{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Scattering cross-section calculation\n", "\n", "This tutorial will show you how to compute the radar cross section (RCS) for a dielectric sphere by sampling scattered near fields on a closed surface surrounding the sphere, and transforming them to observation points far away.\n", "\n", "This example demonstrates the usefulness of the near field to far field transformation for reducing the simulation size needed for structures involving lots of empty space.\n", "\n", "To obtain the scattered field, we will run two simulations:\n", "* one with the sphere to compute the total near field on a closed surface around the sphere, and\n", "* one without the sphere, to compute just the incident field and subtract it from the above to get the scattered field.\n", "\n", "Note that in the future, this process will be simplified with the introduction of a total-field/scattered-field source, requiring only one simulation. Stay tuned!\n", "\n", "Then, we'll show how to use a near-field to far-field transformation in `Tidy3D` to compute the RCS for the sphere either on the cloud during the simulation run, or on your local machine afterwards." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.265889Z", "iopub.status.busy": "2023-02-03T05:58:29.265567Z", "iopub.status.idle": "2023-02-03T05:58:29.948459Z", "shell.execute_reply": "2023-02-03T05:58:29.948099Z" } }, "outputs": [ { "data": { "text/html": [ "
[23:58:29] WARNING  This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on   __init__.py:103\n",
       "                    PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on,                \n",
       "                    please use 'pip install tidy3d' instead.                                                       \n",
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           INFO     Using client version: 1.9.0rc1                                                  __init__.py:121\n",
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This cuts down on the data required vs computing the full volume because only the fields on the enclosing surface are required to get the far field information.\n", "\n", "The near fields will be captured on these surfaces; after the simulation, they will be used to compute far fields on your local machine." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.957974Z", "iopub.status.busy": "2023-02-03T05:58:29.957875Z", "iopub.status.idle": "2023-02-03T05:58:29.959935Z", "shell.execute_reply": "2023-02-03T05:58:29.959668Z" } }, "outputs": [], "source": [ "# create a set of surface monitors around the sphere for local computation of far fields\n", "mon_size = 2 * radius + 2 * buffer_mon\n", "monitors_near = td.FieldMonitor.surfaces(\n", " center=center, size=[mon_size, mon_size, mon_size], freqs=[f0], name=\"near_field\"\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll make a [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor) which is used for computing far-fields directly on the server during the simulation run with `Tidy3D`'s full hardware optimization, making it extremely fast. Note that in this case, `surfaces()` must not be used because the solver already knows to use only surface tangential fields to compute the far fields.\n", "\n", "With this approach, the near-field data associated with the monitor is used to compute the far fields, and only the far field data is downloaded and returned to the user. Then, we'll show how to easily and quickly retrieve various quantities such as power and radar cross section from the computed far fields.\n", "\n", "Note that for server-side calculation of far fields, this is the only monitor required. The monitor's `size` and `center` fields specify the location where near fields will be sampled, while its `theta` and `phi` fields specify the far-field observation angles. Therefore, all the information needed is contained within [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor) (one can also use [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionCartesianMonitor) or [FieldProjectionKSpaceMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionKSpaceMonitor) to specify the far-field observation grid in different coordinate systems)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.961403Z", "iopub.status.busy": "2023-02-03T05:58:29.961303Z", "iopub.status.idle": "2023-02-03T05:58:29.963782Z", "shell.execute_reply": "2023-02-03T05:58:29.963516Z" } }, "outputs": [], "source": [ "# set the far-field observation angles of interest\n", "num_theta = 300\n", "num_phi = 2\n", "thetas = np.linspace(0, np.pi, num_theta)\n", "phis = np.linspace(0, np.pi / 2, num_phi)\n", "\n", "# create the far field monitor for server-side computation of far fields\n", "monitor_far = td.FieldProjectionAngleMonitor(\n", " center=center,\n", " size=[mon_size, mon_size, mon_size],\n", " freqs=[f0],\n", " name=\"far_field\",\n", " custom_origin=center,\n", " phi=list(phis),\n", " theta=list(thetas),\n", " far_field_approx=True, # we leave this to its default value of 'True' because we are interested in fields sufficiently\n", " # far away that the far field approximations can be invoked to speed up the calculation\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also create another near-field monitor where the fields are automatically downsampled based on given sampling rates. The rationale is that we may be able to approximate the far fields fairly well even if the resolution of near-fields is not very fine. This downsampled is achieved by supplying the `interval_space` field in the [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldMonitor.html), and can be useful for further reducing the amount of data that needs to be downloaded from the server, while still leading to accurate far fields, as shown below.\n", "\n", "Here, we downsample by a factor of 2 along the x and y dimensions, and a factor of 3 along z.\n", "\n", "Note that the downsampling feature can be useful if downloading near fields to compute far fields on your local machine, but is unnecessary if you choose to use server-side far field computations." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.965097Z", "iopub.status.busy": "2023-02-03T05:58:29.965001Z", "iopub.status.idle": "2023-02-03T05:58:29.966980Z", "shell.execute_reply": "2023-02-03T05:58:29.966709Z" } }, "outputs": [], "source": [ "# create a set of surface monitors around the sphere\n", "monitors_downsampled = td.FieldMonitor.surfaces(\n", " center=center,\n", " size=[mon_size, mon_size, mon_size],\n", " freqs=[f0],\n", " name=\"near_field_downsampled\",\n", " interval_space=(\n", " 2,\n", " 2,\n", " 3,\n", " ), # these are the (x, y, z) factors by which fields are downsampled\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Simulation\n", "\n", "Now we can put everything together and define the two simulation classes: with the sphere to get the total field, and without the sphere to get the incident field. A uniform grid with a fixed grid size is used in both simulations to allow easily subtracting the incident field in the empty simulation from the total field in the actual simulation. We will also apply symmetries which reduce the computational complexity of the problem." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.968347Z", "iopub.status.busy": "2023-02-03T05:58:29.968255Z", "iopub.status.idle": "2023-02-03T05:58:29.977554Z", "shell.execute_reply": "2023-02-03T05:58:29.977179Z" } }, "outputs": [], "source": [ "monitors = monitors_near + [monitor_far] + monitors_downsampled\n", "\n", "sim = td.Simulation(\n", " size=sim_size,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),\n", " structures=geometry,\n", " sources=[source],\n", " monitors=monitors,\n", " run_time=run_time,\n", " boundary_spec=boundary_spec,\n", " symmetry=(-1, 1, 0),\n", ")\n", "\n", "# Here, we add the sphere as an override structure for the messhing such\n", "# that the grids of the two simulations match.\n", "sim_empty = td.Simulation(\n", " size=sim_size,\n", " grid_spec=td.GridSpec.auto(\n", " min_steps_per_wvl=min_steps_per_wvl,\n", " override_structures=geometry,\n", " ),\n", " structures=[],\n", " sources=[source],\n", " monitors=monitors,\n", " run_time=run_time,\n", " boundary_spec=boundary_spec,\n", " symmetry=(-1, 1, 0),\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize Geometry\n", "\n", "Let's take a look and make sure everything is defined properly in both simulations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:58:29.978921Z", "iopub.status.busy": "2023-02-03T05:58:29.978842Z", "iopub.status.idle": "2023-02-03T05:58:30.118525Z", "shell.execute_reply": "2023-02-03T05:58:30.118274Z" } }, "outputs": [ { "data": { "text/html": [ "
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[23:58:33] INFO     Maximum FlexUnit cost: 0.125                                                      webapi.py:253\n",
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[23:58:36] INFO     status = preprocess                                                               webapi.py:274\n",
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[23:58:40] INFO     starting up solver                                                                webapi.py:278\n",
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[23:58:50] INFO     running solver                                                                    webapi.py:284\n",
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[00:00:21] INFO     loading SimulationData from data/sphereRCS_empty.hdf5                             webapi.py:415\n",
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   "source": [
    "# Run simulation\n",
    "import tidy3d.web as web\n",
    "\n",
    "sim_data = web.run(sim, task_name=\"sphereRCS\", path=\"data/sphereRCS.hdf5\")\n",
    "sim_empty_data = web.run(\n",
    "    sim_empty, task_name=\"sphereRCS_empty\", path=\"data/sphereRCS_empty.hdf5\"\n",
    ")\n"
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   "source": [
    "## Setting Up the Local Near2Far Computation\n",
    "\n",
    "To set up the near-to-far transformation locally, we need to grab the fields on each surface of the near-field [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldMonitor.html) objects.\n",
    "\n",
    "So, we simply create a [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjector.html) object and pass in the surface monitors as shown below.  Note that we also need to pass the normal directions of each of the monitors in the list.\n",
    "\n",
    "In addition to storing the near field data, this object will compute the surface currents and provide various methods for projecting the far field quantities.\n",
    "\n",
    "We can optionally pass in the number of points per wavelength in the background medium with which to sample fields on the monitors. The default is 10 points per wavelength. This can reduce computation times significantly. By default, 10 points per wavelength are used.\n",
    "\n",
    "One can also pass in coordinates for the local origin of the set of monitors; the far-field observation points will be defined with respect to this origin. By default, the local origin is set to the average of the centers of all surface monitors passed in.\n",
    "\n",
