckqaz4/TeoBXgYRwiyHK8qY5dwp30ojkgkQMQjm+nF7e06vqnnFP7JPU5BaNTnHavp3GyEQSt9qRSNt5LMcvlW/WT7jv1t7LNIpanfGpjLO/4JvBK5PLeqs49D7A0mbhyKTCo0rlPhopLwVXvrwCDA2MumU4gcJUwVwGDeiAv7rRO4qn30LDN5MApEU+fKm9KEI4/PkU6XpLeVP4pqE/gwL23KoEBLZfsYarH+k53LjCm1yvo/R5jt7G566xeRZHqOblVPv2dNpgnnfA7x8a9msTpRMs4HZL/5J+tp/7XNjkd2nDyuPizE/dYJ+nKJT767JQM0zXX13R9illTOxwntuViCetTR4wfBPr9qTriPkKOM2dDWG5Xf1PeoQ7cyVXHA8e8Y8CUd5wuE6hxZV07Sf9Nyt/uCcdVnFJ9uL1czu8v8hQhp9PT4FEJDnDpALpA1mVcIk8D53homl7INhkJ2099K4CcAqvTQ+Ihya2bPJ3xcdnFAQ+SA1M9ProM4Hg/F2ywvgYWyp1ADcunshPtgMYpCbO+e5LnWLqx0DHrskmXfMJO48m6SXbVYQIp07ixzk4yn7YYqG7dGDv+J757lqwTKeu7timz87GVvHpvN6b0+LoDBxxrN6ZTbHaAI4FKt6w59ZN4VB5c7kj6aFJwwlnUogTYGYP5T8tMPKt8yrvbfJ9sgvGdYMoRAZwrN+l8FW/Tb96j3otcTNLfO/0yJlI308la5sCLnMHq7xO4SoHPtTktQaT6adAmB+dJhh3QMPFNmt434pyfbXWAndb3mYxcIOl7nDXYkVMT2/SkkgLEFMg4RuQtLYXRsfS1AkmvO3bnAuP0UOB42kl02jaffFu/GlycfRAwJD5d0nR8pt9qX+TXBVWOAcEBY0Tb1ASudv0z2bDyn+yUfsO+dxKF68/Zv+uP39l+el3N1D750PLtWzuJaYorjs8EVJwNngOynJ7cCdgpZitgdMvviU93L42l04eTZ7Iv6ncVizjWjLPcC8u2Jx04uj5jhs/V7dxGHaac4MZpun4xM1huapTOtQJYCcFqu86IXWJzQdkNQkr6Wm4Kiisn3AGCk6PQOfqaAxI8HpscjMFvShApgDoAQ9053bjEOQGBdkB3uIH8USdOnkQuqGlfSm6MHRjQ8gQVU0Bn8utxZXLV8Xb7tZwvaF9uP0+yZyZqBnkusa2SkdOnA+1pI7azRdqWC9opebU+HJjX8pMNTfpq+dQH6C9uQ7Ty0GXIwxT/GBeaD+rd2coEALS/FNd3bGmVEF0sdn0wSTsbcX7b1/r1El1O36O1MzZKyf5Xcif9uj5TfSfbikfGOfUFp+9JjhvTjpNL++0YTzmm8XIzhCtbuqdPEbrkUcQgN713ZAIkvOccz+1Zck/aWk+/u6Q9GVDiZZVQ+jrBQUosDhRRX+4pQPntTcN0jqQPbT+tubsEtiLnbI7fHRvb0fPkyJRJn/CnDaqUZ5JzZ1Mu9cHgwSSm/LZ98A+77pALxEzCTh4HsJooJ3U2gRwnp/NHBRoTuJrs0rWhPDpdKb/0Icqa/IW6VpsjL4n/NHM8Je2i9J6j1Z7IprQMXZ98/5WLM+1/nEVJuqN/TzLvxKMVyEo80F41njAO9fisHnSd3nfsahVvEvBx8nEjOnWS8twUQ68xq7473tpfykXsx8XEi3/R6CpZOsdjnbShjYkoBbMuz09Oh9PZVkap7bnfk6xsi47Gftro6Zhs320Edom4r6vundG7Y//Oqaf9IJNz6nfnHNOMBZO+W5NPwUGD4gTGXKByAXjSjbvXbab9hWk8HFjQdrk0mMgll7Sxvm2PS/RuzJWPBLJSEmNbLuG5jf16JH8ah2QH/UnAsQs0KEvzM22KdjN8ya8Y35z/6/1p7FVPDsg5ML/yD+pHN16znOrLgYxELk66WJgS7KQv5c/V7TFlfwn8sC2CAgLMSb+Op10/4mk9J6/mHPppAkL00dWS3Q1sZOKL+nF24+yWPN4NkHXr78GaAAaTjVMy6xIYTEF0cm4al/bnHFG/rzbzadmUfF2QXxlEAlWqmwQ0Jz7orK4NTT5TMNAZlN0N3dQBk1r3OT31aB+cWlbdaF9TEif4YnDUT21DaRWQlDfOjvHU7GpDdvIDlWVKCJSb5bufaUnSkYLj1cOM46Hvuf2FLrGoLadk6sbC2b8CINq001m3RzvW+wQxKhP1ovJx6TbFium+zijRj1XHq1i8CwZWyd+NQVp2Y78cS24md32kJM77KRbXtdahswVXn2Po+FBKcYRlXN8uxnNmScHQtAfMtUe9uBgxye/K01e1rhtHl19dPNH2LxZgqUJ3A4IbGJfUV06uZSbAtKo7BRq9thP8WIc88ckyGfAUZIrc8XIHGLps0jmdjft/kqM/nLFSORjsKCv70yTmgkABkZTwkn67vNtHuBs4UqDW+mlm1snMwOkCtgYn8k6geQ7AUl5TPY6H6iZtxFa+2A7l5tKKEkFqAlcES1Ns2PHrNI7Jbp2etN1pGXblV84OnD67H84ME2QlSn7v5HJxPPmbA1gEgU6PO/FG+VH5nB33P+5v7et9UnoFslrPes09/LFv114TH1ydrp0+NN5zhltjXfJjLef0O+X4m8Ws4WRPK2I5zR+aEy72jz0nBRalBLQDhnYMKgXPyam0veSEqa46uwaTtNlSnVDf3cK9VtNTh/bDPVXulQk7Bu0SOuWdgoNzzBXQUN6mUy2qQ16fnN8BSgZv198UcBzfSTZeI+jZHRPaFeXQ9vtpm210wnC2MI0V65NHt6Fd92a5tlgmJcKu42xE9eLscwI19CnytWu76bu26wA823MJ1YESZ6fqF6pDxwtBOPlMMZt61e86K7eiCcCxbccX42cCobRPNzvOck5+1Zn2P4E5/tY63Oqx0oXTgVvaU15ZbtoDxRyi4+jyJe1wspemndhJQLrSTbehn9T9Thv3LMDS3yQa3QoUqUNwz4Wrq4ZMx2ciajCSEvQqWSbw5pKvytB8af8JnE1r9S4I8z515fh/OImGOlFdO91Tp6p3pysXsFY8JD2syrukrDz3py4dJbtN9k95XN0de9MlN+rHHe5QUvtKQC7VoS8qDymBsR2OeZLflSPoILii7jmW5NvFDfoI5U/yacJrXtwDQwL6WsbJlJIJdcrEu7Mfj7LxN6+7Mes+ne3Tt5U0xjmw4HTIcVzNKBGIJNtUfaa2nI+6OMrv5D0BOcej8xeOebIR8kO9sG6RLsu7/Y2rGHczgCuX97tP+v5uHE/2cbEAqz8pYDpV5JxYSQfa/VkOfRLeUSrBFeu5pO6SvUvQSfa+7tqmHDS+BACYJCZHn2aHHFBYkUu4XJ5iQKKDK7nk6dogD5RVyQWTHbn4ewd8pTrKixsfVz/ZWpV1f9eNvLngt+LdgR3ytNJt8odJN+TXzUy4f44XlyQT3y7OaPzhn+fZ0Y+CjaksaQJcK3tp33Z+onHFtaH+m/pxnyzrxp3Jc4pRqQ7rarxTXWs5F2NXs9fan8qTDlCwn9RmeiDjGO/6f/1zm8Snh6qUyyibroL0Z8WaKSdQpvvMPr5VfKNduXiw0o9eT9sFLnKTO53BJQZN1KxbxOnKnvXpctNpJlJK2HTONHj83HEcd3JH5dRAMs1UpaTaRsXZJgYPBgXH/6Q/5zRu7Z7LUyvSIJ/AiuNB+574d0mHbTk5NTmxTjrs4Ph2Y96f6ck7AQSduXLtu+Sq19VeVN8EtyQXwFUXtCVnxykJTf62AjWuvdV9l8xd+510EmhMCdAt//GTY5x4Vd92QKJ/E9jpGKxOlSb/oMyTPbs9gQ5sJbDb31fjRD+fYhkT7ar9/t0P3kU7KyXUBf077S1z/k3dNKDiderAxaUpLzW4antJsZSz3I7c+Ew0gS/Hq4sPCYitgN2bk27vnfEHHXTQQQcddNBBbyV0q+/B6icGTu/yqbdJ0bh7StiZUZruT09OfL1AmmbUp6BplkP7cLML6SnPTaMrT272Sa/vzgC5Wa6E9qfZA/dk656G3VNwajPNClAHbkx2ZiSmJ99J9h4bzqamp1GOZZoxSifoyHeyIzc+aaZFZzman+lkE2nS1e7sVaq7sq0izs7s2uq094e2657806wH+df66YmbvLlZgzQz4U6G0v4489Cnxhy/5J16SWOV9klN45j6SrNnUzxS+dQfuV1CeWPbPNxAnXOmeGcLCm0z6SbFHsb3iZTPZFvKN2Xkpn83rhrL0jgpP0Uut6SY0fdWNNnMbc9SvUUtERbtGAwHQgfEBRM6V5dLScgZhkssDtQxmLu9Di44at/JGJwBOlCh/bq2mUxWScwFQurN0QSSdMo51XXLCH2PiTTx6XSsgU2Dm9Oltjftyeqp/QRAyIPyTB4cv93HKhlr3yyjn8qf/ktB3R08cHs7GPyTvs6x/wSEmuhvWo7LZRMY0PqMJ9Sr8zenxwkAJGCWQExqZ9Jd0hnvNR/6J5O0XY7z6mDETsxJ4DLpQK933+SNMlF/ugRH39KytKOJJp/idZKL8c4OKB/twcUO1waBkpPFjTfjInPpJEfqY3WAYzeWub6meKdtpnpvFQBrBTZcENHyuuk01W1jSWCGQG0VKNU5CQJopExSLpFRN85wONuh7e+QC2Lsbyo38ZnuOVlSQKrPFBQSIJoSqQNYbHNKUI4PbrJmIKceXYDqf2m/lOMh6YS63El0rm3q1G0kJh+aOFfjPMnjxs8lEgeKUkLSclo++X9ROlGsZRvInRO41Q6VB+otnYRUfax8jjy42OrAQQI4CVRMdahLt1co9bNKvrQzxtWkk2mT9Uqf/Z2AjHFlikWJnA+5GDiNLXW+Aldst/vnzJX2Qfl2ZHW83gyvwUh26/pPMk4g7G7RXXtNQwoYBDoJ7VORbpZiAldMHi6YdX3WcShZDSgBFhrSCpTQIFeBiHKsDG4V4CbjZHLgdT61TAF+4sU5vZNvAlmOdyeLS2pJ9j5UwQCVxlF56k8eQaecyTecHMnek/2l+6vZTk02Chy6X5Yn/2xnSowaC/p3X9OEMul4J1m75DKV3xkHx7/ed3IrYFgl6gk0J5m1nitP21X+pwMPjH0ce7fkrf05/hkvUmx0S6SOvymu8MHJ6YU88YCQAmlt140Bx1eBwy6YTnlKwYeLkSugTJlV9ik2THH2Zniwc/WcPyYc4PSrfSUgdpEAi3uvVkjUBRBddqHx6B+z5QDSySanSnUmgMUlLOf8/M5r7I/gjX0mR3E8umCWgIXjbeIzyUNed4CzBgoX0NmOu6dtuqPx6Z/rY3JiBsTJnt2pQ6e3FFhTkOJn8qXdmVk3/k5PyZ74JL0K6skfmNxVB0xoLgFOfU2yOB/U+NAyum0BtBXlW/mcgj4BhdN1Ajpabxprp29XljpwQJFlnQ9P9rrqn7xrHf7WmKs6p904XtO9NLbUzWTjU0LfBToqI/mlbU526OJ8t8UHPvrhZDsreW8wW5zqnWMjqa/k59TBRQEsvoq/P1Ny43dXXpWlx4+nTX5a1xmro11ncfIwyacEOhkCHckF40SpDSebq+vq6b4qF8RcOz32PB7OsUwBVPWum3RpD9puAmkJMDh9uWCa+nP22fwycU5Li2y767mxdvZEv+Dsg/Mx13bP0FHPrt+Wqe1C38vFzfuJ3Fg4Hfd3J09qa4o5bJd2wzGp33pkP+m/eZzshGPBcUjxMdlt0ofTT9dlvEzgZqVrva988hDIKmkmOdJDleqToH4nvqWYRV9VPjTu0Vbo3wmcaHkXO1JeSv3uEn2esdXZZwLXSSZX7mYxOzvlWdeW+70q17nj4gAW18t3QME5RqP10tILkyXva7kpELuAydkL5+QJWJEnl9S1L0dTgFzRZJQqwznvsko8pOCXllFoC27zrHNaFyRS0OcYT/qbbCfpgDK4jf96LyWzKfGyT/fUSr3qP7VfLm3sBERe02Snm9Cn9tgOeaGMLefKJ9wY7/JPHbhESt76H4G1szkHhBNvTjbVQ9JtkpOzjWxnxy+YhJkknTw7II2yuERPPaQ2XBxZ+bi2yT8x5uKl8tXbB1xsJ9+O/xQ32J/2m9pK9QmynA6435A8T3s0XXs35qGRdpxsWHWxiiFpjFum1YPePf0md15zydw55sogUtvJuJLzF/GJq7+7pKbgyi2LaLmVXvQFhgoOXIIkD85Qp35Zr/tyT2Yu4SdeVk5E+VYB2Y1NJ+3+JJBKY6X8sO0d0KTB2gV//T4F1dVmfga3FU1Plwp09H5aqnVLDImUX+Vb+5nsfvLDxEvSb/LNZAdJX27M2Odk+x3ACZhc26ovN8uosS8lzCk56dhMeum45ZK2O1zj9JWW06c4lmJ5AprJp9yn07crt/JTzu41KTjV08WsRxk1BjF2ubwxyUZ74Wla1tFrasv0tcRLl3d7y6YcdLOYXWX8duUYe3dsyfn5xQIsl8wnkNV1lNSJU+CeAEW3vZsEug6TNz/7Lb87x5rZHw2GTs2AvgqWDlhOAdLxRqeZyrpANYEk1RHr63WnK23DBS/qiXJPicnJdg65wOgAQOJNy3AMJ/5SEGyawNx0ylD7Tf7ItrSszlgrn5OOXLsM5pzip06VHxfwXXxhPHKyOj6d7qnPFPi1bOurf6eN++cs5bn7XJZ2D5IaH7l/0ckzJbz+nWZEEwBkmSSvG/cEDnYBgLMdV67102X1wXQCb1pXH8hdP84WKcNOnHI5Q79zRj2BK73HiQR36OQmPKy4Mdf7iRI/ei3FVf1+bmy/JwBWLy+tEv+OgvV4vLaVnja6H3cU2vUxORbrOEC0ej0A20mgqNvXviYduaBCHbiAqWVTUGAgS7rTzx4T8pQ2UrIdp6vue9osP+mI7a5AEQPLlIC1bAJ306yMkupoeuJiUulrbjkq2XVKjvye+ldKr1OYdMl2kq3pg1Hr2AGF5P+Ob6cTbTuN4yoJqe0rnw5YOb7c2LjknZI/AXRTL10l/3VLjZOfOD2Qbzcmruw5wMD9Jk8s6wDWaqa4yyR+XSzhwQvGdl2Kn3wy+af7VBtLelnpw+kuxappqTa1cxNWANiOA2ArYKz3XB51vnxRAIun/xy5RFnk1oJJycA0AOyAq9RHSsqOT10iIH+ub+coek33PqWgmtbDCTqYAGn0LqBMzsMxS0af5HX3pzr6W/l1uk08prFIwINtON6VDwYRJmwn61QmAR8uU0xBWdti8J/07tpIY+PscfLJVT+pzgpUTUFU5XfLXwnMOr3Rl0hpn5+zVz48UV4CPgcOXDJJPpkSK+Vi+5PPputqA7RRF3Ndn6vEmMCV9r2yffY9AQFS2gNHP9SZK+1j0osS7UV1NOUYtstyac9mmtnSMXF+Oi3zNqVc3veSXfNgVcrHTi9uT+fFLBHq98mA1BldEHOGrADLHQ93TpuStuOdAY68qIP1STn3mgY388aAm4y3yD39TolJ200BRdvb3fszJReOLx0pAQ3lqT91XMkz9c5j4c5O3GEEp480s+DAgupikmvnRJT2NwXhpDf6lfLEALwaB5Vb+3D2RRnoI5OcbMclQgc4JtLyHO8pITldka/2cScbyS2/sE22zbFjUi5K4GqybdptAjRuqXCKfzvk9JnqTrEqydKfzk60bxe3nB6mGODKTv7p/CXx7HSbcqRr3y1VOj673gS6tB/VH0FWAoY34SGeY6O8sK7ykXIBdbTjwxe/yT0ZpDuVk9DwBCxcPxqwWD6BNv7mADHZp1M4qX7LnAAVA95Oku1P9pWCHJ9eUiBjH/w+6X8yehKdzQX1la61b9f+RJ1AtV09GcRE454mVTcTWGAdlxRcvfQ96WEnMSa9TPraGUsHHshf+s1El9pKPuna0/I6vqn/5EtO/l3Q4PTmZFOARZDIuq6vacloSkrOPieAkXjp+zv2lkBTIucfSR7qla8jUFot2zr+eM8BiFW9LkcAlMjtT6St7sYDytrtu5li5mS2Sx2lGOVs263+pO1AXZ5xmj7NfHKbdFdPEfZ1GpSicM4EpWTq0PuUgNxUofadgkQCCHRKHWgHLqZgmfialh27nH6ma7y3CiYpELEegxjBW+vF8TCNS9KF/sHa3SSSEq/eU/DGwKLtO52o/hgE+rpLoC4g63ee7lT+CDIdyFOelQ/SNM4TMKTPOV4mPtLpJ9WX22M5ybAbVJ2ukx3ow1DajJ70M/HB8aMekp2tYofTo4IMlaP51d9uv9BqT6DqfxU3WI86SfLofX66pVkHwrRMasuNxY498SShy0VOrq477d91lPzc3dMyrk0dw2Rnui3B8ah+8oCZbVUdtZ40V7h8SrnUVt21NPa3CbJuHWDtgIn+l15OqmVdAON0Og2JydwFnwRIdgCRGtAqWDog6IKZA4k7ASQBRsq1CtiTszFIOX5Yd+XYdEjHM3mnnpzNKTjR9leBhzbCMXA8a/8ERuxnZ2+A9jPZw04ScnxqG66+6zOBiwlo6Kc+NOi9aVnN0SppTeTsebWnxrXvEoCLT1PyS3bH1yBMNuPGKtmO8qDlmPycjSsf2oaLe1N92m3rQX3E6Zb6c/K4vrTMBHyoF7ajfFIWzv448OAewtl+sn0Xh5I+U75ZjcuKUtxzen/APLDyn8qWiPfcPkqWdQ9Kt0W3u+ProIMOOuiggw466K2Abv09WP29yM006MxNegpzTzTdTio/zRDsoGg+/U1PNEXplRFs2/XJY75JR25mwLU/zQZNs1f6BLmSIxH1kp4GnWyuDMutlo107LscbYtPkm7pJz1da5/TE+ekn9Wmzf7tXv2xsrfEE3Wd+Kbdp5lD8uy+p9krt1SVnkhXM4xON0nuxOtqlortOH+bYkqaGXF6bj0l3pOOdKakbSzJ7ZZnpxkG1QeXbrQv+qx+Um+k1Lcbj5XfJTt3Ns1yLt5Pfu/4TXa5Gk9XL+Uh95lslmU057Ivyuq2O+zK9YC8iJXjR127ZWrqRXl0PKkfnOND9+xrGtL+m6YUsFrJLkg4Wk1ZpkDnkt0UKKfp4lXCoqzOUVP95ODTxkPyN7Wf+nNG7AId7yWnTuO0GmM3Vq7Nqd0EFth2Gk+nk3Syi7qYTpi5hOFsRAOP28OV+OS9yR5SQnJ62Q1c1Ff/S2OXbI12RD5dItZ2k152fJDtKFBJNk8/nJIQE53KNAEJ5d+dYuZySrKxHZlXv1NSdAmw/rn9dy5WUKfpn2uLcqSyel/5n2K2jqtrS8eAPuPGgbyyHQeylJxf6tjs2LjLm7RPB/Sog+vhT15xf3XryNmA29Kzyu9cHky8XMSLRlURfGot6vv6RKXJhNcm46DhMVD1d73nZhLS4LHdvpechfy6xOUSmNZNxsQTdYk//k5JZgqglLP16QzZOVs6tp4SdQJMGpwmORJwSLaT+p9eX6GbolU3nDlrO+a+OBew0z4xXptmC5oHBkQnL8dTydm1C3JJ1xNNCS7Vd7pLsq0oAawEPlzycPyt+nD1U0JnfxMYSmPFtlKSdDbp5HL6T7qhTrtvjRec3UgzstSD28PnaAJAST43bpTHAUbWpV5XoID1NHZqHHBjO42HHrph2yvf6UNF999/v82NHOvU5k0A91pPQRDBluqXdSZ/n3LoRQAsJaecc5Sy+p6C7047u5trV8HNGZd7PYTWO1cPOgPAV1m4YMLES57ZZ6rLhMMA4+QlwHIBN236JIhy+kv8pgCtwMPJ5uol+0oB18nV9bXMrtOvdKC0Okmj11wfk1wuWUxLUORL3yOlOuX3lKjcPfLlKNktyySdaYCnrvh7shH248jpw812avlJbgUwLD/FUKevJIcrV6QPHi5G0ia5WuFsJMUtJ7vTj7OdqW0nv5NFQaOCxBRjnU7Jd7K3NAZOXu1Tl3UTWHGxrHxWX1/jclLbGnXSlOzK+b6Wd3HJzUy5HO8edC/2FKFTDsvR8ZNCVsnBtTklcbajQID8T7wlg+9PF4TYp7ZDw9D76X1Qk8HpfW1nl+gUTW76mzw4h3Rjzv7Ytysz9ePqp8A9HZF2/afgpPedLKxPoMJ3waS22D+nwBVM6hg54ri68XHAxy3/JwDidKQzm9PJtWQ/7GcVC1LsWAXfVZB29uWS6ArcO90lmdg2k6fT0+Qfzq66TTdTPtkMk7XzlSSTXmccYR5xdV0cdkRbm8pSRiez2jNXbro//e5sMsWxFMtde5PcTpdqm4yD1LXjgzI8sPiLIF2HPpPK6Zg30NO46XR0m8uBdx1gOSebjJSDOAUsNyDue5flRjt9pxL7a0pI3DlDAgLk2/XZgYwgxcniZHSgbCqX+E1Omq5TD4mPZAt6PyU8B470SPmkF0fkj7ZFZ1UnT8HPgQK97oKbyjHJ4GamtB3ODjrb5PIjE5F7NQqfelPQ30lklJs27uo5WoGklLTYRhrDigk6ZruUbN6BA9fu7qGH5DvkY0qybEttSPlmgkuU4u4UM1THU1xQvtyMisq0oh3bYHnnDwQmnL1JcTjZqKvj4qr+Zjutx/T+KZWJvE45uGiKA9TtA/gLF/on39h/klHb6zanwz4kbc/p+mJnsJxi0ovLktJd+yQHTNpBpnIuMOk1ZxzKL8u7YOb63wlCTerkLjgxqFNfHUzZFuWtT54eSRu6J8CSyAVH5Y18Uy/luPqkqPw5eVI7UxnVmQZ190fMHd8JlLiE1e0mQJQ2xrvfSUbOIO/YJWVws1a091Rf7S3ty3PE9qYkSX6mBOPaVfCXdJD05RIq+5lskPy1nejYTYdaks5Y1vWj/Wkccb4xJUSWc+PqkjSTs17rsg2ueql5dQDKxfdzeEq26/zXUQIsem8FoijLtGSs1/o6l4ddPEptqR84QEQZEy/Xg/zT+NE2ydfkVyr3tJf2nt+D5QSjM6ZlEQ5oSggcSBpASnIuSWmZKWhNiYnG5pySAV11NU1xtvG4QMx2qf/VRkcnL196ygRFXtP+opSkXWJJyUblT3sd0risEngCWdxLkmxjJRevsz+2u8PzOX01f042resSSlPyO+pxCppajuRsUduc+tf4QDvXe11vSrRur562o3WTrF1Gn/7PWYZ2Pp3in8rk2nZ+tAJaThY3Jo6PSS4HKlNM5qskWieJdwf6nL61DGdcKEPikd/TvkunG9qh06UDLufMcurvNH4cc8Y4jlHKHS4mPoC84XJzl9sFTMwBq1iziqEX88ee01OX++QUepoZ0HZWyU7rOMPUAJiMkdTO7k7RUUb3nQ49BS8XoLiXSpMLE73KzJOcLphOScPp3DmslledroKWjoELoARWKdg5vp28rkwiFwiS3hIoILlgy/oKGpTXFJT7nrM/VzeNZ99zJ7ZcENaxUZt0tuYSgdMpdaR24pbvmYBXfaQ+p31rCYD0PTdOmuimsXBypqSdYqqWU9tJACu1ozY4ASXWVT/f0fnEE23T2ZDrY4oHO/XVDiib3u8ylNvFuW4jnbx2Y67ginaT9ObigPKiMjF3uLiRYgN1xXIPhP1YzBXOvlb9uRxw7vhf1CnCyUD7nkuYHaCnpO8ChDPgvscyrKttp35p3JMsk7x6zzlc4rMdhTNGNHIXoKZlWW0jPck7UNDXGTzIbxoP6sTpL4ErtsPgp0nflZ+cUQOc8sDxm8CPa59jTlL9JFveTba7NjpRmn3RgN3fNWBTHvLkEmna3Nzf3Ybcc/QyxZL+1NcKaL0Uv+gXqd0JdNDHGB/SDORkuwmgaFLrh7JkXy6OJEp+MPmb013qb4rXTsaJ6APOZycff+QjH/mgrbCce9WAk5v+n3iiD+xS4kvHNIEcF+ddDncx7Ma0rdsCuO3CxfOduOf0cTfB1V19TUMRja+oN5bqKQEG9J09LqrYbpPHf52RJiN2QYflaHSO/+k0mCYotr9Kkq6+8pGSd3pC6k8CI8czSXl2gV0dVEHK1A7r9liqjleJLwFGlT2BM6dL2gGDzCoJubYIJoq4sZN6c7pN/VHGbj8lF9bt+wzWKfBxDDmDMiUNHWuWcW2TmDgcEHLfea3673jEV2/sHqxYgY30Sg/qm7FIydlzSpRJDxznNHs32YoruwJ+ru3JvtLStdafEqsrk8BVEWeNnc/ygcv5tIsZ+k9j2TRWSW7GwBW4ID/sWz+pn7Th3YGvaxMzWl/pZKr2q8DM2cPKjpy8F7lEqIrpzckaQOt7vcysqDcwunZW/XR7NBYXeCbnX/VJp1f53LIh+Uxg04Gz5FDkcQqYrr5zJtVR87F7QoZ7lVwdx3MamwQiUhKfdDC9kyUF9h1dOpnYlgYwV4Y61+Du+lbwUMQnZfKcdOV07Npg0HQJIvlKSvKp39Rm8tkJLLk6SU8EJK5c/6vYRf7dDIbjaxoHJ8NKv0zSzrcmHyG/O/FEr02Jzu1tcvrR9p1tKS8dXydetL0Up5IOyUsRN9S7Nle/lQhQOj7wZKRrb5W7kq3sAAyXLydbdW0znisxH/Z37ifuT5d3HDkbb5p852IBVitVj2/ydQm6LHVOEFejJVhJQIPfeZ8Jzcno+uU98up41jLplIoz8BRgpvopaNa/Hg89saMOwqfp9PSl45+eQhxwcIFV+aMjdvuTnLTFpFO2xT09UwJNsnOMk94V0Dof0PK7SavvO7CzksOBqySn63fV15TsXdBle04v7nUUqU8HIimD80+NK0XTEpHzEZe0HFDe0W2X4Wkxx2cCB+TX6THxk5Jb0m9/52EmdzDB6WEV51Revo8qxRi27x5w0+GdRFOcoF24/YTkz/lB8qlpBlJjposR7jrlcjzSDq4NeFIdpnzL77Rn/b2a9XN54eL+FqFuup5Ainv9gDPSneDpAM0EWFyg0DbS+zeSka0CU7eb5NkBSGqoUyBOCY98aXstc4GrJLvjyyUmLq3okm0nFh4Nd7wx6LskRSDm5Hb7e/rTJe0U4CijozS+lM8lROVnWoZYjYXziSlJpSBL/VLOJD/15HTP8gy2/KQPUBYG56THlIS0TZesyB9nrLkhmQmBRJ+gDNQVy7ilS7UlgnXqfDqR5eIw9ad2SX0lf+YSsM4MpiTseKBulOfkLwns6HgkGbXOZPc7YCzpSfWTZNb6Dhy5+KZbZ1w8qM/pUIXKlfi6NvGm7T7tuXJypXhBX5oeBNJJ8zc33d3XnB500EEHHXTQQQddIN3qEmE9mTRqLmrU6WZ+Vu/fcZ86E8K+Fbm6PS3TzI/rLz2Fubo7s0rpKXrqm0/YKxlSm5xVaNIpay59OJ6dDHzK0afkSUdNq03/6Ul70od7KqLt6b3Uvz5Zuqcr1+80ziT3tO3Ge5q50SUHPbnj6rnZEN0HST91Ona60vruX5pdaP7d073qMvGQZhrSkzfJzWao/BzPxKv2ob7EsvqUrTP+q7iw4vtc2pk12uHDzWRMfk8f0nZcHxyDlBtSG9OsE+urPqZ2JntnHRfL6J8uNtPmk24m/rgM63ijn0+xkO2z/H3/z451Bivt6U06djNhLUtfSzTJezEAywXDnT0MyZG6vn6fApILzn1dp+bdunTXc4NDo9C2Ob2bnH8afBc8Ek8JLCgP1N0KXKVElpyz9ekch8sB01q8a58HBwhypoTLdhn4p4Cn7e70oaTtkn/qXXncPaGW+izq/lq+tDHYARZ9GEq2s5MYVwmYwbzbpM3TzldtO7t1Sc/Z3erdcMqbJgq1S/cqkeRPji8mOMrndOTio7PrHftd+Ycrxz6VV9r0ypZTzEv3HDCgHpKMrn2nN/af4vUOoHT67L1iO220LpNPs7/+Pk0wOJta6SbFxuvwgJjqM5av9EZ7Izl7bIxwcQCLKFOVyI2EPLLMwNTl9LPbTMkvJQBXjsdHm8dVEGQQdUnckWuXelOHSc7tfqcA4YI7x4AbLlN5tsmZRzpP39PXZ1BXJCa0dhR1mAkEpHadXNN4rNpxiU7HrPez9fU+4OHABG2WAGllU/rZdQkcXPJPCX4n8Ol3+mbSU9KjAwUuqSSfTzqZQIL21cQnbeVtOonGU8HqUyn5aD9uw7PTYdKZjqkrv6sz1bkbV9qGi9euv1X/rr7zqR2Q52xb88UKaLkytM1J1tQuwQLr7rympnOV483J3Ppge6o7d4Jf+eEkRIpXlCe9cmU3pp1jy268nM9eFMByAXsCPS7puiTGMmyDSSMlC/LkAgWXuDSguE2uiaduS9tMCcvVdeXODWIuMJEm0Mrr6kBpQy311K/rSPwyabhEQnkYWM4NFu4dYAzK2ndfm4CDJtki98dPU9KgzCxDPvlb7Zn6Sbp0477S6wpUTXaWiAncyTjVc/wRbLpkwHr8Nz18TMFd4wPjgCtPWdx4JfC3As1Oh7zPGO1kVn/h5uikV8dHkmsFLp1NMian7SP6PcVf2t8E8Dm2Lu9NMSzpycnavLgxanmTjfW1dNhnl1J+aKLsnMV0IE3JjQvjcJdzn0oJ2F3EH3teOUV6GkwBxQVGF+B0UBP4mQaHaN45j5vtYdBJ7fM3HduBUqcLF7QTyHF7qnaDjfJFsMqEPgGNrqtPYi5ION0T2LC8BhfKp2Xck5+zIRcEEihRXvV68gEn9ypgKQ8pKLHOpCM3BpNdTEHMyZaC2pRkU8AlEHZ8KJ9pLHsGlfHFBW8majfOaTtAf18BG8en8+1kS5RPARZPJmo598CoumDbHH/3MOL2zyZbmcZ7si/HqwMfBJ0t86p9tuWAweQzzBFJxnOIsiT/SrZBWdJ+2OT7TW5iQP1mymfXAYA6nnfKTv6k/d8msLorAMtdY7J0hrhrlE65Kbm6gJCCfNpP1MTlNNeWAyOOL2eM3YdLbvqUps7jjmuzffaZwIsbO7cB1+kmjbELTkx0juepH1dPN3arrt2MI3XAJ/HJQZ1c09My66X7rr7jNenB6bSvueDodM1ykyxTYGSZaYzTLHG3vaM3l0gcP2zL9aNJjf4yLVFTJ4wlO+SAmtrZpG/qw8ns+nMxQftTvbp+1IeoA+pe+U5LSCl/kC+dmaQPulknrev6Ur7cQ/q5Sdvp0cmZbCMBLMf/5KO0FycvfY7+4GzOtXtjHmC0zSIepCJPzJnOb1NsSv1f3J/KSYK38Gm2pb7rEpLbl5AMxiXxpt1TYM4gkgy8xzLJ+JVH5Uf7ZjBiO1NQncq4Ovw3BTj2xcDKAOIciUFS6zEAJyehEzlAotT7obredLpxSkhJH47//q28MimwvtZbBQfamdMzeUyAjTxPoGmyPbab7IZ8unqs78Z28l0X+N0sZtKh04k7aOFsnAk/AacpufanmxHXJWc+kBEgTHpPiUtloN7d3sDkLylGO0Dkkil10f8cAKKtpv03KVY5fpXP5GfUBdvUdlVG6s3JoDI6fqe9Vbyf+uv206njpF+2kfxJ47nTT7IR0sST1lN/vMhN7k5xKZA5g6ZDu+Cl1xMIS4lgBxw4PtiH20tFPaQ2ldyRbdc3jYr3tM8JOHY5F0wTuYSR+tIyrp3E5wokuSSuCSWdxNPkymCltpOe0FVXyusq4JIPnQlcgSeVU+XgvZ0kodTypidI9u9k0KTGcg7kESC4fpL9UD63FOz2miVfm8DAyha1TbWV9lmCJhdrqCeOafIxLcuE6ZJj8qUpVml9J3PSEX3MJXSNa65dXWZMIIvARMGK05+SAwLOvqkLd3iBZdgHDwo4vaf4nOzP6YF9pv1OK5pyGPnT9ie/vw4nmXXMXJ+Myxxj5YV2Tvs4Rwf37BLhyqHPCTSsv5rdSgPBdp0RJHkYWBmwnSOzbb2/mwQ0UGmAcUHc6WtyINU3p7FZRvtf6U5P/rlAnXhxwM8BG01201JtAidTEtYA4ZycdA7QYTBlOyn4pMS3Y0tMhu4gCvvvsVNbdnXI+055p6+kD8rP77r/cGcseS9RiiW7PKjtp43SBAfObl2sdDreAVXO9iYdcEy4zE4eUtwhIKRdcpzSWPB3kict2U+ALIGanjHUhxOnGx1vAu9ux+kqjY+Lmc4m+nUPO4BRicuO9EnyTV1P8ZXlnO+5OOfac21zf123pfFq5yHynl0iLJqSvH5v5XPpJCXl+uzlHjWC/peWA9mGC17OoEnaD51mMqAqr38ni06UwJHr3xGTZ0om7l6RLjvQwFVeyqdyuH5cEnd8uSChgZd1XR2XhByIdJt1nY65gZcJbeLLtZX2nTj+nQ6nPVJuszHHhjI4Sglb+XLjpu1OtszrGuh3wAH71oBKuakfLefig8rIMivdKkBYJTfqsvnr+iv9qc5cEm9yS+HUq/vd/TgQkco5ULCKVV0mLU/pb63j9tlxzNOeVAccaFvJf5vcGJMHrT/5uvZ7Tux3+7KYbyYQ5Pxa5Ujtqi+kVzhcLx4itc0EstLDe91z4LX/Mcde7B97LuJyFwOjThsX9d+mKkrOzY3maaDctSkB673ma7X52bXH4MEEy8FPTjD1N/GQkr0m65WRJ126tqZpauo1Oa3Wcby5BEeaEps63rQJmrxPvLky1NUUuFNATvqj3qgnvb+a0XEzLo63lLQoG/l2bTo/cuPJwwoOyDggRl2R92RXyR+1jZR03OGBIj1Y4WaFNcY4HXK8WHfl852Eko24MXH8rfxV9ebsx/FNm+e2iCS3a9f1kfjUtpNtu8TsgImOd7J/B9QSAHNy8r5+ql25PMp7tJOV3SWZ05hebzwIaXn6NPXjyjl9qR9wq01RioMXAbAYpFjGKZGG7QKIC8op8LJOMly26faNOGNRPh3PNPrkwM4BUmB1QZ58OB10GXVQpyNn0Kug5fTT36fxm4IyA6Cr29c5bU9ZdO+TsxVNJpzh4KmXiSfaepLB2Yn26fTiEoPTRwcb3dTPtlUn1KX2y0357h1mSecpqOs1ztRwCZx9Jb8mudjjdObKOx3TjpMtOd0k21RiDHI2mvpIvsllFPY3JdcUd5NtOj5Yl/vU1L9WS3XOF12/q0TMe2kc3V5Fl8M4Pgls6G+X75J9ub7UHlW3yV5cfptys5OR/U98KhE807ZcDKJuup3JH1Ju3Mlb9xzA0mTmgEZRX3fT9ZNjdFnW4VPvVD8Fb+XLgQBnjK689psMnXX46gdHqi+lnalipw9+17HS5bSUMOlkKYGkJOjkc4HT2cTKVibHckCgg3fROceVVxuYtZwrQ2rdO306YOVkToBC+WfgU/CvCUx9OZ0C4kwH7c0dLuB46H0mC8ri/E3L0UZT3yyXjo+noL9Lbqxo445HjWdpjyH1o7IUuVl+BwTYLuOz8rSrD+pXt3V0/fTnYjj+nOGiztKSodPPyhZUb1wpcf0kntmv6pD6p75TDEz+k8Y58THph+2wPcYJzRcuv7pTmBo7mhyAcrk66Vpjh7Z5kTNYLuHzmjqyLllMG5bTAKa9Bl2e32ngzjE5kJqcHF+UVflTmp6KdowyJXrXDr8z6e7s3UqAaXIURwQqqzaZ2F0g3pHdyeqWbVRHCcCsAmmqN7XHvvW6OyHngJerm77vHNhYte30quW4H2wKpBwL2gl1xL6pH7bDgwpMpNyrQj7p9072NL76SZ/etQe3tcDN4qiM1KM+MFGXiZJOyKuLgezDxRjVO3WdkqcCnqbWDR8SHD8pj9BOur7+oWIdR+f/04oAyzpbJsCadJEAYsqRU8xNv7V8mmV08eLa9OdskvdSH5MOU7v0udug2+vpoIMOOuiggw466K2E7somdz6F6vU0i1I0TYlPT5q8lp7I9XuakXAzY+4paXp623lSJM+uD531cuvRu+33p9OLexJ3M058QtEnEM4ykT8+VfMp2D2d6ZONG3unf+3PfXfHfJ2unO6cvvWJiU/ptEVXl7rSp33uD0u6cDM+qyfCNLvinniV0gwS/+lyn5sJ4WsgEk2zV7tl01PuNBtGfTqd6OzHVD/FL5anDE4uxzt1n2bWXD3qhzNOXa5+87Sxxu1JT0l2JyN5T/sgNUa7/XupXc5waZzT+9y6sfJhN6tCHrT8FPfcPdpvj8mkz+TTLg/od9XHlCsp2405AJXkWeGCc2zJ8X6bdOvvwZoGhsGA9bqMkgIaZ0hqtGz3nNMECnR4Mm4abN53xpPuaZ8dwNypOIIwJi5nWKvElo67Jr2okzAgOeClfExjkQJNamvSqWvD6Wq1L4oO6xKgficQ3jkmnwBWSkZJFw7EJR9zfGh7SRdTgpna1Hsp+Ok1PkSQj6muK6t6oD8TDDmdT3xPPDk/5UbtVSKg3zgbYfLnuKdEtgNGqUMCsHSie4ohySZdv+4gxNRWiiOTPlblEthx9ZTvLpfiBgGM3mO/vOf6VD1qfY1zLiYxhmts1zJ6yMWN8Q0etqclulXsSSeWEzBMMe7iABZnXVLgr8HiE+yucpxTppmdFQBxpGDHAcDkyK7vc14n0TpJvCrwc8ExGa0afAJnBG+uPb3uAs6UEFPgnDb1a1+Opx3A0HK5p1ZHlC3ZTwowDJoT/47vlNy0rUQ7tj3J7vzKgdCUQKe2lccVEEyBm/EkJRjWbTvT18BQp1Pidzyx/ST/jp5c4iYImvaA0Zfaj11ccP0waaXYtjpl50CPu0dZKL97uGQ7TrcJCDl7XQFA6neyeW3P5bMENpyeHDn7Ul6TDosaAFNv/HNLKU4T8Kxe//GA9DfpXvORa8v5Z9IJr98NsHWrAGsVdIrcZnGnHDeYPKnoyjkj0foM1GoMaeZBZXOBOp3GalolWSYMBtcUwFxQd86WEqT2Q9mTgWvfTh9JPq3jgANlWYETp1/9nsDLqm2OXeKbPGg9ghPXVwJqSXdTOXdP7ZqgaQKLyv+KH7ateqEfTH7pfCB9sh7vpSTh7EHbcHpM8rp2yCtPJjq7Sv26spMd9BLy5C9OthRDHZhxeiV46n+qd9UjQQH95OEkRuYRNyMzgbPp0AXld8QtEsqDa1NjrrPBNwUcqD6dDzs/U3I6TMS4/oDMaNZvBXIqIycwdoBUig0s6+LvxQEsGnQKFK2waQrcJWOCo90ArMtwEwBSoNRlOWDargYXHeBJbmfQEz8JGDlHcYmJU8VTgmM7PAKr/OoTdJqx4ziuEq4L3kn3dEzdl5F0wMTp7I59KW8pILiAk3TsbNfZvQOn1BcpJU3nUwx0Wq9OUjm745OnyrICtY5/Z+v080nmlS5cEnP6cGBxNYNBXrvd3QC/A4S4b9DpycWGlNgTwNBYlnhhH7RpxkGtp3F1ZRNKaW8jfY1JXuNRAlkptqQY4nTM/jiTk2JtAjjpnovNrv2UqyYZlcrvXR8O8CRydrkaC/3OPlOMov0k3V3Mn8qhoxW5AMRB5lMHA1+X1TIEaMpD4sslK5bhfQccpwTqBlqDdpomd4BiNb3sDC4ReVaekrMT7Di+W0d9f3p1hnPM5LwJ3HCclLh/zs0mkp/VgQvHR5LLBXBnZ2wrLY24IOXA+O4rQSb7mMZGr7uE7/jVBOPAlhtDF2wnn6XOnA87kOT0o2NVnwowJ3ByLrBzNqFlGQ+VZwIb+gSB106MI38u5nBcnV6c37Y8q7ik5ZV0r1ff4/YNLZP8yBH15+6Tx/pU/bj9wQlAqp6crCpPsjPKlnKryz8sw/HXfO32biWg5uLQqqzz/ymus67qmn3s+ug9e4owJbkUEPvTBWK223UTAOpySdnThnlen5L+OSjZyc4A0demDdJsb3VtcuCUqB1gowMwgK7kWwEMN1atC7UL1nGBRK9zWTfJ4gJR39sFWTp+jj/Hw/S0nSjZndO5Gw9Xb0pwUxtM+GzD2fiunRZxU7obpwQgtL/pcAXrcRZ6mrWb2lrZ/UQTyFJeyRd9RmVgvZR8m9Ipz8knXGx0tqhlaTeMQVO81bZ5gCDpNfmX8+cUA1P8dm0pjxO4WuWile04/klcwndjoXakEwIpNzQ5kMR2d3jcAbmp3Cp2XtRrGiisM9Yux2P7bNMNFIN21+PTFx2B/JC63JS4U3BP1ym3ewqiA7r66XoKXtQB69DxNWA73ZLUEacyyRH52d+5GZ+8srwjLmEmcOXGmRuGGVySLTdxGUNtr77rFHyyMb1GfTraDcSunkvCqkedAUq6ULkTv24M6K/JD1JS1PFz8YdJWOtSXjdeLJ90yARMe01trcZtAmkuMaZE4/SaeEy87I5LkT449Es7E7+pD46tA3YuRjkQ43zSxWMFpg44pb4TD6zn5Hb2QODGeg6wuPaYr9x39/Cg+k4nYl1fjpzOSQmETrhg2u5y0QBrcgA+ZegJhd12nXHtBBg3wOS1N4umtrWt1D5lJN87IC7xrYY16YrfE680aFf2TpH2y2SsM566j2OafXA61Dpu2aDlTMGdG3SnMenven3HBnkt1Ve+XNB0PKgcnDmeeHJBjbNqOkZadwIS5I1jrD6lvpfseJrVcf1qe9T1pG8G7gRMCN7ctojEmyMXYxKA5LUu68aSvxMoIKghb65N/U59qA+2fmhPKcbpWHRd2g0/3awnAUMCHPqZ5DsH4LGO8j7FE/dP9bALshJQIb8uxiawpXF3d2Y4Ef1RSeWd/CDdv02QdddeNOo2iTNAMkmqU2hb+sn+3LJMSkoTvzSgHYCi7UyJ17VBXugI/JMNWq5BSl/bQfJTnykBTfy1rphkXT87DkSn1kTPQKr2o9e5uZRBSoNFGpsJJE20U4d2TgBE3bg+pk37OgOpfazk2ClDvjj+tAH6iJ4q6teSaKB2IIvyahxZ6Trdd75JALqqq/LpDPS0p3GHmCRoFypbqjvt4Un1ElDSa85n2WbX61la/skZgg3WczpwoMzlB63De2nLivKfgBLzE3WYxoc8994xbW8CSOk+bYJ8k7cUl1JsUFn5kDnFeLar4FmvO1rlrnTN6clNklzMJncn+LSEpGWb0oBzDVnLEt1PiZNtuyTHNlzy0DbpOHQiOmEyIAUK/UkQ4eRP+lslTRfoKLvjbRpXF4CYvKbTRim4N7+tW+6967KTrXGsUmDXtlLCmdpPYHuybQcspvoEA0zKtNfVfrJdUOASifbjfJQz1bQ7XTZtXbDdXm7qa5oA2J9LCNSjA/jOP6kXB7KcHiffdEnMxZs0m666TLaWxkzb2bVr3ktxlEDY+bHqgH64k1jVvmkDacxTmQQaXFyZ+Ei6I0BR0LHaWuE+Hbha0coXXNxjrJ7yZlOKK1Of/N7tuLqrXDa1eVFvctffaSCSsfb9HWVNRuhAidvvMQWAnXK6Tp1kU/mY5NwpCO2PG+A1IDERJR2tAr4G20nH9S+BqyQ3HZXBUfWTppwT7wS6fW03SCf71MCv1/Qz8TgFmZ0gxXYSSFvV4zXKynF1IEP71jZcH1rXtcdkRFt2Adj5QQI7Kx1SdvVZnphi+9Q35dcHhWSn5Et5cLqgb7Btl9Q5o+l0MtnNFEdSHFcbmU7ycTw6djmQmsa06zk5qFctnxL9ZCParn42uVk5jlmyV31YcPEr9T/F76SvHZ91bab202GC6xB3eG3HhzkDRTnInx4ISW1ezHuwkqE1MaC4WalJOTvBRo2AAb3L7YC2hKIJrJhEErFuladxOMessjW93FPMzshXAZA62zHA5NzkMe0rcGPk2iNVXb6DySVgbWsVMJgIXJvkgd8dQEl9JWCkbabEO+llhxzgmcoo306XrO+SZxFnYvjdyZMOIbhxmvaKqLzq624PHu1Vgb+bFUr879xLgEb5db4ztcv7tG/1vRTP3AMoyzEu82HQzXyvZGV7LhY739Pf0xJo6r/rOj7V/lxcWbUxjSF5VtvmJnbHE+VwY+9062TX7zvxRssozzpDqeRiSVF/37GRSX/O7vThZrVSdlFLhCm5E2AVJZTNuqug5drUOuzDgRkXnJxDpQ2LapRcgmD59OkcvcDVIx/5yCUQc3pisEj7lJJO2SfbTn2z/3SfpH2wf521c7OG05ix/UlnlDElAeohgTcnhwNYWpe/3fWJGEQdAGEQdOVTECf/K3ty/jjpyYEvpy9HXaf3ADkfX82GrE5KMVbsJHPHP3WR/CbFsN1+NVHSnhjDHGk9F8ucDbk4sdKNsy/Kl2wvye3ad+26usm2E3Bw+mu9O3/SOt0+wZfLj6rTdLhisg13n/c4GZLqXodlyCm3T/xTz0nHztb4/c1Nt7fb66CDDjrooIMOOuithO7Km9zd9f7kDFbaxJ0QuHt6cU8SiT/XV5M7Kq5PMSRF6Prk4I55p1mc6Umcx5Opr/TESJn1KcGdBEpPN3wiOecJkXvEtC0nQ5PbvM6nOj2Fxrb5O+k3yeNmLNwT+DRTM43tqq7bT+CeIFM996SrfpeeNCmjtpVOb/Epc7L56Wn5nAMP2vb09Jpe01F0//332z8TpDb7cMYu2aMbj0mHaWbM6c/pR/Xq+OcWg/T6g/4+zcyyTlo61DFzs1TTTBP16HyLbU6xQfPLNEa79q66djp0spb9uaU2t8eJs1I6VqpvxlUnf/KtNFbUV/PuZpWuF3HSzVqpH07bX5xM9JNpH+/FLBEyuTnFuPKra46m6ccJNNR1DepdtvdE8Q9Van985YRzxCIFNC6hOXlXPLM8nU37p1PSWWjE5D0lau1fgZ/T5Q6ITMloAijpGuukAOruOXDW5E4vpnZVB7t8MyAxoWm/CSSlpKxBmac3dQnNAR3ajtOn41sDqCaABOJcYFSZUjCnfbj9jOyPfFJXBO/0E7d3Ji2fuHGgLlS+HZtJ91xf1Dc3oCefo17Vx2lnGlOcXC7Bsn9nF0lGxx/tb+d1J04Op8skSwIoGhNXeSyBn1RP4z3z7JQXJvti7FRbSW2mPFaU4i+vu/xIeVY5lu3y3kWfIlQgs3L8yal4bwJtSbl0ZgZPPR3HJNPl+L6eaWOt8qXGlBJJcgAXwBNwWoEJ/a56V8DgkooDWjxun4L1Cog4/tw91UMKrv19lbicjCtdTQ6uZVXmnbbdPQbwcwGWs1/aqQMMU6BKOp3k1XIJsBelmTdHKSkqSKMtuAcA5V+vtW5qv2O3Sd/XRNqkwGVnf6a75/hL8rukmPrR9h0g2tF1y03gkHSobTDm7cTrxAtn8xm/XB6hryQZ3Ssx2FYCWBw/gp5V/SlOqe/y9SbUOQ9sqNz9ihMXO9h3t6G+222qPzhy/TuawK/KQf6mU4bN204suScBljMuJRdctG5qk21M7Tknc4lABzKBGCcbeWDQcQElyb3Tvksc/HSGqN/b6NzTD/lwutPPKVHsEJ2bwT7ZjN6jgyWHn3SSQEgKjKlOkonB18mYAIMDQBMvtOGkTxesCLCm8UhJLMnFOrzOE0nJnljf2V7PPtNO0zIZg7ICkEpGmkwmH1j5idNNio8qA3U9jUPSXfJNp8fEr/JGYmya4rt+khjDHA/OR9ISMPtN5OzSlUn5yc3ONl/TKcvUpstPTnZXtj8dwHEHOpy+yVf7AIHdpKvrwUddvKceHHjiOHOCw8Xdt4oXjT7cZOsQtpZf9esMJyFbF4gd/zqQ5IVJhzwnYON+0/DTawucbCkB0vDd8ij1k9pyicLpKj3JKzmQpa+uYAKd9KZ917/pdQxvqk25JYAJ+KSy7iQk9cJ2Jj5T4mabbgnS9ePsbSWbk5MJp68pwEoBeYeSTU57x9j3LrVt9zaCTkAJZDjdMMnsEP31XL5JzvaULwcU08xB4kXvpXjS5OKzA2rUA3lI+p7adddc4ib40ToEm3pqkLolT86XpqVGxqIUh1axmrmGD3O9XYZ6X81gaf8pH6Ucy1ysfZE/jlFfu0269T+VQ8XSabkM50AFkanrg9fIiwM9aTlkSpQ66PqEoPVce+nelNQYyBJqd4GQ3508mgwmMOXapUys755cXMBz9rEKgCugoXpTPjSJrhLUBHIoj1sqYUClblzASYkryZeIvsLAPukxjZvjP9V148fytFn6wOTH1HualWKdpKtuQ8smoOdsRB8cqr6Crd2k7gCGi3fObl2s3CG2P+3T2YmR1FeSfac9J/sE2laxINmajtskE/VOcMWDRwQgqhPtyy1/kQ8eSCEvtGEug3OfMHXDrQLKd78AVe2cep/AzLWJidN4sS2XY11ccpRy7kUArGkztwv8TT2QNOAul05VdVm2lQYjBagE4pxsO2U72LpTEnTCFKi03yL9EyFM2ImPFMDowFrWgaqk1wSQJmdISTfp1IEvyqh9uU33Tn/OHjWY9T9uAGdybdKAyv0hSX435e4SIO3W6WPSkdOTfk6zLxMAmsZplWxZxrWpY8ryLjmkmJNmrnlwQfeoOJlUX7SB/j1tM3C8u3iQYpOzWZeIWd+1pe2kcXI+6XzVtZPAGss58LCjL6fXc2kVn5Jttg10XO+yLhY7maZcqG2l/MWxTzO0LkemGfOi9oH2A41jKQZdDzpyYzb5lvKR7Il9Jn1eJMBKzuq+M8m7RJdmWrpM4sHNBkzASgfQTcm6xLlydC0/nbxQp6GxaN8MbLyXDEsTSAJZLOv4mhKv04Nz+NU1N2bJCVNQY99qQ2mvRgr+5wQW5VvHS/t14MH1xzIst3OK0ekygRcFBvQfRy7wqwyqcyen6qj7JgiadKXXJzCSgrzyxiVEPnFPoIQJV/vcGR/yy30mKUHxn9v4nHzOxTW1hQkgkrR/tfnVnjWXhPW388c0lueQs0WO1w5wUJlX7ZNXF99X1O3oTLybBet7PJihfDv/TvGW48Xy/N7E2NfXJlmpsym+ann2t9PXPQ+w1GjTBt0mh75dfb23w4c7OZQMwwXmbifRyhD5W41+Ag2Jh8loXFm2yQTXPPGEiNubs3IMB/woN+8x8DpeyS/7SEHbkba9O8ZOnhVg5NKA0/tqM+ckzxS8VS9axgVi5cHZcuJ5olXCSXW0T90+sAqyri2XwCjPCri4Mqk9+vyklxQbVbb0r+v3rEmy/xTXXGxys1/kr8ckJdudpJ30s7Jf51/nyJnGQesnQDmNO3lIMc6RK5fs3PHj+KZcDuBqWR1nt4ToNtZrH8wTSpMs3L7Bemp7if90bScHXMweLB2EFLBoIEz8XTYlkiaX1Bw/2rfj+xxQ5TbmUsa0iZyBwjmHXp+OsjpeeT0lT73fTpXGQE8gsq+dsdGx5qkbOhb1qPpKY+McNyWoiW+XVJnsd4Gn3qduVf50bbJJtqffNWm6E4LpyZuzTpOvuH5pQ8m+k6xFmtDdmKbTuVObBG96r+2TCWWVBNP4E8Q7HjUWuIet5J/a1gSunN1P+tEx12v6h4hbbxyTnUTmgI/jLwGknfi/ihPq1xz7NJZum0Hqe3ponmxEr7nY5OxQ9eE2mlNfky5cfFQ+0nhP/SulWKkgr3/v+BLb1PZc/r/o92CdC46cw63q9ufDUbALNEkWbbMDI9ep3SbDHX1oUFeHdzNwSY4pSDPYp/02DtCR5+YrJQ2SnhJzoDKNVQoCCXQwcPYn909Rl5NumWzJUyo/gUCWd31zTBgA+5PXVU7Vi1uedCDLkeubvKbk4JI2x88FVS6DuMSi/Se9un54Mknjj+PJyax1tP9dm1B/UFlTIuO46d4f6jwlTUfqU4w5rKcxLR0uSDMaLOeSs+uryzDOpz2W7MvpkrI0iGQOmdp2IGM1g7WyKdfHFHP58Ov4df5F+RJYYVkXk5oeCAeIErhyvO7oRu2MPNMnbptu9TUNTMjO0V1ynspOA+QGMSVhUgIKKZizLQZodz/1r0499at90xFcAkp6nr4n56FB65Ms+XK8M5Gkfvu301EaC+qMM4rUr9ZZPXk5gMNgRr6c7brAre1rPW1vBSxUHm0j6W4qN8mT6k5JyPlCCnwKfFS/Oj78kxxOF6ldp3u93vUVOKR9n8qbJvsp1rlknOyP+k1202+Z798psVFO7YdltA0H7tKS7Y4/VR0uNTp/VV6cTM0HdZT8340zxzPFW5bXPl07BFgOALp45/py93vctZ209caNU9fhycHJd9z+6AS6SLTF5n/SN3U72ZqzObfScPHvwUoD6AKcK+tOuqXEoW24wXcJUHnaGZBkaHovJWS2weuuHNtN/Dgnm/if5FI9uWXC1K5Lzjom6UCDypqOPKc++Y4w7YdJyNlZCmYMRmnjsdogl0LZPvtwM5TTUnfqv/tmf8kf0hgyIE626cYkXeu2Ur+Ov1WwVUqbaFfyuuScToeu7NGBFLXPvqfLbpTrnOVPyudAE9twsVPt2+nMbU9gTD2Xb/UnN+a6v4dLeCn+9n23f1HvpzFUf1eiTt24qjzq0y7GO3kfjm+QN9Iqxk3E+Jf61hhWRNti/OnrfJ+j3p90QnClNkv7v226K+/BanLC08iYEJS4DuySWupj6ncqN8nXhqVGoEldDYBP5C6xuml5FyQc39R1/3YzRpSBhuwCo4IM9/Q4kQtOTGBJ72oXTNDdjnNu5Zt8ruxwJRtlYRDRd8dQFteGu0dZnU60Xwd6KB8PMUyBMc00Oj2kay55JXn4222453dnO0zCrv45AZi6TmCVJxBdclC9dlvcrM4Hscm3pqTs7Gql+5a1Ex9lKmK8U7DiljlTX45Pxrrur0n3f+o/N7ZO/mmmiuOoRPtK9udm75zP0G4SqW4fDml9ApwERlz8S4BTP/lQfC7wc+3rP82V/OfaXoHVi/tbhNN1Gp1LWklZzhkYEJyh0ng4WMkZ3cB20FGnT+Ud4NQ+JzDFoKMOnWaVXGDQ9s8hghTXh/LJcn1vdZQ8JUfqIiU1B647eRGUaJ9si58Moil5prFWfqZxY2BMduhmyljG2VCyK/2edLPSNeV1fNOOXbBt0JFmOJzuHdBs3nVGwwEyJ7+W7XcCMcFSN807++ByhtuL6BLHpEdth3EsjQv17JKw63MCMROf7JM6TnpXHlmm99ns7h/sdjnbMenE5YxVrGA7SQfOhhNP7H+y3V1dPhxwRX3QVl3718PM3aSLBNDIQ/OnsfXh6PVO0voPCB100EEHHXTQQQcddG/MYCnSdU/znJbu+wnNuhmZ/nTT2CTOgOgMhXtC4JO7vg28aJq9ckS0z3tONsc/eZ3aZVtJzulJibykp+4kU5rSV/7TCabV0mTigzMI6ektzeSkOtTjZG9pxkqf9qaZJDdGnN3Re62vyZboQ47cU/uki1X7yTZ6Fqb57t+cmUpPvG4mhve7XTdD5OxJZ5vK392+Hi5dUE96Om1lc04/tCnnO0kH6Ym+ibxTd6tYQT4mXSaZGdPJu9ODxto0q+Lk0mXZ1DZ5db+ZNya/V/66DmffXOxmXW1vmvWe8gbj72qvsl6njlmfdD3IsJMPuOeu63DmV/vS9pKNXexrGlThaY27y/AFeilwOgWzTio/OU/ivYnLROpwdBrXvtbT8snBGHjYZkpkWobf2W634YJTWhbTdlKQpsxORxx/RxNIcMmS/DmQRLtJRKd3DqzX+pSP4zHxyTYJLtzm9VWSmORy/ChNPuTAQlpKcO1OS6mOb9qfk93ZFnnpxJQOODj9NEjiiUGVWcfIjYNLFvQJ1ae27w73uLFYkZNRkzL5d/FMZWH/KVE7QLmykV1ZnO8ngMXldMfXqj+XZ1J9F2MaWOmf1dF9fVNeSm1PvE9j4Hxv6pux2sWApqQb+ulq+ZFbYPSe9tXk/KXrXCzAcoM2DVyXSZv/krJc0JySvrZHB9Lkpu05mjZbal23od1dJ5/TyZzkYI5v/WQAcslIDXwyaqfPlDBT4NUyq1mXJKcjDWIESKs+2XYKKM5mU8BQvU/yqe063dNOp2A/JeQEbJ1dsY7OBuwQA2GytcQvk8pOf04P9LOVLRMc6Sw1Z5DdTBL3Xjl/1PruYcQlxJ0Ekq5TL85eOUtDnTjeqZ8VXw6EKE/Uq9ZPPpUA1oomHTt+XAxNMrdeCljpqzV6dtOdsNZ+9cCUix0dB9j/ruwu/rs8UeRmlFcx6lqAPPcrJj5X8XcCcLurSBfxmoYVwEkOPm3edW2n5KfXNXixHJ1cv6vRu2WqHcScULzK757snfHSaBNopcxq0Az8Kz1PRjolqYm/5CyrBOpsyo1fCgrU2/QkRTlaZ6rLBFDYRvNCnqlfJnaCCz3koMme7fJ7esWAs6/+zkDq9DnpwelEZeR3V4Z24hICibbkwIAr6+yg9euCt1L91o3wzX9ayu069NfVQZlUP/nOZKPKjwIjBVgpRjhAwnYcMHL3NJ4lov1RvlUidbEh2ShBQlOK0Y5PtsvY7YiziEkPHGsXb5K9pHu8n+Khi9tuPK7FlxL4mWhqv/WTxizFwIt70ej0W5XRJ2+SEyRQRENngNRTNtrv1N4UfGj0ybBTQOR3Lb+61tc7sTrDSnrjcoaWWxm6k3e6Nzl+6mtKDn3fgSTWXQEVN7Z0/K6rdYq4hyfZq0sUaYw7gbl7PPVE3nUGi/rmd2eTidQfKacDfeR7arfLE6ROACvJwj7duDpgkXjibwccGHO0XvN1//33b+vEyUMe+E/l4DUH4JigXNKjfalPpCQ2AYkmnV1pSrMg+rmrL8qe6k72k2zG7Ql07a36drF7Ajiau6Z9tpRvAppp3FzMZhnXln7SNu8bXl20E0e0bwfInK06Ge4G3eoS4ZSEmujI/ZnAmEPKO4mDRsWB4yD1Phq3BJNkfTiUgvRqZskFJxegdh3IARCXxLotl6icHlWmSUerwOp4mfpTmVbLx64f3icAU7twutJ2OHOkcroZGuVPr3Ep+hyduaTmyhFIEKCqXaZl8RTg+TvxnuxlJwk4IODKOL/YKa/9qr90Ocd3svtz4oYDlal+SrJJtl0/0jor2VZyOFC4K4cCEAJF8sf2Es8uaScg4/pIuUvjgx6aUL3rgxuJM2YpR6Tx6d9u5szZ7i5pXcbXB8Kb9JXP5CMJRDXYdA9cyefflLx8zwCsJjpsk1uTZZ3JMRISVqeb9qkQVPC+2y/iZNK2kj7In2uDBpL04sqoMab+p3fBrECHo3MMnSBuh3aC+RT02JeTOW2mZBAkrRIp5Wa5VH8KkGq32q6bgVOZEmBJiTvNeHIMHbiY+qFvUedJl/RXXtPfzh6YoEi7CZX60PFR+VwscLa4Ai6qN6cXrZuWXyaf036YhLl3MenG2Uf/Xp3Kdsuhq7FwfWnd5F+J71XMZn/TGPY1la/L9GEJzugQNE3+Rn6cnLTZ9CDk/CXlDgfMXP5rUt6S/+/4I+PL5FN6b3Xy+yKWCBNwmgzStZGuuwSmg+BmxlZtKtUsltu/o3K4IJPk3pErGfKqLgMsHUUdOO0DWoGe5MjTdx0fnsTSvRrnjn9yNlcngTQFVylwuoCgCXVyXvKRZiVX9p824bsE4fzCHcemLthuSi7axuTbzh+U3IPLlBhTUng4ZZIPpzKTX2jZBHCUHyYB2hkBgtN7kpX104OU44X3CK4mfdDmeDjG7Vtqu9ZN3wnAuD6nwz/Ohl0i3onB3ZdrL+2Rcnt2NfbVZy/Buy0nOtOlOtM/Ypz8xcUq0nSNBxTSzHcCNk1OXw0uGfudP64wwwTC7ha4ulWAlTZX69+KmwJ9IgKCFIzVYFwi6nLp3kRTAtC2GPBSWw75rww59acyp0Sp9Sagory4wEfeHH8u8DrQQjoHnCpvbHe1WdgFdbatTq/Jwb0+wPHQsneQYVJ1T5gugSg/XB5x4KvLTcvNKTgyyDtw5fZUdJ2kRzdW9BG2ueObTDjUaV9nWbdnxAGa3Vg1JbXJP7SPNKuX/NTx6k54TmCMeuK/yT84jik5J93tJNNEbYdpf4/TwTSWLhazTZWV+7LcMmWqmwCA6qvtQGP6qm2CJR1n9jfZFG1U/V6vu3ZujH0nfia7cLao/U2x77bB1a0DLBrzbrCcwATv62BNxqLknD4lXjdQE2hKDu2AzdT+DrU+NZmuAF1KqKu+EyByoGJqX+vtOLers+s4q/ZW5Qlk0mnD1E5/uhlDDVZp8zTHl3yRZ73vbCv5YmrDyUVwRZ2pflxAXAVU/U49EEyuEreLNynZTe0kvthuamMFgpP/8frEB8eSNpUS75RQXV3K7GwotZNI7dydENTPVexNOkuvB3C5oNtJQKzr8OGds7IrXaofU16tu8oH2geBNnWSQMrUP2Xk7D1lfyCcVp584OHkP8pDSnZ7UX/smdecEbpyzhCdc3Wb6ak5GZl+9vfVb5UrAamErB0gZAJ3/Lu+3f20T8I5NpP2inZAV+pX+04nMZWnrsvglpLU1E6XYcAnX04mBTLKD5/eUhKnHVD/SSanRw3EKSkkm2Z/rqzjayKCLNffZI/cEznRyje6P03SLoiTfycT256SFnXhfH0FxJL/J/6crbo+0lYBN87OXqYlFvoHeZtkd3LQlvlaiySv68fFSY05KQYnHaZ4S94doCFffa+XROnXSW+MH5PPrPw3jamT0YG2fo/XdEAnjZMuDaqOnA6bVrbndKj83ja4uiuvaUgGk37rNVVoCiYJNKTAnHjTgUp9TNcJIrrNiRy/zvmpBxojA5LTqXOEKWBMbZB31z6DnMqWQA0dT9tNU+/kz713hwCFtuXkITnZ3fg4HSSQNQUB1YUCEl7T76kN6tnprsnpOfmea5tj7ILiRC6BT/0q325sV+BKx0HHb9p3M40h9ZBmZBRI0NYTj0kPzu70e7IPxxd9Jtm01nWzmdSj05cDWqt+V7rQtty4sK8p1mjdKfek37zuAIaCg/QKoJ1lUKcfHiBQGV3s5G/VB/fL6Z+KS7GnqfnXv9AyxQHmQN5TmuJY97U6kX/PniJMGxFT4nEBcTcgp/pahu1qWR67T3XpyFqHm/cc/ymhr8AgZaUcCTho+zRcbce9yiDxpEliSljsl8bORKxyTslVA4jbq0I+d8DTOYBRk4CO/ZQEqAeWoe67XAJyq7Fx4ColSdXnyh9Vfre/je0y0as8LqFQxpQI2bYDdRO5GMSkon9HsMur3CmxaFn6XIoL1CNjkItDLoa4MaZ+tH/aVQIfjs9J9ztxWHlLlBKsGzONS07OpDe9rm06vWjfTt8T2KB9JyDJtjj7k8C/xiK2RX1Ttolv11a6prSKdSlOTLY35WOW2YkD9yzA0uBUpAOvlE4U0AhIyTld4Jwcm+AkGafWXfWpZdP3ZBgaOJzsqwQ46Uzvu+/tuOm9ZC6Y0WlTwOiAkgJqAjQMfC74uATmdEaHp67SGE06b5shwFK97Y4H203jq+0T0Kf2KTfBRH86vZBcIuM9N/49VrqBnjI5vXS7Cuhpp7Qrd83FFpfQ+N0lgqRrtndOkHdj7YBAf3IMp3jn2k/gwy3vTj7b352+UwydvqctHyzD604naqsd42h3zm8nvTFX6HX9dDpYxX3XDk/eUV732gfXP2Mo25rsNcUid6DqBr9T3Hfgztlg0s8KgGlbt0X+XOlBBx100EEHHXTQQW/5M1g8Ht5PJUT93KDmUG0TUTCXqBShp30BRQlJ87g2+2Z9RdIOObuZFfekxX5UjnPQt5v9Sk+zLO+m1ZP8qW+tq8eLU5vszz2Bp6er9FSYnjY56+P4TbNhnNXR65y+137dDEF/pqdIp4/05DjNfvU/Pq27E4tuI7C+B2nn5YOpf/Ky+/Tp9EjdrIizDC3LzixU6kPHTu2S/2izk/2zfXeNM0RT/FjNNnFGw/W1M6PBzzS+u/rUMu49aald8uD6cnGXsYGzPSSdMV3ttV2Nt7bh7IbXk+6od8cDbcfxov31d8bytCWEPnkdYh/H041p98MtGNqf6snFUsUSq9fU3NMAiwpVwYv0N++tAmlyJoIrHaD0bqEpKDHJ0QjpoO4vpLPNFBwdb9MJSZfUVvIk2R04cICA+nBBMAHfSWbnJOke23WBljLySLXy1Nf1HW2UTa+pzeo1BmcX0B1pMOjfCogoMwOy9uEOFOwklMTPxL8LmlNSXgGbboO8uaTn/NDZmEuG9A/tS5di3NKGjhP7oewTGHD6c7pz/sXv7lUfrpyTdwXanL+vgE/qt3l1ekm/J9+Z4t1ka440b6S+k6/w+k5Srzp6Im/SvXvwIT8udybdTTbKd1XpZnaN61N8fsT/28yeToO6HOgwgfMNxr6JGFcv+jUNLgCdO3AuOPUAun1DU7Avck8hKfloANb7WkaRd3qSSrKlaymost6Os7lAoAbNNtyLNGngDoztBOCJ/1V5LaP9O1C7CuDKc9Lh5NRqh8k+00xe6q/HRU/dON5dAKnfPEHj7IHftV+Cqx0go/fJo5Mx1aNfU37VZyeo/pxswM0wdl2ViZuJuZeUDw88XZXk0/LUBWOJA5suWU4x1flmGhf32+k+lXM0gZ4pOe74PdvUlYd0am7y+1W+4D3n79omARbrJp25nJL6dXK5FSInB+1uF6ioHyVw1KR+dc6YU3+MPQSbrM92z7WnewpgaRJWBaUg74yry6REzSDAOrzGequk4dp1balhOONQeZOcCSitgiH1vTIsB4rclOwUTFdl0qf2r3zT8R3PlF9lVXCbeFkFOPbtEqBLOAQ61A0DA+VcjRUfHtgnZUpAj4FwBaDcPeoq2XGS0+lpGg9nR/WvAvjbvd3bna49+tGPPrX7S7/0S6d//TffVN63fdu3vXrkIx95df/991/94i/+4oPtv8M7vMPV4x73uKtf/uVfvvqZn/mZU/0qV+Wr7Bvf+MbTtXTiq/p+zGMec7pf5aodbnuYZE3J1Nnsjm2vbMmVdTEjgY1VuytZU6J1djolccZ3zpq49pWvZJuOfwcszgGIk3+yb+03vZomASzXl/NfzcuTDaR8RjmS3h7x//4cUBpTp89pgsLZ0w6I2rXje+5N7slh1UmITJ3SWL7bd213GQKGyehWAIrEIJESniZH5xCON23T9cs+qQvyQ56dkyRQ5vTUpE8PSTatSz4IsFwSoR5SoHHvFFI+yYOzy2Sbbhw4i6kBgTK3DbjlByYJF1inpOvkJS9uuSI95et3veZeuuhoZcfpnVCqA9eWk79A0BOe8ITT93d/93c/gaof/dEfvfqpn/qpE4BS26h6BaR+za/5NScA9CM/8iMn8FT0fu/3flfPfvazr37u537u6lu+5VuufvAHf/AE3N71Xd/1VPaHfuiHrn7yJ3/yVJ46bx6q/+rnh3/4h0/9J1vkWBIQpPHWRMW2mJjYj/tN3Sdb0rFJCd3xncZtAhmJlxSHdY8n88f0GgOnA+1brzt+HY/MHY5UjwlgTTrUcnyoSv07v1358SSn8uls5sY8ULrxoL30THG30Q9I3Y7GcJU12ZDj52KXCFdKUMVxk7Fr04EtGi6NY9rwTn41mDBIk//koCmpJtndUVfyNelUZUx9JXlJCdDovZ39BQQYCdhpeepd+3T1p0Q2yaS2Q355bWqfzs7x2dGx6kjvOxtyy0uubS4ZOn5WYDbVSbK44MdrrOfKu/fIuQRdoKNmnore8z3f8wSqfv7nf/40C+XGrmaZCgjVLFPNSv3CL/zC6fqTnvSkqw/90A+9+umf/umr//t//+8JfNWs1Lu8y7uc2vyJn/iJhywVKg/Fa7XbwK3amMAr7cyN+aRzt4eUfqDxNLUzgRtHK6BCnlLZlJgdH/T/lJwpryb1tCl7RY636RUzkx6cHyS/YxxJenHjwXzHdjXXKH+tqyR3l9H2WX4H6NwMAGt6LYfTAb8zxkx8XdSb3JtWTkUDTO8q2XXsdD+d/OBAuWSfkjadTg155wQdA59LvlOScjy4tkl0fG2v77n+JkdxfXP8JgDX1/Rp3CVaF2ym9lPA1HJJV6kNBtAkT5dhgKbNuSVE92DgwMMUzMhzep/ORC6ArRLFSiepPe6xoI3piaYGST/+4z9+Ajg1C9VPwqzzsz/7s6cZrq7Xy4R17du+7dtOdet7fRYfNZPVy4Zc9lP9Fairmavqt4CbO0nnkoLTA/lmmR37Sjp38S79pi9Nfaf+VvJMfEx+y/JJd21LSbbVgRK2PYHSFajszxSzUvvNU7KflDscYCIonT6V3JaeiYe0v5djS9I4uMovKRb3ven+Rb3JfSd4s3w/LTKg6aeW7zIkZ+T62w2+C/h0Igf42mHVSDjb4EBcAgzd3/Q0vHJaF1ySftj2dG86Pus+XTvJAVoeFwxYn7qaEpMj1VnaGOrGiNcpA9vvQO/47r5XAc/ZTgq8UwAk3ysfnWRKyWWVlFcxwYEz3dCum85rOa6oAFADnPrsTecK1Atgff/3f//pdwGsji/f/d3ffQJW1UYBtbrX+7SqrQZc3SZtswDW//k//+chgK+WDquO8qrxLelTfZ4+sUNqa+4hwiXKBEAUnO72n3hNSdj9ZqJtwOx4S0tgGt9d3O6600Z090keOZ4uN1BOF0OSj+6cHiR/HFONj/o6Fm5D0LKUU/tPuYx1HwDIo17Io/apfrZ6HY2jlY1dFMBy5AxLB5AzGBo4XMJzbfd3ZxjuSWUCWnqNBuBAwUpmGryWSSCK1xRcKbmgovfYbtq74QKX4yPtFyJPO+Bqus92yPdqiXT67k7fTEAxBXDy2fe1/e5Pn/CmJWzlVdtR3U/7v6b23JgkUEd9nQPKmkfqxY19t6/67Q3tb/M2b3MCQrXM18tzRb1JvfdVFcCpstVOz1S9/du//Wm/VJWpug2GdHN73at6j33sY097tnovV5WvNnUzffNdfT/+8Y8/1a0lwqpT/FY7Va4AWl1zS1YJrKb44MglPLatbU7kAMKqDnnR78mn3W+NzQQAWmcFYijzKkek+oxZq9zR7ab9igrGplhJ3asMzk6cD7vy3IZCQES9qP8xdq10ejM8ALu85QCWy6lultbpMeW8i10i3AEeTRz4HgzugaBxOYXrfU7dK8DYWVMmPzQSOoZLWlouGUYKGkp6osXd1z53AIwGj8lgmfj4zx2hdRubp2Dpggj17nSWAlvaAO/ad8FN+U46ZhnaiAYM8pb6S8mBwVzbSnxpPZ5w27V1d13bdTNx1MkE5mljTT17VeCo9joVWKkluSr3Pu/zPqcyv+7X/brTTFIt9dWm9AJTVbZAT+2pqjpPe9rTrj7wAz/wNMv1rd/6radZp6ICTe/8zu98KlN1q05tfK82Cxy96lWvuvqBH/iBU5u1h6s3yRcVgHrqU5962iRf7dYm+Wq3wGC1WSCulhprH1eVTYmFB0ZUH2nWnm2sxs7FoClZav+pHS2f+COP7j5PjOmMJeOJ8rcTLyewo+X4AOBiVuuMD9gqVwJBDkTovclnHThzMpP3NBYO/KTyym8aRweGruHD09aEFJcJrrSMxn3Hr/J1myDrrrwHiwlwEtglPk2S7slGkz77VtKA1s6ymgJfGbMr6xwmJZ9z0Tc3PvNY6wTsyCOTXdqYTzDLAOMCXkqyyTH7X9Kp0wf5TkuqaiPc18MyzsFTIEjgLyVBFzQYPLVcAoyrWSzalUu2qf0Ephz4c0mIx9n1HnWX/NUl3Jopeq/3eq8TaCkwU4CqNqgXPe95zzvNHhXwqWW+Klsb33tDeyXr93iP9zhtZn/DG95w2sxeIK2oZqsKjFV71Ua1/27v9m5Xz33uc09LkAWQfuzHfuw0o1X9FRCr5cbmrzbDF8CqdguIVbu9Sb7arLqTXqlPPR2nOpiSnwNjjrTNKbnSL1egjm3whJ/jOSW/ZBsuxk/gc8oBzl8nkOhsV3XpcohrW/nmWBDsrfpXe0myU2eUnzTZjtoY9eTA1X3YS9Wv0VD52IZ+TvFjh1/X5sW9psFNiToU7JxJ708o1iFxpZTwFbhpe65+CjLJwJiUpmS2k5SdntTgpxMuNHynsy6jL0xMOts1WgeqVgHwnCcNlV//qR44a+mC+cTfBLAme9D6O22m8U37Dwiykj61/enQCOVjn3xq55KB04fqVhNBko2yaNn6XsCnZoLqs/4VeOqZpO/4ju84LfPVXqoCNbWcVyCqZr0LDFXZAj41c1WzUvW9QVK1XSCqytS1msHqsr0xvvqr79VmzWB13apTdV/xilec+q371X5RXW8wphvvJ2LCrPJVdzosw2Tv7qekrvaRwM7Eq7bf9TkDmb7rg8EE+CY/0f7TfrPkp9P1Ccyy3+lgjrbt/M6162KMkzvx7MCO62Mlq5Zz8pL/SaZrscE0npNtTiCVs/gqz84hs4tYIlRKYMUhZK2jZVft8/dkZFPdFf9u4FOwIrkAMvF2Do9dzhm5S2JJDzuOpHXdCSpXbtWO9p2CiJOLfCeAQ4fW5UyXBJQP5ZWANQU59s8NvGmsE+Bxunb+4saCACe9gsQFOsrGvWs7fsaErH02wNc2W1c1m9T7rBq0fNd3fdepTIGgut4ArPjql4PW76r/Pd/zPaf3WTVQK0BUVL8LFPXG9uqvNsPXjFS/pqHaqt9Vpv513eLnNa95zQnoFT9Vpvdy9QtKq2z1z4cXt3RNW3cPTqpj6lHbZvLiEi5twy3l75DzPY3l6mt9n/sPtT8FmbzuPqkXpZRD3LXk35O8Dhi6vrWtVfye7qfYSt7cflKNU4lH1y5znYsFiecb037bvT5wue0FKaZqeTdbrrxqexcLsBSBrgYjKSVt5lVi+w8HPGlZLrlM8k3O6xxvFciI2hkImBRdv+63C4QazHqf27TpOiX1VTA7l9xpq8QLeXBBmM7J4JgSDIMKQakmKPY18arXEtB1M1VOHwnEuH57f8t0isgBLtpjehBaye0SgCbc3tDeJ/EKrNS12tBe+6AK9BSwqc/e1K7gp+pV2VrSa7BTVG3W0mHdL9DTG9VrOa/a7Zmp4qP2e9WLRgsk9cb3rl/lWqY+YdinF0uOar/fDl/XW+Z+O3zbSb8qon0u6SslwbQBnm2tDn9Me2tW/BBs6DV+799q67Q9BZaub7Y5gSLnI5NfaP/Jhp28SWfMQzsxcQJlGh8UpDJuOP0n0OLkV+IKD/XJWLpqjwCriOORxlwfEmjXq/x8m7Q+lnLQQQcddNBBBx100Fv+JnddDlk9UU0INiHl/lw90UyzCORjNQ2r9dKMkms7bUpMT147s1s707761Mh72mZ/57S+44lPITszlYnPdF+faLUv5ZVlnFxpD5F+6iyWky3xnGaU3BOvlk26Il9uL9bOcen+rjNW01NnenJ3T/etxzSjwXbcU7XTMU8M9ibxpzzlKQ+e7NPTgkX91vXaO1UzTu/7vu97KlszUt/+7d9+2p/1wR/8wVcf93Efd1omfOlLX3r16le/+lS3TgZ+wAd8wKnd2stVn7UZ/uM//uNPy5JVtt6VVZvkn/nMZ57a/N//+3+f6tam9j6F2HvCtP+aWXvlK1/5YLnaPF8zYEXFa78lXsd6sgc3Jv190nmX4Tul2PbO767HWW7H/zRLpP6hS+bOP9I+nbR/1vnHKj63TJSF97nX080KcbaJpH6t8cvVmWZqGCco1zTbpv1yK4TqUHXME6WaM8jHDcaHm+MdH+SdWwWcja/i8s7M4T0LsHoDNpNW2tvSR3MduSUJrtUnQ6WBpkFJ912CcHUofyIHGtw0cDqRlYzbJULtRw8eOH6T0bNvyrALppTnBMLogCnwaJu6zOLGrmXvT4IQF2DYxypgJoCl/TjgtNOH2h73MDhdqr27I+V9bzpFq+OUbIu8EmiTmKDYVy9fFsB67/d+7wf/pE2BkgI4v/7X//oT6CnQVUCp3j9VVCcGq2yBo/os0PRhH/ZhpzIFekrGpz/96VcvfvGLTxvRa99Uv6ahQM+HfMiHnNqtvVzFR7164UUvetGpnwJitWG9yj3nOc85tVmAqajK11Jg9Vd9F5Arfmt5sQBWla39XMVDgcECgv3+Lf2zOitwNSVpB0g43m6Md057JnLASq9rHKfNuT7cQR3KwBjp+nex0NEEvFycJ09TXHZJP+kqASbqc8pj6dUn7rfqZvfVLpqzGb+mmHUz5NcUi7ofZ8NOX26ygnq7WIClTlOU9jQxSdCwp0Fpcsc+m1xb7FcpgavVXhjy6va4uPrsW+sk43D11NkmEJN0q/e7LRcYtC6BEwNBEYFAUUr63ZZLCLQPF0xT8E5HojleDgSlYMkyaZxcUFR5mnR2Qe9zozDHL+lWeeLGcacDyjclIVc3JRzXvpv9UBkKJPWG9AZYBWYK7BSIKeBTZfo0n/6umawCUfVOqipb4KbATM1CvexlLzuVfe1rX3tqv6hmwqps1e0/1vyd3/mdV1/5lV95mmGqevXqhwJKdVqwyxXVXq7iufrrE4PF6+tf//oHy/YpxJK5TyEW9YlF1d1k385f3Cb5ZMv0d/p+19O9PRxLZw8J3O0CHwegXCxxMUrLpQMDyW6ZuKcHlmTTTh43I6n1GZ9VJs4IK68au5pfjZM8Ta06db6o11Zj7uSc4tl1yHm0NUfJ1lnG5fwUCy9+k7t+T8l6+q5Bw7Xr+nQBPAV9t2Q0JRilZMCKyNXoOc3q2l8BK5e0GWBYhhvY6Rjq+NTBri7YHnlzJ5kYlOkkOo7OBlyw06DDseas5w5wc7IzkCZ7TAHT6YB9td070DTZiAvu/ZttpKBOXpM8qzopMHOmQ+sXyOlN7DUzVLqupb4CS7UUV+BIN7lXmX5relEBqAJHfQqwbP/rvu7rTtcLiNXsVZeten1SsNqtNr/+67/+dOqwrhUoqjr9p3Sqzf4TPX29TyH2ZvfqhycLS4Yq2wBL/6xOAxt3otO9XiONA++nuJJ8i+V2++G47rTh7iXwzbLMBwo4Up+pHT1BTF5SXG/+OJOcYgaXx/S7AzZdxq0Q1L0+UNH3ewuA/jkn7b/rJeCT9OT6nupcb84GJtCceOp6bsO9wxTaZwJyF/uaBqeMtAas95kMeH/HCJzxMMG54+8p0TIIECSwL750LS3XTbrQttI9Tchc5lN9Tc6SAhJ1NyX01DbbcIGfslIubTM5mwuyCvhUH5NOUhtOlpXcU7JJCdC1q2WmIJP4SO07m0/jNPWp4+T4KB+ofUlFdUqvkkb/GZs6sVdLagVM+sRgAZKafapy/TqFrl+nBqu9qq9/vqZmmHqZse4V2NGTgUXVzzu+4zs+WK5f81B1+5Rfla2TgU984hNP1xscVbnmtf+sTt2rOv3ZJwuLn+KrTy8WqCue61/dr+s9W9ek8cGNUxNtNyVSveZmkatNzva6em5GNNkY/dPFI+U5JWmWY4xzJ9MSOXDoYkeKhToufLDVVQTXVvPu9kK62NTf9UWdmqcaWOn9pENnR0qUmTl31X6Ty5U7QDyR07MDwJNsFwew+ESRnMcZmbvvBpHG0HVWCc0lMF5LzkZZuu8ErPhUqv218aanpwnYUL5ui7xRBnVO11b3vUrq1PUERl1inkDoRDo+7onVjWXildeTzHRw/ZecOiVFred0Sl0weNOnHO+aKNkGdZ6O6U8Aizzqby2XZlXrsxJCAZui2rNUAKMBVG0Sr43ntYzWG9rrjeu9mb1mkqrtKlf0a3/trz2VraW9Akr1ZveP/uiPPpX7qq/6qtNsVL2F/f3f//1Py4W9JFhUe7VqQ3sBntrQXmVrT1dvaK/+67P2adUm+Vryq3JFuvm9N7TX7FgBvuK1/6xOy1R/qqeWLYuqbO3tqrK1Sb5n1vqVEz02akutU46Plk3gN8UgbSvZv5blHjrGqgRW1B4cP9pW4r//EWzskMszLuaSZ/2tscDxmnhhrFCAqjGZM1Pab9drXttv67NnQalj8uDk5H0Xu5OeXB5y37WdnbZcG6on0gow7trIPQWw2lic4ybE2cpwG2H7PgOMc4T+TAnhnFOAk/MQWK0crEmdS6eoXT0lTp+rTE6uqSzbd8a/+u4OHmgAcnxosO02dKx2nEEDzfROlf7uliaTjpRYzo11CjYTqFwFKP3NF5K6/t248f60fKJ1EoAnbw4g8qncyauJumZ0impDe88MFYCqP4nz/Oc//wSQCtDU0l1vfO+ZpfpX4KboN/yG33AqWyCp/hU4+5RP+ZQTYPve7/3eUxtVtsBUfa9lv9bHM57xjNPm9wI79eLSuleb1uvUYbXVZevvE9bG9ypXQK6oAFGddizg1Pu0ClAVGOz3aPULUuv3s571rFObRVW2eOmyBbxKzgZ+ybbcWPQnl0MSmOe4us3CtCn924D6jzF5akvzgYIGZzOMh5xxY9yY/Jh8p1je91IsUn2mWLkDsKg7/nPxlPuqajx6zBV8T4eCeH3KldQJ9aF+rHxOfVEPiVw/DhwnnrtM6yLNmt7TAKuewnYVmoIBQdDO+nRKYAQGDA5so78nXjUgqGEr3+pMrn5qW+WdErdLlEyWDLrqjE43abycLqkH6k95cnpNY8g+nIwuWKXAyd+c9t6xz5U+OO6O1OmT3LyewL+2NxHtaApKtFv3pO6SRdKL41dBY7+Is/c29V6mAikFdmq2qe71BvYCJP129AItvdm8AE+VLUBVQKVOCdY+qvrdb2UvoFR//qZATLVT14pq43zNSFXZ173udQ+eEqxXMVSfxUu1WX285CUvOZWrWa6i6rMAV81GVXtVtvd2VRslU10vPdbvl7/85Q/eL977e81AVNmSq8F0OiGlema8K3IzlykWuPr0zfSg68bUgWqWd77L/V+T7CxLf1ZenM6oO23HyeH41rb0u6ununSgVmeuXFxw7dJPCdQmudh+ioPTMvAuXYf+WSbRpP9UV+W5GwDr+ubhaOphUO2PcANd1Nd1EDURVMDpaU8CGKJuDuJkYLrRnOWdsT6oNDiWGl47CGVSEOdAl+rCyehk4P6HSV5tm+TkZj+OXHBRI1ZeVNeqVzpN2gvCpyG330DbTMB4pZNkm6QpgE3Or2Al1XdJkrrmkoLTueNp97r2Vfbc/7pv2hyTI2edNXlwDHsca/9SUX3W/f6zNjVb9U7v9E6n372hveJJLadVew1MqkxRLTX2O7MKoNWMUM1CFRir5cUCLrVPqvZR9R9g7r1QVbZewVB9FQir+1Wu9kwViKsZqGqz+qqlvCrXwK6+d7vVXoHB4q3kq31b/ed3dF9Y99v7wbps6ar3nKXZDCYUTc70DcYTHRu36TrFv7rGN/8721Wb1PF2PqV7ajmD1Xbnlrp6Oaz+9YwadUU+GDsdT2rHDvCo7aoPsQ0Xg8hLj1OPUcurvJJ0DAgmtV8H1AhwXKylb+oYtU60nIt/3b/rq4i5QfVJ4rhPANaVdbbcD3MX9R6sZIQuodEpnQM7p3MJg2VS0tzBmjRIbd8FFHUiyqdydlskBjcHANI+jInYP51eeefY7fThglrL0KekVs6UgCKvObkduNGyE3Bt4pMOx517Ymirk107GRy/rl/2TZDjbMr5QQrgLNNB3Nmps+f+dE+K7pRi20kvh1Xw6z7Kbwqk9Kb3fsVBA6z+8zdVvl802sCp2us/BF1Lcf23Cx/1qEedyhQQ6z+Vo4G/Zqj6pF/9OZve0F589Mb3PtHYm9eL+s/i1H6rXrosuaqN4rXK9p6qBpC9v6rb0JOFLjZODwOuHIE0YyrrsY6W6/tuttvZnrPpHV92vCVifEz97+iI/KTE7cAgKelOdTj5v4shbgZGQc4q3ig/2g/BE8spcXXG6diN4fUwxpSXcjD3OBuecsHdpFsDWBV8itITe6JkhK6OCwqTo+6CnRWIcwk79e0c14EbV89tKCXfjueVDhLviR/qKemx5dJNqCkoEQg4J3TyKLhIQdOVr0/ONCpwcXLydCGXzMhvCq7UL4NGassFIaf/pC93AkjrpcTnlgYm4Ka6c0k7zb6pvrpszebUTE/NLPXJwaLe+N7LdXWv9lQVfczHfMypXG9Sr/1WvUm+3s5eAK7ezl4b32sZr8sV1eb33qReS4gFlKrdarM3tNdrF3qTfJcrKsD13Oc+9+oTPuETTmXr3Vm18b3K1sb7mv2qDfXVZwGx3pdVVDNrdZ9vV09gwSWjfnBJMcTVd2OpbSd7cbbm4ohr15H6QMuxA4SKmPDJR8obbXPUl4t3Sm6WXeXsOk5Wxnnnc9xfpXUJsjgLt5O3HPBRGyCA03GgLie7aJpiOGMax8LlZjfGzmbJ684LzO9ZgKWbIvkENCUgl6SmQSRNoKx/c03cBZwVKk5ARe+nhM82aIi6jJKcNvHinEqJyXcn0CRduLFkUk3tk0cdE7ecoXI6PSTZJxtQAJIcmEu9XIqYbDOBPwdQtYzblM+x0eVzZ6+TDVBnjv/6V/bHpRKVz+lZE0Fa0k7L/V22wEhtdO/ZqPpXG9w//MM//ASkaimulg5r03jRJ3/yJ59mrAo0FdCpuvXW9ypbv6t8gaMXvvCFp+8FmHqZr04M1gnBKlegp3h52tOedvWJn/iJp31TBdDqs8DRs5/97FOb9SLRopqNqj/hU6cQa8N7nTjsje9VttqsssVbLYP2ax6Kig/6Le3KJRP6kC6X045dfbXBCVi4fYpuOc3NbiV74b0p1juetY2UTKf+9NoUJ1RGyj9Rik1u/JxuCBhSH87XXb9F3K7C8l0mxcmd/pnH7lv8tQzqg2VT3lGgWaRL13qffpW2vFzEe7BawBTMk0O5JNTl9NMtlWjZFVAiIEi8OoeewFXiV4NTKq/7KZwOHB861evATRNBBdtys0PUlXNobZdG74Ik23FO7mRg4GKidht5qRf2T4ekrJx1YkJIgYK2tArSvO/0nJLdys4n/TWpL+m9h7s07xKTa1/v9X6Jmt3pU4U1y9N/tqaAVe91KqBUVG9NL8BUQKhmuGovVYGdaqOu17XaNP+//tf/Ov3+vu/7vgdfl1CA6FWvetWp3bpXM1T1UtOajaqZpzqFWO30xvf6Xu0X9d86/NIv/dIHN79XOwWqvvmbv/lUttooUFUz+rUhvt+h1W9y58Me914l+0rjn7YPJJBAH9BrbmaFY0e/mcBO0+q+S9TOVx3fSU7nq8kf3T4n2i15oFyr+o5cO4xD1MEUW7Sey6tTrmSsS+PL3NNE22Fbu8utO/mbv6dxuZhN7vUUSgVxkNOGPCZDrrv3pzO05PQkGq7b4zIBKK5N64yTCwwuWSag1ps5uReGe1ymjdMT0JgAaQo6qic6W/Ogb4t3wXkyPY7lqn4KuNQ1r1FXqW4DXH36Uxlpk7rvzrVL/l0wI78K0pxsbvw1iThaBTTqu0BAb55l4qPPqsx8+WGydcpaS4S1t6o3vvf+rNo/1S8ELTBT92oju/4twgJBBVx6Q3mBmQJjJUNtUNfN7L1UV2XrX4GgKlv7oapszVj1n92pe/U6hSrXG++Lut1ezuw/i1NlS4YuWzNwtS+rN77rW+A1kekGa9ppSkj0QdqW+q1b+tXftOcE6tr29CCE3kvATu8TdKgO3FIe/TeBETej0fW6TG+Un8CSs0/lP/GRYir1yZkW7YttJR1xrFRezSM608PctQKFtBHHs8tf9w1/Do3jznvapvoBD22kGSx3wKMPxry56XbfG3/QQQcddNBBBx30VkC3+qJRPhk3uSea/lSEz/vpaWjVbn9PT87su58K3BPhzhOfeyrRGZl+SZybLdt94lf+nX7TbJErzycm9sOnEbafZlu07X6qoVzuCUp1y5k4N6OV9NTUMyn9z+0J7CcdlZlt64bTyZZoB+lJl3XdvfQ0q3rV35zl3PGdpLc0Q+q+p760HZWhxqRmdYrqs/Xar2/oP5VTcaRkrN99arBOFNbsTy+31YxWv3y0ZolqBqlmsOrpvZ5ca7ZI/4Zh1e0/lVOzUn26sJ9yq8+aCdOThf3qiCrTp/56dq1fr9CyVHs1s1VtdFvNv/6pnH4Krzr9XrB+VUU6DJHGqvXuZrPdTEE6yOBisfNpraP9k9+Jd87uOHnVZqc4pW1Rpomm2JHyF/1m8vem3vPpiH7J2ON05nIA9UT50oxU4imNnZvRSzZR5HJKyjWURf2gc/I541HkTjff8wCLCk+O3vdZVz+1DQUqDAZ0MJccyZ8bsAmgOCLwIP/qWJrs2f/Er9MVZU6607KpnAtsNPQVaFDi2HMa28npAvRkQymQUG61jx4P59zuRIs65zStn2w4BSEGveQbLkg4nVCHU/u7CTvtXXN1V75OXZYf1KbvotoUXgClAFCVe+pTn/rgn7WpfVcFiuoU4Ed91EedluHqz9/UC0RrM3tRnRissrXvqpbz+sRg7anqE4NVtk729R6rBmd1YvAFL3jBaSnwK77iK057rmrJsdosfqpsgaoq97Ef+7GnNqtcUb2YtDbf12b7/hM81X+9Wb5OIVbZL//yLz+dLKxydVqx/1B07eeqTfUFuuo1D8VP/dZ3GU1j45KKfqrN065SgnTxi3ykfp19JJ61Xy6duYewqS2VJ51IS/phDFqBMacTlzuY3Js35pr0nX0xFyjwWOlbc8wO7ZbTceNhh4mcLRW5XKPXE55IOXK6fzGb3B0AWg2gOnqTC9puQFwi770Cqa8JRacgov0WJVDX5TjIDsxoWQIvF8SSsTlnZDnymsqpfFrPvRvFJfXV2LkxoH6SHTAJsD7ld+PH38ojZ25c213PBfZVclTAwSTjgkvqO8my4oG24K7zsISzHeXNzXQ63dZnzRjVrFBRncTrWab6V2CoQEptEK+ZngIlBWL0z9/UZ70OoahOAVbZ2kReOq0/f1N/1qZAU73SoYBO/6mcKle/G+hU2XrNQoGbOjFY96vsc57znAfb1JOFBeyqXFHdr31dH/RBH/Rg2aL6szrVZm2mrw30tZG+/1ROlWtwVmCx/25hgbgCdM4OnK2nhFPEmfXVKSqtz9cC0FadDZJPBxT6M8XSVV5wPu9yBe87GVVH6UFW+U4HCXaJcZHjRv/Xfigzc9wKjGouSasBU75QflwZ/bzZmOVK40+e2a6TMcVmynqbIOtWXzRKWhm9luvP6Z72o8EkJRjXVgpU5It13VP9BDK0T/dEQYdJDqGyU35tx93f0Y06Pf/+mHtFQQI6q4DJTZc79RIQmOq4IJLkLmpe+Lc03cwpbW4aYxccaHuqb8rFAN18pBOjKSGpPOSbOlYZSW783TKI2hPl6FmkAlH1vZffCuwUMCrAUUCqrtcsUM36FIgpkNN/0qaoZo5qg3qfAqy6X/u1X3sCWFW2wFSBnHp/VX2vNvslp3W6sMpWn1W2ZqL6ZGHxVW32acH6Uzl9CrGo+ip+6sRgfS/AVLwWAPuyL/uyU9kCg8Vr1fnGb/zGU/tFda9mu2rMq7+Sv0429pvcaQfnJAnVufP5FDvbBgmunJ8nn3PJkXwxtqo9Jx9nfa27k3ynU2srGfQaP8mHm31MfWqcZfxh22oTLh47naWY5PJTX0sxtftzvE36v17IzvL8rrwzHqc8ejfp1k4R1t4GAg83FevQuwMqCYwxmGhAqRM7fQqqyyq5WYp0QorkeJyMyh1b7d/pTx24JDgBP2fsfZ+6STM03b778xjcg7QDYjV4M1FMfEzBqPlydZkM1D4YxBnwdaq7x6J1p+8bcgCr+1M9qW2QTwYMfS0HbSSBWvJFmVUn/JMcVa9Pqmo/E4jWNnV/lvbbPOkpTPp/Ub1dvfZKFdUyWQGL3rNU+6dqH1P/qZqqU7NVNbNUoKReg1BgpP9YdJ8uLEBU9WtGqE4Y1rUCQFWnZsv6T+pUuT7NVzNL9a+AUYGsqqMnCwuMVZt9CrFm2HoWqr5XuToxXe32i0Orn96vVSCv5Kp9YyVv78GqayVf7b8qXfTfWOx9X4yTCaDT7/o6Tw7qWPGBRu1PbVzHOJ1QZN+8zvJ8MFXfZF88jUy7Y9ymLTtfZ1KfgBln8KkXys4ZJ+1X5VRdMC7ry5B1Bab70z+Z4/ZHTiDSxWB3342pys7xT+PxCDmFPeU1xmxtP/3JLcZe8q92Utf0j6hfxAwWEw+fioqcIagh0/Cn5M06dEQHahzYmQCMlnFyOZ7S1DrBo+trSnATUneOQtlo+EzQTv8sm/ph3bTPjcGVsvNTnXzSnXsCd3uYphkcJ6OTmZvMOXaknQSllIAW+yDQIRDte5oYOOPk+qH/qP0oaQB0463tFxW4q+WxogIe/c6rqt9vcK/vBThKlgI+BWx6I3jVV4BWVCCm/5ROgZ1+h1aBGP1bhA1kiqpsgbX+Q9P9JvkCSfW9N8n3n8rpzezdX/+Nwn6vVbVd8qs8JXfx0RvsW8fVfv/dw2q7x0TLcNwSOb9knOoHTb5aQUHMtN0hjaXyyvtabkqqqS/nt91WihHJH/szgRCWUVkmH6UMU05J1xOY7fb4gK7fCTqUd21D9ZnikKub9DPlp3PJjaH+1jKs5/TRNAHOi/hbhBR4SjyT0TunoTFoMnDgiss89c8ZruvbGaH21cQnkiSz23fTPPLpiPwk+VeG7pxK+3OAqomzMu6fq8vEn3jb4VnldHY1AVUG6h63FU8uuLrrlJkJcce2XBJwIHBFtAX3xNinfNUvtK6zWefT9E/3ZE/563uBntrXVFQbymuJrTap14xPAa/aAN+zTUW98b2AUy3XFeh53vOed7rXm9Rf9rKXnZbbakN5l+2N51W2/1ROletlvipbm9+rn1pCLGBUb4GvNmsJsjap136pJz/5yac9VAXGqv+iAmlVtja/1+xXbX6vDfX9p3IKJNZSY/FWAK//WHRRv0m+9oBVu9V/LTVW/X4BqdoVZ7WcXaySdwMrzib0J/19mql2/TGGsw9eV3tzvkufTfHYtevIAQba56qNRIwjbixcDOAYu9l8t+mbfbMvlSX1q781J/Y1zo4lsOPs8Do8GDpgpnEjxfvWg+vTyZ+A9sUALLePQJcLlFIg19+OOIiaMOm4NFIHFJSfdJ+BjYbB+wxcjv/mp/Uzgb6ksxWlWR+COm17cjb3UtXE1+41vZfA5ARand00kGKf7sknOa3jl9e5BKP2r0lMgb2WSQAs6Ye2qbNVDGju9SBdtpK5e/CgrG4PS+LRBUy2UbM/7/M+73O61yf+ah9VgaKaPSrgU7M6PQtUAOf5z3/+CawUcClA8oEf+IGn+i9+8YtPYKjful5l68/qFOjp8gW4apN6Xav9XP03DgsM1anDWkrsvw9Y4Kg3qReY0o3vVa/KFRUorA3tH/ERH3Hqp8BU/1mdOjFY5Yqnkq1AY13vpYrai1Vy9eb3Kvua17zmdE+3ChDocC+bs52U3Og7E0hZAatV7GQfzkaYBNVfHc+OD9ce72n91UOV68vpzhHjycRHkxvnJi4hav+MZU4Hel99m/1o2w3CtdwE7ijvDsDqa7xX5GLiDhaY6twmyLrV92BpEFBa/dZrU1DnAOravQsCab2WxrAL7rpsWmaZprNdgCLYcUnNBZ/JAVJgSMHZ1eV6tgIFOi/HwjmWG0PXRtJTCnKdgLrcNDPj9DclE5ZngJsAlvLOh4yV87OfpJcpSXa/vS+CrwJw+yo4Ng5QOntKY00/LGBXgKqoAU9tBq8ZngIhda9mqfqN630isMr1JvUGJN/wDd9wAja9Sb3AUs1GVRtVtoBQb2avdgo49SnCKvvKV77yVK7/rE2Bqq/5mq85zYb1fq8qV7Nh/ed3iqps1S2Q1ACp+qwZr3qXV33vN8GXbLUkWICxqIFW8VL8F2CssiV/6cbZKR8SGdcc4OCyPsfV7fN0WwE4zi52TTGu+Vfe1JbcLBf9ZMobWj49oLpET/9WPbltC0kv1A/HxPnxCoR0O7o3jvE/Aa30oOPKkycnq6MpBhUpz8rHaoWH+lnpk/aZYuZFbXKvJ9RWzDQIJ6aCE2hC13taTxWuSY0vl1RSQ11NYXPAUkBTmp7A2nC4YVsNUR3YBT9Xjp8OmFFG5+AuYBJgtX65ydABLO0r9c+NuEz4/JMIfLJS0vbS5tSur39Kokj/9IeOkwYK9qFy930FMPoUqsnMyZpkaTm1XW4kV16cDdW/SvBVvvb49H4g508OUDlfW+0LUhvqcex/FR9qyayolgN731UBqtr0XfcLaNT14r32OtW/3nhefRew6fdoFWApQFQy8c/q6J+/qfYKIDXQ6T+rU7Nlukm+/lVfBZyqbJWrjfL6p3K6r+KrrhdAq88qq38qp+rXkmj9671fPTNX5WovWf2usr353cU6t0ma13X83aeOqbPL9i/akIstzqcdYOfp2CqjvpZkcMvsLqf08if9cxUDGTfdNgjKRx1o2/S51jEBHLeuEDx1uzyI0npjHON4Olug/h2AcnEtkYJm5gEljrXmPepD6zBuJADebRYRiOqf+eql+Yt60WiRJmUapfueED+vTaSDrwZMxEzQ43hxAIkO6BI9DYd9OZ6c/nZkZkBzwIxA0fVBg1U9ujoMCI7n1EfSHQNrB2e2p3L19dQX6/NfWv9nHwRrru+d/lyZBNb73k47rKf91xhWgi/g0rMk+rfwnLxT224cne9qm0woPYvTp30LZPTm9ZoBaj4bkNW/0nvLoG9y743oXb+AS/XRb4mvQFszUd1mlSuqTeYFnEo/Baj6fgGu3rBewLRAVAHB4rn7rTZ7Y3vXq7IFuno5sPqutvpN7j0j18Cr5e6N7Z2AmOyZPBnjWu8p+XJcNU6kJahVXWcTrOPqrXxnasvFN5dbXEzXlQQlt6yeYiX1pv417VlTeRn7XfvKm4s9bnxTXErxhbyqflKeTjmT5VY02Ynje8qZxBouR7/VvAfL/eYTyZQoaYjaj5saTkrWgeL+JBc8HIihnM5I0nS7Mw4X3Dh9vmqnnX1yrK7v+uvrfGLooKLLYLvAmbpYAeA0Vk5mZzNuyeucsUj9p6Dogql7YHDBwZGzv6ks5Vd9axl9bYkeB5/4dny4ZLDaJ8L2CkT0i0Zrtqhfs1D09Kc//fSvftdyXQGQ2qReG89r9umrv/qrT0t3/Sb32izfZQuo1AtFP/IjP/K0rNdl62WmtaG8N7P3DFa/Ib7KfuVXfuWpbO3hqj1btcRXZXsze+0Vq1myfpN7bUivt77Xni2WrT1cteRX786qcr3xvvd+1Tu9SpYCeD1bVm3rRnT1FzdbrLru7zrDwbHsT+5DcjadxtyVSWOuMSzZQWpnKutyhQIoF8tTnKGv7PCZ/Jty9KfjwYE4V1d5YxxV/3b6S/p08TKBF8Z37d/JraSxwfcpOD0AAQAASURBVOXbVc5w8jU5Xgkuu26aXbunAZbOPKTAS6OnIaa9S0XTtKRD3K5M4kcHn9PlE3qnIdIYnT7IB42Xm6FT8KHBTcmf9dzvKVBRb24fh9N10hf7SaB4F1CoLNpOWvZwunFBKyUZ9pOcWoMZ2yhaLT07nqtOLal1WwUcaLP12bMy/eqC3uej/Snf9E1nmymBqc70nvJe/TTAKvDRM0NVp8BQbRyvfVW17FZ7qGpDe21mL9BSe5wKKFW9otrQXgCpXvRZZetU4id90iedwFLt76rZoipbIK3arH/6JvcXvvCFp/1btcergFJtaH/uc597Ajz1r2a+CsTVn7+pNmsvVlEtH9b7tupN7tV/tVtyFDisstVm7dGqewWiauN7v+29QFf1VbNt/ZqH+u3imgMUtEe1L30NAw8w0L6mBJlsblVHy9BPdEvE1Ifrh/FSy6QYknzJ+bSrn2TRNrRuKuP070CFyy0EQ2n/467+WGeyB83FRW5bQNKjs93Uvv7md8ZNzu66nHU3wNWtA6xkbCvA0dd4nQE6GQYRLdfjV4DPAaUdpE6awN2qDxrPqt/kYCzDNhNwWAFTV9aBL8enG5NdgNHlOBvlxkidUvmc9gU68KMyOaDJum5cJ6DrgCZ5Tk/pvXzWga+X/pwee/aKgae/6340BvWe8dp5qmQAZJneH9FLhAUsekmuqGZ26m8QFpgqIFXXCyi94hWvOIGYAjkFpPo0X705vTfJV5tV93/+z/95Klfla+N7fRYwqrbrX4Gmopp1qg3t/Yb4Al5Vr2aYCrBV2Zpdq3L1NxD7tGJR9Vdli6+SocoXSKzThPUm9+L/ta997YN/3qfGqWfp+q3zPe69ub9fgEp7anvTGeQEsJLe0wMCyympr6Zyzu5dW7tx1MUYra8J1wGNBK4480MZ2Q75Uj7S/sMdH3afrr3Ur+pR9+G5/lg/gSbmWAeQ9Ddj22oVqEnjQjqcoZ+so7/5ve5zZWV68H9z0O3CuYMOOuiggw466KC3Arq1U4Q1/U+UqQi8SY+hTuvJKySdNnW6Uw5VpvejuGUjh5S1zWlGpqmncvUN1y1vn17jCUI+FaQnMacnJ4fbCDqNBftJY8InQZ4M0Y3q+qTCpxenb+qdelP9Ul9u/LVtnQFT21DZuv40Je1sdJqB4p/d4X4l8pqeRt2MYW3irncs9bV+S7njtajGqZYU+3UNulTYJ92KahZMT+Hodx1XPoVyCUPlVf1U//0m9/rszd51T08B1sxQtVVLcfXOqn5lQt2rMkW1Ab1PDFY7/PM3pY8qW/udqn7NNPUerDqBWP/0dGFtRq+TgN1myVPt6cnCoqpTbfaJwf6zPrXk13/Auf+WYc1elZw9u1izan1Cssaj+qh7jBdqG1yipT2kU2VpFlXbd/Y2xU/GKdpA8gvOxnGfoJKT0c1QMR4rv6ob2maKP/19NQul5VazJClvNPFPWaUDDMkH3dKri4tu9or+St0oH7rHr8ltEbnGNhu+GqZI/5TOBEtoY5TJjZvmpKI++XtRm9x3sZxTkEv2nAqlY9B5UrJ1v7VvAh5OZ6a2/j/2/qXXtm5Jz0K9NlmlhIQSS8ZgKGDJWAgMFhI1LFHhD1OghmSwJbuAbHzJtLEw/IrUXkexjmPrPc953uh9fibXzm/u2aSpMWbvrUWLiBaXt7dLH9/g9Gs8VyCxwPf0P+XP78Yb65p8GfiufqbwNM4FBFMXU9rplxbs2ubTfGkmQZ/pwmSl02a/dOArQaUcqSNea3w0PSdvJsvIuqfXtiSQSxvcNpPMB2zsibZJ9PvqgDkBN0Bg6exyYvLC4N943k/WS13uEuGAlr0/Dx77szUDOOZvrg2vA1bm3gKTBWj8qZz5G7mG7p7sm71O8zoG9renC3dv2vS1P5UzNHZv2P5W4vCxOl5d7unCfa3EyDaf+buC8314XoC1CXX6HZrNHlvhuBAQPPnjFvPFtDc+AGed3JDf7KABrCkEf9eDivFqdVi3/Xg7wYP5el5rPF16zU/qxOhaXG06tf4u3pIXi4EEWO0BOGM57YalxfAt13aDvN4+s4+lZ/jhSS+/2je5v3UIQ9hW/wIOlgQTLE0hSMoZlwRX5In9tUCWdJfG/J+JKh2BszB7n7rh9yY79dwCFJ9A2O4JZLGO9ZVB1GafLNiZw5szZV07cdlsqdFL3XD82UdLfDZuFjxNVuPV+kt6q7+dhdq9UxkYd6Z0wei0GbAxgGRmfWYP0ACW/F2+/W4zrzn2xj8BZiaw1Kl9z2Q+AGdmgPbdWHN9fipnNo/PjNbsuRpe57TelDndN7Na8xM4ewpwNq9P2/n5nQEx+/M3s2l9TgvmT+XkicU9BTinEGdP1pwYnLrz1vnZPD/1huaUqTsb7PN04dTNE4uzl2tmvPancvY1DXNtZwkJigk4rsM8aesZz2gvF0iwMcmxa7GEbYxGAwTJk/n6G7Bgvp2xNTf858nKlc0OFDBh53V7uGexeJHjyBxj7Rtd5q1LN9l35hmTgQep2F/Suvj8/gC4aJ+tXHme9UjvAsqfEmC9SfgXuHqb0JJeS1QEWmmo1zHd6yQCneU6hUUZCe4oswUvJjoCpk1STTfJxwXmWqFeWrJ9M+6tzhPgfDoZYv2bY1/J4Yn2FWBM18mLgayngGXt03YGOE3ZJb4FSPujylNnX245SX6WsxZc7QzYPuUzgC9Qm7JLheZnlqC5PLzf2TaD/Sy7zSsYZmZp30U1QGh+Kmc2me+b3P/m3/ybP9rMKcABWLO5fQDY1B2QNHVno/meLJzfDJxrA9D2LfJ5unDrzmsX/of/4X/4QW82t8+S4ixPzsnC6WdPAg4PU/fv/J2/8+NU4NSdU49//Md//OPU49SbE4/T1yw5zlLk0N8li3wZMm2PSePJPpJO6ruBG+uT9phxqo2zXW/8XrbCPpMntm0+1HhJGQiuGAct7psfsr+83uIXr7fN5RfISjBk+m4+ydWd9HHj982YX7nqWxmbjF2mu/ZpuuD3lkc+JcAyRzNlceks/5oTt/Lk1BYwOLOydDgL1XhYPtNo0oBbwHkKgBYkMgk1gEX+TUfJG3lpQJA02rg2hzFHN1otMNLhW5BjYUBuoMv6aY7bgmALNqRlexZYmu4X7CzQnFmQqTugZPc+jkyTyPfFnfPSzgVIA1hm6WvA1QAlvs19kv7Un2sLyKafWX6bewPOZmaJy4fcy8HPtkdyZdrvU2/f1j48DoiZazPjM69RGAAz4HDqDACaMj+SPDNHeQpwTgwOMBpwMzRmdurv/t2/+6POgJ7dkzHAak79Td35G/mm7s5y7c/qTL2ZjZpZqX2X1dSd2aypu6cLh+7QH1A7dYfm8Dr6371jOw4Esy0xW2FcYMxsKwjNzizmps9a31d8zniStnzFNotZZv9bt/nIJV/bA8k2BA+kZ8DN+Lb8YbP4Vzyw9i2vWKG+OaZtRSMf1M0OjJ8tticrywJF0yP3dbK08b7A9qfb5L5vUjbQRCP7wRiMvz09bGFwyU2M7aimDXgClKXZljX4vw2oOTBBRRYDIrYhc2lzY+Du5bAN501mGweCQXN43qMuDFAmD22Tqe3T4VNa0xuvWRJvwSv5sXYNmLX+l2fOSpLHFuQtWDMIz76fSd6TsGf2aV9tsD87M5vBBzTt+6PGFsYX53PfOD50ZkZlZr0m4e9eqOlrlueG1nwfgDD3dr/T0khwsGWXKlcHnJ2hHPuTOVOSzm5+H0A3AG9A1pTZ5zR/I9vwNbRmRih/KmcAzMg3+6IGaO4m+eErN55PvV0KnXoj89QdcDR1t6+ZQZtZram7b3zfzeyrr6E5tHdD/XwO/6PzaTdjMJ8zbqPvnS2c/hbc7lJW7hPaws269NkEqbkE014kS/uiLzbgw3ho4Co/ra3RYI5IW7AYRdthPV4jKHhaFrRyAUmjm0uT1MUW2wPbgEWOCzeLpz5afuVPdzGHZBxcHvZnepaX/Lmb1EmLzb85ZgjZJ/PFmwdQA3a28X7r7GtZPt1P5VhiMoBiTzkNkZI+aU1ZkEVHzO98gqazLx1D2iZvGk+2byAhlyqTBnl4k/QvMNnaZp/5A7PZZuvkPZPlCQiTjyw2c5jtjYYFuEwSS3fHgw7LIM9gzORgiYLF7htQt+/NXywRTbKeWabhaZf49udbJpDsXqqVfwDR+MFc3/azjDX7sAaszIzPlAEHO0O2QGCD+p7w203aexpxZ8amv9wATn9gotuZsl3W3FO90/9c32XNlS9PFG69BZc7S5QzdrvxPU9F7szXfF87m83vA5ym3f78zvCaP4mzm9n3haCrg53JG5rD3yag/fmc3WM1sg6gnb+lObLN9+l3/hZM7otiU38XAMh6ud+I7Szemb3R5p72+1hc5z3yazaf/mAPWvRNo2/+RTlt9t/i/iUrebWYTEBMPbX4nW0oWz54Z9sGRnh/x/Oq364tLXsgbTnJdGX5JctT7iNNG2uz50+5B4v7dKZQwebA+X9b6rJ21yDw2tIgIMkBzsRme7Sag/MaeUj6LTAtXxZYrC6nzpuBNqBBeU0OPiGYzE9Gzz05nNFqcraAaTJmACJAacXAYyaZNt7GzxvbyP6uZVMm20mi+YPBC0x2pmfB5IKALQMS5m/aDugYEDIJf9ptX0M7Z7N2pmru7wm7BVRzbWgsgNnXQyxQv+Sf7wM6ZpYofypnN7TPBvHd0D5LcHN9QFBufJ/yn/6n/+mPz6k7IGuWEIfP2Xg+P38zy3a58X3exj71Zvlwdbg/gTOzT/Mi0aw7s4D7szpDczbJD83/+X/+n3+03c3vy+u8tX1//mbB2MyATRmZ/sbf+Bu/433qzr0Zg6k7wHg2yg/gTXCbfmKxIm1oS5sxp423eJl22L5zU7QVAw4N/DQ72f9TfgNj1qbJYL6Xscl4IZ2WtEnnDTBkzmH/aQNvwdUWmzzIGLx65ezZE8AxgPUb0HgLzJv+WkzMe9aePHxKgHU5Uwu+7QnoMiCry7/2NJRA63LEtlZs/RtfF4DM2S7yfyX9rJ8zYW1/AUHdW16t32sJrMnKYLBgIWlv/x+ZLWp6Yf87/g2wG43LMa8gb3o2+2yyJC9MKDs7MgAhl5VWl0NrZmV2ZmXqD7Ca2aoBRzOLMyBlwMMAgEnsuy9o2uxrBnZ5YAHV0JiltLk27fInd7bt8sKxs+SxpwWnzO/57Yb2KQNaFiANIBoQODNIU2/2j+10//5Uzn/73/63P/ZWbd3//D//z39sfJ/23Pg+9WaJcIHkbH7Pn8oZgDMb7P/W3/pbP/ZSzfXhawDS/L7gP//n//x3P5WTG9qnr/0JnwFNAwZnSXD3ek294WvqTZn9Wrv5fd6xNXWHf7Mvxohmq1eMYt0r3rV4lLadD7gcW4u/xke2tTZP8cjkuh5Ira49fD3lI9Ik8Mu6LTawztV/Aqykc83KJ2/5gJj1Mx4aT+Q7c43tx8rS7GHvNfswELyfqWtrk9/fgLxfPcBqe6FY2ixWJsWPABozKhoeHcueOnL9msadRtJ4YH3Kx+/mcAa0CBTeAFC2t6CaDvSGzpsAxPsN/JGuJecWxNJWWqBqiX83WxqopQ2zbRYD4ZbI+GdymZyUdYDPgJ7hf5fGpsxMzwCRAWAzKzOAZIHSzDbt8t8AgdkfZHviZnluZpfm3tQZ0DV7ogakDY0BFjMTM9cXnE1/M2uWT8T0+fSh4WGBEn8qZ2Z2/uk//ac/AMcCwqm778EacDQ0Z8ZnysxyDU9DZ3QxQOnv//2//zuAtD9pM5vZt97yPXX/l//lf/lRbze0z/d9vUL+/M3/9D/9Tz/o7E/lDJ3d0D76WJ1MfyP38Dx/w+vU+3t/7+/97vTivoB098PwHVtMJi25WAJpNmrlKdYY6HlDz/bgbNy6wMi2TXBgspkOnpJpywGNlt27+mesWH6vMbJCX09eqNurbuYobqFIe3oLdHI85jsftL+XWdALTLW4mwB47+cspgG8lot/Zvlpm9wnIO9T9e7ryI2vW8wofsfsi2lQS7Kc9t1BWX7ySXs3CBpg4QbBLZY80liT96RB+bKv/c7AlLMT27ato5tuW2BkvVzfN50TZLagYc6UcmV/Oe7cuGn709p+iWtJhG1y7FK2fJPytl0+bAk2dci9EQTjtInLD5Ie9bH9j2/NDMkk9knMA5a2z/mh4nkP0wCeAQJzb9rMnqR9Yef8DQ+zlDWAaZcXB0xNf0Nj3js1gGnAxoCCAXM7KzNAYq7tPq8pO5u1pwsX0E39obn7kVZG7sEaOgNOdulw/gYsTV+7SXxfJLpAbH8sembCpu4Am6E/s0Wz8X3qDQgbulM3N74v3/mG9gE9u6F99DW6zbfD803uQ2v3ey1gzKXUBaCjj6mzG9+nDJCb71NvdDTtpu7OTOZSEDcx0+btoeK6vuUpUTcbpr1aEs6Xk5qPk4bxk7Ev4xJjidEycHSBKwMgpiu2v4CijUfbWkHdrszUGXXEGE3w0cajAUzmiRZX39D4dmxZaXoymVdusz3mmb2fY7l7Kj/VDBY3bvP7FjOQVB6BS7Z7M9tCOtvP8tMckWAt5aKB07Gu5PnEY+rK+m8GlXLl9wQqFnBTL5dOLTg8geLkMelafxtQ+aSUU9LUb/Ju/FgS4lgaYGvjQd6zZGAzGzZboX7Z937fJLzy7uzVJPdJyHN9l+p2I/gkpgFM+xqHAQbTZgHbLtHN3y5jDUjZfV37JvL9nOv7NvL9njOpQ3vK7sOaPvcN6gu6EvDO5wK7/Zw6A7aG1wVl2+eASW7s31mo3Yc2fO1Jx5152lOTA874xvdtO8Bo34g/PA/AmiXJ3Pg+vK1+cpyWj3zv2Oh0eB0+dk/V3FtdTllAv3zvtRZTWjFfSJvOepffWiE9s03zi+Y7DfClv1KWjA8ZW8jDE5ijbxK0NEB5XaMMti8t+dsDVwYUsj77zPEkwExAaPGQ42L9NhtrMa+N//eyEvMEjK5xs7pm66RvPvCpAFb+Nt2UFZRLNfZUcaFmDjKfPCzxMsm3RGq8kIY5uBme9bXyt5J8bJI0B7S9VgkmbEaHxtaCbdaxjf0sT8H6CrYri4Fw64tjnvXNfmgzT4X2egGilP8C0JZI+L3RX/0sUFlwsUt3O5uzPymTv3G3vzW4cg2omFmY+T5JPwHUtt2fp9mlrmm/J9uG36nPN8YvsJrZoQEV86Q4gG3oTN3hfT4X6LVgmEfDZylyX6kwS3pzfTaS59vZ53Pf/TX1d7Zpysy+zQtAB2zu29n/u//uv/vxotKZ0ZqN77u8OHWH7gCyoTt8zob2qT9LhvOOq1kWnA31Q3NmuWa5cMpuaJ/ZtgFZQ2Pkm3pDc3ifpcbdpL+b2adM/zOLla+24LibHTbbYvukscVmoG1f4o5N26B98UQaDeRYncvfbAa5yZ4zyRbnLE486a6Bw0aTOSbbkVYDMKuTjUucTb90nu13LC0vvrGvFuNTTxbbvj2sVFleuGLp0iSYvGLszwZZP/W3CNuU6mUc23Y/GZBt8Ijms63R2id40mn9sG2T167RaQw0kUYCptQXl6zIL5eoLiN84xDcD3I5YgZROjILZ6eyD/JE/syuOK1soNGAns2YGm+tWLDK/98E4tQtry0fu+S0r1pYYLXJOe1hyr4WYJfdht78P5u3Z4ZoaO8b0SfJL8Da2at9LcOOZ84sDmDKmaTtd4DGboLf3xLc2Z1pmy80tUSysg79mT0aXgeoDZCc67PxfX++ZjfoD7CaMhvY9xUMU2aJdEDS1B0QNNdng/n/+D/+j3/pT/7kT37s79p3Wc1+tdnQPmBqQM/wPgDpv//v//sfdWdD+wC1eb/Yf/Ff/Bc/6s1+qilDY2Set7zP9+F1ZJr/p+7Um2XauTf6GRr7W48rm9krtyvwwYu2ZLbaHmzyO332yTeWN6NpscPiEn2Sslz+ZMt4FmfegCjWzz7tAfYJbLTyFoSufp7G0nSfWxRa/Gv6bXSb/kjDAN/3kl/Mnpo+Tb9PecVkbf7xKTa55+DZ00QDRXlv618AK/83YMXS9lu1PhqoYJ8tcebMSH5/Cg7UCY24BRoDlUzg5gCm8/yes00X7zlL2YJG9muBstkKdd3W7CkP9WZP8wRYLTA9lasON25uMR0tD9Nml5Z2j87MHO2s0cxa7TLfAJlZCpvZqqk34GBno6bt9DngaerNJ31hwNnyOH3sTNi+O2poD6CZezNjM31P2Vc6LPCb9svryp26saA+9KfezPLsBvf9YebZ9zQ/cTOAaTe+776KqbvvrZq6s0l+6s7nbnyf/2cz+wCemZHazeYDggZ8Dt0BbkNjXqEwM2hTd/4GiM5P4Yyud//ZlAGPA5L2J3Z28/202cMAu5l9Zg13+TL1lQAk9fJkZ0zMBlozRpnP2wPNEyihD+a9/J5Jn8tzCZas/9wMb/JcWycu4EU5Wqyl/i0Wcrw4C8kVDJPV6OVYXu2prysGZmk2wDFnPjTAlTGSy6zfjxxPeS5em96yXLpdWX5W+XlQ7qt8la/yVb7KV/kqX+UPpPy0U4SzZ4QnF9qenjaDtWXu81X9nDJ+Oq1wPZnlbIg9ZTW0zScxts0ZCKLppxmgC63zKTKXHcmrLXlxCTH5zvYXr8Y3Zd9+n/ZNJA27vrwmrZV7l7SsDds220pd2c+3GG+06488JTW7576blTdPkW1fM+syL8Oc03LzXqeZYZkysz/7bqWdwZqZlllinOtDb2ZUdgZlZlpmA/n2u5u6p8xs1cyS7VvQZ0ZqZnbm3VEzm7OzQVN2uXA3pu+m+/bEarMEU0bOWUbbl6IO70N39jnNkuC+BmHo7ynE3bS+L00dnmdJbn/TME8Wjn5mNmw3yI+M+4b1uTd1R8bpa38qZ+pOPJt6I1++jHV/tmhmyXZz/tCc+vuj2nsKcg8ZTNk9cLkHy/YZceYkT222JTfGxG1vP99iY5P9sq7tebW2nFlJmrnNYflKHfAQUcqUM2SU3WaGuG2C/Cedtsepndi0vjIGsr7JYDNxT76SumTe4rhxSwllaXHxmsHK9vl2gLf6YiHPbWaR9U1PxueU9fdPs0Q4pU0LXtPZ17S1DcSWXIpKOjTALazT+mg8mawEcRdgegpq7T5P01GfDDZ08AyKFsSzfdvAeBk/ZUme7X7y0Mbe9EFQdwX7i74F5qzfbOMCWJYYyDf1mkEwAdXem+Q8SXva7Cb2PO23+6jmc08PTtllul0STJmGxgCs2TM0ZfYTDSAZUDF0Bzzs7+8NeNm9X3N/9krNslee5NuXkw5dJpw27hyr3bCfehq6A2oWkMz93fg/ZQDNvkNq7u2+sz0ZOH3t6xlWjgUbewJw94xN3dHTLvkNvX0B647zgscZk+l7X5exe9emzfSz7wXbpVMG+XzQIShKu3x60Eld8UGKPm7FwEpLvBYzWmGc2msmI+k3OVPe9NG8v31YvqBs+9CSdWwz/uoyQbDRTv1d+nqTa570kLLm3lfLc5R361s+eVsYu7+/WMJLGS4A1fYAksaVmy+ePgXA2kJF0fho4DQSM0bSf0L7TGwGsLLwfuuXQSwH3ZKz0Uzjp7FeoIN82uZ301NzJAbCdoiAPzhqwI59mu7200Bgk7np+ik42HhmMGx809nNdq5Tm7Yxtz21rp4H9OxsxwKBfffV1BugMABgNlzPbMwk733Vwb5vak/xpT3sO5p2o/Vc2xeITplZnikzQ7QzWXvSbekOkJh9Sztrs0Bu+Bqeh/e5vu904jht4d480/PS3ZOFM4s1cg5/A7BmM/qU+RxgOKf7Rjf7moWdgRpae7Jw9mvNz9/sicOlO7LOveFpTgDOqcHZPzWb3Kfu/gj2vnphyvAwdYfu9DN7t2bf1YDS/bHomV0cftvsPffAmU6u/auXL7z1EYLhBfdtQ3u2a/3s9/wt2JbIr43d7YQj5TE7swedBvYMvG3hPrJ9gLAHUANwBq4oaytv7+3eSaPPvnlwxYBji5f2mXqz+Gr52Ort96d8n3mSJWm2Op/uPVhbTOEGQtYI2C4ToQ3+myCQ323wyeMTAr4GsYFF8p39Xsa1TyrklcWAYwNXLTC+DUTs04IY+zH5zMmedMk+SLvx1OTiOPEk1H63YHTJlH3wlGXa29Lb2ar9uZsFtPvDwTtzlb8Qn0sgS3t/c3A3yU+7uTZAYWeF5t5u2p5+BkwMONqlsPwpnJRh+hla29fcz+XMHDMbn7nWHqwMYA1g2tOC+yPJ+VM5AzbnZORsup+lvJmVGyA09YfW1P1v/pv/5nenBXeZb8DQ3Jt2C1LnBON//V//1z+WXWcJdO4NwJpTgANsd6P68DAvZd0Tg/O3JwYHrM44LpBrm/wbIGibxC1epC6v2HUlJPOta6ky21/8PdHbuswXLVcY7+SPsYqy04/N/7P9+lTGgitvcAwsdjWfeFsaoDTaPHRk+dXy2P6f8jd7tVwzpeUCA1e819o3Wiwt7n8agGXKusDV1iXC5hOBlRzct+AonSGNzgynGW8CNbZ7Al7ko/GeT3H79LTXGRzIqxl9/v8EQqin/b8dJX8q5hB0HAt0FtwsMLMvA5TNJqlH/lkisdmv5Yv7SUxW8jZl+sllq11qyuWumSWaa7vMt6AmZ68GlEyin/9nNmYA2dQdsDDJf+rO7MrMskyZGZydfVpgtScHd/ZqPnM2bWdzBoTsO7rs6Tj3gNh4XfsO92WgAwT3paAj3/6u38wyzUzegJnhcWaWBvzs7xsO7QFVU2/0MLzuzNvU39c+LHAaULU/lbN72KbN7sfaesPD/lTO0B397QzXyLv9c+8obcRs7ml/DuOQzTxcy9a0d3tIeJP4DRBbPOSYX0kx6TzFcIsnBoLS/tIGud/reijgTGLaOPk3np70yfvZR37mH8eV8Sz1wT6uOGXjYjppwGrLPoylDJdtULYsjClLn6dUp7R+PtV7sKZcBsvv7Z4Z8tW+JTwGHG5Ez75aP3REa5POsG2e5GxgM9teR3+nEHywjunG6Nh1c+TcyN7AAr+TDwOWV4C75H8Kxhxj9mkAK+VnEksatsxhejR+d9xyX9CCqt0DtJvHmQT2vVG7UX0S+r7jaYDR/mTMXJuEPwBqwMIAkP3R6NTnviV+Z6RmhmdmdKbezhBx4/3KsoBv94zNtaEzPO5sV+4Xs6BKvc214XkA0gCX3ec0ffzDf/gPf9SbzfYDunZP2L7JfWf6hs4sHw4Ymv73/VNTps3qYTepDxD70z/909/pbXme7zsmO+ZTd/sf3Qxv831/Ymf3bqXdbNv0Gy6PXhvYeS112HzC/JeJMa8bz/SLNm7svyVSymO8Wr/XdcbaFsszRreN9aSReeKShffe6Kj9/1bm5D8fOm0rx5T10/Tj1J1tVDfgZvx8Lw/5zGUNAFpMbYeRUuf5IGNg7NMBrKeEt/XadTuJYfTpFLlxr/W19HOPwIWeLxkbuGogJ8v2l+8LSuPOvSrmyBb8Nmi84d8AI2mbHvMeAdZTgEheCRisz6zDTZnXODEZGG+pZz6hXv00Xqk767uByayXICT5HKCye6gm8Q94mSWx/V2+PSU493Z2ZpL+brqepD/Jf0/pDd2hMWVmtgao7ZvYl96CowWBAzyG7tzbZbUFUsPLLG/O9QEk0/dcH7p74pOb2Kkn6nzGZejtJvEFlrvMlzN9+76vqb/0xsd3E/z+7RvoZzlvT/zN9T00sN838OdPAG3ZfWgLHFe+3HvG2bzLXm2vH+3NgFazJS7tNHBm9OyAQo7R1uX/b+Rkab5F/7z6brHrqp96eYr5GecILFoMTr6egOYFpCzum77Mb1I2gqgFV/mglMCaq0jZ39YzwPntWC4lz+sjb2YVW0xOng0UfsQWf1UvGm3FkicdyPZbmaI4tXk9DWSfdhz3idePyLV1L6fYOmmkPD2x17IdA4MFGm4iZt8XIHriefXXAG4auh0Np+xb354imUDM4cmf6dg+nw5X0E5sfA0oWF8fKSvj/sxMLg8OgJl9P8PTvmAzwcSefJu/ndGZewsWVm8DeOYvT7btG+N3PIZG7s/an+dZmfet8FN2g/sAvZlRm76HlwEgCzoWXPHXDJ4S2/bHH4+fjeRT9keYB/Tlj3ZvH1t39p7tTNcCrL/+1//6j03q03ZmuUanC1hnRmquj9zJ8+owN9+PHmcJcTe0b30mqbQLO+VnydSSRbv/Vq9viu01YozOz7cAi6DhAii2/NPoESg1QEqgYbJZaXrOvsmrxRST9Yq7Bh4pm8n1VI+xzQBoA0WU13T8m+MN7waeTJ8txpqtmfymv08DsBryfoMyW0KzOrnMZ2Aj6+9nGlczdDN4G2w6txkVAyINtf0RkKTMDNZE+K2QpoGBRsP6t/tvnyoYSOmU1OUWAsgWVAlYGfhakMi9CRaE3gKoK3jOtX2CpD7n2syszKzSgJJJ9gOyBhTNZusBXwNaZtlvkvpueF+AMbR3H9eeSlx+Zulwlv0GIMw+o91HNZ/7eoIFRkN3Tx/mDM7QnP1dO/u1Jw93c/zOmO3fzvTwvU+tmB3nGA0/O2s3P3ezP1Uz/HKstu7IvG+HX33shvZZ5puf1hk9T90BsXNAYOjtKxhyPJePmQHbn8pZPS7Ayifza5bX7I8Js8Wc1FezaQNK7PMCI8ZvS9pX7Ez9NXoWA9m/ASnSMT6n8GBK03GzO6PNco1zXrM4T1k2zhlw5J8BFeog/y4ARN4uAHiNw/dob8veLT8aL6ZX04vJ82lnsN6ApLx+GSRLM5K8zwGdskuDdGpbdzYZ2Ne2teVG8mGypwFe+qAjZgDNvq7j1Rfwop5MXgZRA6ZPRk0Hf9v2Glv7JHh7KstT2giXBSzINp5N97THXT5bwJQv7ZzrMxu0+4oGqOzLM3cT97YZwDNlgMMAnXyR5gCGATgLgqZM+2kz/SyNAQU7Yzb3t8z3AXoDjhboDd2/+lf/6u+AypTZLL8/eDz95O8lrj6af9GuzB6yzW58n7I/R7Ob8Kn3ubY/aTPAb+qtfAOsZsZqgObS2Z8ImjZTnzNi+7mzhDN2Uy9PfOYsliVPsw/qgqdP7QHSEoqBk/l7M6PdABLHowEro33J25K6zeRP4cPOE8hqOqYuts9Gw2JfA2GMCVduyzaWyy7AmDxce6bys63aNMBv5QKjW3IrR9OFtaP8NiZmM/zO/j71e7AaqqShP4ErG1QiYQ7c01ISg1dD09bvXM8nW+6Xag6bvDUjy36pg+uEZp7Yyn7JzwX4rM4FFljHkpHJbm349GLgxk7mtSC/OrHEY20umtxkf+kidWL2RIA1YCffZTV95SbrfefUJP0BBQMAdtlvZlpmxmXKLFcNABuws6ftdtP6lJ2VmtmaARkLmKbM/7OMNvzs7xsO/QFTsww3POx+rgU4O4O1AGNPN+6ypiXKa6xMv5bgRid7+nGWMXdD+QKs9KGtO2Bo6639/W//2//243cKh+9959cuN46+90Sl2fPINqBt6M73nfVjvesQiIEEgqzUnx3UsPj6kYeKjCkNKKUs5PFprK6+Gz+WmC/gYPWe4hd5tvhP8NJOBTfAwX1wrV6Od/bbdEjZ0kY4Royf2W77aDHV+qWumr6+l1OW5kumzwbG0r753er/zPJ7eZM7rzFINcO7kHIOnCFXGit52H4Jrt6UXJLcz+TDjND4yyOspp+nxL20n4Je8m3H4e3/pLPt6IB0bOqg0U5+TE8WWBkErkBA0EZA1QKOtc3NnHuNfDxtwGVf1OFuKF+gs+Bk37m0J9dWF5PI9+dkpuTP3QwY2h8dnmS/y1sLFnYP1ACIaTf95L6sAWoDpubU3r7BfdsuyBvAMqAiTyBOP7vfK2eId2N9S1DUh+mn+c7O0m2/rWzdfBv79rEvGM2xHZ4ZG6zM9dVL2kzaGv9oIy2+ZR/5ncmkgSzTrdVp/ZM3PrQ1YGdxiaX5K+/nye82Bk0m88HWT4tZjAUJ/pJGi3WXzE/1t82VA5K3N4cSTC4DV43f7M9k+35MhpjujEY+eKfe2T/fy5f3ms992j1Yb65bgN36e80MgEZjyjUHSlrcv9T2Zy19AhDK9yS/6SOdxGQ3HTR5m24vEEOeTN8NYGUd65f3LABmPZuBs2tWeCKF40p500Z4ejP75X2j1fR58T3gY39DL2eZFnht0l8wOp8Drua3BqfNzDJNgp8lrikz87RgZ8HUgqipv8vY8zlgbK7ve53m+85QDfDYGavdLL9AZUHWAovU++p5eNxZtl0qTN8yPdEmWjBvyf2pEAQk72snbZn/Gs+0mRavrmvmD+y3xYaMZQ3IXfoyX7geOC2ZZpv0G8avN2V5bTNwTLopB2eYrV+uKqTeCZYMJDCn2MM19cTxafpfehy/a8WCNPJ/niKkvjaeXGPN/MQ+mA+2NJukPkiHWzKyTcv52eeVKz/tKUIzsi1PQYizM0nTEC7pNfDCQTaDMKC33xlMm5EbT1bP6FPuttTwJuE0Hth/A6G2Z4F8W3/mGKYvm50inStg5zgz6Nmx36TfQBIDIb9bsk6alhBtnHbGhLofvvd03r7/Ka/v97k+szBT+BM7U2d/U3Df5TT159osBw6gmL1HU+ben/zJn/xuuXJ/t283qucy0vK+gGRf5bBgbviY5cO5ticPG8BiMrXgnbZk45jt7P/0cx4Q4ZhYkqMNPNlL45H2fPkddU06TKTNxy9/SV45vtme+qC8CbAunrP/i88FWS2P8HrGzLenfi2eNtBiecF4MD9P/ZLf1FU7+d0AI/khyKWvmE4oTzu12fZUGR+/iZlHgh6zuZQtHyap22bjLV9k+59Vft4bt77KV/kqX+WrfJWv8lX+QMpPf5O7XWsosz0B7P/XE4k95Tbae63xecnSntIaYuamc3tiJf+X3trTaD51t30j7ekjn1rt6XOfRvLtuTZefOq9eCYPuSxr8tmMGWcWrtd0mF3kddsLyCcnznzs5zV219NTs5l8mpuN57uRfMq+6HM2ss/9fe/VnorbVzLMUt7cmzGbje//0X/0H/3uRNyedNvN3ruXafdb7f4pziztvq+ZpdoN3bPBfsqcdpyyPw+T771aWTiDxSUdG+NWbPak6bH9/zQmNuuQhad+WwxrPp06pv2avXG2jfK2Yu14r83m0G+umbenfUDX+DKOUJecwWixrOmGNG1G7rKXln+MvtHgzO11yo6zPNuecpsurthvsjQbv/bqtrj6m/Br8mb5wVancgYrecl+my2/8e9PCbAyCFGZnJ6kkV5Ko2FmEG997rXLCJsTJp/kgUkijc6W1xooWL28AYDJJ0Hckx6bbG8C9sVLyt5kJh/mpFnSLj6SaBiMki8LUgZ41q6SXgs0NgZtyt2COunm5vZ9W/kCo9kAv5vc95ULC3Tm3Uyzb2pB0NDYPVVLZ9/2vqcVp+w+q6E7S4gDtHaT+lybl2rOT+8MrTk9NzS23+lz6u9PzuzrIHJv1hUYL7ujn+fBA9Nt6rVttDYe+H8DwPl9N9saLfMx64Mg5dp7SMDV+LIkSF/I2Lj2S1B9jRtlteR/+YsVymfFwAb5NdDOGGWxOfl4iimU6SPFYt4TcGg23XKVjR0B1Zu8mDTf5uZvL/b6Ug+kSV4vvp74+YOYwTJ0m2Bovu/TtTkaDeYKWkmzHXcl7RbIs68nh6Vs+b6tpL26IGjYuvmjzg2s8NpbAzNjJm0+TWcfBDuknYFr67dZBuv7AoPUFWXJcbTXKlwgjYE55eOsC2Vpdpn6ylOjBraoi6m/IIj8DeCZPVRDb8DWnkCcdrtnavdtzYnBnb3aWaoFalO27faxv284LzodEDX0p599XcNcm831+0LS3Vw/bXdDe/JDsNo2MLfkRRr0RbNVfr8K2zRfZ93mn1MYC0i7xS7rN8GL6acBweyTchEQUc7W1vSV7dPfORNhtLNd3rvaGO/mQ6bb9eONUQbWnuLUVdr4tphph3eMj0u+FotJd783UJXjcPmR9UM63yWeZSylrIy7+b3xe/nS9jPl7StLfrXvwaJxMeAkCFmnJPhgsdcbtGCyfT3xt0Z8GfMFdshDbrZkMEj5LRAbuCO4aTylHI1/C5zUZUtqjYcEV21Z9AoSpGXteeJz+5xiTzs222jXTD+Xbs2en2S7TjlRX7lZf2aJZilu3+E0ZV/0OWBofsR4N7nv0mHOWO2pwuRzac/s177iYQHd9Df0Z1Zqru2rHgZozesbdvkxf7h5Z3IWvC092sqCrOZjDQwnsGX7vL807HSU2W3a0rY1W7/soY1/xoCWSGxZsM3MfRRAXj5O+S4fbQmacpNvm2k0nV0J/PKtBiyfdMTYvNdaXL3i1tUHC1dosm/LFfmakBZvjT+zZYup7eDXFG40z3b8/gRgvheQ/EQ3fe8pxrLt21Pnv/ofe7a/Lbl0cCHY/D+PUdtU5xoOjbUZ4JMRmVwN6LAteWf/SY96ayDJQOVH+G9t2Ift/+Cek9aOb8lPGgwwHKOUPXnONk/BlvxQL0svZbyCzZMc5DH5ZJJOgJA62BeCTpn7+xqEfZ3CzBzNtf3x5t0HNfT2+5R9K3sGmu1/wNgAqv1twJF5aM9P70yZvgag7dvOcylx2swLPef/nf1Ke9gfex76mRiYVCxopr4vv81rlugNwLZj9K1fs9stXJrZccuHQnsAMDkvf+B9O2HLeg0Amn0yuaZvN36op9SFLQG3GNR0Y/wbQDCw2vqw8ibZNjtp9vpE22LclhzbjZsWVzhmuWe1Aayn5c+lmTw+yWD9Wcy2wnF/A0Kt7VM/f1BLhC2wbclBvupzEJ8cuiVCtqXhtsE3h9o2fHtzGuoFkixQkGcGTwN3lhhYmq7omOm0lpwsOV4Gz7ZMCE/FggcTzhPoa7ZofD3xYHWtf6PFpJ32PMF1N4wP0Nqltrm/77La92PN91mi+2f/7J/9ADT7BvUEOruHaq7vC0EHTM1P3uzP5uwLSwe8TZl2OzuVPxOzwGH6nToLBIfm2n7OhO2MF3/oe2W1hMwZBfoGZ3ZsjBNQbb20ZZsVzr6vvS22CXyujf5m3Ib26G6B6xVD7NryuXISAO2DC5/M24OBgSkDjW98qOmEwIHXTObVc3uVQvLE9o3Ppzzwpv6lJ8sJ5OWj8dBorW7s8Adt4gmI5r0GpvJhwGSmvq5xp11sMVlt7J/iqwEr07vx8wf1UznmPNeSTdZrhmRJ32hlW67P2rtDsthGxCtZ7zW+24T0DTTRGSiDgazkIR3nShoM4MabjQXpGu8ENW1MyFsDYBbsbCzMMa2fvd8cO/lvQKHpiLzae2Eu3qfuApv5nP1N+y6p3Ki+pwfTXqbMEt/8APJudp8N6ftD0QOwBpANgJo2A6L2PVhDa67vScLZgzWAan+8eUHavFNrZrBmtmuXLLOkXi1QN4C6s9NPgGF11JJA6t2WCq2+HYzJ2XLKtfyOPncGcPQ4s3w7i9h433u2DLNLrQuemj62NPm2tOTMRNcS9NKwOJPxO2nb6TDKbVs8WNr+pJYjMi6y7urSgDzj5gViLG6zriX6ywYpH+ns+BEgZd7bPbsGQiw2Zl/NPu3var8l65rdZb2nnLD3SaP1bW3+IN7k/uTA9r+BqNxvs4reOgySBBvGA4GCGZOBDhr48pJOmtPuLQGQpumDJQOwJR46+LZ5ok/glDxvIG8AYUsDhqTDthmI2N6usXCs2e/WyXFjn+TL2lEvrVBPV9IZugNq9seL5/6e3lugNUmbCX91vS8XnTL1Z1ZlNsDPzNRc35+12d823FN+u/Q3v0u4AGv3UO3P5gyYGoA2dXa/1dRbXhZ8pi3mxnzKzITH8XtKpJcNp65zxsfGzzY7py2MjKPHPXgzMuXvHE7Z5dv/8D/8D3/8PzNYA4b35GeOu8VD3iMIb/tgrDCJZ4Lj7N3yRZA1ba8TZnm9+c4V5w3YXbQt7tp+H9J6OlBjAOsjG6FNtuSR/RrIoXxWh3uwWuFybcrOtnwwafUIgJmvdja9zQp/kyVF2mhOPhhWyOtvcyNl+rQA61J8G+AshnSnGChJh8wB414sgg1D5+bUBijS8C/5LAARsFwytyCV/1N/V2JiSX7sKX51bn1kgDVZ2U8rBny4cd2KLS1nALHZu5SrBcTUC4MVdUP62daWW0k/wfLOfOT+JQaofd/V3hsQsO+gmjIgbfdLDb3Z/L5LiLtJfgFDLj/uzNeCrH3/1vzt+E+bnU1bwLaga8r+HE5byrhshzq8ZnBboS2nPWdcyEMY+33kTd0OaP3jP/7jHzNUI/PsfRvdMgmNzANa5/oArE08y8/ob8Zoyv4YdbZffW27HGs+1KR+9rslUwJ72mvqpx0WSn1anw1AcSzejFmLK1c76iL7abZldalD0iE9+jj1YX03YMU2XE1pM4+X7teeko7J23LwVS9j1c7K25Lmt2M2L2dpLbZfoPOtPWXfn/6nckwZNFwDCXvfjCnpW5B5AnCbnGxWwQAMadlTYba/pu2ZQDhjZKeOMlimQzIQmc7bOFggTV0xKJgeL2D8FvDRFuiMGbw4hlcg2zq2MZkyN74zoDQwybr73fbssD4BLfe8Je/zfU8TJkAaIDBLgVt3gNQs6c2M0yT3Seqb+KevmXWZ2ald+tsZrykz4zX3BzBMu3/9r//1j2Wv3Y+VwCoDeeqbtmv6sgTWQMRb/TZ7vdoTYM1s3SyL7n62eefX/M3vPI5ONpnkzwMNYBo9jb529iqB3r/77/67f+kv/+W//IP+gN0BvlN2X1wuYyY4M56bblrJuHHp2+i2GEg/MP9tQIv0DcCYbRiPBijbuFu/Fjtam71vBxj23pWrrH/G8pQpAVLqhYdjsk3ySID1tBx75erUs8XTbPftBQCyB4K2hSZ10fpqfTzln08xg9Ucun0ySPJJKZXKZJTXzCmocKPZnKQZHv/PwE2gYgO+vO3pEe4ZMfnNGC8Qld9t31R+v/bCGaA1vbXATUfddgRQBrDIB/VDXmxcruDXrrNtLtm0RNhO+HCcbFmKn0l36u5syG4gnyS9iX3rDZgagLC0d/lwrk2Sn9muAQ0zGzPAYEDW2kUu/U39oT3/5+zKjN3UW7r5mgazDY6b6bwtGaUOcjb68tPU4fDEcSAva4sj5yyrzn6qAVMDhkavU28OBOysVp72HD3Oy1cHcO4b9ZfWjtcsIc5+uBm3ne1KHnZMh/7QGFq2v+wCKmYzjBu5vHz5aCtphy3pJT3GftYzgNLomZxZ2gxKA1dNviZ/zihnnGc8fdsPZecf994RlFm7vZ4Ai/Z+6cW+Z9u1Scunl95/8+IkY+Ymy3HUBft4yhWfepN7GgaN+G0w3pJGbYawxnUZeJanDeikn5/NSFNO26RKelvXksw19cr/L10aDUs419hYHZPbHCr74cwTlylsLwT7Nh4a2CJAouzZln1cbc0OnsYpk1QLsqsDo7uvY9gN7vvi0J0NmbLX9weeJ8FPUt9lrN1btD8cPbSGxpTZazWJeH/qZgHCfA4gG8CxJwmHfo5lHgQwwNiWOvZ/7qlsdpXjbDbPcWyzj/StubZvyh9ZBuzsO8Xm2uhswFeejhxANvvUZlZq9LsbjUfHM9M4fzPLNbT2ZOYuA09fU38PJAz90f//8//8P7+bWTRdpQwZa8xeWqxKm6Mdpg7Nhtd+zd9sLMi7yUF+mi9n35n4CTQugNb4pF6Mt5aXDEzavSY/91qZ7u2h7RpD4z+vX7mEJcfl0t+3F0C6TY60/tJXtxBPULan8fo0r2nI4LgB0jZcNiUzkXGQaRQZNHMQmvEkMn8yMAsET8l1g7MBodRL8pp6Yt9tqvfJ8fndAEtzzPxMWpdOeT/1kGOYMqf+2B+XZDm2DDrkk3ZgcrGP7GtnJLI9geLTdwvCBCD25Jl1F1zN9938vrzaCbz9CZ2dcZnkPqBhl/n2XVe7L2ho5mmkAVIzQzMga2jtu68SjLSHCIIcHkBoYNj0xXq0E2vTEpWNy84g79Lo6GWX+6aMDmc/1vye44zBnrqcegOKdt/bLt/ubNjMcM3/Mxs2tGcmbH/3MZdsp+6A4aE9dcnn5RstHl6JpfmJ6ftKrnav+Vyj3WhaIbjK9gSkb/t4Aib5aXI13T3FzJQhfZltTG9JgzO7FsttNcn45TgacDHwdNnklCtnWZ5nPM5Yu/mRB36s2Nh+GoDVTqkkIn1C1VnMsK00Z7ekfYESJu23a8H8n4Apr1vypizc52XOb85BXi59pfNkUmSiz/G0J0mCRPLCKfU3gbhNl29STCdqSdVswBz8ml6/dGu2xad8C2YZTAh4jW4GYgKE3Ue1QGzB1ICAAQ3ztycTN8nntP/+P2BieN+EtcuQe7owgd7ylOPJYmNipdWxcbjixFVWVwzOs+/q3//3//0fOtz9Zvuqin3P1f7m4si9v904up39WVOm3sxmjY5GXwOYhubQmnEYEDZ6y/HbH+7e33WcmbCskzqwhytLytSb+Vfq8wIPWbf5GW2c+1rfjNVlI80XWrxOHhJYtP2y7KP5fZNvi82Osz/K1QDEBXYvfT35WouzV+ykjWTc3ras973IT5mN56Sxs8n2cGYx2Xhv9/+8yrtfPv0qX+WrfJWv8lW+ylf5Kn8xlwhz+Ss3ChK9XjNT+78dKbY+ScOmRq+non/b6USbtbA9RtRPtn+aPrWpYJP32hTZeDcdUIf5Uzh58qm1t1nFdt/kyTY765czWG2WMXm3Gb83syO0zfbExe+cun8rq/H99OTGDbdT9pUKO9uyT4LTZpb+9rTczNjsy0L3bfLzubNW83qCoT119/1OO/tCHkzOtcO2TMH6O475ZJxj1Hz7jd9OvVkunVcvjA6m7AtY53cdZyl0Tl/uXqnRxcxKzSnN+f//+r/+rx8zUXN/dbo0p85f+2t/7QfvU29mCWd2a/Q2f7tvbXW9upyZqz/90z/9ofNps/fzQEWLeZfMTz7HujtLxv5sHLifjm2umTCOh12jz07hFgOLNYynVx5Yu2wzryyMp7zX5LxmddsM4s6wZs54Mzv0NBYtFiUvlotS5+aDLf5/P2bZko7NktlmetJI+ZpOrlneT7EHa0oCKyZkM8Z0pAYYsn1zpK2T/yf9DGJZ9/qe1xggrr7eBpL9blOoHwUFzaHMESkP26bOOD4W9BqwY2DhZmOTZ8qOFfesNXmoI+OFOmtj9wTsU7ds2+oRHJi+k2cDE/uZL7acz/y5ln231bYbWQZIzL6ffT/WvpZg2uxS4ACPfS2D8fEEaGjfl22lbrhkcum90bektf/PUt5sKt93h82+qF0SXFA5OhrQNHoagDWvr9hXVkz9pb96HOA0gHXo729Izstdp/7U3eXD+T7LhVPm/tTbZcJp294P9AYAUI9PpenfYlrzIcbrp77b+LNNi73Nv9N/LC5M4Sm16zUfb0sDJFmarbNO8rb/XwDQdNj6XtCWsdpA2wVW3sS/7wKwmh9fS8nNxmwbxgWufh/lp78Hq62nbrkG7hqEBiqeEK/x90aGJ/kux2p8J8JuBpaFG+atXSuX/szRjG8Cm6TLNjb+7amkHUtvpY3nBVjJx8X7072PBOM34/Nk3xlsOFZT9lQheZvxmuQ+f/lS0AUTu5dq9bH7f/Io/vSZL+HMF5OymF8n+H4CWKS1Mrej3m94oE53X9WAoykzIzWzSH/yJ3/yo58BSHN/ZqQGZM3/s0F9Zq1mhmlkX13MAYC//bf/9o8N8DNr9a/+1b/6MeM39feHuHdWcN72Pt9Xr/sj2sPT9LN7vGZs9g37TScry/XakKy/dt/2RlpynOttn2rq9nrovRL209hdPmZ+nvG/2WHWexNfGjjg9Typmr6X/ZkMb2075c0YkPtgja/9zofStv+p6aSNNXX1/cVD15Q9SJNjQVCW97Ov/LSTwFnXDtR9yp/KMfBh/3OwaVBse7WzJG48Nt7b//bdDKw5kBlT1ic90jXeWv8sqVP+/5SY0sGMb3s/TvLHDZ102KR3yZjvW7L21s74mGKAkTK/BXoXOGt6JQ1e5/iYvdHHmCDzlQ77kDMAYGZtdsN7JvN85UMewsgfjx6wtrpMfp9kfaNTA7Zps29KA6z7Tq+ZWZrl0ykDnPL1CQOuBgzNBvXdxD66mu8jex4omKXGWRacv9HjP/pH/+jHDNXUG8A0LxmdujO7tTOCC053lmzK1JsZrqk7pwj37fumX9PpU7zambHLVu1ULJNdtk0dW9yy+/wkAHyKcyb/llyGtuRMOhaDP5KzjJ7F8uYDLXe89fM3fsExuJadL7/MWH3FoixP9C99mg3tvawzZW2bfZntfjqAlaepphjgeAtytlyDZEaYhtjQ7FNyN36IrK92F+pvhvdWL2+BWAO1OQ1tASLr03DzHoHAxQtpMuAyAE3J2a+1qzyBaUGoBW0miUu/Jv/W4xOU0bXklLK2INraZN9md/b7hpusF2TtqcKl03SQCWtfsLk/h3P13eyddt7kvU5ktf5ouxyv+b4zU3NtluimLHDKfkbW2TM112ZJcADpvv9rTgfu8uLw+S/+xb/4cX8+p+6+ymL6+o//4//4B1AbADf3ps9951j+aPbwNKBuPqfky0ipw0vPeT3to8XFtwmvtbU2jAHcE2XgKv2acYXys9+M7ezn2hdovFLnJhvtattwVvwqGXttNslOkRpfKUfLbQZQtnCLTNLM+i1OWw7I0oD1lo/kCJbUubXZOj8TXP30F41SCQaunoJoAy4cNDornS0d0Pp7A7JYElg8Jeq9n5v97T7lI0BtRk5jN6NuIKtdN97mj7+31hInafBlkc1xnoIs+2664F6eBoTMLtKOzG4zgXFPhwV2SwxG6xqDBvCs/sq/b3U3Xc71We7K1zQkP/lahn2h6W52tyUs8kud5XWOl/ksx+OjwTLpj6wzUzRAZr7PCz0XYG2fC9gHDO0BgJF73wg/s1KzH+uv/JW/8qPNgK5//I//8Y86A4r2x7p3yW9muGa/14Cof/pP/+mP5cOVYWas5v7MVs3s1y4rDo1dIrRE23Rs+qZv5Bhw2Yi29RbMNf9q40Xf5OGnKzaYv5o9TaHNmz1ZMd6zXz6Q8s98zfqzfMM69sqYposFa0+xk33bq3OM5xaH8wF3C23Nyhv7uvK19ZVt3gDdTwGwMkm9TRKWNPN+0s9+LtDB0hzzl8j3ZEzWrtFKRzb+Ujf2fpBWMnCYo11ts38eVKCjZ1/5f/bPE3Z77dKl9bH8pJ7IK+vbki2BGttS72ZfGVypN9PNysz3wiSPFqyfEoUlBtrI8rovIV2wlC8y5Z65BRlmd1bsPsfnyU/NHi66rG9AekEUdbbLoDOrNDoZ4LUvEN1N7Lt3a/dLDXCad1fthvXpe2lM3Z21GiA14C51PbNhu0S49YaHmW3c5duMBxfoNPuw2QnTfYs11Ps1lozv3OvV2tq+TMprSbaBpqyX2wCom6eY32KE5TCTyXT/kVhNWfK7ATPWNTq0pZRr61jMbfo1et8feLxoWmx/0sVT+dQAy2ZeaFw2qJlU0lg/Cq7eoF+j15B0c6g03qtf46MFTurqKSmZsSePrV32l321pJ0Bka+boO5aYDQA0HR88WlAsemrBZT9zNccWHB4clTj2/RIvsxuk+fmM29KJgTKu/f3p1py83vqaXWQr3jg/UsPTS8pz1XX6rdxN39M2xz+Z0ZqXokw13bmaq4P2BldDDCa/VSjk3ldwy6t/nv/3r/34zUOc31A1b5cdGet9u3tQ3frzrUBTf/kn/yTH3R3n9fuwZrZrNmvNTT2rfEJOjIh21urmx+b71riNBBjtO1+K+Znxt8VpwhmSNv6S54beLrkTJBnMhA0PpWMj9cPeTcZTa4LUDVQnPdzxnLlvGJJu38B1G8vwaPFsmZzv2Rc39z7lADrAkdbbz+fUG3SeHoKajSMhzVG9tH6fjI2S57Joz3tPJUWRM0w20Zk03UDja0vynmVrNcCcQs25nwGBgm8nviygPdLAJbphEH7SeY3NpB29xRYGFDZfv52Y/faSM6a7LFuHue+kuilp7e+zHot4VFnb5LFvpYi+5iZpplhmpN+M/s0s0qru1k+nc/ZczU/kzNAaTbIz4nDKft29mm779aamaoBYtPX/E39AWuTYIfe0FieBlxNnT3J2QC36eppz0/aLmPMymdxrsXjxovFnjeJNvki8Eremkykb4Ag6XApjDLnTHh7DQx1dfFk4K3NqL+NCYxvpJl17FrqvM38P+Xpls+o9ylPuXHrtNLAVYslxtvPBlk/9T1Y/J5gaP9v4IU0jCYdJe+1IDLFglmj0zYTtyTxxCd5NFCwPFq5guAFrrJt9s3+WoDPfvZpncZsoOwybgMfb5yi1bdAdYFFo0va2fZpHx/Hwuzc7LEFToJJ0mqBqgXslWHp58tDbTx4gMB0bXqlPTcbt/r8bHtaLt0zWSefu0S3s0L7CoUBPgOYZnZq9kQN8FnANcBo/p8N6/P7gvsuq/mJm31dw3/5X/6XP0DW7O2a6zN7tfuqpiy4GqCWYzYzaetLe3Bjvu/PGmVpccp0yLiVAINxsSXMp6Sf4MWO8BvNpJcJf9uT1iUv5eY9AqwLIKTdWB9mo42fN/GrAaGmP7vXQN5bnlqst9Je7XC1/SY2c8Wm/Z92buNyxXTLjZ9yDxYTAPebpCJWabY/56Jp19excr8FNzxfT4zr8JbUcmAvo99gQT4pswGjC7GTxlMd0jTZkp9WmFzNAS6+6Ihs0xzdEjzvG2BkUDW7ocwX/ScgR3C2/PCBgqf2mk55QtGe6C/dXsli668vmj03v2uBjTSeAFUrBiAZdPfaxaPxlO32Te77OoWZpZq9T7Mfapbz5vpsZp/rA64GeO3pwH3b+gKnAVZ//a//9b/0H/wH/8EPUPTP//k//wHC5vsAuFk2nM/5yx/eTj4nVg2gmxeXzljv0uG+NLYdjrE4sbqyE6KXvlM/luhM1/QJxro3yThB9FMcSjqMDY2/5IX2lf71FAszDpjer7xlMlp+aLJe5clvLb+kTb2h2+pdsWbLlf/2O4uN60eA0lvg+CleNLr/U2nmFAkC9tobxba9WhdvHxnATEqZQDnl+qaYI9IR2oma5uCXsT6VdJLWngDz6ofOlrrL/kjLQCzly3p2j3QaICa93Wf0Zq+FjRtp55M5N7HbtD5nBO2BgKe+TB/UdQZytm2AlmAv9dXG/vIjA1ukY68VsFm0i4cn2116A3QGVE0ZIDWzTTN7NaBm+BigM0Bq6uyrFAZgzczUAKr8aaGZvRqa86b3oTEzWAPQ9vTmtP+v/qv/6gf4GhqzXLh7sKbf4W1A1/A+defVDsPT//l//p+/e7P86uc6NUpQRPsxnTSAStt6A5QbkLriPfs0GRrPF5jMhy1ukWgJ/uLt0hHlu8blihmpn+Z7LG/3hdkYcp8f9fA2vtvYfisxffu9APUF0K1Oiy1PND7NKUL7noWOlXUTyOz/7ckh76/D7XIA/zfkbgPdQKE5uiWBt4CP8pCuJcTLWW3mxpIXS3MuA6LXjEpr3wLIm5L6ZZBsdLlPpQW1DObUaztxRn0xYGedvZcvSTWg2vb+5TheezYuWyZvlw9kckpen/amtHrUN3W0hQC8nYR7k/Qt4A8gGoA075vaVy1MH3MScN89NTNI8yqGKTN7NIBoZqMGDA3YGTr7otEBTbNkOGDqH/yDf/BjaXHqzczTyDLAaUDabJz/z/6z/+xHm9n0vgBrf6NwgNX+VuGUXR7M8bcj+PZ3gWbT07VUaPEv2/6SRGYxhvzbydq8z9lqS95bbLWE9mk2xjb01yb7GzDadGK0GshrscyAYxvbppurPIGrq7wFjk+giiXHkfJ/JP/+6vZgZWAwdHyBKzOuhlTNkBJcpdOyj6ST/VgwY5/cQ0VA02TOz+TFlrmugNkADfXSxoe8k1ZzfHsSoZ7YrgUx8mTjzv0T5Mv6Wx6X3u5tyX7sz0CSjV1LEFf9+WPiaAmHsrRAm/p5w8/yYEvnliioc/4QLUFA8sRgbf2krjmLlbN2pGOzgCzZx8wKDYAZIDMAa2ew9qdpZnlw6syS4IChof8v/+W//DHrlG9nHxoLhPb1C3NvNrMPUNtN9Psi0dHX/IzO9DEvI90XnO4LSfMlo3NvZsCGlz11SFtPOW1Gc/XIMeeDKtvxlQmMmU2vTzHm6Tp9Kfu1eG+A0myQvra6yz1eV6yyWE7+Lf+sLo3v7IP9pV6v/JTjTL3lvaxvs8MWS1tcI9+UrcnxrTwENuBoMhqP9p3XaN8/q7w/a/pVvspX+Spf5at8la/yVf7ivmi0odCcJeATsNF5eprZ9vkExieypUf69iRmswB5KiWfolYWInl7Kkg6fPLJJwZ7WZ7JbjK12SDymzxd+t6SS2kpH/XUnpSMn6fZmezP7KjNtNn3a/bKaOUTsdUxGchn8wPqhHsTWpts1/RyPb3lLEfKxbFtOrlmLVJnaX+mQ9NFkzn9i21S7ibvvCh0lgVnFmqW8vZN7vsW9f0pnPl/ZpdmqXA2p0+9nQ2f2a2ZbdoN8jPDNHuv9sTgLiHO/am7y5D/+//+v/+lv//3//7vNq6vrqfe8DOzXTMTNnRmFmz2cg2tHCOLARzv1NVHZrA4W8P4aXHGYkYbh6f/k0azK84+cW9exvb8nisZ2cb6TZnZd8YPi79vZMhi/CaNq1is5dgm32/jU9bbA2Kc6bb4s/d/W04aWsk+m3x2vem81fvZM1g/FWBNacF/Co/jXgbLDXnpaG2pgYClBXeWC1hlHQIpk33L08Y+0rAEx6DKgGH7iExO6owB1JYF5v7qk3sjltfVfRZutmWitWDA6+T16X/TA8eEcl5J4AJhWTgOpodm32nL+w6q5ZFH7i0gmq3Q9k1XlLONPfm1kraw+83eHt+35NaSBts2efb68DDLerNfajeQ7ysW9qdwZr/U7K0agDX7rgY87Wb2BU2z5Pef/Cf/ye+WCKfevBNrQNHU3986HFoDzgY0DVia/Va5fDhllxBnw/0Cq1mK3J8k4v63C2AyzrVicTd1fyXRp6T9JpGZve2n9Ztlk30mffKSD7jNZ5gX3to1aVgSpwxPhXKbjxoYbHp/8oF28KHxzFzN7T4m/xb7/pQj3/Dz9IqRFj8+LcDa0kDIFnPiDNaWoGwT7NXvU3DO71ciN6BG8NCMOYMBaSVgSlpvkskbPZAGSzrT5dhNnzYr0vqw/gn8jH8bpxbwFmxZEMjxedo4zuDMMTcA1gKE6c3GhgcV3gR/6snuPSXii+8tPDxAPixp2HgmnXwCtrE0PrLw/UmUe1/6OUBpwMz+6PKAmQVg87uBs69qQNa8amHazKxTvoB0QNKApynzXqwFanNvANzf/Jt/80f9AUvzO4Pzt+/DGj5mk/2UPTE4/O4+r+Fp6vF3CFvsbHbWwIWV3FDewPUTwMprxtsFGuiPlri3Hcc46eRMSyu0OSstjpgeCYqazAaerM5+tthn/9s+PMYCiwktNrT4bOAl/zJn/BYA1vTS7tNmGaPTBmyvHnX9BiP8KgGWLSVZnQvImKKfBj6dKOtb4t3vLbG3BMGkmoNJ3hofBkQsYTdUfukoeWz8PDmyOT8dycaL7d8AM9Zh3zkjmXzwpN51ei3HJkFLjp0lDTvmbXo1J6cu0tYMsFk/bWxNR6ZLS2bGI+s/JfUWiNN+V7brqZO0U7439pV6tUSzfMzS3L70c0HMlGm3J/gGMM1rFwaI7bLf3N8fZp52s2E9f1JoN+LPzNi0/1t/62/9oPG//q//64+ZsgFOA9gGWM1s1f6O4X4fWvM3dRJcNSBg8enJ/pquVzf7Jvmkn7bA5cVrPJq9GW1rk3VzLO3kH/0n41OL2600UHV9p+6Tjwa0mCueYnIDTRmX+FNKWcfGoMWHjE9bJ2Nv80/Lf98lF22+b1shsr9mv5zBtBjBpcpr3H/V78FqoOg6ZdHa596n/aSzcWBawrVBZnLhXwMAW38/r3rZf7azvVAtudm9CxSyDQOCBaGnsbiSq8lhAKIFjtY3QTP3B2Sx4EWe7BTkNQPyRvfkm/xaglgA0oJMsz8b89TtZaP7ee03uQI9+U6+kgbrvz0FePF/JeVGb+oOiJgZqf1/k9IAq1mumxmqAVS7tDl7sAYUzf/zff5mpmn2ZM2S3/Yxs067vDj150Wl09cAtHlL+4K66WOAWv5Q9L/6V//qx+fuAVt98hSglQaoaGNX2b6414rtm8+bX/Nz7YRjY2DLwFLzzaR/LWszFmzfth+rJWzKSt4JrBrostxgcdnkpF+mndj1KWvLlMtynsm+dDi7yQmJxncW6sXAXNbLYnHFaO61/cwHvE87gzXlKRlksaCwgScNwJbYkva17HMBAwsS+WmzXenobwbTAmIDOlcQMCBDgNWCxVNiZH3qroGop4SQBn8lAgM35DdLOlPySBCwZRLsFZiNJxuPnLK+gGQbj9y3lgCLerBxZ4Jk3/b0mfebbp/GxfzW9GS6IHDc+vnZgIH5J/XE//dzf9h6N6hvmdmmWdqbMqBoZpzm/VYDhgZ8zWzWAKAFQgOo9ids5nPozszVALBJaH/37/7dH8uOA8J2Q/vOkM37tbb/AVezvLg/KL3vzJoy12ZGiz+VY2ORupnCmcL1Adu7lDSyniX9p/Ewvbdx3u/MC9znY0mY+aPZd7Mp6sNiZpMlabcDQlMufhqP11KWAYv8nvzZZvOmTwOvBh45o5p5Lh8GfvuwyZ35OWNCk5t5cP7yfYKp69bntWz8q3+T+1PwbMa9bT4CkiwZveEzE8aV7KescTVgmPfTaZ6ctTmggZcLYGWAouOsc9BBTZYGMJJW9nu1o6MzETw5idHjEnT+WRAxO+PMEZ3REkP22cCuTXNfweQJlJosTzSpk6dEuZ8JFpsMb4rVa7w0G2w+Rr8wPsnL/qTNgKmRccHPzCwNyBpQNPulZtZplu/m1OEE8gFQ806qfTHoLiVOmf/nbza2z6nBAWcDnAaIDb3pd+oPvel/ANwuTU7d6WvqTR8zAzYnFKfuAK+ZLVvwlXLTJlJPmUBThwmuqBebhbz2M1msfPJZA3P8pB/m/RzjBi5WrwQRZmspq90zvrkEzzjTZKWcb/znia+8bvudcuYprxPUWHwxnRtg5N7W7O+3xx5No51tDBAmnR3nrGt28zZOfYqfyuGsjz2NWBtLYEwu9hTwVsG2rpx8WgDPPpLvK3iYA1JXTwmC9a/2F42mG0tavNeA6xPI4rhy1meBX/J5BZX8tGCxtNKGaDfZ117nDKSByIu3VjKYNFtodtJszXyCdfjd5MpPS8SWKJLfy89awG46Yj/JS9tX2Qr9cWjNDNFf/at/9ce1/cmaAVrzqoXZBzUn/mamapYB5/vUnw3tA8IG8My9AVP7g80DgLbuAKUBVgPGZvZpysyYTfsBddN2ANwsIW7bXRYc8DUAa0Da9JU/AM3ZTdPxk96v+MEYau0u4PE0DmyXPk+QQlt/AliN/hRblqRPGFhrcloyTz9sOW37vh4gmt6veJzXCKSazbQ4lPqyHJiyUvas91v0yxzO/Jy0GzhLwEyw1z6fxuPTLBGukIZ2s1yA4o3xTrHZolaeAEDjg/y0YsmPxkjn5gm8liBbIDDj4ve89iY5UZ4mE+W6EroBrGw/hU5EJ09Qdk3VJ90Gsp70SNtrY9GCIeu1vt8E4bxuMvK+8dx8y/qwRHyBQp4EMxmbDKTJpapLH8Yf6w5v+86r+X9nsGZWaYDP+uCAnbm/r0qYGaX5nKW9qTNAaQDVlH29wywbDr39DcIBVnN9f0h6Zs6m7Bvjp0y9mbka+vtqh5nVmhOOsyw5/fA0oclsttZsqQGiy3ZtDNiXjW2LnVlniiX7NsZmM/TTjDGML3ZgZXl4AlitTYull19taXvtGKuzHsfQQFYrjEPZB3l4ape8Nj/9VmbFSNfAGfMh80uLd38wAGsFs5MFDTw0MJB1m5PtJ2k3YNSSjg2W7etp/Wd9C1wmhwHQJyBF+fZ6my00fkmXvL0JcKx3jWHqhvpNRzOHy/vbfvdsNNDwFDiuYoGDusinLtO1bWy/9NeS2wWEnvizWdq2ZLTtchyyzcpkfWd/5Lnp5iloX3ox/i/7ndmiAS8DXOb6zjIN0NnxG3A1S4Oz/2qAz846zefOKk27OR04ZWadZu/VvHJhZrgGYA2N+X+WEYfmzIz93//3//0DlA24Wt4GXA2omuXJ6W+u7w9AT7190WgDNG3cqacppNNioYFT68dofdTH3iRIyx0GhjKGJD/ZPvd3bUn/NV3adoHGM/XxZvmQda5PAo3sz77b/9l3+lar13KMAZlWvv2bOvtuvzfbQQgajYenfjOOfdo9WG+U39CubWKnI9jmPLbZe42XBjayj6xn/RiA4NS3ASzKnvWTfvZn07CU5cmxDIga8Lt4usAvEzL5uhzLeKANbN8ENqbLywZbcDT5kr+LRspvyaeBgEaHciXAtGBj9nztyzOdcCbqSqIt8S0AbrKwro1BBknqodlf8p2fMxu0m8ebTexeqAE8M6s0S4cDjhY4zeb32VO1Pwy9m9wHjA1Y2ndczVLfLEUOSPrTP/3TH8Bp+p7/p96U+ZxZsZ3hmvsD9nZfVvMRJgvawaWbLbZPy0CV8ZDXGevMn8hz2lRLnNxsn/RafGygJGkQZF3+bPea3VAn11I7gSUB01Ox2Nh8lPq9YkzGVra/4jz5mNL0xr19LadmjuNe2y02w32tfHzKH3tuA5+KTiBiJ/9MYVvfSnMQM7YWVGwgr70pTWarmwmMvGXiSaNMmZg4sm5L5saj8WVJNXV9gYtrxuNNUKSM5Hn7SBqU3cAw++AYW3Iymd/o0YJ7/l39k749vSftC8xwRpD9kN+noGTjbzzY/ZSBQNCCv/mozYqxGL+8ziTIvgcwzezRLNXN96EzAGjuD3j6y3/5L/+YnZqN7FMGgM33uTfAbZYEZwZsQNfQGdA0gGnB0/S5y4UD1gZgzfXZxzUgbpYZ9+Rg81HK3Oyy2dsU22vYbOGXFvaftJlwsz9uus+2OSNreYA2Q3vZGNFke7J/6mrLtYzL6+Y3bPMUG4wXWzUxXixfND/MmLrXKavp4zcyrpYb83+eSnyKLfZpdZsePhXAassR+y6aluTeGCLBhv09AQ9LBvn/8HkZbfKeNC7dWCEPmwhy07YBIOu/yWkAogUiApYrKLUkeAXY/N6clTwlz7z/RKPR3dL2/ZitZLAmnZT9GqM3fUyxAwC077c+ZG3Js8nBe80mrr6b3q3+xc8TPcqa9/i6htHtzCwNsJm/2YT+f/wf/8cPIDVvdp+/WVqcWatZvtvXNUzJVykMvdnMPu0GUM0m+t2XNfTHVmbWagHWnBgcvv7Fv/gX/z8b5RNkXONkY3AlnGZfTPzU7RuQdfl3lnyw5JKzjXvGlbbcTR8hEGD9xt/Kv68AoD5bzOJ4md4MRHxExwQ+xkc7iU465Il9WLzKOJd02kzjW19NvjlObdn2qRjY+yV0/sIDrJz5SWRNBD1lHak5KJMI7137ddYBWnKjE6WDtuTAaza79ZQoGgo3J/glTsUAtW1WH098WPA153tKAkyyDeCRn+bQpG3A6prxaDb2BKysGJjZ/s0+LJmRL+PFfKPpO2lMv/tw8AS07HrTiSWrTCDNDpsts24Dy0+gbvV67dMZfczy3+x9Wlr7A8sDcOZvgNHQGLA0M1IDkAZUzebz5GeWC2c5cEHW7Mead13taxb27fHT79SdvwV2uxQ4dabv3ROWsuT7fi69G4h4Gs+m3yvm2dhyAzbptrhry760swQ7ueF/x5LL3ZuYt9Bv2irEUyyyZfWmZyvJ7/5vS+qm39bn5UcfLRcwzM/8jdS9/hSDnnKfxW6bXbZ+2t4q+sanBljmyFssgO+ntcn/bQMiDYXB/dp4+HavEK9xOjSXylo7MzDy3f6n0yevdNgMUMmvJbGcKTOe3xrqG6CSibAFeQNLyz8LZ54MpBqfdr2BJNMz9W9HjXka5klv5CNpv0lg/J6JqNlZfl5P1waWc1wMNLY9Q0x4Uy9/IHqD+dK1GYmmu8t/ZzlvANb8DmDyMzNT+xM4y9csFc4M1J4S3HdbDeiaMrNbf+Wv/JXf/aDzzHBNnZkF2/dbDaCaPgd4zQzX9jF7u/a1Dvs7hds3x5YAyWSm3A1EZx+kb3Uv0HYtw2Vcybb7yTFikqVtmv0bfZv9M3t544vXwyx1Qvuk/qinjLdX3LX/Oetj/DS53uS4vZ7jlCDL5LXYwNJilfHAeiZf4/HCDX/e5ee9M/6rfJWv8lW+ylf5Kl/lD6T8tBms3V/V0CcLEXB78rGn6zYV+IRk+XTDpzo7Jsr6yVd7ymo8N76oE/KRT1TtadVmD3JGpb1C4Bof6oJPiPYEwRmftjyw/19P6q1fTinzaZ06aLJRTo7VNc5tBiuX6PYe7fZJzjZzZOPIMSLNa8YrX5GRPLSZL5ttoR7fHA7ZPu2EbOoo7zNG2NNw0+ks6c0M05SZNZvvOXs2M1TzmoWZedpZppnhmvv7nqxtOzNSc2/qzLLfljmN+Md//Mc/7s8M1ey9mlms3b81+65mVmyWBjlLu8X29rSndPOzLOknNqOSdr3/2yzRyt1i8rZJG8rZjxYPt16L8W9jo9VL+bJ/69t02/pv8ZcxrfkS4x6vm7w2dtmGums5Lu+/yUn2v8ne7LjNnlnefsrLWYffr5mvTwuwpjTnNwXv9Sdg1gJF9pv1Wr/maAwULakkrUsWAyiU1eRom/0Y4DJJma7N6ZvRU3+mp6RrY9Ec3egkUGAwt4327dTO24DRnM5sosnVABjbchxN9hYIzGZWR09L8M22L2D25G95z45aXwGNvmK8Nn7zu9W1YE95tgzgmaW8XaqburtMNzJN3Jq9UrMJfTal/8t/+S9/tydr6A3A2n1Us3Q4m9T3BOAu9c0m+tmPNW98HwC1y4Fzb19SOvX3ZaI2HhzvtQHzB/MJ831LsjmebWN4G9f2nqhcOmRbnoq22HDFmaSfcbMl2WbHT/t33sSv7Zcb8NuDhwEw6/NNjnySe9td+9ZMB2/jOXlr/P0GsYpt38TFK8+zUMftgN2nOUX4ZAB2j+++uMBQS35XUDFjsUR4bbx/4iXlZwA0OgZ0nsAadZYzEHufr0tI3t6cAGnF2plBX46Z7Z7Giv8/BQeju3rOz+QndWdJgjI/BWnWo/M3Phm4ec82+Dadp9zbZmcSrkR8geocg3bSlHZK2VoSJQ+XfS6N/X0yC/YMyjN7NSBnTvrl+CSNqbPvrRoQNP/P9ZF13nk1M1NT5vuAtZm5GnC1YGlPJu57t+b+gLHZ/L4b4peu8dn03vSYMSsfVBpYZT8txj6Nkx3HZ3vWs7zAfatvYq75EeWk/TC2sp8riTfARXB62fcV82wPl8Uo088FNnjQi/LylHqLwQ0EbXvmsG/yMEe7ynxF2ewh0vSY8jzp6VP+FiGV2hJztlulPwEyDh4Nn1PibMuyhrYnVi7DNRkYSK4klbKyfau/PFriupxgis1YsN51vwUHcyyrb/1lkHxTn/1n+y326gQbh6bDtlk1+27LjgR/Nj6mw5aw9pMzGATGNuaWiFY/HD/qoiWcJxsh308J3pawk/8cj7xvfdnD0OUfTWdDZ2aX/sk/+Se/e59VvvhzZqfmHVZT5pTh1J/N8Nn/gKvZ9L4nEwdk7YxZAngmvr3OsSFAp+wc57WR68ACl4FTB+y72bz5uvnAlozV5pstrrK0PHIVs4UEFm3LhJW3fLINfTr9+qmtyfImx5COPTi27TzZhuNzLTdnsViT9MzG2XaLtWV8MhD3M2exfupvEW6xwLefBFKGPg08WBDNPQt532Zq7N0eOSh0vva0xoRkhUaQcrHvp2IJuzn8BSyvNlddS8IXn7x/1X8bLHZMaVcX/SuxvinWLgPT3M9femcQu04ZNfBLPdsT3QUE8/6bcUsQ1wLd9b3pyq49JakMji2AZzLnbN/16hT6b9LaN77vT+qw3cxU7T6qqbuvW+DM91wfgJXxiDZOeY0vPt0TuHB87cm/jYfxYv0ayLVEbLStNJ6Mx+bbV2J/6vttnLb/jSfKc/kFASz5NT/Ka2/8IWkRxHG26pJ5Z3Ztn9sFDL8LeLL9hMY7bZGxznyEMrPutXr0qwVYO13eHHi/00BbgjfESjp2neDoKuyHAWvr2CzSZfAXcLFlPYI8k5PGZqDvjRM2wNGA0RWgkl4LGk8B+umJI+leoPepmCNbP0yKWb+BCtrO8sqHiaRz2bD1+QaMJh/XcuYTQG+JOoFMu5+FyaXZigV+0uHnBRYsfhAQkM7GL47xzG79s3/2z378P3uxZnZqN6pffNlrLMijzcIZn78UOOSnJaungzMcC455ymC6ttjzEb9l3+T3SUdZ7HUOLSaY/TNH5HXqusVhi9nUF+Nd5qP0J/K19RNYcXyY15LvBCfMTW/j629l5nUL+2FJ3V6x5Wq7PLzh91cHsC5jbwFz75mxGQ0COL5X54kWkyeNzJZjmmxPfDaAtbzmOrYBPaO1Txg00pa0m3PQ2ZuuUmctMDwlAbYxvgxYXnQtEWQ/pG+BlMGSdmHB7q3c2caSiY3LNZ6WEFpJW25AhHrcdvZgkTNFSf/iP/trwKaNh9mK0ScvpquWzJvdk858zgzW7LtanU65EskWm6mf/znTT51cNKmbp6STYDjHcmV50l/yZTq/ZGBb86ns22RgXGB8ND3mNfZhG+5Ntxw3O1DQ7Imy5b3mR5kXnmLL5Q88Xc3N+Ka3tGVulTFZU7YtaTdLwza7X+CKYPI6xHHFtLz/6QCWGU8bHAuyltRs0zZpPQEr+5+D9ARSjD8abjrLFbymvEHaTHrbrtUh38u77eF5Y4QWRN8GF9Joda+gyzapR4LU7KsF7LzfgNW15t/keAJepgOOv8mTNJue897bfSWXDZOffJ0BZWIgNL6u4Hxtsr34p80/7VEjqLFESr6z7v6fhwXSXlrSfwMYUv8GDBMk5Yb21AV9xPRJfjnuBh7zkwnV5L18xZI9/xgfTa7l1WzcvrfxzvYtXnA83uS3pqfL7p7GzP5an+1aykBQd81GkobJ9i1isuXwi7+GEVrO4Rgnry2WfVqANeVCrM05pjCYtCBqAfHijW3fyHQF0DcJlYm9gavW1yVTm3Wjbq/kdTkAwcw1y8e+zaHb01Zz3qSXdrN8NZmva6aHFsSa7prttsMaTCDXMvTKepXkJYOkLbc+2bnJnGN+BfUdjwZiWv8t2b0ZwwRWXBbZ+7YklH28CfD7meAjf6eUddPWW4K7YhTrpkwrD2cFjN83sxMsDegwaTGWZT9WJ3nJeGDgKvvjlgnK3QCp+SjBGYuBzga6Lp3sPfLZYkLqPnVq+/BavjE5GS/JYxvnK07Q1tpWnO8HYKW85Mf6Mh6aL/8+yk8FWFOuYPWEVJNGBlG25WCnoq0vou0p10a4C9C8AWUtAWdSooFf/VLmlImgrCUoa9+SZrbLmYyk32ZLLJGwnycQcDkNT2VlAnw7NnaiyoI127WgTjoWQFrAT5627dvTRsl3fiefb4vpYvm9QHjKyO9XXxdwbX1e+1OSVntIIf0WV4ymxZms/8THk04sYV/1yBOvNZ+k7Fe/zQ4JYo038rPFHtIWiLBdk9cAR7tO3sw/Ln97ssUGdi9Zsu82O7x1rtUOmxV82qJg/3MMDTQ2+/1W9oU1AHT5ROvrGm/28TMB108HWFuenI5PyARVFgTMmRlEbJNcApv935LelUiMJvk2edNBrkBmjpc0LEFTV1e5AojdewIc2XcLLk/LJwaIrwDZeGtt2ph+5JSJjdkVvE1OfnIWk0/rLbFbXzsOKW+jY3xam2ZrTT+5obbpq41T+hZBpvFn+mjF/PaSxeRugZ59mLxW3/q7EhD1acCJfpa6TB4yvtqBkTY+zf6SXs5iPYGRjB3JA2Nkztq1mTvT2dWvxWba3EfG+4otpjvzaQNB1ofFBc5KMnc2/q48R/mZgy9dfz8eWNv4PeWaC9j9vstPfdEoy9v9HVvnMo7LybNOMyCCLG54vGSx/2l410m4K/jQeTgt3k6iGV+mDxp38s/Ezn7ouCkrx+gNaGq6tH5bmwtYtWRly5EXgOC1p9NL1jb55Vi8AT4tqLSAv3yyjo1/k9N4auOcR7H5sMTlzzcnSG1M2S/1trOqTPjsi7a/36/N6legT2BkPFqyeAJKNgYNrGTf3DOVtBlLOOtHmuYXWwieGR8uG+LnldwtpibfywvbGQ82BunH7cHmGiOOPds2HZrvt3hl+jc65D/5JL3GQ4v/preLv2/FF6jPJ71yudb4ufht9vWpZrDSuKmsPM1yJZilw/+vRNgAhSVTPsFdpy0aqLqSNGVoDsX7Zkjc0HvpxgIt+aZOLGm3IGngirLb/rqWuJvezIEZIGxMGh068dLIIN7avXHYBkRSfgIL6ixLm/1betT9BdCe9GMBsPlKm2kz3yHIMt8x/lKXLSgnj7v5nDSsjwTLfIB5CuJW5xojq5v3nhK66YTfR4Z8ULW+M3YkULHY0+SzWGJ9tRhsumE9ypV2Q54N8NlYMc9ceqLMScN4T/4aOLHSNpM3sHWBlac4sNffyp3l4q/18xssSz7FTurZ8krzF35/0vuv/k3uTGLtuDIH+M0ylwX/LRdoYT2ChLxntNnOaD8ZIp3SHL4ZUPafjmKJjvRy+rsBvFasz5aoGZDMmd8uISRN9jXf0wmZuO10UUsitq/BAEGTte2buJJ1CwDWtiU0C7CkawGuzbA+JYeWbEz3yV/K8aakfzGhNqCSPFmiSbtv7WlnLRlf+sl75jetbtPDGzshuKCtXInX/NNAA9u22ENbNODE+jn7yFOR/KRfXA/D9r9tCTA5eaAqZXkCY+kHFudNBo4FafI+Y/FlC5f+TV/Wp+WkK8Z9Ox6685o9gFM/zY+IG37f5ffPwVf5Kl/lq3yVr/JVvsonK7+XF41ee3t4LWcE3swCZF+c1WozLPaUxpkQtiPt6/UAWd+ewPZ+bgbmU057kuHrKihro2FPX+TdxqXJZfq2pwh70st19TYzxP6WP5OT/NvTldG7nizTFq6nNdrCkwxW2A/7Z38XbZu1uJ4kr6P67YnV7qdfPI0p+Wh+yqdy+mrOkiUftOl2wjXr037n+rXxO6/b/81uTQesyw3cfMJPmpTNZgSusbuWntlP6oeF7zyiXE032Xbp20wFZWDszD5bXLEY2Gwul04ZC+aP74uifq9ZJOqEBxAyb1oeM73wvWzUh/nxpV/6C/cn2raGLW2WjfefYorFQ8aDK7aR3qc+Rfg0hWhrtVziuQyFQZOBxgZr6zJgX7zTeZMvGqcNPKe/+ZqBLTZNfp2csWTcAIiNhzky617JfNvZfg6TozmhOUO7xj+rY4m1gZa0Nzq9BaYGzi7wZ7p6A0ZaeQPerqDZeG1B0IL8Wx6bHo0/S1DJs8llDyrWj40bfdaS87WBPPtiHDN6TOD8zmUpS7YEgBnzuGfJwEW+U8uS5TUuHN+nmNRswT6f/Cf5b+/cukqLF2kDA1YI4FvyN9pXDDY7bDHF5M/6ZrfJd75Sx2KhjVHLNc2GLG9+C58kDcuTeb+B/pZfbSw+Yg+/SoCVAcIAxxQDKrxO47aB2v+T7n4m+HlKFhYYm8O0GSSWt4ZyBWOWy4FZJ+mY0ZnBt4TUkof9/5GAcQUh27NndOmkV9823m2Wo9HIwxmtf5OVMj6Nz3Xv0gXBYgvQVjjWbxKN2bHRyjZ5Cmzb2YlD8x8ClKRvs7LJA2cM2Le9MuCpEChYrMnr9mqE5CPjp73ugm1TXusr67VDRRwP0sz+2beNlcXZ7NP8kKXFQdKxem/Gzfoj4LQXs/LhKMcrDxnYycSmQ8ae5Im+/haEtbpXO+PziiEG/r+Jj+//zU6e8hF5tpldtm/yfRqAdQGGS3A6pR1BvYzUNsW24MSATrr2DpkGXrJO49OchXwa/+0I8VNCI01eN2BxBVhLStTVtWmT9K9iDmbJ3eRePp5m1Ux+ymPy5S/N2/hfswL2/5OPNLDYEteU9qbxK/C2oHUBRwuc6bvUCdsuvUxUxi9BCHmmDz3VSwAz31dfHw3KF9A0e+B300+zyyyW9JeP1JfZGWewbNbrKRkybpl9P4GBZp8tb1DP9r/5+huwkbwRkFvy5x/tvS3Bp16ajpYHnmw1f7h0aTTb9S05U9zaXPGAJXXUTq3md4sBpHfxYz70BwGwngY2r9kAbDHl0dHn73qR2mU0pGvvF0leSHP+DNCk8WSyNhBnhcClgTFD8U9j0ILlBa6y7/zh6eyjneZ7k0CuoPh2TDOBmo2YDTFQGp2nQHXdY4ClPB8JDOSDgNee9NiftW0BNHmzZc4nnTQA02Q2+VjPxnZ426WeNl4GtNLOrTQen4BR+uolA+1h+c8YZHuQNuZ8RLe818AU5TTZTPY3NpS0Lh8wn7lksxzyBlwYqDEZKUvG8pYv2L/5g8UPymezYmxnOrp0QLqXbo2nLE0Gy5Wsf+UC+qfl5u3L+vy0P5XTgvybZLSf1/Hd9v0KZNYnpzuvgGy0ua+FAY9GnqDE+mklHZMO2vRw6YaBYZNTFiaIlG3b8h083JR/yfZL9NAcdv8nsGr9sq9tY4HWkrnxau2aXM1On2Q1kMZxbYmXtmH9WdC2hGzLUGx3+Tr1aveog/Z6AOOTcjEuWSJm35SD/LQkabaSieLSAceGezYZB1rCInBbn7T6l/6v5G86N56y/xabTadWrL3Z2puDVo3GE8/JNx+WUydPY914yVxjumUOaH5+2TELX6PT6je9fC8HEPKBr/lak5nfWx4nAPuUAOtKJnm9KWg/qegroLV3QT31bcsS5Kc5VUt6mZy3j+T5KZCYw5ij5BLYpR+jRV4yCGUftvmb+0TS4NuUtiUg09tTMT08JYbW/k0wbwE3l1SmtCVs0xHvc7bTEtPTvhvS4skfA4Nvgo/Jnf9TzkwyzR4pv/V1FTt5lX3mtUuGZnft/ytG5CdtusUP/m9AKWXJ3wA1XtjWdGF8k1/q5SmuUCZLgM1faZcXn6a31n/TC/ML+Wh2avs0TaYrn10ytL4bwFg6OSlggKTFOQLRBgJbyRxgY/hbiQHmo9QdebJJClt63f83J16z958GYD0Fpsv49zMHyY7OmlPbRs2nku3pDFfyzeBOOZjYEr0/OTTbWjIjfxc90lxdbtDOOlMy0BFk2UknkyfHwpZqngK6yZ7FThEx6Bkgo6xtLN6AAtqmLUvbTIkFRfLJTfTbB+k1G2HwTZ03OViPJ/carxxH6/tJn+Y/xqsBrAtw7D0+6KyOcpxasT6uMbSN5G8SnSU920ZgY9fAjCUj8pyz2C1eG6CwJN76Tv74cNhkeaLZbDLHMhNt8/MrHjNmPunH4orFPuu7ARLmEVv9yG0byavRMVviQ1yTM+nY7NX3iAG2RSTpXHF5dUAal7/k958Jrn4v78GyxGnOcAUuUxLvX7NXBGkW5Fiu2YgmI6dEWSf7TuO04PfUN78/BQh7miCtxg/r8o+Oy6WHnEVogSb/mmx5zXhsIIY6tbEwOZ+ctNk1HwIuUNLGi3ykLBYMyVfKT3Bl+yyMn5Y4TZd8oCH9ltTsu/XDOi1eTLFgnGOZdblsRF45/jbmpuM38ljJBzXTafufMi2Na2me/mpA7oopb+S69JIJOMfF7GtpNd1bfbbNAwwWh9766RXrLz20tvmwStud762vy99a3e2DMpJWe4B7U74dD1Rv6TD28KHV4lLz359ZfuoMFo1pStv4nW3ebJzlwNM57X5zPv7fQN+WKwDbwNqGxLd9NT1dRmTAgonGdMtge/FgtE0fBp4s+V19GMAijTYeWZ5mFJquqdPsN2fnzG4tsFJXTwmKfGViTF5Wxiw8iHDZQfJuJ9Is6V/2y/16bMN6RoP6SZoE7sn/FYhTT7RBS7aXnbBsu1ymaHGn2VTq+XpwodzUm31v/LY4bHson2Je89VtZ7z+krhoPna1eQKKb0v68gXIrj6NR/NF5jGzddK1PEE+ze6uGGu80/++vXgY+AgYNXnsuunj/62x/gsNsOxpqA2WDbLVy/8J1togt+DzlMhJJz9Tngs1M6lOId9v96tw46EdKb8SIOXlhtnk7zo1yX4acMi2DNrGU0uKqV9zKvJmgWL19LR23wJm49FsKfclpB/Y6aLWj4FGC5Ycq+SJeyQM5LH/HaO2ub8dAuH4849j9RQACQZJN/WT9823mj3ad5tRWV0+xYgsJj/le0Pj6VqLmU/1OIbmw6vPC7xlX61Pi2tvk1+LI08A8imfZDuzV+tj/572+j7F8cYrae89s9+kz5jFPNF4pT4NoFAXyQ/jwbeHWTD20cbzrd4oY/ODN772q53BsgTa6nCgngbAkm1TqNVPPlj3iRbbUY4MyHnPkvhlXO0+N+bbqaLrqTMdkcHoClhJywIRZWhjacE+6bUxacXsLPXEQNACaHNWAwtNX6b7xmOTofnEmyCSAHxPgloStf4XBDK5Mgm0wD6fewK1BfiPJtm3dS1+WPBvcYk2urJkvQS0jbbZiNmcgQ/TTfPHpxkEymt8TdnN8/kyTYLzdljAPi1ONDku+1jeWsnE/VQu38rveY0zq/x8yiPtHvPD3h/9P8Udo9X4aA9ejN1POYj+ZLJ9JLZlybgx5SnWbZtL/mvF6FO+pqEFuqeg2wY/79uSI43nGrAn5zSQZYabsxOUmYOcht6M5eJrHZSbqvOE0ZsgYQGqfSfdpMExzKWMTUht42zKkuNmG8XZb7tmfLCuyfAGVCXPySv1aQmtJVPyfwU76ydLbnDNn/tI20gZDJy1PTytz+t6k4Nt+dkCdtbJGdi8xwMZ6YfZLvWQ/W6d1QVBUgO5pGH823falc1isF/Gm2tzsvnFfrfXq1iMZtI23m38noCt6fMpRpGegRfT9ZUTzPevWGg8UwfJo/XTgCTrMDaSJ9q3+QhlfxNreN/GsvnmFON5r2d5ihvZxvKl+UXGruvgyq8WYGWQbgi8BSszjDcBykrj4Y0hXPQYdBoKN57NuNORUn9Zli6PC6ehtaROui1gvUmCT8mEAT/7pt4bwDL9MQE+2RGBcNvo3xIC/2+gaflm8rHZj5Z4rsL7zX5Mp3xSTt5sFtTACPdSXWD5jS1S5zY70nw+x7QBrAyu9HHbh2cyczwvv7UEx3G2BNHGmPaU1xhzdixb4dhufUvIxu/SaDbI7282f5usF5i5aFDXLe5tXbNn3r/4Nr830GI+1XJds/VLVisXfer3Cahkv1f+Yj7IYjp44wtNV2xDOjt+TzP1f57l50G5r/JVvspX+Spf5at8lT+Q8lNnsN6UfIog2rYnwmy3aLohXs58PB1ZvmZkklZ7gv/IkwnpXDMaify5adl0wlmhpcn3UF1PS2/KEw9brpOJyavJnHzZDFbqjxtyOU5tGv6S/3oaz1kpe2rdp+Uck7RXysQnPNpvK9dsEfVjbXjNngxJ3+w7ZTV+KE/bA9j0fj2FW9xo9s0N/FvXTunl/+SB/zNGpA5s/Iy29csZhDZ+vP/2kA1j8EU/aVN/lOuJx5TvaQbLYrfp5oqPnLE1/722Jly6oMycVaE+yIf1aXpueau1u3hNnq42V25qPH00n5guaBuM92ybM1gfxSG/6je5m6HsdZ4qezr1Q/qZvJoBG0+XYTyBI7veln+aXhIkmBFdSWJKO8nSEm2rZ7xZnRYELDDnZ3OKpHvVNSBiDsfxpy6tpI6eQMzc2zHm2v5ln0y0BFt2stD2PeUn9WMB0pY6CLKaTq6N3PQh2gj7aAGZS7zNRtq4JE+2z4RLMy0Gpcyk2ezVZHrrd8aDJemkSzudYmCZ+jfgkPvxth8eyEm6/P5Lkqfp7oo/GfvS19JP6TNPvp90qFfmnfyfsudYt5jEYvvWnoBNypV0m39c9MyHzb9t/J/G2/whS4tLV8y1HP6Uy5c26z/F9V81wDKnZ+EgXk+RpJ/9bFszMtIwIEFDfhoUM1pzgsZ77vNYOm3fmbUl7SdHJe9Mjm/0YwnbHNM23KY97PU2Vmkve7+dsEk69oRqINXG7goUlkAp6zX+T8n8jT1e9UyHU9qTM+s+AbeP2hb5aEDC2jLpNRn4/7UHcku+fsIC8ba1VxM0OS49MX6x38uuqEv6gyVCi7NtPBJ4UOdtbFLmlhgN+F1x6tIZfTfjJH3/srvkm7rMa0+yXXyabyYPdvL2ie+nuk0+4zlj486sz/eWM7c9D1LRT1q+/FYekNsM4UfiE0GulSu+fgqAlQ6/R7anXAj5I4abdUnPjNZej2DB0frm9zb7RsewJanLEQ04sc/9ZH+kn+CN9KfYEzuX1FrSSSdpY8vSEsiTI+y97ac5agsQjQ8G2+0/6TG48Og6dfwmODBAmAzJI8es0WSftI8GGlp5m/ivazZWjZe9l6+VYMJLXsgXZyZId3WZM4d7/wIAyQvvU578f4qdLk2a5I/0n/hqS75N/iYbv1vcbby3Pp4S79M1A8BXEmdMJEAzvzE+TFeX7p4AQCuXvkzONitMmd/qd8oTuLKcmPSegMu3g8Zlu0+5Mv83fdkBq0/5mgYGHwtSqRyeALA9Si0w2/4PGp4hfhov678BZqRtspFvOyK/bVJmOg0dcf6YsJLvpq/lIafOV/920ir5ZXvqO+VioLOEafc4dpt8k94mzWxrDpf6Z+BM+VaX1E0e+70SYhuj7I+gowWcvGe0qT8CaytvgMSlr7yXT6/kJfnPVwDkw5b5afqjnRKlPbfA2hJk3s/f37RxSfpXcr62JlwJ4gInDRQYTStZx7YuUIctBjVfMr4ZVzKOXzybLZjeGyAmLdbNkjHRZtnfLKOnv1ksI48t/lpsZnnKfZbXWD/jZRuPdi1lvWKK8fmtzDJbf5ftmx6b3phnqMdP+R6sLVeySCcyx7yS45S25MLkfvGS9degnozq4pH9mtGT5vVqhvzeErBdNzBjIHH1uEtxbbbL9NmCiPGYzm5BhkGS9PKaHTe3pSIGRLYhaJuy75BaXXCcrb3pncXam+3wsx09TvmsbwPfxkvy1OzsSg4mhyXyliySfo7vpTv2bbZJ/tv+tin2MMX+WjK4QIvprcUNixlvwU7Wzf5ylpl0qYNLx9QbH7BYyHez3bb/jfH3AhK8bvzuJ+MC40azo8b7U32zGavD8hYQ2cMOaVpMYR/NRnKv8LVi8+1BxmzXJgDIU/p20xdl4srSzyw/fYkwlWnFQIwFYwtarTwFpK2Tdfe7zeCwPzoll46yzVNCZLLPZSADQGxv9cxReL/xlTSvAGsOSDpTGNyfgi6v2R6cNiYcuwzeT8FldU/6tmG4AZ/LZqwwSBlf+X/Kdu21SbBhyYTlLVDYuo0eg+LqL09TWjHAwuBt+rR9IRdo2O+cvWibY7NcvkBQwKREIHHRNFBhSdBAG3WX9dreqGaDzdfscEIDANf7iDgmyeMFRFOnVpoNvI0bl901IJef7NcAZOroqRjNy7YZ500f1/jZXj/6dBbLP2/113LNXm/j+FE9PsXhXzXAakZ3tWsn8p6CnBnOhXRbImt8seSeDnO0p0LD45FhcxgGIQM2GTyfktpT8DO9ZF+Z5PJ68sl9NXbqq42t2Q31cNW/7If8JN9MErY/zRIU9XgBm6bfJ7skuKW/UOYnf9uyS7AmX9NjXuOD1I7T0L1mIxgnLLBakH4TG97YP8coPy9dpm7Sd22JPd+qTz73M2fNn2ISAVaTwWSmjdB+Uib6+ZRd8s3xbgdb2owy9ZD9NvDYxoHy7Uy86eHioT0oJgBMPee4sS9rS/08FdPDW9BBfbSYQhtmXH0au+Zf3zFGGVefAFf7vOL5U/+fDmDZst2bZG4DO+V6wrBE04Jh3mvgrzk8+bO/7OsKdiaHBUr2zc90kAQbV99Jgzpp40THYJKnTk0uG2NLRleyTDnJDwPZG/vjmG2wzJ8OMT1Sv1fAt9Jsmf/TlsyvuNGee5iazEyOqdO2VP8U1JaXBdU2JuyL9NvhkKeA+ZS05r4tHTz5SPvf5M59jJaULhr5nf5xAY6kbXHAxtuAWatHvrnHydrSn8gjdZdJ/m0CNvux2Mj+Saf1kf0nQLZ9wmxjsYn9GW/0MQNnVmy8M6ZnPeu3rcC0mL4x5m359uIVMU0m7oHNe8mX5Sfy/ek2uZuzm/GZs2fdpzXVdOanoGzJOPmzes04WqIz4EX+WyBJnb0JqkbD+muFjmiOxX4MyK0T5Fhw3MzZr4DdAlfTRUvmXF5LmWxjc9uDRz5s6cvG/QJgljyX58Y3fYUzm+ZL1D9lJm8tuJpMBkDNzyxRZ39mD5ddPMnBe4wJTfY34JfXdkN/Agby0PhuAIX3CCBSJksqafuMYc12m5yk/ZTYGoBgHfLf+jbaBiisL+qTvPHE8MX3U4ynXi9d0rdZP/0n43QbpxY7L7CZAMtoXfk7+2HJ+g0ctbI850wkHyRTd9nuI/38qt+DlQppT8R2CmfbW6C+0LgBhf3M69l3o5XtLHg14HQZW6P7Jvk2Wh/p9+InP1uQaKAqdWFLQXSKDPqNf7tuekoaBoKzL1vSMNsg3ayXpQWOC1w1G6O+29Nh2q3x3eRP3RtgIT8W2K/Czb7UF8eGy2Fvnk4vW7GkQ5CZ+rt8qvnlEwBI27Z4dj3xG4i6AAPHMONj8xObGSTv5Kd9Gj8tzja9Wn+7EmF6Mb6ve0/JdnllTLDtALb8Tdotlhsoufh/is38v8lotsD/2wGx1IPF9isufyv58SmGNP4Ysy6fYKy1GPdpABYBTwuMdtTenmKb8uiEVgwkMSBc4OyXFgs4Gwyvjb/k0eTe/9/0m22znSWc1m/rn4nDTt01em90+yZQXEmvBSkuKecnv18JZf9sb5n1vdfTFp6SwVNy4KfNHFpQfPv0TdDCILrfr43cWW9ne/J6A8jmQ0//M+60cb30y3jQbGzHPsfcxvU61GP2xTE0HpgASWev2VP+0zK/xVjyYfxw03gDJilbS8bWz5VsWYf3N+62QyLGWzvRnDQv+QjQrrhitpo+RRBoNrntzZ/exnmbEGl+1GxhCsFRfl7+lN/TFy1vtbjxM4HV72WJ8CnRZGnouBVLsBbIWt8WJFpQI0+XoZh8nHmjk5u8TN7GwxO4YuJpwbL1zXYWNC/wTHotEJFuCxzZ7gk47L3suyUq+557ahovlJeg05Lt1d5oMbgwWBv/BEPJj+noWgq10myG39sspV1nAmFyuxIur+d42x6mN/y3elmWLuVJ/+aLeGm3zXbTX66TqsnLE40tuVnbeDHgZYmNfBu9BrDSt8wOLh/9SOKkXeWDrfWbhQ/+qbvUJcfQbN7sKXmjr5KexTErjM3JO/NBtrliGOtafEo6U8yWUsYW53l/aZh/c5KCcfL3UX7qKUIK2pJMU4w51ZvAzwGispvj2tKLJQPj8U3gZvsnR2nXLdHy0xyxgQ/j25yNyY51bcznO0+nWPC0n8LJ+9bOEmhzrtTH282Wpr8r6NNGuA/NZjSy3gWyyNPlV/t9TwW2jca5dGr6oj4ZyHiNurPj78n7ZUdTbAkxv1OmlI3Jq+mR7VmoV/ML8r7AKp+wmdx4iMJ4Snm20KbYnktsSyNPiFKO9GuzacY7ixnNdk0u+jMTaSbR1s8vSaCNptUz/lP3b+LmlpxZNB3kQwVndlm39WXAhP5kbZ9iWMbWleVaamYhAHtj81bMLq78le1+ZvnYNv6v8lW+ylf5Kl/lq3yVr/IX7xRhfje0mU+X/PmK9kSVhU8aT0/FU/YJk+3ZZz65LGp/Qu/JY87asI491TR+7ekwnzTaLJbxln1eT3TZx4zLPnHv0/A+Kb15siJvtqGZT41mJ09Pzfz+NMvSdGT9vnlqtT1Y1If1YTNYjd+LjrU1nT49tbY6+bRNmnk9Z8ZsiYl8tCft9L+0NZvFam2f4gj9p+nU/LEtn41/8AW7e3/jTuqKenk7u2bycqwum81PLt83Hhqt5H37ph/w9RWps2u82K/NYrUYZPmixa03crZZfPZ5xUSbjcm2bUaby96XjOwz/7ftM9Zf81nG/rex5qMzSkbj0mv+f9nGp1kitP8tgL1JgJchWfC0xMF6NOwLcLDfN4n2epM5N6Smfmy/xMpp+rsM24IRNwe2sUlQ2ZyNwcr6+0ggfOsgDQi9BSAXD3tt+50x/KM/+qP/v6RBvhtQYj/Gn8lz+cq1f/F67QVle6OL5C+XoNKG2n4hSzoEGslzJvr5zn0guW/nTdKnbV5jbfHiCujcO2j7QmjHCUjtHv3HfI0yW0JNINoOUZhPJx3bg2N65neCpvSLfFDjUk/K9eRDtmXhKflmvDeZt9hpdfbdABr1aTq3ey2m5GsKrq0NV06iPthHszuCYNK1wwJTWk74SH7isjpjHPPk1rli26eewbqcpgWdvWeD8JH+zImXbj5l0wGTTguU5Itr1E8zRAZyyA/3hlny23pPIJX7QlK/TdcWMNqYZQBq49L0SvnZvyUl8p28N7shLUu6Kd/up9k2+a6jrctZTib41PVH7fi6bwHoDc0345OFftFmR56S84KrDMwW1JeGxYQdA/rzfrdxTJ5srN+e7qMsBq5sVsKSlMUk9s9+LIZR5pSX/p2fxkfak8VC8tZKS8o5i9Vm+wwEcRzT35joL3C2sk2xfhvvmScyvrX9gIzrlvyZNzhOeY/0t2/yc4FMA1im88v289rTg8S3Y0a2+WvqLx+wKIcdirOTsDaWn24Gy5If62xpjtIS4NVvSwZMdnTWdTwGyUu+/MvA9pT4LIBlf3nPNgzv9eTrCWTZ/zRgyspN6skbg7g5lz1FN7D0JJOBlqRpcuR1A7WWhDKIXAH+Ai82o/GU8K5xsMR6gUvKY7rkyb1mP+S9Bekr+V5+a7xZnfSzlMFshHzZBmLy3cAOeZzPjRUJrthn+jjtqNmC6ZB02neO0ZO9ZX8tCV9tW6xfvTQ/TADNGVDjL32vAYLmO0mDyddko/1sXZs1YRwyfT35i8Uni/NGt/Wd/V4PF+b3LY5bzGOe+yZ+bHnuKRaQD7Mji/dZ782K1K/2PVgtyG2h4prjX1PVS3MdtQGAa6mOs07bhsHy7WlD8rE0SMv4SUNsyTnr0bgvtM4EZTSvdslrC+gtUdhT5qUrmw1qMlIGLtU1PfNaCwCbmOf/tJcnfWXitX6ajaTcbQzegCzjx8AD5bXvBDYt6LE/Fo6R8dt4e+K7yZl9GtggT5l4r4Sds8GXnzL5s475DPu2ZHIBAuq8yfCUtJqOzWdMptXTlDajRh45A75tLcZSFssxrMN9b+mrZpeN3jWeWy9Pb5IHjpflTOvH5LJisr+pR91sSf0zptne5N887Ou7eHmTL3n/aUvDp1si3PImoE9JRbak2RB10qRxtOBmqH6vcdOuFQt6jQfKaNcsoVIOC7zk5QILLQlwA2rrN+vbzIeBnykEV+aQW2/u5yEEAzOWQFtyYB/kr+m/BbgLVGTJ2dDUQwZSykB5Gu9Mxk3WC1xd9JsOL11TF7mkmnWaPyWP5M382fztab9R09HWYZJroGXvGbiyze9Lt/l4LkFvoqe+Kc+bZNvs23RjoJ665/JnS4grRxu31EfqjHGBdA0EX3nkSS823qZz2nqLxbaJ/9L9VZq9U/dP4MV4v/pK+fZ/PrTm+Fl8/i1yyWW3xg/tP3laOhtXLE8lT290/asFWAxkl5FusST5FCSa46XRG60EBS2RWrAkrSnc/MvZi2x/AZmW3K1NCwbUvwVoyvoUmMiXgaQMxDsuDDxPfeWyQpsuJ9+XzKk7SwpWLn091WXiNQD0FBwNJD19b20bz20MTJ4c12YDxn8bN9Ln/Rboed/6bHEl61lCMR7z/7bVYO/zz+5nP7nRP2nTVy5d0qdYnyCGunqThDOO2Xhse9uIbHWtz+ST/FPe5KnZAXV1ga28v/pngjZQQ76vlZBmX7Tr1MklX+atNktzgZq93uzZYteVpz+i7+/l4ajVY1xtMlvuvOzvU+zBak775KxW2ma/bXOBjWsWxNb/G4BpfNJILRizbzpQGoQZryU5BtdWLIAa2LmAwQVmnvplsDQHS7o7JnN6L1/K2JIaZaKu2e7SySVz0rJgnLxbeQIzxovxxQDfZH1jx9mWuqHNWRLMz2YPNgZZ12LBU/A1HTYwwnHjMv9es+UpA3xPuqVdU2/c53nxbP/z+puxoM6u5MNk23RJ+Smz8Uleubeo2TsfvJ7yxmUD1NUV35lbbIsIARbHxOKP8ZM5bkEEl6ubDdMGE/hd4733mk9eeiHPLXYlH1uuWJQl7ZD+ynock8vGP8UMVtsIvKUlxWactn6etIy+ORONgKDlaVByyjQNoD11cB+CyU49XEGKicr0bAGg6Sn5N+CXDnIFzpagsp2Buq3Ha5n4LHG0oJP38n7qgnxksURGELXB9gIuVwCnbp6AofHZZDWZOXaXjV/gM3V92auBNCbgJqfJcvHHe+xjabQHH9pOAzPWF/m5AEjaekuslP8NCNr/OR5c0mNbxpGMjR9JTJfsZr+2YTzH5skfaUfsp+nr4p3fm+9b+4yRxrvF+ryX359mApMedZDjzGvZvoHRzBEGHhuQM5l+A1+ziYxr3NqsHGVqcYP9fvrfIjRlXIZLx7TEy/oNjGQ/bSasJabGWzMS9pWApZ0yos5an+wng6K1twRgtPKa9cOZGquX07amy6fCAG96plx0JLOZ5If2+GR/DGBJy54grw392TavW2C8gMUT2LgCz95/s7ySoIB7Kigjl0fM1trv8jVAkrJYojZfv3yJ/Lc6Rt/qUtY2NqkbJq3Un43FU3KwuEMw9+TrTcetWLzhWBJ8X30vPdtWYXZAedv/rW8bA9qPxVnr402uID1rS7s0udhP47XZ3n6+ARwJFudzX01j220s3nw7APKbnMB6udWE/pp6aP78aX+L8DI8S5I0WCbPdky08dA+DfQxkew1o2/BnoOZxrj7LZKerdkbKL0CZcpvn3bNeLfkQTrJV2tr9K9ZHgv0pmcb7+bUZksM5uTN+Gonw8j3lUzYZ/JidCmrjcdHA5QBSgvOH+E/N/BPsdOVqZu2H9F4pbyttESR+uSM95PMDNjkhTPQ5DOBzfJhJ2Ft/G0fqcl2ycy/5LPN0OX/HwFZzYa5nGz6fAOwbEnpijP0oyef4pi+sb8Wb/jAwbG+trc0eyZwaWPaQKjJksDp4oc5ir+dyRzdwNX3F+Nh+T99IHlp9pT6sfjy6d/kboF6r1Oh+5eBaQ2Jg2fB9GmqP2mZY13T2UbPwJoFoGzfQIsl22YoT86U3y14tkRtgYq8vjVaq8ck1myCshiNpvPsuwXFpcHZhazLJRbuZ7AlGEsulJU212zwqZj9pi2aDjiOrGPX6Z/Up/lnPrBY8iCNlrCvNmxvdtvqtjb53Xys8cWxWHvJk8kmI0/Q8YGq8Zoysa+rWKziPWuTpS1pXuVKfBdP2T77vfaKkr82U2U8WRzgZ37nsi9l47hx5vei/ca+r8L6dlL80iNpMOYxl1pfzQevQlt4Wj3Zemz/kVz1q3yT+xsjaO14LT8N1e5gpxFxb1XSo3NsocHY02TyYYP5lCySB5PLjKMFHUt6pMvluyejI3B9GkcLLK3Oda0lsUYvkxB55PIs22W9fUJLmRtQb7rItjaVTRCS1xoo3s83STM/W9J8O462ofQCSVffbGPj9BQwrR8rb3ztCby9+d9kWR01gJW8J6hnArEkyr4ugMrE+caXPpqomTR574lms5EnMJl9M/buvfx5mScZsv8W4+27AaI5kGOAyfpaHvjgnnqlji9/az5hQG9zZdpJmw1OuzRw9hQLvx8/8WP6tRyaPnTxm/p8mgn+dDNYea0JbKDkbWLJdkyeVu9Ct+kM7WSR8WEJ+XJyArwnYMe2+f/bTfrsI/V8gR2TwxzeQCGXSN4E1GtDPmUnHwxadrDApp/z0/YdXSAgnXv3K2RJIPVkd6kPA06WxCygJ0i8giD7SCB4JYbVberdZlAaYLBCv7H+mxxGO8ctD0s0oGK2xbFPcETwRNppF5x9Mf00H7Jxt1n1/d9+jSLvU282DlnaHjH2YQdhWtwnT1ecZV9NXxfYeBuztjwBAwInS+YcN8acN4e2LI+w/pv8ZO2vgwWNho37E8iaYnbN6xzL3P9ldVl4eOKNXP9vlp+3nf6rfJWv8lW+ylf5Kl/lD6T81J/KsSfkaxYgv3MDuCHwpEk0bE8Zxtu24VOnPUlc8tnThj11NXRt05vUDdvwuvGz12yPDOVoT3/t6cRmZPjUwhmsq17j356Yko+ceeFSSy4T2hObjTflox7aDEjOVND2r1kCznSkrbQntqcxMx5W/uupsz015+xC9pl18kewKVtr8+ZJ1vhj+8uOdkkhx526bnFj+bbltr3XbNnsxMbtaabnrU/yx7Sp78vGLV6brbSN+BZDG+8mg81Yk/7bmRbr13RB22i8N797kyuan+ahkCu3teu0Z/NNWx3JeGo28kafb5fdvv8bmhMXqGfSJs+ro2mbG+w3TprszRc/JcCypQIO6tajomz/ypZGM79zWeT6nL92LDj7SKe7Bq05KAMUX2dw0aO8l+MZIHhbGi/tuiUz8mcAq41z9mV0F0BdyxBt6aXx2IDGJSu/Uz/tNFArCfRWL0vHEpXx3O4nwFzf4JHnFowuIGZ64H7IrGf0yT8TVUtctJG2f8notweN1JHJ3gAWeWvx45L7qbSkZKfWTI4GTAxgrYwZn5qfPoGdC3xcYLQlXMrFvvKh3HyQPmv54gJY5DlBusWVbUubefIrgkryv/JZPjW6TXbm4VZsgzn3GrbyXYA55aTMT7aZP5HD/Vg8VPJme8+nAFimjDcDY0bL7w3Rp9FYgrSAwz7b/9yk2hyKyZOJr+mChrW0Wv0Gapp8LOakl0wpF/eWtCfCJh95sMCdAeU6KfUmMWf/FjysmE5zfLKtBcbLdlsiIk32+9HCpDmFPtJsjPbLxGuJifpjuca70Ul61vflewQlK//KnTEk7TrvZcBum8jNpq49TMar3W8z+harLmC1PKYuk85TnGmJnWOx/6cvpL7oI9R3FosTe50zx8lfxlsDGlaue1dd81fG+ZTF9MexbA8qzHXGj000ZFvSbnGKtvyUk7+VFYeWs0xvzH9Jq+Xv1XfLzZ9yibApPetMIfiywEX6Wy/pc1rZeGnA40r2DIRTOBOQ95sh2uxEFpPbAlLjd683ENIAAelfIObpyXGXKJoeCSoan5Sr6elyXKNJvltf6chmQ83J0wYzsNvspSV/C6DX2JP3BsauhNt4uPqx4EUfXEBi4CG/X3xd49M+W0Cev7FPS1aXzkxe49mSEu/l/5fcH/H5VrfF3Sw8yXqNsS31pH1f/kR6zd4ue2j9plzW3kCItbfc1OJgxpnVC0HN9pObr20ML782+7P65o88XJP51uzCJiUo5xU/rF7mE9PLU0zKwtUL8mxbFH5m+ek/lWMlwQb3yFwOkW1Ji/83Wlks+FnATMdhssjAkm2v9e2WbNPg29MqHcqu2WkuOpLpnOPSEsLFl+0bauPLBJM0np5gGRxSn9m36Wu/8y9lskBLmgSPb/qhDKZbA4j0AQumbGNj05Zc2v/XXkiTKRNwJpb0G5PzWoIzHVynHp8AS9bhUsc1o8mEbOPUQNfq8u2YtQTFYnZLvzJ+zOZN/83W9/98yOSpuKvvpZGxwZZ+zGcaoGu6ZPsGWppuuf+w1W/bU1LmBkBbXGd+bDHrigk2ju0741/6yT44t3G54sDup2q8ZGEdy7nNz7INc8SnfZN7M+inQco6l5LSgFtwsUTO9tb3U9LjZkUbVBvkDBIEWeznKkmnyUcZL9rpqFzWuADSOh33gBBcNbDBcb54peO2IPvkYE86bgn1euLPdkaPT1hveFgbMbqZBFLmvM+k0nRggIU2edlY1mm63/8zGdmMZ4Iy04nxbuCKIMV4py1t35tUlp+sQ33k95ZkL70lrwQYBrAu3Zq+kgfOOJvvmY6o86u/tAE+LCVNgqy8njJyH6fxarxRZ7Zv7cpDRu8CM0+xNetYnsl6fLCxOgNa0n4tttjSqcnU+E975D5OG99vGNf0p8xTDSSRl9TzG9uyuPUpAVbbpJt1plwOmIXG08BAKt+mtJcWl/iuAecgm6OZcRvgY/3U0RWAqRsLvhbomxzGZwuyTJx7jU8znCq3/Vhvlx+zbFvqtwWpvGe0PtJv0stkYHuynugncHgKFBdtBlsLQvx/+tiAnDSuIPcWnLb6fCdTJnazkS3cuEq+DBhe471Pz5YMknc+LDVgYn0YX3k9fYUyGbhI2i05t5iR17g8wxl3S2SNduMlH7Au22Z7JmPGS4u7JuOT3rJO/p91DdiZ7Iylxi+B4RMvba/V2uS1ItL6IM8pJ689PUxb7L9s9tuLZb+m75ZbW+4yX7j6/1QzWJeQb4I0jTcDkwXHbcOgTUff6eicusxkkDyRNp2LQPJJ5gayuJRyGVV+T9CyiStnBVbON8nK9J7/W4JKnRFgkcecJrYktn3YkiyDIIs5ZX6mvnm/BSfTu+2xYp0LZGV/154G4yX5zBmON0DI6lzJxDYGp6wtcNuyRtoPk2jyZsE3abQxafKm/9p2BPO3TI5Jk/HsSurZpo0N7ZMJw+RqACHp8JrJxXG79Ehd0A9bbDCa80e7av2a7V0zm9n+jT8wR5nNUYdNJuYp4/uKtwkwc5z4QGZyNPlM3uYfLBYLrxjZ+vku8l9xz+KO0eWYvZH/0/5UzpQ3DmEbYp8UZMb9BBiSJ9s099Qm69vekSeHszqsdyXuZpikzx/j/bM/+zMdC0ug2a8FUCbJNhuRyY26ptzZD8FhzjLusg35274oF0sLMm+SoV1vCZK6TBuzwNxK0rmeNLduo8vEQdoclysR0t4a8GL/ZmucfbZ9iZTB+LG66eO2rNESMhN69m1L+lm/yXvJbw8U9K2rXLPCLQabrxvvFouoxy2c0c5+rrh1xWaTodlXk8/qtJhtdCw2pj1cfT+NC2nsw/8W7u99ihsGPoyfa79hyr33mSepl9/I4Z3mf2nz5I1+RLlbvkrZf1/lp/9UDgHJE8LkwD0loDXOK0Ey6DeH2c80+KekZFO7V5JKYzQwwr5aMf4NpCT93E/ypHfqxIAkdZf6S3nzBZSsx/oGTlOPey9BlgE68kV+n4JTa5NJl3baCoEXbc1kyM8sDGiXrVz9mk1mu+ZP5LPxaHsS2WfbEG2b8c1eyH+zHYsRuVTZEn/KkHsLG8Da2dm2H+4NCDI7IP95/7K7BlyZwJJ+9rvfqWtb9s9iMZN0sy3H32KRtc82b0GRPaA85STyynjXNqE3ninPk59a/GsgK6/ZOJEec4bZ11OuoKzfY0zz4bqtPrWYQr+mvaeP2ri165/uTe68RgMwBachvEmGaxyWXJPW/m9PZc1xkgYByDWIthyX7Xgig3XYh8lFXlrCMBpMEA1g2Ub1VjiFf+k6gz51YzJmobOxz6tkkmjJK+lYQHsT/E0uC5KNR/rLBQDYD3XIoG1yWH/kd2kkD6Yzu9Y+3wL5BDfZd9MpbcKSLvtvAMd0a0Ai7X2X6ps8LXlNyY3Bly5babEh+04/oF4bfYt3F/i4+GPb5Osj8l7+SSDZZLBxt5jPPvkgS/299QeLu2lfzG2c5Sdv1Av3aVlO4qoBQdA1nqnn3yJn5wqE6aGVlp8Zs5in3tL/1QOsViwhNBpJx06AkE4aZDMmM66LXw5o65dG1ZIogZAZUH42/bTEutfT0E3mq1gisbYtaeX9pJFjmMGJslpwMx2bbNm3JTADOkaHtnfZaStJ2wJfCwhNbtYxGVqyaGOa9nIlVrNlTvM3/tp9q2d62CRhenjSxfJom9RTBwm03xzEsLhA4Je6axvkzUZpp60/0rgSusUZJmsmR44vbbclO+u//W+yN59u+cLGvvXZ+GTdS9cZuzg+BHZvAUnLLUk7eaRPs43x1cAV9Zeg7IkXXvsmcaVteXmKqcwbT9uILvv/1ACrgZgrMaeB2+bUbN8SwBo7+892FsQaOLHpf3uKabK3BJ513+ioJco2fX/tgUo9fdQYOU6U03REflNHbwEWE5o5G3VFuVNfDDy/RA+t3yxcerb2yUvOMGTSTX1Qpway2CZP9lk9ykFdPenAxjJ18jbJ7v/rj7Z3w2yF/GcySt6oA84oE4BYv5SZtNMHmn+Y/Hs/5bW+ab//NgnlStisZwn+Kq2+JX/b/2d+Q1rMD09gzOJJfibfpGegY7dDNJ7aGNnKS/qd7QV8GquMF6ZLa9d0a7JeceDbAe7Sp9Insz7lb/73BK5+dvm9vGiUiLMFpvzf7mWwYd2897QHh/eYgAmy9jt5SyNvy4mWDPJ0CJ3GZmX42faBXI58AYgMaG1Nuxmv1bk2YhNUNfDT2mdfOU4p90WnJcGkyfqUv9G/gE+2taRJG0s+OdvZEvibQtu8kh155lg/gRm7l7rm8s3ea5vr074skdD/li55vkADN7832dtnvineQKzxxWtMRlehH5veGH+bPtLeqYOrz6vOky9uWVuwpaxs0/TJMUhgQj4or9kF887V535fcNXs2uSiPzQ+rz2JV6E+yVMbt6b3jFVWpxXLP5bPs2Tse2uDFr8/Eh9/VZvcaTxMwKbclpCsHpVp+3lsKW55Io29njxvHdvvwXYEMzbAV8K2gP0G3JGXtnTWQNIVzIwv9ptytqTL70+JqzkN+bap+AsM0Rasz/3fxpgyZ9unRGp9WwC8lpLeBLKmg6ZHO8GW/to2y1oip8wX723JP/tvydBoZ3vaz1OCSZpvZhkb7afEYHox/ZlvWd/k6824XHy/TUa03aT/FCOvpH7ZF0FI4zX52cMw1JP5IMfNxoMxmQ9K+fCcumZ/jW/GmOXpyeZNH097gC8faPrnuL/xk8te954Vm+FiG8tTF80/7/J83verfJWv8lW+ylf5Kl/lq/zFfpN7vtdjUW+i/mvq75olsv/bk4fNlLWnIT492f95nVPbb3Vj8l1PqdY35Ws85z3qwGaUqEd7ajMZ2hP4NfPy9NSUdC/9tieZt7Md1+wIZz64jHSV62meR5fbWO++I3uivXRqY0O5bbYkZ9LaEyqfwq/lxuTh0pk9qVq79nSd91IGm2W56NkMVHv6Nhpm+/Qh4yXb5547ymhy0zYu3Te+2vaLJuv85UuaW1wz3V1jmjJZzEsaZndtjK2f3JvaZGi85li22HXFZNYzW2s5jGOY9e1kr+kv5WyzVVuHMZJ7ke0VD1PIe8YU5mCTjXuvcsybvt/m4U/7moa3e2auQb/62M8ruds7OcyQWj985QP7f+v8V9B7SoDmZAR6FrRY6CTGM48evzVic5LGz5UILztpxWyB901XJsN+tkTVeM6gsPcysNuhCfJowJb8PumRfDU5d6yvhGm6afXMZp78vemitaMPbOHya0u8tp/EdN2Wo0mDCSfrEGARrCcfbV8n61kyM57Y72U/7MNkNvDDflocNZ9oICALl67zM4uBIh70oSzNt+xgRdJM2zMw9ASujF+2Z72rbhtX0rqu5XU+BD4Bm98K+GpA0XTP+Mo+d48j+XxrQ58CYOVTQUtkzRgIcva6Pf2RzgUEGKwy2ZEWjYj8Jyi7wMDbvRjbPpNcc2oDWTaDdiUk9kkQZPIyEFLWy1E5tqTbgGj+b/TzugWEKyB9JNiQr6RrAMuSmwW763Ue1NXbQh7t/hNosmSZ3wnGbXzI+/4O4qUP6+8t709BN+81n2eyZB2LQbx3jWXyxZOh5KOdsmry5rjt/7Z/03RpgMT0YO3b7IV9tjhq95v9UZ+WUFtM243vFkOZ/DkR0ArpmMxv4kpbaaHdPfGRNN+0ueJ2u0dZk8fvAYC52vGkixavlq7Fpidf/0js/FXPYD05qjkbA871NG9BzPq+gkkbEEseTLI0JAu4hsrNMa1vM6CnoGh0mRh5DNae8hgAzMD3e3uXTuOjndxKXS5fWS4nyzr8fcmPJGir82QrrEtdv6n3ET6eQFQDMpetN36S9tX26qcB02bXe6+9i8xs1fRh9U0n+8mHHIsrppM3iS1ppn0bYGg+bLykbvnOoDdJmvQvnpNPG7uPJraMm9Rl6vfNwRazXQNcrd2VpAnIrjieEw1N5gtMG597jwAnabIP9vcmxpB2ex0R7es7JjByppG8Wgzj2L2J3a18tP6vBmDlk8KlSA5a3nsyqqs/Fmt7PRXlNePbiiWeKxiTXktMdNarZP+N57zflp3Maa9lheWvgdcM9BZwGEjNyWx62WYsGfDsFFwLMm9AFvuy1wZQhwZYDFyyD+onZb4SPgv1n2NC4NzG+CkhNQDQxmXbWqKy68aT6erSC+3PgHzWa3tETL/5ZzPXT36fJcf4Ok31hlbTS7P1CyyZz139NZ6bLpoc1G+Oi8WUFmN2bGx2KumbfxFsNJBC2Z70xPFt+mq06Ftv+jE9MU7ad+5/arb07djqkvUN8Fl5o0/GmY/slf1VAqzmhIloDRVzI9xlPEzG63zZz5YrEV8DZrxfgKA5aPLc5GE744GJzZJFo298sF+bQm/O1/THMTOHvMAU+bSjyrSTpG+8UE6zu9amyWIAyvSbtJiELTmYPp/2/ZnMjefkoyUH46kFRgNwTxv4LZmaPbOOtbvAh+mV72IzMECbegryjAHt3UVvC8eKPNg7nix+7Ezc28T9BLBaXGqlgSba0tODcvLK1y8wb2QeYH+NJ+qZuuGKBAGetWsPCVnsVQ8me8sFJp/9TwBp1+nPLT5fdvCtPGDzGmny/rZ7AqAcwwTf13u2PgXAuvYGtYRkgYjGxwCZ/eZ3m9LkZkdLjtkvSwNVW5/y0aBaf80JG5hjnbYXIutcxt4SCp98SOdKLBcYuxJWGw/SspJ0LAmRt5TtWkb5SHJk4rb7Tw8htsRsPmUnwEjTgqIFOQu2SS954v4K4/EKpMYvAcFHQMlTsmnJgH5qoIK2//SdfZNHJrnkhWOcPtX2VDXAwHG45L4SpfGY/ZnNGEjhd8Zu6r/lBOqKMl/872fzA/aX8lL/b3SXvkw9UD88CJF1DOTxvsXz5YHlabxzTJP22tRlW1uYY5NG8t9OZL6JGxm7OS4/G2T91De5Xwa+Jdeo21HUNO4MNHmawBKvGci23ePFRMnNKEwOM74WaLKYUZrMBmBI48lJLsDVrrG/dmSdezyMnyfjps4pgyUOjgnt5CnYNpptj8aVuO0JNcfvKTA0+8qn9KdxzwDV/IB6afa9Y21g3QK8BfH0qfluU/Vml0wOpsPmCy3WNADEGGHJlkAi/cFkIJ+NJmW2Mco4Yks7bZaDfH20mEzse/l52jNmY0fb5RgvGNnYzhhk+lkbY+K3cc5+zcdN5tw71ABMFuuL/ba+zLcz3uYEg9l948/si/y3rQ6kv3UzHmQf347Tozk+2a/xn/evlaPmQ0/j9Cl+KucCGzTwcSrOTHH61ALm3s9Pa0NDZeBjAGtA6KqffLdNuckb6SZ9Cz402o8ArFYn61lAMuBxPcldAbfx1JI+eTUQ3hLBpRvjNe3taQmu2TTrN5DDxG732L7NrjWAse2uQMx7DITJJ/cFMbjm2O33tNkGLvJ7s5ELpF2JJK9lUrromS3tdS772ZiYv5gd0G6pu9buihGsa0uD7OMCD3YSzMbabIx80RYa3y0GNX2Tlj20N/+wsTB+UlabYUq75f4wAgrq7M1D6FsQYWNNn6I/NFBGvzLffBsHLe9ewM10nuPcDk5dMeZT/tjzKqPtF1iF2gbqbWenD7IPGtFbY9w6OWjpVMkfDakl65TFHIiJqpVsl/JkP6k7JuXmaAxG1FcL6FOuzdWXo04x3V5BL+nQ4Rj4rkSTMzKUl45rPJg9XXrO0pI09Wx17H/Ka/20gHf5hdXnFL7pLpONydWS6raz5a5Ljqb3luCtjSXelCt9yWyLvmO215ZjyN/TMqglbfpXi3HNVqbY3qQcS/NjzqbmOJIP9pdyGOBmG/o8x8no24MR+W3+YIU6sPFP+imXLVW9ic9bbPvLpVNebwDoat9AT/KevkF7ZOz4LnGW/TT5qOe2XG4xZzFD08+nWyLMAMQgw6B/bWazYHEZnRk4n7gtmObgbX1OnVqQbYHDHNt45zULDNRNOjRlMT0kjQQcNs3L8ubpw4zaeLz6aWNM22k6toSe/ZrzWwAgALDxyHEwW7TEaYG2AYgWdCzItKfgFoBbQrcnw/TfqzARXfQZkE1P9tlke6P/lN188iOzz+zLkvCVxC95mn29AQVvxqL5FPt846ONdtsbaHHc+GLcsj2zpJFL0tc4J+8tdl3AxWJB9pFxcm2bD8FtZjHprx3uPXtQv3RLe8k2bWyzbvML6oP8txWAHD/mevopZyxz/HNi4doHmp+fcgaLyWQ/M9lt3VHULhG2oGLOsuUKFObobZM8eTXalK8lFBqR8W7BlIn2KgbAnoJ78v8mYF+0Grhg2wwaTQYLrOyLs3ZJo9F9KjlL2k6+GU0GfdZpPDIIGBBImVn3Wmp5M/bX074tydNO3oAQk4UJw2zCkoKVtwnQ+LFATLnI6xPISbpcRnzyMYtvliz5eSU4o8U+qQfuY7K2TafsnwDDaLGN6cRm28w/aEsGEC3GEug0YGD6pDwEAaRB/VGOlivTpy//MNs2/b3dl5qFMhNIst9v5UGUY8GSKw7X+JlOM6fnHuufCbJ+KsCyBMMEa4h2r+esSTOATADNCLgh0JKRBbQWSNk3E9PWM3lJi3JZEDDQwWRgBm18W+Lk/y1pUkdtL5vpKfum3lku3nNqus2IPQVx421/L/PNHg+jafX5/ZLzAioNZFl/2aaBMNp7C+ymtwxmqX8mAPpiAzWXzAYw3gCWLRakm22kv+6D3ta1p2ejZfbPBJl1zX/e+HKTmXw1u+c4Uk6brW7+ZQDDeGP/ea8t01/LfUmfCd1AFsePuuYY0/ZarGFso+7s/qUT6s5ASfZDsEQ6Ld/uPeZL48HaUlcs346HZrPP5J8zZXtt+8vrbLtjmf51xdZf/Sb3VO6ToIY4r2Rr7XNwst+nJMT+0xksCbViQewpKWQdS1AX6EsDNLksCTTnpwzWz5Z9QkgjZnJg+zczHuw/6dkyHvtrCTSDajpq0wPH8aN8G70r4FgyoD9wjJrczUaaXo3f5Iv0DTRt+wyAtgRv/Vy6bD5hclKnRi+DsPXJvwQd+VR86eGNjJZwd5/TxjDaKXn9SEzJGJE8t72RHHuCF9osZ34Jeow301Pz3/3OWcXUny0Ztfh+6cr6NXCQddq+2tTlPsRZLjGQkwDGYsITIGI/Lb61PYP83wBW1v2OWNuA1OXDfGijLLZX28qloz/P8nPfuvVVvspX+Spf5at8la/yB1B++gyWPW3kU8/e45opkXZbk7ViT2Ztyt+eQt7OFhDNt1kuPpXwuz1p2PXW735vMyDk28aGfF2zWHufY7rLK0Y/x+CJp9R39rl7RJpu2xNz6/9JR3zaMv02OWxzvz0VU5+8f520+4geOIPWnn6vKXWb4WH7Zt9XXfpO1uc2Afry1dcbm2a95G3j0v5xOabFn/akbzylbk1+zqxkfW59yHt5je3ztzlNBtqrzWAxRlFvzUYuPWWbjC3ZH9vYsqQt572dyeA4UMZrKfQpHlCfnAXMutTJ2z5MBua9S3aLQdbX09iabaT/5vUszF1PsTlpZMxstvGpAFYmGRqL1TWH2sF4Wl5qjrptuWclp2tbMLTSjP2pjRmSfW/9rRwMMCsHf9CY+mwAkX/sI/nL/WykO/3/0R/9kW6gbkCF/PKa6eECF8t/Cwa0CQJ+a5P/N0fea2YbudRzATSTKfdYUA/5/Slpk6cmoyWzpEMdNjC1crexerJ36imXofN1AZT1KUlv+6cAz/04f/Znf6a20vTQ/KfZWeqryW66MRtt/bTE+WT/BFZM1hwDs5UnnbX6li+yfTvZ3Pq1GGS2viDA9MLkfcU0lss3P1rSxo1/ypL2QluwQ2BGs+mUMnxHO/rE8mKxoI2n9cm+bTmf7T/lHqy3QXWVs+3yXta1dm/64P+bZA0APcmTPJK+0TJHuALx8kfDt9k3zjrwJNDSTmdLWfaPPLVESx1OmcTFU6BN9qbTSxcWGMy5M1i08UkHtePST/0yCZNnk7XpPfvgGBJgXSdfSZsytyPdpEHeqIu0EQtctg+PNvXkr2nXO3uU8jTeLQlkEm4HTi497MwVHxjIa7NH0jS98FrGwDcbdS25ky7b2wNQ/m/goeko6+0b2Nt4tGI2kfGF/WT9FsOf8kb2y3sEWOyPkwZNTvNF8t8AHWm3/XhJkwCENk8dsq3RzHaWE5rdf0NflrvYxsbCAFWz+RabP+Upwmvjn31vS3dWGGyv/tYw7eRVm2a/EieduSV/M3DK3PRjjrtJIg3J6PBe1rHNqKxD489A0ALgR2YYn+5fTka+34CNtI+2qdcKnZj22ZKu6ZNjxVmJN0mMAdRktifrK/BZcrfEcYGmRrfJnnzm9eS1JTJunF9a1jd1mIciLAY1XTRdvyk5bvQto2M+TSBD+kzQ2UdLgNZ3q2dJztouuHqK+/TvvZZAeK8zZlN3zUbNT3ifY9zswsbCYlTO3jf9NhqUZUt7XUgbg2usLh+xOEGdr2xGzz6nXLHqTX9PMdr6eLLVT/Um9y1XIrEEavRS0UyUGWxJ50oE+cn+LDHQMBv9BEN2r5UW7K2ezW7lpwGPlMmcxfRmS7zGJ0+8UEd06Dclx73Rpm1RXwmu2nS4jUvymUnabMHoWZ0WoE3ui24rTD6kR6B2Jcz2rqtm92nzBnCe2ud9nuC9wJ7ZxhYuj7wJxpYELdYwYV7J3vbSNdkvvVk8ab/J2pLUZX9P8c32N5mtz8yf6eGKh8mXnQhkOzsVSV1x3KxOAnSbLTQfaa/+eVO4wtBm0ZPv1JFt/bhAjYFBi6VXjLYH9qcTmyxtrC8beYMFLBZcOv10e7CegvnWTae5pkNZ6ERtUK4jz/k9nfxp+tkSwbZ9AnDWf967QN720ei2YJJ9phFaYG5gtSW3C2CZ7p7swfRhbVYWAizyn3vIrj6s7HJVBsVmbxbEroSWdv9U5yn4M/iYPlNPDVgYoDDg0fSWe88a6CMfpsuVme97uxJKXqdebQnzAgApm41BA2Kpu8turVw6tmR50TW/