Abstract. We present optical data obtained with the 1.05 m telescope of the Torino Astronomical Observatory for five X-ray selected BL Lacertae objects. The data are in the Johnson's B, V , and Cousins' R bands. As the observing periods include the pointings of the Satellite per Astronomia X "Beppo" (BeppoSAX), our optical information will be comparable with the X-ray observations for a better understanding of the properties of these objects. The present data also provide optical information on sources that have been rarely observed in the optical band. Variability on short time scales (a few days) was found only for 1ES 1959+650.
<p>In thirty-one years of observations, the Hubble Space Telescope (HST) has produced a vast archive of thousands of targeted observations. This includes galaxies,&#160; clusters of galaxies, and gravitational lenses. Occasionally, closer objects such as Solar System bodies or artificial satellites cross the telescope's field of view during the observations, leaving trails in the images. On one hand, these trails can impact the observations. The standard data processing pipeline (DrizzlePac) cleans cosmic rays artifacts (Hoffmann et al., 2021), also removing asteroid trails, but it leaves residuals in the combined images. On the other hand, this is a great opportunity for the Solar System small bodies science, considering the already existing images from the huge HST Archive, containing more than 100 Tb of data and spanning three decades.&#160;</p><p>Our project is focused on studying serendipitous asteroid trails appearing in archival HST images. We used images from two instruments, namely the Advanced Camera for Surveys and Wide Field Camera 3, the ultraviolet and visible channels. These images were acquired between 2002 and 2021. We built an online citizen science project on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org), launched on International Asteroid Day 2019, to identify the asteroid trails in the images (Kruk et al., in prep.). This project involved more than 11,000 people in search for asteroids, providing 2 million classifications for 150,000 images over a period of one year. The labels provided by the volunteers were used to train an automated classifier based on a deep learning algorithm, Google Cloud AutoML Vision. We recovered 2,400 trails in the HST images in total.&#160;</p><p>The asteroid trails appear curved as viewed by HST, because of Hubble&#8217;s motion around the Earth every 90 minutes. One example is asteroid 2001 SE101 passing in front of the Crab Nebula in a rare cosmic coincidence, discovered by citizen scientist Melina Th&#233;venot and shown in Figure 1. The project also contributed to other serendipitous discoveries, such as new strong gravitational lenses in the background of some famous HST targets.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.ec984bd41ea069331202261/sdaolpUECMynit/1202CSPE&app=m&a=0&c=4d0a7574074b80ef68437340d6523257&ct=x&pn=gepj.elif&d=1" alt="" width="700" height="549"></p><p>Figure 1: Trail of asteroid 2001 SE101 passing in front of the Crab Nebula, M1 in 2005. The trail appears curved because of the motion of HST around the Earth. ESA Image of the Week: http://www.esa.int/ESA_Multimedia/Images/2019/10/Foreground_asteroid_passing_the_Crab_Nebula. Credit: ESA/Hubble & NASA, M. Th&#233;venot.&#160;</p><p>We further analysed the asteroid trails in order to obtain their astrometry and photometry with a customised algorithm. We validated the trails visually, finding 1,700 trails presumably of Solar System objects. Their distribution in the sky is shown in Figure 2. We extracted each trail from the images by using a fixed-width aperture, which was moved along the trail.