    "To see the usefulness of downsampling the fields recorded on monitors, we'll also run the near-to-far transformation with downsampled fields to compare the RCS.\n",
    "\n",
    "Finally, to get a sense of performance, we'll measure the time it takes to compute the far fields locally, and compare it to the time it took to compute them on the server."
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   "source": [
    "import time\n",
    "\n",
    "# first, we construct the classes which compute far fields locally on your machine\n",
    "n2f = td.FieldProjector.from_near_field_monitors(\n",
    "    sim_data=sim_data,\n",
    "    near_monitors=monitors_near,  # only supply the non-downsampled surface monitors as sources\n",
    "    normal_dirs=[\"-\", \"+\", \"-\", \"+\", \"-\", \"+\"],\n",
    "    pts_per_wavelength=10,\n",
    ")\n",
    "\n",
    "# do the same for the downsampled monitors\n",
    "n2f_downsampled = td.FieldProjector.from_near_field_monitors(\n",
    "    sim_data=sim_data,\n",
    "    near_monitors=monitors_downsampled,  # only supply the downsampled surface monitors as sources\n",
    "    normal_dirs=[\"-\", \"+\", \"-\", \"+\", \"-\", \"+\"],\n",
    "    pts_per_wavelength=10,\n",
    ")\n",
    "\n",
    "# we'll also project the fields from the empty simulation into the far field to get the scattered far field\n",
    "n2f_empty = td.FieldProjector.from_near_field_monitors(\n",
    "    sim_data=sim_empty_data,\n",
    "    near_monitors=monitors_near,  # only supply the non-downsampled surface monitors as sources\n",
    "    normal_dirs=[\"-\", \"+\", \"-\", \"+\", \"-\", \"+\"],\n",
    "    pts_per_wavelength=10,\n",
    ")\n",
    "\n",
    "# now, we retrieve the far fields in all three cases by passing in our far field monitor from before\n",
    "start = time.time()\n",
    "far_fields = n2f.project_fields(monitor_far)\n",
    "end = time.time()\n",
    "n2f_time = end - start\n",
    "\n",
    "start = time.time()\n",
    "far_fields_downsampled = n2f_downsampled.project_fields(monitor_far)\n",
    "end = time.time()\n",
    "n2f_downsampled_time = end - start\n",
    "\n",
    "start = time.time()\n",
    "far_fields_empty = n2f_empty.project_fields(monitor_far)\n",
    "end = time.time()\n",
    "n2f_empty_time = end - start\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison\n",
    "\n",
    "We can see below that the local computation of far fields takes about the same time with and without downsampling, because the fields are anyway resampled on the near-field box prior to the computation. The real benefit of downsampling is reducing the amount of data that is stored and downloaded.\n",
    "\n",
    "The server-side computation is extremely fast, as expected, and requires downloading no near-field data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T06:00:25.141770Z",
     "iopub.status.busy": "2023-02-03T06:00:25.141690Z",
     "iopub.status.idle": "2023-02-03T06:00:25.143895Z",
     "shell.execute_reply": "2023-02-03T06:00:25.143639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local near-to-far: 0.9768528938293457 s\n",
      "Local near-to-far with downsampling: 0.7064340114593506 s\n",
      "Server-side near-to-far: 0.0803 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "n2f_server_time = float(\n",
    "    sim_data.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "\n",
    "print(f\"Local near-to-far: {n2f_time} s\")\n",
    "print(f\"Local near-to-far with downsampling: {n2f_downsampled_time} s\")\n",
    "print(f\"Server-side near-to-far: {n2f_server_time} s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Far Field Data for the Server-Side Computations\n",
    "\n",
    "Recall that we also computed scattered far fields on the server; let's extract that data too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T06:00:25.147880Z",
     "iopub.status.busy": "2023-02-03T06:00:25.147761Z",
     "iopub.status.idle": "2023-02-03T06:00:25.158428Z",
     "shell.execute_reply": "2023-02-03T06:00:25.158169Z"
    }
   },
   "outputs": [],
   "source": [
    "far_fields_server = sim_data[monitor_far.name]\n",
    "far_fields_empty_server = sim_empty_data[monitor_far.name]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Scattered Far Fields\n",
    "Now, for both client- and server-side fields, compute the scattered far fields by subtracting out the results of the empty simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T06:00:25.159888Z",
     "iopub.status.busy": "2023-02-03T06:00:25.159811Z",
     "iopub.status.idle": "2023-02-03T06:00:25.161848Z",
     "shell.execute_reply": "2023-02-03T06:00:25.161596Z"
    }
   },
   "outputs": [],
   "source": [
    "# scattered fields for client-side projections\n",
    "for field, field_empty in zip(\n",
    "    far_fields.field_components.values(), far_fields_empty.field_components.values()\n",
    "):\n",
    "    field.values -= field_empty.values\n",
    "\n",
    "# scattered fields for server-side projections\n",
    "for field, field_empty in zip(\n",
    "    far_fields_server.field_components.values(),\n",
    "    far_fields_empty_server.field_components.values(),\n",
    "):\n",
    "    field.values -= field_empty.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the RCS\n",
    "\n",
    "Now that we have the far fields computed in three different ways (locally, locally with downsampling, and remotely on the server), various far field quantities can be extracted.\n",
    "\n",
    "For this example, we use `FieldProjectionAngleData.radar_cross_section` to get the RCS at the previously-specified `theta,phi` points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T06:00:25.163294Z",
     "iopub.status.busy": "2023-02-03T06:00:25.163217Z",
     "iopub.status.idle": "2023-02-03T06:00:25.167281Z",
     "shell.execute_reply": "2023-02-03T06:00:25.167054Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the RCS for the local, local downsampled and server-side cases\n",
    "RCS = far_fields.radar_cross_section.sel(f=f0).values\n",
    "RCS_downsampled = far_fields_downsampled.radar_cross_section.sel(f=f0).values\n",
    "RCS_server = far_fields_server.radar_cross_section.sel(f=f0).values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Results\n",
    "Now we can plot the RCS and compare it to the analytical RCS computed via the Mie series.\n",
    "\n",
    "The results match very well! As expected, there are minor deviations due to the FDTD discretization.\n",
    "\n",
    "Notice that the downsampled monitors also yield fairly accurate results with less than an eighth of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T06:00:25.168816Z",
     "iopub.status.busy": "2023-02-03T06:00:25.168738Z",
     "iopub.status.idle": "2023-02-03T06:00:25.444382Z",
     "shell.execute_reply": "2023-02-03T06:00:25.444128Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yk1PKkJW3kJ6eHuem/YYmwo5btom839OAhPPnoEsXiI/XdXgiH/Dx8aF48eJ5koxrk6enJ8WLF8/2RD5CCCGELklC/paq7liO37pugTgzPMslMKGVARw5AuPH6zo0oWMxMTFcu3Yt25Ph5AcjR44kKChIrkAJIYR4o0hC/hbr9m4zxjjMA2B54wQ2VQdWrICzZ3UbmNApX19fEhMT38iEXAghhHgTyU2db7nvPvoEz0//4YL5LwzuaESE00d8lAuTrYg3R926dZFbS4QQQoi8I1fIBecWrMXskRPxd6sy5cgu7t27p+uQhBBCCCHeGpKQC/T19Lkycy+1L+sRHhhAv379SLx3D4YNg9hYXYcnhBBCCFGgSUIuAChdzI7ftm3D3NwcDw8PrjRuAKtWwdSpug5NCCGEEKJAk4RcpKhQoQJzvl4EnZpTr3ssoSbA0qWwb5+uQxNCCCGEKLAkIRepfDC0H/rlbhJj9QS3dmXUhYMGwcOHOo1LCCGEEKKgkoRcpGJpas7aTj9Bkh5eNW+ztkYxePRITcqTknQdnhBCCCFEgSMJuUhjQHM3qkf1AmB4Kw1h5kawfz8sW6bbwIQQQgghCiBJyEW6Tn75E3rhJUmwekhz1xrqwlWrICFBt4EJIYQQQhQwkpCLdFmYmLO4+RIALjh7M8etN3h5gYHMJSWEEEIIoU2SkIsMjevYkxLBrSGqMPPuBfAwMlLXIQkhhBBCFDiSkItMnZyxnuru5Yi9fJLJkyerN3b+8AMEBek6NCGEEEKIAkEScpGpMkVLsHrF/wDYsGEDj3r0gJEjYexYHUcmhBBCCFEwSEIuXql+/fr06TsAarTi3fAkFH19+O03+PNPXYcm8oCbmxuGhobExMToOhSRQ/Hx8cyaNYuKFStibGyMRqNh165deXb827dvo9FoGDx4cJ4dMzvye3xCiIJPEnKRJY16dYZuh7jRaC+/Ne+gLvz4Y4iK0mlcIvd5e3tTtWpVTExMdHJ8Ly8v2rVrh7W1NYUKFaJhw4Zs27ZNJ7HoWmBgIMuWLaNNmzaULl0aIyMj7Ozs6NatG2fOnMlwu8WLFzN79mzs7e2ZNGkSM2fOpEqVKnkYuRBCiMxIyQyRJR937ML0A/WIKOpFvyLP6OFQGs3dAFiyBKZP13V4IpfcvXuXx48f89577+nk+EePHsXNzQ0TExN69+6NhYUFO3bsoFevXty9e5eJEyfqJC5dWb58OQsWLKB8+fK0adOGokWLcv36dXbt2sWuXbvYtGkTvXr1SrPdnj17MDc35+DBgxgZGekgciGEEJmRK+QiSzQaDds+/BYUDQlVDzGlekt1xddfw/37ug1O5Jrz588D4OTklOfHTkhIYNiwYejp6eHh4cFPP/3E4sWL8fHxoVKlSkybNo07d+7keVy6VL9+fdzd3blx4warVq1i/vz5bN++naNHj6Kvr8/IkSOJjY1Ns939+/cpUqSIJONCCJFPSUIussytdiMcQt0AWFj4NnF168GzZ/D55zqOTGhTTEwM8+bNo0qVKnTv3h2AyZMn4+TkxO7du/MsjiNHjnDz5k369u1L7dq1U5ZbWVkxbdo04uLiWL9+/Wsdw93dHY1Gw6xZszh37hytW7fGwsICKysrunTpwu3bt9PdzsPDg44dO2Jra4uxsTEVK1Zk+vTpRL00hCsuLo7ly5fj5uaGg4MDxsbGFCtWjK5du+Lt7Z1pPCdPnqRNmzZYW1uj0WgA6Nq1K82aNUuzXdOmTWnevDmhoaFcunQpZfmsWbPQaDT4+/tz584dNBoNGo2GMmXKpIqvU6dOWokvJ9auXUuDBg0wNzfH3NycBg0asG7dugzbe3h40LlzZ4oXL46xsTEODg507dqV48ePp7TJbr8LIYSuSUIusuXPiUshSR8qHaW3VX1o3FituiIKhNDQUJo0acLnn39OuXLlsLOzQ09Pj379+uHn50eXLl3w9PTMk1jc3d0BaNOmTZp1bm7qB8Njx45p5VheXl64uLhgZGTEiBEjqFu3Lrt27aJVq1ZpbmZduXIlrq6unDhxgvbt2zN27FhKlSrF3Llzad26NXFxcSltQ0JCGDduHLGxsbRr147x48fj6urK3r17ady4MV5eXunGc/LkSVxdXdFoNAwfPjzdYSgvMzQ0BMDghcm7XF1dmTlzJlZWVlhZWTFz5kxmzpzJuHHj8jy+9IwdO5ahQ4cSGBjIBx98wAcffEBgYCBDhgzhk08+SdP+22+/xdXVlYMHD9K6dWsmTpxIixYt8PHxYfv27SntcnpeQgihM4pIJTw8XAGU8PDwPD/2pUuX8vyYOVFtSleFWSgMbKLc9r+v63C0wsfHR/Hz81Oio6PTXR8ZmfHj5U0yaxsVlfO2z56l306b3NzcFEBZu3atoiiKUqJECaVq1aqKoijKihUrFEB5//33M9x+6dKlysyZM7P88Pb2VhRFUaJePllFUbp3764Ayrlz59I9lrm5ueLg4PBa53v06FEFUABly5YtqdYNGDBAAZTNmzenLLt8+bJiYGCg1KpVSwkODk7Vfv78+QqgLFq0KGVZTEyMcu/evTTH9fX1VczNzZVWrVplGM+aNWuyfB537txRjI2NlRIlSigJCQlp1js6OiqOjo5plifH93L/azs+f39/BVAGDRqUsuzYsWMKoFStWlUJCwtLWR4SEqJUqlRJARQPD4+U5RcuXFD09PQUe3t7xd/fP9X+k5KSlMDAwDTn9bKMziu9+PJKen/7Iu/kVf9HR0dn+h7ztnpT8p6cyk5OKQn5SyQhf7Ubj28rhsOqKZSrqIyfMP6/FXFxugvqNb0qIYeMH+3apW5rZpZx22bNUre1tc24bd26qds6OqbfTlv27t2rAEr//v0VRVGUoKAgBVD69eunKIqiPHnyRAGU8uXLZ7gPR0fHlIQtK4/kxD+9N8XWrVsrgHL9+vV0j2Vvb69YWlq+1jknJ5guLi4ZrpswYULKsrFjx6ZJFJMlJiYqRYsWVerUqZOlY3fs2FExMjJS4l543SQf09nZOcvnEBcXp7i4uCiAsmHDhnTbZJSQJ0uv/7UVn6Kkn/AOHTpUAZStW7emaf/rr78qgDJ06NCUZSNHjsz2B4H0pHdekpC/vSQh1603Je/JqezklFJlRWRbeVtH/uy6mPd+fo8fVv7A1I/HUPTHH2HPHvD2hudfnYs3y+rVqwH4+OOPgbQ3dJqamgKgp5fxSLeMxlznd3Xq1EmzrFSpUgCEhYWlLDt9+jQA+/fv5/Dhw2m2MTQ05OrVq6mWXbhwgYULF3L8+HGCgoKIj49PtT44OJgSJUqkWlavXr0sxZ2UlMTgwYPx8PBg2LBhDBgwIEvbvRzf/PnzOXXqlNbjy0zyWG5XV9c065o3b54SW7KzZ88C6Q9hSk9O+l0IIXRFEnKRI25ubtSrVw8vLy8WL/2Or7dugkePYMMG+OADXYendZGRGa/T10/9/NGjjNu+nMtmlr++3NbPT70mnlvc3d2xsbGhfv36wH8Jk7OzM6DWwAYoX7587gXxAisrKwDCw8PTXR8REUHhwoW1cixLS8s0y5LHYicmJqYsCwkJAWDu3LlZ2u/Jkydp0aIFoCaSFStWxNzcPGViHh8fn3SrohQvXvyV+05KSmLo0KFs2rSJ/v3788MPP2QppryK71UiIiLQ09OjaNGi6e5fo9EQERGRsiw8PByNRpOlJDqn5yWEELoiCbnIEY1Gw6hxUxny888siDjNlI9GYT1nFnz1FQwYAAWsvFqhQrpva2aW9bbZFRoaypMnT6hWrVpKxYyXr5Dv3bsXICXRSc+yZctSXVF+lc6dO6eqoPKiihUrAnD9+vU0V7CDgoKIjIxM+fCQV5IT94iICCwsLF7Zfu7cucTGxuLp6UmTJk1SrTt9+jQ+Pj7pbveqqiVJSUkMGTKEDRs20KdPH9atW5fpNxeviu/QoUO0bNlSa/FlhaWlJUlJSTx+/JhixYqlWvfo0SMURUn1Qcna2hpFUXjw4AElS5bMdN857XchhNAVSchFjrV0awJ+/cAwmva+LThhZ6de8l23DoYP13V4IhuSrwJHR0enLPP29qZs2bJYW1sTHx/PTz/9hKGhIf37989wP8uWLctWbfAyZcpkmJA3a9aM+fPnc+DAAXr37p1q3f79+1Pa5KUGDRpw/vx5Tp8+TevWrV/Z/ubNm9jY2KRJCqOiolI+8GTXi8l4r169+OWXX9B/+WuaLEqOr3HjxlqLL6ucnJzw9vbG3d2dnj17plqXXGHnxb+N+vXrc+7cOQ4cOMCQIUMy3Xdu9LsQQuQmKXsocsyhSFGqxXQG4KTJcSJGjVFXzJ0L8nXwG8XW1pZixYrh7++Pl5cX4eHh+Pv74+TkRFJSEhMnTuTy5ct89tlnmQ4ZuH37Nop6s3iWHoMHD85wXy1btqRcuXJs2rQp1Vji8PBw5s2bh5GREQMHDkyzXXI5vuSkTptGjRqFgYEBY8aMISAgIM