zv9TZvql2QOBZN43PRiPFse4n9HsmP3ZK3n20w4kPPl41ssHMW45sNcAXQCpxRsbjyxma0/LX08xfz8ZK5/i1Bta34r9NP5Mzo/YrtnH1c8fxI89v0m6V2BqCSW/04HNeFq/7OOpf8pjyYDtL6NjMKNTpn7au6+ox+QxaUy59hK1gJT6pYy2h876vXSRATIDGD8p9xtd52xcc9qULa8vX8mHgZVMxBkUpxAImg4tkO3+tmsflslhCae9a8YOmRidq57ZoNnLJS+X8tKX2lI0E3z6Ruqs7cMiLePVZvQu2a54k98t8V5JlHxmW7t20TK/vxJrq8OHmZbMLQ4xfhhAoh1NoR3TNtvBEtoq+eBKQNP9k66yburEHqaewIEBCtN1ymX0WCf9xtq1sTMZv4G33DtpvnH59EfyrvlPa/MpX9Mw5TKgFqSeAszeb694sLr83oJp3ifo4f0M4EyExpP1v9cteLc++eqJy6Dt+/aXgYlAhv0mP1fCpNNz/FsSoK6SF9JN/bRlUeO9gQDb+NvG403yy9myBA1vkjx5sCWTpwCSoHDHl6/RSLlae/5/BdqU3fpYOfhwQEBtoKbZ2xtAQ9234Jztmg9l35wFtsMMTUeZ3JqOGQuTF9Mj+75O+7F/7lFMGubH5Lcl6KfEvHp8WgJrwGRjGPniZ9Nz2sb8z+UlA2RPurgAAm2xgYykyT2bFq8a+M/+3+bUFr8tBti13+JwCWf8rzjW9Et7fSPTGxzxqwRYv0SBU2ywmtGw/rX36OKxBZAnoGaBl4mQvLH/p+TA/mzJJIPEU9LLPm1a/ok3jgcd+wJJxpPRvxIMnWuv2dg/bVK+bNOWAK+EYQGUfwYUyZPJaIm96dKWTPIUnCWapLHLNXm/Bbqn4JVJhDNoyef6nyXWS6dNB09tWS4gdNGljFnebEe4lk+N7xYLzCZ3zG3/JB/MMhE+nWLccUrfNT9jPEh5syRvjDm5TNd8rSV86mu/XxvRWx/JT9IwPmwG31YzLNZeoNBikMVJ2wLR9HHZdZanWEWZf4vDQOSRsdIKddzi7kf4/nQA6w2AyMJBeZuI6CyXIbf77Qmo1W8J1WQ1w2b9FvizHoGVBTHrM9taGwOzTBytfgM8VloQbsGQpSX65KPx3JIjx6DZGwOX2VImMD5NLnCxWTL2ZWPJJ1j23WZmLvunbixxmM7J717PJPP0RE6bt2BMXi/AYbw2uclPS7bGz5Of5RKXASLzHeqeYNRKO4CwPOQfafHBLPWQvLTtAykfbdrKU2I0EGH3yC/b2X7VLQlAKMvb3JTfm/5saTP7N/oED1f8uzaWt/hlvpL3su0VP+z/N7PQ3x5AVct/BsieHkrs2s8EWj8NYF2zJaw3pRl5Uw4Nkgn2evKhk7aNgxbIn4J6LgVZMaMhreY0116BNLjmpOlsfMJNp7BlHD6lZJtNHJcjM7Eu4MgZuSc9G4hqPBkP1OnK+BYQZmJaOslLAhWOm80gZcC0hGGBpG3SboEyeWf7Jp9tOmZfGVDJM5er3zzMmJ/md9qQ8UWae31tgjMvy0vz/xaT2gyfJexmj0kjwVIexMhxpR2QpgHsbPPGRjIeXnE39XXNOpoerzh12UHylfZntBnPqOcmN3XE0kBTix+XHpJO04HlNfPFllMMMD0BksvfTA5r+5uXb2rf/xvfHP/8s1WnzEFPY/mpANaVfKdsvZwSnkIlX2DnSji81gaMia9tBk56NMh2vxVO4dNYrM/kpwFX8kw5uSGUeuLTcQvITKZvE1XKR5C2cll9A5l0qnR0zhiljNSl6cvsyPridwMyuVew2ZWNl/VP3t4mBtZtdk1+rT33mDExmD6fkpH56ZZrn02LCxxb+2ygxPTJ+o3vS6Z2f+0m46CNN2XL9gQiOVaWPAkuGYONd7annizOUl8XqDAZc3atjXvWp85s+bPZzaVn04n5H2eDrxjY+iNv1Ov8v7/gwQfi5M0+3/DR9JS0W576d/7NXuTMb1d8IM0p+bDBv0tPSavN8n4KgDXlKfBnYFiFtACSJQNJC5pMyC3A2LRjGm06JvkmT9k3p3ONRxoadUL6FrxMT9RJGnpe59g0EGNJPft4WiZ4ClANWNuYXps5Kc++Iyj5NR03XVPmpqMm7xWIU0YDFs2u03avpG52cfHLREvwbJtVaUcXuEleWmJs7czWWO+NbPQDFm48tv7z+2UHbQy2n5bY2v6/lkhIM+k2WfmW+ivJ0i6vfUbsg7wyXpmdW13K9lQYk/ZwR8Zzs5vLp81HTU9P/vbU7ilGMybZYQfjt/Fu/bS8miUBkOnpewFU1nfTOfdzGR/mCy3XfKpThAZo2hIPE+gU7juxjbEcxJyhYVJoPF4A421yvZJJXr9A2Ror92NYImnBrMn5ZIQ208OkRoDz5DSmh7anJOvmzFPywmnh5MeWZu0psunpo2Cp6Ym0GYCa7T/1Rz0Y7eSh9WclQdXyM8l3f4Nv+ubpQwMFDRC+3Zht9kQf2xdY0t4MvHJ8CCaSp2xns9e214ixoCUq0xlt5A1IszEzuha7WtzhviHzb7Mx0r72d7ZEy7Hl9QuYrd4af1NsvNonddhAQLtH/tpYXnpk2xZv2nhTZ9a+8Xvpofnbk8zfir7yWsuP2ZZg2eRP+k96+PMs786Hf5Wv8lW+ylf5Kl/lq3yVv9hvcucMiD2ZEAlzFivpbb2rv5wJyicF1s2+DI3brFt7arAnkrf8Jt9b3+7vtGzy3FB7e/Ilj+SfT4VcIrQZrOTFZrCog/3OkyfZj42VXeMYsp/kty1lts3uNhtgT8j2VGu63Zkg6sqW+0xvtAXTI3VjdZKu2VkuH1E37em8zfhwVqXJZfaT42l2/nZWx2ZdqOO1FR7y4KwXY4H54tMMRvLAwxNvltuvmHb1mX2bD5t/WyyizqiHdnDgSYarbv5tf5Y/bIWDPJsuKJ/FdeM/5d2+OXvOMbCYyzo2dpd+yL/ZR84IWQ5seqLM5PONLVob21eZM8jMby2W7P0p1z67T/VbhFQQFWKncRIY0WGXhhXSbsrffq9gSCdoAZnyWt9X0Hsy4gyCU3Y/DEHIFdSN7lMdbpzfZGl6oo5bEqfsWfJ3y7Y/8tSAg+n6qmdJhX1lnwRu5rQcp5XBwF7W3fFk3zb2DPwcf0vOLSnsvQZoLl1/pDzRaICJujS7NX83OqzfEp8lDhvjxscTuCHPSS9tjD7O0gBD3nuKB9eYGgAh7SZDJk07WfzG/y55M77w9DHBvNlx8niN2eUDrHvx2uQxmWysTHdJm7o0vVIftqep8WU0eP+N/f2mHDYycJk0E0DbHmjbi0v/+dQAKz/TKS2wUlE5OPs03QIeA2kWS0Km/CuBZBDc9m9RsiVjfnIPSDod6VqQeVNS9qvd5fBZp+n8opt1OC4EJ9aOpSWBFqz2s40ZgUqCrJXZ+jI+rA/u7bENt0nTAmiT7wIkFoCvoEwemozGN3lpSagF9wZ8TD88aUe6lKdtxM4gvgDhDQB7AlBNn5TB9Nb6JNAhjWvzdtKlXlKPbfMy+c4/gi1re8VI68fqGZgyWYcH/gj2U4xPXozeR0qTv8XBiwZ5p61nrmj+2P7IG2NvszPWJT9TdgwMLF4n4Bl3Wi5kIf1PDbCeEsLe3wS2//OIcRoWUewbgEEjyn6zvKWzPF7T+DQGfn9abrySXusv6V/J5k2AMWDAYMAZxTYzcNmDgR1z/BYc34xh0rek12gRbHHM9vMpQTaa2d7qN9CW+jaemt2ZvE92ZkklddIA1tZriYGznFZsBoQ8PumI7RO0mo6YZNju6nf/T76TZtMx+zZ9X3baYkeLCWzL7RErP2MIdUpwdR144Pc3Cc/GKvmxQy3mWzaGF9BJkJn3jb+mS+OLsrccZ/GbdZpeLHYmr03vBq6sf4t111h+K9tJqJv2UJB0COzsgIPll59dfvoM1pOQLRC0k0vNQPNaA1W8Rh6uQJ10dkaNfCT95aMl/WxrejAAl/Vt34M5sunZwArvJe8NXG3hk2JLCm0snkBQBnS2M/6zpC6pq0snTXYDlAwg1IXxYSCGdds4mc21oJJAJ3lqQTWX5y2IJ/h4Coxs+ybJNlBGWdo4vw38NkbNBy+e8v8LNLHNJok2+5C8PhWz5fxs8SDjB9sSgOR3PnClPxFoGK+WbM0XDUgkbwbeTEbrn/5tcefJlrKNzXKT10ar+fklj9E1HZj/tz4YD674Yzb/G9nLnHSSF84SXye+W46wBzXigmZDn2oPVgNRWRgcM9C3QGAAq/HQgt1HgEDyfwEQlo8MsvFD+Uz2bHMFxyc+bbN4M/rGyxVYp+QTCDfQE4AYwMrZE5vxtPG7XndhAYMzESZftuWsJmlT1pTn4vtp7JLflJV0sn2bms9xb8H8Smjbj/lN/t8ADRM0A2ujyT2Vq+u3yzo2xvRz87f2FN0AWH5nbDRgk3phaboh3Van+Yzx0WJv2vRep9+8KQZunmKV6afZe+ohy9MKiMU9o/02D9C/n7ZiNN4tXjzZHfnc/i8/Jq2MtZZjflMOXrUYwDi839OunnLn1k06xAufFmBNeWNAlvzyyc4AVqPdEn0DB8nD26CQ/LwNBu2+BVkLOPnZHOepL6NhDmABhAF922SSufSQ7fLJnfIZWONSBBOWORVlM/CybVoCbaAq7yV4yhmq/Uu5DdjQRpNvmwm4EmvKlMGp6XTvtSU7C3rks41do/UUFwwY2PuMLGhy1q0lnKZPytSST8YoAjGTx2i3E5bGe7Pd5NFiRLOV1Nd+XqC7AYq1+UyGu++mLak3QMdrLRayGLh8U++y1auv1G+LFa00YLQPA5SZS618aDCg8lSyTm4+b3nMcmbT23d58LG2U2YVaH8pwnjnDD7HrOUC9vMzwdXvBWDl/+bsLbjxXjOgCzgZSMhigfQp8Cett0HgTbAyYyJoaQE4nSWd5koODUA96aDVoePZGFuiMNmN5wYICRqmmPwXKGkzHW2sWGz2ioDw8gfTyV5rgYx74CyBUBa7z6R/AUAmB5aWZK5gaDKvfA1cWTJIu0saHwFy5O2tXeZpUM68z/1r03cCE+rpSmAGyK74YHSWxtPD5QUeLhBnNmi/rGB1kzbvX6Doyh/XA5vZ2VvaOX4W/xjXbY/aG12sT9hM0dWO39/EuzamfCCdYg9e3/FwRBvPg2vX2DZft/xoxWT4dDNYqfj2pNSCxxvlUIltc+41ENdygvHw0VMlW8iD8cWg/hTkbRmVQbo5ogWEi2ebsbJN8Mbnfj6NBeszCbwJLNf1t+DNEq0lMiaB1Ac3gxsQvRJGS36WFJK+bS5NfsmH6aSBmLfFZL2SWUs6lqCekr59bwCE37Mftmt9pV0wgbe9YReAatepi7xvMfMCLrTJq/+W9K4N7Vf8ajGg+bgl4hwj+lFrz7+kZ2DO+G706fds04CRyf7U71POYBvey/ppDxYrmMtt3Gjj34Xe/m6i4YDL121GPtskjTcx/1OdInwSmgNtxp91DaBYsGiOarSeaO4149lkuZKQOQoN6AJDlMkMs81iWZAy/Zjuudz11pgveaxfS0Ss04Kh0SNNCyxvNhrbdbO7LAuuOBV+8cX2l41ddkJ+Uta0u48CKOr+yRfoi6uPKVxGssB8JUULsGzbeGqAIdu0Zem8Rh3kElmzNdNls7u26dj0cO29a/Z7jf8V//hQl79ZaTpnv1Ysbl+xO5Nv0rX42Hi47Krp2vb8US8tF3DvKdtdwOvp+lM8Nv7om1n3KQ8+9TUl2+/7Dqek/DaGOb7tRHE7CPTRmParBVgszdkvA2Kg5sxRW6vNcimYU7vcV9EcNZ9SaaxPg8pEt21yWaQl7r2fzsDlIvKS/G9dntC8CkGBJX7y8GZMCdYYMLOOBS3TywVIGk9vgnG2TZuzTfo5htzYfgXf5NkCjgGmpmsmm2bTljipN/La7Mz8u/FB2ch7Bt92as1k5p4S6tVK9tFom24p94Kr3INkieGJ3n4yPjD+mT6zL7a9bIn8MA6ZvG/GJPkwG8j7T7oxmVJXF+28x4MxJjt9vsWerbsPUX/0R3907gVtcpgOrpzW2lhJMHztZb1iJOVhnG+njr/h93SzL4tDZvPWv9Ex/n4muPq9A6yPgC7en8In7ky8ZtT7yYFggODTg70PpCXh/JV28m3FaK2Dkv5TANi6+fR4BS/qYKdsucmVgOYCLauv7ftavqGuW0Jsba+kaYDjbQAyXbXyFBz2s23wZNCmri+5c4/ZBbAyiRjPWc94yn5b+6bj5ifZF/394sEODLR9WQbceL35yPaVwMKSgsme+65sb489OBl4aQCo0W6fbUzMxtJOkx9uXueYGH8WN9kXfZ71G5C54oPZRQLcrNcO11hdK9Qv7cVoXnHuyffzgcd4ugCI9WP+9xQ76bvUw5WnvkEP2cb0nXzkeFHPlgez3Zux/NUDLArbgrwFZRpE1re2BiSsP6Pf7i0tM+7kmU/9jd9LNvJrp0qsNOfd/2mw7Ks5MPluvHCGxk66NJlJ9woALWnk/xzvTUp53/Rictq9y7lbAExAabKnvJf9twRj98wvnk6JmbwpM9u3cdw2/J52zUSX9NLf9iFgZgX2Wj6IvBk7GyfyeOmg+f0bf+YpuzYGb+KFAazcXE87bDbFOuzT4qht3mef5rek0ewo79MOGB+SXibfXIbnqsTWvXLC5WMtP+V9gtHmS3v9mim+gAt5brxaeZKdNOdv43mLwU3mb8fJ86d8Rh9qMchkMdv4g/ix56YYGu9lVNcTEhOJGVMaNR2QtLjnyAww+bDkYHxm/wQ37IvtzfG27RoUlzuyXpvGZRBkf2839NO4KTP1Rt5MN9ZHsycunaY+rT51SH6fxjDpJJhL/g1MWN9bj/w1gEXdPQEK7nnIds3eEhjs3wWsWqE8T6eYsv4uuVE225tkoIL1bRyajxsIeSMveX1KLg3U8H7q/vJXxhO2bTIY4LK21E9eu4D8BXAYf594zfqcQbpAjvHTQGzjm/ZPnfAhM+s2+ZI+4wjjs9E0mZhTrH/mBAPSXFIlz4xB35B79sE7fZszjOaz2zdz+lv7+NngasovO/r2Vb7KV/kqX+WrfJWv8lX+Yv3Ys800WT3e55Oqzb4YMjfkaiemkj97KkkEzU2rnH3iVCpnAdivPUnbBm8rfGI3PVIOPjEY8m9PmNemUJOpPT2YzPmXMyqNZj4dPT2htJku08/1REud2rFhzmDlPY6TyZazb1aHPCcPNi606zYrZvsabfaKS3PpA+yX/O7n3LPlY+MvdU3fvJ5+qTOLGaZXypY8XzMhNiuwtJ5mvS4bftP2um4zd5Qr29BOOM6Xj+em/rnf9nWRT/aZ9C+btjGhTbc9WBxj6rrFVvZrPt7iY+rcZNp7tGebmW4zsZTRvtOv9nu+PoHy0cfzf9t7toW6sZOCdm2/t78sl75/H7NXv7fXNGyhET45cxrUlbR50ogbLp+MP3nZYqd1sr90sJSDe34syVuAu/RmPJIGncoS9FOieAItFvTpmE9jzuusw/qp4yt5t4MGdvy5JWae3jTZGbzXzhpQSXqm2wUv+fuWCdos+BtYTv3/Ul1buZKkyUmezJ/32hvf3jamP/OrbZc0khb7s9O0Fp+av5ou34AK05n1ze/tWgMGBliegA8/0+Y/Gi9NvisZX328ORjALRJNtv2zfT02BqSTPpg23uLN5SctHtFW2sO69dNK5p8ci1zGM31lfeOZy6LfjrxDu7xis8W4N/Tt+6cCWG0TqyVnu7/GspsXL/SaCdgCxJVIsm0mS+sj21yJ32gzcV/OlPdpgC1hZH/cq0a+3wZ+6rL1dwWBfTq6EvlTwjc9UmctuWZAMBocy3xCZN080bbBudkU5ct+TC7uH7t8g3K2Pq+2T7bLGTILnmYzWSf1NYW2YQcQaPe05euEH2WlDnI/R+ObOuVf6sJmXHMsr2TI8saOjNcr6XAjdd5rhxaaH2Vpryahzmysfim44hg0UNSKxRiCDBvvlJe0poyOJ8ZdJyNpx803mxxmg+aX1M2V8ywe/NIctG1/G/mzASXTUbZNealv48N0sOXaD/jpfovwSrKrpFRiO31jASD7tAG1knQI4t4E3TUIMyqrR70QdNnsy5NzWOLOfmmQpMtPLoMawGIATT5yrCzYX4HkLbgy3hoQufTXkmwGVQvC5JntybeNFeu1J3MbPystqGQAMjBjSYHJI3mn3E0fBFdNd2ZXJhtPy106abZ98c3+sp8G6Joc3JTbbDX7MD8jvxcP5IPXWuI0O2Gfxsd+Jm07EGHAxOzhktNK+kbyww3h7KOdhiNtHjYx30z5r9k92lDawDXbxv6bTzNmU59Gz8bzqe5TG47F95JPmz1aLH2KOWbfBGuXjL9qgGVBm8pYp2ASuE4rPQE2Jv68boDCjDjbpDPstbyX/yffl6M/6e1yECZq+z/r87sF/aZnO0p8ndJsDpG0nhz6zfWPJBobX2tDngm0eKr0ia/rWgOsbEd+L1mupMkTQQz2FngzGV6A1Pyx+Xr+XYC16eHSa/ZpT7TtfoshbxI9C4EF9flkt/xusYbx5ikRtrFIum3MWCcLT6AasKIeWh8m71MxGWyWyPyCfT8BUH5vDyZXjOH19cs8IXs9jGd/PAX8BLCy/aXbdj+vX363pcU1s/Gsn/eu3NJkzVyYnz+z/F4A1lWHA5TJjMEw6bbpeQ5WM5j2JnMzWM5OTbGlhgRZNBgCoqaHLM3wjL71ZfLz/zZLkXVNz6a3BtSyDZOs8WPJiDIxGDX9Mug9JfgLCGa/H00KFuQIbi4gYXK0egau7OmdgbuNa5udpV3z/6x7+eWlQ8rVivVvfsaHhtQTeWjBmzJvO77OwuSgDkmHhTPled22NFwl70+bnBlcmlnnzQynyW/2efFkoLABoqzX6pi9Xf0/7R2yWEVdve1z5d33u+0WCo5F9mN8Nb288YHWLn2McXI+EzCnHBfdKYwJVy5pvLdxaa/E2HqWuz8NwGoI3AbD6q6CrmBptGxmKtu3/STcvMyAlLxufc7opAEy2VHeCyxO4VQ1dWVyX+Aq5TWkb3Js3WtqnwcM6JgpS/LFYPyUKIxPtrMg8UZ3DUi04J3fzb7TBi6aWZdBx641mRiM+NcSAPswAEo61tboXKUBxrcJ6pfUNR+jf7b9Nq19ypP92D279ibhrX5sqTT5Nxka39T9xXPS5pgnDQONzVbf5ADqY+kZ6CDY23rmS5Zbml03+aw8xRb2nXq1pb6WP5OnN4VxkfxedHLCwHJW0v8ecT1lTT6s7yfdX/bG/kz25mOfYgbrKblssWn1KVyasbb5f0vCyUsOCJOzBcoMFBxsCyLkyZzf+P4lJQNo0nwKUtk+n5JtP9C24R6H5P0CRy3BXMspT/qgU1mbBhbs+/6fT2TU0/L8FOiufRXsL3X3BH6sjhVLaja+prO2h8vsvSXPZnutPR9SnsboyTYMBJDfj/jb1ScBDE9+tvGijs1/9wFu61vsyETdNvtm3aSVde1gzJOubWMy49GlN+Pzsu+Mp0ziGdsYW7bwQdba8Xrqx0Ak+TO985O0DZiaLgzIWExvukz5GsB5E2csLjTg+13yP/tmrrW9wDa+F8hsY/SpAdZVJ5VGI9ygxScz0spBYOJuyTL5yPv7SeNtwf8psVjJ+txUfdF9MpoWjKkj1jW9kU/+n/q9QI7xbs79JlBcIODq79JdS8ippwxotj/pKamwjtm6yU6bu8am8cGAZIF86uRPjmSwY/KmH5rur+RmCd7s9dKhXbeElvffJBDK1ZJYxoe9x9nzlvze2uHVfvfwEADvvdw/aAcdGH+3GD3TjcWQS8bLt5oemk1wRshyie3Jav1Qvhb3zc73egI/o0VQ8nTK0vIB+7e422Joq2vyNBmvGP/9xeweHxhY2kqJ9ftkl63ep/qxZzoqFZWOQYDVlgCnZLIzQzQ+mCxbUHwKvnR+0mUdS5BpcNQLg0UDSsZzS3BJt5XWdvua5GuAwurzurV54oGy5f8NnJA/44uzDFZMBgbsK5mT9xYMnnRA26Zf0JapqwtUGMCiDWdAZz3SMl1nu5wdvMaA/t+SA+s/xYzUC5ffL9oNvCc908UVl8ymzY7amCVfHEfqYn9uiDZjcS8B2RPfbwCC6TN1kz517Qfdvs3fKZdt/qeurnFYOs2/59MeCBrvjFVpSwRkbw5gJK9Zj3bAdi1mXiDr7SnLKx58x0M8+7u2n7z1t+SNtvxpAZaBq7eJvRlAAwENjFjdKRlg/m3BiQGjdi+daPdU8NTklfTNUZkYjTcGcdLce3zyoz4Y5C4d5/VMzg0EXYGByxGXPVmQt0RqfL2VpenT2raARr5o+2xDIJHtU45sY0tMV/Bqekz9XLRon20/0BTOqFgiou5s+ZVggwE39Ut74nYBS5CWGFmaLt8Ed+onZ+z50Gl0U3dNh8YXARb18MRzxp63sX7rMNbmYYykb/w1fphcaWtXnE55cuYvZcq29FdeN3tImm1WubXj9wsQXbHF6jaAZP5uM3QJlK7+OEZb18pTLqfNs49G51O9B+vJybdeu0YHeEqApkw7RbAzMeStGcYT+HkjpxUzDnMm2yBJR25Oksb/BNxS1nZkmO2szgU6DJQYkGDbKwE3vloSYrJsT+KmTyuN373H/lriML28ka8F4CxM1vZEb/voWv/mo2mf7fROs6OZYbls7Gm8ec/0bPpn/fb9SnJmNx+Jd7yeD14tyVi7y9fJJwFVlpSvAQvT5VuZDSSk3ZjspHWBo+yHNt2AxqWj/P9awmrlsk8+APBBz3JU47/piDnsktkAKeNj8m1bSn5z/JRV011ez4dC01uj/bPB1F+oF41ags1kbqj0I0mODr9B255CkhafWm0wr8Sap+baPpV/m5J8t2nuq+QemraZevvJ73bq8ZLnGidzbHN4c5qsS4CZfedxXdqX9UsgaWNNu7ATkpYs9v+PgDn2Tfqmx5bgbVyyb/KfemvAyHhtNsGg2eilXTfwST1y1tl0b8mZdJu+TG+t7ZWcjN6TXi992uza9Z0J0vzF9jIyHq88bQN982nK/qY82culw5Tnisd2L22aPp70n8AzaVH+J9DbbIAxr8XL1vc1Juw/80W2a6+RuPi5fI1xmLLZNoUWm7O02Pxp92BZoCJgaI7zZMzsY0pbSkqF54A1R9sgZPfyM4sFMUuaV4C/6DKAUadGa/e9GLiiEea42DIh+eHYTrH3izWAc9Wz+gQETH4Jst4GkgxO1MHe51Om6aL1lYWbyLN900nr5wmMUi8EMPTBJoMlgdRDG1tLWpTjkjfpN5+zQE6baAC49beFibbpI/u4fL0BNfPpbN8SEZc4U1fbby67se+1a4uRybf561Oiu2z1mkmnjXJ8n8BcxlyOCWe3TL7VRX5yTBrfyeNTTDf5mr7Ml9jnU34w/TU744SEAVfyZPJ9ewH2Gggir9wPR9zQYqDJ+ilnsC6nYWkDdpXL4deps94VEK//yeMTQGyzZ2akbN+cNYOlzUgknaelAgtET7Kz0PkYlNpTMnWx48TEmJtzWd82d18gi8XGwXTApGO6TCDG9nvvaf/BU1AgeGKQpEzXqdvWryWYpzamkwYkWlDl/5THNvY34NtsPPtoermSh4G3S6asfx0r55i1e41+k+MaEwNYly4sbj8l2yZzs62cTb72gVrfVsy/SMMOCrSYmXUMCOV1yzVPMZUxba/tA2+CfsbaJz9pwI1/7Hvb8/CWyfK9zKQ9xbvmF6RtvpD6sgmNnwmupvgrT7/KV/kqX+WrfJWv8lW+yq9jiZBPOg1NPj0RclZm67LYbFJ7Grr4MHpLw96/sk+ednrhmhWx6V570kxd7Fq4zWBcT2FT8mnG9NjG63oKMP1eT7ssqY+2JPt2WWHrvp15yaci8s1paaNpMxTs52mmxGyeusv6ea/py55yOdOQNNoTu/GfdS9dmw3zSfmaHdm++MqA9QG+r8vk4z0bK278Z/umD6OfdbN+O5l7xbAmD/XKduTp8psnGVu/qb/2WgGziysPmA0Yj+SLS/lNXs4k8d1g1s8bHe1Y2TJa9pfXuIzZ9EVZed1mnKxO6oc2wr16LJzBSp6sv6exfmvL7dSnxbVma7+P8tNfNMrAZUko/7+c1Az3LcgivRYYyZsFzB18W46yd4TQ8Ix/C8jb9g3QoQxsa/et3gWIroCXdK7gmnzY+Ld2BlhMlusUDpOsycDNlRl8nuyDCcB0YQH3Soap1zd9brGTYVyCbSCN9C/gYgnWkjHBWEtepsP8HGDVAIvpwfRKv7ts8UoQ7VrKsvK35WP20UBOs9usY7HlyRcb3/s/x82StNFtpwCzL9rkk26onytftGsEWOTXQFLqxuKL6ffpFQ8pu8lk+m390N7aJADjmx0uMX1zq4HFDZOTJe+3fLvfl78W9688QP29nWD5VQEsnkxqjscA/mRQWaxtc8hGjzSfnugbf2mIBogYlMwoGehbYjP5zMgu2bduGjvlNKOnU6STWh0LVqaTxnNzKAsGLAymUzY576s6jI+mX+P30q2NZwtET0HJdEEe+T3lz1NQbMtgaf004LKHGpjwlm5uxiZYIN/NX5J3S9xv44eVy9dbIeCw9hYHGtixMaGuGm9XPDJb2JIHWRI4sJ2dQEx6Bgga/9QBwRH/z7a2QX1prG/vfs2Uq+mctMy26J9v8gBnxi6/bqdnGR+sz2a3FsO3n6cTuARST7kueclx+PbSB5s8iRnSnppvmX+lLdnBq0/zY89tkKdwMGlY5vBtCSf7ZWlOZn0yMXJzH+nSmVvy48yITU1vHzlDZjynDlJ/VyDPetkX+bclkzT2HJOW2K7EbDxRL3RuC5jsw4Bcgqvkm5s2Gewaj20cTM4LBLRXYHxEV9anFfMp8te+b/3UkY0/eXkCaFeQtKR2bcyekmNs/ZhOLh0a301v1EVLdFcSuGyJ/V1Jr+ksdUSZbKxaoibPaRNvS9K0Q0J5Lf00ASH9msl9YydtLHWTuk67ugAZx5DX89RmyysEcoypFqOTD45Fo5Myp46uwwMtviYvlHlp52Gkb0fepiykz3stVjTatE8b8091itASYZbLOc2QLBm0AGWB4qnPKdd+iad3UTXHyrJOxrqG2klzv+cTJ/Vk/Flgp44yAJjBG81LF63Q8I3+3uOTtSUS8mN9pd4JsPJ9aS3YfDSJXADwSqDk/Qq4fALONhet5KUF1UwUHwEvRifpZZ2c3UqZmq01G74CctNr8mKzI6l3LlVQX80PLn9k4iPt5r9mh7Rn0jObaImr6bA9bNGX29i/KVe8276Hvv0GY/K4Ornkyz4JfIzvlqw/4gPL1+4fNN9qoGP/py3aEutlnwRZrW8r5ps5Y/cb8akrRz/l0G1ve5tNv7TNrP9pARa/m1It0NOYzbgbfQsUFjAvsMf615o6aVgysr1ZND4mPvZnIIB8EGxRR0nv2stiwelyQJPBaDWdMfka709r6XTGpWuBtfVvfZkt8noL5m/twmTJ72+DYNJsIIq0CHSY/LmHMpPRU8JZGpkQze4M3NF/Lxuysb38uyVx6uipj6RHu0idc4Y0dftmf5r1t/fyj7To75TPwBFtlDy05GjjdM3UX5vmmw+tvvhQ1OJO02PautkY+126+cLNZg+2Nzf5XPtnXDf95P9Xvsm+W7ykX5HGFc+yfQNY32SW95eUzP9Pe/nod83eP/V7sNKQLKmkMi+lXKi31TeAZcUCbksy1kf+TyO0/SdJKx3CjMUSQNsvQPTepoSpbybVRtNKAxO8bwGPMrJua3PxkvrMJQbT6RubuDbicvwvWpZYbJO5yf1EO9st7RZM8/NK7Ly+tHaj+ZWQ7Jptvt3/G4h8A65yIz3HmPZgvsT4QP9N+dseMIKJBmay2PIaddr8wACW3bdYsHposyfU3er2STfGiwENymqbqEk/5Wozim/0cekuP5m3nmJqi1+Xvb0BrY1ntjOg3HhoOYttCdSanbyN3VuebL7pwcaQdK7PT/ljzzRSIkxLmOZgNrg0BgMIb0DZBWqu9hZozOiMBq/ZNL8l5QsYmX6bITKwmaO+ASFJ00AawY7de6Lb9EZbsIDJzd3JX9oegwn72ntPAC/b2vfkiwDwDah40hl958nujGfT5xPIuOg3wPW0L231Q9tqgXOBwLXnzoJvArTL75hcm+8tv2+ShgGUVp6AV9q2AazWZj+vWGUz+MZX9tXi7wXKaPONtrXNMTden8YjbdBy16X3VgywUJac8aJ9NwCz37k0Zzxe+cfyHemYbSSf+8D1TXyG5crfrZ7JQv9mnd9X+emvabBrb4LzR1BnOpptkr6M54l3Cw4MWtm3tW8BzYzHnPAJGCU99n0BLDPaJruNi/HLdstv7juwOi1pku7FpyX/p6Rl7xdbmrsUsf/nzE3WZR+X7W/7pxmgHCMDhCYD+bekz2TBRGL8UsY2jtlv8rLfWyA0f9kxSR4JQk3+rbf6yh92b/oynfFe/m8+1mKCjWujZ3ptPsZ2KXvze4s9+b9tFP9oyRjVkncDU1aaHdpKB+2TPyDe/K3F7TbO13hZHNvvdkDDeM9rK8f2YTGOOr5yaMrQ9vxa3SeZrF2WlPvKw02nlufSDkiL9T7ti0bX0bhMtp9PSDfr8X4qLwe8BZdGs82oXI6fNNoyz9axIGPGzM8WXC5gZA7K/rj/gHTocC2IXDrKcUkwYfWpQ/ZpPDa+mBiY+Lgs18BdBl077dTAFUvqkzzQ/tIOn/TaAnTyZHqz/YdMJK1P8vYENOhvzY7ZD5PI8p17uCzJ0jdSp2lH2S9nD2gvllRMV2/qNVuzBNnsJsvT/pS3fayeGFe2D8pqsjfbbvxY0rT+OZ6M+Yyvl+5ZLO41X6BtNR9tcYKy2GxZs435n7Ghyclxpk7n//x92tRPm521/ZDph/bw/O1hJjvrPtlxym2y8sGrjeenAlhE6zl4uRfADJWB5QJgFjgvQLZljaIFUUseLJkIWrDd0p6+LdFZcLW6ZkSXvuhwb2f7LkNlAKOjtgCd5cm5WhC0pH4FWAv+DIjkk3bYxoP/29jksuAFyk1O1ntK7uTV9tvQb6yfrGP3je+mP+rCeG3Jkxtp25IEZwZTl0yQq5em0wtQsVgcuXzTdJKyps4t+bYZHLNt8p+nQxNc0lftwYK8E1hRJ/lJfqgjAwnkPcf/qZivml20vXsmS+qmxdqsz0JAYHwajy3mtf+T1+zb6hmvBpj4YLrXdvy/RX3LLbR503PWo0/nHu6ka7n1ygmfZpN7C6AEWFMsMFuwzJeGWeA3ozHa2X8LdE/lTcClQzXHbIZm95o8TFJsw6DS6DbHtPpMmvmZ9Wy6Pu9fgYr8tI3rlgiyje3pSXnJA+3U5Df+Wr2PlpZwbHxsg3XqyWyp8X3J04Ddk78wKT8VSy7ss/XRZrEb30920Pq/kjB5n3s8gWYgrPWxY5wgiCDdNs2b3Vzg0xLgdVBmvzcfSXpP4JL8tutPgCCvGShI2Q1cmU6yTdPJG1vjyVFuGUhZbbmX43yBLSsml/k9czavJz2T9bfyGqKk1/z62stlvt0A5eWnn2aJMIsNWNtL1OrvddLdduZgpvQL1dqAPRks+d7P3IdgR3EvQzdeLwe6ANolIw240TdQ3ORmv/kkzOWd1E3SsuTBexakm63QlqiD7D/7uYIL+zTHfzNDYSd4bK/XG5tkckg9boI2H8t+DKBQRvPDp+Bo7Zava3woR/Nfs5/LHnjtKbk3GWwsDGDx8wKQb5KCJdYLZFG319H6a5z3vsXXi++WaLlETh3mdxvbpdN4zTpNb5eslDPlZ7Gc1V4xsXUSeBt/UzJefjQHLK9XjG75k9tJmt+ZPr6VmfiL3+y3tWNMa3r4iC/96gHWlpYYnoy4gaWsa+0NyHD/ixk0/7eElXWYOG0JhHK9SZYfAUgm8xta5nwG8iwwPjmN6fMy+NUdnch4SDrtpM9Tf6bDJ1lM301mXtuZvMZ78vM2QbSl2DZ+7Kftf7t00eqazTcQY8CaICD5bu8gSh+8kvDWvRJM86umhytpNyDb+mW95rNsu9/bkp614wOL7Ql86p/0fkliY7xJnaV8lkifYhpjj7W1+GkPPMaz0WL9lMNso13fYsthDQw95T97ACIPlDHHx2RsOvp2yJT8Zi4hX6Rl/eUeSrP7nwmupvSXknyVr/JVvspX+Spf5at8lV/He7C2GOLfJQui/K2z6NSOYrZZBJuZ4qyITbfaU2cWm/1a5M2nipwZsFkxytF0s/8/Ifp8yrAn4jYWnKUiP/lpT7msez3x2xOb8fX0FJ/3V787s2H729qMg/XXZn+MD5Y2+5F07V1XPCrNNk2nHOcmU+rEltlI/9qASx1eT6fk3WZ6eHovecxZkXYgxWgaD2ZPNmth+jS5cjztnVO0hfSdt7M6b67R31ef9NU2azJldLt/zVZNX0knZyGaXTUaqSOeZPyIHuzatXyU35tsFtuan+a9rGNx2nIX9Wf8JX273myLPDzFstRP5jCLndzXav7EQv3ZDFbTUcaDLW2f3bZ943O/6h97ZkLMOs057NPqNVCUg0cFp6G9PUHy5uRjykpnskS915hUWgIzvlKe/W7TrTTY1tbGKWVp08yNty3tlEkrTQdpVw18PenL+G5BhH1f5eqvvX8qfyjVdGZ9vAF7KR8fKBroyNNlpi8bkwuQma8bDX5v1yhjiyWX/5rdGG/ZNj/Z57Us12KBle3/esWBJZ0cs2yfMnPJj3aQB0AYD43P/SSfVzI1WakjLnM3sJHX23Im44vZvem0yXMdyGr6ofxtH1XGw7b3MvXZYnD732iQRzsBypifp05ZJ+n9JurZfercTvJmjmZOu+S8MMGn3IOVSnoyjFYsoBtCZlDJfRgWuJKn5JN1WnC7Akq24YbtBnqeAr8FbD4Zp2Fm4WbFpmeT2XRwJYw349qCFB0u9bDFjgBb/1fgoR5sFvIKuKTbAncDbks/E+RlOxakmh3lTEbSaXplYNz/2yyIAZHs02YSr7aUx6492W2CBurkSlhPoKDp2OikHTRfIs9NFvLTwPaUp8S3/JgNWl3OJlFmA1mXnM0OMi8woVLGrMO6+cl2DVQan5Sz6Zs2ecWJlHHlSPltRcT6y3hvDzUme5PxyktPOY3X07b+Hbxfy/yN+ieAuuIDx7J9/yUY41e9yd0cpim7bWbcPztxYoGCG2KZSFqCMr6ZiJMnq2uGaKcxki/SMkNjQHuz7EBnugI79fl0FJl8Mqhlf3TuVhp/5JNB9hpLszPr80k3JgPHI/vj7MIUbuA3md/wsLQMJCV/tky99dj3nnCiTrOeHWzIOnlSsQX4S4csHLenRJhJiwmjgRfaLn2UiShLeyWE6e4jwf4JuFgytLrNrjN+TGmvbUkgkNct/lo8MttOOyKAWl4aGLD8sXT30/TMGMSYzntXnsrrSd/0TVtqucByXNMtfbr5gvmAyZTginHhTTzedr8pudnyG2crqb+cILFxa/pqY/xpfyrnrZDpSKawHcDr5NFeb05LA2j7v8y50kDa8WY6kiXNFrSvZGS6MtBgQYSB2oKbyc6panOQdMzG85vgZTJbGwaVBCsrV7MFc7iWeMiXyWbBlk+pK78t3ywAIzBL3ZMXSyJLpwGsa2wsGbCeJQ4mBtbNGcaUowF2BtwroFJ+o5Vtzb85flcSoo3lJwGu+VHq5NJB9ml/SWvL8tb0tWA5fZ6yWQLM+oyZrJ97Yuz07wWweM3u8aEl6/O66chOBF46ZZ8cl1ZaHNzvMxbXyXLj2WKtxYJr/xJ5s3p23/yF/pe5mLOobMOl9I2HVzF/Tflpo3vffOdTAqxMwBeA2fp5zQI/r2VfdCZ7iuZg7yBbEEjekoYts7CuBYKsfy2VpuHavTfB13h5cijKQgBGXhr9NnZWpxXKwjFM58yxflMsQBE4vJmd4LjwEAUTnOlg/5iY2j4c9p/jZE+elNf4Jt2UwZYIuRH5Sddvgt2bZZIp9nDF/hof1AsT9yXT5f/b1pZXtzRfaPVIu/kLbd9ij4GW7M/ATyZMAyhZN4Fa2nEmvxYz838ma45L0xN91nS0n+kDbbzTJ0mj6e4J0KRsnF21HEE5n3LfxQsf1q64b300f8+6bbvOlgRDabNt/1wrKYetDqR+t/5H6P+qANaux6YxNWe14NsGgAP3FPyyjvXxFKwtKbUBM+dtDsSEx4SZhthAkslqejG5th71YLoxXZrMlJt9mrN/xPgZALjP7gm4pcxJ72mMWBpQMd1mfQN3T5tare8p7emPgd18i9eengAJ4rIvk5MzddanJbaW7LZ+s0HTwdqG+Xx+WgK+6LbrBKDJr+mL4IHj1pZZWZ7qtcTMe/b/JfsTGFxd7Es1WY/fmax5f/83WWw2zOw+geBTHGv5IPuweGv33ur/KX4ZXYtfLUay7psZpMaTXfteHt7YL2fuG43ruo0R+zLg96kAVirRDI4bYq+9KVeieJuY2j6kFvzf0r6KDXBOF18ObfcygOW1/EwZGASejK0lQbbn2ORyg5V9um3O3oIng4XJdYEkk8XaXfph4iA9JomkT8Bhsl/A04AAkxk3yNuY0YYaIM3k1JLMBTrb8uabsTAdGr9vgUAml6u/BsLeAJ3Vv+1Ns/G2Okvf7Jt0rFwzuBtj3yaZBH0po9VrfnrNgl0x+wILl/ymXwKp1PP+2QN/xrE3sf+KR9QX7fgCJNnG+iHd/J/2e+0bTn7Mjrg0zL4b/98fZswyPmZutqXsVrZdix3MGZ/yNQ1bWjDfe1naYFkSsmC+iueTSgaPJ2NpBt0SQH6+SficBmfCy2tMEjk9yqlsGjaf7iyZWJ8XWDP9r67tNAhp0vgvYGZ920bxp3KN48re7jMo8ymb453JZfnNcbRA14BAq89AbYAm73PszKcacFoZrsBJO6AMT0nrlwTBp4Tb5DL+8mmX42i+mf20sWFyZ3/XHq6tcxWOoY2JzWLaWJkMTc8WezMePcU+0mo28bS3yGi1nJLXnnh7e/8JEG6fnDlrMXevMRawjZVm6xaz2c5AX8Zy7uu87NvsvclqQKrds/Gn3mwsPmKLvyqA1ZYJ3gRmC45MQuvQSSf7zpeRvVnrfQIfec3uWRBtzt7AmxULFGk4XAqhAbcg3Yy7BbKnYN/08nTN+OB3A1e0hUZn7zfbY5KzYgnncuTVOxMR+crlpMv+kvbTTCGTP3WY/V17vQyEXU/Db/REnzY/afbOfq5rBCnXMmz6SbanDzWd0zYpBx/mEmBdiXn7zh+3p7zs0/rKsbNEZwmf8jwlaYs1Sc9WJkxm9m91Eug/+esFFM23mg2ZXT7Fu2x77btizGMcaDyZzgyAUF7TTwNwb3Tc+PouW1Y4Jm+Bj/H6FOcypr7t51cFsP7sz/5MjcU289pMUwb0FnibQeX1Nn2fxQzvAijNIJMW6fH+06BnICZATZkYaLNf0rtAkhk+AUPWo5Hn044F+a3TZmqS5jXebYYk69j3BmIawLC+G6BInWRfBtxoOzbOScP2Eezy+7ajXDZ+7MuAKksDAC2g0u+ajV9bAZIe79P2mKQsgeWm/axD2g0QJMixpGj8ttISa97fz7nfYk+Wa69ZS+b09fwjSOSG5Es2A99NL1cyb/G1rUg0+VocMT9dXVppYMR8m98NVLTDEOTP7DnrMN81oJU0clwtlnIyguCQ/Vg++15mmuzQzBNoTFlbPm00nvr4VQOsmUGyTef85OBl3SfQMMVA3JbdZJkb7tsnk94TkLkcnQbxNPP21nCSJ0usKc/o/zK8jwQPOiflbwGHumkBieDnCWClLq7AY/xN4T6h1o48Jg9bvy2zPOnoyfGt7ttZlSd6/P8JZKWur6Rt9a0OvzPh5NgQYBJkXTrb9lafiSH9m75gPJg8ORb8bvp/Kk/2xDo5S3XtCbvsss342XiY/mknF8Bh3DM/Mj3ZTB0fRFsMYaHd5vek18DIlfytj2xv/nvZSMszpGljRv21fMP4bP6QbcjzFuaUNotnPvU0JowPWzhmpPHplginNMXn98ugMpk1h6GxtuBogC0H4ukJxr5nHTOIRu+ptMDUgrUZ394zflqQY1/23XTx5sSG8WlBp+mBSZnLJ1dA536vpzGwMc57b4LqFcS4wfaJl4ufFpSzjfkDQdrF69tk3ZKwJT8DIReQ5jKX6cG+t9nB/b78cK8cdWX9Pe05499bu0sab8GHJe0rBrW4Sz/N/i0WPs1M2IZ5xp/Gq42j+dybwwz73Q5imH+08gRGGv9ZWr3Wd7ML8pByWQ5ufmv95vg2mS49fX/I9W9s0/i7cAB97E18/dW+yd32t9hg7/VmCC1Z2akbBvCmYBqPDbY5jO2BWVqcIrfTXU0XlNmup04bD2/KBa7e8GBgLoPUR4s5zpMT2WbzxhP7yOnxlvguAJH9pT7eLoW90RF18hSQSZNLPVnHjrU32gRLDVCkTTIYMhFfY0swlIcFLPFnoQ08JcltY/tFqfsLBDT6pg/j2frbzxYvUld2Si9ptsSZbVshTxkvzd+utsl3q28A1fqjjHktDwGlzaef5g+IM57ajCV5SXoZT9q+teQ19zL+ktIAGHNam5ygjzRw1fprvvBN4qMtzVuuv2ICeaFubQnffPdnlF8+ql/lq3yVr/JVvspX+Spf5S/Gaxr4ZHDNVLQnX3tqsePTLK2fhm5tRuLNU1midZ4My992o374FN1mUuyJ9poKbU847Z7VtVN69sTCPWnX03KTy/pvsyT5Z3sgriWb+Zsn1/mz4+TGD59u2+wj29vT+C+R86qTdW0Wg7qwVxFQ1iafPSGbPbSn2KfZq+TRZhWuGYvtu8WKLZx9yetNx5S32UjyRr1d9974Ro6vvQblLW/tXur7TTx4iiVPr1Bh/GXcbTZ6vS5gbWzb7AtOefjmOlHafK3p7MpP1JHZnM2SvZltsRkx6sH03MbrignXIbMslt+mWN5+ykHXFgP6Amewciy2nyd7/NW+aHR/cNkSlIEkJs2cxuXSXEtm28eTcdGpLz5b0HwCRG3q1ZxuZb8MOad+OcX8xjkZvJqMDKhTMlixjclsQZTOQmdowYqJv+nVkjjth8uDJgv7edLrFYjtOm1s6NsR95SL1w1oUhb+n0E9be0NWEw95vU3wJM0ra7ZZUsYyRN1mjzaEvIFrDN4G+8WRxp9q9NotnFgbLxAL8fnCViZDdLHso/Vi+nPQAdpbr3mn82/qRsu3w0/mSdaorcx4UNJe6UGdb797v/XQRf2mXzaxvwn/0kQmvLbPjfTJ/lp+YC6o5x5nydNv70EckaL/PDhLOMdY9/W3/LEz6f4qZwEWZaomChsViLbX8GKdNco6JSWPBv4uMCVJeB01NwL0MoaRUvkef0y3KdN0wxiTMrGQwuEBoRa0DU9k2ZLnCZn1tmxJbBqgdHqcKybjswWDWAkrxmAWuC3/7NdAgwGF9O79U+d2fVWKPeVwI2nFsAtyRvPTX9sd8nzBEDIX9Pp8tL6N7u4wIbxe9nuJVM7pZX1n2In2z3NWjWfz/of6a/xTXqcmaI+rK98OM2+aGOmP4t5HF+zhaYj2yP1piQwy2s5e0fAwTxEgMw89mQzlLfliynsp81eXTbQ4qodRmi28ql/7LklV6vLa4vwzRCtTUsWdIp0yjSsNqjG+/LXNsqmIZDO20CUfbX/aZhPCbO1awEi67dElvcskVz0DbhYvwSQ1LnNWlBXbZyv5MMgayeQ2E8GAKNNv7DS7tFmr0KbMKDBvkz/LXm9BSXXe5L2f+Mjn87fLtM1UNbsjLbQ4oDpLW3T6FHneb+9+sH6zTpZLwF4yrvbEpIGdWU8mR7sBKY9OL7xIbtuqxgpn8X8/NuHrAUPWe+y84wnOwmQ9bhKQH9/sh/Tuemf8pkdsN6Tv9phq9QZ9ZX6bwCq+X6zHcuzbwEWDySwXtLhSgtz/c8uP/UUYQNBrNOUQ2d6s35LugnQeI/Ju/FDemy/9J/W901GK+zjrbxMqJdO3oAa8n71u/W4/JY6zhmnpG1JiA5n0+lTbA+YrcmTl6RlurbxThpX0CMNBosGLvM+5Sd/xif1yTqNV0seLVFQLkswSdf44HhuST9d0LAA662NtjFp+zAYY1J+0ymXgwxkWDJt/ZJ3jtUlJ2euPppYDGClDLb/pr3GoAEI1slkm3bQlvcJmky/jBGMhUtrv6c/UV72lf+/edA3W2JpscXo2XgZuGtj8sYeGthmv9Zni03fZO+q5Z8WP1gnxzbvt1i2323J/lMArFROAx1bso49qTIp7rWr36SxzstpZdJqTvGmrytIsg0TWAtiV9v8P/lMMEMjTR73qY0JI5NgS7xbl3rkUhZlySlqA0nZR7ZhwLS/5N+Sfc5wtYRkQIT8pGy5rysLAbsFOvLexvEpUD/Rbfpoid9symjZ/6sL6thky7Y2K5gAgvpl8M7+TZ5sZ75vIKnZmQX0liySPu2NAKDxa7oynVm8uMbK/PCNjb0FD82+0ravmRS2v8AX9cxlsuSV9kqgQL1PsVUIArmPlDb2Zj9Xzmv8W1teMx4sVtj9vZZbelq++gYQ22JCy6W2JP/kj4YdPuUMVhZTYEsQqdS2Zk6a+T+DL9ff+bTzFGDyc+sxmbB+1rNrrXD6PcHQFfTT0ZZ+Tn1ficNknT+baUo+qfsGXJpeLgDDJ6PmwBZ4LRCT9wZCjH6OC/+4d4NtyX/rxwqTGXluAKx9fwrgVkwGk6MFyEbz6Z7NaLTl3yuZt+9vwNkULr890b/AlbW1Mc7rHGMCrOzDgGhL/ry37cmPAaw3vpN1s9j//HuaFWE8T7ptzG3G1JKwgYwEWoyLT/nj4o+y2ZiYLHuvxRTmzLZPLftMsPQ0zuTLJgS+v3gg2c82y9Ri9lMOzRWlyz4/DcCi83PQGtCxhG4nipLu26BuIMAM2njKtjmIdE4b2DdBP43dnuCXB74sb48mJz3TO2XZ+qYT1tsAaA7XkuwTTcpNh30CguyPs6CZINsspslpwIx9m94skJH3NgYs5h92365xXEy3xovZrumrJQPSsET+NHVvek7+L0BHf2QiNVDDmVzTS9JOeqZTAyXtPpNH1skEyQemtDvuUUz+mp6e9EddZn3ez2vUm9Gnbi4ft9LiIosBBhtv6rH5m/G0cfYCRM0nLxkp21O+unKf8ZP2RVsxuvSnNnv1XWafsy3/zL6MT94z3T3p6dMtEdKonoyiOYk58dW2OfEUO3FhYCPpWl9MuPzpluSdfFkCsD5a0KGRGsI3XVpiejtumegZLM3Rmgymk+F/ASJ10gBI062NnenYxj9tJGcJ+FSUNKxPbrp/E0Sb3pqNZKK4kprp3/q5/KcFOCsXuErdZP02a5GAOB8gpnAmI4O16ZwJ5drk3Og08GFyWtwy/bJc42B1zRcvuhYfGh3GtCdfzj5sidf0+URz6Vm8NBlyvyd1kjaYD8cZdyz+t5OGHBsDAEnPlnYbuHgbxy8bs4dV2u2lcyuczedY/BZ7J5nr3uadpW3jSP1u27af+lMCrJYYt9gAmaE00GUGaEb1NsFZv5awaCxXQG/8533SI5i55GiJ3Ryd7Z5AEfdGtMDW+mBfWyeXNBJc2UtZbXybDq1f3mvXmkxG70m3SZOBnnSazSdtC9INJNlyVkuel223xM7x2WJLLJd+Muna8g1lz9nN5HXrGWhjeRrjxgP5MzBIX7e+CM4oh/FuiX7rrH1tYsnYZzppNmIbza+Yaf7Y/JYzRU3e1meT33wo713LpZQ1AVkbg/lL0MD9R43vJitLiw8XP42e7X/ddtQ19cI+qGfmrLTD3/4bnfPk7y+ZReW9jAO2L+v6/jPLT/+x57ZhekoGBAv0rG//ZyG4upIzDcyM1oIEeUkn5Vp38msBgXJmXw0UsH4Lxqx/1Wn/cwzegtSnAMJx4jJLBkhb3mOSMiBKvrNO0zn104JJk63pqQEeBjGCUPoHxzqTBGdzmn3vsrIle9sAzv8ZZFlyHFsgpF6Wf6tjiSCvPxUbr+zLErT1nzLQLkwWxrJLtiedXqAt+zSQ205PJg/ZjgdV2nL9xjzzqaeZHtNH85nmlxfAsg38tg+JY9RsIAFE7oedsg+FqW+L/5SVOkkeGmCy+Eh9ZR6y3GpjYLxeeYr6p25+i6XB/GR/TeYL/BFksR11dWGGX/UMViq17RG4guSbwJJ0LBEaENk6DRQ1HrJ8NLg/TRFbsre+LOCTR6tj11sdu9b4Mh7MIVkaeFkdMJAxCLVigYu8Zr2nTczNptiXyZTXLHjZRu5sn0/I1ueTvviE3ZKV8djkvE7kWF9vNppe4Kbp8KldG09et71X7N9mzVg/5b026NtDAkFWi0MtLlgCNdmt8EBJju91iCKTfFsmfwOkqIPU46WL7KPZhfFjAPCpMF4/xYp9iDH+G8BsxeIAgclTnmy8mowXLfabMXR/gux6yNs8snWuMWgAq8UxtttiYPTTvaahJT0WUzQH/mrTAAf5YALbuhYMll9OH/PPaCTvNNwW1Kk/e1rKJ8drSZABszl2A0R5sIBPqyb7BVLbmGxJOWwG4NJVK5Z4klby3JYUrhnRxpMF0RYUroC4fDH5fEQPOZbURdvw3QKXbQBudtfGnHy9LRaM26zdpQfydMlrSc34uvhdWtZvm518SiIpj/1vfvrEd8p60TSgZdeegOjWS+C59n49eJAHxsO3YIt0bRnL9DMxmH6YvkS+bSaHMSj1YX1e8ZYPiIxlCYJIN+k86Zp8Ju/ca/cdS3oZZ3b2ycaSfadNNh+/xuoJO3yKU4R07mswm+JMUQaYWiC0UxKGjs1x2C6vGw2TlSf50gCvRNOCFdutg+WeJm4WN8O0JNgCPu+9AQ8mi41/OiwB1hMYoJ7aOGThE/sTWDC6HIdrjMhb6nr7v8aGwcLs22Tl5tC9x+XHVp5sx3R22YL5sBXqtAXJDPDJZwMKxncL0G9AAnW0n6tXW+5/kt3ks3Fgv1va0owl8/Q3jvFTEiOfbNv4szidftAAx6XDy3fIZ9LP+E5ftT53w3/zQ+PVcsUV92lbNo5ZOHtI4EOaT/mYsYT3qT+T+/sRN6iTN/7B2Pe0DehNPvrzKj/3h3m+ylf5Kl/lq3yVr/JV/gDKT10ibKenrC5Le/pnnbYJk08Mudcg6Wcbo9+eNp5mcfLpsO1fsj1g++SbsvPJZ/9vex/aE8Rey6cGmy1h26YDPgnnDEk7dt9msIwmZTae2xOf1eOSTD7B7lg98ZZ6fLIP6rzVMTq0AeqO9G1vlM1K2F/jqz252vf2RPpGD6zHYvsWTUectW2ljTHbcubZbJntqYc2Zo0X8kB9Zz1esyf9KZwdskMd9Ane54lik4Ntm96oK+4xymVxa9d0vtf5agjTE2eEbCytX5PZ5Hya3WpxxfpInaTM2a/NxLeVgKyXs6w2482xSPvi57fIS4xJlLGdOraYzu/Npqxts9dP8WPP+8mA+EvpZLFXMRiweAJHtt5/DYiBqvb/teHVjp2vQV+yp4GnLAyib2SxehZ88nsz2jeByYK68Xkl4ezf9hVYMLBAmPy+2cORpR3RvsCY6YZJzeTMemzbxsx0zn4ucNWSTtpyS/oX2LtKC5omr9E0nk23b3z96UQj+dpPnt7K5TomIfZ92VKTn3I/JaAnWZJe0jS9M7k2nlvf5IE+adeebDb9pgE0658gku//opwpG/VuOmt286SvpMUJgr2foIZ9G8Did8snTVeUj7H2GyY0qF/TvU2QWM68ABu/k9efWX7qHqwEETTiX1poJO1/C7imbBpNC04N7OR9czZrb8efSa/RtHeLEGQ1A2tOZMeqLRmbbC24mhxZ78n4LQhbgv8lhUfRN6A2GbMvgqvkdcsb2UyHV3Az+k8JpF1/Sq6W4K79hCw2A3HpK+kZD/nJhGs0KBMBT9uQSzpvkmR+5mGQxl/j9219yraflpj4qpwn/V86uHjjHi4Dacaz7aVpQDHrp6xNjgtgWR2+DqblARbj+erXYsqbWLmlgay1b/ZvoM0eID4aVy0+fjt81/SZeWuL4QOCZ+PBdPL7KD91BivBhA2qDfKboGltrgBox7HNuVnHElIzkreJMw2KTsm2CZZWBtOf0Wz1yctHHWqP5C5v7UmFbU1e3qdcyaMtUbBeAw15L9tnIOXyUgNYRp+8tFdxMFBc9OkLpEWwY3xdgOUKQi3pXyDAAnnySFuxzbQmgwXpi8b+5TKFHZC49Gb6aTGGid5AWfpxAx+Z+Jo9XDq6kk3zrwZ+SJvX7EGH+vlIghsaG1PaCcKlmbnkDVjJ742ujeEVoywGPQEsi1ntGvuiTVw29lRsvFMGHpKwXGZ5M+v8pvy4+X4+xb20gyu3m/4uOT8VwLIlvPzk9f1uxmZGtvcNZOS9dJ7r5Wekm4n3bQDewH6BLMr3FKCNrxY0OU3caDXnYf/s+3pnFGVsYIf909lMZnPKK/k3h2MwsCCUrOSIAAAQIUlEQVRgSZjjlEk8C2dILMCQdpOx6aO9s+htMbtpAS8BSvJDUPAUgNMv2tIqdXwFVn5PwJnJN2mQP7PByw4aP83Wsv7lt/v/AIynVwWYT9OOs2/jKf/PmbxMjpcfJx3zX46vxe0GSmgHBhqszeUHFqONzhY7gU0bMZvJutn2sl+TyV5Rk75uOiGdpzjMe+wv79u9fH2PvUbmt2XrBn1hwXLywFc+mA6frn+0zq/6NQ3mNBf44GDkDxovvS2WvPJaDiINIB2zAYarXIHDnNDAxbUB3j4ZPOlslrC28D1IJs8WSwZ2zcarJaG9RxDYNpZTTy3BGR+UKV9jYYHP6DegcAGKBFj70j2+O8d0boHOniQZbC1hUAcGSHIsnpJN8sq9ai3Y0xaa3djSgAXWllR5Pf3cgFzTvSWRrGNjlTqymMRiiT0/+T31lPdpF1wZaHyaHtNOuem40SEYYpzjMumlE3uNgMUZ3rMkTFtiLDKfyNjSVjam5ItDW+xtMdzuUZ783x4yuKXEeGnxxSYdSIevOWl8Z/ulvTbEOP5b/BZr8kk7TP0yV1u8zv+fYt2nBlhUoBmCJTV7grDpcyZiGuY6/JtA1QKx3TNa6QgmI5PilbRb3/akZjLndRsP6q8BTTNS7jExcGNBOWWgY/PHdtl3S2BPfJjM1CmTa7MH02vaVvbD2av9biDDylMSJm3WybrtNJUt29OHml7a2LBc4/EUMM2GmlwXSDIazR9Z5ylIXzxf4M9kYnuzD/LZVgkaf2ZHrG9jZjxYe157OtVp9tl4T/qcISZQoiyk02IktztcY2V0GI/y2qUv2goLD0MZT1fbq0576DQdMT/ntQS8vy1vcrdxow5sheHSPXMT9XrFg0/xY88JjGzZpAWlLTmgT4H0CvaNv+0jP5P+FWyMb/L+EZ5MjgawLDhRrwR7OR6UicaZ9PZ7W1tv42h8US/XGLzVUQNgLKlH2tPSIY/tODF5sbG3QNv4NLu59MV+CR6yXxuzDHKkdyV2o0G+TP4pBujyXgumH7ER7rfK9s1OLJg3cNUC9rWnk/8nHaP1xqc4rk90LkBlfb6JY2Y/U3iAIOtnsXzQxqjZeivmB42Plo/exJLkbcoT6GWueMoPzRepgwukWZ0mexuLxs/qOen+JrYD5KSDyZy+8ZG8nvUYo83XPuUMVgbUN2vm3I9AQ70MnkbXHGDvW/kIILLEyQRlAdsCeJvdynrksSXcp2BxBdk3OngzDpxdsJMvtjxqAZEJiX3QyTjlT5uw5GX9JU9cKmt6earT2jVZW4JlMLVl1raf5yl5770ci6clF6OT9LjXwmz82nez8lwBM+2MT/zWz1XSZuw769LnGriyvSV2IvXNpnz6gC0bM4aa72c71rmS3ZW0qXPGStOlycs6LYabTV9AmzySHv+3fbgEr6T7UT+5eMixvh5EKNcbsPQmx17ysc82JhZjrFCvVw5lbvlI3P00v0XYkOsUGmEz+suxchrajOvqz5xgab0xAANWDOaUizKRlk2Zb13OAOR9M3wz5Ctom3EnT+1+0rIkYkGICaftM6BN0J6yfp4ceyOXjTtlbsHKdGugptk87dVmN5Jm6rQltRwDs1PqmH2lrhKg5GZWAs42TqkT2gCBTvpK8kSgdy1lZ5vUMX8Eu8Uh888LYFj/LW5cCZX2ShtmMsn/L5CQtC/emixPdcwfWr03OjM9vwEGLXFfYKHFkFZM97ac2Gzs4iN5tViTeaHFXGtHPs3m7UGk+daWtlWn+dR36IQ+Sh1Z7GHseLJP0/WnXSJsg5Z18lpLdmZEDPZc381iiZf0jEerl3R2A/MFBIx+0mVgYBA1gGO6pmMykTbZTM6sl/T3BXwW4E0PdI6sl7w+AazG8+pnZ0lytsQCbrM5K20T7RUgyJslgBaQn5Z8W+C+knrWHxn4e2oml/H5pLMma/JIEMWncspigMHsyfz9ij9Nx+bzT/1e+m7gIL8T8F58cqyYsGgPF23ybvJlv3kv29oMwi+xF/LEa2YXZm+ckcv+9jMf7Ki7xq/ZIuNVzsq3jeNZl/fbWLX4YPySnh3CyFhr8Yi03owV9fMd/ndtes/2OR55YML6Mv9pOenTASxOkV+BwBxlr+/gbB0uA9jSEGm3fvM7B9j2B+T9jyROo5PlcizTD4Fl1r+CtYE+c/4WnPL+lTysfQMQHBtrz/6uWR/yb/83MJB927KmyWmJp9kt5TRbYR9XwGegYd9pk21sLHGz7zZOWcfsyhK8Bd3ma6bv1Dt9nDy1fRgpR44Ll63n2gDStofEQJ0lpBZ3rk3VNmO5/CzfaVtt24XxeSVHLqE+gZ7U/fJky6HGE8etxda8d/FpemDc2FNvqTsm+dbW9MCZRvq95Q0DAU/+Tllbe8YFi/mcuUqdEKBufZsVZ7z7Vh6Ok++l13zoqZAvA7n5+WkBlg0qg+4T6KFy0jBsb8xFi/1avQwY85en25oDLW0rlvB534yy6ZF8NtlbIsyk1vSVPBiQSFpvAiED7jpxBqdLB1fS3aTYEkIDgy1YPtlDS+T2P/X9ZiMm7Z4HEt7wTb3nWGbSS7majrOPfdN9W5Jv9mj6TP4IsMwmchxN/5aoW8Dfkv1wGSXlylk/JufkrS21pJ3abMJlTwReljybrz8lbJbs69pzl/GUOqBdMPFaXH6KW81vzFcvYHHt9Wt657gwLjZg1tqYPba+jB519rSfkONAXXG86c9cum62e+W4Lesj7aErv/Ph0HCA2Yvlmk//JndTCK9v/SkNTLxJBubAT7xdCSIT+N7nmrAFftKgc1wBweQhjwxqLTgtv9nGjI4GbuNkyYd8Up6sT6NfmqzL8aHOGt8fLSZnymB8XIDqyaaM/iXHG9mufmy8891cyfPVV/PXxk+TjzbW/jcQldcI8FJ+8vo0g8JP2imTu/Ga96/Eu/xcSfkCRQZWrN9G3/63MWjgiMmWdJMHyml5YOtaDGPMJx36qNHN8SRfpG1j9hHg8xFfbfnrqa9mYwQedq/F1daH8Wn+ecWfKzfuvfageeni4j/bcB/wzyoff/XzV/kqX+WrfJWv8lW+ylf5izGD9Ud/9P/tik8ley1LQ/OcJbI6ScNmIt48XVwbEvO+lZ2JaUuf7WnENugn329otSe8NrVq9O1pIZ8Kec9k+ehTnT0h53XK9fYobpPpmg0zWVmX+xWo26u99XnNYth101Pa5DUdnr4znzOLdc1ImT218jSblZ9v6FxxgLOdbE8dk27zB85wjH52po+boXMvysXvfjd+Ljtrp2/zz/bIWOHycpuxSf3Svnk/dUm59xrf6s1ToKaDNn7XTN7So2xNDspgs2VtjGwPMHlssybm65f9kGf6aGvb7KDJzLYcm7ZndPVBGanD38gKRdMH9WhxvOUt9kuaucT/qQDW7tmwIP8m+bIsLQMimQhtMC9w1vbBZB+NFgNgM2C22UFPY0nDZjAwOtwgy/ucek3axhudOhNJ0rqc/UryDEwM/smP6Zg851IOQVIbK7OTBn6eQNBT+Qj45TiSjpUn4NKCY9pq7oHKdk+bpo2/p8Ro9dLmL9pv+bj0mHXoJ/lDwwsQ0rcJqlpcS5rNrhpf63dP9ez/ZruZoN+CKwPrTPpNHsac7TdPHlMnF1A0H6HsJkvKQbu/ZKM9Zq5h7KMumQfIJ8tlp8lPA38t11zF7JHAywAWcwHtvwE22gQ3yvP+FqP31peYuz4Sy361e7CyXGi2KTCDHdvyKYagxxzoKTmRt3QGOsYbQ28Jx+TK+/nZ+t7CjZ9GtyWgBmj4dJ80rP4VTNpsSQNGrJvBK4MoZSOd1Qv3ATanNpBKHlo78myAP4PNm6PiHDPruwGpC2BxjEln+c+2BmIu4MSEY4nC+rVE8qZd9mkJtiXz5s8NlJCf7e8pkBsIy+/NL98kbNPBfmeSaWCDbTmOU1qyNbrUo41fu/dEizwnKLZZuOzTACPlIdhoev7o2DDuWJ4xHZnszUavfNpsytpep/0YS5/ufS85hON07dN7Ktlvrix9SoBlBm5B/Y0htUCwtCyRpaOZYbLfpGkBN5MNnfAp0T3x346Ts78MOjkFu8XAwJbryHzybnxfPJlMVprTWNJuILI52ZPzteSY+jRHTjsy+SyA5b0L1PCklgUd6qYFOcpKG73GkXJcib7xmzTyu4HY9SXTD+s9Aarkn2P5lBjY3/+nvXPbbRyGgWj3/z/aizwQGAxmKLfQS4tzgCCJK8sUxZttOX0W22t9b3r3cTXb8bat/7RPwm12K4ZSv66bk+3o/h4nU9+6/6m4Sj+Z4WPy9+TTSXeqo9RnetJ0u9ux6WYrdLxNu7qejqWFRIrlpzyafGF7yjfJNTJvy2iSL2y2fCqoUozSv71dUvLrfwerOY9+1klLj19uAUoNK93S0klPTywmNMkko0+G3Ma5BeKWfH2s7Sxg2vj95VNR1JJaIgXet8VMC+D6PRWopyLKg3kqjL5zLNVpOtPxZNWKGn1P9t4KCB3LRioCUhHVAvHs55833TWbbPIlWf0kyJPC9N38qsnUihd/UqzFlGST8/J/Tq/J3k9imr70+5YQ0pjSrX33v1TQbWxxS4/jfXocdjm2eJ3sa1tP5jrQn8lJbZsO021Otbv0NGfSa/KvdgWqFXauxxRH01V83c9zXZJZdZ6Om3TnMrkfzXZdl5j0n3LJU3w72Ufqz2VKY2s+lX677k/+L8ITGrBmTdK8/B5uMkA3vNk27WZb+92lTa5WUCRjTH93WbXv70y6ttXk4WeKp+NvQaIlby8wtzG7zKc23nZL0G2c/nrTzrdrQHc5NBinAs+PtxU3KsOb4sb79uSr/Xi7Nh9+bJUrjU2Pm2Ta5nRLPG2/lgBOx/SArAXzZ7uvd/Q+XQ/ah8af5AMp3ni7luTaWPz28YxF5U7+73Ew2byPVfuYGJn8b+vH14N+3k/+pOPwMUxC9ytMm0wfRm/6Mxuqv+YrKe/oZ9XRVlz535ucSX/eZ2IrNrS/N9tTDGiFkK5LVJ3qXPrFjOcwV00u7y/50inWz+ck259ag3WaPG07k5euNLnz+r7NqFtAcRm8j5S82qR6AP6gTp4mtxlNc940LpXHk/fIkILJliw9OPul1m0xYgrYSV61hdSXz3Nbq9Tm/JQEk000XbuczaZTkejbm11v+FiSLlpATjbt27cknMa/yZxs3W+p+MvlTd/b+FofniznbNbna7NjLV7Uv2fbNn9tfCpfG3OSLx3Xx5/s9+0Toj6etu5ms9Xk49sTfpuORt/z0n11kXSKXSk5J937cg+1l+QTSYdDi4l6rBSX3FZdXu9Px3ea15Yb2ny1E2i3wzaX7vfP4t/p3du7baf4qm2bzt7G2Zv8e1rEAgAAAIAfwQ+NAgAAAFyGAgsAAADgMhRYAAAAAJehwAIAAAC4DAUWAAAAwGUosAAAAAAuQ4EFAAAAcBkKLAAAAIDLUGABAAAAXIYCCwAAAOAyFFgAAAAAl6HAAgAAALgMBRYAAADAZSiwAAAAAL7u8h8DjNIjyWnrswAAAABJRU5ErkJggg==' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(figsize=(6, 6))\n",
     "ax.imshow(image, cmap=\"gray\", interpolation=\"none\")\n",
@@ -224,16 +277,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "24ffc973",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "DevFailed",
+     "evalue": "DevFailed[\n    DevError[\n        desc = autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        origin = Traceback (most recent call last):\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\server.py\", line 1790, in wrapped_command_method\n                return get_worker().execute(cmd_method, *args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\green.py\", line 110, in execute\n                return fn(*args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\Microscope.py\", line 212, in acquire_images\n                unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, [0.0, 0.0, 1.0, 1.0])\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\ThermoMicroscope.py\", line 192, in _acquire_stem_image_advanced\n                adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope\\_acquisition.py\", line 107, in acquire_stem_images_advanced\n                call_response = self.__application_client._perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope_client.py\", line 242, in _perform_call\n                call_response = self.__endpoint.perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_core\\orc\\engines.py\", line 206, in perform_call\n                raise api_exception\n            autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        reason = PyDs_PythonError\n        severity = ERR\n    ],\n    DevError[\n        desc = Cannot execute command\n        origin = class CORBA::Any *__cdecl PyCmd::execute(class Tango::DeviceImpl *,const class CORBA::Any &) at (C:\\gitlab-runner\\builds\\ehTiiTbyF\\4\\tango-controls\\pytango\\ext\\server\\command.cpp:87)\n        reason = PyDs_UnexpectedFailure\n        severity = ERR\n    ],\n    DevError[\n        desc = Failed to execute command_inout on device asyncroscopy/microscope/default, command acquire_images\n        origin = virtual DeviceData Tango::Connection::command_inout(const std::string &, const DeviceData &) at (/Users/runner/miniforge3/conda-bld/cpptango_1758200193404/work/src/client/devapi_base.cpp:2029)\n        reason = API_CommandFailed\n        severity = ERR\n    ]\n]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mDevFailed\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m scan.dwell_time = \u001b[32m1e-6\u001b[39m\n\u001b[32m      2\u001b[39m scan.imsize = \u001b[32m512\u001b[39m\n\u001b[32m      3\u001b[39m scan.scan_region = [\u001b[32m0\u001b[39m, \u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m]\n\u001b[32m      4\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m keys = json.loads(microscope.acquire_images([\u001b[33m\"HAADF\"\u001b[39m, \u001b[33m\"BF\"\u001b[39m]))\n\u001b[32m      6\u001b[39m images = []\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;28;01min\u001b[39;00m keys:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/device_proxy.py:358\u001b[39m, in \u001b[36m__get_command_func.<locals>.f\u001b[39m\u001b[34m(*args, **kwds)\u001b[39m\n\u001b[32m    357\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mf\u001b[39m(*args, **kwds):\n\u001b[32m--> \u001b[39m\u001b[32m358\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcommand_inout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/green.py:231\u001b[39m, in \u001b[36mgreen.<locals>.decorator.<locals>.greener\u001b[39m\u001b[34m(obj, *args, **kwargs)\u001b[39m\n\u001b[32m    229\u001b[39m green_mode = access(\u001b[33m\"\u001b[39m\u001b[33mgreen_mode\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    230\u001b[39m executor = get_object_executor(obj, green_mode)\n\u001b[32m--> \u001b[39m\u001b[32m231\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/green.py:121\u001b[39m, in \u001b[36mAbstractExecutor.run\u001b[39m\u001b[34m(self, fn, args, kwargs, wait, timeout)\u001b[39m\n\u001b[32m    119\u001b[39m \u001b[38;5;66;03m# Synchronous (no delegation)\u001b[39;00m\n\u001b[32m    120\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.asynchronous \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.in_executor_context():\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    122\u001b[39m \u001b[38;5;66;03m# Asynchronous delegation\u001b[39;00m\n\u001b[32m    123\u001b[39m accessor = \u001b[38;5;28mself\u001b[39m.delegate(fn, *args, **kwargs)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/connection.py:72\u001b[39m, in \u001b[36m__Connection__command_inout\u001b[39m\u001b[34m(self, name, cmd_param)\u001b[39m\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__Connection__command_inout\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, cmd_param=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m     39\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     40\u001b[39m \u001b[33;03m    command_inout( self, cmd_name, cmd_param=None, __GREEN_KWARGS__) -> any\u001b[39;00m\n\u001b[32m     41\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m     70\u001b[39m \u001b[33;03m        For commands with a DEV_STRING input argument, invalid data will now raise TypeError instead of SystemError.\u001b[39;00m\n\u001b[32m     71\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m     r = \u001b[43mConnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcommand_inout_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmd_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     73\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(r, DeviceData):\n\u001b[32m     74\u001b[39m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/asyncroscopy/.venv/lib/python3.12/site-packages/tango/connection.py:112\u001b[39m, in \u001b[36m__Connection__command_inout_raw\u001b[39m\u001b[34m(self, cmd_name, cmd_param)\u001b[39m\n\u001b[32m     86\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     87\u001b[39m \u001b[33;03mcommand_inout_raw( self, cmd_name, cmd_param=None) -> DeviceData\u001b[39;00m\n\u001b[32m     88\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    109\u001b[39m \u001b[33;03m    For commands with a DEV_STRING input argument, invalid data will now raise TypeError instead of SystemError.\u001b[39;00m\n\u001b[32m    110\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    111\u001b[39m param = _get_command_inout_param(\u001b[38;5;28mself\u001b[39m, cmd_name, cmd_param)\n\u001b[32m--> \u001b[39m\u001b[32m112\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__command_inout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[31mDevFailed\u001b[39m: DevFailed[\n    DevError[\n        desc = autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        origin = Traceback (most recent call last):\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\server.py\", line 1790, in wrapped_command_method\n                return get_worker().execute(cmd_method, *args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\tango\\green.py\", line 110, in execute\n                return fn(*args, **kwargs)\n                       ^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\Microscope.py\", line 212, in acquire_images\n                unique_ids = self._acquire_stem_image_advanced(scan.imsize, scan.dwell_time, detector_names, [0.0, 0.0, 1.0, 1.0])\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\asyncroscopy\\ThermoMicroscope.py\", line 192, in _acquire_stem_image_advanced\n                adorned = self._microscope.acquisition.acquire_stem_images_advanced(settings)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope\\_acquisition.py\", line 107, in acquire_stem_images_advanced\n                call_response = self.__application_client._perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_tem_microscope_client\\tem_microscope_client.py\", line 242, in _perform_call\n                call_response = self.__endpoint.perform_call(call_request)\n                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              File \"C:\\Users\\Supervisor\\Documents\\GitHub\\asyncroscopy\\.venv\\Lib\\site-packages\\autoscript_core\\orc\\engines.py\", line 206, in perform_call\n                raise api_exception\n            autoscript_core.common.ApplicationServerException: An unexpected error occurred in the application server.\r\n            Scanning detector 'BF' not found.\n        reason = PyDs_PythonError\n        severity = ERR\n    ],\n    DevError[\n        desc = Cannot execute command\n        origin = class CORBA::Any *__cdecl PyCmd::execute(class Tango::DeviceImpl *,const class CORBA::Any &) at (C:\\gitlab-runner\\builds\\ehTiiTbyF\\4\\tango-controls\\pytango\\ext\\server\\command.cpp:87)\n        reason = PyDs_UnexpectedFailure\n        severity = ERR\n    ],\n    DevError[\n        desc = Failed to execute command_inout on device asyncroscopy/microscope/default, command acquire_images\n        origin = virtual DeviceData Tango::Connection::command_inout(const std::string &, const DeviceData &) at (/Users/runner/miniforge3/conda-bld/cpptango_1758200193404/work/src/client/devapi_base.cpp:2029)\n        reason = API_CommandFailed\n        severity = ERR\n    ]\n]"
+     ]
+    }
+   ],
    "source": [
     "scan.dwell_time = 1e-6\n",
     "scan.imsize = 512\n",
     "scan.scan_region = [0, 0, 1, 1]\n",
     "\n",
-    "keys = json.loads(microscope.acquire_images([\"HAADF\", \"BF\"]))\n",
+    "# think we can delete this from the \n",
+    "scan.haadf = True\n",
+    "scan.bf = True\n",
+    "\n",
+    "keys = json.loads(microscope.acquire_images(detector_list = [\"HAADF\", \"BF\"]))\n",
     "images = []\n",
     "\n",
     "for key in keys:\n",
@@ -275,10 +349,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e8cc27a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiled key  : stem_image_HAADF_20260528T074825597771.tiff\n",
+      "Metadata   : {'ImageWidth': 512, 'ImageLength': 102, 'BitsPerSample': 16, 'Compression': 1, 'PhotometricInterpretation': 1, 'StripOffsets': [15750], 'RowsPerStrip': 102, 'StripByteCounts': [104448], 'PlanarConfiguration': 1, 'ResolutionUnit': 3, 'FEI_TITAN': '<?xml version=\\'1.0\\' encoding=\\'utf-8\\'?>\\n<Metadata xmlns:nil=\"http://schemas.fei.com/Metadata/v1/2013/07\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\">\\n  <Core>\\n    <Guid>fbb5dabc-1c93-4d89-a747-087cca7234de</Guid>\\n    <ApplicationSoftware>AutoScript TEM</ApplicationSoftware>\\n    <ApplicationSoftwareVersion>1.15.0.484</ApplicationSoftwareVersion>\\n  </Core>\\n  <Instrument>\\n    <ControlSoftwareVersion>3.21.1</ControlSoftwareVersion>\\n    <Manufacturer>FEI Company</Manufacturer>\\n    <InstrumentClass>Titan</InstrumentClass>\\n    <InstrumentModel>Spectra</InstrumentModel>\\n    <InstrumentID>4018</InstrumentID>\\n    <ComputerName>TITAN52340180</ComputerName>\\n  </Instrument>\\n  <Acquisition>\\n    <AcquisitionStartDatetime>2026-05-28T11:48:25Z</AcquisitionStartDatetime>\\n    <AcquisitionDatetime>2026-05-28T11:48:25.5877447Z</AcquisitionDatetime>\\n    <SourceType>XFEG</SourceType>\\n  </Acquisition>\\n  <Optics>\\n    <Apertures>\\n      <Aperture>\\n        <Number>1</Number>\\n        <Name>C1</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>0.002</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>2</Number>\\n        <Name>C2</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>7e-05</Diameter>\\n        <Index>1</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>3</Number>\\n        <Name>C3</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>Cicular</Type>\\n        <Diameter>0.002</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>4</Number>\\n        <Name>OBJ</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>None</Type>\\n        <Diameter>0</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n      <Aperture>\\n        <Number>5</Number>\\n        <Name>SA</Name>\\n        <MechanismType>Motorized</MechanismType>\\n        <Type>None</Type>\\n        <Diameter>0</Diameter>\\n        <Index>0</Index>\\n      </Aperture>\\n    </Apertures>\\n    <GunLensSetting>785.380554</GunLensSetting>\\n    <ExtractorVoltage>3600.03662</ExtractorVoltage>\\n    <AccelerationVoltage>200000</AccelerationVoltage>\\n    <SpotIndex>7</SpotIndex>\\n    <C2LensIntensity>0.194291203</C2LensIntensity>\\n    <C3LensIntensity>0.35150091</C3LensIntensity>\\n    <C1LensIntensity>-0.451997455</C1LensIntensity>\\n    <ObjectiveLensIntensity>0.823973011</ObjectiveLensIntensity>\\n    <IntermediateLensIntensity>0.0603362652</IntermediateLensIntensity>\\n    <DiffractionLensIntensity>0.191390786</DiffractionLensIntensity>\\n    <Projector1LensIntensity>0.2809157</Projector1LensIntensity>\\n    <Projector2LensIntensity>0.910340794</Projector2LensIntensity>\\n    <LorentzLensIntensity>0</LorentzLensIntensity>\\n    <MiniCondenserLensIntensity>0.343435914</MiniCondenserLensIntensity>\\n    <BeamDiameter>0</BeamDiameter>\\n    <BeamConvergence>0.0300123314</BeamConvergence>\\n    <ScreenCurrent>0</ScreenCurrent>\\n    <LastMeasuredScreenCurrent>2.42726296e-10</LastMeasuredScreenCurrent>\\n    <IlluminationIntensityNormalized>0.194291203</IlluminationIntensityNormalized>\\n    <FullScanFieldOfView>\\n      <X>3.27940413e-08</X>\\n      <Y>3.27940413e-08</Y>\\n    </FullScanFieldOfView>\\n    <BeamTilt>\\n      <X>4.05897982e-18</X>\\n      <Y>-6.81691992e-18</Y>\\n    </BeamTilt>\\n    <ImageShift>\\n      <X>0</X>\\n      <Y>0</Y>\\n    </ImageShift>\\n    <BeamShift>\\n      <X>0</X>\\n      <Y>0</Y>\\n    </BeamShift>\\n    <SampleTiltCorrectionOn>false</SampleTiltCorrectionOn>\\n    <Focus>2.60934069e-05</Focus>\\n    <STEMFocus>2.60934069e-08</STEMFocus>\\n    <Defocus>2.60934069e-08</Defocus>\\n    <NominalMagnification>2560000</NominalMagnification>\\n    <HighMagnificationMode>None</HighMagnificationMode>\\n    <OperatingMode>STEM</OperatingMode>\\n    <TEMOperatingSubMode>None</TEMOperatingSubMode>\\n    <ProjectorMode>Diffraction</ProjectorMode>\\n    <EFTEMOn>false</EFTEMOn>\\n    <ObjectiveLensMode>HM</ObjectiveLensMode>\\n    <IlluminationMode>Probe</IlluminationMode>\\n    <ProbeMode>Nanoprobe</ProbeMode>\\n    <CameraLength>0.091</CameraLength>\\n  </Optics>\\n  <StageSettings>\\n    <StagePosition>\\n      <X>-4.0947825e-05</X>\\n      <Y>-2.652768e-05</Y>\\n      <Z>-8.998404e-05</Z>\\n      <Tilt>\\n        <Alpha>-3.136e-06</Alpha>\\n      </Tilt>\\n    </StagePosition>\\n    <HolderType>SingleTilt</HolderType>\\n  </StageSettings>\\n  <ScanSettings>\\n    <DwellTime>1e-06</DwellTime>\\n    <ScanSize>\\n      <Width>512</Width>\\n      <Height>512</Height>\\n    </ScanSize>\\n    <ScanArea>\\n      <X>0</X>\\n      <Y>204</Y>\\n      <Width>512</Width>\\n      <Height>102</Height>\\n    </ScanArea>\\n    <MainsLockOn>false</MainsLockOn>\\n    <LineTime>0.000779</LineTime>\\n    <LineIntegrationCount>1</LineIntegrationCount>\\n    <LineInterlacing>1</LineInterlacing>\\n    <FrameTime>0.081047</FrameTime>\\n    <ScanRotation>0</ScanRotation>\\n  </ScanSettings>\\n  <VacuumProperties>\\n    <VacuumMode>Ready</VacuumMode>\\n  </VacuumProperties>\\n  <Detectors>\\n    <ScanningDetector>\\n      <DetectorName>BF-S</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>25.935</Gain>\\n      <Offset>0</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0</Begin>\\n        <End>0.029320892</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <ScanningDetector>\\n      <DetectorName>DF-S</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>false</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>21.75261</Gain>\\n      <Offset>0</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0</Begin>\\n        <End>0</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <ScanningDetector>\\n      <DetectorName>HAADF</DetectorName>\\n      <DetectorType>ScanningDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>true</Enabled>\\n      <Gain>31.1406715</Gain>\\n      <Offset>-1.752</Offset>\\n      <CollectionAngleRange>\\n        <Begin>0.0728785469</Begin>\\n        <End>0.2</End>\\n      </CollectionAngleRange>\\n    </ScanningDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG21</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>0.785398163</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.36</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG22</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>2.35619449</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.17</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG23</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>3.92699082</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>24.35</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <AnalyticalDetector>\\n      <DetectorName>SuperXG24</DetectorName>\\n      <DetectorType>AnalyticalDetector</DetectorType>\\n      <Inserted>true</Inserted>\\n      <Enabled>false</Enabled>\\n      <ElevationAngle>0.31415927</ElevationAngle>\\n      <AzimuthAngle>5.49778714</AzimuthAngle>\\n      <CollectionAngle>0.7</CollectionAngle>\\n      <Dispersion>10</Dispersion>\\n      <PulseProcessTime>3e-06</PulseProcessTime>\\n      <ShutterState>Closed</ShutterState>\\n      <OffsetEnergy>-250</OffsetEnergy>\\n      <ElectronicsNoise>25.46</ElectronicsNoise>\\n    </AnalyticalDetector>\\n    <ImagingDetector>\\n      <DetectorName>BM-Ceta</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Binning>\\n        <X>2</X>\\n        <Y>2</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>0</X>\\n        <Y>0</Y>\\n        <Width>2048</Width>\\n        <Height>2048</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.5</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"PostSpecimen\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n    <ImagingDetector>\\n      <DetectorName>EF-CCD</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Binning>\\n        <X>2</X>\\n        <Y>2</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>0</X>\\n        <Y>0</Y>\\n        <Width>2048</Width>\\n        <Height>2048</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.5</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"PostSpecimen\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n    <ImagingDetector>\\n      <DetectorName>Flucam</DetectorName>\\n      <DetectorType>ImagingDetector</DetectorType>\\n      <Gain>0.7</Gain>\\n      <Binning>\\n        <X>1</X>\\n        <Y>1</Y>\\n      </Binning>\\n      <ReadoutArea>\\n        <X>256</X>\\n        <Y>256</Y>\\n        <Width>512</Width>\\n        <Height>512</Height>\\n      </ReadoutArea>\\n      <ExposureTime>0.0064</ExposureTime>\\n      <Shutters>\\n        <Shutter type=\"Electrostatic\" position=\"None\"/>\\n      </Shutters>\\n      <DarkGainCorrectionType>DarkGain</DarkGainCorrectionType>\\n    </ImagingDetector>\\n  </Detectors>\\n  <BinaryResult>\\n    <Detector>HAADF</Detector>\\n    <PixelSize>\\n      <X unit=\"m\" unitPrefixPower=\"1\">6.40508648e-11</X>\\n      <Y unit=\"m\" unitPrefixPower=\"1\">6.40508648e-11</Y>\\n    </PixelSize>\\n    <Offset>\\n      <X>-1.63970214e-08</X>\\n      <Y>-3.27940408e-09</Y>\\n    </Offset>\\n    <DetectorIndex>5</DetectorIndex>\\n  </BinaryResult>\\n  <CustomPropertyGroup>\\n    <CustomProperties scope=\"\">\\n      <CustomProperty name=\"Detectors[SuperX1].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].IncidentAngle\" value=\"0.09459684\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].DistanceToSample\" value=\"12.42\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].CommercialName\" value=\"Super-X G2\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].DetectorConfigID\" value=\"3101e35a-08ec-4b3d-9108-6aa5d0e49281\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C1].Name\" value=\"2000\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C2].Name\" value=\"70\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[C3].Name\" value=\"2000\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[OBJ].Name\" value=\"None\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Aperture[SA].Name\" value=\"None\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"StemMagnification\" value=\"2550000\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Optics.MonoSpotSize\" value=\"&lt;=11\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[EF-CCD].CommercialName\" value=\"Continuum 1065\" rawdatatype=\"String\"/>\\n      <CustomProperty name=\"Detectors[EF-CCD].ElectronCounted\" value=\"False\" rawdatatype=\"Boolean\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX1].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX2].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX3].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].SpectrumBeginEnergy\" value=\"120\" rawdatatype=\"Int\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].PulsePairResolutionTime\" value=\"5E-07\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].BilatThresholdHi\" value=\"0.005039\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].KMin\" value=\"120\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Detectors[SuperX4].KMax\" value=\"180\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A11\" value=\"1\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A12\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A13\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A21\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A22\" value=\"1\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"Scan.ScanTransformation.A23\" value=\"0\" rawdatatype=\"Double\"/>\\n      <CustomProperty name=\"IlluminationIntensity\" value=\"0\" type=\"double\" rawdatatype=\"Double\"/>\\n    </CustomProperties>\\n  </CustomPropertyGroup>\\n</Metadata>'}\n",
+      "Image shape: (102, 512)\n",
+      "Image dtype: uint16\n"
+     ]
+    }
+   ],
    "source": [
     "scan.Activate([\"haadf\"])\n",
     "scan.dwell_time = 1e-6\n",
@@ -298,10 +383,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "6418befe",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f72552e843f2413e9f92507fce6f4937",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(figsize=(6, 5))\n",
     "im = ax.imshow(cropped_image, cmap=\"gray\")\n",
@@ -310,6 +421,14 @@
     "plt.colorbar(im, ax=ax, fraction=0.046)\n",
     "plt.tight_layout()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52350cf1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/scripts/run_servers.py b/scripts/run_servers.py
index 698ae79..90e9ebc 100755
--- a/scripts/run_servers.py
+++ b/scripts/run_servers.py
@@ -77,6 +77,9 @@ def pid(self) -> int:
     def running(self) -> bool:
         return self.process.poll() is None
 
