{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2024 Massachusetts Institute of Technology\n",
    "\n",
    "SPDX-License-Identifier: MIT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observing a Satellite with a Custom Ground Sensor\n",
    "In the previous example, we learned how to use `madlib` utilities to define and propagate a satellite. Now we will use `madlib` to define a custom ground-based optical sensor and use it to generate realistic astrometric observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Satellites\n",
    "Let's quickly define 3 satellites:\n",
    "1. A LEO object similar to the ISS\n",
    "2. A GEO satellite at 0 degrees longitude\n",
    "3. A GEO satellite at 270 degrees longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import madlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_mjd = 60197.5\n",
    "\n",
    "leo_alt_km = 430.0\n",
    "leo_speed_kms = 27500.0 / 3600  # Converting from km/h to km/s\n",
    "earth_radius_km = 6378.0\n",
    "\n",
    "leo_pos_teted = np.array([leo_alt_km + earth_radius_km, 0, 0])\n",
    "leo_vel_teted = leo_speed_kms * np.array([0, 2**-0.5, 2**-0.5])\n",
    "\n",
    "leo_satellite = madlib.Satellite(epoch=epoch_mjd, pos=leo_pos_teted, vel=leo_vel_teted)\n",
    "\n",
    "geo_satellite_0 = madlib.Satellite.from_GEO_longitude(lon=0, epoch=epoch_mjd)\n",
    "geo_satellite_270 = madlib.Satellite.from_GEO_longitude(lon=270, epoch=epoch_mjd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Sensor\n",
    "Now let's take a look at how `madlib` lets us define sensors. Currently, only optical sensors are supported, and these sensors can only measure satellite's angular positions, not their brightnesses or radial distances. `madlib` lets us create both ground-based sensors and sensors in Earth orbit, but we'll focus on the former for now.\n",
    "\n",
    "Ground-based optical sensors are defined with the following parameters:\n",
    "* **lat** - The geodetic latitude of the sensor, in degrees\n",
    "* **lon** - The geodetic longitude of the sensor, in degrees (measured East of the Prime Meridian)\n",
    "* **alt** - The altitude of the sensor, in km (measured above WGS-84 reference ellipsoid)\n",
    "* **dra** - Metric accuracy of the sensor in the right ascension direction, measured in **arcseconds\n",
    "* **ddec** - Metric accuracy of the sensor in the declination direction, measured in arcseconds\n",
    "* **obs_per_collect** - Typical number of observations per collect (either fixed or randomly sampled from an interval)\n",
    "* **obs_time_spacing** - The time in seconds between observations within a collect\n",
    "* **collect_gap_mean** - Average time in seconds between collects\n",
    "* **collect_gap_std** - Standard deviation of seconds between collects\n",
    "* **obs_limits** - Various limits on what the sensor can observe, such as elevation limits\n",
    "* **id** - A unique sensor ID string\n",
    "\n",
    "Let's go step-by-step through these parameters to build an imaginary sensor near the [Haystack Observatory](https://en.wikipedia.org/wiki/Haystack_Observatory):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor_1_params = {}\n",
    "\n",
    "# We'll start with the ID\n",
    "sensor_1_params[\"id\"] = \"Needle\"\n",
    "\n",
    "# In degrees, the coordinates of Haystack are 42.6233 N, 71.4882 W\n",
    "sensor_1_params[\"lat\"] = 42.6233\n",
    "sensor_1_params[\"lon\"] = -71.4882\n",
    "\n",
    "# The altitude of the observatory is 131 m\n",
    "sensor_1_params[\"alt\"] = 0.131\n",
    "\n",
    "# Our imaginary telescope has mediocre imaginary funding, so our astrometric\n",
    "# measurements are accurate to within ten arcseconds.\n",
    "sensor_1_params[\"dra\"] = 10.0\n",
    "sensor_1_params[\"ddec\"] = 10.0\n",
    "\n",
    "# At each pointing of our scanning pattern, we'll take 3 observations, spaced 1 second apart\n",
    "sensor_1_params[\"obs_per_collect\"] = 3\n",
    "sensor_1_params[\"obs_time_spacing\"] = 1\n",
    "\n",
    "# On average, we'll return to each pointing every 1 minute, give or take 5 seconds\n",
    "sensor_1_params[\"collect_gap_mean\"] = 60\n",
    "sensor_1_params[\"collect_gap_std\"] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Additional parameters allow you to simulate systematic errors in the sensor's reported position or have the sensor mistakenly observe an object near the target, but these are outside the scope of the current example.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation Limits\n",
    "In `madlib` you can pass in a dict of limiting behaviors that restrict the observations of your sensor. For instance, you may be modeling a sensor that cannot point below a certain elevation, can only observe a certain range of azimuth angles, or is very sensitive to the background brightness of early twilight.\n",
    "\n",
    "Common supported observation limits are\n",
    "* **dec** - Declination (in degrees)\n",
    "* **az** - Azimuth (in degrees)\n",
    "* **el** - Elevation angle (in degrees)\n",
    "* **range_** - Distance to observed object (in km)\n",
    "* **sun_el** - Elevation angle of the sun (in degrees)\n",
    "\n",
    "Our sensor will have a minimum pointing elevation of 15 degrees, and it will only be effective during astronomical twilight (when the sun is 18 or more degrees below the horizon).\n",
    "\n",
    "We define each limit as a tuple with `(minimum allowed value, maximum allowed value)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Any observations that would fall outside of these limits will be ignored and won't be returned by our sensor's .observe function.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_limits = {\"el\": (15, 90), \"sun_el\": (-90, -18)}\n",
    "\n",
    "sensor_1_params[\"obs_limits\"] = obs_limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can finally define our `GroundOpticalSensor` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor_1 = madlib.GroundOpticalSensor(**sensor_1_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Observations with a Sensor\n",
    "We are ready to use our sensor to simulate observations of our satellites. Let's simulate 72 consecutive hours starting at the satellites' epoch.\n",
    "\n",
    "Currently, `madlib` only supports observing a single satellite at a time, so let's start with our GEO object at 270 degrees longitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_mjd = epoch_mjd\n",
    "end_mjd = start_mjd + 3  # 3 days, or 72 hours, of observation time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ObservationCollection(pos_observed=array([Observation(mjd=60198.0288381947, ra=-92.0985296777439, dec=-6.491351755392359, az=206.33020117688616, el=37.37237039534833, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.02884976877, ra=-92.09310934444511, dec=-6.495381442626195, az=206.32706878736045, el=37.36896969043159, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.004127898483183, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.0295410948, ra=-91.83790200462018, dec=-6.499259734608577, az=206.31879623112025, el=37.36714952148726, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.170430318712338, sun_separation=None, sensor_id='Needle'),\n",
      "       ...,\n",
      "       Observation(mjd=60200.36442202112, ra=31.068356708980904, dec=-6.504800722471203, az=206.26007204578138, el=37.377489729597784, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.01409052688083, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.3644335952, ra=31.065913859048276, dec=-6.506723954447802, az=206.26709977919722, el=37.37351692455588, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.011280472840635, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.36444516927, ra=31.06955902322227, dec=-6.505854376470046, az=206.2680878859745, el=37.374164928452124, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.008470351212612, sun_separation=None, sensor_id='Needle')],\n",