&#160; The position and corresponding flux were obtained for each point along the trail. The calibration was performed using the WCS (World Coordinate System) information stored in the header. As a by-product of this algorithm, we were able to derive partial light curves. The apparent magnitude of the corresponding Solar System object was obtained by integrating all the flux along the trail.</p><p>We used the SkyBoT service provided by IMCCE/Paris Observatory and the JPL HORIZONS online solar system data and ephemeris for identifying the known objects. We computed the ephemerides taking into account the position of HST. Despite using the largest databases of minor bodies, we only matched 300 trails with already known asteroids, taking into account the orbital uncertainties and their apparent motion. Therefore, our data contains 1,400 unknown objects or objects with very large orbital uncertainties.&#160; This is not surprising,&#160; since most of the apparent magnitudes of our trails (Figure 3) are fainter than magnitude 22, which is the approximate limit for the asteroid discovery surveys performed with ground-based telescopes. Most of these objects will correspond to main-belt objects with sizes <1 km, thus will help us characterise the distribution of small size asteroids in the Main Belt, a population poorly explored by current studies.&#160;</p><p>This project demonstrates the power of combining novel tools such as citizen science and artificial intelligence to efficiently explore archival images and obtain important results, with the invaluable help of Zooniverse volunteers, beyond the original scope of the Hubble observations.&#160;</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.666737e61ea063681202261/sdaolpUECMynit/1202CSPE&app=m&a=0&c=ad4fa7d50feb3749bfee5cd931b1625c&ct=x&pn=gnp.elif&d=1" alt="" width="700" height="539"></p><p>Figure&#160; 2: Sky distribution of the asteroids detected in HST observations. The vast majority of asteroids are in the Ecliptic plane (denoted with red). The two gaps are due to the lack of HST images in the Galactic Plane.&#160;</p><p>&#160;</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.5b8160171ea064091202261/sdaolpUECMynit/1202CSPE&app=m&a=0&c=9cef2fc703d9f1aae3c9a489a03680d4&ct=x&pn=gnp.elif&d=1" alt="" width="700" height="516"></p><p>Figure 3: The apparent magnitude distribution of the Solar System objects identified in HST observations. The majority of the identified asteroids have magnitudes >22, fainter than the detection capabilities of many ground-based surveys.&#160;</p><p><strong>References:</strong></p><p>Hoffmann, S. L., Mack, J., et al., 2021, &#8220;The DrizzlePac Handbook&#8221;, Version 2.0, (Baltimore: STScI).</p>
<p align="justify">A fraction of near-Earth asteroids has the orbital elements similar to those of comets, but a visual aspect as any other point-like source. These are potentially dormant comets nuclei who entered in a period of inactivity. Their study can provide a new understanding of the final state in which volatile-rich objects reside and of the existing organic material or water content distribution from the early Solar System.</p> <p align="justify">Dynamically, cometary orbits can be filtered by their Tisserand parameter with respect to Jupiter (T<sub>Jup</sub>). With few exceptions, comets have T<sub>Jup</sub> < 3 while asteroids displays T<sub>Jup</sub> > 3. Although the value of T<sub>Jup</sub> can indicate whether or not the asteroid crosses the Jupiter's orbit, this is not enough to outline a cometary orbit. Tancredi (2014) had developed a method to classify asteroids on cometary orbits (ACOs), based only on orbital elements, which doesn't require any numerical time integration. Beside Tisserand criterion, this algorithm rejects all samples in mean-resonant motion, with large orbital uncertainties and with large minimum orbital intersection distances (MOID) among giant planets.</p> <p align="justify">We seek to make a statistical analysis of the potentially dormant (extinct) comets from near-Earth objects population (NEACOs), using the spectral observations over the visible and near-infrared wavelength interval. The aim of this work is to constraint the fraction of dormant comets orbiting in the near-Earth space. For this study, we&#8217;ve compiled a catalog with 149 spectra of near-Earth asteroids (NEAs) with T<sub>Jup</sub> < 3. This sample represents 10% out of all known asteroids which obey the T<sub>Jup</sub> criterium (Fig. 1).</p> <p align="justify"><img src="data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAAfQAAAGTCAIAAABRRXc/AAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAABmJLR0QA/wD/AP+gvaeTAAAAB3RJTUUH5AYXDgsk1uZ5ogAAgABJREFUeNrtnXV4FMcbx9/ZPUsul4u7JxDBCV7cpcW9WJFihQrSlhotlBbquJZSpAUKxaG4SyhOEiTufrlczm93fn8svyONEcLlLpfM5+HhSfZm52bnLt+dfecVhDEGAoFAINQtKEsPgEAgEAimh4g7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdhIg7gUAg1EGIuBMIBEIdxJrEHWNs6SEQCATCq2IeKeNZ+jKrys2bN3fu3GVnJzZ5z3q9nqZpirKm+5xVwzAMxpjHs5rvnrWDMdbr9QKBwNIDqUdUoircxzFnzhxfX98aHYPV/IGlp6fLZAUDBvQ37U2PYZjNmzd36dIlIiKCZVlLX2Xdh6Koc+fOZWdnjx49mky4GUAI5ebm7ty58+2337axsbH0cOoFCKHNmze3bt26efPmZb/kBoNhy5YtcrmciPv/B8rjhYSE9OrVy7TdGgyGM2fOtGvXrk2bNpa+xPqCTCZLSkrq2bOnpQdSX8jIyLh06VKPHj3s7OwsPZb6wpkzZ9q0adO5c+eyLxkMhiNHjiCEanoMVmOLQAg9evTo+PHjCoXCtN3a2NgQE4E54fP5IpHI0qOoR3BfcjOoCcGIjY0NTdNlj2dlZR04cCAzM9MMdmBrEjWapoVCoWm/oxRFTZs2zcnJydIXV4/o3LmzVqu19CjqEc7OznPmzCE3VHMyefJkqVRa9jhFUSKRyDw7fFYj7hjjBg0adO/e3bTdIoT8/PwsfXH1C3IrNTMCgSAwMNDSo6hfVGRPd3Nz69u377Fjx8yw4WQ1ZhkCgUAgVB2rWblXBMuySqVSrVYT1wvCq0DTtFgsJrZpQp3BusUdY5yfn69Wq21tbcvdviAQqgjDMDk5OY6OjuWaSgkEq8O6xV2n0ymVSk9PT6FQaOmxEKwehUKRk5ODMZZKpWT9TrB2rNvmrtfreTweCb0jmASRSKTT6e7cuZObm2vpsRAIr4p1izvGGCFEFlkEk8B9lzQaTWpqKklkRLB2rNssUxqlElSqmupcKAR7e0tfIaFmwRgzDKNSqRiGIaFtBKumbn19T5+GI0egJgIEWBYiI2HaNKh425Zl2aKiIs5iawxSUKvVWq2Ws+FqNBqlUlnyFISQvb19SRFRKBQMwxCbrwUha3ZC3aBuiXtREaSlAUUBxmCqP1GEACFgWfD3r7yhTCabNm1aTk7Od9991759e+7gzp07T58+vWnTJolEcuz48WVff2306sEYSySSlStXNmrUiDsil8vnzJmTnp6+du3a0NBQS88mgUCwYqxJ3OVyeUZGhqurK5/PL78FJ8QUBa6uYG9vAn1HCDQayMwEvf6FDwR6vf7hw4dxcXFr165t0aIFF+2dkZERHR1tMBgAICc7OzExcfHixR4eHhhjjLFAIPD09DT2cPPmzTNnzqhUqmNHjxFxJxDqHmq1OjMzU61Wm+HR3GrEHSEUHR194MCBUaNGOTs7V9aUomDkSOjUCV49rImi4OlTWL4c5PIX3iq47bi+fftev379woULffr04Q6WbCMWi19//fVyY8ExxgcPHGzRokVgYOChw4emTJ1i/38Tf25u7pkzZxISEgQCQYMGDbp27Up8sQkEayQjI+Ovv/5KS0sj4v4cjHH79u1nzZr14qYIgY0NiE1U1kMship/DCzLduvWzcXFZd26dR07dhSXGQO3YC/33NTU1PPnz7/73ruNGjUaO2bszZs3e/ToAQCFhYVz5sx5/PhxixYtGIY5deqUs7Nzx44da2qiCQRCjREcHPz+++8nJyebIaLeasQdyqyCK8S0G2Iv0xsGsLGxmTFjxptvvvnPP/8MHTq01PgLCgrmz58vsbcHjFmW9fHx+fDDDx0cHADg7NmzeoO+e/fu7u7uwSHBBw4e7N69O0LowYMHN27c2L59e8eOHTHGhYWFxK+fQLBezFb0zbr93M1K1W4tLMt26NChZ8+e69atUyqVpW5IFEU5ODg4Oz3D6BWj0WgOHDjQsWPHgIAAsVg8cODAc2fPpqamAoC9vT1C6MCBA//++69MJnNwcBCb6qGEQCDUXaxp5W5hXmYJ//bbb48ePfrgwYMlxR1j7ODo+OmnnwYFBZVqHxMTc+PGjT59+mzbtg0AsrOzU1NTz184P2H8hEaNGn3+2eebNm86fPiwVCod0H/A7Hdmu7i4WHo6CARCraYuirtpdyqq1VtkZOQbb7yxcePGpk2b/ucprII7xNGjR/V6fUJCQmJiIgAghKRS6cEDB0eNHCUUCie9NWnwkMEJCQnnz5//6aef+AL+okWLzDihBALB+qiL4o4xFBRAZqZpvGVycp718zIqzxV4Gjly5OHDhzmTuhGDwcAwjHE7hc/n5+XlHTlyZNz48UuXLOEO0jS9Z8+ezz77LCYmJjw83GAwODg4tGzZMiIi4uzZs/Hx8VzeBUtPNIFAqL3URXFnWdi7Fw4fNo2fu15fxZQGpTxhGjVqNHz48K+++qpZs2bGg3l5eXPnzrWzs+Na8ni8+fPn5+TkJCcnf/PNNyXrF3fr3s3mG5sjR47IZLKffvqpWbNmrq6ucXFxT548mThxIlF2AoFQOXVL3DEGlgWEoLDQlBGqAMCyL3wOkEgk8z74oHXr1sYjU6dOFYvFjo6ONjY2ANCmbdtFixYZ1+wYY5qmJRKJRqP5/PPPW7VqVbI3by/vr778iqKoBg0a9OrVKzY2NjU11cnJafXq1SavNUggEOoedUvcPTygWTMTu0IaCQio/HWxWFzKDd/X13fhwoXGX1u2aNGyRYuyJ4aFhXXq1KnUQZqmR40axf08d+5cjDHLshRFkTU7gUCoCnVL3Lt1g86da0TcuawGliv2hBAipaYIBELVqVvizuMBSdNKIBAItTOI6c6dO5999tn9+/ctPRACgUCwVmqduBcUFCxfvnzNmjXR0dGWHguBQCBYKzVixDAYDEqlkqIoOzu7shuAer0+OydHo1Y7ODg4OzuXarB161Z3d/emTZuaIbEOgUAg1FVMuXLHGF+8ePHTTz8dMWJEnz593n///eLi4lJtUlNT33vvve7duvXo0aNPnz5r167VaDTGV69du3b9+vVp06aR3FgEAoHwKphy5c6y7I4dO44dOxYQEBAXF4cx5opUGFEoFAsXLjx79ux7770XERFx8NDBTz75hKbpGTNmAEBhYeH69evHjB0bGBhIlu0EAoHwKphS3CmKWrhw4aJFiyQSyahRowoLC0s1uHTp0pEjRz755JOPPvoIALp27ZqZkbl27dpBgwZ5enru2LGjqKioQUjI48ePVSpVRkaGTCZzdHS09BQRCASC9WFKcUcIhYSEAIBGoyk31ubs2bMikah///7cr1Kp9PXXX1+4cOH9+/c9PT2zs7NzcnI++ugjvV7/5MmT33//3d3dfcKECVUfwE24+S/8i8D0YT4YcBAE9YJeVMWGLJVKtXfvXo1GM2TIEDc3N+5gVFTU06dPhw8fLhQKHz58eObMGYZhjFkKhELhsGHDjJX2DAbDoUOHZDLZ8OHDq11r6f79+6dOneJ+5lzjGYYBAIqievfubazX+oro9fqzZ89G3YjKyMzg8/mtW7fu379/uRWyNBoNlzUhLS2tRYsWZcO1CARCTWA+r3CtVvvk8RN3d3cPDw/jQa5S6NOnT/v06bNgwYJZs2YhhBQKxeTJk0eNGjVixIiXeou7cHc7bOcBD4Mp45gQIAMYukP3ntCzkmYKheLrr79++vSpUqn84IMPuIP//PPPnj17+vfvLxQKr1y58uGHH7Zt29bW1pZLRGNnZ9erVy+juCckJMyfPz8rK8vV1W3gwDeqN9r09PSTJ08ihFiWvXfvHkKoWbNmCCGKoho3bmwqcVcoFBs3btTr9V5eXjk5uZ9++unRo0fXrl3r5ORUquWFixfnzpnDMEx6evrMmTOJuBMI5sF84q7X6/ML8iUSSclaE1KplMfj5efnA4C9vT1XNVQikYSHhwcFBXEpWYyoVKqCggKGYcRisa2tbdm3QIBooCmg0LNS2QgDLvn/Sw3YeAoLLA10VR4IEEUFBATs2LFj8ODBZZO2Y4xdXV03btwYHBzMbSoghHgloq5OnDghFosjIyMPHPh7wID+xpBUjLFcLs/Pz+fz+Y6OjuX6IBnp06dPjx49EEJarXb06NECgWDnzp1cV7yXjPDCGOfm5ubn5zds2LBUfKxUKt2wYYNUKuXz+Rjj3bt3z549+9q1awMGDCjVSXhY2Pr1611cXKZMmUK2Ugj1GYVCodFoGIbR6XR1qoYqt79K03TJ/Obcz6X2XW1tbZcvX15K2SmKOnPmTEZGBsuyI0eOfOON8he2nI4PgkFNoMmrr98RoHRI/wP+UICC6/wF18iyw4cPv3Dhwu+//7548eJyOkRIIBCUK7LFxcWHDx8eMGBAo0aNli5dmpCQ0KBBAwBgGGbnzp2bNm3SarUURTk7Oy9btqxZs2b5+fl///13q1atmjdvXmqiOF8jjDFFUdyvL5u6oLi4+P79+0eOHDl16lTr1q1/+eWXUj3QNG0sGIIQCg4OFgqFer2+bFd+fn5+fn4KhYK7DbziJ0IgWCksy+7cufPy5csAEB8fb4Zie+YTd5qm7SR2sgKZVqs1CrdarWZZtmSqWwBACJXdR2VZtnv37vPmzSvbvhQUUE2gSQ/oYZJhP4JHf8FfVWzMYuzn5zdlypSff/551KhR4eHhpRpoNJqoqKj09HTOLCMWi5s0acJp8e3bt+Pj4z///PPg4ODly5efOHGCE/dHjx4tW7Zs/PjxgwYN0uv1Tx4/4Z5asrOzv/zyyw8//LCUuL8KOp3uyZMnJ0+ePHHiRGpqanBw8KRJk/r168fn88ttn5aWlpiYWFRUtGPHjjZt2rRv395UIyEQ6hgURY0ZM2bw4MEMw3z++ed1qkC2SCTy8/WLexrHFQLlDqanpxsMBj8/v6r0IBaLXV1dK29TDfOLyRkxYsS2bds2b978ww8//GdsFCWTyT777DOhUMhleQwJCdm8eTO3+3rw4MGAgIDmzZtLJJKuXbsePHTorbfesrOzy8vLU6vV7dq1a9y4MQC0+H9eSS8vrx9//JE7WHU4C49YLC6l1wqF4siRI4cPH46OjnZ2du7Zs2ffvn3Dw8NLPT+V4ty5cz/++GN+fj7DMN99951xG5lAIJRFKpVKpVK9Xs8pQE2/nfnEnaKotm3b7tmz5969e4GBgQDAsuylS5cdHByaNm36wtMRQlFRUVu2bBk6dKiZ/SONd4uqmN0xxk5OTm+//faSJUsmTpxIlbBmYJZ1cXHZsGGDv78/t3IXCATcDmRGRsbp06cHDhyo1+tlMtlrr722/+/9d+/e7dixY+PGjSMjI2fMmNG6devWrVv37t07PDycK7T9shvOAJCTk/P5559/+OGHpbYEnjx58sG8D1RK1fvvvz9l8hQfX5+q2AQHDx7cpUuXoqKi7du3f/755z4+Pp07dzbnR0MgWBdJSUkHDhxISEgwg1nGxG+QlZV148aNmzdvyuVypVJ5+/btqKiotLQ07tU+ffoEBAT88MMP9+/fl8vlBw4c2Lt37xtvvNGwYcMX9owxDg4O7tGjR8n92HKaWXrZzjFo0CD/gIANGzZotdqSKsnn8/39/YOCgoKDg0NCQvz8/Dj7+8WLF588ebJnz56+ffv26dPn22+/zcvNO3z4MAA4Oztv2LBhyZIlLi4uf/7559ChQ0+ePFntgaWmpiYlJZX1swwLC1u9avWQIUMOHTo0YeKEpUuXRkVFqV5Ugkoikfj5+TVu3HjBggW2trYnTpyw9MQTCLUaV1fXPn36uLm5Wd/K/cCBAx9//DFFUWq1GmM8cuRIlmUXLFjAFXQODAz85ptvFixYMGjQIBcXl9TU1MjIlh9++GFFJt1SODs7B7yoYkZNOLlXA0dHx5kzZixatCgoOLjULbrsilin0+3fv79du3bvvPMOQoir0HT06NF//vnn3Xff9fLycnV1HT169OjRo3NyckaNGrVv374+ffpgjFUqlUAgeKlUDU+ePPH09Cwr7mKxeNiwYQMHDkxISDh16tTx48f/+OMPPz+/Pn36vP7665z1vxIMBoOBYYyXplKpEEJlTTqk0gihniMWixs0aGAstFmjmFjc+/XrFxgYyP0NczrFrbiNDd54442wsLBLly4VFBQEh4R06dy53MiXcikoKEhJSfHw8KhEzky+cufcb6rUEuOSdUL69u27c+fOgwcPlqyhqlKpzp456+nlyc0MRVGtWrXKzs6+du3al19+OWzYMGNLLy+vY8ePX7p0qWmzZndu327ZsqVEIklNS5UXyT08PBBCSUlJ77777vjx4ys3zpT6Dj1+/DgwMLAin0g+nx8aGhoaGjp58uTo6Ohjx47t3r376dOnP//8c6k5f/DgwZUrVyIjI+0kkkKZbMuWLfLCwp49egKAXq9fuHChRCJZunQpTdNFRUU3btwoLCwsKChISEg4cuSIo6Nj27ZtX9Yvk0CoAyiVyrS0NKVSaX2ukP7+/v7+/pU0QAg1bNiwKnaYsic+fvz4+PHjI0aMKBsp87wZIAz4GBy7D/dN4gopA5ka1FXRdx6PFxgYaNwrlkgks2bNevr0qb+/P+dH6Ojo6OzsvHLVSuPnyufzV6xYkZGRERwc3LVr15K9NWrUqGuXLvfu3fPx8d2wYYNarRYKhRqNpnmz5m+//TYAGAwGbq+1khnz9vYu+VRkMBji4+MHDhz4wmuxtbXlTPzvvPNOQUFBWU9KvV7/5+4/16xZw2KMWdbZ2fnHH3/s3KUzAGCMCwsLjc4AmZmZy5Yt40IZ4uPjP/744+bNmzdv3pyIO6Eekp2Tc/jo0cysLDOIO7IW1+MjR45wRTxKHlQoFEVFRd7e3tyvm2HzNthGAcUCa6olPAJEAcUA0w26LYbFNFToMM6yrEwms7W1NZojDAZDYWEhRVGOjo4IIbVarVAoSp0llUp1Op1er+falHypqKiIYRh7e3u5XJ6VlaVUKh0cHHx9fUUikbFzsVhckUMLxrioqIh7C+5IXl7e+PHjly5dGhkZ+YrTwnndZGdnKxQKsVjs5eVlfBfuJYQQd8RgMMhkMuPXDGPMhWLVQhONwWB4+vRpXFycg4ND+/btye2HUA4FBZCfDwBQ3S+wwWCY+803sz/8sFFERI2OtE59fXnAE4CAS/9SMja18l9f2IbrmfeiueIijP4zHh7PGOkDADY2NuUKsVAoLLdDLl4XAJycnMo+rJTqvCxGeTWSnp7Osqyvr++rTzVCyMHBwfiYUvalkuN8oQMrgWA1nDsHe/bAq/i6sCwkJb1SD1XDmsRdoVDk5OQ4OTlVtKTqCT3DIKwmHGYQICdwompf4aqX4vqN6/7+/pUYtQgEwgvQakGhAIoCloWXN3toWTZTq1X/14muhrAacUcI3bt3b/fu3WPHjq1oD9YHfHzAx9IjrY3IZLJ169ZdvXr1ww8/JNYGAqH6cGmrKAqCg8HV9WX1PV2t/j09PVmno6zOW6bmwBi/9tprc+bMsfRArBKapps1bzZ06FAuDSeBQKgmnCgjBG+8AT16AMO81NkBAB/r9Tnz5jEveWI1sBpxB+Il/QrY29sP6D/g1fshEAjPoGlACF7yOZgCoM0lZdYk7gQCgfASKBTPPFtMBUWBXP7q3ZjHR9FqxB0hlJycfP369aZNm5abzJ1AIBD+w82b8NtvgHG13RbLQa1+FUeXgoKCu3fv5uXlkQ3V/yCXy9PT08vm0SUQCIRy0GqhoAAAAGMwVYpdoyhXa/WtVqvT0tLUajUR9+dgjJs2bVoyQJ9AIBBeAKeh3t7g5lY9OS4fHg+qnDelJN7e3mPHjr1+/XqdyudOIBAIFgBj6N0bBg9+Wc+WykAIXrK6mfmpU+KuU+j0Sv2r91MuPBFP6CB89X4IBIK5oeln/+oT1iTuLMtyu8wVmauif4u+u/4uok1vzMIMDugd0OW7LhSvwr0UlmULCwsBQCqVGjNtqVQqrVbr4OCAENJoNKVyy3CR+iWjiuRyOcuyUqm0bC5/g8GQm5srk8l4PJ6bm5tUKi05DxqNJisrS6VS2dvbu7u7VzGL8gtRqVRKpbLscYSQvb39S2UbJhAIXClp4i3zHxBC165dW7t27ejRoyuKUFVmKXNiciigTJuBAAFigXVs6Fh5rzKZbMqUKdnZ2d9///1rr73GHdy5c+epU6c2b95sb29/9OjRpUuXGqUcYyyRSFavXt2oUSPuiFwunzVrVnp6+vr168PCwkp2Hh0dvWrVqqioKL3egBA4ODgMHTp06tSpXGLoK1eu/PLLL0+ePMGAKURFRka+//77L1uBr1x27ty5bt067l7FWQm5u45QKFyxYkWHDh1MOM8EQp0nISFh7969cXFxdapA9ivCbaiOHDmybKGJ5yBAgBBCdh52AolpFpWMhlGkKzCDX7i7rdfrY2Ji4uLi1q1bFxkZyeVuzMzMjI2N5aLRcnNzU1JTly5Z4uHhYSyz5+npaewhKirq/PnzarX66NGjJcX9wYMHEydOFIlE8+fPDw8P1+v1N27cOHLkSM+ePRs3bnzhwoUpU6Y0adLku+++c/dwT4hP+OmnnyZOnLh9+/aIV04717FjR0dHR4qiVCrVkiVLgoODp02bxrIsj8crVaiPQHgl0tIgKspkPi0AQFHw6JGlr6o03IbqkydPrK9YR40ikUiqkl+QoqgOn3cIHRHKMq/6RUE0yv43+/Dow+pC9YsbI4Qoql+/fjdu3Dh//nzfvn2hjAVJbGvbt29froRsKTDGBw4ciIyMDAwMPHz48NSpU7nbmE6n+/7773U63R9//GFMHtCmTZthw4bZ29srFIply5b5+vpu2LDB3d0dAJo1bRYeHj5kyJAff/xx/fr1PB6vsLDw9OnTsbGxNE0HBgb26NGj6pWsw8PDOd/ToqKitWvXBgcHDxkyxPiqXC6/evVqdHS0SqUKCgrq2bOnh4cHAGRmZl6/fr1NmzZcNmalUnnu3Dl/f/8mTZpwr54+fTohIcHDw6Nnz55cLReGYS5fvuzk5MSy7JkzZ1iW7dGjR/PmzbkJxBjHxMScPXtWJpOFhYX16NGDe3orKiq6fPlyo0aNoqOjb9++7evr+/rrr9vZ2Z09e/b27dve3t6vv/66i4uLQqE4f/58s2bNjKXYi4uLL1y40Lhx48rLDxDMR1ISbNsGBoMp++TywNSmyHaRSOTl5SUSiYi4VwsEAqlA5CwySWciRxGiqvrlYBmma9eurq6ua9eu7dixo52dXakG3IK93HNTUlIuXLjwwQcfREREHDp06ObNmz179uSOnzt3burUqaXSwnh5eQHAtWvXbt26tXz5ck7ZOcLCwoYMGfLHH3+kp6d7eHh89NFHly9fbtO2LU1RFy5cEIvFVanXUZWRnzt3buXKlVwl2IMHD/7xxx8bN2709vamaXrDhg379+9ft26dnZ3dli1b1q9fv3XrVgCIi4ubMWOGTCZr2rTpmTNnNmzYsHr16g4dOuh0um+//VahUDg5Odnb2ycnJ2/atOm3335r3749ABw7dmzBggXe3t7e3t579+7dvXv3mjVrPDw80tLS3n333dDQUB6PJ7Kx2bJly8WLF4OCgq5duyaRSH799dcrV66sXr