36sLCwVHWuHR0dCQ0N5fLlyynLEhMTmTRpEo8fP8728ZOHqWzYsIEePXqwcePGHCfjL8bn5+enlfiyY9CgQQDMnj07zdCU2bNnp2oD8NFHH6Gvr8/06dPTfOhTFIX7L0xQpu1+F0KI3CZXyMVr2Tb+K6r/tBUqeNDN5z0O2ttDQACsWSP1yd8wkyZNYvLkybRv3x4XFxcURSEgIICGDRvi5eVFnz59UhKlvGBgYMCqVatwc3PDxcWF3r17Y2FhwY4dO7hz5w6LFi1KmeDmRUlJSSnba1v16tX5/vvvGTlyJJUrV6Zdu3aUL1+ep0+fcuvWLY4dO8bgwYNTxnOPGTOGAwcO0KRJE3r27ImJiQnu7u4EBgbi6uqa7Q8Nc+bMYf369Zibm1OpUiW++uqrNG0yGwb0suT4WrVqpZX4ssPFxYUxY8awfPlyqlevTrdu3VAUhR07dnDv3j3Gjh2Li4tLSvsaNWqwbNkyxo4dyzvvvEPnzp1xdHQkKCgIDw8P2rdvz7Jly1Kdl7b6XQghcl0uVHl5o0nZw+xznOSm1iXv2kmJWviNWouvfHlFSUzUdWhZ9qqyh2+Lb775RilTpoyip6enAIqxsbFSt25dZd26dbl63MxKj505c0Zp27atYmlpqZiamir169dPUzM8WVJSkmJjY6OUKVNGiY+Pf+Vxk8v4zZw5M826zErhnT17Vundu7dib2+vGBoaKra2toqzs7MyZcoU5cqVK6nabt++XXF2dlbMzMwUW1tbpWfPnsrNmzeVQYMGKUCqmtqZxaMoSso2mT2SS0m+KLOyh9u3b1dq166tlfgykllfrlmzRqlXr55iZmammJmZKfXq1cu0tOHRo0eVDh06KDY2NoqRkZFSqlQppVu3bsqJEyfSnFdW+13KHr69pOyhbr2peU9WZSen1ChKbtZtePNERERgZWVFeHh4upUXcpOvry/Vq1fP02Nqg/u1szTf3AASDRj4cDnrrf1hzBh4XjbuTXDx4kUMDQ0pW7YsJiYmug5H5/r06cOWLVt48OABdnZ2uX686OjolLKKr8PX15caNWqwYsUKRo0apYXI3g7a6n+RfdL3upVX/R8TE4O/v7+8x7zkTc17sio7OaWMIRevzbVSfazD6oJ+ApsCj5A4b94blYyLtHx8fChevHieJOPa5OnpSfHixRk6dKiuQxFCCCGyTBJyoRULOnyBnnd9Ek4fZc+ePf+teKFqh3gzxMTEcO3atSyPQ85PRo4cSVBQkFyBEkII8UaRhFxoxfCWnZhU2RWeBKs3Vvn7Q8eO0KhR7s5mI7TO19eXxMTENzIhF0IIId5EkpALrfn444/R19fH3d2diwF34ehR8PGBQ4d0HZrIhrp166IoCl9//bWuQxFCCCHeCpKQC61xcHCgdvsh0LUFXRf+CR98oK5YtEi3gQkhhBBC5GOSkAutMq5lCDWPcLPIGR706gd6enDgAFy8qOvQhBBCCCHyJUnIhVatGT0ZFA2UP87Q709At27qiiVLdBuYEEIIIUQ+VaAS8jJlyqDRaFI9ZBxs3qpcvAzFn6qz6x144kX82LHqik2b4IWprYUQQgghhKpAJeSgTi394MGDlMeYMWN0HdJbZ37X8QAk1dzPMq9QaNoU4uPh+XTiQgghhBDiPwa6DkDbLCws3rjJTAqagY06MHxXCRLMH7BwzwE+nTULrl2DgQN1HZoQQgghRL5T4K6Qf/311xQpUgQnJye++eYbEhISMm0fGxtLREREqod4Pfp6+rQrribfwWXOcKN0efjoIzAz03FkQgghhBD5T4G6Qj527FicnZ2xsbHh5MmTTJ06lQcPHrAkkxsK58+fz+zZs9Ms9/Pzw9zcPDfDTSM4OBhfX988PWZuGf2uG3u2bCbpX4UFC7/hk7EfqSsUBTQa3QaXjrCwMKysrIiJiUGRiYzyXEJCAtEyq6vOSP/rjvS9buVV/8fGxhIXF8e1a9fQ0ytw10JzrCDlPemJjIzMcluNks+zjylTprBgwYJM21y5coUqVaqkWb5mzRpGjBhBZGQkxsbG6W4bGxtLbGxsyvOIiAgcHBwIDw/H0tLy9YLPJl9fX6pXr56nx8xNmzdvpm/fvpQqVYrbt2+jv24dLFsGq1dD/fq6Di+VixcvYmhoSNmyZWXadR2Ijo7G1NRU12G8taT/dUf6Xrfyqv9jYmLw9/eX95iXFLS852URERFYWVllKafM91fIJ06cyODBgzNtU65cuXSXN2jQgISEBG7fvk3lypXTbWNsbJxhsi5eT5cuXbCxseHevXvs37+fdseOga8vfP99vkvIhRBCCCF0Jd8n5EWLFqVo0aI52vbChQvo6elRrFgxLUclssLExIQuPT5g9fFLjFu2j3ZzRsEvv8CWLbB0KRQurOsQhRBCCCF0rsAMZDp16hTLli3Dx8eHW7du8euvvzJ+/Hj69+9PYUn8dOaWfQL02Mf1Yj48KO0INWtCbKxal1wIIYQQQhSchNzY2JgtW7bQrFkz3nnnHebOncv48eP56aefdB3aW23JkOc3c5Y/weQvd8GHH6rPf/5ZvcFTCCGEEOItV2AScmdnZ06fPk1YWBjR0dH4+fkxdepUGR+uY7UdKmERXhP0EvntihdK375gbAw+PnD+vK7DE1ng5uaGoaEhMTExug5F5FB8fDyzZs2iYsWKGBsbo9Fo2LVrV54d//bt22g0mlfeD6Qr2oovv5/n2ya3fx/y+xbaVGAScpF/DXYeBEBsZW+OXfKHrl3VFatW6TAqkVXe3t5UrVpVZ5UBvLy8aNeuHdbW1hQqVIiGDRuybds2ncSia4GBgSxbtow2bdpQunRpjIyMsLOzo1u3bpw5cybD7RYvXszs2bOxt7dn0qRJzJw5M93KVEIIIXQj39/UKd58X3QZyPIFn4H9BaZ/e5DjnwyHmJj/EnORb929e5fHjx/z3nvv6eT4R48exc3NDRMTE3r37o2FhQU7duygV69e3L17l4kTJ+okLl1Zvnw5CxYsoHz58rRp04aiRYty/fp1du3axa5du9i0aRO9evVKs92ePXswNzfn4MGDGBkZ6SByIYQQmZGEXOS6ooVssY9y4b7FEU5HXSCx6WT0XV11HZbIgvPPhxU5OTnl+bETEhIYNmwYenp6eHh4ULt2bQBmzJhB/fr1mTZtGt27d8fR0THPY9OV+vXr4+7uTrNmzVIt9/T0pGXLlowcOZLOnTunGap3//59ihQpIsm4EELkUzJkReSJ8S2GAZBoFcDRo+66DUZkKiYmhnnz5lGlShW6d+8OwOTJk3FycmL37t15FseRI0e4efMmffv2TUnGAaysrJg2bRpxcXGsX7/+tY7h7u6ORqNh1qxZnDt3jtatW2NhYYGVlRVdunTh9u3b6W7n4eFBx44dsbW1xdjYmIoVKzJ9+nSioqJStYuLi2P58uW4ubnh4OCAsbExxYoVo2vXrnh7e2caz8mTJ2nTpg3W1tZons9u27Vr1zTJOEDTpk1p3rw5oaGhXLp0KWX5rFmz0Gg0+Pv7c+fOHTQaDRqNhjJlyqSKr1OnTlqJLyfWrl1LgwYNMDc3x9zcnAYNGrBu3boM23t4eNC5c2eKFy+OsbExDg4OdO3alePHj6e0yW6/Z1diYiILFiygQoUKmJiYUKFCBebPn09SUlK2z/WXX35J1SY0NBR9fX06dOiQavmFCxdSfn83btxItc7V1RVTU9OUSe5y8ne9Y8cOmjVrRrFixTAxMcHe3p5WrVqxY8eOlDav+/fcvHlzLCwsKFq0KKNGjUqZIfOvv/6iUaNGFCpUiOLFizN58mQSEhIy3Nfx48dxdXXFwsICa2trunXrlqZPXiX5NZx8Hhm9hiHnv28hskMScpEnPm7Tma73BsFvp9m8+XnJw2vX4IsvIChIt8GJFKGhoTRp0oTPP/+ccuXKYWdnh56eHv369cPPz48uXbrg6emZJ7G4u7sD0KZNmzTr3NzcADh27JhWjuXl5YWLiwtGRkaMGDGCunXrsmvXLlq1apXmZtaVK1fi6urKiRMnaN++PWPHjqVUqVLMnTuX1q1bExcXl9I2JCSEcePGERsbS7t27Rg/fjyurq7s3buXxo0b4+XllW48J0+exNXVFY1Gw/Dhw9MdhvIyQ0NDAAwM/vvi09XVlZkzZ2JlZYWVlRUzZ85k5syZjBs3Ls/jS8/YsWMZOnQogYGBfPDBB3zwwQcEBgYyZMgQPvnkkzTtv/32W1xdXTl48CCtW7dm4sSJtGjRAh8fH7Zv357SLqfnlVXDhw9nypQpJCUlMXr0aNzc3FiyZEm6Mb/qXEeMGJFqu8KFC1OrVi08PT1JTExMWX706NF0f46JieH06dM0atQozTcjWf27XrlyJd27d+f69et06dKFCRMm0LZtW4KCgti5c2dKu5z265kzZ2jZsiVWVlaMGDGC0qVLs3LlSoYNG8bWrVtTvukaMWIE1tbWfPPNN8ybNy/dfZ0+fTplX2PGjKFZs2bs3LmTxo0bc+vWrQz7/0Uvvobbtm2b6WsYcvb7FiLbFJFKeHi4Aijh4eF5fuxLly7l+THz0rFjxxRAsbKyUqKjoxWlUSNFAUVZvFjXoSk+Pj6Kn5+fGld6IiMzfry8TWZto6Jy3vbZs/TbaZGbm5sCKGvXrlUURVFKlCihVK1aVVEURVmxYoUCKO+//36G2y9dulSZOXNmlh/e3t6KoihK1MvnqihK9+7dFUA5d+5cuscyNzdXHBwcXut8jx49qgAKoGzZsiXVugEDBiiAsnnz5pRlly9fVgwMDJRatWopwcHBqdrPnz9fAZRFixalLIuJiVHu3buX5ri+vr6Kubm50qpVqwzjWbNmTZbP486dO4qxsbFSokQJJSEhIc16R0dHxdHRMc3y5Phe7n9tx+fv768AyqBBg1KWJf9/ULVqVSUsLCxleUhIiFKpUiUFUDw8PFKWX7hwQdHT01Ps7e0Vf3//VPtPSkpSAgMD05zXyzI6r/Tiy0hyH9SqVUuJfOH1d+/ePcXW1jbd/WR2rhUrVkxzrhMmTFAA5cyZMynLOnbsqFSqVElxcHBQ+vTpk7L88OHDCqDMmTMnTYxZ/bt2dnZWjIyMlIcPH6Y53xf/zl/n73nXrl0py+Pi4pSaNWsqGo1GsbW1Vc6ePZuyLiIiQilWrJhiY2OjxMXFpbuvH374IdVxfvjhBwVQOnTokGp5er/Xl1/DL/7tp/cazsnvOz3R0dGZv8e8pQp63pOdnFIS8pdIQp57EhMTlVKlSikYOyprN/yuKCtXqgl5zZq6Du3VCblaNT39R7t2qduamWXctlmz1G1tbTNuW7du6raOjum305K9e/cqgNK/f39FURQlKChIAZR+/fopiqIoT548UQClfPnyGe7D0dEx5U0zK4/kxD+9hLx169YKoFy/fj3dY9nb2yuWlpavdc7Jb7YuLi4ZrpswYULKsrFjx6ZJnpIlJiYqRYsWVerUqZOlY3fs2FExMjJKN+lwdnbO8jnExcUpLi4uCqBs2LAh3TYZJeTJ0ut/bcWnKOknRkOHDlUAZevWrWna//rrrwqgDB06NGXZyJEjs/1BID3pnVd2EvIhQ4YogLJjx44067788st095PZua5duzbNue7evVsBlPnz5yuKoigJCQmKlZWVMmLECGXgwIGKnZ1dStvp06en+ZvM7t+1s7OzUqhQISUkJOSV55+RzP5emjdvnqb9nDlzFEAZMmRImnXJ/XXr1q00+6pUqZKSmJiYqn1iYqJSsWJFRaPRKI8ePUpZnt7v9eXX8It/++m9hnPy+06PJOTpK+h5T3ZySrmpU+QZPT09nrWsAw5/M2fTEQZvmgNjx8LFi+DrC9Wr6zrEt9rq1asB+Pjjj4G0N3SampoC6u8xIxmNuc7v6tSpk2ZZqVKlAAgLC0tZdvr0aQD279/P4cOH02xjaGjI1atXUy27cOECCxcu5Pjx4wQFBREfH59qfXBwMCVKlEi1rF69elmKOykpicGDB+Ph4cGwYcMYMGBAlrZ7Ob758+dz6tQprceXmeQxx67p3ODdvHnzlNiSnT17Fkh/CFN6ctLvWeHj4wOo4/Zflt4yyPxcXVxcUuJ9cZm+vj5Hjx5lypQpeHt7Ex4eTosWLYiKimLDhg1cuXKFqlWrcvToUUxNTWnQoEGafWf177p3795MnjyZ6tWr07dvX5o3b06TJk2wtLRMs31O+vXFe0CSJbfJbN39+/cpW7ZsqnXvvvtumv+D9PT0ePfdd7l+/To+Pj60atUqzT6Tvfwajo+PTxnuBWlfwzn5fQuRE5KQizxVpmQRQg3i8De5SYS+PpbvvQd//glbtsBXX+k6vIxFRma8Tl8/9fNHjzJu+3Iym1kC+3JbP79cnd3U3d0dGxsb6tevD/yXRDg7OwNqDWyA8uXL51oML7KysgIgPDw83fUREREULlxYK8dKL/FIHov94jjekJAQAObOnZul/Z48eZIWLVoAaiJZsWJFzM3NUybm8fHxSbkR70XFixd/5b6TkpIYOnQomzZton///vzwww9Ziimv4nuViIgI9PT0KFq0aLr712g0REREpCwLDw9Ho9FkKYnO6XllRXh4OHp6etja2qYbd3qye66WlpY4Oztz4sQJ4uPjOXr0KBqNhubNm6fcdHj06FEcHR05e/YszZo1S7eCTlb/ridNmkSRIkVYuXIlixcvZtGiRRgYGNC+fXuWLl2akhTntF8ziyOzdS8n+8n9lZ7k5Rn9f5Esu6/hnPy+hcgJSchFnprWeSA99q6Byqf44ee/mNynj5qQb94MX34Jr1GtIVcVKqT7tmZmWW+bTaGhoTx58oRq1aqlVMx4+Qr53r17AVLekNOzbNmyVFfeXqVz587pXiEDqFixIgDXr19Pc6UvKCiIyMjIlA8PeSU5eYiIiMDCwuKV7efOnUtsbCyenp40adIk1brTp0+nXH172auqliQlJTFkyBA2bNhAnz59WLduXabfXLwqvkOHDtGyZUutxZcVlpaWJCUl8fjxY4oVK5Zq3aNHj1AUJVWyZm1tjaIoPHjwgJIlS2a675z2e1ZYWVmRlJREcHBwmgT74cOH6W6T3XMF9VsCLy8vzp49i7u7O++8807K8cqWLcvRo0epWLEi8fHxKd8o5JRGo2Ho0KEMHTqUJ0+e4OnpyebNm9m2bRvXr1/n4sWL6Ovr52q/ZlVGfZy8PPmDfEZefg1HR0enfPuXnpz8voXICamyIvJUlzpN0IsuAqZhfLf7DHTsqCaat27BuXO6Du+tlXy1LLkMGahXyMuWLYu1tTXx8fH89NNPGBoa0r9//wz3s2zZMmbPnp3lx4tf078subzfgQMH0qzbv39/qjZ5JXlYQPLX3q9y8+ZNbGxs0iQvUVFRKR94suvFZLxXr1788ssv6L/8LU0WJcfXuHFjrcWXVckf9JKr6bwoedmLH9aSP3yl9/fwstzo92S1atUCSLfaUEYViDI71+RtXv5gmpxkHzhwAE9Pz1QfhFu0aIG7uztHjhwB0h8Kk1NFihShc+fObN26lRYtWuDn55dSUjA3+zWrTpw4kabcYFJSEidPnkSj0aT8fjKS3ddwTn7fQuSEJOQiT+nr6eNsqtbXDbQM4ElMDHTqBFZWcPOmjqN7e9na2lKsWDH8/f3x8vIiPDwcf39/nJycSEpKYuLEiVy+fJnPPvss0yEDt2/fRlFvFs/SY/DgwRnuq2XLlpQrV45NmzalStzDw8OZN28eRkZGDBw4MM12yeX40kt+XteoUaMwMDBgzJgxBAQEpFkfFhaWqh6zo6MjoaGhXL58OWVZYmIikyZN4vHjx9k+fvIwlQ0bNtCjRw82btyY42T8xfj8/Py0El92DBo0CIDZs2enGZoye/bsVG0APvroI/T19Zk+fTp37txJtS9FUbh//37Kc233+4uSx+nPmTOHZ8+epSwPDAzk22+/TXebzM41ubzfi+cK0KRJEwwMDFi5ciVPnz5NlZA3b96c4OBgVq9eTaFChV57TL+7uzvKS8Ph4uPjU4Z3mJiYALnbr1l17do1fv7551TLfv75Z65du0b79u3THRb0ouy+hnPy+xYiJ2TIishzn3XsT4/d66HKcVatOcRnS5fCunXwUg1dkbcmTZrE5MmTad++PS4uLiiKQkBAAA0bNsTLy4s+ffqkJEp5wcDAgFWrVuHm5oaLiwu9e/fGwsKCHTt2cOfOHRYtWpQywc2Lkq+evViPW1uqV6/O999/z8iRI6lcuTLt2rWjfPnyPH36lFu3bnHs2DEGDx6cMp57zJgxHDhwgCZNmtCzZ09MTExwd3cnMDAQV1fXbH9omDNnDuvXr8fc3JxKlSrxVTr3XWQ2DOhlyfG1atVKK/Flh4uLC2PGjGH58uVUr16dbt26oSgKO3bs4N69e4wdOzblhkeAGjVqsGzZMsaOHcs777xD586dcXR0JCgoCA8PD9q3b8+yZctSnZe2+v1FzZs3Z8iQIaxdu5YaNWrQpUsXYmNj2bp1Kw0bNmTPnj2vfa4A5ubm1KtXj1OnTqGnp5fq26Dkq+ePHz/Gzc0t1U2JOdG5c2csLS1p2LAhjo6OxMfHc/DgQfz8/FLNhpub/ZpVbm5ujB07lr179/LOO+9w+fJldu/eja2tbZYS5Jdfw25ublSqVCnD13BOft9C5Ii2S7y86aTsYe6LS4hT9KZYK8xCKdtioq7DURQlC2UP3xLffPONUqZMGUVPT08BFGNjY6Vu3brKunXrcvW46ZXdS3bmzBmlbdu2iqWlpWJqaqrUr18/TW3lZElJSYqNjY1SpkwZJT4+/pXHTS6lNnPmzDTrMiuFd/bsWaV3796Kvb29YmhoqNja2irOzs7KlClTlCtXrqRqu337dsXZ2VkxMzNTbG1tlZ49eyo3b95UBg0apACpampnFo+iKCnbZPZILiX5oszKHm7fvl2pXbu2VuLLSGZ9uWbNGqVevXqKmZmZYmZmptSrVy/T0oZHjx5VOnTooNjY2ChGRkZKqVKllG7duiknTpxIc15Z7ffslD1UFLUM4fz585Vy5copRkZGSrly5ZR58+YpN27cyHQ/6Z3ryzW1XzRt2jQFSLeUZnKt9uTSiC/3UXb+rr///nulU6dOiqOjo2JiYqIUKVJEqV+/vrJy5cpUZQwVRXt/z8nlHtP7e505c6YCKEePHk13X56enkqzZs2UQoUKKZaWlkqXLl3SLY+alddwiRIlXvkazunv+0VS9jB9BT3vyU5OqVGUXCzb8AaKiIjAysqK8PDwdO/+zk2+vr5Uf0tK/3X5Ziq7tu/D+MY9Qu7ewczMTK0g8vAh2NnleTwXL17E0NCQsmXLpnw9+zbr06cPW7Zs4cGDB9jlwe/jVTdWZZWvry81atRgxYoVjBo1SguRvR201f8i+6Tvs8bd3Z3mzZszc+ZMZs2apbX95lX/x8TE4O/vL+8xLynoeU92ckoZQy504vdJ8yjzKIzYkGD1Ji0/P6hQAerXz9XSfiJrfHx8KF68eJ4k49rk6elJ8eLFGTp0qK5DEUIIIbJMEnKhExqNhs6dOwOwa9cuKFtWvTp+9y54eek0trddTEwM165dy/I45Pxk5MiRBAUFyRUoIYQQbxRJyIXO1KjvBs7t+fVKFAmGhtC+vbpixw7dBvaW8/X1JTEx8Y1MyIUQQog3kVRZETrz0DQSOv1FQlhpDh3ypG337rBtG2zfDl9/nX8nCSrg6tatm6YEmhBC6JKrq6v8vyQKNLlCLnRmzHvtIN4ErANYsPYwtGsHJibqJEG+vroOTwghhBAiT0hCLnTG3NgM28hGAJwMvo5iZgatWqkr//hDh5EJIYQQQuQdSciFTnV5530A4hxvc/68N7yvPufPP3UYlRBCCCFE3pGEXOjUhI6d1B8c/mHZD0ehY0cYORK+/FK3gQkhhBBC5BG5qVPoVBW7spg8rUCMxQ3+uHwViheH77/XdVhCCCGEEHlGrpALnWtSvD0k6fHU0J87d+7oOhwhhBBCiDwlCbnQudUfTKXe0YbgcZj9+/erM3UePw6TJ0NkpK7DE0IIIYTIVZKQC50rbVOcTm3eA2Dfvn3qwkGD4Jtv4PBhHUYmhBBCCJH7JCEX+ULbtm0B2H/wAvEJCf/N2rl3rw6jEkIIIYTIfZKQi3zhIQYwtC5RvYpw/PgpdZIgUBNymZ1NCCGEEAWYJOQiX6hVwQ5KnwOHc6z85SQ0awampnDvnszaKYQQQogCTRJykS+UsrbDLKwGAPtvXVaT8RYt1JUybEUIIYQQBZgk5CLfaFxUvbEzolggDx48kHHkQgghhHgrSEIu8o1RrbuoP5T/hx07D8F7aoLO9esQG6u7wN5ybm5uGBoaEhMTo+tQRA7Fx8cza9YsKlasiLGxMRqNhl27duk6LCGEEM9JQi7yjU516qGJsQaTCH7+ywvKlIGLF9Vx5MbGug7vreXt7U3VqlUxMTHRyfG9vLxo164d1tbWFCpUiIYNG7Jt2zadxKJrgYGBLFu2jDZt2lC6dGmMjIyws7OjW7dunDlzJsPtFi9ezOzZs7G3t2fSpEnMnDmTKlWq5GHkQgghMmOg6wCESKavp0+J2EbcN/mby9H3SExMRL9GDV2H9Va7e/cujx8/5r3kbyvy2NGjR3Fzc8PExITevXtjYWHBjh076NWrF3fv3mXixIk6iUtXli9fzoIFCyhfvjxt2rShaNGiXL9+nV27drFr1y42bdpEr1690my3Z88ezM3NOXjwIEZGRjqIXAghRGYkIRf5Sv963fnmzyASA29w7tw5GjRooK5QFNBodBvcW+j8+fMAODk55fmxExISGDZsGHp6enh4eFC7dm0AZsyYQf369Zk2bRrdu3fH0dExz2PTlfr16+Pu7k6zZs1SLff09KRly5aMHDmSzp07Y/zSN0r379+nSJEikowLIUQ+JUNWRL6yoM9QuiWUh2uX1Fk7FQWGD4eSJeHmTV2H91aIiYlh3rx5VKlShe7duwMwefJknJyc2L17d57FceTIEW7evEnfvn1TknEAKysrpk2bRlxcHOvXr3+tY7i7u6PRaJg1axbnzp2jdevWWFhYYGVlRZcuXbh9+3a623l4eNCxY0dsbW0xNjamYsWKTJ8+naioqFTt4uLiWL58OW5ubjg4OGBsbEyxYsXo2rUr3t7emcZz8uRJ2rRpg7W1NZrnH0a7du2aJhkHaNq0Kc2bNyc0NJRLly6lLJ81axYajQZ/f3/u3LmDRqNBo9FQpkyZVPF16tRJK/FlZseOHTRr1oxixYphYmKCvb09rVq1YseOHTnu38zi8fT0RKPRMHTo0HTjefToEYaGhrz77ru5cnwhhMgOSchFvpM8a+e+ffvUq+LXrsGDB3DggI4jK/hCQ0Np0qQJn3/+OeXKlcPOzg49