+# TODO: Break out alternate configs into .yaml files or somthing
+# TODO: ex. UTKSpectra300.yaml, DigitatlTwin.yaml, WDTwin.yaml
+# TODO: --debug flag where all the terminal outputs of the servers show in this termainl (or are saved to file)
 
 MICROSCOPE_MODES = {
     "real": ("ThermoMicroscope", "asyncroscopy.ThermoMicroscope"),
diff --git a/tests/conftest.py b/tests/conftest.py
index 4bed298..0916d02 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -194,22 +194,22 @@ def thermo_proxy(tango_ctx):
 @pytest.fixture
 def patched_single_image(monkeypatch: pytest.MonkeyPatch) -> None:
     """
-    Patch ThermoMicroscope._acquire_stem_image so acquire_scanned_image() works
+    Patch ThermoMicroscope._acquire_scanned_image so acquire_scanned_image() works
     without AutoScript/hardware.
     """
-    def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list):
+    def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list = ["haadf"], scan_region: list[float] = [0.0, 0.0, 1.0, 1.0]):
         # Deterministic image makes tests stable
         arr = np.arange(imsize * imsize, dtype=np.uint16)
         return FakeAdornedImage(arr.reshape(imsize, imsize))
 
     monkeypatch.setattr(
         ThermoMicroscope,
-        "_acquire_stem_image",
+        "_acquire_scanned_image",
         fake_acquire,
     )
     monkeypatch.setattr(
         DigitalTwin,
-        "_acquire_stem_image",
+        "_acquire_scanned_image",
         fake_acquire,
     )
 