      "      dtype=object), pos_truth=array([Observation(mjd=60198.0288381947, ra=-92.0948106922608, dec=-6.495278711207822, az=206.3241865981276, el=37.36987491594943, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.02884976877, ra=-92.0906322839648, dec=-6.495278860479825, az=206.3241861400855, el=37.36987488448717, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.004127898483183, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.0295410948, ra=-91.84105351416645, dec=-6.495287793125771, az=206.3241588364332, el=37.36987297238249, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.170430318712338, sun_separation=None, sensor_id='Needle'),\n",
      "       ...,\n",
      "       Observation(mjd=60200.36442202112, ra=31.06438041711439, dec=-6.505064397304026, az=206.2646603431709, el=37.375945986503446, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.01409052688083, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.3644335952, ra=31.068558760114822, dec=-6.505064550623255, az=206.26465996320638, el=37.375945928968584, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.011280472840635, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.36444516927, ra=31.072737094358484, dec=-6.50506470386718, az=206.264659591647, el=37.37594586919653, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.008470351212612, sun_separation=None, sensor_id='Needle')],\n",
      "      dtype=object), pos_expected=array([Observation(mjd=60198.0288381947, ra=-92.0948106922608, dec=-6.495278711207822, az=206.3241865981276, el=37.36987491594943, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.02884976877, ra=-92.0906322839648, dec=-6.495278860479825, az=206.3241861400855, el=37.36987488448717, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.004127898483183, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60198.0295410948, ra=-91.84105351416645, dec=-6.495287793125771, az=206.3241588364332, el=37.36987297238249, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.170430318712338, sun_separation=None, sensor_id='Needle'),\n",
      "       ...,\n",
      "       Observation(mjd=60200.36442202112, ra=31.06438041711439, dec=-6.505064397304026, az=206.2646603431709, el=37.375945986503446, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.01409052688083, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.3644335952, ra=31.068558760114822, dec=-6.505064550623255, az=206.26465996320638, el=37.375945928968584, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.011280472840635, sun_separation=None, sensor_id='Needle'),\n",
      "       Observation(mjd=60200.36444516927, ra=31.072737094358484, dec=-6.50506470386718, az=206.264659591647, el=37.37594586919653, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.008470351212612, sun_separation=None, sensor_id='Needle')],\n",
      "      dtype=object))\n"
     ]
    }
   ],
   "source": [
    "geo_observations_270 = sensor_1.observe(\n",
    "    target_satellite=geo_satellite_270, times=(start_mjd, end_mjd)\n",
    ")\n",
    "print(geo_observations_270)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output we received is an `ObservationCollection`, which is itself holds individual `Observation` objects.\n",
    "\n",
    "An `ObservationCollection` holds two types of data in numpy arrays:\n",
    "1. **pos_observed** - The observed position of the satellite at each valid observation time.\n",
    "2. **pos_truth** - The *actual* position of the satellite at each valid observation time, ignoring sensor noise.\n",
    "3. **pos_expected** - The position where the satellite was *expected* to be at each valid observation time, ignoring sensor noise and systematic errors. This is used for calculating residuals.\n",
    "\n",
    "Note that `pos_expected` only follows the target satellite's original orbit, unaware of any maneuvers. It also does not apply any systematic errors in the target satellite's orbit or the sensor's position.\n",
    "\n",
    "Each `Observation` in those arrays can have the following fields for an optical, ground-based sensor:\n",
    "* **mjd** - Timestamp of observation in MJD format\n",
    "* **ra** - Right Ascension of satellite, in degrees\n",
    "* **dec** - Declination of satellite, in degrees\n",
    "* **az** - Azimuth angle of satellite, in degrees\n",
    "* **el** - Elevation angle of satellite, in degrees\n",
    "* **sun_el** - Elevation angle of the sun at the observation time, in degrees\n",
    "\n",
    "Let's examine these results in more detail. First, how many *valid* observations were collected for this object?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4364\n"
     ]
    }
   ],
   "source": [
    "print(geo_observations_270.count_valid_observations())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What were the measurements for the first observation? How do those compare to the ground truth and expected measurements? (Note that we have ground truth for the satellite's range and range rate, but our optical sensor cannot measure those values.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation:  Observation(mjd=60198.0288381947, ra=-92.0985296777439, dec=-6.491351755392359, az=206.33020117688616, el=37.37237039534833, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle')\n",
      "Ground Truth: Observation(mjd=60198.0288381947, ra=-92.0948106922608, dec=-6.495278711207822, az=206.3241865981276, el=37.36987491594943, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle')\n",
      "Expected:     Observation(mjd=60198.0288381947, ra=-92.0948106922608, dec=-6.495278711207822, az=206.3241865981276, el=37.36987491594943, range_=None, range_rate=None, lat=None, lon=None, sun_el=-18.001341475664166, sun_separation=None, sensor_id='Needle')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Observation: \", geo_observations_270.pos_observed[0])\n",
    "print(\"Ground Truth:\", geo_observations_270.pos_truth[0])\n",
    "print(\"Expected:    \", geo_observations_270.pos_expected[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation Limits\n",
    "What happens if we instead use the same telescope to look at the GEO object at 0 degrees longitude?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "geo_observations_0 = sensor_1.observe(target_satellite=geo_satellite_0, times=(start_mjd, end_mjd))\n",
    "print(geo_observations_0.count_valid_observations())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zero observations were returned! This particular satellite is never within our sensor's field of view, so it is simply never observed. It's important to remember this edge case when working with sensor observations in `madlib`: your `ObservationCollection` objects will often be empty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Observations\n",
    "Let's plot the measured RA and DEC of the GEO satellite at 270 degrees longitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = np.array([o.mjd for o in geo_observations_270.pos_observed])\n",
    "times = 24 * (times - min(times))  # Convert the times from days to hours\n",
    "ra = [o.ra for o in geo_observations_270.pos_observed]\n",
    "dec = [o.dec for o in geo_observations_270.pos_observed]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(9, 9))\n",
    "\n",
    "_ = ax1.plot(times, ra, \"ob\", ms=5, lw=2)\n",
    "ax1.set_ylabel(\"Right Ascension (degrees)\", fontsize=16)\n",
    "_ = ax2.plot(times, dec, \"ob\", ms=5, lw=2)\n",
    "ax2.set_ylabel(\"Declination (degrees)\", fontsize=16)\n",
    "ax2.set_xlabel(\"Time (Hours)\", fontsize=16)\n",
    "ax1.grid()\n",
    "ax2.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how, by specifying a maximum sun elevation at which our sensor can operate, we've created gaps in the data during daytime hours. Also, the 10-arcsecond noise of our sensor is very evident in the plot of our declination measurements.\n",
    "\n",
    "Let's compare this to a similar plot of observations of the LEO object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yqVDB3DgAAABQlEyHiYceekjffPON9u3b54p64KRz5/zHo6LcUwcAAACQG9NhYsSIEerWrZs6deqkJUuW6OLFi66oC5JGjZIsFknKeRe2xWIfBwAAADzF9G5O9evXl81m05EjR/Twww/r4YcfVtWqVVWmTJkccy0WC2cwCiEiQpo/374NrO3/t3Wy2NOF5s+X6tf3ZHUAAAAo6UyHiYMHDzr+nPkLb3Jycq5zM38RRsFFR0vt2l3WmDEH5OtbX3XrWhQbS5AAAACA55kOEwcOHHBFHchH/frSww//rB496sjPj+12AQAA4B1Mh4natWu7og4AAAAAxYzX/2fu9evX66677lJISIgsFouWLl2aZdxms2ns2LGqUaOGypQpo6ioKCUlJWWZc+bMGfXr109BQUGqUKGCYmNjde7cOTf+FAAAAMC1x+vDxPnz59WsWTPNmTMn1/Fp06Zp9uzZevvtt7V582aVLVtW3bp1y7KrVL9+/bR7924lJCRoxYoVWr9+vQYPHuyuHwEAAAC4Jpm+zKlu3boFmufv76/g4GC1adNG/fv3V4sWLQr0vu7du6t79+65jtlsNs2aNUsvvvii7r77bknSokWLVK1aNS1dulR9+vTRzz//rJUrV2rr1q1q3bq1JOn1119Xjx49NH36dIWEhBSoDgAAAABZuWw3J4vF4tjNKbvMsd9++00bNmzQ66+/rvHjx+vFF180tfaBAwd0/PhxRTk9va18+fJq166dNm7cqD59+mjjxo2qUKGCI0hIUlRUlHx8fLR582bdc889uX52enq60tPTHa/T0tIkSVarVVar1VTdhZW5nrvXhfeiJ+CMfkB29ASc0Q9wlr0fzPaFS3ZzeuuttzR9+nT17t1b/fr1U3h4uCwWiw4ePKjFixfr008/1VNPPaWePXtqzZo1euWVVzRu3Di1a9dOXbp0Mbz28ePHJUnVqlXLcrxatWqOsePHj6tq1apZxkuVKqVKlSo55uRmypQpmjBhQo7jq1atUmBgoOGazUhISPDIuvBe9ASc0Q/Ijp6AM/oBzjL74cKFC6Y+x3SY2L17t1599VV99NFH6t27d5axpk2bqmfPnvr0009133336ZZbbtHYsWPVvHlz9erVS2+++aapMFGURo8erZEjRzpep6WlKTQ0VF27dlVQUJBba7FarUpISFCXLl3k5+fn1rXhnegJOKMfkB09AWf0A5xl74fMq2+MMh0mpk2bpjZt2uQIEs7uvfdetWnTRq+++qruvPNO9ezZUw0aNNCWLVtMrV29enVJ9ofk1ahRw3E8OTlZzZs3d8w5ceJElvddvnxZZ86ccbw/NwEBAQoICMhx3M/Pz2N/ET25NrwTPQFn9AOyoyfgjH6As8x+MNsTpndz2rlzp+oX4HHM9evX165duxyvGzRooNOnT5tau06dOqpevboSExMdx9LS0rR582ZFRkZKkiIjI5WSkqJt27Y55qxZs0YZGRlq166dqfUBAACAksz0mYmMjAzt37//qvP279+vjIwMx2s/Pz+VLl36qu87d+6c9u7d63h94MAB7dy5U5UqVVJYWJhGjBihSZMmKSIiQnXq1NGYMWMUEhKiXr16SZIaNWqk22+/XYMGDdLbb78tq9WqYcOGqU+fPuzkBAAAAJhg+sxEs2bNtHnzZi1btizPOcuWLdOmTZsclx5J0pEjR1SlSpWrfv7333+vFi1aOLaSHTlypFq0aKGxY8dKkp599lk98cQTGjx4sNq0aaNz585p5cqVWYLK4sWL1bBhQ3Xu3Fk9evRQhw4d9M477xj8iQEAAABILjgzMWrUKN1zzz365z//qQceeEB9+/bNspvTkiVL9MEHH8hisejpp5+WJKWkpGjHjh26//77r/r5HTt2zHPLWcm+7ezEiRM1ceLEPOdUqlRJS5YsKfwPBwAAACBPpsPE3XffrVdeeUUvvPCClixZkuOXdpvNJh8fH02ePNnxYLlTp07pX//6l26//XazywMAAADwENNhQrJfatS1a1e9/vrrWr9+vX7//XdJUs2aNXXLLbdo2LBhatmypWN+/fr1NW7cOFcsDQAAAMBDXBImJKl58+aKi4tz1ccBAAAA8HKmb8AGAAAAUDK57MyEzWbTl19+qQ0bNujkyZNq166dYmJiJEknT57Un3/+qXr16snX19dVSwIAAADwIJeEiV27dumBBx5QUlKSbDabLBaLrFarI0wkJCTo4Ycf1tKlS3XXXXe5YkkAAAAAHmb6Mqfff/9dUVFR+u2339S9e3dNmzYtx1auvXr1kp+fnz7//HOzywEAAADwEqbDxMsvv6zTp09r1qxZWrFihUaNGpVjTmBgoJo1a6atW7eaXQ4AAACAlzAdJlauXKmGDRtq+PDh+c4LDw/XH3/8YXY5AAAAAF7CdJg4duyYmjRpctV5FotFaWlpZpcDAAAA4CVMh4myZcvq5MmTV5134MABVapUyexyAAAAALyE6TDRpEkTbdu2TadOncpzzqFDh7Rr1y61atXK7HIAAAAAvITpMPHQQw/p7NmzGjhwoC5cuJBj/NKlS3r88cdltVr10EMPmV0OAAAAgJcw/ZyJAQMGaPHixVq2bJkaNmyo22+/XZL92RPDhw/XsmXLdPjwYUVFRemBBx4wXTAAAAAA72A6TPj6+mr58uV69NFH9cEHH2jevHmSpB07dmjHjh2SpN69eys+Pt7sUgAAAMA1IylJmj9fOnhQCg+XYmKkiAhPV1U4LnkC9nXXXafFixdrzJgx+uKLL7R//35lZGQoNDRU3bt3V/PmzV2xDAAAAHBNiI+XBg6UbDb7l8UiTZsmxcVJ0dGerq7gXBImMjVs2FANGzZ05UcCAAAA15SkJCk21h4iMmWGipgYqUMHqX59z9VXGKZvwAYAAABQcNOnZw0Szmw2+3hxUegzE4cPHza1YFhYmKn3AwCKn4QEaexY6dgxKSREmjhR6tLF01UBgGd8803+4+vXu6cOVyh0mAgPD5fFYjG0mMVi0eXLlw29FwBQPMXE2K8NznT4sNS1q/0U///v2QEAJcqlS+bGvUmhw0RYWFiuYeLQoUOOP5cvX16SlJqaKskeIjgjAQAlT0JC1iDhLC5OevBBqXNn99YEAJ523XX5j5cr5546XKHQ90wcPHhQBw4ccHzt27dPrVu3VnBwsGbOnKkzZ87ozz//dHzNmjVLwcHBat26tfbt21cUPwMAwEs9+2z+46NGuacOAPAmtWrlP16zpnvqcAXTN2DPnDlTy5cv15o1a/Tkk0+qQoUKjrHy5ctr+PDhSkxM1LJly/Taa6+ZXQ4AUIzs329uHACuRU2a2LeCzY3FYh8vLkyHiQULFujWW2/VP/7xjzzn/OMf/1DHjh21cOFCs8sBAIoRPz9z4wBwLYqJyT9MxMa6tx4zTIeJffv2KTg4+KrzKleurP38JyhDkpKkd99tpIce8tXo0fbXAFAcREXlP86OTgBKoogI+31jPj6Sr2/W73FxxecZE5ILHlpXtmxZbdmyRTabLc9dnmw2m7Zu3aqyZcuaXa7EsT8dsZRstohi/XREACXTSy9JH36Y/zgAlETR0faH08XFSQcPSuHh9jMSxSlISC44M9GxY0ft379fzzzzjK5cuZJj/MqVK3r22We1b98+dezY0exyJUrm0xEzMiyy2SySLLLZpIwM++mxvXs9XSEA5C8iIu/dnOLji9//aQKAK9WvL02ZIr3/vv17cfx3oukzExMnTtTKlSs1c+ZMffLJJ7r//vtVp04dSfadnz766CMdPnxYZcuW1YQJE0wXXJIU5OmIb7/t3poAoLCulf/6BgDIyXSYaNSokb788kv17dtXhw8fzrFjk81mU82aNbV48WLdcMMNZpcrURIT8x9fvdo9dQCAWZn/9Q0AcG0xfZmTJHXo0EFJSUl67733FBsbq27duqlbt26KiYnRokWLlJSUpFtuucUVS+Vw5coVjRkzRnXq1FGZMmVUr149vfTSS7I5/Sd9m82msWPHqkaNGipTpoyioqKUVAzuYk5JMTcOAAAAFCXTZyYyBQQEqG/fvurbt6+rPrJApk6dqrfeeksLFy5U48aN9f3332vAgAGOZ1xI0rRp0zR79mwtXLhQderU0ZgxY9StWzft2bNHpUuXdmu9hVGhgnT6dP7jAAAAgKe4LEx4yoYNG3T33XfrjjvukCSFh4fr/fff15YtWyTZz0rMmjVLL774ou6++25J0qJFi1StWjUtXbpUffr08VjtV9O5s5TfQ8OvtuUiAAAAUJQKfZnTX3/95ZKFXfU5N954oxITE/Xbb79Jknbt2qVvv/1W3bt3lyQdOHBAx48fV5TTb97ly5dXu3bttHHjRpfUUFRGjcp8oEnOu7AtFvs4AAAA4CmFPjNRr149jR07VoMGDZKvr2+hF7x8+bL+85//aPLkyTp27Fih35/d888/r7S0NDVs2FC+vr66cuWKJk+erH79+kmSjh8/LkmqVq1alvdVq1bNMZab9PR0paenO16npaVJkqxWq6xWq+m6CyI8XJo716JBg3wd94BkPspj7twrql3bJjeVAi+T2YPu6kV4N/oB2dETcEY/wFn2fjDbF4UOEyEhIXr88cc1efJkPfLII3rkkUcUERFx1ff9+uuvWrBggd59910dO3ZMrVq1MlRwdh999JEWL16sJUuWqHHjxtq5c6dGjBihkJAQPfLII4Y/d8qUKbluZbtq1SoFBgaaKblQgoOlN98sq9Wrw3TiRKCqVr2gqKjDCg4+ry++cFsZ8FIJCQmeLgFehH5AdvQEnNEPcJbZDxcuXDD1ORabLa8nGeTOZrNp3rx5evHFF3Xy5ElZLBbVqlVLkZGRatSokSpXrqygoCClpaXp9OnT2rNnjzZu3KijR4/KZrOpSpUqmjx5smJjY/N8YnZhhIaG6vnnn9fQoUMdxyZNmqT33ntPv/zyi/bv36969eppx44dat68uWPOrbfequbNm+vf//53rp+b25mJ0NBQnTp1SkFBQabrLgyr1aqEhAR16dJFfn5+bl0b3omegDP6AdnRE3BGP8BZ9n5IS0tTcHCwUlNTDf2OW+gzExaLRYMGDdLDDz+sd999V2+88YZ+/PFHHTlyJNdwkJlVmjZtqmHDhqlfv34qU6ZMoQvNy4ULF+Tjk/XWD19fX2VkZEiS6tSpo+rVqysxMdERJtLS0rR582YNGTIkz88NCAhQQEBAjuN+fn4e+4voybXhnegJOKMfkB09AWf0A5xl9oPZnjC8m1Pp0qU1aNAgDRo0SAcPHtSaNWu0Y8cOJScnKzU1VRUqVFDVqlXVsmVL3XbbbQoPDzdVaF7uuusuTZ48WWFhYWrcuLF27NihGTNmKCYmRpI9/IwYMUKTJk1SRESEY2vYkJAQ9erVq0hqAgAAAEoCl2wNGx4e7vjl3d1ef/11jRkzRo8//rhOnDihkJAQPfrooxo7dqxjzrPPPqvz589r8ODBSklJUYcOHbRy5UqvfsYEAAAA4O2K/XMmypUrp1mzZmnWrFl5zrFYLJo4caImTpzovsIAAACAa1yhnzMBAAAAABJhAgAAAIBBhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGOKyh9alp6fr+++/19GjR3Xx4sU85/Xv399VSwIAAADwIJeEidmzZ2v8+PFKTU296lzCBAAAAHBtMB0m3n33XY0YMUKS1LBhQzVq1EhBQUFmPxYAAACAlzMdJmbNmiWLxaL4+HjOOgAAAAAliOkbsH/++We1b9+eIAEAAACUMKbDROnSpRUeHu6CUgAAAAAUJ6bDROvWrZWUlOSKWgAAAAAUI6bDxOjRo7Vt2zZ9+eWXrqgHAAAAQDFh+gbsevXq6cUXX9Q999yj4cOH684771RYWJh8fHLPKWFhYWaXBAAAAOAFTIeJ8PBwWSwW2Ww2vfbaa3rttdfynGuxWHT58mWzSwIAAADwAqbDRFhYmCwWiytqAQAAAFCMmA4TBw8edEEZAAAAAIob0zdgAwAAACiZTJ+ZAAAgP0lJ0vz50sGDUni4FBMjRUR4uioAgCu4LEycPn1ac+fO1dq1a3X06FFJUs2aNdWpUycNHDhQlStXdtVSAIBiIj5eio2VbLa/j02dag8X0dEeKwsA4CIuCROrVq3Sgw8+qJSUFNmc/h9jz549Wr16tV599VUtWbJEXbt2dcVyAIBiICkpZ5CQ7K9jYqQOHaT69T1TGwDANUyHiaSkJN177726cOGCmjZtqgEDBqhevXqSpP3792vBggXauXOn7r33Xu3YsUMRnNsGgBJhzJicQSKTzWYff/9999YEAHAt02HilVde0YULFzR+/HiNHTs2x/jw4cP10ksvady4cZo6darmzZtndkkAQDGwenX+4wkJ7qkDAFB0TO/mlJiYqAYNGuQaJDKNGTNGDRo00Oqr/T8LAOCaYbWaGwcAeD/TYeL48eNq2bLlVee1bNlSx48fN7scAKCYqFMn//G6dd1TBwCg6JgOE2XLltWJEyeuOu/EiRMqW7as2eUAAMXEq6/mPz59unvqAAAUHdNhonnz5lq/fr1+/PHHPOf88MMPWrdunZo3b252OQBAMdGli303p9zExkqdO7u3HgCA65kOE4MGDZLValVUVJTefPNNnTt3zjF27tw5vfHGG+rSpYuuXLmiwYMHm10uV0ePHtVDDz2kypUrq0yZMmrSpIm+//57x7jNZtPYsWNVo0YNlSlTRlFRUUpKSiqSWgAAf5s3z34jdvv2UliY/fvq1fbjAIDiz/RuTn369NGXX36pd999V0888YSeeOIJxwPqTp8+Lcn+y3z//v31wAMPmF0uhz///FM33XSTbrvtNn355ZeqUqWKkpKSVLFiRcecadOmafbs2Vq4cKHq1KmjMWPGqFu3btqzZ49Kly7t8poAAH/r3JmzEABwrXLJQ+sWLlyoyMhITZ8+Xfv379epU6ccY/Xq1dOoUaP06KOPumKpHKZOnarQ0FDFx8c7jtVxuuvPZrNp1qxZevHFF3X33XdLkhYtWqRq1app6dKl6tOnT5HUBQAAAFzrXBImJOmxxx7TY489pqNHj+ro0aOSpJo1a6pmzZquWiJXy5YtU7du3XTfffdp3bp1qlmzph5//HENGjRIknTgwAEdP35cUVFRjveUL19e7dq108aNG/MME+np6UpPT3e8TktLkyRZrVZZ3byfYeZ67l4X3ouegDP6AdnRE3BGP8BZ9n4w2xcWmy2v55MWD5mXKY0cOVL33Xeftm7dqieffFJvv/22HnnkEW3YsEE33XSTjh07pho1ajjed//998tisejDDz/M9XPHjx+vCRMm5Di+ZMkSBQYGFs0PAwAAALjRhQsX1LdvX6WmpiooKKjQ7y/2YcLf31+tW7fWhg0bHMeGDx+urVu3auPGjYbDRG5nJkJDQ3Xq1ClD/6DNsFqtSkhIUJcuXeTn5+fWteGd6Ak4ox+QHT0BZ/QDnGXvh7S0NAUHBxsOE4W+zGnRokWSpHvuuUflypVzvC6o/v37F3bJfNWoUUM33HBDlmONGjXSf//7X0lS9erVJUnJyclZwkRycnK+W9UGBAQoICAgx3E/Pz+P/UX05NrwTvQEnNEPyI6egDP6Ac4y+8FsTxQ6TERHR8tisah9+/YqV66c43VBuTpM3HTTTfr111+zHPvtt99Uu3ZtSfabsatXr67ExERHeEhLS9PmzZs1ZMgQl9YCAAAAlCSFDhP9+/eXxWJR+fLls7z2lKeeeko33nijXn75Zd1///3asmWL3nnnHb3zzjuSJIvFohEjRmjSpEmKiIhwbA0bEhKiXr16eaxuAAAAoLgrdJhYsGBBvq/drU2bNvrss880evRoTZw4UXXq1NGsWbPUr18/x5xnn31W58+f1+DBg5WSkqIOHTpo5cqVPGMCAAAAMMFlW8N60p133qk777wzz3GLxaKJEydq4sSJbqwKAAAAuLb5FPUCqampKuYbRgEAAADIhekw8dNPP2n27Nn67bffshxfu3at6tSpo0qVKqlq1aoevxwKAAAAgGuZDhOzZ8/WyJEjVaZMGcex06dPq1evXjp06JBsNptOnz6tgQMHaseOHWaXAwAAAOAlTIeJ7777To0bN1ZoaKjj2LvvvquzZ8/q0UcfVUpKihYtWqSMjAy9/vrrZpcDAAAA4CVMh4nk5GSFhYVlOZaQkCBfX19NmjRJQUFBeuihh9SiRQtt3LjR7HIAAAAAvITpMJGWluZ45kSmzZs3q3nz5qpcubLjWEREhI4ePWp2OQAAAABewnSYCAoKyhISfv75Z505c0Y33nhjjrmefLgdAAAAANcyHSaaN2+uDRs2aO/evZKkuLg4WSwW3XrrrVnmHThwQDVq1DC7HAAAAAAvYfqhdY8++qjWrFmjVq1aqW7duvrhhx9UtWpV3XHHHY45Z8+e1c6dO3XXXXeZXa7ESUqS5s710aZNrfTddz4aNEiKiPB0VQAAAIALzkzcd999Gj9+vC5fvqxdu3apdu3a+vjjjxUQEOCY89FHH8lqteY4W4H8xcdLDRtK06f76Jtvamr6dB81bCjxyA4AAAB4A9NnJiRp7Nixev7555WWlqbg4OAc4126dNGOHTtUr149VyxXIiQlSbGxkv3h4fZ7TWw2+1dMjNShg1S/vkdLBAAAQAln+sxEJn9//1yDhCSFhYWpWbNmuu6661y13DVv+vTMIJGTzWYfBwAAADzJZWECrpWYmP/46tXuqQMAAADIi0suc7py5Yo++eQTrV69WkePHtXFixdznWexWJR4td+SIUlKSTE3DgAAABQ102EiNTVV3bp109atW2XL67qc/8dzJgquQgXp9On8xwEAAABPMh0mxowZoy1btqhmzZp64okn1KhRIwUFBbmithKtc2dp3768x6Oi3FcLAAAAkBvTYWLp0qWqUKGCNm3apJo1a7qiJkgaNUqaOzf3m7AtFvs4AAAA4Emmb8BOTk7WTTfdRJBwsYgIaf58e3CwWGySbLJYbLJY7MfZFhYAAACeZvrMRLVq1VS6dGlX1IJsoqPtz5N4550Mbdr0h9q3r6HBg30JEgAAAPAKpsPEXXfdpc8++0xWq1V+fn6uqAlO6teXJk/O0BdfbFOPHj3k5+fr6ZIAAAAASS64zGnChAkqVaqUhgwZkueWsAAAAACuPabPTLz55pvq2rWr4uPjlZCQoM6dOyssLEw+PjlzisVi0ZgxY8wuCQAAAMALmA4T48ePl8Vikc1m05EjR7RgwYIcczLHCRMAAADAtcN0mBg3bpwr6gAAAABQzBAmAAAAABhi+gZsAAAAACWT6TMTzlJTU7V161adPHlStWvX1o033ujKjwcAAADgRVxyZuLs2bMaOHCgqlatqm7duumhhx7SvHnzHOPz5s1TSEiINm/e7IrlAADFSEKCFBkp1a5t/56Q4OmKAACuYjpM/PXXX+rYsaPmz5+vihUrqnv37rLZbFnm3HnnnUpOTtbSpUvNLgcAKEZiYqSuXaVNm6TDh+3fu3aVBg70dGUAAFcwHSZmzJihHTt26MEHH9S+ffu0YsWKHHOqV6+uRo0aae3atWaXAwAUEwkJUnx87mNxcVJionvrAQC4nukw8eGHH6p69eqKi4tT2bJl85x3/fXX6/fffze73FW98sorslgsGjFihOPYxYsXNXToUFWuXFnXXXedevfureTk5CKvBQBKsmefzX981Cj31AEAKDqmw8S+ffvUtm1blS5dOt95gYGBOnXqlNnl8rV161b95z//UdOmTbMcf+qpp7R8+XJ9/PHHWrdunY4dO6Z77723SGsBgJJu/35z4wAA72c6TPj6+spqtV513u+//57vmQuzzp07p379+mnu3LmqWLGi43hqaqri4uI0Y8YMderUSa1atVJ8fLw2bNigTZs2FVk9AFDS+fmZGwcAeD/TW8PWq1dPu3bt0uXLl1WqVO4fd+7cOf3www+64YYbzC6Xp6FDh+qOO+5QVFSUJk2a5Di+bds2Wa1WRUVFOY41bNhQYWFh2rhxo9q3b5/r56Wnpys9Pd3xOi0tTZJktVoLFJ5cKXM9d68L70VPwJm39kOnTj76+GMfSZZcRm3q1ClDVmuGu8sqEby1J+AZ9AOcZe8Hs31hOkz07NlTkyZN0qRJkzR+/Phc50yaNEmpqam65557zC6Xqw8++EDbt2/X1q1bc4wdP35c/v7+qlChQpbj1apV0/Hjx/P8zClTpmjChAk5jq9atUqBgYGmazYigf0UkQ09AWfe1g+33VZWH3/cWZJNWQOFfce/Tp3W6osvznuitBLD23oCnkU/wFlmP1y4cMHU55gOE0899ZTi4+P10ksvaefOnbr//vslScnJyfr000/10Ucf6eOPP1Z4eLgee+wxs8vlcOTIET355JNKSEi46n0bhTF69GiNHDnS8TotLU2hoaHq2rWrgoKCXLZOQVitViUkJKhLly7y47oAiJ5AVt7cD/7+VzRwoK8yA0SmefOuqH//Wz1TVAngzT0B96Mf4Cx7P2RefWOU6TBRoUIFrVy5Uj179tSyZcu0fPlyWSwWrVy5UitXrpTNZlPt2rW1fPnyIrlnYtu2bTpx4oRatmzpOHblyhWtX79eb7zxhr766itdunRJKSkpWc5OJCcnq3r16nl+bkBAgAICAnIc9/Pz89hfRE+uDe9ET8CZN/ZDbKx06632rWAPHpTCw+3H6tc3/X8/KABv7Al4Dv0AZ5n9YLYnXPJv8xtuuEE//fSTFixYoC+++EL79+9XRkaGQkND1b17dw0ePLjILg3q3LmzfvzxxyzHBgwYoIYNG+q5555TaGio/Pz8lJiYqN69e0uSfv31Vx0+fFiRkZFFUhMA4G/160tTpni6CgBAUXDZfxoqXbq0HnvssSK5lCk/5cqV0z/+8Y8sx8qWLavKlSs7jsfGxmrkyJGqVKmSgoKC9MQTTygyMjLPm68BAAAAXF2JOM88c+ZM+fj4qHfv3kpPT1e3bt305ptverosAAAAoFgzHSZ2796t//73v7rrrrvUokWLXOds375dK1as0P3336+GDRuaXfKqvv766yyvS5curTlz5mjOnDlFvjYAAABQUph+aN2bb76piRMnKjg4OM85wcHBmjBhgt5++22zywEAAADwEqbDxNdff62mTZsqNDQ0zzlhYWFq1qyZEhMTzS4HAAAAwEuYDhO///676tate9V5devW1dGjR80uBwAAAMBLmA4Tly9flo/P1T/Gx8dHFy9eNLscAAAAAC9hOkyEhoZq69atV523detWhYSEmF0OAAAAgJcwHSY6deqkw4cP57vV6ltvvaVDhw6pU6dOZpcDAAAA4CVMh4mnnnpK/v7+Gj58uJ566int2bNHV65c0ZUrV7Rnzx499dRTGj58uPz9/TVy5EhX1AwAAAAUe0lJ0ujR0oMP2r8nJXm6osIz/ZyJiIgIxcXFacCAAZo9e7Zmz56dZdxms6lUqVKaO3euW54xAQAAAHi7+Hhp4EDJZrN/WSzStGlSXJwUHe3p6grO9JkJSerbt682btyonj17KjAwUDabTTabTWXKlNHdd9+tDRs26OGHH3bFUgAAAECxlpQkxcZKGRn2ICHZv2dkSDEx0t69nq2vMEyfmcjUsmVLffbZZ8rIyNDp06clSZUrVy7QTk8AAABASTF9+t8hIjubzT5eXJ717LIwkcnHx0dVqlRx9ccCAAAA14Rvvsl/fP1699ThCi4PE87WrVunnTt3qnbt2urZsydnKQAAAFDiXbpkbtybmP7tfsGCBWrZsqW+/fbbLMefeOIJderUSSNHjlTv3r11++2368qVK2aXAwAAAIq1667Lf7xcOffU4Qqmw8Qnn3yiffv2qU2bNo5j33//vebMmaPSpUvr7rvvVs2aNZWYmKgPPvjA7HIAAABAsVarVv7jNWu6pw5XMB0mfvrpJzVp0kQBAQGOYx988IEsFoveffddffrpp9qyZYtKly6t+fPnm10OAAAAKNaaNLFvBZsbi8U+XlyYDhOnT59WrWzxav369QoKClKvXr0kSdWrV9fNN9+svcVpnysAAACgCMTE5B8mYmPdW48ZpsOE1WrNci9Eenq6du3apRtvvDHLDddVqlTRiRMnzC5X4iQlSS+84KPXXmulF17wKZZPRgQAAMDfIiLsD6fz8ZF8fbN+j4uT6tf3dIUFZ3o3p5CQEO3evdvxet26dbJarbrxxhuzzEtLS1P58uXNLlei/P1kRB/ZbDX17bfSa68VvycjAgAAIKvoaKlDB/vvdQcPSuHh9jMSxSlISC4IEx07dtTChQv1yiuvqHv37ho3bpwsFotuv/32LPN++umnHJdDIW+ZT0a0P9DEfh4s83HrMTH25ituzQYAAIC/1a8vTZni6SrMMX2Z07/+9S9dd911euGFF9SyZUtt3rxZUVFRatWqlWPOb7/9pgMHDqh9+/ZmlysxCvJkRAAAAMCTTJ+ZqF+/vjZs2KDXXntNJ06cUNu2bfXMM89kmZOYmKhmzZrpjjvuMLtciZGYmP/46tXuqQMAAADIi0uegN24ceN8t30dMmSIhgwZ4oqlSoyUFHPjAAAAQFEzfZlTQe3YsUPDhg1z13LFXoUK5sYBAACAolakYSIlJUVvvPGGWrZsqdatW+utt94qyuWuKZ075z8eFeWeOgAAAIC8uOQyp+wSExMVFxenpUuXKj09XTabTf7+/urRo0dRLHdNGjVKmjs395uwLRb7OAAAAOBJLgsTR44cUXx8vBYsWKBDhw5Jkmw2m9q2bav+/furT58+qlSpkquWu+ZFREjz59u3gZVsstkyn5Ro0fz5bAsLAAAAzzMVJqxWqz777DPNnz9fiYmJysjIkM1mU3BwsK5cuaKUlBRt2rTJVbWWOJkPM3nnnQxt2vSH2revocGDfQkSAAAA8AqG7pn48ccfNWLECIWEhOjBBx/UqlWrJEndu3fXJ598omPHjumGG25waaElVf360uTJGXr66W2aPDmDIAEAAACvUegzE23atNH27dsl2S9jql+/vgYMGKBHHnlEISEhLi8QAAAAgHcq9JmJbdu2SZJq1qypVatW6bffftPo0aM9FiSmTJmiNm3aqFy5cqpatap69eqlX3/9NcucixcvaujQoapcubKuu+469e7dW8nJyR6pFwAAALhWFDpMlCpVSjabTUePHlWfPn00YsQI7dq1qyhqK5B169Zp6NCh2rRpkxISEmS1WtW1a1edP3/eMeepp57S8uXL9fHHH2vdunU6duyY7r33Xo/VDAAAAFwLCh0mfv/9d02dOlUNGjTQmTNnNHv27CzPkUhNTS2KOvO0cuVKRUdHq3HjxmrWrJkWLFigw4cPO86gpKamKi4uTjNmzFCnTp3UqlUrxcfHa8OGDdwcDgAAAJhQ6HsmqlatqmeeeUbPPPOMNmzYoLlz5+qTTz7R9u3btWPHDj399NPq1auX/vjjj6Ko96oyw0zmNrTbtm2T1WpVlNNT3ho2bKiwsDBt3LhR7du3z/Vz0tPTlZ6e7nidlpYmyb6DldVqLaryc5W5nrvXhfeiJ+CMfkB29ASc0Q9wlr0fzPaFxWbL7bFohXPu3Dm9//77iouL05YtW+wfbH8ogp5//nn1799fDRo0MLvMVWVkZKhnz55KSUnRt99+K0lasmSJBgwYkCUYSFLbtm112223aerUqbl+1vjx4zVhwoQcx5csWaLAwEDXFw8AAAC42YULF9S3b1+lpqYqKCio0O93SZhwtnv3bs2bN0+LFy/WqVOnHKGiTZs2io6O1mOPPebK5bIYMmSIvvzyS3377beqVauWJONhIrczE6GhoTp16pShf9BmWK1WJSQkqEuXLvLz83Pr2vBO9ASc0Q/Ijp6AM/oBzrL3Q1pamoKDgw2HCZc9ATtT48aNNXPmTE2bNk1Lly7VvHnzlJiYqC1btmjr1q1FFiaGDRumFStWaP369Y4gIUnVq1fXpUuXlJKSogoVKjiOJycnq3r16nl+XkBAgAICAnIc9/Pz89hfRE+uDe9ET8AZ/YDs6Ak4ox/gLLMfzPaEoYfWFYSfn5/uu+8+ffXVVzpw4IDGjRunsLAwl69js9k0bNgwffbZZ1qzZo3q1KmTZbxVq1by8/NTYmKi49ivv/6qw4cPKzIy0uX1AAAAACVFkYUJZ6GhoRo3bpwOHDjg8s8eOnSo3nvvPS1ZskTlypXT8ePHdfz4cf3111+SpPLlyys2NlYjR47U2rVrtW3bNg0YMECRkZF53nwNAAAA4OpcfpmTu7311luSpI4dO2Y5Hh8fr+joaEnSzJkz5ePjo969eys9PV3dunXTm2++6eZKAQAAgGtLsQ8TBbl/vHTp0pozZ47mzJnjhooAAACAksEtlzkBAAAAuPYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhpsPE4cOHdebMmavO+/PPP3X48GGzywEAAADwEqbDRJ06dfTMM89cdd6zzz6runXrml0OAAAAgJcwHSZsNluBtmfNnAsAAADg2uC2eybOnj0rf39/dy0HAAAAoIgV+UPrMjIytHv3bq1Zs0ZhYWFFvRwAAAAANzF0ZsLX19fxJUkLFy7Mcsz5y8/PT82bN9fp06d17733urR4AAAAAJ5j6MyE870PFosl33sh/Pz8VKtWLfXu3VsTJkwwshwAAAAAL2QoTGRkZDj+7OPjo+joaM2fP99lRQEAAADwfqbvmRg3bpxatGjhiloAAAAAFCMuCRMAAAAASh63bQ0LAAAA4NrikjBx+PBhDRkyRBEREQoMDMxzZ6dSpYp8J1oAAAAAbmL6t/tffvlFN910k1JSUq76hGuegA0AAABcO0yfmXjhhRf0559/qmvXrtq0aZNSU1OVkZGR5xcAAACAa4PpMxPr1q1TWFiYPv/8c/n7+7uiJgAAAADFgOkzExcuXFDbtm0JEgAAAEAJYzpM1K1bV+fPn3dFLQAAAACKEdNh4uGHH9b69et18uRJV9QDAAAAoJgwHSaefvppRUZGqnv37vrpp59cURMAAACAYqDQN2B36tQpxzGr1art27erefPmCgsLU1hYmHx8cuYUi8WixMREY5UCAAAA8CqFDhNff/11nmMZGRk6ePCgDh48mOu4xWIp7HIAAAAAvFShw8TatWuLog4AAACgxEhKkubPlw4elMLDpZgYKSLC01UVXqHDxK233loUdQAAAAAlQny8NHCgZLPZvywWado0KS5Oio72dHWFY/oGbAAAAAAFk5QkxcZKGRn2ICHZv2dk2M9O7N3r2foKizABAAAAuMn06X+HiOxsNvt4cVLoy5yyy213p9z4+/srODhYrVu31oMPPqhq1aqZXRoAAAAoVq62senq1e6pw1VMh4nM3Z0yd2qy5RK1LBaL4/j777+vF154QW+99Zb69+9vdvlCmTNnjl599VUdP35czZo10+uvv662bdu6tQYAAACUXCkp5sa9jenLnNauXaunn35aNptNrVu31syZM/XZZ59p6dKlmjVrltq2bSubzaaRI0dq0aJFeuSRR3Tx4kUNHDhQW7ZsccXPUCAffvihRo4cqXHjxmn79u1q1qyZunXrphMnTritBgAAAJRsFSqYG/c2psOEv7+//v3vf2vGjBnavHmznnzySd19993q2bOnhg8frk2bNmnmzJmaPXu26tatq/nz5+vtt9/W5cuX9e9//9sVP0OBzJgxQ4MGDdKAAQN0ww036O2331ZgYKDmz5/vthoAAABQsnXunP94VJR76nAV05c5vfTSS2rYsKFGjBiR55wnn3xScXFxmjRpkr744gsNHDhQU6ZM0bfffmt2+QK5dOmStm3bptGjRzuO+fj4KCoqShs3bsz1Penp6UpPT3e8TktLk2R/2rfVai3agrPJXM/d68J70RNwRj8gO3oCzugH7/Lkk9LcuaX+/yZs5wc622SxSE8+eVlF+T9V9n4w2xemw8SWLVvUrVu3q85r0qSJVq5cKcl+D8UNN9ygxKvdgeIip06d0pUrV3Lc9F2tWjX98ssvub5nypQpmjBhQo7jq1atUmBgYJHUeTUJCQkeWRfei56AM/oB2dETcEY/eI9hw0L1+ustJNmyHd+h3347ot9+K/oaMvvhwoULpj7HdJj466+/9Mcff1x13h9//KGLFy86XpctW1alSplevsiMHj1aI0eOdLxOS0tTaGiounbtqqCgILfWYrValZCQoC5dusjPz8+ta8M70RNwRj8gO3oCzugH79OjhzRkyGXFx/vo0CGLate2acCADNWv30RSkyJdO3s/ZF59Y5Tp3+YbNWqkb775Rps3b1a7du1ynbN582Z98803at68uePY0aNHFRwcbHb5AgkODpavr6+Sk5OzHE9OTlb16tVzfU9AQIACAgJyHPfz8/PYX0RPrg3vRE/AGf2A7OgJOKMfvEujRvanXv/N163rZ/aD2Z4wfQP2448/ritXrqhr164aM2aMfv75Z/3111/666+/9Msvv2js2LHq1q2bMjIyNGTIEEn20yk7duxQq1atzC5fIP7+/mrVqlWWy6oyMjKUmJioyMhIt9QAAAAAXGtMn5mIiYnR999/r7ffflsvv/yyXn755RxzbDabHn30UcXExEiSDh48qPvvv199+vQxu3yBjRw5Uo888ohat26ttm3batasWTp//rwGDBjgthoAAACAa4lLblp48803dfvtt+vf//63Nm7c6Lg3IiAgQJGRkRo+fLh69erlmH/DDTcoPj7eFUsX2AMPPKCTJ09q7NixOn78uJo3b66VK1fyJG4AAADAIJfdAd2zZ0/17NlTV65c0alTpyRJlStX9qqbrIcNG6Zhw4Z5ugwAAADgmuDy3/R9fX35r/0AAABACWD6BmwAAAAAJVOhz0xMnDhRkv2SoUqVKjleF4TFYtGYMWMKuyQAAAAAL1ToMDF+/HhZLBb16dNHlSpVcry22Wx5vidznDABAAAAXDsKHSbGjh0ri8XieOBc5msAAAAAJYuhMxP5vQYAAABQMnADNgAAAABD3BYmUlJS9MILL7hrOQAAAABFrMjDRFpamsaNG6fw8HC98sorRb0cAAAAADcx/NC6bdu2afny5UpOTla1atXUs2dPtWzZ0jF+8eJFzZgxQ9OnT1dqaqpsNptuuOEGlxQNAAAAwPMMhYlRo0Zp5syZWY699NJLGjNmjMaPH6+tW7fqgQce0KFDh2Sz2RQWFqbx48erf//+LikaAAAAgOcVOkz873//04wZMyRJQUFBioiIUFpamvbv36+XXnpJDRo00JAhQ5SWlqZKlSrpxRdf1OOPPy5/f3+XFw8AAADAcwodJubOnStJeuKJJzRt2jQFBARIkn7++Wf17t1bjzzyiC5fvqzbbrtNH374oeN5FAAAAACuLYW+AXvbtm0KDw/XzJkzHUFCkho1aqRZs2bp8uXLCgoK0tKlSwkSAAAAQC4WLJBq1pTKlrV/X7DA0xUZU+gwcfLkSbVo0UI+Pjnf2r59e0nSzTffrHLlypmvDgAAALjG3HyzNGCAdOyYdOGC/fuAAdItt3i6ssIrdJi4dOmSypcvn+tYUFCQJKlKlSrmqgIAAACuQQsWSN9+m/vYN99Iixa5tRzTeAI2AAAA4CZXe4bz6NHuqcNVDG0Nu3fvXi3KJzblN872sAAAACipUlLMjXsbQ2Hiu+++03fffZfrmMViyXPcYrEQJgAAAFBiVahgv08iv/HipNBhIiwsTBaLpShqAQAAAK5pkyfbb7bOy5Qp7qvFFQodJg4ePFgEZQAAAADXvuhoaf58+83W2d18s1TcLuLhBmwAAADAjdavlxYulEJCpMBA+/eFC+3HixtD90wAAAAAMK5//+J3FiI3nJkAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGMJD6wrIZrNJktLS0ty+ttVq1YULF5SWliY/Pz+3rw/vQ0/AGf2A7OgJOKMf4Cx7P2T+bpv5u25hESYK6OzZs5Kk0NBQD1cCAAAAuNbZs2dVvnz5Qr/PYjMaQ0qYjIwMHTt2TOXKlZPFYnHr2mlpaQoNDdWRI0cUFBTk1rXhnegJOKMfkB09AWf0A5xl7webzaazZ88qJCREPj6FvwOCMxMF5OPjo1q1anm0hqCgIP4lgCzoCTijH5AdPQFn9AOcOfeDkTMSmbgBGwAAAIAhhAkAAAAAhhAmioGAgACNGzdOAQEBni4FXoKegDP6AdnRE3BGP8CZq/uBG7ABAAAAGMKZCQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGECQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYEgpTxdQXGRkZOjYsWMqV66cLBaLp8sBAAAATLPZbDp79qxCQkLk41P48wyEiQI6duyYQkNDPV0GAAAA4HJHjhxRrVq1Cv0+wkQBlStXTpL9H3RQUJBb17ZarVq1apW6du0qPz8/t64N70RPwBn9gOzoCTijH+Asez+kpaUpNDTU8btuYREmCijz0qagoCCPhInAwEAFBQXxLwFIoieQFf2A7OgJOKMf4CyvfjB6GT83YAMAAAAwhDABAAAAwBDCBAAAAABDCBMAAAAADCFMAAAAADCEMAEAAADAEMIEAAAAAEMIEwAAAIAHJCRIkZFS7dr27wkJnq6o8AgTAAAAgJvFxEhdu0qbNkmHD9u/d+0qDRzo6coKhzABAAAAuFFCghQfn/tYXJyUmOjeeswgTAAAAABu9Oyz+Y+PGuWeOlyBMAEAAAC40f795sa9CWECAAAAcCM/P3Pj3oQwAQAAALhRVFT+4126uKcOVyBMAAAAAG700kvmxr0JYQIAAACAIYQJAAAAwI3GjDE37k0IEwAAAIAbrV6d/3hxehI2YQIAAABwI6vV3Lg3KfZhYsqUKWrTpo3KlSunqlWrqlevXvr111+zzLl48aKGDh2qypUr67rrrlPv3r2VnJzsoYoBAABQktWta27cmxT7MLFu3ToNHTpUmzZtUkJCgqxWq7p27arz58875jz11FNavny5Pv74Y61bt07Hjh3Tvffe68GqAQAAUFJNm5b/+PTp7qnDFUp5ugCzVq5cmeX1ggULVLVqVW3btk233HKLUlNTFRcXpyVLlqhTp06SpPj4eDVq1EibNm1S+/btPVE2AAAASqguXaTYWCkuLudYbKzUubP7azKq2IeJ7FJTUyVJlSpVkiRt27ZNVqtVUU5PB2nYsKHCwsK0cePGPMNEenq60tPTHa/T0tIkSVarVVY3X8iWuZ6714X3oifgjH5AdvQEnNEP3umtt6R//lMaP95Xx45ZFBJi0/jxV9S5c9HeM5G9H8z2hcVms9lMV+UlMjIy1LNnT6WkpOjbb7+VJC1ZskQDBgzIEgwkqW3btrrttts0derUXD9r/PjxmjBhQo7jS5YsUWBgoOuLBwAAANzswoUL6tu3r1JTUxUUFFTo919TZyaGDh2qn376yREkzBg9erRGjhzpeJ2WlqbQ0FB17drV0D9oM6xWqxISEtSlSxf5+fm5dW14J3oCzugHZEdPwBn9AGfZ+yHz6hujrpkwMWzYMK1YsULr169XrVq1HMerV6+uS5cuKSUlRRUqVHAcT05OVvXq1fP8vICAAAUEBOQ47ufn57G/iJ5cG96JnoAz+gHZ0RNwRj/AWWY/mO2JYr+bk81m07Bhw/TZZ59pzZo1qlOnTpbxVq1ayc/PT4mJiY5jv/76qw4fPqzIyEh3lwsAAABcM4r9mYmhQ4dqyZIl+vzzz1WuXDkdP35cklS+fHmVKVNG5cuXV2xsrEaOHKlKlSopKChITzzxhCIjI9nJCQAAAB6TlCTNny8dPCiFh0sxMVJEhKerKpxiHybeeustSVLHjh2zHI+Pj1d0dLQkaebMmfLx8VHv3r2Vnp6ubt266c0333RzpQAAAIBdfLx9G1hJstkki0WaOtUeLv7/V9hiodiHiYJsRlW6dGnNmTNHc+bMcUNFAAAAQN6SkuxBwvnX2Mw/x8RIHTpI9et7prbCKvb3TAAAAADFyfTpWYOEM5uteD0BmzABAAAAuNE33+Q/vn69e+pwBcIEAAAAAEMIEwAAAIAb3Xxz/uO33OKeOlyBMAEAAAC40ahR9t2bcmOx2MeLC8IEAAAA4EYREfYtYH187OEh88vHx368uOzkJF0DW8MCAAAAxU10tH0L2Li4vx9aFxtbvIKEZDBMpKSk6KuvvlJiYqK2b9+u5ORk/fnnn6pYsaKqVaumVq1aqVOnTurWrZsqVKjg4pIBAACA4q9+fWnKFE9XYU6hwsSPP/6of//733r//fd18eLFHA+Mu3Dhgo4ePart27dr3rx5Kl26tPr27asnnnhCTZs2dWnhAAAAADyrQGHixIkTGj16tBYuXKiMjAwFBwfrjjvu0I033qjGjRurcuXKCgoKUmpqqk6fPq2ffvpJGzZs0Pr16xUXF6f4+HhFR0fr5ZdfVtWqVYv6ZwIAAADgBgUKExERETp79qzuvPNOxcbG6o477lCpUnm/tWvXrho5cqQuX76s5cuXa/78+Zo/f77++9//6s8//3RZ8QAAAAA8p0C7ObVt21Zbt27VsmXLdPfdd+cbJJyVKlVK99xzj5YvX67NmzerdevWpooFAAAA4D0KlAoSEhJML9SmTRuXfA4AAAAA78BzJgAAAAAYUiRhIjU1NcdOTwAAAACuLYbCxE8//aTZs2frt99+y3J87dq1qlOnjipVqqSqVatqwYIFrqgRAAAAgBcyFCZmz56tkSNHqkyZMo5jp0+fVq9evXTo0CHZbDadPn1aAwcO1I4dO1xWLAAAAADvYShMfPfdd2rcuLFCQ0Mdx959912dPXtWjz76qFJSUrRo0SJlZGTo9ddfd1mxAAAAALyHoTCRnJyssLCwLMcSEhLk6+urSZMmKSgoSA899JBatGihjRs3uqRQAAAAAN7FUJhIS0tT+fLlsxzbvHmzmjdvrsqVKzuORURE6OjRo+YqBAAAAOCVDIWJoKCgLCHh559/1pkzZ3TjjTfmmGuxWIxXBwAAAMBrGQoTzZs314YNG7R3715JUlxcnCwWi2699dYs8w4cOKAaNWqYrxIAAACA1zEUJh599FFZrVa1atVKLVq00MyZM1W1alXdcccdjjlnz57Vzp079Y9//MNlxQIAAADwHobCxH333afx48fr8uXL2rVrl2rXrq2PP/5YAQEBjjkfffSRrFZrjrMVAAAAAK4NpYy+cezYsXr++eeVlpam4ODgHONdunTRjh07VK9ePVMFAgAAAPBOhsOEJPn7++caJCQpLCwsx/axAAAAAK4dpsKEJO3Zs0cbNmzQyZMn1bhxY/Xs2VOSlJGRocuXL8vf3990kQAAAAC8j6F7JiTpyJEjioqKUpMmTfToo4/qxRdf1NKlSx3jc+fOVZkyZZSYmOiKOgEAAAB4GUNh4syZM7r11lu1Zs0aNW7cWEOGDJHNZssy5/7775ePj4+WLVvmkkIBAAAAeBdDYWLq1Kk6ePCgRo0apV27dumNN97IMadixYpq0qSJvv32W9NFAgAAAPA+hsLE559/rvDwcL3yyiv5PuG6bt26OnbsmOHiAAAAAHgvQ2Hi0KFDatmypXx88n+7v7+/zpw5Y6gwAAAAAN7NUJgoXbq0zp49e9V5hw8fVvny5Y0sAQAAAMDLGQoTDRs21Pbt23X+/Pk855w6dUq7du1S06ZNDRcHAAAAwHsZChP//Oc/dfr0aY0cOVIZGRm5znnmmWd04cIFPfDAA6YKBAAAAOCdDD20bujQoVq4cKHmzZunbdu26d5775Uk7du3TzNmzNDHH3+sLVu2qHnz5oqOjnZlvQAAAAC8hKEwUbp0aX311Ve67777tGHDBu3YsUOS9O233+rbb7+VzWZTmzZttHTpUvn5+bm0YAAAAADewVCYkKQaNWro22+/1VdffaX//e9/2r9/vzIyMhQaGqru3bvr7rvvznfbWAAAAADFm+Ewkalbt27q1q2bK2oBAAAAUIwYugEbAAAAAEyFiX379unZZ59Vhw4d1KBBAz377LOOsc2bN+udd95Ramqq6SIBAAAAeB/DlzktXLhQjz32mNLT0yVJFotFp06dcoxfuHBBQ4YMkb+/Pzs6AQAAANcgQ2cmNm3apIEDB8rf31/Tpk3T5s2bZbPZssy59dZbVb58eS1fvtwlhQIAAADwLobOTEybNk02m03/+9//1KFDh1zn+Pj4qHnz5tqzZ4+pAgEAAAB4J0NnJr777ju1bds2zyCRqXr16vrjjz8MFQYAAADAuxkKEykpKQoLC7vqvL/++kuXLl0ysgQAAAAAL2coTFSuXFmHDh266ry9e/eqevXqRpYolPXr1+uuu+5SSEiILBaLli5dmmXcZrNp7NixqlGjhsqUKaOoqCglJSUVeV0AAADAtcxQmGjfvr2+//577d69O8853333nXbv3n3VS6Fc4fz582rWrJnmzJmT6/i0adM0e/Zsvf3229q8ebPKli2rbt266eLFi0VeGwAAAHCtMhQmhg4dqitXrqh3797auXNnjvGff/5ZMTExslgsevzxx83WeFXdu3fXpEmTdM899+QYs9lsmjVrll588UXdfffdatq0qRYtWqRjx47lOIMBAAAAoOAM7ebUuXNnjRw5UjNmzFCrVq1Ur149WSwWffXVV2ratKn27NmjjIwMPfvss2rfvr2ray6UAwcO6Pjx44qKinIcK1++vNq1a6eNGzeqT58+ub4vPT3d8QwNSUpLS5MkWa1WWa3Woi06m8z13L0uvBc9AWf0A7KjJ+CMfoCz7P1gti8MP7Ru+vTpatCggcaPH6+9e/dKkv744w/98ccfCg4O1rhx4zR06FBTxbnC8ePHJUnVqlXLcrxatWqOsdxMmTJFEyZMyHF81apVCgwMdG2RBZSQkOCRdeG96Ak4ox+QHT0BZ/QDnGX2w4ULF0x9juEwIUmDBg3SwIEDtWPHDu3fv18ZGRkKDQ1VmzZtVKqUqY/2uNGjR2vkyJGO12lpaQoNDVXXrl0VFBTk1lqsVqsSEhLUpUsX+fn5uXVteCd6As7oB2RHT8AZ/QBn2fsh8+obowz9xt+pUyfVqlVLixYtksViUcuWLdWyZUtThRSVzN2kkpOTVaNGDcfx5ORkNW/ePM/3BQQEKCAgIMdxPz8/j/1F9OTa8E70BJzRD8iOnoAz+gHOMvvBbE8YugF7w4YNxeb5EXXq1FH16tWVmJjoOJaWlqbNmzcrMjLSg5UV3MKFUkxMV1WoUEo1a0oLFni6IgAAAMDgmYlatWpluTnZ086dO+e4b0Oy33S9c+dOVapUSWFhYRoxYoQmTZqkiIgI1alTR2PGjFFISIh69erluaIL6OabpW+/LaXM/6kuXJAGDJDmz5fWr/dsbQAAACjZDJ2ZuPPOO/XNN9/o/Pnzrq7HkO+//14tWrRQixYtJEkjR45UixYtNHbsWEnSs88+qyeeeEKDBw9WmzZtdO7cOa1cuVKlS5f2ZNlXtWCB9O23kmRx+rL75htp0SLP1AUAAABIBsPEuHHjVL58ed17770FehJ2UevYsaNsNluOrwX/fz2QxWLRxIkTdfz4cV28eFGrV6/W9ddf79miC+CFF/IfHz3aPXUAAAAAuTF0mdPTTz+txo0ba8WKFWrQoIFatGih8PBwlSlTJsdci8WiuLg404WWRCkp5sYBAACAomQoTCxYsEAWi/2Sm0uXLmnz5s3avHlzrnMJE8ZVqGC/RyK/cQAAABRPSUn2+2APHpTCw6WYGCkiwtNVFY6hMBEfH+/qOpCLyZPtN1tLNjnfL5FpyhR3VwQAAABXiI+XBg6ULBbJZrN/nzZNiouToqM9XV3BGQoTjzzyiKvrQC6io+1p9ZtvJHugkDJDxc03S/37e6gwAAAAGJaUZA8SGRk5x2JjpQ4dpPr13V+XEYZuwIb7rF8vxcVdVqVKFxUYKIWE2J87wbawAAAAxdP8+fmPF6c7BAydmYB7PfywVLnyKvXo0YMnVwIAABRzP/6Y+1kJyX78xx/dW48ZhsJETExMgeb5+/srODhYrVu3Vo8ePeTv729kOQAAAOCacfq0uXFvYng3J0mOHZ1sNluW8ezHLRaLqlWrpoULF6pLly5GawUAAACKvas9N9nLn6ucheHdnLZt26Y33nhDNWvW1D//+U+Fh4fLYrHo4MGD+u9//6sjR45o6NChqlatmtasWaOvv/5avXr10rZt29SwYUNX/xwAAABAsXDxorlxb2IoTLRt21ZDhw7ViBEjNHXq1BzX8U+dOlXPP/+8/vOf/2jLli168cUXNX78eE2cOFEzZszQO++845LiAQAAgOKmcmVz497E0G5O48aNU40aNfTaa6/lekOwn5+fpk+frho1amjcuHGSpBdeeEE1atTQmjVrzFUMAAAAFGNNmtifK5Ebi8U+XlwYChPr1q1TmzZtHPdG5MZisahNmzZa//97mPr5+alJkyY6duyYsUoBAACAa0BMTP5hIjbWvfWYYShMpKWl6c8//7zqvJSUFJ09e9bxumLFivkGEAAAAOBaFxFhf5aEj4/k65v1e1xc8XlgnWTwnol69erp66+/1t69e1U/j582KSlJa9euzTL+xx9/qHJxuggMAAAAKALR0fYnXcfFSQcPSuHh9jMSxSlISAbPTAwYMEDp6enq2LGj4uLidOHCBcfYX3/9pfnz56tTp066dOmSoqOjJUlWq1W7du1S06ZNXVI4AAAAUJzVry9NmSK9/779e3ELEpLBMxMjRozQ119/rf/9738aPHiwBg8erODgYFksFp08eVKS/RkTPXr00IgRIyRJe/bsUfPmzdWvXz+XFQ8AAADAcwydmfD19dWyZcs0a9Ys1alTRzabTSdPntSJEydks9lUu3ZtzZgxQ8uWLZOvr68kqVmzZlq7dq0efPBBl/4AAAAAADzD0JkJyb5b0/DhwzV8+HAdPXpUv//+uySpZs2aqlWrlssKBAAAAOCdDIcJZzVr1lTNmjVd8VEAAAAAignTYSI1NVVbt27VyZMnVbt2bd14442uqAsAAACAlzN0z4QknT17VgMHDlTVqlXVrVs3PfTQQ5o3b55jfN68eQoJCdHmzZtdUmhJtnq19NxzN6tevVKKjJQSEjxdEQAAAGAwTPz111/q2LGj5s+fr4oVK6p79+6y2WxZ5tx5551KTk7W0qVLXVFniRUTI/XoUUq//lpRR45ImzZJXbtKAwd6ujIAAACUdIbCxIwZM7Rjxw49+OCD2rdvn1asWJFjTvXq1dWoUSOtXbvWdJElVUKCFB8vSRanL7u4OCkx0UOFAQAAADIYJj788ENVr15dcXFxKlu2bJ7zrr/+escuTyi8Z5/Nf3zUKPfUAQAAAOTGUJjYt2+f2rZtq9KlS+c7LzAwUKdOnTJUGKT9+82NAwAAAEXJ8EPrrFbrVef9/vvv+Z65QP78/MyNAwAAAEXJUJioV6+edu3apcuXL+c559y5c/rhhx/UqFEjw8WVdFFR+Y936eKeOgAAAIDcGAoTPXv21B9//KFJkyblOWfSpElKTU3VPffcY7i4ku6llzL/ZLvKOAAAAOB+hsLEU089pZo1a+qll15Sr169tGTJEklScnKyPv30U/Xp00evvvqqwsPD9dhjj7m04JIkIiJzNyfJHij+DhXx8VL9+p6oCgAAALAz9ATsChUqaOXKlerZs6eWLVum5cuXy2KxaOXKlVq5cqVsNptq166t5cuXc8+ESdHRUrt2lzVmzAH5+tZX3boWxcYSJAAAAOB5hsKEJN1www366aeftGDBAn3xxRfav3+/MjIyFBoaqu7du2vw4MEKDAx0Za0lVv360sMP/6wePerIz8/wQ8sBAAAAlzIcJiSpdOnSeuyxx7iUCQAAACiB+M/cAAAAAAwhTAAAAAAwpECXOdWtW9fwAhaLRfv27TP8fgAAAADeqUBh4uDBg7ket1gsstlyfwZC5pjFYjFcHAAAAADvVaDLnA4cOJDja+TIkZKku+++W59++ql27NihHTt26LPPPlOvXr0kSSNHjtT+/fuLrHgAAAAAnlOgMxO1a9fO8nrZsmWaOXOm3nvvPT344INZxpo1a6a7775bH3zwgfr166cOHTrkeD8AAACA4s/QDdjTp09X69atcwQJZ3369FHr1q312muvGS4OAAAAgPcyFCZ++OEHRUREXHVeRESEfvjhByNLAAAAAPByhsJERkZGgXZo2rdvX543aAMAAAAo3gyFiRYtWmjLli367LPP8pyzdOlSbd68WS1atDBcHAAAAADvVaAbsLN75pln9M033+j+++/X/fffr379+qlOnTqS7NvILl68WB999JEsFoueeeYZlxYMAAAAwDsYChN33nmnXnvtNT377LP64IMP9MEHH2QZt9ls8vX11dSpU3XnnXe6pFAAAAAA3sXQZU6S9NRTT2n79u2KiYlRvXr1FBAQoICAANWtW1cxMTH6/vvvNWrUKFfWCgAAAMCLGDozkalJkyaaO3euq2oBAAAAUIwYPjNRHM2ZM0fh4eEqXbq02rVrpy1btni6JAAAAKDYKjFh4sMPP9TIkSM1btw4bd++Xc2aNVO3bt104sQJT5cGAAAAFEsFChN333239uzZY2qhn376ST179jT1GWbMmDFDgwYN0oABA3TDDTfo7bffVmBgoObPn++xmgAAAIDirEBhYs2aNWrWrJkefPBBrV69usAfbrPZ9NVXX+m+++5T8+bN9fXXXxut05RLly5p27ZtioqKchzz8fFRVFSUNm7c6JGaAAAAgOKuQDdg//bbbxo1apTef/99ffTRRwoJCVGnTp0UGRmpRo0aqXLlygoKClJaWppOnz6tPXv2aOPGjVqzZo3++OMP2Ww29e3bV9OmTSvqnydXp06d0pUrV1StWrUsx6tVq6Zffvkl1/ekp6crPT3d8TotLU2SZLVaZbVai67YXGSu5+514b3oCTijH5AdPQFn9AOcZe8Hs31hsdlstoJO3rp1q2bNmqX//ve/unTpkiwWS55zbTab/Pz8dN9992nEiBFq3bq1qULNOHbsmGrWrKkNGzYoMjLScfzZZ5/VunXrtHnz5hzvGT9+vCZMmJDj+JIlSxQYGFik9QIAAADucOHCBfXt21epqakKCgoq9PsLFSYynThxQl988YXWrFmjHTt2KDk5WampqapQoYKqVq2qli1b6rbbblOPHj1UtWrVQhflapcuXVJgYKA++eQT9erVy3H8kUceUUpKij7//PMc78ntzERoaKhOnTpl6B+0GVarVQkJCerSpYv8/Pzcuja8Ez0BZ/QDsqMn4Ix+gLPs/ZCWlqbg4GDDYcLQcyaqVq2q6OhoRUdHG3m72/n7+6tVq1ZKTEx0hImMjAwlJiZq2LBhub4n8yF82fn5+XnsL6In14Z3oifgjH5AdvQEnNEPcJbZD2Z7wtRD64qTkSNH6pFHHlHr1q3Vtm1bzZo1S+fPn9eAAQM8XRoAAABQLJWYMPHAAw/o5MmTGjt2rI4fP67mzZtr5cqVOW7KBgAAAFAwJSZMSNKwYcPyvKwJAAAAQOGUmCdgAwAAAHAtwgQAAAAAQwgTAAAAAAwhTAAAAAAwhDABAAAAwBDCBAAAAABDTG8Ne+XKFZ0+fVoXL17Mc05YWJjZZQAAAAB4GcNhYuvWrRo7dqzWrVun9PT0POdZLBZdvnzZ6DIAAAAAvJShMLFp0yZ16tTJcTaiYsWKCgoKcmlhAAAAALyboTAxbtw4Xbx4UTExMZo8ebKqVavm6roAAAAAeDlDYWLz5s1q0KCB5s6dK4vF4uqaAAAAABQDhnZzunz5spo3b06QAAAAAEowQ2GiYcOGOnXqlKtrAQAAAFCMGAoTgwcP1jfffKN9+/a5uh7kIilJevfdRnroIV+NHm1/DQAAAHia4TDx4IMPqkuXLvriiy905coVV9eF/xcfLzVpUkqffhqhjz6yaOpUqWFDacECT1cGAACAks7QDdh169aVJB08eFB33XWXSpUqpRo1asjHJ2c2sVgsnMEwKClJio2VbLa/702x2exfMTFShw5S/foeLBAAAAAlmqEwcfDgQcefbTabrFarDh8+nOtcbtI2bvp0e3DIjc1mH3/7bffWBAAAAGQyFCYOHDjg6jqQi8TE/MdXr3ZPHQAAAEBuDIWJ2rVru7oO5CIlxdw4AAAAUJQM3YAN96hQwdw4AAAAUJQMnZlwtmnTJq1du1ZHjx6VJNWsWVO33Xab2rdvb7q4kq5zZym/e9ejotxXCwAAAFwnKUmaP186eFAKD7dvrhMR4emqCs9wmDh8+LD69eunDRs2SLLfiC39fcP1TTfdpPfee09hYWEuKLNkGjVKmjs3859t1hvZLRb7OAAAAIqX+Hhp4MC/d+m0WKRp06S4OCk62tPVFY6hMJGSkqLbbrtNBw4cUOnSpdWtWzfVq1dPkrR//36tXLlS3377rTp37qzvv/9e5cuXd2nRJUVEhD2xxsTkDGvz57MtLAAAQHHz99b/fx8rzlv/GwoTr732mg4cOKAePXronXfeUUhISJbx48ePa9CgQfriiy/02muvaeLEiS4ptiSKjpbatbusMWMOyNe3vurWtSg2tng1GQAAAOyuta3/Dd2A/dlnn6lKlSr66KOPcgQJSapevbo+/PBDBQcH69NPPzVdZElXv7708MM/6733rmjKFIIEAABAcXWtbf1vKEwcOHBAt956qwIDA/OcExgYqFtvvZVnUgAAAAD/71rb+t9QmPD19ZXVar3qvMuXL8vHh91nAQAAAOna2/rf0G/6ERER+vrrr5WST3Q6c+aM1q5dq+uvv95obQAAAMA1pXPn/MeL29b/hsLEfffdp9TUVN1xxx3avXt3jvEff/xRd955p9LS0vTAAw+YLhIAAAC4FowaZd8KNjfFcet/Q7s5Pfnkk/rwww+1ceNGNWvWTC1atFCdOnUk2beG3blzpzIyMtS8eXMNHz7cpQUDAAAAxZXz1v/S38+ZkIrn1v+GwkSZMmW0Zs0aDRkyRJ988om2bdumbdu2OcZ9fHz0wAMPaM6cOSpdurTLigUAAACKu+ho+/Mk4uL+fgJ2cd363/ATsCtWrKgPPvhAR44c0fr163X06FFJUs2aNXXLLbcoNDTUZUUCAAAA15L69aUpUzxdhXmGw0Sm0NBQ9evXzxW1AAAAAChG2LcVAAAAgCEFOjNx+PBhSfZLmHx9fR2vCyosLKzwlQEAAADwagUKE+Hh4fLx8dGePXt0/fXXKzw8XJa89rTKxmKx6PLly6aKBAAAAOB9ChQmwsLCZLFY5Ofnl+U1AAAFkZRk3/Iwc9eSmBj79ogAgOKtQGHi4MGD+b4GACAv8fHSwIH2vdQz91OfNs2+JWJ0tKerAwCYwQ3YAIAik5Rk3zs9I8MeJCT794wM+9mJvXs9Wx8AwBxDYWLixIlatmzZVectX75cEydONLIEAOAaMH363yEiO5vNPg4AKL4MhYnx48dr6dKlV523bNkyTZgwwcgSAIBrQGJi/uOrV7unDgBA0SjSy5wyMjK4URsASrCUFHPjAADvVqRh4siRI7ruuuuKcgkAgBerUMHcOADAuxVoNydJWrRoUZbXe/fuzXEs0+XLl7V7926tXbtWkZGR5ioEABRbnTtL+/blPR4V5b5aAACuV+AwER0dneWSpe+++07fffddnvNtNpt8fHw0atQocxUCAIqtUaOkuXNzvwnbYrGPAwCKrwKHif79+zvCxMKFC1WvXj3ddNNNuc719/dXrVq11KtXLzVp0sQ1lQIAip2ICPvD6mJi7K8znzMh2Y/Xr++52gAA5hU4TCxYsMDx54ULF6pDhw6aP39+UdQEALiGREdLHTrYH1KX+QTs2FiCBABcCwocJpwdOHDAa26snjx5sv73v/9p586d8vf3V0ouW4McPnxYQ4YM0dq1a3XdddfpkUce0ZQpU1SqlKEfHwBQSPXrS1OmeLoKAICrGfptunbt2q6uw7BLly7pvvvuU2RkpOLi4nKMX7lyRXfccYeqV6+uDRs26I8//lD//v3l5+enl19+2QMVAwAAANcG0/9p/vz589q7d6/S0tJky+Mxp7fccovZZfKU+VA858uwnK1atUp79uzR6tWrVa1aNTVv3lwvvfSSnnvuOY0fP17+/v5FVhsAAABwLTMcJvbv368nn3xSK1euVEZGRp7zLBaLLl++bHQZ0zZu3KgmTZqoWrVqjmPdunXTkCFDtHv3brVo0SLX96Wnpys9Pd3xOi0tTZJktVpltVqLtuhsMtdz97rwXvQEnNEPyI6egDP6Ac6y94PZvjAUJv744w9FRkbq5MmTCgkJ0eXLl3XixAlFRkYqKSlJp06dksViUWRkpPz8/EwVaNbx48ezBAlJjtfHjx/P831TpkxxnPVwtmrVKgUGBrq2yAJKSEjwyLrwXvQEnNEPyI6egDP6Ac4y++HChQumPsdQmHjllVd08uRJ/etf/9KkSZM0YMAALVq0yPHcia+++kpDhgxRmTJl9OWXXxb6859//nlNnTo13zk///yzGjZsaKT8Ahk9erRGjhzpeJ2WlqbQ0FB17dpVQUFBRbZubqxWqxISEtSlSxePhzN4B3oCzugHZEdPwBn9AGfZ+yHz6hujDIWJr776SjVr1sz1v9xL9suIvvzySzVt2lSvvfaannvuuUJ9/tNPP63o6Oh859StW7dAn1W9enVt2bIly7Hk5GTHWF4CAgIUEBCQ47ifn5/H/iJ6cm14J3oCzugHZEdPwBn9AGeZ/WC2JwyFicOHD6tz587y9fWVJPn4+EiSLl++7NhutUGDBrr55pu1ZMmSQoeJKlWqqEqVKkZKyyEyMlKTJ0/WiRMnVLVqVUn20zpBQUG64YYbXLIGAAAAUBL5GHmTn5+fypYt63id+edTp05lmVe1alXt37/fRHlXd/jwYe3cuVOHDx/WlStXtHPnTu3cuVPnzp2TJHXt2lU33HCDHn74Ye3atUtfffWVXnzxRQ0dOjTXMw8AAAAACsZQmAgJCdGRI0ccr+vUqSNJ+v7777PM2717d5HfrDx27Fi1aNFC48aN07lz59SiRQu1aNHCUYuvr69WrFghX19fRUZG6qGHHlL//v01ceLEIq0LAAAAuNYZusypVatW+uKLLxyXNXXu3Fk2m03PP/+86tSpo7CwML3++uv68ccfFRUV5eqas1iwYEGez5jIVLt2bX3xxRdFWgcAAABQ0hg6M3H77bcrJSVFK1eulCQ1bdpUvXr10p49e9S0aVNVqFBBY8aMkY+Pj8aNG+fSggEAAAB4B0Nhok+fPjpy5Ig6duzoOPbee+9p2LBhqlq1qkqVKqUmTZro448/1k033eSqWgEAAIBrQkKCFBkp1a5t/15cHwNi6DKnUqVKqWbNmlmOBQYGavbs2Zo9e7ZLCgMAAACuRTExUnz8368PH5a6dpViY6V58zxXlxGGzkwAAAAAKLyEhKxBwllcnJSY6N56zCJMAAAAAG7y7LP5j48a5Z46XMXQZU6SdOXKFX3yySdavXq1jh49qosXL+Y6z2KxKLG4RSwAAACgCFztEWxF/Ig2lzMUJlJTU9WtWzdt3bpVNpst37kWi8VQYQAAAMC1xs/P3Li3MRQmxowZoy1btqhmzZp64okn1KhRIwUFBbm6NgAAAOCaEhUlffhh3uNdurivFlcwFCaWLl2qChUqaNOmTTl2dQIAAACQu5deyj9MvPSS+2pxBUM3YCcnJ+umm24iSAAAAACFEBGR925O8fFS/frurccsQ2cmqlWrptKlS7u6FgAAAOCaFx0tdehg3wr24EEpPNz+jIniFiQkg2Hirrvu0meffSar1Sq/4naXCAAAAOBh9etLU6Z4ugrzDF3mNGHCBJUqVUpDhgzJc0tYAAAAANc2Q2cm3nzzTXXt2lXx8fFKSEhQ586dFRYWJh+fnNnEYrFozJgxpgsFAAAA4F0MhYnx48fLYrHIZrPpyJEjWrBgQY45meOECQAAAODaZChMjBs3ztV1AAAAAChmCBMAAAAADDF0AzYAAAAAECYAAAAAGFKgy5wWLVokSbrnnntUrlw5x+uC6t+/f+ErAwAAAODVChQmoqOjZbFY1L59e5UrV87xuqAIEwAAAMC1p0Bhon///rJYLCpfvnyW1wAAAABKrgKFiezPkcjtuRIAAAAAShZuwAYAAABgCGECAAAAgCEFuszp8OHDphYJCwsz9X4AAAAA3qdAYSI8PNzwDdcWi0WXL1829F5ISUnS3Lk+2rSplb77zkeDBkkREZ6uCgAAAChgmAgLC2P3Jg+Ij5cGDpRsNh/ZbDX17bfSa69JcXFSdLSnqwMAAEBJV6AwcfDgwSIuA9klJUmxsZLNJkn2IGez2b9iYqQOHaT69T1aIgAAAEo4bsD2UtOnZwaJnGw2+zgAAADgSYQJL5WYmP/46tXuqQMAAADIi6EwsXHjRsXExGjDhg15zvnuu+8UExOjLVu2GC6uJEtJMTcOAAAAFDVDYeKdd97RkiVLdP311+c55/rrr9fixYs1b948w8WVZBUqmBsHAAAAipqhMLFhwwY1b95cwcHBec6pUqWKWrRooW+++cZwcSVZ5875j0dFuacOAAAAIC+GwsTRo0cVHh5+1Xm1a9fWsWPHjCxR4o0aJeW1G6/FYh8HAAAAPMlQmPDx8dGlS5euOs9qtfLAOoMiIqT58+3BwWKxSbLJYrHJYrEfZ1tYAAAAeFqBnjORXXh4uDZu3KjLly+rVKncP+Ly5cvauHGjateubarAkiw62v48iXfeydCmTX+offsaGjzYlyABAAAAr2DozES3bt104sQJjR8/Ps85EyZM0IkTJ3T77bcbrQ2yn4GYPDlDTz+9TZMnZxAkAAAA4DUMnZl46qmnFBcXpylTpmj37t0aNGiQGjZsKEn65ZdfNHfuXC1btkzlypXTyJEjXVowAKB4SUqyX5558KAUHi7FxNgv5QQAFH+GwkRISIg+/vhj9e7dW59//rmWLVuWZdxms6lcuXL6+OOPVatWLZcUCgAofuLjpYEDJZvN/mWxSNOmSXFx9ks5AQDFm+EnYHfu3Fk//fSTnnzySTVo0EClS5dW6dKldf311+vJJ5/UTz/9pC5duriyVgBAMZKUJMXGShkZ9iAh2b9nZNjPTuzd69n6AADmGTozkalWrVqaOXOmq2oBAFxDpk//O0RkZ7PZx99+2701AQBcy/CZCQAA8pOYmP/46tXuqQMAUHRMnZmw2Wz68ssvtWHDBp08eVLt2rVTTEyMJOnkyZP6888/Va9ePfn6+rqkWABA8ZGSYm4cAOD9DIeJXbt26YEHHlBSUpJsNpssFousVqsjTCQkJOjhhx/W0qVLddddd7msYABA8VChgnT6dP7jAIDizdBlTr///ruioqL022+/qXv37po2bZps2S6M7dWrl/z8/PT555+7pFAAQPHSuXP+41FR7qkDAFB0DIWJl19+WadPn9asWbO0YsUKjRo1KsecwMBANWvWTFu3bjVdZF4OHjyo2NhY1alTR2XKlFG9evU0btw4Xbp0Kcu8H374QTfffLNKly6t0NBQTZs2rchqAgDYjRpl3wo2NxaLfRwAULwZChMrV65Uw4YNNXz48HznhYeH648//jBUWEH88ssvysjI0H/+8x/t3r1bM2fO1Ntvv61//etfjjlpaWnq2rWrateurW3btunVV1/V+PHj9c477xRZXQAA+4Pp5s+3B4fMUJH55/nzpfr1PVsfAMA8Q/dMHDt2THffffdV51ksFqWlpRlZokBuv/123X777Y7XdevW1a+//qq33npL06dPlyQtXrxYly5d0vz58+Xv76/GjRtr586dmjFjhgYPHlxktQEA7A+m69DB/pC6zCdgx8YSJADgWmEoTJQtW1YnT5686rwDBw6oUqVKRpYwLDU1NcuaGzdu1C233CJ/f3/HsW7dumnq1Kn6888/VbFiRbfWBwAlTf360pQpnq4CAFAUDIWJJk2aaNu2bTp16pSCg4NznXPo0CHt2rXLrU/B3rt3r15//XXHWQlJOn78uOrUqZNlXrVq1RxjeYWJ9PR0paenO15nnmGxWq2yWq2uLj1fmeu5e114L3oCzugHZEdPwBn9AGfZ+8FsXxgKEw899JDWr1+vgQMHasmSJQoMDMwyfunSJT3++OOyWq166KGHCv35zz//vKZOnZrvnJ9//lkNGzZ0vD569Khuv/123XfffRo0aFCh18xuypQpmjBhQo7jq1atyvHzuktCQoJH1oX3oifgjH5AdvQEnNEPcJbZDxcuXDD1ORZb9j1dC+DKlSuKiorSunXrVKtWLd1+++2aN2+eWrRooZtuuknLli3T4cOHFRUVpVWrVhW6qJMnT+p0fpuTy35/ROalS8eOHVPHjh3Vvn17LViwQD4+f99X3r9/f6WlpWnp0qWOY2vXrlWnTp105syZQp2ZCA0N1alTpxQUFFTon8kMq9WqhIQEdenSRX5+fm5dG96JnoAz+gHZ0RNwRj/AWfZ+SEtLU3BwsFJTUw39jmvozISvr6+WL1+uRx99VB988IHmzZsnSdqxY4d27NghSerdu7fi4+ONfLyqVKmiKlWqFGju0aNHddttt6lVq1aKj4/PEiQkKTIyUi+88IKsVqvjL1BCQoIaNGiQ7/0SAQEBCggIyHHcz8/PY38RPbk2vBM9AWf0A7KjJ+CMfoCzzH4w2xOGn4B93XXXafHixRozZoy++OIL7d+/XxkZGQoNDVX37t3VvHlzU4UVxNGjR9WxY0fVrl1b06dPz3JTePXq1SVJffv21YQJExQbG6vnnntOP/30k/79739r5syZRV4fAAAAcC0zHCYyNWzYMMu9C+6UkJCgvXv3au/evapVq1aWscyrt8qXL69Vq1Zp6NChatWqlYKDgzV27Fi2hQUAAABMMh0mPCk6OlrR0dFXnde0aVN98803RV8QAAAAUIIYChM7duxQQkKCdu/erdOnT8tisahSpUpq0qSJunbtqqZNm7q6TgAAAABeplBh4tChQxo4cKDWrFnjOJZ5OZHFYpEkPffcc+rataveeecdhYaGurBUAAAAAN6kwGHiwIEDuummm5ScnCybzaZKlSqpZcuWCg4OVkZGhk6dOqUdO3bozz//1KpVq3TjjTfq22+/Ve3atYuyfgAAAAAeUuAwERMTo+PHjysiIkKzZs1S9+7dc533v//9T0899ZT27t2r2NhYrV692mXFAgAAAPAePlefIm3dulXr1q3T9ddfry1btuQZJCTpjjvu0NatWxUREaG1a9dq27ZtLisWAAAAgPcoUJj46KOPZLFYNGvWLJUvX/6q88uXL69Zs2bJZrPpo48+Ml0kAAAAAO9ToDCxbds2VaxYUbfffnuBP7h79+6qVKmStm7darg4AAAAAN6rQGEiKSlJLVq0KPSHt2zZUklJSYV+HwAAAADvV6AbsFNTUxUcHFzoDw8ODlZqamqh34e/JSVJc+f6aNOmVvruOx8NGiRFRHi6KgAAAKCAZybOnz+vMmXKFPrDAwICdP78+UK/D3bx8VKDBtKrr/rom29q6tVXfdSggbRggacrAwAAAAoYJjIfTAf3SUqSYmMl+z96i+PLZpNiYqS9ez1bHwAAAFDg50zs3btXixYtKtSH7+U3XsPGjMkMEjnZbPbx9993b00AAACAswKHie+++07fffddoT7cZrPJYrEUuihIV3vWX0KCe+oAAAAA8lKgMBEWFkYocDOr1dw4AAAAUNQKFCYOHjxYxGUguzp1pF278h6vW9d9tQAAAAC5KdAN2HC/V1/Nf3z6dPfUAQAAAOSFMOGlunSx7+ZkZ3P6sh/v3NlDhQEAAAD/jzDhxebNs9+I3a6dTVWq/KV27Wxavdp+HAAAAPC0Au/mBM/o3Fm65ZYr+uKLBPXo0UN+fuQ/AAAAeAd+MwUAAABgCGECAAAAgCGECQAAAACGECYAAAAAGEKYAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgAAAAAYAhhAgAAAIAhhAkAAAAAhhAmAAAAABhCmAAAAABgCGECAAAAgCGlPF0AAODalpQkzZ8vHTwohYdLMTFSRISnqwIAuAJhAgBQZOLjpdhYyWb7+9jUqfZwER3tsbIAAC7CZU4AgCKRlJQzSEj21zEx0t69nqkLAOA6hAkAQJEYMyZnkMhks9nHAQDFG2ECAFAkVq/OfzwhwT11AACKDmECAFAkrFZz4wAA70eYAAAUiTp18h+vW9c9dQAAig5hAgBQJF59Nf/x6dPdUwcAoOgQJgAARaJLF/tuTrmJjZU6d3ZvPQAA1yNMAACKzLx59hux27eXwsLs31evth8HABR/PLQOAFCkOnfmLAQAXKs4MwEAAADAEMIEAAAAAEMIEwAAAAAMKfZhomfPngoLC1Pp0qVVo0YNPfzwwzp27FiWOT/88INuvvlmlS5dWqGhoZo2bZqHqgUAAACuHcU+TNx222366KOP9Ouvv+q///2v9u3bp3/+85+O8bS0NHXt2lW1a9fWtm3b9Oqrr2r8+PF65513PFg1AAAAUPwV+92cnnrqKcefa9eureeff169evWS1WqVn5+fFi9erEuXLmn+/Pny9/dX48aNtXPnTs2YMUODBw/2YOUAAABA8Vbsw4SzM2fOaPHixbrxxhvl5+cnSdq4caNuueUW+fv7O+Z169ZNU6dO1Z9//qmKFSvm+lnp6elKT093vE5LS5MkWa1WWa3WIvwpcspcz93rwnvRE3BGPyA7egLO6Ac4y94PZvvimggTzz33nN544w1duHBB7du314oVKxxjx48fV506dbLMr1atmmMsrzAxZcoUTZgwIcfxVatWKTAw0IXVF1xCQoJH1oX3oifgjH5AdvQEnNEPcJbZDxcuXDD1ORabzWZzRUGu9Pzzz2vq1Kn5zvn555/VsGFDSdKpU6d05swZHTp0SBMmTFD58uW1YsUKWSwWde3aVXXq1NF//vMfx3v37Nmjxo0ba8+ePWrUqFGun5/9zERqaqrCwsJ04MABlStXzgU/ZcFZrVatXbtWt912m+OMC0o2egLO6AdkR0/AGf0AZ9n74ezZs6pTp45SUlJUvnz5Qn+eV56ZePrppxUdHZ3vnLp16zr+HBwcrODgYF1//fVq1KiRQkNDtWnTJkVGRqp69epKTk7O8t7M19WrV8/z8wMCAhQQEOB4nXmZU/azHAAAAEBxd/bs2WsnTFSpUkVVqlQx9N6MjAxJcpxViIyM1AsvvOC4IVuyn9Zp0KBBnpc45SYkJERHjhxRuXLlZLFYDNVmVFpamkJDQ3XkyBEFBQW5dW14J3oCzugHZEdPwBn9AGfZ+8Fms+ns2bMKCQkx9HleGSYKavPmzdq6das6dOigihUrat++fRozZozq1aunyMhISVLfvn01YcIExcbG6rnnntNPP/2kf//735o5c2ah1vLx8VGtWrWK4scosKCgIP4lgCzoCTijH5AdPQFn9AOcOfeDkTMSmYr1cyYCAwP16aefqnPnzmrQoIFiY2PVtGlTrVu3znGJUvny5bVq1SodOHBArVq10tNPP62xY8eyLSwAAABgUrE+M9GkSROtWbPmqvOaNm2qb775xg0VAQAAACVHsT4zUVIEBARo3LhxWW4IR8lGT8AZ/YDs6Ak4ox/gzNX94JVbwwIAAADwfpyZAAAAAGAIYQIAAACAIYQJAAAAAIYQJgAAAAAYQpgoBubMmaPw8HCVLl1a7dq105YtWzxdEtxg/fr1uuuuuxQSEiKLxaKlS5dmGbfZbBo7dqxq1KihMmXKKCoqSklJSZ4pFkVuypQpatOmjcqVK6eqVauqV69e+vXXX7PMuXjxooYOHarKlSvruuuuU+/evZWcnOyhilHU3nrrLTVt2tTx4KnIyEh9+eWXjnH6oWR75ZVXZLFYNGLECMcxeqJkGT9+vCwWS5avhg0bOsZd1Q+ECS/34YcfauTIkRo3bpy2b9+uZs2aqVu3bjpx4oSnS0MRO3/+vJo1a6Y5c+bkOj5t2jTNnv1/7d17UFTlGwfw7yIsNxFYQhAUWhLBy4jGTSKvSyGSeAETNSP0HxFJMWrsQsD8cGw0UfGGTQ5YDlnggGWNhBcwRAhwaMCUgWQ0BNQERBEE2fP7w2FrZxfDdXHJ/X5mdoZ93/ec8+zhmWUfznnfTUVaWhpKS0thbm6OwMBAdHV1PeNI6VkoLCxEdHQ0SkpKkJ+fj56eHrz++uvo6OhQjImNjcUPP/yArKwsFBYWorGxEYsXL9Zh1DSYRo8ejc8++wwVFRUoLy/HnDlzsGDBAly8eBEA80GflZWV4cCBA5g8ebJSO3NC/0ycOBFNTU2KR1FRkaJPa/kg0JDm4+MjREdHK5739vYKDg4OwpYtW3QYFT1rAIScnBzFc7lcLtjb2wvbtm1TtLW1tQnGxsbCN998o4MI6Vm7efOmAEAoLCwUBOHR79/IyEjIyspSjLl06ZIAQDh//ryuwqRnzNraWvjyyy+ZD3rs7t27gqurq5Cfny/MnDlTWL9+vSAIfI/QRwkJCYKHh4faPm3mA69MDGHd3d2oqKhAQECAos3AwAABAQE4f/68DiMjXauvr0dzc7NSblhaWsLX15e5oSfu3LkDAJBIJACAiooK9PT0KOWEu7s7nJycmBN6oLe3F0eOHEFHRwf8/PyYD3osOjoawcHBSr97gO8R+qq2thYODg5wcXHBihUrcO3aNQDazQdDrUZMWvXXX3+ht7cXdnZ2Su12dna4fPmyjqKioaC5uRkA1OZGXx89v+RyOTZs2AB/f39MmjQJwKOcEIvFsLKyUhrLnHi+VVVVwc/PD11dXRg+fDhycnIwYcIEVFZWMh/00JEjR3DhwgWUlZWp9PE9Qv/4+voiIyMDbm5uaGpqQlJSEqZPn47q6mqt5gOLCSKi/5jo6GhUV1cr3ftK+snNzQ2VlZW4c+cOsrOzERERgcLCQl2HRTrw559/Yv369cjPz4eJiYmuw6EhICgoSPHz5MmT4evrC2dnZ3z33XcwNTXV2nF4m9MQ9sILL2DYsGEqM+tv3LgBe3t7HUVFQ0Hf75+5oX/WrVuH48eP48yZMxg9erSi3d7eHt3d3Whra1Maz5x4vonFYowdOxaenp7YsmULPDw8sGvXLuaDHqqoqMDNmzfx8ssvw9DQEIaGhigsLERqaioMDQ1hZ2fHnNBzVlZWGDduHOrq6rT6HsFiYggTi8Xw9PTEqVOnFG1yuRynTp2Cn5+fDiMjXZNKpbC3t1fKjfb2dpSWljI3nlOCIGDdunXIycnB6dOnIZVKlfo9PT1hZGSklBM1NTW4du0ac0KPyOVyPHjwgPmgh2QyGaqqqlBZWal4eHl5YcWKFYqfmRP67d69e/jjjz8watQorb5H8DanIW7jxo2IiIiAl5cXfHx8sHPnTnR0dCAyMlLXodEgu3fvHurq6hTP6+vrUVlZCYlEAicnJ2zYsAHJyclwdXWFVCpFfHw8HBwcsHDhQt0FTYMmOjoamZmZOHbsGCwsLBT3tFpaWsLU1BSWlpZYvXo1Nm7cCIlEghEjRiAmJgZ+fn6YNm2ajqOnwfDhhx8iKCgITk5OuHv3LjIzM1FQUIC8vDzmgx6ysLBQzKHqY25uDhsbG0U7c0K/xMXFYf78+XB2dkZjYyMSEhIwbNgwLFu2TLvvEU+x4hQ9I7t37xacnJwEsVgs+Pj4CCUlJboOiZ6BM2fOCABUHhEREYIgPFoeNj4+XrCzsxOMjY0FmUwm1NTU6DZoGjTqcgGAkJ6erhjT2dkprF27VrC2thbMzMyERYsWCU1NTboLmgbVqlWrBGdnZ0EsFgu2traCTCYTfv75Z0U/84H+uTSsIDAn9M3SpUuFUaNGCWKxWHB0dBSWLl0q1NXVKfq1lQ8iQRAELRZBRERERESkJzhngoiIiIiINMJigoiIiIiINMJigoiIiIiINMJigoiIiIiINMJigoiIiIiINMJigoiIiIiINMJigoiIiIiINMJigohIB0Qi0RM/Zs2aBQCYNWsWRCIRCgoKdPoatGHXrl0QiUQ4evSoUntiYqLSa+5PRkYGRCIRXnzxxcELUkcOHz4MkUiEffv26ToUIqJ+Geo6ACIifRQREaHS1tzcjLy8vH773d3dBz2uZ+nWrVtITEyEt7c3QkNDdR3OkLN8+XJs3boV8fHxCA8Ph0Qi0XVIREQqWEwQEelARkaGSltBQYGimFDX3+err77C/fv34eTkNEjRPRtJSUloa2tDYmKirkMZkgwMDJCQkICwsDAkJycjJSVF1yEREangbU5ERP8xTk5OcHd3h5mZma5D0VhbWxsyMjLg6OiIuXPn6jqcISskJAS2trY4ePAg7t27p+twiIhUsJggIvqP6W/OxDvvvAORSISMjAzU1NRg6dKlGDlyJMzNzeHt7Y1jx44pxpaWlio+qJqamsLPzw+nTp3q95idnZ3Yvn07pk2bBisrK5iYmMDNzQ0ffPABbt++/cSvIT09HR0dHVi5ciUMDAbvT1FDQwNiYmLg6uoKExMTWFpawt/fHwcOHEBvb6/K+L65Gv1dLSkoKFA7l+Of7ffv38enn36K8ePHw8zMTGk+R21tLVatWgWpVApjY2MMHz4czs7OCA4ORnp6usrxjIyMsHz5crS3t+Prr79+mlNBRDQoWEwQET1nLly4AE9PT/z222+QyWTw8PBAeXk5Fi1ahOzsbOTm5mL69OloaGiATCaDm5sbSkpKMHfuXBQVFansr7GxEb6+voiLi0NtbS28vb0xb948PHjwANu2bYOXlxeuXr36RDHm5uYCAAICArTxktUqKyuDh4cH9uzZg+7ubixcuBCvvPIKLly4gDVr1iA4OBjd3d1aPWZXVxdmzZqFlJQUSKVShISEwNXVFQBQXV0NLy8vpKenw9jYGG+88QbmzZsHR0dHnD17Frt27VK7z9deew3A3+eMiGgo4ZwJIqLnzO7du5GcnIyPPvoIIpFI0fbuu+8iNjYWHR0dOHjwIFauXKnYJjY2Fjt37kRSUhLy8/MV7YIg4M0330RVVRVWr16NHTt2wMLCAgDw8OFDbNq0Cdu3b0dkZCROnz49oPg6OztRUlICAwMD+Pj4aPGV/+3BgwdYsmQJWlpasGbNGqSmpsLIyAgAcOXKFchkMuTl5SEpKQmbN2/W2nFLS0sxefJk1NXVwd7eXqkvJSUF7e3tSE5Oxscff6zU19nZibKyMrX79PPzg0gkQlFREbq7uyEWi7UWLxHR0+KVCSKi54yPj49SIQEAUVFRkEgkaGhoQEBAgFIhAQCffPIJAODs2bPo6elRtOfl5eHcuXOYMmUK0tLSFIUEABgaGmLr1q2YNGkSzpw5g+rq6gHFd/HiRXR3d2P06NFK+1OnsLDwscvlRkZGqt0uKysLV69ehYODA3bu3KkoJADAxcUFn3/+OYBHRVZXV9eA4h6oPXv2qBQSAHDjxg0AwLx581T6TE1NMWPGDLX7k0gksLe3x/3793H58mWtxkpE9LR4ZYKI6DkTFBSkVEgAjz74S6VStLS0qP0wa2NjA4lEgpaWFty+fVvxYfjHH38EAISGhsLQUPVPhoGBAWbMmIHq6moUFxdj0qRJ/xpf34dqGxubfx1rZ2f32AnadXV1OHfunEp733yS8PBwGBsbq/QvXrwY1tbWaG1tRUVFBfz9/f81loEYOXIkpk+frrbPx8cHP/30E6KiopCUlISZM2fCxMRkQPu1sbFBU1OT4twREQ0VLCaIiJ4z/S0ZO3z48Mf2W1hYoKWlRek/9VeuXAEAxMfHIz4+/rHHvXXr1oDiu3PnDgBgxIgR/zrW3d39scvkZmRkqC0mrl+/DgCQSqVqtxOJRJBKpWhtbVWM1YbHfXne+++/j6KiIpw8eRJz586FkZERPDw8MGPGDISHh8Pb27vfbfvOVWtrq9ZiJSLSBhYTRETPmX9bHelJVk+Sy+UAgFdffRUvvfTSY8dOnDhxQPu0srICALS3tw84jqGi73z0x9TUtN8+MzMz5Ofno6ysDCdOnEBxcTGKi4tRXl6OlJQUrF27Fnv37lW7bV8BZm1trXnwRESDgMUEERH1a8yYMQCABQsWIC4uTiv7HDlyJABotKTsQDk6OgL4+8qKOvX19UpjASgmN9+9e1ftNk+6apU63t7eiqsQDx8+RG5uLt5++23s27cPYWFhmD17tso2fefKzs7uqY9PRKRNnIBNRET9CgoKAvBoQrMgCFrZ58SJEyEWi9HQ0NDvh/an1fc9EN9++63aCdY5OTlobW2FhYUFPD09Fe19hcWlS5fU7rdvDom2GBoaIiwsDIGBgQCAyspKlTG3b99Gc3MzzMzMMH78eK0en4joabGYICKifi1YsADe3t749ddfERkZqXZeRGtrK9LS0vDw4cMB7dPU1BTTpk2DXC5HaWmptkMGACxZsgROTk5obGzExo0blWKrr6/He++9BwCIiYlRmgQ9Z84cGBgYIC8vD4WFhYp2QRCQmpqKo0ePahzTvn37UFNTo9Le3NyM8vJyAICzs7NKf3FxMYBHt5r9c1UqIqKhgMUEERH1y8DAALm5uZgyZQoOHToEqVQKf39/LFu2DKGhoZg6dSpsbW0RFRU14GICABYuXAgASt9poU3GxsbIzs6GRCLB/v37MXbsWISHhyM4OBgTJkxAfX09AgMDkZCQoLTdmDFjEBMTA7lcDplMhtmzZyM0NBSurq6Ii4vDpk2bNI7piy++gLu7O1xcXBASEoK33noLgYGBcHFxQUNDA+bMmYOQkBCV7U6ePAng73NGRDSUsJggIqLHcnBwQElJCdLS0uDj44OamhpkZ2crvi17zZo1yMvLG/AypwAQGRkJc3NzHD58GL29vYMSt7e3NyorKxEdHY1hw4YhJycHv/zyC6ZOnYr9+/fj+PHjar8AbseOHdi+fTvGjRuH4uJiFBQUYMKECSgpKVHcjqSJzZs3IyoqClZWVigpKUFWVhZ+//13+Pr64tChQzhx4oTK8rs9PT3IzMzEiBEjVL4bhIhoKBAJ2roJloiI6AmsW7cOe/fuxffff4/58+frOpwh6ejRowgLC0NsbCxSUlJ0HQ4RkQoWE0REpBO3bt3CuHHjMHbsWJSVlek6nCFHLpdjypQpuH79OmprayGRSHQdEhGRCt7mREREOmFra4vExESUl5cjOztb1+EMOZmZmaiqqsL//vc/FhJENGTxygQREREREWmEVyaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgjLCaIiIiIiEgj/weJZyPvG9PoTgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leo_observations = sensor_1.observe(target_satellite=leo_satellite, times=(start_mjd, end_mjd))\n",
    "\n",
    "times = np.array([o.mjd for o in leo_observations.pos_observed])\n",
    "times = 24 * (times - min(times))  # Convert the times from days to hours\n",
    "ra = [o.ra for o in leo_observations.pos_observed]\n",
    "dec = [o.dec for o in leo_observations.pos_observed]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(9, 9))\n",
    "\n",
    "_ = ax1.plot(times, ra, \"ob\", ms=5, lw=2)\n",
    "ax1.set_ylabel(\"Right Ascension (degrees)\", fontsize=16)\n",
    "_ = ax2.plot(times, dec, \"ob\", ms=5, lw=2)\n",
    "ax2.set_ylabel(\"Declination (degrees)\", fontsize=16)\n",
    "ax2.set_xlabel(\"Time (Hours)\", fontsize=16)\n",
    "ax1.grid()\n",
    "ax2.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This fast-moving object is, of course, much more difficult to observe than the stationary GEO satellite, and we only obtain a handful of observations before it leaves the field of view. Zooming in, you can see how observations are clustered around collection times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(9, 9))\n",
    "\n",
    "_ = ax1.plot(60 * times[:12], ra[:12], \"ob\", ms=5, lw=2)\n",
    "ax1.set_ylabel(\"Right Ascension (degrees)\", fontsize=16)\n",
    "_ = ax2.plot(60 * times[:12], dec[:12], \"ob\", ms=5, lw=2)\n",
    "ax2.set_ylabel(\"Declination (degrees)\", fontsize=16)\n",
    "ax2.set_xlabel(\"Time (Minutes)\", fontsize=16)\n",
    "ax1.grid()\n",
    "ax2.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensor parameter \"obs_per_collect\" was set to 3, and \"obs_time_spacing\" was set to 1, so each of these collections contains 3 observations spaced 1 second apart.\n",
    "\n",
    "The parameters \"collect_gap_mean\" and \"collect_gap_std\" were set to 60 and 5, respectively, so there is on average a 60-second gap in between collections, with 5 seconds of normally distributed uncertainty.\n",
    "\n",
    "Using these four parameters allows you to roughly approximate the behaviors of sensors that periodically sweep across different pointings in the sky."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "maddg",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}