PT369euHn58fXbp0wdPTM09icXd3B6BNmzZp1rm5uQFw7NgxrRzLy8sLFxcXjIyMGDFiBHXr1mXXrl20atUqzc2sK1euxNXVlRMnTtC+fXvGjh1LqVKlmDt3Lq1btyYuLi6lbUhICOPGjSM2NpZ27doxfvx4XF1d2bt3L40bN8bLyyvdeE6ePImrqysajYbhw4enOwzlZYaGhgAYGPz3xaerqyszZ87EysoKKysrZs6cycyZMxk3blyexrdy5Uq6d+/O9evX6dKlCxMmTKBt27YEBQWxc+fOHPdvZvE0adKEMmXKsGPHjnRvSN68eTMJCQkMGDAgV44vhBDZoohUwsPDFUAJDw/P82NfunQpz4+ZH928GaBgU1ehZDslJCREUebOVRRQlM6dc+2YPj4+ip+fnxIdHZ3u+sjYyAwf0fHRWW4bFReV47bP4p6l206b3NzcFEBZu3atoiiKUqJECaVq1aqKoijKihUrFEB5//33M9x+6dKlysyZM7P88Pb2VhRFUaKiotLsq3v37gqgnDt3Lt1jmZubKw4ODq91vkePHlUABVC2bNmSat2AAQMUQNm8eXPKssuXLysGBgZKrVq1lODg4FTt58+frwDKokWLUpbFxMQo9+7dS3NcX19fxdzcXGnVqlWG8axZsybL53Hnzh3F2NhYKVGihJKQkJBmvaOjo+Lo6JhmeXJ8L/e/tuNzdnZWjIyMlIcPH6ZZ92I/Zrd/XxXP9OnTFUDZunVrmnV16tRRjIyMlCdPnuTa8bMivb99kXfyqv+jo6MzfY95WxX0vCc7OaUk5C+RhFz35v25SWEWCh/WVbZv/0NRzp5VE3JLS0WJj8+VY74qIWcWGT7a/douVVuzuWYZtm22tlmqtrYLbTNsW/enuqnaOi51TLedtuzdu1cBlP79+yuKoihBQUEKoPTr109RFEV58uSJAijly5fPcB+Ojo4pCUpWHsmJf3pviq1bt1YA5fr16+key97eXrG0tHytc05OqFxcXDJcN2HChJRlY8eOVQDFw8MjTfvExESlaNGiSp06dbJ07I4dOypGRkZKXFxcmmM6Oztn+Rzi4uIUFxcXBVA2bNiQbpuMEvJk6fW/tuJTFDUhL1SokPoBOxPZ7d9XxfPvv/8qgNKxY8dUy/38/BRA6fzSh3xtHz8rJCHXLUnIdaug5z3ZySnlpk6R7/Rt0oRp5wH782zcfpluG9tD4cIQGgrnz0P9+roOsUBavXo1AB9//DGQ9oZOU1NTAPT0Mh7pltGY6/yuTp06aZaVKlUKgLCwsJRlp0+fBmD//v0cPnw4zTaGhoZcvXo11bILFy6wcOFCjh8/TlBQEPHx8anWBwcHU6JEiVTL6tWrl6W4k5KSGDx4MB4eHgwbNizN8IusuHDhAvPnz+fUqVNajy9Z7969mTx5MtWrV6dv3740b96cJk2aYGlpmapdTvo3s3gqVapE/fr12bdvH8HBwdja2gKwceNGgDT9pe3jCyFEVklCLvIdx8IOGEeWJdbcH/fbN0FfH1xc4I8/wN1dJwl55NTIDNfp6+mnev5o0qMM2+ppUieztz+5neW2fqP9UBQlkyhfj7u7OzY2NtR/3r/JN/U5OzsDag1sgPLly+daDC+ysrICIDw8PN31ERERFC5cWCvHejkxhP/GYicmJqYsCwkJAWDu3LlZ2u/Jkydp0aIFoI6Fr1ixIubm5ikT8/j4+BCbzqRXxYsXf+W+k5KSGDp0KJs2baJ///788MMPWYopr+J70aRJkyhSpAgrV65k8eLFLFq0CAMDA9q3b8/SpUspW7YskP3+zUo8AwYM4OzZs2zdupXRo0ejKAq//vorhQsXpn3ybMDP5cbxhRAiKyQhF/lSLYvmnFX8CbMO5smTJxRxdVUT8qNHYfLkPI+nkFEhnbc1MzTLctvsCg0N5cmTJ1SrVi2lQsTLV8j37t0LkJLApWfZsmWprii/SufOnVNVUHlRxYoVAbh+/XqaK9hBQUFERkamfHjIK8mJe0REBBYWFq9sP3fuXGJjY/H09KRJkyap1p0+fRofH590t3tVlY6kpCSGDBnChg0b6NOnD+vWrcv0m4tXxXfo0CFatmyptfjSaz906FCGDh3KkydP8PT0ZPPmzWzbto3r169z8eJF9PX1s92/WYmnd+/eTJgwgY0bNzJ69Gg8PDy4c+cOI0aMSFMeMjeOL4QQWSEJuciXetR9j7Nea6DcNQ4f9qBnixZQuzakM7RAvL7kq8DR0dEpy7y9vSlbtizW1tbEx8fz008/YWhoSP/+/TPcz7Jly7hz506Wj1umTJkME/JmzZoxf/58Dhw4QO/evVOt279/f0qbvNSgQQPOnz/P6dOnad269Svb37x5ExsbmzTJeFRUVMoHnux6MRnv1asXv/zyC/r6+q/eMJP4GjdurLX4XqVIkSJ07tyZzp07ExwczJEjR7hx4waVK1fOdv9mha2tLW3btmX37t3cuHEjZbhKen/HuXF8IYTICil7KPKlQS7N1R+KXeGXnd5QsyZ4e8NXX+k2sALK1taWYsWK4e/vj5eXF+Hh4fj7++Pk5ERSUhITJ07k8uXLfPbZZ2nGE7/o9u3bKOrN4ll6DB48OMN9tWzZknLlyrFp0yYuXLiQsjw8PJx58+ZhZGTEwIED02yXXH4uuWyiNo0aNQoDAwPGjBlDQEBAmvVhYWGp6nc7OjoSGhrK5cuXU5YlJiYyadIkHj9+nO3jJw9T2bBhAz169GDjxo05TsZfjM/Pz08r8WXE3d09zXCr+Pj4lCEiJiYmQPb7N6uSx4qvWrWK3377jbJly6Zbfzy3ji+EEK8iV8hFvlTUvAhm4e8QZXUZj3syIVBemDRpEpMnT6Z9+/a4uLigKAoBAQE0bNgQLy8v+vTpw+zZs/MsHgMDA1atWoWbmxsuLi707t0bCwsLduzYwZ07d1i0aFHKBDcvSkpKStle26pXr87333/PyJEjqVy5Mu3ataN8+fI8ffqUW7ducezYMQYPHpwynnvMmDEcOHCAJk2a0LNnT0xMTHB3dycwMBBXV9dsf2iYM2cO69evx9zcnEqVKvFVOh9QMxsG9LLk+Fq1aqWV+DLSuXNnLC0tadiwIY6OjsTHx3Pw4EH8/PxSzbaa3f7Nqo4dO2JlZcWSJUuIj49n7Nix6Q4zya3jCyHEK+VGmZc3mZQ9zD9mb92oUKmcgrHmv/rFUVGK8rx2tTa9quzh2+Kbb75RypQpo+jp6SmAYmxsrNStW1dZt25drh43s9JjZ86cUdq2batYWloqpqamSv369dPUDE+WlJSk2NjYKGXKlFHis1AiM7ls3cyZM9Os8/f3VwBl0KBBadadPXtW6d27t2Jvb68YGhoqtra2irOzszJlyhTlypUrqdpu375dcXZ2VszMzBRbW1ulZ8+eys2bN5VBgwYpgOLv75+leBRFSdkms0dyKckXZVb2cPv27Urt2rW1El9Gvv/+e6VTp06Ko6OjYmJiohQpUkSpX7++snLlylRlFZNltX+zE8+HH36Y0kf//vtvpm1z4/gZkbKHuiVlD3WroOc92ckpNYqSi2Ub3kARERFYWVkRHh6ebuWF3OTr60v16tXz9Jj5Xa1atbh48SJbt26lZ926ULUqGBqqJRCfz0yoDRcvXsTQ0JCyZcumfH3+NuvTpw9btmzhwYMH2NnZ5frxoqOjU8oqvg5fX19q1KjBihUrGDVqlBYieztoq/9F9knf61Ze9X9MTAz+/v7yHvOSgp73ZCenlDHkIl9r3lwdS3706FEoUwYsLODZMzh3TreBFXA+Pj4UL148T5JxbfL09KR48eIMHTpU16EIIYQQWfbGJORz586lcePGmJmZYW1tnW6bgIAA2rdvj5mZGcWKFePTTz8lISEhbwMVWhVdpCo078BWrzDQ04Pkqhq5cMOeUMXExHDt2rUsj0POT0aOHElQUJBcgRJCCPFGeWMS8ri4OHr06MHIkSPTXZ+YmEj79u2Ji4vj5MmTrF+/nnXr1jFjxow8jlRo02XlMjTbQ6hdOA8ePABXV3WFJOS5xtfXl8TExDcyIRdCCCHeRG9MlZXk6g7r1q1Ld/2BAwfw8/Pj0KFDFC9enNq1a/Pll1/y2WefMWvWLIyMjPIwWqEtXeo254TXcnC8zeHDx+j/fAgLx49DXBzI71Xr6tatm6szggohhBAitTfmCvmrnDp1iho1aqSawtjNzY2IiIhUNYBfFhsbS0RERKqHyD/6JU+oUuwKm3ddgmrVwNYWoqJkHLkQQggh4MkTOHIEfvoJ5syBTz6BAQOgfXto3hxOnNB1hK/0xlwhf5WgoKBUyTiQ8jwoKCjD7ebPn59ubWU/Pz/Mzc21G+QrBAcH4+vrm6fHfBMYPy1HrMUtjt0KwNfPDwcnJ6wOHuTh1q081lIlnLCwMKysrIiJiZGrwzqQkJCQapZQkbek/3VH+l638qr/Y2NjiYuL49q1a+jpFZhroa8t23lPUhJGt29jdvEikY0bk1CsGAA2mzZhP39+hpvdOXeOp1ZWrxtutkVGRma5rU4T8ilTprBgwYJM21y5coUqVarkWgxTp05lwoQJKc8jIiJwcHCgWrVqUvYwn6hu6cI/yi2eFYnCxsYGq3Hj4L33KN6uHcUrV9bKMZLLHpqYmMgNgTogpd90S/pfd6TvdSuv+l+j0WBkZCRlD1+SpbwnPBz274c9e2DvXvVqOMAvv0CLFurPLVrA9u1QsSKULAlFiqiPwoXB3BzHd9+FUqVy92TSkZ1RFzpNyCdOnJjp1NkA5cqVy9K+7OzsOHv2bKplDx8+TFmXEWNjY4yNjbN0DKEbHWu68o/POigdiKfnCXr16gHt2uk6LCGEEELkFl9fGD9eLeLwYsU8U1OoWxdeHMXQqhVcu5bnIWqTThPyokWLUrRoUa3sq1GjRsydO5dHjx5R7PlXGAcPHsTS0pJq1app5RhCN/q824RZPoDNDfYf9FET8oIuKQmePlVrrsfEQGIiKApYWsIbVhtcCCGEyJLwcEgeWmJjo44LT0pSJwXs0EF9NGqUo4kBFUVBo9FoOWDteWPGkAcEBBASEkJAQACJiYlcuHABgAoVKmBubk6bNm2oVq0aAwYMYOHChQQFBTF9+nRGjx4tV8DfcBVty/FlicV88dFELtXZD3wFd++qL1R7e2jdWmvH0vn48bg4CApSv5JLTEy7/sWqMooC/v7qf16FC6t12oUQQuQ7On9vyc8UBf7+G+bPV69+HzigLre3hw0boEEDqFAhw80TExPx8/Pj6tWr3Lx5k5s3b3Lr1i2ePHlCZGQkz549IzIyEj09PcLDw/PopLIv2wl5UlISx44dw9PTkzt37hAVFUXRokVxcnKiVatWODg45EaczJgxg/Xr16c8d3JyAtQZHF1dXdHX12fPnj2MHDmSRo0aUahQIQYNGsScOXNyJR6RdzQaDYPe68EXykS8vb159uwZhTZtgilToGtXrSTkGo0GjUbDs2fPdDueU08PQkLUZNzQUJ2Z1MwMDAxAo0mdkD99qrYNCYF799Qr50WLSmIuhBD5zLNnz9BoNBjm4MpugZWUhNXff0O/fnDxorrMxARCQ9WLTKCue0lCQgLHjx/n2LFjnDx5ktOnT/83VlsDVAasAGugKGAIJILmoCZfXyXXKFn82BYdHc3ixYtZuXIlISEh1K5dG3t7e0xNTQkJCcHX15f79+/Tpk0bZsyYQcOGDXM79lwRERGBlZUV4eHhclNnPlO6dGnu3r3P4cMHaGFkBE2bQvHi8OCBmqy+Bl9fX4oUKUJYWBiWlpZYWlpiYGCQNy/cl+upP32qnk+hQpmfV1wchIWpCXny+DojI7VPLC1fu0/yUkxMjNzopEPS/7ojfa9budn/iqKQkJCQUlLZ2tqaEiVK5Mqx3jgnTsC4cf+VLzY3h48+UpeVLJmmeUxMDAcPHuT333/njz//IFQJhVJACSAKzC+YU6NGDcqVL8dv5X8jThOXantLI0v+HfIvxYsXz9OEPDs5ZZavkFeqVIlGjRrx888/07p163Q/5d25c4dNmzbRu3dvPv/8c4YNG5b96IVIx+OnoTxqWhOsLPl7/3lazP5YTT4fPoRbt6B8+dc+hp2dHaampjx69Cjv6tFHRqoJdZEiagL+osePs7YPIyM1OQ8PV6+s37+vfu1na/vGXC2Pi4uTybt0SPpfd6TvdSsv+l9fX58SJUpgpYOye/nSzp3qt9tAYqFC6E+eDGPG/HdV/AVXr15l5cqVrNu0jogiEVABGAC80LScRTn+PfovBgZqShu/PZ7EpERszWwxMzTDzNAMS2PLTAt85AdZTsgPHDhA1apVM23j6OjI1KlTmTRpEgEBAa8dnBDJbApZElfmGBhF8ufRG3xjYgJ16sCpU+onbS0k5BqNBmtra6ysrEhMTCThxbu6c8PmzZBcA79DB1i06PX29+yZOinCmjVQuTJs2aIOdXkDXLt2jbJly+o6jLeW9L/uSN/rVm73v4GBAfr6+vl2mIROvPee+p7dsiXX+vShqqtrqtVJSUns2rWLFStWcOTIEXXhJ6RKwg31DKljX4c6JergVKxeSjKemAjGf24lOBjOB0NwsPrWaGkJk6/nzenlVJbfrV+VjL/I0NCQ8lpIkIRIpq+nT9GYejwyOsrNuMckJiai37ixmpCfPAkDB2rtWBqNBgMDg5QXeK743//UKwKgzii2ZEm6V7MVJfXIk02bwN0jgSJFNJSy18fZGZyc1GF3mJjA1Klqcm9g8F9JqMREdUhLPr65WU9PT7621yHpf92Rvtct6f88cPMmfPstLF0K+vrqe9XFi2BmRuILkwIpisLOnTuZsnAK182vw1H199OxY0f0q+lzMfYibcq1paKeG/HXXDm7z5zfT8CDRvDBDnUf+vqwY4c6mfeLYmLy8HxzKMcZR0xMDBcvXuTRo0ckJSWlWtepU6fXDkyIlzUu5cquiKMklgzl8uXL1Hz3XVi8+I2YEjeVP/+EsWPVn6dMgXnz0oz3vnQJFq8K4K+bv1PRbQeNYxvw5PIT3N07c9tEgV5dwd8OTjqjd78+jYt0YFQ3J7p21WBco0bq482apU6qsHUryJU4IYQQeUFRYOVK+PRTNUMuXRomTVLXmZm90Ezhr7/+YsLSCVwveh3aAhro07APXw//GgeH0mzbEYvpH0ZsmqEhLCz1Ya5eTf180SI157e1VUeDWljkqEpinstRQr5v3z4GDhxIcHBwmnUajYbE9Mq1CfGautVrxq7DQOmrHD16gpq91TFoXL4MERHqd1L5nbc39Omj/kc1YkSaZNzXV+GjBe6c4GuocABsIDgETv1xHLwB7kEdY9AoYPEALP4iqdJfHGcmx09U4f3bTVg1ej62lrbqDkND4Ycf1O/tnJ3VS+zvvaeTUxdCCPGWiIyEoUPht9/U582bQ/fuaZoFBAQw9MuheJl7gct/y9uUacNkt8mUtisNwJezjLl8WV1naQnvvgtNmqgPZ+fU+xw5MjdOKPflKCEfM2YMPXr0YMaMGRQvXlzbMQmRrq716zPggAFYPmDH4St88klx2LdPfTW+Cck4qFP9RkWppRqXL09JxuPiYNL8G/zv1kiUCofUtooGAqwxuRNPo/L1aN6lOfb29phbmvMo0o1rj65xNtCLS89uE23/BIpe5Y+4qxx12cbnfT+nYcOxNGlSGL1//oFeveD0aXU4y5Il6hV6GdMohBBC227cgM6d1YtlhobwzTfqEM0XhmVGR0cza8EsvrnxDUo1tdifvqJP53J9KRkwhePfVaNKr/92+dFH6rQbXbqo8wLp6+fxOeWBHCXkDx8+ZMKECZKMizxlZmRKocjqPLO6wPmHQepCNzfdBpVdixerU/62a5fyHVp4OLRoF8r5Zs5Q7ikkGML5QjQzqsXEoRNp27btK2vXel/xZuZvMzl08xARPhF85rMaGE+FWr7s2/EO5Y8dUy8brFmjlpW6ckX9QPAmfI8nhBDizXDokHolPDxcnRtjxw5o3DhVkxMnTjBo0CBu3roJI0CTpKFjyX5Yen/D1nl2xMer7Xbs+K8M+ccf5/F56ECOaqJ1794dd3d3LYcixKs5274LD8rxLPIOgYGBug4n+zQa6NsXrK1TFunrR/Eo4F84+wHcLE4Nz6qcnL4X953udOzYMUsTSThVdeLPGX8SsTqC9evXU7RoMzAL5Eab5lSc3I+5/wuAVavUKxUaDfz4I7z/vjp0RgghhNAGW1v1K99GjeCff1Il43FxcfSY0YOmzZty8+ZNStqXZLDNF3S+f4W/Rv3Cxh/UZLxxY3WCzueVEd8aObpC/r///Y8ePXrg6elJjRo10iQMY5NvWBNCy45NXU7dunU573eWEydO0LNDB1iwQJ1cYNeu/HnF9/FjtbzhrFnqf1bPhUSHcO/+PT7o/QH37vmh/6gkX876hE/XfJrjCi8GBgYMHDiQ3r170+WLj9hrEoJSczPTA07xZ9eleGyZhHGlSuplh969ZdiKEEII7aldG44cUct/vVDZy8PHg04/dyK8aDg0hgGlBzB+/Arq1zcnIUF9H2rTBr74Qh0X/jbK0bv+5s2bOXDgACYmJri7u6eqr6nRaCQhF7lGo9Hw7rvvcv78eTUh794dvvtOnbHy4kW1Nnl+M2mS+nH/6lX16zzgL/dHDDjYkmeR14i7HEeRIkXYuXM1TZs21cohjYyM+GvBGradbEffHaNILHybs5Y9sXtvNlc3f0rxGzfUGT2Txcbm67KIQggh8qGkJPj8c+jWTR2OCfDSTO2frPmE725+p05jnwB9evRhw+gNALi6hmNgYMXMmWk2e+vkaMjK559/zuzZswkPD+f27dv4+/unPG7duqXtGIVI5d133wX9Ehw6dkW9SaRRI3VFfix/ePSomoxrNDB3LgDnL0fQ6bc2hBr5EmdoQKnKpTh79qzWkvEX9WzcnQezLlMyog7oxxPWbBpVxnTgieELn8UfPoQaNWD1aq0fXwghRAGVkKBWUvn6a2jfXh03/oLI2EicZzvz3d3vwAgKPSlGrVMXWNRlU0qbBQvu8fffkoxDDhPyuLg4evXqhd4bMi23KFgWeB+HqU/wM7EkMjJSrX8E+S8hT0z8b/Kfjz6CBg148DCeJt91I6mYD0TaYH+gBCf+OEG5cuVyLYyiFkW5u8gLN+MBAIS9s5+qw6ty7949tcHPP8P16/DhhzB5snrFQwghhMhIUhIMGwbr16slT775BqysUlb/c+cfSs4qiTfeoIDDjb7E/hiIz+FazJr1326MjOQ+pmQ5yqgHDRrE1q1btR2LEFlS0aE4GMRBqUj+efGmkRMn8tdNihs2qGWfCheGuXNJTIS6M0YTbX8I4kwp/LcNJ3cfpnTp0rkeikajYd+UDcyvOx/DIEMe73lMw4bvcujQTfXrxpkz1YbffKPeIf/sWa7HJIQQ4g2kKDB+PKxbpybjv/2Warbse/fu0advHyL0IiDSAPuDO7i78VcS4gzo1AnmzNFd6PlZjsaQJyYmsnDhQvbv30/NmjXT3NS5ZMkSrQQnRHrer9OQbfuBUtfw8PiHZhNGqP8pBAbC3bvqbGC6Fh0NM2aoP0+bBoUL0+vLDdy3/xkUDQa77DmwfjOOjo55GtaU9lPo9U4v2h7pyLVr03Fzs2L3Hn/azZoFlSrBkCGwcye4uMDu3WBvn6fxCSGEyOdmz1bv3QI1Ke/SJWXVv//+S5s2bQgICMDsaQcSA3/kfog9lpbqJgMHSi2BjOToCvmlS5dwcnJCT08PX19fvL29Ux4XLlzQcohCpNa+dj110pzC/uw5egcKFYJatdSVp07pNrhky5fDvXvg4AAff4y7Zxw7njy/Cu1ek02z5lM3+QaYPFa2TFn27TuGiUlNkhquo/3GERw6el0tx3jkiFoJ5vx5qF8f/Px0EqMQQoh8aPNmNSEH9X2uf38AEpIS6LOhDw17NCQgIAA7uylEXdpNbIg9LVrApUswaJAk45nJ0RXyo0ePajsOIbLMysQKs2eViDL/l0shj1EUBU3jxursYE+e6Do81YAB8OiRWvXFxISL3mGw5iA0GM/ommXo0aOHTsMrW7YIuz0DaL3rCzCMofWPn+Bp/D+avPsunDmjzuiZlAQlSug0TiGEEPlIx47QsiW4uqbM1vM09iktf2yJV6gXuEEtk1r8vmUiHTqoIyBnziyYM2tqW86KHb8kIiKCI0eOUKVKFapUqaKNXQqRqSoWjTiv/Et0kRju3r1L6XnzYNmy/POqL1ECFi0CQFEU/v67Dzy9QvWgkiza/ZuOg1O1quvEiifrGX18AFT9G5cl4zhnthzn2uXg5Em1lGThwv9toChyeUMIId5m5ubw99/wfK6MiNgIGq1ohN9TP4jT8I5/NTwOeWBpack//4CpqY7jfYPkaMhKz549+d///gdAdHQ0devWpWfPntSoUYMdO3ZoNUAh0tO84vMbOUs95PTp02BhkT+S8ZduKvV95MunP3/Kvn37MDYOYtu2VZiYmOgouLRGufVkYd1VkGiAUmM39WdMJigoVJ1JtEyZ/xquWKFWikme01gIIcTb4eFD+Omn/97fDA1BoyEsJoxG3z9PxqMN0PvlKP3remNpaQlIMp5dOUrIPTw8Umom79y5E0VRCAsL47vvvuOrr77SaoBCpKd3oyZYBjaFK3GcPn0m9Updlu374Qdo2xbOnCEhMQmXRR+w+MFiaABffPEFVatW1V1sGfj0/QF8XvU7UDQk1tlG3Y87ERsb+1+Du3fVO+p/+gnatVOvnAshhCj44uLUSX9GjODFeoUh0SE0WtkIvwg/iDKCDSdIutuMw4cNSUzUXbhvshwl5OHh4djY2ACwb98+unXrhpmZGe3bt+f69etaDVCI9NQtU5X/NR0GXuc4c+a0uvDbb6F8edBVlZ/ERHWYyv794OXFqB82ElroLMSaUyGuFp9++qlu4sqCr/qMpJvlZwAEVj9O15FdUZKvhjg4wI4d6s2zhw6pEzHJBGBCCFGwKYo6TvzECbXGeN++KavG7hjL1Yir8MwU1p+FB/Xp0wf++it/fFn9JspRQu7g4MCpU6d49uwZ+/bto02bNgCEhobmq6/jRcHW8PnUXv/88w9xcXEQE6MmirqqtLJnj3p8GxuCuvRg9R01wcXDmQ0rVmJkZKSbuLLot/Hz6GTXCc0+DXvX7uXzz2f+t7JjRzh+HEqWhKtXoUEDdZy5EEKIgmnNGnXiOI1Gra5SuTIA169f58DkA3CpOqzzgoe1GD8eNm6EfP42l6/lKCEfN24c/fr1o1SpUtjb2+Pq6gqoQ1lq1KihzfiEyFDJkuUpVMqVWNsmXLx4Ub1yC2pCrosJgpYtU/8dNoweq5aSVCgIQhzpUdqRRsmx5WMajYY/RvzBquGrgJrMn9+fzz/f/V+D2rXh7FlwdobgYGjRQv1PWgghRMHy778wdqz687x58N57KIpCSEgI7du35/GDYbDjEjx+h6+/Vr+YlsnbX0+Oum/UqFGcPn2aNWvWcPz4cfSe/xbKlSsnY8hFnllz/G+efegO7wdy8uQZqFtXvfP7wQMICMjbYC5eBHd30NfnStfuHI//FgD9w44sXjA3b2N5TUOHDqV69Q1gass87138tsvzv5X29uDhAe+/D7GxcP++7gIVQgihfbGx0KcPREWpJQ4nT0ZRFD744wOcJjpx/fp1rK3VOzYXLIDPPtNxvAVEjsse1qlThzp16qRa1r59+9cOSIis6tqgLmNOAUX/5a/D1xk71kydIOiff9Sr5Hk5C+a33z4Pqiv9/lwLhjEQUIvPOjfFwcEh7+LQkuPHq1Pk04YkljxHr813qFNzHeXKlVJXFiqkjinfuVO92UcIIUTB4e4OPj5QpAhs2AB6ekw//DlrfdZCaTArb4bHzveJiYF69XQdbMGR7Svk169fZ8eOHfj7+wPw119/4eLiQr169Zg7d+5/N4IJkcvsrYpj+Kw0aBTO3g1SF744bCWvhIWlDN0IGziWS3vehScVMD1ty5Q39NKBlZU+q3rNh0RDlGqHqTnqM2JiYv5roK+vzviQXJc8IgKGD88/EzMJIYTIGTc3NSnfuBHs7VnjvYZ5x+ep6/aYsON/O6hRo4Yk41qWrYR8586dVKtWjb59+1K1alU2bNhA9+7dKVSoEMWLF2fWrFksXLgwt2IVIo0yBvUBCDOPJjg4WDcJuZERLF0Kffpg2qoBRYLOwv9m8kX/VlhYWORdHFo2uGUr+tuow22eNdyMc+/PMv7APWyYevPPu+/CnTt5GKUQQgita9oU2rbl9L3TjNg9Ql3m/gWlHgXQqFFb3cZWQGUrIZ87dy6TJ08mJiaGlStX8tFHHzF//nz+/vtv9uzZw4oVK1i3bl0uhSpEWo0dn08QVDKcM2fOQOPGULMmNGyYdzd2mpmpNVo3bWLL1k08fPgttkXGM2bMx3lz/Fz0y7hPqRTRBzQKV6qu58PPlqbfcMYMtTziv/+qv4NLl/I2UCGEEK/n66/V/8Ofe/D0AZ03dSZBSYArHeHYLFq0KMrzeX+ElmUrIf/3338ZOnQoGo2GQYMGERcXR6tWrVLWt2nThjtydUzkoc51G6g/lLqC5/F/1NklfXzgu+/yfJr3BccX8Olvn4IhfPrpp5ibm+fp8XOLz9frMH1cB0zCWRM1md0Hdqdt9M47ahnEd95Rb/Rs2hQ8PdO2E0IIkf/8/TdMnapW0Xr8mMSkRLpu7crD6IfwuCLs/JUWzf+rgii0L1sJ+bNnz1K+gtfT08PU1BQzM7OU9aampqln+BMil7nVdIJEQzB/xN4TOvgwOH8+rFzJg7u3mbrvSx7Xe0yhaoUZNWpU3seSS0wMjbj0xW5M483AIJGBowam3EOSSqlSahLepAmEh0Pr1vDHH3kfsBBCiKyLjISPPlJ//ugjKFoUfT19DC5YQGQR2LKHymVN+P13PakznouylZBrNBo0L3w0evm5EHnN1NCULuafwdaK+F/eRVJSkroiNhZye9bYqCi1PuuoUXz241cohs8gqBqj244uMFfHk5UvXgKP4ceodbYWYTfDcHObRVRUVNqGhQvDgQP/lUX8+GOIjs77gIUQQmTNF1+opYIdHWHOHADWr/+F4ys+hm8DKEJpDh40xMpKx3EWcNlKyBVFoVKlStjY2GBjY0NkZCROTk4pz6tUqZJbcQqRoW0TZmJ2J5DIkBD+/fdfdciKlRW4uOTuOPLduyEykphyjmxK+BMAzckKjB83OveOqUN1S9flzy27MTH5hevX1+Pq9r/0b/I0NYXt22HMGPVrUFPTvA9WCCHEq3l5qUM8AX74gbsJIZy6cIqPPpoHNEA/yYj9+014A6v3vnGyVYd87dq1uRWHEDlmYGBA3bp18fDw4PTp01Tt3RsSEyEoSK34UaZM7hx440YAfnCrRaLpnxBhT99apbCzs8ud4+UDpUs70K2bC7/6rcar8QKGfJbAuoXT0jY0MPjvP/lkgYFQsmTeBCqEECJz8fHw4YeQlAR9+xLfuiXd1zTB+6Y38TbxNC3/CRMm/MpLU86IXJKthHzQoEG5FYcQOZaYlEhMyXehqRUHj1xkyJAh4OSkfvI/dSp3EvLgYNi3D4D5JrfUZefq8vkPb35llVdZu64UO8euIcoshPUhP9LitxoM7NEx843c3aFdO/Uu/uTpmIUQQujO6tXqLNM2NrB0KXOOzeHs/bMAWBlYsWXLEuzt9XUc5Nsj2xMDCZHfaDQazpVZCS13434ljyYI2rYNEhI41bQSj6x8IcEIpyQrqlatmjvHy0cMDfTwnrYLTbgD2AQweM9s/r1+I/ONjh1Tx5J/8gl8/33eBCqEECJjQ4bAwoXw7bd4Rv/LPM/kyX8a8MOCH7C3t9dtfG+ZLF8hL1y4cJZv4AwJCclxQEJkl55Gj2LxdQkyPsQDvWdERkZi3rChOmQitxLy58NVLti0h6tVITqeLyZ0yZ1j5UOVShXll3a/0/9oM5Ry/+D86cc83vR7qqpLqcyYATEx6hXy0aPB0FCdTEgIIYRuGBvDp58SFhNGv5U1SSIJLgzC6Ppy2rZ9cye1e1NlOSFftmxZys9Pnjzhq6++ws3NjUbPr0SeOnWK/fv388UXX2g9SCFepY5dY/6KPASlojl37hyuyVfIL1xQr8xq88bCuDj1Kz4jI56Vqw1L62JTxJOO218xbKOA6deqLkd8lrMm8gOinPbz7shunF+3N/0P7hqNWpEmLg6WLFEnUjI0hMGD8zxuIYR4qwUHg7W1eq8P8OmBT7kbcRdCysPe5axdq2BtrdMI30pZHrIyaNCglMeJEyeYM2cOmzdvZuzYsYwdO5bNmzczZ84cjh07lpvxCpGuttWfTxBU8jYnT55WyzfZ2UFCApw7p92DGRnBnj3w6BHbT34P9GPiBAcMDLJ1S0aBsHriUGqEqTXXLzjsY8o3UzJurNHAokVqKURFgaFDYdOmPIpUCCEEAAMHqjNae3nhftudVd6r1OW71vFey1D69pWpOHUhR2PI9+/fT9u2bdMsb9u2LYcOHXrtoITIru6N6qs/2N5g37GbavI3fjwsWKAm57ngS8/vOHP9DAYGBnzwwQe5cow3gffi73jH8B3whIVTFrJr166MG2s06lCiESPUpPz333O3NKUQQoj/7NunlqO9cQOsralgVQGDqy7g9RGWYRXZtq20riN8a+UoIS9SpAh/pDMD3x9//EGRIkVeOyghssvO0hajyLIA/PMgWK2PPXmy+iitxf9g7t2DgAB8/O8y49xMGAOtu7SmePHi2jvGG0ZfTx+fKT6MfGckKOXp3j2SEyf+yXgDjUa9sfPHH2HzZpmHWQgh8kJCAkycqP48ZgxUrMikkedI2HIM9i7jt99MKWBz2r1RcvQd++zZs/nwww9xd3enQQN1qMCZM2fYt28fP//8s1YDFCKryho14F/8iSocSUBAAI65cWV88WJYtowfBreAMgrcdubjwQW/1OGr6Ovps2TJd6xf/5CoeGuazRqK3w9zqVS+Qvob6OnB8OH/PVcUuHULypXLm4CFEOJts2YN+PlBkSI8/Wwcwf7+7NhxDYDO71+lTZtaOg7w7ZajK+SDBw/mxIkTWFpa8vvvv/P7779jaWnJ8ePHGSw3aQkd+aHf51Q83ACOH+LMmTPqwrt31YooDx68/gESEmDLFhRgi+W/AJhes8PNze31910AmJgYsHmzFfTpSGKTbdSc3p/w8PBXb5iYiP3s2Wrt+PPncz9QIYR428TGwpdfApD4xXRa/dGdRt82IsHkM2rX/oStW2vqOECR4zrkDRo04Ndff+X8+fOcP3+eX3/9NeVquRC64FqtOm1q1oUkOH36tLqwTx8YMCBlEp/XcuQIBAVxrqIVYTaBkGDM0EbO6OvLxAnJOnUy58OqIyFJn9gqZyg34n2ioqIy3yguDmN/f4iIgDZt1Cs4QgghtGfVKnXIZcmS/FhPw9n7Z3lo+hADQwM2bfoIIyMZOqhrWU7Inz17lq0dZ7e9ENqQ/KEwJSF3dVX/dXd//Z3/+isAi+o7qM//rcOEUUNef78FzM9TevBuyEIAQqoeo+yHHYiNjc14A1NT7qxYAXXrwpMn0Lq1OnxFCCGEdjx/DwydMo7pHnPUZUdsmDxy8lsxod2bIMsJeYUKFfj66695kMlX/4qicPDgQd577z2+++47rQQoRHa4P0iA91tzJsSGuLi41An561TziIqC338nQQ922QcCUDLYgXIy5jldnt9NoPpd9evRR5WPUmG4G/Hx8Rm2TzI3V7/FqF4d7t+Hli0hMDCvwhVCiIJt2zY4cIAvy90lNDYEHlXD4IIH06Z9ruvIxHNZvqnT3d2dadOmMWvWLGrVqkXdunWxt7fHxMSE0NBQ/Pz8OHXqFAYGBkydOpURI0bkZtxCpOtKrDc4HSQp9j18fHyo16iROgFNQADcvg1ly+Zsx3/+CZGRXKxWkjiDCHhWhAmdmmk19oJEowGfn6ZTdUgi18rN4l65YzSc2BCvZV7o6WVwHaBIEThwAFxc1JJcrVrBsWNQrFiexi6EEAWORsM1Z0e+W/G9+nz/UqZMDqNQoQxmVxZ5LstXyCtXrsyOHTu4du0aPXv2JDAwkO3bt/Pzzz/j7u5OyZIl+fnnn7l9+zajRo2ScbVCJ5pXbKj+UOqBOmylUCGo/7xG+esMW9m2DYC4kt3gm4uw8RMGDej5esEWcHp64LdmJv2LfgDP4Py283zwwQckJCRkvFGJEnDoEDg4qEm5l1feBSyEEAXNyZMQFgbAxP2TSFQS4Fp7bMP1mTOnsW5jE6lk+6bO0qVLM3HiRHbt2oW3tzdXr17l+PHjLF++nA4dOkgiLnSqR+PnNxbb+XH42GX1Z22MI9+wAX75heMV4iGhAm1rXZWa+1mgrw+/jFrFT7V+Qu+xHuvWFaVRo2+JiYnJeCNHRzUp37UL2rfPs1iFEKJAefYMOneGMmUIPnMUj2v/QKIBHJjJ+vU2aGQOiHwlx1VWhMiPajmUQy+mCBjE4Xn9obpQGwm5uTkhXduxfO9fQCJDh3Z+vUDfMsP6DWPmzGPAQs49ccJmjDN+d65kvEGlSqmT8aAgyCyJF0IIkdqPP8Ljx1CkCIbl3yVq4VnYtAfn0pdp185J19GJl0hCLgoUjUaDXWI9AEJM4nn06BE0bgxbt8LZs6+176b/a0tAxwDMqpjRoUMHbYT7Vpk+vQmdut6ALgOJLnWF6t+6sP7g5ldv6O+v/g579oRMbgwVQgjxXHw8LF2q/jx1KmMm3CYhuiTcLMPmzXL/U370xiTkc+fOpXHjxpiZmWFtbZ1uG41Gk+axZcuWvA1U6Fyd4o3UH0rFcOLECTAzU5O5nExvf/Mm1KrFrXkz8Is6B7bQvG4fTE1NtRv0W0BPD3Ztr8Bw01/hSUUUq2AGew6k5ezeJCRlMq787l11Yqfdu2HgQEhMzLughRDiTbRlC9y7R2yJYmx2MsLrnz7A53Tp4kmlSjksbiBy1RuTkMfFxdGjRw9GjhyZabu1a9fy4MGDlEfnzp3zJkCRb7St8XwcuWkIHh4er7ezTZvg4kXW/LMFNArcqcuEIf1eP8i3lEYDP37VjF9cDqLx6wz6CRxhK41Wd2TfpQwmb3JxgR07wMBAfZP56KPXK2EphBAFmaLAokUArBrZgL57BnG1znkKF17J6tXddBycyMgbk5DPnj2b8ePHU6NGjUzbWVtbY2dnl/IwMTHJowhFfjHE1ZUfy/4Mv3rj6empLkxMhIUL1fHkWZnOHdT/1H75BYCfS0UCYPxvWVxd5eu+19W/uyN3Fm2n2IklEGNFjM093tv+HvO/n59+FZZ27dQPR3p66oxzH38MSUl5H7gQQuR3Bw/CxYtEW5nxpdHzoZpXYNq0aRQuXFi3sYkMZSshDw4O5s6dO6mWXb58mSFDhtCzZ082bdqk1eByYvTo0dja2lK/fn3WrFmDIlfS3jqmRsa0a9EWAG9vbyIiItRyH2vXqnWtDxzI2o7c3eH6dbzLmvHI5gEkGNGjWvWM62iLbHFw0OfBvvHMKXYAy9uW6hvG6GmULz+W778/zIOnL01C1qOHmowDfP899O0Lmc0AKoQQb6MLF0Bfnx+G1+FhzEMIs8P6dtNXjjAQupWtzGLMmDGpZuB89OgRTZs2xcvLi9jYWAYPHswvz68o6sKcOXPYtm0bBw8epFu3bowaNYrly5dnuk1sbCwRERGpHuLNV6pUKcqWLUtSkhmnTp1SFybfiLlnT9Z28uOPACxzeT4b57UmTB7bRcuRvt309OCL8fU59skxFr+7GCsrRwICvmL05+Ww/8YBxy+c+N/J7wmNDlU3GDIEfv1VnezpyhWpvCKEEC+bPJlnVy8xx+J5JSuPLxg1vA+FChXSbVwiU1meqRPg9OnTrFu3LuX5hg0bsLGx4cKFCxgYGLBo0SJWrFjBgAEDsrS/KVOmsGDBgkzbXLlyhSpVqmRpf1988UXKz05OTjx79oxvvvmGsWPHZrjN/PnzmT17dprlfn5+mJubZ+m42hIcHIyvr2+eHrOgOuZ3hzstigOF2b59ByVLlsSsWjXKAQl//slVHx/1qvlzL/e9/pMnVN6xA0UDW4vdB8DCvyQajUZ+R7kgLCyMNi3b4FjKhfHjA7hb1Av0EwngAmMOjmbMvk8om+hMbyc3mpZrRJWVK4ktW5aEu3fVmz7Fa5H/e3RH+l63Cmr/r7m6lrCkYAgph/HVSN5f6Zovz7Og9n+yyMjILLfVKNkY02FqasrVq1dxdHQEoF27dlSvXp2FCxcCcO3aNRo1asSTJ0+ytL/Hjx+/sm25cuUwMjJKeb5u3TrGjRtH2POZpzLz119/0aFDB2JiYjA2Nk63TWxsLLEvfO0dERGBg4MD4eHhWFpaZuk8tMXX15fq1avn6TELqpuPHlBhpT0oGmrte58Lp3eqZaDs7CAkBPbuhffeS2mfpu8XLIApU4h2qo9Z+FfwzjY+q1GTr+eO0cHZFHwv9/+ZM+F8NMUDn8RzKO/sAruLqdpPLzWd/k1GUaxYCcwsYjFa+ROapk2hdu28DbyAkP97dEf6XrcKVP8HBUFYGE/LlqTkN2V5mvgEdq5kxvuxzJ79ia6jS1eB6v90REREYGVllaWcMltXyC0tLQkLC0tJyM+ePcsHH3yQsl6j0aRKbl+laNGiFC1aNDshZMuFCxcoXLhwhsk4gLGxcabrxZupfLESGEdWINb8BpfCnxETE6Pe4Dt4MCxZAitWpErI02jaFHr0ILJRY5jwBdxqxMffy3CVvNKggRXeRzvy7Fk7Fi7swi97L3HbzAel7PdgF81XI7/iq/iHwE+U79CZmEr7aOypT+XK/XhvyAQalKmOvp7MGiyEeIssWwYLFhD4xShig8uBgQ0m18P59NPRuo5MZEG2xpA3bNiQ7777jqSkJLZv387Tp09p0aJFyvpr167h4OCg9SABAgICuHDhAgEBASQmJnLhwgUuXLiQ8nXA7t27WbVqFb6+vty4cYOVK1cyb948xoyRK5pvq8omajWUJAc9jh8/ri786CP137171QlnMtK4MWzbxqqYaOAMzZv7UKpUqdwNWKRRqJA+s2fX5taZAcQdXMCxwfv5JO4T6tSsA5QH4LbdIwIt4bd3EvnKYAPv/lIbo+k2NPi2JctOLyM2QW78FEIUcLGxsHo1AKGxLYlbeRZWHWDSBKM8H34rciZbV8i//PJLWrZsycaNG0lISEhTQmfLli00a5Y7JeFmzJjB+vXrU547OanTvh49ehRXV1cMDQ1ZsWIF48ePR1EUKlSowJIlSxg2bFiuxCPyv9YVXbnovxocH7Bv3z5atWoFFSuqU7Kbmb1y1sddV3ex6NIiKAr9+/fPo6hFRgwM9HFxaYqLS1MAwsPD+fvvg/y2aygn/mpCVdMjGJb25ZQDRBpHcDbsCP/sOkbg74EkJX7E06dladnuGZ3fs0C+FBNCFCg7dkBwMJQqxdQTanloE+V3Jk+WHOhNka2EvGbNmly5coUTJ05gZ2dHgwYNUq3v3bs31apV02qAydatW5fqhtKXtW3blrZt2+bKscWbaXBzFxb7A/aX2fWbVfI8CfDnn2p5j/R8+61aveOLL/hk41JCKoeg/6gk3brJZAr5jZWVFb17t6Z379YAPHv2DL8ff8Rx9hzuFQrHvQwcTUpk0ZlFwPugKcPPhaqjv6cszc1HM29AF+rVydZ/gUIIkT+tXIkCrPiwJk/+6gaUYdw4FywsLHQdmciibL8b2dra8v7776e7rn379q8dkBDaUt2hNMbPKhBb6AY3FSMCAgIoXbp06mQ8IeG/5ydPwrRpEBXF/TqVCdBXh7nU4kOsrKx0cAYiOwoVKkS9CRNg2DCKTZuG8//+R+OxY7Gq8ISdO5cSZb0LLO+RaB3AIY5x6JeylF04kcX9htGlk9Er9y+EEPnSpUtw/Dh/V9ZjDHvBBUz8rjNx4hpdRyayIVtjyI8cOUK1atXSrdUdHh7OO++889/MiELkA81su2N0uy7EBLJ///7UKx8/htatYdw4rPbsUWeDjIoCNzfmmcSAXhIENGT2OFedxC5yyMICli8HHx8aLlvGxo0bCQvbgm+3OH7eVRrzYw4QZQ6F/fGv9jG9jldg/fn1MomYEOLN9MMPACxoV0R9fgkGDxiMra2tDoMS2ZWthHzZsmUMGzYs3dItVlZWjBgxgiVLlmgtOCFe1/7J8/m8bEe4fZV9+/alXnnwoDob5/LlOEydCuHh0KQJyo4dbLik3q9geLU27dq55H3g4vXVrAkaDQCGSUm88/vvfHjxNhEnH3HicDmqHbWFSHPiC91l8O7BfPXLV4SHK5ne6yuEEPlKYiL89Rf/lAAPq8eQaACnGzJu3DhdRyayKVsJuY+PT6bjtNu0acM///zz2kEJoU3Jf7OHDh0i/sUbOfv2VcsfNmtGVI0aMGAA7N7Nyce+PC30LyQY0650bfQyGm8u3hxGRvDTT9C4MZrYWBr/c5HLx4IJ2GxGi+NGGF+GGYNmUL36H1SrlsSiRer7nBBC5Gv6+uDnx6yPGqnPfXvSqn4lKleurNu4RLZlK9N4+PAhhoaGGa43MDDg8ePHrx2UENpUu3YdLEq7EmFRlQMHDqReOWoUuLtza9Mm2LABrK2ZuHmpuu5yR+bPaJX3AQvt02jUIUnHj8OJE2o9ejMzHAIfcfhQHB43amNsXIh79woRoxfCpxfa0aj9de7c0XXgQgiRudtxj/gr6az65GRVpk0brNN4RM5kKyEvWbJkplOcXrx4kRIlSrx2UEJo05RffufpUHdo/zRV6cyM+J22gagilAyqRdWq5XM/QJF3NBq1xvzatfDgAfz8MzRqRP3vluPrewFX1/nYtqwLFf/mvHNt3um+hb17dR20EEKkIzISFIX5h/+HokmEm62paHkJV1dXXUcmciBbCXm7du344osviImJSbMuOjqamTNn0qFDB60FJ4Q2DHdT61Zj78fOIxcICQnJsG1ISCgxf9vB4pPMH9k8jyIUOmFpCR9+qFbXadKEChUqcOTIYbbE2NH0DiQaR/GsQx+WLmzK/8Zd462/5zMoCDZuVL9V2rLlv+W3b0OxYlCnDvTsCUuXwvnzSIcJkcvGjYN33uHfY/9CoiGccWX69PZont87I94s2UrIp0+fTkhICJUqVWLhwoX88ccf/PHHHyxYsIDKlSsTEhLC559/nluxCpEjVUrZUSRCTa4TapViy4vJxEtWr15FfPxMalTrSf/+jfMqRJFPaDQaWs5Zxo7Ibnx8Sn1TO9T8OKcDKhPWtqM65OVtSjRjYtRvE5o1A3t79T6LlSvh1CkA4uLiCAoMVCsWnT8Pv/0GEyaoyXnFijBrFjx8qNtzEKIgioqCbdvgyhWMfhoHS+5hHXyF3r176zoykUPZSsiLFy/OyZMnqV69OlOnTqVLly506dKFadOmUb16dY4fP07x4sVzK1YhcuzDWqPVH+pcYvX6jem22XVpF/N3zAdg/PhP5CrD26phQ4pu2878jY/oetERvST4tRZ0KrmHxwO6vXKG1wIhNhbmzwdHRxg6FDw8QFGIrVmbI7W60X9nSUwLncC4eH0c2jShFtCR+kxhLHv0nYjSGMHNmzB7tnrjtBBCu/74A54+5VnxshyKbQbPwhj/SWWMjGROhTdVtstHODo6snfvXoKDgzlz5gynT58mODiYvXv3UrZsWQIDA3MjTiFey6ze76P3zB4KBXM+OokjR46kWp+QlEC/jaMJfS8UW7ei9OnTR0eRivzC3NaW3367RW+lL3oxcNUSNha2xvNMEo8fo14p9/PTdZi5Q09PvTL+6BGKgwMnO35CveI7MAlaTUvrGvzaajcx49rAOB8S62kIsLZmn0EDFhSaRsepflh8WIMazbryc7GquO3R4/hx9Yo6Dx+qk3EJIV7P+vUEWIF39/ooVEVPbwTDhn2g66jEa8hxPbfChQtTr1496tevT+HChQkKCmLMmDFUrFhRm/EJoRUmRgY0NR2hPqkXyYgRI4iOjk5Zv8JzA1Fm9yGqCMMabcPExERHkYr8RE9Pj1/n/MrCqot4usOYCd76tGgRR+PGSYSu2gHVq8OgQXDvnq5DfX03b/5X69HQEL77jlOj52Md9ifv3qnDuffnwqg60HwWOB4HoygM9Yz4fMbnhIaGcvfud0z77iwYxJJU8h98m//O8FFXOFD7CE0/vEDr1h8T8/776vAXKfYuRM7dvw8HD7LwXWhadCu43qBTJ2spqvGGy1ZCHhoaSp8+fbC1tcXe3p7vvvuOpKQkZsyYQbly5fDy8mLt2rW5FasQr+Wb3sPVG18KRXDj7i1mz54NwCm/U6y+rf7dmpz7kK++cNVhlCI/mjhwIgc3HaRQIRMSEkK5ofcXK786qF4l37ABqlWDVavezPHlioL1zp3qRErPh5c8efKEwVu20HjFUiL0jKHTh2B/HgONIR0rdWRVx1VcHnWZ6M+j+LLFlwDY2cGXQ9pzfcx11nRaQxvHTugphuDoCX1G4enwPZdvnIGTJ1Fq1VK/chdCZN+vvxJpkMQGJ331+R0YPny4bmMSr80gO42nTJnCyZMnGTx4MPv372f8+PHs27cPPT09jhw5QsOGDXMrTiFeW72qdowrs45ahRSGLO/PggUL2HN0D34NAlGKhMHjqnzb/yNkHiCRnqZNm+Lu/hPNPmxBVKcAPn9qz/FDW/nDeAmG/5yBYcPg99/VUoolS+o63Kx5+hRGjqTUr78CoBx158uwuixf/T7BAcFoNBpa1N1L1RoTsC9hyfA6wyliViTD3elp9KhgU4EKNhUY4jSEwIhAFh9fzopz3xJXKpZOibAVaPL0KUqXLmjmzoUpU1JmVBVCvIKiwPr1bKkOTw0T4UkJSiUY06ZNG11HJl5TthLyv//+m3Xr1tGiRQs+/vhjypUrR+3atZk3b15uxSeEVi0d2heAq96X+GbLP1xud0hdEV6K7tG/MWxwGd0FJ/K9unXrsvvnn2n9a3uSCgfwd+tJ1PA8zPnZf2A2bzr8/bc6jOWnn6BHD12Hm7kLF9Qyhdevo+jrEzJuJrW2dCXwyR8wIISyx8qy6ZtNr3WhpaRlSZa0+5rx747m1N1T3LW6S8tPA1msxDKS79GfNg3u3oX//Q/5JCxEFi1ezJK9fYEQONeR4cNKoa+vr+uoxGvK1v+A9+/fp2rVqgCUKVMGExMT+vfvnyuBCZGbvv76a9zaTFefhDnS+uEWtq16Ry7UiVdqUa8FnkMPox9qClZ3+bdhRxr81Y9Ij/NQrx6EhUGhQroOM3M7dqgTJF2/Dg4O/D72J4qv7kZgy7HQ8nMwTKLT1E5a+9bTwcqBntV7MnHiRN7r9BljSvWn6MgSXC2CWkZxwgStHEeIAk+j4XTVIlyxCYEEY/B5xNChQ3UdldCCbCXkiqJgYPDfRXV9fX1MTU21HpQQeWF4h3cZFxPKj9VusugzK0nGRZY1rtmYw4P+Ru+pMdj+i59zS+4VtVInGTp0CNq1+69xfiuTePs29O4N0dHQti1LB/1F961VSPygFZQ7grHGhHXvr2Ppe0tz5fC//14MuyEfE1rsAbU+MOdwCVPut3HLlWMJURDN2v2z+oNfB9o316PkmzJETmQqW0NWFEWhZcuWKUl5dHQ0HTt2TFP38vz589qLUIhc0rmTAZ07WQPg66vbWMSbp5lTM3ZGb6PzH91IsrtCw2/f4dacW9i0bPlfo9u3oXlzWLIEunTRWayplCkDS5agXL9Bv6DRbN7hC0P6g2E05S0q8tfA3VS2rZxrh9fT0+Dz2d/UXfIed83O02pAYSw+8eSasxN2dna5dlwh3niHD5O0729O6v8FpoC3LSOXd9R1VEJLspWQz5w5M9Xz999/X6vBCCHEm6RT406sj17DwMMDCb8VTt9efRk1cjcdOxqq37h8842alHftCpMnw9y5YJCt/3a149EjiIyEcuXU52PGMHrUfjafDYIh3QBoUbolO/v+jqWxZa6HU6xQMXwnH+Wdr1twz+wfnnZZhXNrhVs/dMDEz0+9QVYIkdrq1eht3swXBqOZXKEVxaKX0ratTLxVULxWQi6EEG+7AS0HYJhoyNBvh7I/qin79xkyfnwSixfroVm2DExNYfFiWLgQzp2DLVugaNG8C9DXFzp2BGNjdcr7woVZsmQJK1dOBM0Mijx0plXDimzsvxEDvbz7sGBpbMmlzw5RZV4zHha6SHDz7wlpu5ASUaApWTL1sB8h3nbR0bB7NwDbEwbA1Vg+muEqN3MWIHJbuxBCvKbebXqz8/ed6OvfAr0Elu7bxddfK+oEO4sWwbZt6o2eR46AszOcPZs3ge3cCY0aqVfpExOJDAhh2hfzmfjpRACmTonnwfLTTHeenqfJeDJrE2sufHqAIvoliC8SwcB3k9AkJalj3K9cyfN4hMi39u8nISqSuBL2ePEL8DUDBgzQdVRCiyQhF0IILXBzc+PXzW2gV2Xo1Y1pfyznxx+fTxTUo4eahFeqpM7q2bQpuLvnXjBJSTBjhjpUJjISmjcn9O9TVOqQxPwbf0FnfWbPmc28efMw1DfMvTiywM6iOO7DD9DUtCnHDsMx9NT66D16QFSUTmMTIt/Yvp01TlBhQCiK0wrq139ChQoVdB2V0CJJyIUQQkt69ehF53o11SfvfcJH/9vC9u3PV1arBl5e6s2dNWpAbk2kFh4O778PX6ozaDJuHA9/2U8llzAeuH4MVU6gX0NDp2Gdcuf4OVC9WHWOfXrs/+3dd3gUVdvH8e+mhzQSQqghEELvIEjvTTqKIBZQARFRQRBFHxV9FBGk6AsoRRQeQalSBKX3rhQhdKSXAIGQhBBCSOb9YyCCBAghySTZ3+e69trdmTNn7j2Oy52zZ86hSZO+dGQZYbacsGcPvPmm1aGJWO/aNViwgB/Lw8kcseAOzz33nNVRSRpTQi4ikoZ+6fsLtZ1rm2/ad6XTe4tZtuzmTm9vcw7wZcvAzc3cFh9vLtKTVgYOhIULzTHjU6Zwst8ISlQ6RnizbhCyFGfDlSVdFlMxb8W0O2casNls9Or1GedtdahZ+0mO5AQmTTLH3IvYs6VLOeoYzfogwADbHmc6depkdVSSxpSQi4ikIZvNxpr31lA6sQw4xpPYsQNfz/r+9gLg6/vP+88+gypVoE8fiIp69AAGDTLrW7+ewzWfo1Slw0S2fQEKr8UVd1a9vIJGwY0eXI8FWrf2oEzf3hxt9D2N2waSaANmzbI6LBFrXbzIlMdymK+PVqfJ4w3JkyePtTFJmktVQv7mm2/yf//3f3dtHzNmDH379n3UmEREsjQHmwPbPviTgtcLgUsMi3J245sZ39xd0DDgxAlzzPf//Z85xvzrr82fqFMiPh5+/BFee+2fbXnzwh9/EOpWgXKPHyamQycouIUcNi829FhHrUK10uZDppNZ774L8e4cLXKS2tUaETFhotUhiVjKePFFvqx8c/Gfv3Lz/PMarpIdpSohnzNnDrVq3f2lXrNmTWYnDZgUEbFfbs5uhH6wi1xxucAZ+nzUh7lz13DixG2FbDb44QdYuhRCQuDcOejbFwoVMp/37Lm7YsMwh7j897/mvOJdupjLzy9fnlQk9to13nyzP9e8DkDuPXg75GRzzw1UyV8lnT/1oyuVpyj9yn0GwKZGm2n78niLIxKx1u+7/uRqjkMQ747Lkf20a9fO6pAkHaQqIb948SI+Pj53bff29iY8PPyRgxIRyQ583H3Y9e4uGhxvwI2D/jz1lD81a8Zw7ty/CjZpYibfEyZAYCBcuGD2lN/ewbFtm3kzqJcXVKpkDk05dQry5IHPP4ebnSSXLl2iSZMmrFo1Guczn/JO0Mds7bWZcnnKZdwHf0RfduxLQGw1cIlhncdKNi3bBB9+CJcuWR2aSMb6+2++WDjVfL2/Jk+2rIKXl5e1MUm6SFVCHhISwuLFi+/a/vvvvxN8ayU4EREhv09+fp/0O/XrN8IwcnA6/ihV6/9NWNi/Crq4mCtUHjli3pTZsSPUq/fP/rNnzUV+YmIgRw5o08bsXT92DN57D9zdGTXqIqWbdGXDoQ3kzJmTFStGMrT7+5TwL5GRH/mROdgcWPL6FEhwhhLLWfvOE+ZY+/fftzo0kYwTFwdVqtDv+5/x3N4RdrppdpVsLFUrQfTr14/XX3+dCxcu0LBhQwBWrFjBiBEj+Oqrr9IyPhGRLM/V1ZXFiydRv2MXNpdcxsmYvJSuMZstS0tTrNi/Cjs5QcuW5uN2lSrB4sVQpAgULmwm8DcZBrz5Zhhjlv4JHVbicN2FWR1nUadqnXT/bOmlYsGSdC3WkylHxvBx3Uje/gscx4+HF19MvykjRTKT5cshMpJqkfmIOTIVX99yNGvWzOqoJJ2kqof85ZdfZsSIEUyaNIkGDRrQoEEDpk6dyrfffkuPHj3SOkYRkSzP1dWV78Z/hJtTPOTeR8STTSnXaAu//ZaQsgoKFIBmzcwbP29Lxi9fhmrVTjFm03x4pi24XKVOiZpUr5D1k9axnb6gvEN5rs2AX9xvzjLRt6/5F4hIdjd3LgBLPepisIxnnmmIs7O1C3lJ+kn1tIe9evXi1KlTnDt3jqioKI4cOUKXLl3SMjYRkWylTN4y7B8Qil9iAHifJu75JrQcMIeJE1N37838+bEEBp/kzwIfQutXwSGRzqU6s+ylpXi6eKZx9BnPw8WDzW9vJr9jKd68+ibXnFxhyxaYMcPq0ETSV2IixqKF/LceTCmwEGhJhw4drI5K0tEjz0OeO3duPD2z/he/iEhGCMoZxKGB+yjpVhpco6FjJ3ovDObLr7/kxo0bKarDMAyaNz9Guze2c+XZZlBpMjbDxqf1P2Xa09Nwdsw+vWju7u5UqDCDMIbwgcvz5saBA1M+NaRIVrR9O384nmNQA1jdIQbf3L7UrVvX6qgkHaV4DHnlypVZsWIFvr6+VKpUCZvNds+y27dvT5PgRESyIz93P3YP+IvX5rzGxL0Tia8czTuz3uG7bybh7b2Arl0L0aqVG4UL/3PM5csAMSxduogvv/ySP/98Gtrsh9z7yOnkyy/PzqFBkQbWfKB09t2kEhTo3pMRlabx5GQfah4/DqNHw4ABVoeW8fbsgXnzYN8+OH7c3ObtDUWLQocOoKQte1i4kNmlb74+AG1atMHJKVW3/UkWkeL/um3btsXV1TXp9f0SchERuT8nBycmPD2BtgfbMmDOAMIOhnHwQiGgOH9eWsYbnwXgHBGMu1s8cdediHOIwymkDTeiNsMxcHc/Txu/XvhV7sVnjT/Dz93P6o+UbvLnc6Fg6cOccr7GszWLs4+SuD/5pNVhZbz58+F+c1AXKvRPQp6QYM7SkSNHhoQmactY+Cvzat58s/8x2g9ub2k8kv5SnJAPGjQo6fXHH3+cHrGIiNidlsVb0vK9lkT1jmLMmOmMHDmSi61Ggt9p4hOciL/mC07XwDWaG4D7KXfeCniLPn36EBAQYHX4GWZmr1HU/LECx8vsolloF9YWLWp1SBmvYUOzN7xuXahd25xtx9HR/Plkzx5o1OifsvPnm9Novv469OsHyawdIpnXujf6cOhYV7jhgtupMJo2bWp1SJLOUvX7R3BwMH/88Qe5cuW6Y/vly5epXLkyR44cSZPgRETshbe3N++//wp9377KE1Pms+VsOHGOceBxIalMkEcQz3d+ns8afmZhpNaoEVyeoCvNOO65hHUOywkLe5G8eXOZM65k119sV62CSZNgyhQz8fbyMldzdXN78LGzZpkLKf33vzBmjDlXfe/e4O6e/nHLIxt69rT54mglWjQqgLv+u2V7qUrIjx07RkLC3VN1xcXFcerUqUcOSkTEXuVwycGaHmuIT4jnXMw5ImIjcHZ0JsgnCHdn+/5HeWbvL3l88lIovZg3egxnlsdRKFkSsuOvtnPmQKdO5tCTqlWhTx9ze0qScYCpU+HJJ+Gjj2D/fnO8/VdfmSu8vvSSOd+9ZFrrzs8HH2B/Dtr31XAVe/BQ/0cuWLAg6fWSJUvwue0nsISEBFasWEGRIkXSLjoRETvl7OhMQe+CFPQuaHUomUa1wuUofLUJxzyW8qfHaJgRAzlzQv/+Zu9xdrF4MXTubCbjnTvDK688fB2OjvD009C+Pfz4o/lHy4kTZl2//w6//JLmYUsaiIvjfJ9+xHkdBMDh8GFa/nuRMMmWHiohb3fzZhKbzUbXrl3v2Ofs7EzhwoUZMWJEmgUnIiJyuzl9h1Bl4lKOBcdwrKA/hU+Fw7hx2WfGlW3bzCQ6Ph46djSTaUfH1Nfn5GT2iD/7LHz7LXz2mbna6S3ZechPVrRmDQHjv+GQLR9BeX6kUbX/4evra3VUkgEeah7yxMREEhMTKVSoEOfPn096n5iYSFxcHAcOHKBVq1bpFauIiNi5yvkr08W7C3wNY2Nvrtg5cmT2mJc8IsKcuvDaNWje/NGT8du5upqrnB49Cq1b/7N95Ejz/V9/pc155NEsXAjAYqMVhO2nfXsNV7EXqVoY6OjRo/j7+6d1LCIiIg807tVx5HRpwNcXvyLSKzeEhZk3PmZ13bvDsWNQpAj89BO4uKT9Oby8/ukRj4uDYcPMJLBiRXMWl3nzzKEykvEMg8SFv2IAi9gDTKNt27ZWRyUZJNV3dcTExLBmzRpOnDjB9evX79j35ptvPnJgIiIiyXF3d6d48S/YurUaHzmt4Wu+M29Y7NEDHB55AWrr9OgBmzfDzJmQEcMUXF1h3Trzxs/Zs81ZXVatMv8g6N7d7K0vXjz94xDTvn0scTxGj35wfsdGqsdWJ3/+/FZHJRkkVQn5jh07aNGiBVevXiUmJgY/Pz/Cw8PJkSMHAQEBSshFRCRd/ecLd9r+WJfR/vv47NsceO3fD8uWQbNmVoeWes2bw5EjZqKcTmJjzSnLjx+HM2fgypXiXC/1M7kGfEFvYzy2iRPMYS3/+Y85beLw4eaB167BjRvg6Zlusdm9hQtZVBxOewMe0K5ZO6sjkgyUqoT8rbfeonXr1owbNw4fHx82b96Ms7Mzzz//PH1uTc0kIiKSTp6oWxLH3w6S4BFOj4p1mN66FVSrZnVYqXPlyj+Jbjom402ax7Fi/58YgesgYDf47wevM5AjHOIDeH34afjoQ5g+nZcWfsbK8J349/uUJqWq0cspnKAeXaF8eahRAx5/3JyOsXjxtBvnbucSlyxmUbmbbw6503KIZlexJ6n6bW/nzp30798fBwcHHB0diYuLIzAwkGHDhvH++++ndYwiIiJ3cHZ0pmXAcwDMKBPFgbZdM2aYRxrL8ccf5oqbkyenab2XLpnrARk373s1DIMtj5XFeKk2NH4Pyv8E+beDVxg43sAjx62AcsDLL/NzkCcniqxgu89HDD3TnCLHXqDmiwkM9tzBoenfQNeuULq0Oe1kvXrwxx9pGr/dSUhg9/nDHPMF2w1n8sflo0yZMlZHJRkoVQm5s7MzDjfH6QUEBHDixAkAfHx8OHnyZNpFJyIicg+TXnsf4t0g3190+XCa1eE8PMMg71dfwcWLsGVLmlQZFQXvvw+FisbyxqQf2LrVzMhtNhttyjQip7Mfld0qU/NqTSruq0jwsmByTcmFbVwUnp6e5MqVi+DgYAIPuFHkUEv8zzTFKSoYw8FgUyB80AhqdvUmsXYtM3m/cgXWrr3zj6ElS2DsWPOvAkkZR0cGPv0GAMaxMrRu1gSbpqO0K6kaslKpUiX++OMPihUrRr169fjoo48IDw/nxx9/pGzZsmkdo4iIyF38PXJR/FozDjrP509WkPBjAI7Tppo3KGaFsc7z55Nj1y4zsR006JGqMgxzYpb+bydyLv/38PIg8D7DHxeCiFkJM2fOZOnCX7h85hLbjXsnyjExMVy6dMkcR347nwJ4VqjGtaKXCPAozY0VX+Hi6EjC3r38d96X9M3vR1JKPm6cOVtL//7m4kQDB4J6ex9oU/hSyAUccqfF+y2sDkcyWKoS8s8//5zo6GgABg8eTJcuXejVqxfFihVj0qRJaRqgiIjIvXzz0rs0nj2fxJKLOfHxdoocCYOffzZnLMnMEhLMrmyAt96CvHlTXVVEhLkA5+w1e6F1Dyi0EQBfmy9Dhr3AmXVnksq6ublRvXp1qlSpQqlSpQgKCsLf3x9PT08cHBy4du0aly9f5uzZsxw5coS9e/eyfft2QkNDubJ2LqyFvWwg/88zefbZZ7kSWIofEn/k8yELeLPyQD5r1Qf3pk3NhP6vv2DqVJg2zVzkaPhwKKiVZ5Nz9nwMkT7rAHA6fppGjRpZHJFktFQl5I899ljS64CAABYvXpxmAYmIiKRUozI1yDM9D+eczjGwAsw4grkiZffumXsFyunTYd8+bvj44PQIq4wmJEDtOgZ73cdBz77gdB1XXHHZ6ELEiggiEiLw8vKiU6dOdOjQgQYNGuCSivnNL1++zNq1a1m0aBHz58/n3LlzjB49GoI7QbNy3Mizm5G73mPizm+Z2H40nXrthD//hC++gDlzYMYMWLTInPe8V69Uf95s6exZjIpleaJSEL/n8aVBeW88PDysjkoyWKrGkDds2JDLly/ftT0qKoqGDRs+akx3OXbsGN26daNIkSK4u7tTtGhRBg0adNf857t27aJOnTq4ubkl3WQqIiLZ2ycNPoFZsPLXcxiurrBjR+a+ydAwYOhQAC526QI+PqmuytERCvTqDq1eA6freJ31Im5kHNFLoykcWJhvvvmGM2fOMHHiRJo1a5aqZBwgZ86ctGnThvHjx3P69GkWL15Mx44dcToxB8bFwi9vweVCRDuc4Jn5bak9+knOlypkDh/auRNq1jTHm2fmP5Kssnw5+c9f4pMl3vC/Z3ii+RNWRyQWSFVCvnr16ruSYYBr166xbt26Rw7q3/bv309iYiLjx49nz549jBo1inHjxt0xo0tUVBRNmzYlKCiIbdu28eWXX/Lxxx8zYcKENI9HREQyj54Ne1LTpwHhNzqyPt/j5sbM/N3/xx+wezd4enKxU6dUVREV9c/rV+u3xNFwhCUQPT4aPyc/vvnmGw4cOECvXr3wTOPx9I6OjjRr1owZM2Zw9OhR3n3nKTyPTISxJWBdL0hwYsOluTw2vD2GYUCFCuYCRIsXQ8+eaRpLtrBsmflEXWAxzbLyXPqSag81ZGXXrl1Jr/fu3UtYWFjS+4SEBBYvXkyBAgXSLrqbmjdvTvPmzZPeBwcHc+DAAb799luG31y0YNq0aVy/fp3vv/8eFxcXypQpw86dOxk5ciSvvPJKmsckIiKZR7Fin7FxY00+PvUTK1hrDpH4+mvIjD/9V6tm9hrv2UNiKnrHv/0WPv3UXFTTw+MUQ18eSsKeBIiBrl27Mnz4cPz9/dM+7mQULFiQL774grfffpthw4bx1VeTiA99G9otpti1RsTFxeHm5mauoHp7onnpEjzzDHz5pZmw2yvD4OLG5ewsAstPjCJ/nkBKlSpldVRigYdKyCtWrIjNZsNmsyU7NMXd3d0cU5YBIiMj8fPzS3q/adMm6tate8fPcc2aNWPo0KFERETgmwXnpxURkZT5+NOSTDn6ESvLzOP4rwUIOnXaHLvcpYvVoSWvQgXzERr6UIdNngyvvX8M2r3Ix18NYfW8JwkLCyNnzpxMnDWRDh06pEu4D+Lv78+wYcN49dVXefvtt5k7IY6VxqdU3DyTn376ibOukdQMroiv+81/i995x+wZrl8f1q+331lYDh9moedZXmwPDscNutJI0x3aqYdKyI8ePYphGAQHB7N161Zy586dtM/FxYWAgAAcM2DFrsOHDzN69Oik3nGAsLAwihQpcke5PHnyJO27V0IeFxdHXFxc0vuo238HFBGRLCGooC8ulaZy3fcobxetw6xixSFfPqvDultsLLi7p+rQ336Dbv1Owov1IedxZhypjRGWSNmyZVmwYMFd/wZaITg4mF9++YW5c+fy2muvceDAAao92ZaE58PJ516Ejb1/o3DOwuaMK3v2wObN0Lw5bNwIgYFWh5/xVq1iWVHzZeJxaNqjqbXxiGUeKiEPCgoCIDExMU1OPnDgQIbevLHlXvbt20fJkiWT3p8+fZrmzZvz9NNP0yMNprUaMmQIn3zyyV3b9+7dm+bj7h4kPDyc0IfsLZG0oba3ltrfWtml/Wt51mUVR5kdEsfq50fj7+/40D3Q6cnx8mWKt2hBVIMGnPnwQww3txS3/b59bnR5xY/EZ5+AnMfhIhhzE6lTpw7Dhg0jJiYmU/03LFasGLNmzWLQoEGs3BMCsYs467yP0iOrMbXpeIrnLIbj8OEU6dIFtyNHuNawIUemTiUxg4cYWX3t5/9lLqtu/Thw1J8CBQpkqv+O6c3q9k9vV65cSXFZm2HcWlg35aZMmYK/vz8tW7YE4J133mHChAmULl2an3/+OSlxf5ALFy5w8eLF+5YJDg5OGoZy5swZ6tevT/Xq1Zk8eXLSaqEAXbp0ISoqinnz5iVtW7VqFQ0bNuTSpUsP1UMeGBhIZGQk3t7eKfocaSU0NFQLK1lEbW8ttb+1skv7n7p8msBRQeCQwFNn5zJ7XDurQ7rT8OEwYABUqgTbtoHNlqK2Dw+HKtWuc6JuMyiyGqKB76BT8078+OOPODs7Z0j4qWEYBmPHfsMb/zkOzy6BvLtwTfBj46vLqZy/Epw4ATVqwJkz0LmzOWd5Bg7ZsPraX/HmABrnGo7thjPlfy/Lzj+3WxaLFaxu//QWFRWFj49PinLKVM2y8vnnn+N+8ye3TZs2MWbMGIYNG4a/vz9vvfVWiuvJnTs3JUuWvO/jVjJ++vRp6tevT5UqVfjhhx/uSMYBatSowdq1a4mPj0/atmzZMkqUKHHf8eOurq54e3vf8RARkaynYM4C5IqoBMDCk0sgLAwmTjSnGbRaYqK5giXAa689VNL53ntwonRfMxmPs8FU6NK2C9OmTcvUyTiAzWbj9dd7s3Zhazxm1YfTVYlzvESNcQ34K2wXFCoEM2ea8zf+/HPmnh0nHXztHwyAcbI4zRtruIo9S1VCfvLkSUJCQgCYN28eHTp04JVXXmHIkCHpMu3hrWS8UKFCDB8+nAsXLhAWFnbHLC/PPvssLi4udOvWjT179jBjxgy+/vpr+vXrl+bxiIhI5vRG3ecAiCsxh/jiIeYSln/+aXFUwPLl8Pff5pzjnTs/1KGPdZsKVb8FA5ht0L5meyZNmpQh92yllTp16rD7j774/1YPTtbgumMkNb5pzPHLx6FWLRgyBEqWNF/bkc3nVpkvjrrTuHFja4MRS6UqIff09EwaarJ06VKaNGkCmEvyxsbGpl10Ny1btozDhw+zYsUKChYsSL58+ZIet/j4+LB06VKOHj1KlSpV6N+/Px999JGmPBQRsSPvtnsVx+tO4HOBaTVu/hvx00/WBgXwww/m8/PPP/RUjDXylMf1vCusggYFG/DTTz/h5JSqhbYtVaRIEfbtfJfAdY0hrAKx+ytzdPfNjrX+/c0/nLLx8IV/i9m0g3CPlQA4ngynZs2aFkckVkpVQt6kSRO6d+9O9+7dOXjwIC1atABgz549FC5cOC3jA+DFF1/EMIxkH7crX74869at49q1a5w6dYp33303zWMREZHMy83JjXpedWEnrLx0cwG7GTPMNeatcvkyzJ1rvn7ppRQdEhkJU6dCXNx1Xu/6OnHj4yh6pihz5swx5/XOovz9/Qn9422KrG0GszfRvk1zdu/ebc5TfvsfKg9xM1yWZBg4tnqCrT9eosCSbtQoFEiOHDmsjkoslKqEfOzYsdSoUYMLFy4wZ84ccuXKBcC2bdvo/JA/xYmIiKSln1/+GaeFTkz/M5p4Lx84exbWrLEuoOnTIS4OypWDypVTdMi778IL/fZSs+Ym1q1bh7eHNwt/XZgt1tTw9vbmr80fUKN6GS5fvkzTZk0ZteQbs5Ptxg1z1aPChc0bPbOrw4dxu3SOcmHOhG96nMYNGlkdkVgsVb955cyZkzFjxty1PbnpA0VERDJSQEAAJUuOITT0JX516ciTzDeHrSSzoF2GaNPG7CUvUCBFN3OuXg3jVyyG3k+wfbO5+vWUKVPumAI4q/Py8mLRokXUrlOHvSUT6Le5Nxfjovis1TvmhOsXL8JHH8F331kdavq4eb/dnw6FiEtcQMOG71gckFgtxT3ku3btSpp/fNeuXfd9iIiIWKlN22KQbzfvFTETWmbPNnuprZA/PwwcCC+88MCisbHQrVc0tOp5c8tp+vTpQ7t27dI1RCv4+voyf95SHM4+BcDgPz9k7bFNMHKkWeD77yGb5hTGurW83gJGlT+Mm+dyHn/8catDEouluIe8YsWKhIWFERAQQMWKFbHZbHeM4b713mazkWDlWD0REbF7z/TMy+fOZTiY6MDfp/wpeikKduyA6tWtDu2+vvgCjhR5H3KegAgbFS9VYNiwYVaHlW5CQvIz++0XeHJ6KJSeT5Px7Tn3yUFyduxoToc4YAAsWWJ1mGlu/18rGdsWHOKh4Z+1k6Z4FvuV4h7yo0ePkjt37qTXR44c4ejRo0mPW++PHDmSbsGKiIikRLnA0riFFwOHRDqUb2nOSZ7RybhhmNMuTp0K1649sPiRIzDkx81QbSwAzoud+GnyT9k+WWvfvgS98r8Fl4pyPccF6g7pYE6D6OwMS5eaY3iykzNnWOtwEgDbKRuN6mv8uDxEQh4UFITt5ti3oKCg+z5ERESs1qyAmejsLHCIS0bOjA9gxw5zYaJXXjFvVnyAvm8lEN+kN9gM2AlDegyhVKlS6R9nJvDNqHqE7P4AEh3Y7bCCYXsWQo8e5s7Bg60NLq2tW8e6m6lSwvFK1KtXz9p4JFNI1Swrty93f/LkST766CMGDBiQLosCiYiIpMbwl/uDYYOgjfxn2HpzY0aOI58+3Xxu1Qo8PR9YvOjT30H+7XDNgccuP0bfvn3TN75MZsvsF3Dc3BuAges/4tJrPcDJyZwh59gxa4NLQ3FVarIgyJwtx/nsZapUqWJxRJIZPFRCvnv3bgoXLnzzDvaS7Ny5k6pVqzJq1CgmTJhAgwYNmDdvXjqFKiIiknIhASF4XjRnJlm7fSI8/jg89VTGnDwx0Zz/HKBTpxQd4nDjIMSAw2qYPGZyllqJMy34+TnyY7f3YPfTGD+1ZvD3U80FlQ4fNqdBzCYWHksk2icCEpyoUTAw2w9JkpR5qIT8nXfeoVy5cqxdu5b69evTqlUrWrZsSWRkJBEREfTs2ZMvvvgivWIVERF5KO2LmStJnwiZBVu3mmOSIyLS/8SbN8OJE2bP+M3F8+4lMRFiYmKYPWg2jIa3679NmTJl0j/GTKhzx3z89NTLcGoqo0aNZF1QEBQqZHVYaWrG5pujCc4WpmGdBtYGI5nGQyXkf/zxB4MHD6ZWrVoMHz6cM2fO8Nprr+Hg4ICDgwNvvPEG+/fvT69YRUREHsrnLwyARLjicY2TJYMgPh7mz0//E9/qHW/XDtzd71ns2jUbFSpAmzYbOXHiHEF5ghj04aD0jy8T69y5OS+99BKGYfDs688REXXzD6hLl6wNLC3s20dk6DTz9Qkv6tata208kmk8VEJ+6dIl8ubNC4CnpyceHh53rBrm6+tLdHR02kYoIiKSSgV9C9LqbCsYCTu985gbbyXL6SUhwZyyD+CZZ+5bdNYsP0LzDWDluUMAjBw5UkuoA6NGjcKzUQVOtTlHuw87Q8uWEBSUMb9upKPEufNYMmMxE4e1wXGLofnHJclD39Rp+9cqY/9+LyIikpm81v41SHyRgdv7mBuWLzdXgkwv589D0aLg5wdNmtyzWGwsjF94DGoNhw69qfpEedq3b59+cWUhPj4++Hh0BafrrHVfz5nj++HKFXNMeRYWs3wTAHuu1qRqKS/98SVJUrww0C0vvvgirq6uAFy7do1XX30VDw8PAOKsWgVNRETkHho3boyzswt7bzRgf8CnlDy/H+bNg27d0ueE+fLB+vUQGQn3uWFvwgSIrPiZ+eYvB8YPHq9Ortss+ORNqkz4AfLs5qmS19i0Bxg7Fvr0gax4w6th4LnbTMg3sZT69WpaHJBkJg/VQ961a1cCAgLMv1x9fHj++efJnz9/0vuAgAC6dOmSXrGKiIg8NGdnZ4o8OQ3eKkTPCn7mxltDStKTj889d127Bp9+vxWK/waJNp70b0elSpXSP6YspHJFR+pdM+cg31z6HPvyu5mrJ61YYXFkqfT33wwvHk6DLrCt9Epq1apldUSSiTxUD/kPWfynIhERsU8tm+fn4PHTbCrpzKUb7fDr8Vz6nOjECciRA/z971ts4kS4WPYTAGy7HRgxckT6xJPF/fRZawoObIpRdCnda3iyYc41mDIFmja1OrSHt2kTy4NhdTCwD6pn9MqxkqmlamEgERGRrOTDp/tDvAvxuY7xTMEe0KFD+pxo0CBzyMrYsfcskpAAn/3wT+/4swWfoXA2mmc7LeXPD8/l/xiAjWWiOJgL+OUXczhQFnN11UY2FzRfFzQCyZ07t7UBSaaihFxERLI9Xw9fckeWBmD1haUkJqbDSeLiYO5cuHEDype/ZzFHR8jTsS8ADqGODBs4LB2CyT6+fqcGDodaQoIr8/O7meN9MmLIURrbuWstUW7geN2FeqU03aHcSQm5iIjYhZertQYgvvivbJ22FwYPhnPn0u4ES5aYPbcFCsB9xgcbhkHsmgtwAtr6tiZ//vxpF0M25OcHH1f/msC5L/Hn7mvMbN48xaufZiZfNOoBgO10ELVqaPy43EkJuYiI2IX3nuoP113B7wiXR7WCDz4whz+klenTzeeOHcEh+X9er1+HefPmcXjxYTxnetK3S9+0O3829uHrRRn3ZStmAi+uWUPY1atWh/TQ/oj8C4AbJ3NQo0YNi6ORzEYJuYiI2AWfHD4E3ygAwLflrpkb02row9WrsGCB+foeiwElJECFCgYvveQM5KFv3774+fmlzfntwBNPPEH16tWJ9b3Oh0M/tDqch5KQAOec/gDA5fxVypYta3FEktkoIRcREbvR/bHusBn2bb65qvSaNXD27KNX/NtvEBMDRYpA1arJFpk3D/aH9CKy4jq8Amz079//0c9rR2w2G1frlIBXEtgVOpuYzp2tDinFzr07lGKXr+IQ602V/HlwcnroZWAkm1NCLiIidmNA+wH4/+nPoYM+nA4sC4YBc+Y8esUzZpjPHTtCMov7GAZ8MvogVJkADYbxwmtPkjNnzkc/r53JE2uuZHrgscskzJ1uzkueBeT4eQoHph2l+dB3afCYbuiUuykhFxERu+Hk5ES9em8CJ/jq9PPmxrQYtjJ2LHzzDbz4YrK7166F3V4jwWbg8Lc7n7352aOf0w593bs1XChJpBv8rwLE/vij1SE9WHQ03mf2A/An1zT/uCRLCbmIiNiVbj2qQeFZjGt0lEQb5jL3p08/WqUBAdCrF5QsmezuT0eGQcXJAHTM3xZfX99HO5+dKlXSgTJXXwPgm6oQ8d1EiyNKgW3buOpicBwHzjNBCbkkSwm5iIjYlTr1akHnF7lSazzTipcEDw/YtSvdzrd7N6y4Mgac4rCddmPEm1qV81F88mRXbHEe7MsNB5xOE3fggNUh3d/WrZR5Dar2SSSwiosWBJJkKSEXERG74unmSeDVEgC8EfwYUYfPwxNPpK6yiAho2NAcspKQkGyREaOvQFVz5c6mHvU07/gjat/CG69jLwAwthrs/CxzD/8J27meEznhQk6oWaam1eFIJqWEXERE7E6v2ubCMpFlljH910eoaM4cWLUKxo0zl+BMhkfdj8H9MoS7Meb1MY9wMgFzivdXKr4OwOaCEPPbfAzDsDiqe1t92Jzu0OOCB/Wq17M4GsmslJCLiIjdeavNW9hic4DnOb6cvcCcBuXUqYevaOLNMcwvvHDPIod/2w3boWZiZUKKhqQyYrnde93L0Hn/RPZ+BRGXotmyebPVISUvNpbtPpEAxJ8O0oJAck9KyEVExO64ubhRPL4cAOdyTCexVBmoWNFcSjOldu2CrVvByQm6dr1rd3w87N27j2XTl8ECmNg7C9yAmEX4+cFPP3Xj7WefpwMwekwm/eXB3Z0xhesAkHjeSQsCyT0pIRcREbv0drPuAESXnE9sTARcvAi/PsT4lVu9423bQp48d+3+4guoW9cVw6hPu3btKF26dFqELbfYbPTs2xdsMHPBTMLCwqyO6C6RkQaxvjsAKJ0zpxYEkntSQi4iInbppcYv4XTFCa4aLKtxc7rC0aNTdnBsLEydar5+5ZW7dl+9CsPm/srF+h9AHmf+85//pFHUcrvVYSdx6OePa8M8/G/KFKvDucuyLafB4wIkOtK0YjWrw5FMTAm5iIjYJUcHR17P8Tp8DbPOJWI4O8OaNbBly4MP/vlnuHwZCheGxo3v2v3993Cl0lAo9zNBrQ/x2GOPpXn8Aru3+JHoFY6tzGlWTxyb6W7uzPt+F5r9UQTP0ErUqlbL6nAkE1NCLiIiduv1Lq8DTflp7Sg2BHU0Nw4d+uADy5Y1pzt87TVz2o/bxMfDfyevhkIb4IYDozpq3vH08tGLdch3MQdXXIEc4axfv97qkP4RG0uN7WtZvOgoPr/UpEqVKlZHJJmYEnIREbFbRYsWpVy5CuBYhh4xT5kb582DBy02U60arFgB/fvftWvGDLhQ3EzCA87mpV3jdmkbtCQJDrZR4ZTZ83yxLEyaNMniiG4TGoqjkcAFchHtdY6CBQtaHZFkYkrIRUTErrV+zQ3652X/UwM5Ubm1OQXiokUpO/hfveOGAR+N3QElFoIBg1t+gs1mS4eo5ZbGpXoBsCMklrnzZxAVFWVxRDft2MGf+WGT00Vq1IzWdSD3pYRcRETs2hudXgOnq+B/kC4uzeGnn+Ctt5IvPGIEDBgAFy4ku3vlSjiabyQAPqf96dauW3qFLTe98EZbSoc5Eu8IAUVcmTNnjtUhARD21waqvgJPDoSKj5WzOhzJ5JSQi4iIXcvrm5ciscUAWJNvM5sKd4ZbvZmJif8UXLjQTMaHD7/n9IgBxUOh3DQA3q83QL2iGSAgrwM1j5lTSiaUtTFt2jSLIzL9eXIrAB6X4PEqj1scjWR2SshFRMTuDWoxwHxRdjofDD1ivj5zBkqXNqdMGT4cnn3WHJPSsye8/HKy9cz8YQYsN/A67sXbz76dQdFL43yd6b0V+q92ZOXKlZw+fdragBIT2Rp98zo6m083dMoDKSEXERG717VxVzwu5gbHeFZGf8/Ro5hJ+IED0K2b2TMeHQ1168L//d9dx9+4AceOXeTr4V/DRpjQdAIODvonNqM89X5nRufrztkc/hiGwfTp060NKCKC9fncAbh+KS+BgYHWxiOZnr4tREREgFcrdgHA9thwvHwvwZAh8OmnEBQEjRrBmDHw++/g4nLXsRMmQKnSLkRHd6RSpUp07Ngxo8O3a04hhbF9N5ECffoAWD5s5bpXLlYF+AJQ3CeXhi7JAykhFxERAQa/MBjHK44YOeL4748fg6srfPABHDsGy5dD796QI8ddx4WFwTsjdnKtax0ocZYhQ4aod9wChmGQ67G82FrkY8feHezbt8+yWLb8dRl8jwHQsIxu6JQH0zeGiIgI4OrsSo88PWAMzPtyHqdPx5KShR/79TeIqfcm5P2L3I3+pGnTpukfrNzlSsQNXvqxL0a1sxCSk9mzZ1sWy29/7DRfXA6gzmO1LYtDsg4l5CIiIjeN7D2SQPdATp58muBgBxYuvH/5X3+Fn/dOgaB1cN2BqV1+1PAEi3iFHeKNfScAsJXyszQhbz+sJ/2W5CXv5tK6oVNSRAm5iIjITe7u7nz++edAHq57H6Frr3PmDZ7J2LsXnu0WDk3MGVrq2+rS9HH1jlumZEmeOOoNgK3YaXbt2cWhQ4cyPo6rV6ly/DAjNoXh/ldhChUqlPExSJajhFxEROQ2zz77LPmf+hZeK0dEjT60bpNIdPSdZRISoN2TN7jS9AXwCMc5woV5786zJF65ycGBioXqki8aEl3joLC7NYsE7d2LI4mcx5e8FVz0i4mkiBJyERGR2zg4OPDhS+8CCVB2Bnu8R1C5MsyaBRERZhlHR7ha4wUothji4ZumY/Hx8rE0bgHvZnVocatTvFh+S4atxO/awc9lYWHuCOo18Mvw80vWpIRcRETkX15t+SodPDuYb5q8y2Hv7+jYKZHBg+H69et88OEHnI6cCUDPvD3p3qK7hdHKLbY6tf9JyEOusG3bNo7ea8xROjmwZy3PdoDXukPlypUz9NySdSkhFxERScaM/jMofqU42Axo0wO6VWfyiReoXLkygz8bDHMTeS7uOca9Ps7qUOWWKlVocMIFpwRwckkEd1iwYEGGhrD1xHYAcoZB1ceqZui5JetSQi4iIpIMBwcHdn62kyYOTeA6UPAPLhaayp49e8iVKxczZ85k6udTrQ5Tbufqinf5qhwYA1P2tofYjE/It185BoBTeFGCgoIy9NySdWWJhPzYsWN069aNIkWK4O7uTtGiRRk0aBDXr1+/o4zNZrvrsXnzZgsjFxGRrMzd1Z2lHy5la5etNLY1prlPc8aPH8/evXt5+umnrQ5PkuE4ZDDBv66n+pdvALBmzRoibg3+T2/x8WwI8AQgh0Nh3dApKeZkdQApsX//fhITExk/fjwhISGEhobSo0cPYmJiGD58+B1lly9fTpkyZZLe58qVK6PDFRGRbKZqiaos+2iZ1WFIStSrB0AwUKpMKfbt3cfixYvp3Llzup869oYzO3M7AlChSEi6n0+yjyyRkDdv3pzmzZsnvQ8ODubAgQN8++23dyXkuXLlIm/evBkdooiIiGQSiYnQekw/DrY5A9dcWbBgQYYk5Jv+ugheZwFo/XitdD+fZB9ZYshKciIjI/Hzu3s6oTZt2hAQEEDt2rUzfNyYiIiIWM829xei1y0mwTUSihTg999/Jz4+Pt3Pu2LjevNFRD7qPF473c8n2UeWTMgPHz7M6NGj6dmzZ9I2T09PRowYwaxZs1i0aBG1a9emXbt2D0zK4+LiiIqKuuMhIiIiWZftu4n0PLDPfB0ST2RkJOvWrUv38z4xehjTf3ah+qrSFC5cON3PJ9mHpUNWBg4cyNChQ+9bZt++fZQsWTLp/enTp2nevDlPP/00PXr0SNru7+9Pv379kt5XrVqVM2fO8OWXX9KmTZt71j9kyBA++eSTu7bv3bsXT0/Ph/k4jyw8PJzQ0NAMPaeY1PbWUvtbS+1vHbV9+ggoXJiGNzurjTynwB2mTp1KQEDAHeXSuv2LnjxC7YTrzMpXnD179qRZvdlVdr/+r1y5kuKyNsMwjHSM5b4uXLjAxYsX71smODgYFxcXAM6cOUP9+vWpXr06kydPxsHh/h38Y8eO5bPPPuPs2bP3LBMXF0dcXFzS+6ioKAIDA4mMjMTb2/shPs2jCw0NpWzZshl6TjGp7a2l9reW2t86avt0smgRtGpFsddcORwQBzO8qOASzM6dO+8olqbtHxUFPuZqrb07D2HsTwPTpt5sLLtf/1FRUfj4+KQop7S0hzx37tzkzp07RWVPnz5NgwYNqFKlCj/88MMDk3GAnTt3ki9fvvuWcXV1xdXVNUUxiIiISBZQ1VyQ54kjcYwOAIID+GvRX4SFhaXbxA/G3r0MrQ15LkDtdpp/XB5Olphl5fTp09SvX5+goCCGDx/OhQsXkvbd+h9rypQpuLi4UKlSJQB++eUXvv/+e7777jtLYhYRERGLBARAUBBNjhxndHVwKHaVRGDp0qV06dIlXU55ZMdq3msMTgkQWrlyupxDsq8skZAvW7aMw4cPc/jwYQoWLHjHvttH3Hz66accP34cJycnSpYsyYwZM+jQoUNGhysiIiJWq1yZer8dJ++pQsRdbk6EwwSWLFmSbgn5is3LIRjyXLRRvGjxdDmHZF9ZYpaVF198EcMwkn3c0rVrV/bu3UtMTAyRkZFs2bJFybiIiIi9qlgR7zg4fKM18954DhLNHvLExMR0Od2OsL8ByHmpiFbolIeWJRJyERERkYfSqxeEheHxwxhq1KiBl5cX4eHhbN++PV1Ot8E3BwA+jkXTpX7J3pSQi4iISPaTOzfkyQNAbGIsZVuXA2DJkiVpfirDgN031yosVloLAsnDU0IuIiIi2dZf+2LI+bk/m4pvBK/0SciPHI8B/0MAPNe4QZrXL9mfEnIRERHJnn74gVJ9OuITdnNCiCAvNm3alOarcv88cwE4xkOcB40eq5WmdYt9UEIuIiIi2VNoKC7LfqP6OXPBHvdShbhx4wYrV65M09METVtF6Fh4d26pFK2TIvJvumpEREQke7q5Nknr89cASAw0lzJP62ErnsdOUeYCBMeUTtN6xX4oIRcREZHsqWJFANofOAlAnPdxyAGLFy++Y+rkR1Xkujlzi1/1wDSrU+xLllgYSEREROShlSgBrq7kuxSD8/lixAccwqGIJ8f2HOPQoUNpcor469f5ru45ikRDk2fUQy6pox5yERERyZ6cnaFsWQCKXSgCgF+FkoC5Cnha2LZ6KWNrwtvNIG/tmmlSp9gfJeQiIiKSfd1MyLtEB1L6yLfU9+oEpF1C/vv8BQAEXIGAgMJpUqfYHyXkIiIikn2VLQteXrxbJS97przKO93qAbBq1Spu3LjxyNXvOnwYgKBw70euS+yXEnIRERHJvt58EyIj4bPPAKhcuTK+vr5ERUURGhr6yNVvc0sAIGdiqUeuS+yXEnIRERHJvlxcwGYD4GjEUYavmUhIy4oAbNq06ZGqjouL46RvLAAFqnR6pLrEvmmWFREREbELnQYt4I9cfcmXtzoAmzdvfqT6tm3bBQFHAWhfs/Yjxyf2Sz3kIiIikr19+CGULMlTkVcBCHfbDzbYtWsX0dHRqa52zqJtkOMiGDYaldOUh5J6SshFREQkezt/Hg4c4Mkb0XDdg3inyxSoWIAbN26wZs2aVFd7bMUlrnxu8Oc4Gx4OLmkYsNgbJeQiIiKSvd2c+rBI5F44/TgA+auXAWD58uWprjZ+3wU84iFfVKA557lIKikhFxERkeytjJl8O+0PJe/1WgBc8TVToNTORx4VFYV/1DSz3hLBaRCk2DMl5CIiIpK93ewh58gRavo/BsAph/3YbDb27t3LmTNnHrrKbdu2sbvVBXq1hOg6+dMyWrFDSshFREQkewsIgNy5wTB4LsAbEh2IdjxO8YrFgdQNW9m8ZTO7y8O4qhAfWCCtIxY7o4RcREREsr/S5iwo9b1P0TZiDZOKRVCnSh0gdcNWftu4izgXcL0BIcWrp2moYn80D7mIiIhkf5UqQWQkfnmcmffmzTnD42rw3XffsXz5cgzDwHZzAaEHMQyDP09EQhUofQGcQoqnY+BiD5SQi4iISPY3atRdmypWrIi7uzthYWHs2bOHsrfGmj/A/v37ifNJAMA/viQE66ZOeTQasiIiIiJ2JSY2nuenvMvTv/WiVgNz1pXffvstxcevXbsWI+AaAF5lXgZ393SJU+yHEnIRERGxHwkJ7N3txLS/fuZIwlZKNioFwMKFC1NcxZo1ayDvaQDqFC+XLmGKfVFCLiIiItmfYcBjj4GHBxV9j+N42hxHfsXXFYANGzZw8eLFFFRjsHJNKM6OsQC00RzkkgaUkIuIiEj2Z7NBbCzExeF85ABFnMyhKlvP7aJcuXIkJiayePHiB1Zz9OhRzp0JYOLXjbk4FIJnz0nvyMUOKCEXERER+1CihPl84AD1ipg95IdiN9GiVQsgZcNW1q5dC1QghMP4xQJFi6ZTsGJPlJCLiIiIfbgtIW9TvSxc8ybeIZrS9c05yn///Xfi4+PvW8WaNWuA8ZR1221uCAlJx4DFXighFxEREftwW0Jeu6YjnDIX9DntGIm/vz+RkZE3e8CTd+PGDRYuXIhj5xg6PB3NX3lQD7mkCSXkIiIiYh9uS8j9/MA3ujrE+HP4aDzt27cH4H//+989D1+7di3hF8MxgmF5UXDzygk+PhkQuGR3SshFRETEPtxKyE+dgpgYPmn2LOvanGfs8/148cUXAZg9ezbR0dHJHj5r1izwK0iiM7jHQ0gurdApaUMJuYiIiNgHPz+oUwc6dIDoaBrUiad2bRtublCjRg1KlCjB1atXmTlz5l2HJiQk8Msvv0DeIADKnwPHkGIZ/Qkkm1JCLiIiIvZj7VqYNQvy5k3aZBgGcQlxSb3kkydPvuuw9evXc/78eRwLuAHgRS147rmMiFjsgBJyERERsVtvfj8Zz0EFePHnt+nSpQsODg6sX7+effv23VHu559/BsAp0FwQKHfp5+CJJzI8XsmelJCLiIiIfUlMhIgIADau9uCq41nWHdtA/vz5adWqFQC9e/cmMTERgF27djFp0iSwQVxAKACtKla3JnbJlpSQi4iIiP1YvRq8vKBRIwCaljJX7DyTsIuouChGjBiBu7s7q1atYsKECSQkJNCjRw9u3LhBvebdsZ2qinu0L086XQXDsPCDSHaihFxERETsR758cPUqHDwIhsETtfNDRBGwJbLp5GZCQkL4/PPPAejXrx+VKlVi69ateHt7067pMIr/OIaYERG4tdRwFUk7SshFRETEfgQHg5MTxMTgdO4cjz0GtlNmL/mi3esBeOONN6hduzaxsbHs3m2uyDl06FAuXPAlhMPYwFwQyGaz6ENIduNkdQAiIiIiGcbZ2UzKDx7E9dgx3BtDkK0Wx5jKikMbAHB0dOT3339n06ZNXL16FU9PTxo2bMjF2HBc3f+GD4GQEGs/h2QrSshFRETEvpQokZSQA9QJqs0x4GDMFuIT4nF2dMbT05MmTZokHXI+5jx5huchJMGHUEdwLVrUktAle9KQFREREbEvN1fsdD16FIDW1UvD0QYUPP8KV+OvJnvIxpMbzWPiEnBNQD3kkqbUQy4iIiL25WZC7nKzh7zFEw6crrmS/Pnvfcg3v5oJ+WPHbvZlqodc0pASchEREbEvlStD27bEFC2KF+DhYT7uZ+vFpeAO9Q7FmBvUQy5pSAm5iIiI2JfKlWHePMJDQ8l72+a4G3EsPLCEivnKUNTvnx7wbSf3EOn+FyQ4U+a5KdD1PBQokPFxS7alMeQiIiJi98LDoWj/l+kwuy0T/px0x74vFv0EgNupJ6jarzP06QMOSqEk7ehqEhEREftjGDiFh5uZOODrCzHb2wDwv+0zMG6uwmkYBr+fMhPyhv7PaepxSRdKyEVERMT+9OxJyQYNYNw4ABwdoXPlVnA9B2FxR1h7fC0AcdcNjIXfwI4X+TifJ0yfDsePWxm5ZENKyEVERMT+FC5sPh88mLTphWc8ILSz+XpuFy5evciqlQ5c/esJ8m39gcfWTITOneHXXy0IWLKzLJOQt2nThkKFCuHm5ka+fPl44YUXOHPmzB1ldu3aRZ06dXBzcyMwMJBhw4ZZFK2IiIhkasWLm88HDiRtql4dAveOgIshnIw6gf+X/nh4JtKiBXTqBLYjf5sFNcOKpLEsk5A3aNCAmTNncuDAAebMmcPff/9Nhw4dkvZHRUXRtGlTgoKC2LZtG19++SUff/wxEyZMsDBqERERyZRuzkXOwYNwc7y4zQaffuADM2dDvBsAe90nsGgRjBxhwOHD5jGag1zSWJaZ9vCtt95Keh0UFMTAgQNp164d8fHxODs7M23aNK5fv87333+Pi4sLZcqUYefOnYwcOZJXXnnFwshFREQk0wkJwbDZsF2+DBcuQEAAAF27wp49Ffhy+nwKP/ELzYs+AYAt7CzExpqDzYOCLAxcsqMs00N+u0uXLjFt2jRq1qyJs7MzAJs2baJu3bq4uLgklWvWrBkHDhwgIiLCqlBFREQkM3J3Jz5fPvP1bcNWAL74AiYObMqhUeMo7Hsz+f775nCVQoXgtlxDJC1kqYT83XffxcPDg1y5cnHixAnmz5+ftC8sLIw8efLcUf7W+7CwsHvWGRcXR1RU1B0PERERyf7ikrmxE8wpxrt3B6fbxxFouIqkI0uHrAwcOJChQ4fet8y+ffsoWbIkAAMGDKBbt24cP36cTz75hC5durBw4UJsjzAp6JAhQ/jkk0/u2r537148PT1TXW9qhIeHExoamqHnFJPa3lpqf2up/a2jtreWc/Xq5AoJIdLNjdgH/HcI2LSJAOCinx9n9d8sTWT36//KlSspLmszbs18b4ELFy5w8eLF+5YJDg6+YxjKLadOnSIwMJCNGzdSo0YNunTpQlRUFPPmzUsqs2rVKho2bMilS5fw9fVNtv64uDji4uKS3kdFRREYGEhkZCTe3t6p+2CpFBoaStmyZTP0nGJS21tL7W8ttb911PbWeqj2P3ECdu6EAgWgSpV0jcteZPfrPyoqCh8fnxTllJb2kOfOnZvcuXOn6tjExESApGS6Ro0a/Oc//0m6yRNg2bJllChR4p7JOICrqyuurq6pikFERETsRKFC5kMkHWSJMeRbtmxhzJgx7Ny5k+PHj7Ny5Uo6d+5M0aJFqVGjBgDPPvssLi4udOvWjT179jBjxgy+/vpr+vXrZ3H0IiIikikZBoSHw4YNcOOG1dGIHcsSCXmOHDn45ZdfaNSoESVKlKBbt26UL1+eNWvWJPVu+/j4sHTpUo4ePUqVKlXo378/H330kaY8FBERkeQZhtnrXbs2HDt273LnzsHgwbBgQYaFJvYlS8xDXq5cOVauXPnAcuXLl2fdunUZEJGIiIhkeQ4OUKwY7NoF+/ffewXOHTvggw+gTBlo0yZjYxS7kCV6yEVERETSRenS5vP9ZvvYt898vjnrm0haU0IuIiIi9qt8efN51657l7mVkJcqlf7xiF1SQi4iIiL2q1w583n37nuX2b/ffFYPuaQTJeQiIiJiv271kO/fD9evJ19GPeSSzpSQi4iIiP0KDAQfH3PawwMH7t4fHm4+AEqUyNjYxG5kiVlWRERERNKFzWbOoOLhAXny3L3/1nCVQoXMMiLpQAm5iIiI2Le33773vmrVzBlYLl3KuHjE7ighFxEREbkXFxdz/nGRdKQx5CIiImLf4uNh61aYNs3qSMROKSEXERER+3b1Kjz+ODz/PERE/LP9xg3o3BlGj4Zr16yLT7I9JeQiIiJi33x8ICjIfP3XX/9s374dpk+HQYPMoSsi6UQJuYiIiMjjj5vPy5b9s23VKvO5bl1wUMok6UdXl4iIiEibNubz/Pn/bFu92nyuXz+joxE7o4RcREREpEULcHSEPXvg77/NGz3XrTP3NWhgbWyS7SkhFxEREfH1hXr1zNfz58O2bRATA35+UK6ctbFJtqeEXERERASgbVvzedkyWL7cfF2vnsaPS7rTFSYiIiIC0LEjLFli9pB36mTOrKLx45IBtFKniIiICEDevOYDoFgx+Phjcx5ykXSmhFxEREQkOe+9Z3UEYic0ZEVERERExEJKyEVERERELKSEXERERETEQkrIRUREREQspIRcRERERMRCSshFRERERCykhFxERERExEJKyEVERERELKSEXERERETEQkrIRUREREQspIRcRERERMRCSshFRERERCykhFxERERExEJKyEVERERELKSEXERERETEQkrIRUREREQspIRcRERERMRCTlYHkNkYhgFAVFRUhp/7ypUrlpxX1PZWU/tbS+1vHbW9tdT+1sru7X/rs93KLe9HCfm/REdHAxAYGGhxJCIiIiKS1UVHR+Pj43PfMjYjJWm7HUlMTOTMmTN4eXlhs9ky7LxRUVEEBgZy8uRJvL29M+y8ora3mtrfWmp/66jtraX2t5Y9tL9hGERHR5M/f34cHO4/Slw95P/i4OBAwYIFLTu/t7d3tr0wMzu1vbXU/tZS+1tHbW8ttb+1snv7P6hn/Bbd1CkiIiIiYiEl5CIiIiIiFlJCnkm4uroyaNAgXF1drQ7F7qjtraX2t5ba3zpqe2up/a2l9r+TbuoUEREREbGQeshFRERERCykhFxERERExEJKyEVERERELKSEPAONHTuWwoUL4+bmxuOPP87WrVvvW37WrFmULFkSNzc3ypUrx2+//ZZBkWY/D9P2kydPxmaz3fFwc3PLwGizl7Vr19K6dWvy58+PzWZj3rx5Dzxm9erVVK5cGVdXV0JCQpg8eXK6x5kdPWzbr169+q5r32azERYWljEBZyNDhgyhatWqeHl5ERAQQLt27Thw4MADj9P3ftpITfvruz/tfPvtt5QvXz5pjvEaNWrw+++/3/cYe7/2lZBnkBkzZtCvXz8GDRrE9u3bqVChAs2aNeP8+fPJlt+4cSOdO3emW7du7Nixg3bt2tGuXTtCQ0MzOPKs72HbHsyFCs6ePZv0OH78eAZGnL3ExMRQoUIFxo4dm6LyR48epWXLljRo0ICdO3fSt29funfvzpIlS9I50uznYdv+lgMHDtxx/QcEBKRThNnXmjVr6N27N5s3b2bZsmXEx8fTtGlTYmJi7nmMvvfTTmraH/Tdn1YKFizIF198wbZt2/jzzz9p2LAhbdu2Zc+ePcmW17UPGJIhqlWrZvTu3TvpfUJCgpE/f35jyJAhyZbv2LGj0bJlyzu2Pf7440bPnj3TNc7s6GHb/ocffjB8fHwyKDr7Ahhz5869b5l33nnHKFOmzB3bOnXqZDRr1iwdI8v+UtL2q1atMgAjIiIiQ2KyJ+fPnzcAY82aNfcso+/99JOS9td3f/ry9fU1vvvuu2T36do3DPWQZ4Dr16+zbds2GjdunLTNwcGBxo0bs2nTpmSP2bRp0x3lAZo1a3bP8pK81LQ9wJUrVwgKCiIwMPC+f9VL2tO1b72KFSuSL18+mjRpwoYNG6wOJ1uIjIwEwM/P755ldO2nn5S0P+i7Pz0kJCQwffp0YmJiqFGjRrJldO1ryEqGCA8PJyEhgTx58tyxPU+ePPccmxkWFvZQ5SV5qWn7EiVK8P333zN//nymTp1KYmIiNWvW5NSpUxkRst2717UfFRVFbGysRVHZh3z58jFu3DjmzJnDnDlzCAwMpH79+mzfvt3q0LK0xMRE+vbtS61atShbtuw9y+l7P32ktP313Z+2du/ejaenJ66urrz66qvMnTuX0qVLJ1tW1z44WR2ASGZTo0aNO/6Kr1mzJqVKlWL8+PF8+umnFkYmkr5KlChBiRIlkt7XrFmTv//+m1GjRvHjjz9aGFnW1rt3b0JDQ1m/fr3VodillLa/vvvTVokSJdi5cyeRkZHMnj2brl27smbNmnsm5fZOPeQZwN/fH0dHR86dO3fH9nPnzpE3b95kj8mbN+9DlZfkpabt/83Z2ZlKlSpx+PDh9AhR/uVe1763tzfu7u4WRWW/qlWrpmv/Ebz++ussXLiQVatWUbBgwfuW1fd+2nuY9v83ffc/GhcXF0JCQqhSpQpDhgyhQoUKfP3118mW1bWvhDxDuLi4UKVKFVasWJG0LTExkRUrVtxzPFWNGjXuKA+wbNmye5aX5KWm7f8tISGB3bt3ky9fvvQKU26jaz9z2blzp679VDAMg9dff525c+eycuVKihQp8sBjdO2nndS0/7/puz9tJSYmEhcXl+w+XftolpWMMn36dMPV1dWYPHmysXfvXuOVV14xcubMaYSFhRmGYRgvvPCCMXDgwKTyGzZsMJycnIzhw4cb+/btMwYNGmQ4Ozsbu3fvtuojZFkP2/affPKJsWTJEuPvv/82tm3bZjzzzDOGm5ubsWfPHqs+QpYWHR1t7Nixw9ixY4cBGCNHjjR27NhhHD9+3DAMwxg4cKDxwgsvJJU/cuSIkSNHDmPAgAHGvn37jLFjxxqOjo7G4sWLrfoIWdbDtv2oUaOMefPmGYcOHTJ2795t9OnTx3BwcDCWL19u1UfIsnr16mX4+PgYq1evNs6ePZv0uHr1alIZfe+nn9S0v777087AgQONNWvWGEePHjV27dplDBw40LDZbMbSpUsNw9C1nxwl5Blo9OjRRqFChQwXFxejWrVqxubNm5P21atXz+jatesd5WfOnGkUL17ccHFxMcqUKWMsWrQogyPOPh6m7fv27ZtUNk+ePEaLFi2M7du3WxB19nBrKr1/P261edeuXY169erddUzFihUNFxcXIzg42Pjhhx8yPO7s4GHbfujQoUbRokUNNzc3w8/Pz6hfv76xcuVKa4LP4pJrd+COa1nf++knNe2v7/608/LLLxtBQUGGi4uLkTt3bqNRo0ZJybhh6NpPjs0wDCPj+uNFREREROR2GkMuIiIiImIhJeQiIiIiIhZSQi4iIiIiYiEl5CIiIiIiFlJCLiIiIiJiISXkIiIiIiIWUkIuIiIiImIhJeQiIiIiIhZSQi4iIiIiYiEl5CIiIiIiFlJCLiKSyRmGwciRIylSpAg5cuSgXbt2REZG3rP8xYsXCQgI4NixY/ett379+vTt2zdtg7XIM888w4gRI6wOQ0QkVZSQi4hkcgMGDODbb79lypQprFu3jm3btvHxxx/fs/zgwYNp27YthQsXzrAYrfbBBx8wePDg+/6hIiKSWSkhFxHJxLZs2cLIkSOZMWMGdevWpUqVKvTo0YPffvst2fJXr15l0qRJdOvWLYMjTd7169cz5Dxly5alaNGiTJ06NUPOJyKSlpSQi4hkYsOHD6dRo0ZUrlw5aVuePHkIDw9Ptvxvv/2Gq6sr1atXv2N7TEwMXbp0wdPTk3z58iU7vCMxMZEhQ4ZQpEgR3N3dqVChArNnz07aHx0dzXPPPYeHhwf58uVj1KhRdw17qV+/Pq+//jp9+/bF39+fZs2apajuB+0HmD17NuXKlcPd3Z1cuXLRuHFjYmJikva3bt2a6dOnp6BVRUQyFyXkIiKZVFxcHIsWLaJ9+/Z3bL927Ro+Pj7JHrNu3TqqVKly1/YBAwawZs0a5s+fz9KlS1m9ejXbt2+/o8yQIUP43//+x7hx49izZw9vvfUWzz//PGvWrAGgX79+bNiwgQULFrBs2TLWrVt3Vx0AU6ZMwcXFhQ0bNjBu3LgU1f2g/WfPnqVz5868/PLL7Nu3j9WrV/Pkk09iGEbSeatVq8bWrVuJi4tLaROLiGQOhoiIZEobN240AMPNzc3w8PBIeri4uBjNmjVL9pi2bdsaL7/88h3boqOjDRcXF2PmzJlJ2y5evGi4u7sbffr0MQzDMK5du2bkyJHD2Lhx4x3HduvWzejcubMRFRVlODs7G7NmzUrad/nyZSNHjhxJdRiGYdSrV8+oVKnSHXU8qO4H7TcMw9i2bZsBGMeOHbtne/31118PLCMikhk5Wfz3gIiI3MPBgwfx8PBg586dd2xv2bIltWrVSvaY2NhY3Nzc7tj2999/c/36dR5//PGkbX5+fpQoUSLp/eHDh7l69SpNmjS549jr169TqVIljhw5Qnx8PNWqVUva5+Pjc0cdt/y7h/5BdT9oP0CFChVo1KgR5cqVo1mzZjRt2pQOHTrg6+ubVN7d3R0wx9GLiGQlSshFRDKpqKgo/P39CQkJSdp2/PhxDh06xFNPPZXsMf7+/kRERDz0ua5cuQLAokWLKFCgwB37XF1duXTpUorr8vDweKi6z5w5c9/9AI6OjixbtoyNGzeydOlSRo8ezX/+8x+2bNlCkSJFAJJizJ07d4pjFRHJDDSGXEQkk/L39ycyMvKOcdKDBw+mRYsWlC5dOtljKlWqxN69e+/YVrRoUZydndmyZUvStoiICA4ePJj0vnTp0ri6unLixAlCQkLueAQGBhIcHIyzszN//PFH0jGRkZF31HEvD6r7Qftvsdls1KpVi08++YQdO3bg4uLC3Llzk/aHhoZSsGBB/P39HxiTiEhmoh5yEZFMqmHDhly7do0vvviCZ555hmnTpvHrr7+ydevWex7TrFkz3nvvPSIiIpKGc3h6etKtWzcGDBhArly5CAgI4D//+Q8ODv/0yXh5efH222/z1ltvkZiYSO3atYmMjGTDhg14e3vTtWtXunbtyoABA/Dz8yMgIIBBgwbh4OCAzWa77+dISd0P2r9lyxZWrFhB06ZNCQgIYMuWLVy4cIFSpUolnWfdunU0bdr0EVtdRCTjKSEXEcmk8uTJw+TJkxkwYACffvopDRs2ZP369Xf0Gv9buXLlqFy5MjNnzqRnz55J27/88kuuXLlC69at8fLyon///nctovPpp5+SO3duhgwZwpEjR8iZMyeVK1fm/fffB2DkyJG8+uqrtGrVCm9vb9555x1Onjx515j15Dyo7gft9/b2Zu3atXz11VdERUURFBTEiBEjeOKJJwBz5pl58+axePHih2tkEZFMwGbc/luoiIhkeYsWLWLAgAGEhobe0Que1mJiYihQoAAjRoywfCGib7/9lrlz57J06VJL4xARSQ31kIuIZDMtW7bk0KFDnD59+r696Q9rx44d7N+/n2rVqhEZGcl///tfANq2bZtm50gtZ2dnRo8ebXUYIiKpoh5yERFJkR07dtC9e3cOHDiAi4sLVapUYeTIkZQrV87q0EREsjQl5CIiIiIiFtK0hyIiIiIiFlJCLiIiIiJiISXkIiIiIiIWUkIuIiIiImIhJeQiIiIiIhZSQi4iIiIiYiEl5CIiIiIiFlJCLiIiIiJiISXkIiIiIiIWUkIuIiIiImIhJeQiIiIiIhb6fyAfYpc6zinhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_db(val):\n",
    "    val = np.abs(val) / np.max(np.abs(val))\n",
    "    return 10.0 * np.log10(val)\n",
    "\n",
    "\n",
    "RCS_phi0 = RCS[0, :, 0]\n",
    "RCS_phi90 = RCS[0, :, 1]\n",
    "\n",
    "RCS_downsampled_phi0 = RCS_downsampled[0, :, 0]\n",
    "RCS_downsampled_phi90 = RCS_downsampled[0, :, 1]\n",
    "\n",
    "RCS_server_phi0 = RCS_server[0, :, 0]\n",
    "RCS_server_phi90 = RCS_server[0, :, 1]\n",
    "\n",
    "# ------ import analytical data from disk ------\n",
    "\n",
    "mie_file_id = \"2lambda_epsr4\"\n",
    "mie_filename_phi0 = \"./data/mie_bRCS_phi0_\" + mie_file_id + \".txt\"\n",
    "mie_filename_phi90 = \"./data/mie_bRCS_phi90_\" + mie_file_id + \".txt\"\n",
    "\n",
    "mie_data_phi0 = np.loadtxt(mie_filename_phi0, delimiter=\"\\t\", skiprows=2)\n",
    "mie_theta_phi0 = np.squeeze(mie_data_phi0[:, [0]])\n",
    "mie_phi0 = np.squeeze(mie_data_phi0[:, [1]])\n",
    "\n",
    "mie_data_phi90 = np.loadtxt(mie_filename_phi90, delimiter=\"\\t\", skiprows=2)\n",
    "mie_theta_phi90 = np.squeeze(mie_data_phi90[:, [0]])\n",
    "mie_phi90 = np.squeeze(mie_data_phi90[:, [1]])\n",
    "\n",
    "# ------ plot for phi = 0 ------\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.5, 5))\n",
    "\n",
    "ax.plot(mie_theta_phi0, to_db(mie_phi0), \"-k\", label=\"$\\\\phi = 0$, Mie\")\n",
    "ax.plot(thetas, to_db(RCS_phi0), \"--b\", label=\"$\\\\phi = 0$, near2far local\")\n",
    "ax.plot(\n",
    "    thetas,\n",
    "    to_db(RCS_downsampled_phi0),\n",
    "    \"--r\",\n",
    "    label=\"$\\\\phi = 0$, near2far local downsampled\",\n",
    ")\n",
    "ax.plot(thetas, to_db(RCS_server_phi0), \"--g\", label=\"$\\\\phi = 0$, near2far server\")\n",
    "ax.set(\n",
    "    xlabel=\"$\\\\theta$ (degrees)\",\n",
    "    ylabel=\"Bistatic RCS (dBsm)\",\n",
    "    yscale=\"linear\",\n",
    "    xscale=\"linear\",\n",
    ")\n",
    "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
    "plt.legend(loc=\"best\", prop={\"size\": 14})\n",
    "plt.tight_layout()\n",
    "\n",
    "# ------ plot for phi = pi/2 ------\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.5, 5))\n",
    "\n",
    "ax.plot(mie_theta_phi90, to_db(mie_phi90), \"-k\", label=\"$\\\\phi = \\\\pi/2$, Mie\")\n",
    "ax.plot(thetas, to_db(RCS_phi90), \"--b\", label=\"$\\\\phi = \\\\pi/2$, near2far local\")\n",
    "ax.plot(\n",
    "    thetas,\n",
    "    to_db(RCS_downsampled_phi90),\n",
    "    \"--r\",\n",
    "    label=\"$\\\\phi = \\\\pi/2$, near2far local downsampled\",\n",
    ")\n",
    "ax.plot(\n",
    "    thetas, to_db(RCS_server_phi90), \"--g\", label=\"$\\\\phi = \\\\pi/2$, near2far server\"\n",
    ")\n",
    "ax.set(\n",
    "    xlabel=\"$\\\\theta$ (degrees)\",\n",
    "    ylabel=\"Bistatic RCS (dBsm)\",\n",
    "    yscale=\"linear\",\n",
    "    xscale=\"linear\",\n",
    ")\n",
    "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
    "plt.legend(loc=\"best\", prop={\"size\": 14})\n",
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Processing surface monitor 'near_field_x+'... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nProcessing surface monitor 'near_field_x+'... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
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🚶  Starting 'sphereRCS'...\n🚶  Starting 'sphereRCS'...
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 86.4% • 3.1/3.6 MB • 5.6 MB/s • 0:00:01\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 86.4% • 3.1/3.6 MB • 5.6 MB/s • 0:00:01
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% done (field decay = 3.42e-11) ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   4% -:--:--\n% done (field decay = 3.42e-11) ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   4% -:--:--
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% done (field decay = 8.57e-06) ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━  60% 0:00:34\n% done (field decay = 8.57e-06) ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━  60% 0:00:34
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Processing surface monitor 'near_field_downsampled_x-'... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nProcessing surface monitor 'near_field_downsampled_x-'... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
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