@@ -218,33 +218,28 @@ def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list):
 def patched_path_acquisition(monkeypatch: pytest.MonkeyPatch, tmp_path):
     calls = []
 
-    def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list):
+    def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list = ["haadf"], scan_region: list[float] = [0.0, 0.0, 1.0, 1.0]):
         calls.append(
             {
                 "imsize": imsize,
                 "dwell_time": dwell_time,
                 "detector_list": list(detector_list),
+                "scan_region": list(scan_region),
             }
         )
-        path = tmp_path / f"stem_{imsize}.tiff"
-        path.write_bytes(b"fake-tiff")
+        path = tmp_path / f"stem_{imsize}.h5"
+        path.write_bytes(b"fake-h5")
         return str(path)
 
-    monkeypatch.setattr(ThermoMicroscope, "_acquire_stem_image", fake_acquire)
+    monkeypatch.setattr(ThermoMicroscope, "_acquire_scanned_image", fake_acquire)
     return calls
 
 
 @pytest.fixture
-def patched_advanced_path_acquisition(monkeypatch: pytest.MonkeyPatch, tmp_path):
+def patched_scanned_path_acquisition(monkeypatch: pytest.MonkeyPatch, tmp_path):
     calls = []
 
-    def fake_acquire(
-        self,
-        imsize: int,
-        dwell_time: float,
-        detector_list: list,
-        scan_region: list[float],
-    ):
+    def fake_acquire(self, imsize: int, dwell_time: float, detector_list: list = ["haadf"], scan_region: list[float] = [0.0, 0.0, 1.0, 1.0]):
         calls.append(
             {
                 "imsize": imsize,
@@ -253,16 +248,16 @@ def fake_acquire(
                 "scan_region": list(scan_region),
             }
         )
-        path = tmp_path / f"stem_advanced_{imsize}.tiff"
-        path.write_bytes(b"fake-advanced-tiff")
-        return [str(path)]
+        path = tmp_path / f"stem_{imsize}.h5"
+        path.write_bytes(b"fake-stem-h5")
+        return str(path)
 
-    monkeypatch.setattr(ThermoMicroscope, "_acquire_stem_image_advanced", fake_acquire)
+    monkeypatch.setattr(ThermoMicroscope, "_acquire_scanned_image", fake_acquire)
     return calls
 
 
 @pytest.fixture
-def patched_stem_data_acquisition(monkeypatch: pytest.MonkeyPatch):
+def patched_scanned_data_acquisition(monkeypatch: pytest.MonkeyPatch):
     calls = []
 
     def fake_acquire(
@@ -282,7 +277,7 @@ def fake_acquire(
         )
         return "fake-stem-data-key"
 
-    monkeypatch.setattr(ThermoMicroscope, "_acquire_stem_data_advanced", fake_acquire)
+    monkeypatch.setattr(ThermoMicroscope, "_acquire_scanned_data_advanced", fake_acquire)
     return calls
 
 
@@ -299,8 +294,8 @@ def fake_acquire(self, imsize: int, exposure_time: float, detector: str, readout
                 "readout_area": readout_area,
             }
         )
-        path = tmp_path / f"camera_{imsize}.tiff"
-        path.write_bytes(b"fake-camera-tiff")
+        path = tmp_path / f"camera_{imsize}.h5"
+        path.write_bytes(b"fake-camera-h5")
         return str(path)
 
     monkeypatch.setattr(ThermoMicroscope, "_acquire_camera_image", fake_acquire)
@@ -313,8 +308,8 @@ def patched_spectrum_path_acquisition(monkeypatch: pytest.MonkeyPatch, tmp_path)
 
     def fake_acquire(self, detector_name: str, exposure_time: float):
         calls.append({"detector_name": detector_name, "exposure_time": exposure_time})
-        path = tmp_path / f"spectrum_{detector_name}.emd"
-        path.write_bytes(b"fake-emd")
+        path = tmp_path / f"spectrum_{detector_name}.h5"
+        path.write_bytes(b"fake-spectrum-h5")
         return str(path)
 
     monkeypatch.setattr(ThermoMicroscope, "_acquire_spectrum", fake_acquire)
diff --git a/tests/test_SCAN.py b/tests/test_SCAN.py
index 324faa4..3e7c930 100644
--- a/tests/test_SCAN.py
+++ b/tests/test_SCAN.py
@@ -31,4 +31,4 @@ class TestSCANState:
 
     def test_initial_state_is_on(self, scan_proxy):
         import tango
-        assert scan_proxy.state() == tango.DevState.ON
\ No newline at end of file
+        assert scan_proxy.state() == tango.DevState.ON
diff --git a/tests/test_data_device.py b/tests/test_data_device.py
index b805ff9..01a9fbc 100644
--- a/tests/test_data_device.py
+++ b/tests/test_data_device.py
@@ -125,8 +125,8 @@ def test_register_path_registers_single_file(
         tmp_path,
     ) -> None:
         registrations = []
-        saved = tmp_path / "frame.tiff"
-        saved.write_bytes(b"fake-tiff")
+        saved = tmp_path / "frame.h5"
+        saved.write_bytes(b"fake-h5")
         data_proxy.host = "127.0.0.1"
         data_proxy.port = 9091
 
@@ -141,7 +141,7 @@ async def fake_register(client, path, **kwargs):
 
         result = data_proxy.register_path(str(saved))
 
-        assert result == "frame.tiff"
+        assert result == "frame.h5"
         assert registrations == [str(saved)]
 
     def test_register_path_returns_windows_tiled_key(
@@ -149,7 +149,7 @@ def test_register_path_returns_windows_tiled_key(
         data_proxy: tango.DeviceProxy,
         monkeypatch,
     ) -> None:
-        windows_path = "D:/microscopedata/tiled/ahoust17/frame.tiff"
+        windows_path = "D:/microscopedata/tiled/ahoust17/frame.h5"
         data_proxy.host = "127.0.0.1"
         data_proxy.port = 9091
 
@@ -162,4 +162,4 @@ async def fake_register(*args, **kwargs):
         monkeypatch.setattr("asyncroscopy.software.DATA.from_uri", fake_from_uri)
         monkeypatch.setattr("asyncroscopy.software.DATA.register", fake_register)
 
-        assert data_proxy.register_path(windows_path) == "frame.tiff"
+        assert data_proxy.register_path(windows_path) == "frame.h5"
diff --git a/tests/test_data_writer.py b/tests/test_data_writer.py
new file mode 100644
index 0000000..49acc30
--- /dev/null
+++ b/tests/test_data_writer.py
@@ -0,0 +1,32 @@
+import types
+
+import h5py
+import numpy as np
+
+from asyncroscopy.software.DataWriter import save_acquisition_hdf5
+
+
+def test_save_acquisition_hdf5_writes_data_and_xml_leaf_attrs(tmp_path):
+    source = types.SimpleNamespace(
+        data=np.array([[1, 2], [3, 4]], dtype=np.uint16),
+        metadata=types.SimpleNamespace(
+            metadata_as_xml="<root><detector>HAADF</detector><empty /></root>",
+        ),
+    )
+    path = tmp_path / "acquisition.h5"
+
+    save_acquisition_hdf5(
+        path,
+        [
+            {
+                "name": "images/HAADF",
+                "source": source,
+                "attrs": {"acquisition_type": "stem_image"},
+            }
+        ],
+    )
+
+    with h5py.File(path, "r") as h5:
+        assert h5["images/HAADF"][()].tolist() == [[1, 2], [3, 4]]
+        assert h5["images/HAADF"].attrs["acquisition_type"] == "stem_image"
+        assert h5["images/HAADF"].attrs["detector"] == "HAADF"
diff --git a/tests/test_digital_twin.py b/tests/test_digital_twin.py
index c088489..c0f5a81 100644
--- a/tests/test_digital_twin.py
+++ b/tests/test_digital_twin.py
@@ -6,13 +6,12 @@
 import os
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-import json
 from pathlib import Path
 
+import h5py
 import numpy as np
 import pytest
 import tango
-from PIL import Image
 
 class TestDigitalTwin:
 
@@ -22,20 +21,19 @@ def test_state_is_on(self, twin_proxy: tango.DeviceProxy):
     def test_manufacturer_is_digital_twin(self, twin_proxy: tango.DeviceProxy):
         assert twin_proxy.manufacturer == "UTKTeam"
 
-    def test_get_image_returns_saved_tiff(self, twin_proxy: tango.DeviceProxy, scan_proxy: tango.DeviceProxy):
+    def test_get_image_returns_saved_hdf5(self, twin_proxy: tango.DeviceProxy, scan_proxy: tango.DeviceProxy):
         scan_proxy.imsize = 32
         scan_proxy.dwell_time = 1e-6
 
-        saved_path = Path(twin_proxy.acquire_scanned_image())
+        saved_path = Path(twin_proxy.acquire_scanned_image(["haadf"]))
 
-        assert saved_path.suffix == ".tiff"
+        assert saved_path.suffix == ".h5"
         assert saved_path.exists()
-        opened = Image.open(saved_path)
-        image = np.asarray(opened)
-        assert image.shape == (32, 32)
-        metadata = json.loads(opened.tag_v2[270])
-        assert metadata["acquisition_type"] == "stem_image"
-        assert metadata["detector"] == "HAADF"
+        with h5py.File(saved_path, "r") as h5:
+            image = h5["image"][()]
+            assert image.shape == (32, 32)
+            assert h5["image"].attrs["acquisition_type"] == "stem_image"
+            assert h5["image"].attrs["detector"] == "HAADF"
 
     def test_stage_navigation_changes_and_restores_view(
         self,
@@ -56,14 +54,17 @@ def fake_stage_render(self, imsize: int, dwell_time: float, detector_list: list)
         scan_proxy.dwell_time = 1e-6
 
         twin_proxy.move_stage([0.0, 0.0, 0.0, 0.0, 0.0])
-        image_a = np.asarray(Image.open(twin_proxy.acquire_scanned_image()))
+        with h5py.File(twin_proxy.acquire_scanned_image(["haadf"]), "r") as h5:
+            image_a = h5["image"][()]
 
         twin_proxy.move_stage([8e-9, -7e-9, 0.0, 0.0, 0.0])
-        image_b = np.asarray(Image.open(twin_proxy.acquire_scanned_image()))
+        with h5py.File(twin_proxy.acquire_scanned_image(["haadf"]), "r") as h5:
+            image_b = h5["image"][()]
         assert not np.array_equal(image_a, image_b)
 
         twin_proxy.move_stage([0.0, 0.0, 0.0, 0.0, 0.0])
-        image_a_again = np.asarray(Image.open(twin_proxy.acquire_scanned_image()))
+        with h5py.File(twin_proxy.acquire_scanned_image(["haadf"]), "r") as h5:
+            image_a_again = h5["image"][()]
         assert np.array_equal(image_a, image_a_again)
 
     def test_spectrum_is_repeatable_at_same_pose_and_beam(
@@ -75,6 +76,8 @@ def test_spectrum_is_repeatable_at_same_pose_and_beam(
         twin_proxy.move_stage([0.0, 0.0, 0.0, 0.0, 0.0])
         twin_proxy.place_beam([0.45, 0.55])
 
-        spec_1 = np.load(twin_proxy.acquire_spectrum("eds"))
-        spec_2 = np.load(twin_proxy.acquire_spectrum("eds"))
+        with h5py.File(twin_proxy.acquire_spectrum("eds"), "r") as h5:
+            spec_1 = h5["spectrum"][()]
+        with h5py.File(twin_proxy.acquire_spectrum("eds"), "r") as h5:
+            spec_2 = h5["spectrum"][()]
         assert spec_1.tolist() == spec_2.tolist()
diff --git a/tests/test_thermo_microscope.py b/tests/test_thermo_microscope.py
index 3d05648..4733cb1 100644
--- a/tests/test_thermo_microscope.py
+++ b/tests/test_thermo_microscope.py
@@ -42,16 +42,17 @@ def test_acquire_scanned_image_returns_saved_path(
         scan_proxy.dwell_time = 1e-6
         scan_proxy.imsize = 512
 
-        saved_path = thermo_proxy.acquire_scanned_image()
+        saved_path = thermo_proxy.acquire_scanned_image(["haadf"])
 
         assert isinstance(saved_path, str)
-        assert saved_path.endswith(".tiff")
-        assert Path(saved_path).read_bytes() == b"fake-tiff"
+        assert saved_path.endswith(".h5")
+        assert Path(saved_path).read_bytes() == b"fake-h5"
         assert patched_path_acquisition == [
             {
                 "imsize": 512,
                 "dwell_time": pytest.approx(1e-6),
                 "detector_list": ["haadf"],
+                "scan_region": [0.0, 0.0, 1.0, 1.0],
             }
         ]
 
@@ -63,32 +64,47 @@ def test_scan_settings_propagate_into_acquisition(
     ) -> None:
         scan_proxy.dwell_time = 2e-6
         scan_proxy.imsize = 256
+        scan_proxy.scan_region = [0.0, 0.0, 1.0, 1.0]
 
-        saved_path = thermo_proxy.acquire_scanned_image()
+        saved_path = thermo_proxy.acquire_scanned_image(["haadf"])
 
         assert Path(saved_path).exists()
         assert patched_path_acquisition[-1] == {
             "imsize": 256,
             "dwell_time": pytest.approx(2e-6),
             "detector_list": ["haadf"],
+            "scan_region": [0.0, 0.0, 1.0, 1.0],
         }
 
-    def test_advanced_scan_settings_propagate_into_acquisition(
+    def test_acquire_scanned_image_accepts_detector_list(
         self,
         thermo_proxy: tango.DeviceProxy,
         scan_proxy: tango.DeviceProxy,
-        patched_advanced_path_acquisition: list[dict],
+        patched_path_acquisition: list[dict],
+    ) -> None:
+        scan_proxy.dwell_time = 2e-6
+        scan_proxy.imsize = 256
+        scan_proxy.scan_region = [0.0, 0.0, 1.0, 1.0]
+
+        saved_path = thermo_proxy.acquire_scanned_image(["haadf", "bf"])
+
+        assert Path(saved_path).exists()
+        assert patched_path_acquisition[-1]["detector_list"] == ["haadf", "bf"]
+
+    def test_scan_region_propagates_into_acquisition(
+        self,
+        thermo_proxy: tango.DeviceProxy,
+        scan_proxy: tango.DeviceProxy,
+        patched_scanned_path_acquisition: list[dict],
     ) -> None:
         scan_proxy.dwell_time = 3e-6
         scan_proxy.imsize = 128
         scan_proxy.scan_region = [0.1, 0.2, 0.3, 0.4]
-        scan_proxy.haadf = True
-        scan_proxy.bf = False
 
-        saved_path = thermo_proxy.acquire_scanned_image_advanced()
+        saved_path = thermo_proxy.acquire_scanned_image(["haadf"])
 
-        assert Path(saved_path).read_bytes() == b"fake-advanced-tiff"
-        assert patched_advanced_path_acquisition == [
+        assert Path(saved_path).read_bytes() == b"fake-stem-h5"
+        assert patched_scanned_path_acquisition == [
             {
                 "imsize": 128,
                 "dwell_time": pytest.approx(3e-6),
@@ -97,10 +113,9 @@ def test_advanced_scan_settings_propagate_into_acquisition(
             }
         ]
 
-    def test_advanced_stem_image_helper_uses_relative_region(self, tmp_path) -> None:
+    def test_scanned_image_helper_uses_relative_region(self, monkeypatch, tmp_path) -> None:
         class FakeImage:
-            def save(self, path: str) -> None:
-                Path(path).write_bytes(b"fake")
+            data = np.array([[1, 2], [3, 4]], dtype=np.uint16)
 
         class FakeAcquisition:
             def __init__(self) -> None:
@@ -115,12 +130,12 @@ def acquire_stem_images_advanced(self, settings):
         microscope._microscope = types.SimpleNamespace(acquisition=acquisition)
         microscope._detector_proxies = {"data": FakeDataServer()}
 
-        def fake_new_path(self, acquisition_type: str, detector: str, data_server):
-            return tmp_path / f"{acquisition_type}_{detector}.tiff"
+        def fake_new_path(device, acquisition_type: str, detector: str, data_server=None, extension="h5"):
+            return tmp_path / f"{acquisition_type}_{detector}.h5"
 
-        microscope._make_filename = types.MethodType(fake_new_path, microscope)
+        monkeypatch.setattr("asyncroscopy.software.DataWriter.acquisition_filename", fake_new_path)
 
-        saved_paths = ThermoMicroscope._acquire_stem_image_advanced(
+        saved_path = ThermoMicroscope._acquire_scanned_image(
             microscope,
             imsize=128,
             dwell_time=4e-6,
@@ -129,7 +144,10 @@ def fake_new_path(self, acquisition_type: str, detector: str, data_server):
         )
 
         settings = acquisition.settings
-        assert saved_paths[0].endswith(".tiff")
+        assert saved_path.endswith(".h5")
+        with h5py.File(saved_path, "r") as h5:
+            assert h5["image"][()].tolist() == [[1, 2], [3, 4]]
+            assert h5["image"].attrs["detector"] == "HAADF"
         assert settings.size == 128
         assert settings.dwell_time == pytest.approx(4e-6)
         assert settings.detector_types == ["HAADF"]
@@ -143,7 +161,7 @@ def test_scanned_data_advanced_settings_propagate_into_acquisition(
         self,
         thermo_proxy: tango.DeviceProxy,
         scan_proxy: tango.DeviceProxy,
-        patched_stem_data_acquisition: list[dict],
+        patched_scanned_data_acquisition: list[dict],
     ) -> None:
         scan_proxy.dwell_time = 10e-3
         scan_proxy.imsize = 128
@@ -152,7 +170,7 @@ def test_scanned_data_advanced_settings_propagate_into_acquisition(
         result = thermo_proxy.acquire_scanned_data_advanced()
 
         assert result == "fake-stem-data-key"
-        assert patched_stem_data_acquisition == [
+        assert patched_scanned_data_acquisition == [
             {
                 "imsize": 128,
                 "dwell_time": pytest.approx(10e-3),
@@ -161,10 +179,9 @@ def test_scanned_data_advanced_settings_propagate_into_acquisition(
             }
         ]
 
-    def test_stem_data_advanced_helper_saves_and_registers_ceta_with_relative_region(self, tmp_path) -> None:
+    def test_scanned_data_advanced_helper_saves_and_registers_ceta_with_relative_region(self, monkeypatch, tmp_path) -> None:
         class FakeImage:
-            def save(self, path: str) -> None:
-                Path(path).write_bytes(b"fake-stem-data")
+            data = np.array([[5, 6], [7, 8]], dtype=np.uint16)
 
         class FakeAcquisition:
             def __init__(self) -> None:
@@ -178,9 +195,13 @@ def acquire_stem_data_advanced(self, settings):
         microscope = ThermoMicroscope.__new__(ThermoMicroscope)
         microscope._microscope = types.SimpleNamespace(acquisition=acquisition)
         microscope._detector_proxies = {"data": FakeDataServer()}
-        microscope._make_filename = types.MethodType(lambda self, acquisition_type, detector, data_server: tmp_path / f"{acquisition_type}_{detector}.tiff", microscope)
 
-        result = ThermoMicroscope._acquire_stem_data_advanced(
+        def fake_new_path(device, acquisition_type: str, detector: str, data_server=None, extension="h5"):
+            return tmp_path / f"{acquisition_type}_{detector}.h5"
+
+        monkeypatch.setattr("asyncroscopy.software.DataWriter.acquisition_filename", fake_new_path)
+
+        result = ThermoMicroscope._acquire_scanned_data_advanced(
             microscope,
             imsize=128,
             dwell_time=10e-3,
@@ -189,7 +210,9 @@ def acquire_stem_data_advanced(self, settings):
         )
 
         settings = acquisition.settings
-        assert Path(result).read_bytes() == b"fake-stem-data"
+        with h5py.File(result, "r") as h5:
+            assert h5["stem_data"][()].tolist() == [[5, 6], [7, 8]]
+            assert h5["stem_data"].attrs["detector"] == "BM-Ceta"
         assert settings.size == 128
         assert settings.dwell_time == pytest.approx(10e-3)
         assert settings.detector_types == [CameraType.BM_CETA]
@@ -211,7 +234,7 @@ def test_camera_settings_propagate_into_acquisition(
 
         saved_path = thermo_proxy.acquire_camera_image()
 
-        assert Path(saved_path).read_bytes() == b"fake-camera-tiff"
+        assert Path(saved_path).read_bytes() == b"fake-camera-h5"
         assert patched_camera_path_acquisition == [
             {
                 "imsize": 2048,
@@ -233,7 +256,7 @@ def test_flucam_settings_propagate_into_acquisition(
 
         saved_path = thermo_proxy.acquire_flucam_image()
 
-        assert Path(saved_path).read_bytes() == b"fake-camera-tiff"
+        assert Path(saved_path).read_bytes() == b"fake-camera-h5"
         assert patched_camera_path_acquisition == [
             {
                 "imsize": 1024,
@@ -253,10 +276,10 @@ def test_spectrum_settings_propagate_into_acquisition(
 
         saved_path = thermo_proxy.acquire_spectrum("eds")
 
-        assert Path(saved_path).read_bytes() == b"fake-emd"
+        assert Path(saved_path).read_bytes() == b"fake-spectrum-h5"
         assert patched_spectrum_path_acquisition == [{"detector_name": "eds", "exposure_time": pytest.approx(0.25)}]
 
-    def test_spectrum_helper_saves_emd_and_registers(self, tmp_path) -> None:
+    def test_spectrum_helper_saves_hdf5_and_registers(self, monkeypatch, tmp_path) -> None:
         class FakeSpectrum:
             data = np.array([1, 2, 3], dtype=np.uint32)
 
@@ -272,13 +295,18 @@ def acquire_spectrum(self, settings):
         microscope = ThermoMicroscope.__new__(ThermoMicroscope)
         microscope._microscope = types.SimpleNamespace(analysis=types.SimpleNamespace(eds=eds))
         microscope._detector_proxies = {"data": FakeDataServer()}
-        microscope._make_filename = types.MethodType(lambda self, acquisition_type, detector, data_server, extension="tiff": tmp_path / f"{acquisition_type}_{detector}.{extension}", microscope)
+
+        def fake_new_path(device, acquisition_type: str, detector: str, data_server=None, extension="h5"):
+            return tmp_path / f"{acquisition_type}_{detector}.{extension}"
+
+        monkeypatch.setattr("asyncroscopy.software.DataWriter.acquisition_filename", fake_new_path)
 
         result = ThermoMicroscope._acquire_spectrum(microscope, "eds", 0.25)
 
-        assert result.endswith(".emd")
-        with h5py.File(result, "r") as emd:
-            assert emd["spectrum"][()].tolist() == [1, 2, 3]
+        assert result.endswith(".h5")
+        with h5py.File(result, "r") as h5:
+            assert h5["spectrum"][()].tolist() == [1, 2, 3]
+            assert h5["spectrum"].attrs["acquisition_type"] == "spectrum"
         assert eds.settings.eds_detector == EdsDetectorType.SUPER_X
         assert eds.settings.exposure_time == pytest.approx(0.25)
         assert eds.settings.exposure_time_type == ExposureTimeType.LIVE_TIME