0aAH755ZfmzZt///333MjPnz//wQcf7N69m4h7bYHLxsUZK0wofFZSr6ImqIviDmCpKtkYYxsbmxkzZowZM+bEiRPDhw8v+SpCKD8///3335dIJBhjFmNfH5+PP/7Y0dERAM6ePcswTLdu3dzd3UNCQg4cONijRw8uLre4uJhb85YlISEBAMqaX5o2bbpu3bqsrCyNRnP69Onvv/9+8ODBAFC2Wsir0KlTp86dO3M5hOPj40eOHPn333+/8847bm5uixcvnjBhwqZNm1q0aLFq1ar58+e3bdsWY7x69ers7Jw9e3aHh4fn5OS89dZby5Yt27NnD0VRer0+P79gzZo1zZo1y8rKGjZs2J9//tm+ffv8/Pyvv/66RcuWa9eskUqlN2/eHD169NatWz/++GMAKCoqkkqlq1atcnJy2rlz54wZM4YPH75t2zZnZ+fdu3cvXLgwOjq6ZcuWgwcP3rhxY2pqqq+vr8Fg2Lt3b9OmTV/dbEUwJVyoUcuW8NprJhNl7p5RL/Pl1VFxtxQIsSzbrl27Xr17rVu3rm/fvqXMMjRNu7m5OTo6YoxZlnV1deX2KjUazYEDBzp27Ojv709R1MCBA9dvWJ+SmuLv56/VaqHigh5qtZqiqLJeK5zFX6PR2EnsbG1tDxw44Ozs3LBhQxcXl7I185RKZXZ2Nvezs7NzZbsa/8XR0TEmJubgwYP5+fksy+p1+ujoaO6ldu3aLVy4cNmyZTwer1evXhMnTgQAuVx++fLl119/nTP1uLm5jR49+rPPPktOTg4KCsIYd+/WjSs56+Hh0bx586SkJIxxXFxcYmLi559/zg2sVatW3bp1O3fu3AcffMBd6ZAhQ7gbTPPmzZ2cnPr06cMZbTirDndp/fv3X7du3alTpyZPnvz06dNr164tWbKkolklWJLAQOjb19KDqAtYk7ir1eqioiKxWEzXen/V6W9PHzly5IEDB0qKO8bYwdHx448/LrsV+TD64Y0bN3r37r1lyxYASE9PT01JPX/u/MSJE52cnChE5eXllftGLi4uXB27Usdzc3Npmra3t/f28l66dOnKlSunTZsmEom6du36/vvvl7JFXLly5cMPP+TuNx9++OGbb75Zxcv8888/v/nmm8DAwICAAB6Pp9NpdTodxs82n/v167dy5cq0tLQJEyZwNxuVSlVcXOzj423swcvLy2AwcGX5EEIOjg7GlwQCAdcVV8TV3cODO87Vhr13755Wq0UI8fl8e8mzklU8Hk8kEnFPQgBAURRCiHPy4TYb9u/f/+abbx45csTe3r5U0VoCwQzo9XqZTKbX60n6gedQFHXr1q3ffvvtzTffdH5R4K/FkwO3aNFi0KBBGzdubNas2X98nip42Dx29BjDMKmpqbt27eIu1tHR8eDBg2PGjAkODvb28T5//vzYsWPL1tmIiIiwt7e/cOFCr169jAf1ev358+cDAgK4/cOBAwd2796dS7u2fPlyg8GwcuXKkqNq1arVpk2bAABjbNxyfCEKhWLt2rV9+vRZtmyZQCDQ6XRxcXHGoGqGYbZs2aLT6fz8/bZs2cKlexOJREKhUCYrNHYik8koiio3Exxn4uccRhmGURQVGV8qKCgQi8V8Ph8/2wp48SM8QmjEiBFTp049derUkSNHBgwYUHKXglCLsPQfb42SnJy8e/fuxMREIu7P4cwdM2bMeGF4DsZYlatSpCkw86pmO0QjZZYSsy/dD0VRU6dOPXny5KFDh0qVtdPr9QaDwSiCfD4/Pz//yJEjXOlqTtFomt6zZ88nn3zy8OHDli1bTpgwYcWKFbt27Ro9ejRngSkqKjp9+nSrVq0aNGgwYsSI7du3d+7SpVfPngghg8Gwa9euU6dOff75587Ozmq1WqfTSaXSRo0ahYeHX7t2LTExUa/Xl7RIlFuj9YXo9XqFQuHq6soN6c6dOzdv3uzfvz/36rFjx3777bdvv/3W29v7rbfe2rp16+zZs6VSaWRk5OnTp6ZPf9vV1VWtVh8+fDggIMDf37+iVBssZoOCgtzd3Y8eOdqhQwcej5eYmHjhwsV+/fpWVBm8Ilq1ahUeHv75558rlUpuB4JQTTA28UYlV7XO2HndJTAwcO7cuampqSS3zH/HyuNVJSQSM/j619dvr7xtgm1VBAaNQafQVaVtKX+SiIiIESNGLF682GglAIDc3Lx33nlHLBZz7fl8/sKFC3NyclJSUt94442SC9hu3bqJxeIDBw60bNly2rRpmZmZn3322d9//x0aGqrX6+/du6dWq7dt24YQmj9/fmZm5swZM7p06eLt7R0XF3f16tXx48dzZu7Y2NjPPvssPDzc09MzPT394qWL77//QbVtzSUvUCqV9ujRY+PGjQqFgqbpf2/+KxAIOOvKo0ePvvjii+HDhw8ZMoTP57/77rs//vhj48aNu3TpMnv27ClTpkycOLFLly737t2Lior64YcfpFKpWq0u1f+zVTmLvby85s6du2TJkgJZQVBQ0IkTJ+ztJVOnToWXzIttY2MzfPjwiRMnjhw50hg4RqgOqalw4ADoTZfqAyHIyTGlk3tthaZpGxsbM0QwgXWJ+wvhntAx4OKcYsgxac+AX6gjEolk4YIFkZGRxiNTp061t7eXSqXcGrNdu3aLF39RsrwWRVEODg56vf6rr75s1apVyd68vLyWLFkCAAzD2Nvbf/311/369Ttz5kxmVqZQIBw4cGDvPn04D3EPDw9uq/DSpUtpaWm+vr4bN27s0qULZ+YODAwcNnTYvfv3omOipRLp999936dPn2rMgEgkmjVrlsf/Dd8AQNP0okWL/P3979+/7+jouPDDhYWFhdxzVVFR0eTJk0eNGsX9ykVgcTvDkZGRO3bs2Lt375MnT3x8fGbPns05O/L5/KlTp3L+nRwDBw4sLCzk/gwmTJjg7e194sSJ+Pj4vn37jhw5krt2d3f3hQsXNmzYkDvF1dV13rx5xl9dXFw+/PDDkuFgDRo0cHd3Hzp0KEmc8EoUFMD586DRmLJPzq3FtLnX6zfIPFkOXp0jR47cuXPns88+K3lQoVAUFRVxkTIAcHf93fub7yOogdwyGAf09O/4dadKcsuYB5ZludVxuYPEGJe7KOBequjEmhtS5We97PqlkgusyrkGg2Hx4sVnz547dOhgudFwBoPh6dOncXFxDg4O7du3J5XEK+TuXVi6FDQa4PFets7cC2BZGDwYJkyw9BXWIAaDYe7cubNnz67px8c69fVtPLlx+NhwqIlnOwpoPmVxZYf/p3Ypl0oUtoZk/YVDMu1Zr3IVJ06c2LBhw4MHDxYvXlyVOGdCZXBhnywLLVrAf4M5TNBttfKkE8pSp8SdJ+DxBHXqigimwsPDo0ePHnPmzOnUqZOlx1KHcHCAiAhiSKmdECkk1AtatGjRokULS4+irmBybxlCDWB5O0PVycvLi4uP1+mq5LtCIBAItY3i4uJHjx4pFAoz+LlbjbgjhOLj48+fO1du7QgCgWA+almqRSsiNzf39OnTOTk5JIjpORjjtm3bct7NBALBkhCzTHUJDAx85513njx5YoYgJqtZuRMIBAKh6ljNyr0q5KkgX10DXu4AGEAiAE9JjXROIFgZxCxjDdQpcd8TAxtvA10DTyMMCz2D4NvuUImnOxcCw7JsSEiIMb4/KyursLCwQYMGNE3n5+enpqYao8YwxjweLzg4mMtGwB1JSEjQ6XQhISFlU+jI5fJHjx5lZmby+fzg4OCgoCBjmCXGODs7OyYmRi6Xu7q6RkREVCNXTLlkZmZmZmZy9kGEnoe8IYSCgoLs7e1NP9cvz+3bt0+dOjVjxgypVHr58uWbN2/OmDHjZTPP1FlkMjh50pTRpAhBdraJSyYRaoA6Je5KHeSogIeAMV2FbARAIWAwyF/01yGTyd56662UlJR169YNGjSIO7hz585Dhw4dOnRIKpUePXp0wcKFjg4OXMpijLG9vf3GjRubNm3KNeaKV6Slpf35559t2rQx9owx/ueff1asWJGRkSGVSlmWVavVnTp1WrJkCZfyd9euXStXrtRqtXZ2dnK53MXF5eOPPy6bTb4a/P333ytXrqRpmmXZoqIiHo/HleQWiUS//PJLLfEZj42N5dKFSqXSmJiYI0eOTJ48mYj7M2QyOHQIZDITr7XNkh2F8CrUKXEHBBQAhSDUGVzFpulSoYOYXNAYXvynwbKsTCYrLCxcv359165ducoSSpWqoKCA2zxRqVQI4Oeff/bz8+Mi6WmaDggIMPZw6dKlpKQkiqIOHTpUUtzPnj379ttvd+zY8bvvvvPz82MY5uHDh7v/3J2dne3i4rJnz56FCxeOnzB+xvQZDg4OWVlZy5cvnzlz5rZt27p06WLshMtp87Kp8EeMGNG5c2eEkEKhmD59eqNGjT799FMuc4Cvr6/xwrmS2RVNC5QXj4oxZhiGpumydyCGYRBCxlNYluXmqmwPLMuW6mHEiBF9+vSRSCSm+fjrAJwJhatgZ3JbCpH4WkzdEncADEAheLslDA6DV66PDTSCO1kw9QhoqvwMOnz48KioqEOHDo0fPx6gtI1eIBCEhoaWWyDbYDD8/fffXbt2DQ0NPXDgwJw5c7iE4yqV6scff/T39//ll1+McfMeHh4dOnTg8/m5ubk//fRTp06dvlz8JZdU0tnZ+bvvvhs8ePBPP/3Upk0bGxubhISE7du3P378mKZpPz+/iRMnGlNrvRBXV1fuTYuKimxtbZ2dnUuWpktNTd2zZ090dLRarfb39x8zZgxXR0mn0+3YscPb27u4uPjIkSMAMHjw4AEDBnA3AK4W9oEDB7Kysuzt7Xv27Dl06FClUvnbb7+1bNkyJibm3LlzvXv3fuutt2Qy2b59+y5fvqzX6yMjI998801uTjDGV65c/eOPXYWFhZ06deKS23BDevr06b1798aPH8/lTSM8g2UhKAgGDQITFrpBCLy8iPG91lLXxJ2DTwOfAr4pVhUCHlR9F5Vl2ZYtW7q7u2/YsKFv375lc5iwLKtQKIqLi43rWbFYzAnT06dPo6Kivv3227CwsF9//fXy5cvDhg0DgMTExNu3by9cuLBUb5yUP3z4MC4ubv78+SXTBbu7uw8cOHD16tVpaWkeHh7vvfdeUVHRwIEDaZp+9OhRYmJi1cXdSLkJ5u7evXv37t2mTZvy+fyzZ89Onjx5+/btEREROp1u+/btOTk5zZo1a9q0aXR09KzZs7cIhX379gWAXX/s+uLzL9q3b9+2bdvc3NyrV6/2799foVCsXbNGbGcXFhYWHh7u6OioUCgWLFhw899/hwwebGNjs3fv3ps3b65bt87BweHKlStTpkxp0aJFhw4doqKibt26ZRze9evXd+zYMWLECCLu/wFjcHaGLl1MnOeLUIupi590TaSFrBoYgMfjTZ069dChQ3/99dfMmTP/My6EcnJyJk2aJBKJMADLskFBQatWrnRxcQGA48ePi8XiDh06uLi4NG/e/O+//x40aBCPx0tPT9dqtVyG27IkpySXsu1wNGzYUKVS5ebmGgyG6OjoVatWcWU0OBOKqa63d+/e/fr149bjY8eOHTp06IEDByIiIhBCDMM4OTn9/PPPbm5uRUVFQ4YMOXToUN++fdPS0r5b8d3gwYO/+eYbbttZq9UKhcLCwkIDw/j5+a1du5azaO3du/fChQs7duxo27YtAPTv33/EiBFnzpwZNmzY+vXrg4OCN2zYIJVK5XL5uHHjHj16ZJxkC334BELtoi6Ku+l2U6sBy7KhoaEjRozYvGXL0KFDUQmjJAaws7MbO3asm5sbZ3N3cHDgVtxFRUWHDx/u0aOHp6cnAAwaNOirr76Ki4sLCwvjbOUV5VBkGbakedoId8RgMLi5uXl6ei5fvjw1NbVly5ZhYWFl7dHZ2dkPHjzgFr+hoaFVr7THsuzx48cvXbokl8sBID09PTk52fjqa6+95ubmBgD29vYhISE5OTkA8Pjx45ycnGHDhhkdiow/UBTVu3dvY3nuixcvIoQePnz4+PFjANDr9RjjBw8edO3a9eHDhxMnTuRaSqXSPn36PH361HKfOYFQG7EmcWcYhmEYrupxZe0st3I3MmnSpAMHDuzcufM/gXwY29nZDRs2rKzN/datW/fu3ePz+XPnzgWAnJyc9PT0f/75JywszMPDg6bp9PT0ct/I09NTr9dnZmaWOp6amioQCJycnJydnVevWr15y+bffvvt+++/DwkJWbJkSanCIA8ePFi0aBG3Pzl//vwqijvGeNWqVb/++uuAAQMiIyNpmr5//z4nwQCAECppKTJuhxYXF/P5AqnUoZzPDSGjsmOMi4qKVCoVJ/HcwVatWjVu3Fij0ajV6pK+ng4ODmTBTrAKWJblisib4b2sRtwpirpx48b69etHjx79wgLZpp25ashGSEjIuHHjduzYUapANi7PeI0xPnTokKOjY0BAgFarxRg7Ojo2bdr00KFDU6ZMCQoKCg0NPX78+Lhx44we8QDA3ecaN27s6el5/Pjx/v37G/1VFArFiRMnIiIiOJlu3qL5ypUr5XJ5TEzM/Pnzf/75599++62kc0vHjh0PHz7M/Vx1PxO5XL53794JEyYsWrQIADQazb59+7iSIMbrKnuWi4uLwaDPyEhv3LiySgUIIScnJ19f319++cXe3t64RUFRVEFBgdRempaWZmyckZFhhmBuAuHVSUxM3Lt3b1xcnBkq7VmNuLMs27Rp01GjRhkXdxWCIb0IHuXBK9fHBgpBouxlvG5KyNm4ceP2799/4MCBkgZxlmHy8vIkEolRjBwdHXNyck6ePDl58uRPPvnE2PLo0aPTpk37999/u3btOnv27Hfffffbb7995513nJ2dWZZNSUnZt2/fkCFDGjZsOG3atOXLlzdr1uzNN9+0tbWVF8p/WfnLv//+u3btWnt7e5lMlpub6+/v7+jo2Kp1q+DgYON2rhGRSFS97UcMoFAoOM/F48eP37hxY3ilpRswxhEREWFhYZx3v7u7u16vz8/P53xgStGvX7+//vprz549EydOFAgEBoMhISGBq+XdvkP7gwcPjh07NjAwMDEx8e+//ybiTrAKfHx83nzzzSdPnphh8W414g4AdnZ23N5jJSAABsOqm7D+tmnelGFBZajq4p3m8YzrVh8fn7feemvOnDlGOxJN0wUFBZMnTxYKhdxHy+fzV6xYkZmZWVRUVKq0aZu2bby9vQ8fPty1a9fhw4cXFRWtXLny2LFjvr6+BoMhNTXV399/3LhxAPD2228rFIrvf/jhjz/+cHV1TU9PLyws/Oqrr7hAqsTEpHfeme3s7Ozu7p6dnZ2cnLxkyZJqVBBFCNE0XXK5IZVKx4we/csvv8Q9jeML+EVFRYGBgcYGpRpzi27uoeSrr76aN2/+4CFDwkLDCgtlDg4Oa9as4fovaV3p1q3b+++9/9NPPx08eNDDw0Mmk2VnZ//4449t27Z955137t+/P3r06IiIiNzcXIlEUlxcXHKcxEpDqJ0IhUJPT0+RSETE/eVgMRgwAAsKU6d8ZzCwL/osHBwc1q9fH1hinT527Njg4GAbGxvOnNKnT5+9e/dyW6lcA4qiQkNDPTw8duzY0aRJk5K9uTi7rF+/3mAwsCzL5/Pffvvtnj17RkVFpaenC4XC8PDwyMhIR0dHALCzs/vkk08GDx588+ZNmUzm7u7erl27kJAQTlvDw8O+W/FdTGyMrFDWtm3b9u3bl3RUrzq2trYrVqwo+diEEJo9e3bjxo0fPHjg5OTUtWvXoqIiTlVFItHXX39d8k48Y8YMrVbLvdqpU6e9e/dcvHgxKytLKpW2adPG1taWpum1a9eW9NHk8/lz353brXu3mzdvyuVyJyenxo0bN2/eHAAaNGiwffv2U6dOFRYWRkZG+vv7JyUlcW/3+uuvt2jRoqS5n0Con9SpAtkHHsORJzXlKtPaC6a1qJHENYRaQt0skJ2YCJ9/Dnl50K4dfPIJ8XO3OKRAdnV4oyH0D8EYm/6RHCGgEVF2Qg2jVMKVK6BUmizsEyHIywONhsSR1kPqlLjTCGiafIkJVotCAXv2QEaGiXO2EGWvl5C1KIFQazAm+TKtHHOFk6zEAEswFXVq5U4gvCK1ws2GZcHLC/r3hzI5/asPRYGHB0niWK+wbnHnykeUDJwhEKoNF6Nr6VEAYAyOjtCvH5CU9IRXwLrv5AKBgGEYjQmrzBDqMWq1Wq1Wc8l8CARrp/yVe35+vlKp9PLy4rzBMjIyDh48mJWV1a5du+7duxszPVkcgUAgkUiys7OFQuHLlqEgEEpiMBgKCgry8/MBgIRBEeoA5Yi7wWD49NNPi4uLN27cyOPx5HL53Llz//77bz6fLxQKv/nmm1mzZtXEUDgv4+TkZK1WGxgYGB4ezq+CzdHJyYnH48XExGi1WktPJsGK4R4B9Xo9TdNSqZSsFQiviA50OtCVTWOox3oGzPF0WI64FxYWXr16ddCgQVwVygsXLpw4cWLWrFmjRo36+eef16xZM2jQIGPckAnJzMz88ssvDQYDRVEJCQljxox57733qvI3Zm9v7+npGRcXp9OZOjKVUJ+gKEooFDo5OZXNj08gvCzH4fhhOFxW3FlgH6FHNNT46qF8cVcqlUHBQdyv58+fd3BwmDNnTsOGDXU63fjx458+fVoT4u7m5vbLL784ODjQNP3XX/u+/fab4cOH+/v7V+VcPz8/FxcXlUpVKzbECNYJQojH40kkkqo8MhLqPEpQ6qCa60UKqFRIjYVYCqhSBSYYxChBaYbE5OWIO1esh2VYAChWFv97699GjRpx1ZDd3Nz4fL5CoaioO71en5eXl56enp6e7uDgwNX5LNkAYxwbG3vhwoWCgoKQkJDu3bsbq8cJhUJ3d3euHqZQKLCzs6v63xhCSCwWl8yISyAQCK/Cn/DnRbhY7SV2ARTwgIcAOYCDLTxPdsQCmwRJZigpVI64Ozo6SiSSqKioCRMm3Lt7LyY6ZsaMGZyJRi6XGwyGirIysSy7fPny7du35+fnFxUVtW3b9vDhww4ODiXbHDhwYMGCBQzDuLi4pKamNmvWbM2aNSEhIdyrKpXqhx9+uH//fnxCwrwPPvDy8qrp6ycQCIRyyYbsp/CUBzwWntkDMGAEqOT/pQ4afwUA9CwmDY2DcV2gi9HObgDDF/gLY581Rzni7uLi0r9//zVr1qSlpSUlJdE0PWDAAO6lu3fvYowr0VwbG5uePXtGRERs3LhRo9GUykqWkJCwaNEiHx+ftWvX+vn5nT17dvr06d8s+2b9hvXcIp3P53fp0sXf3/+ff/45e/bsgAEDSt0bCAQCwTwgQBRQNNAe4CGEKrkIlpR4rgcaaG/wdoXn1e0NYBCC0DIrd4TQu+++q1arz5w5Y2dn98EHH7Ru3RoAtFrt3bt327RpU5HBnaKod999l6ZpnU534MABmUxWqsHJkydTUlK+/fZbLuvsG2+8cfr06T179jx+/Lhx48YAwOfzO/+fwYMH37lzp1u3bjU9BQRCddBq4eRJyM01ZZKv4mJQKEgqGJPAAhsDMXKQV9u6nQu5CJAIRO/Be42gEbfWNso3R7m/GoWbuz2UtMmYk/L93D08PL7//vuioiKapu3t7TmfXx6P9+WXXwoEAnt7+wq74/GggvpqLMtev37dxcWladOmz64coY4dO27atOnBgweNGzfWarUURXFLeLlczjAM2dci1F60Wjh9Gp48MXFMP5dYhuSBeWUMYNgCW+7BvWobzQ1g4NRZAhIpvKgAXO2jwvQDPB6vZA1iAKBp+lWM4BqNJjU11cXFhSsxweHt7c3j8VJSUgDg6tWrW7duDQwM1Ol0ly9f7tChA1eZgUCopXBJvjg5NqEiUxQQL/tXBgM2gEEHOhpoFtiSZpCSi+uyvxoPcp0wwJjBhFITPBf3Xbt2XblyxVgaDSFkzNxi/JVlWalU+v7777+wRHVZGIYpLi4WCoUlA1xFIhFFUVyNtMaNG/fr1y8lJUUsFi9YsKBr1652dnbGlhRFnTt3jnN27NevX9euXS09dQQCAMbg7Axjx4JYbDJxRwikUnj5UoiEsiBAtmDbATrYgV01NJozy7iAy8ueWBYWs3v37L116xYAxMbGmiFK7rm4R0VF/fHHH5xdBSFUXFysUqkEAoGdnR1CSKVSqdVqHo8XHh4+bdq0aog750TMMExJV3SWZTHG3HW6urqOGTOmotMxxkFBQX379mVZNjAwsKbnhUCoEhiDrS20bQsvrNtOeEl0oLsLd1Wgqp7R3AAGOcgxYAlI3oK3fMDHspeDADVt2tTNzY1hmOTkZDNE5DwX908++WTOnDkAQFHUo8ePP/7oo5AGIRPGTwgODubxeBkZGYcPHz506NDs2bN9fKozTXw+38nJKTExUalUGh3SCwsLObfIF56OMfb39+/SpUtNzwiBQKgNFEPxKliVDMnUK+Q3pIGuJUYVhFB4eHh4eLjBYNi/f79ZC2S7urpy8UR6vf6rJUtCQkK2bN5iLIgcGhrauXNnRyenbdu2DR482N3d/WXfSSgUhoWFXb9+PTMz083NjTv4+PFjAGjQoEFVpubJkyenT59u165dSXMNgUCoq3C6jAFX2yucO7eW6DsA5OTkXLt2LSsri6r53PrlbKhmZmZeu3p1/vz50v8+adI03b9fv1+3bImOjq6GuANAt27d1m/YcPTo0aZNmyKEZIWFhw4datCgQbNmzapyem1Jt00gEMwCt9XpDM5doasAnm1CVOR9WMp6wx1BgCQgkYDE0pfy/1GZUcTKEXe9Xq/VagvlhWVfUigUGo2mkvyL586d27t3L8uyjx490mq1H374IZ/P79ev3+uvvw4AnTt3HjRw4M8//2wwGBo3bnzgwIGbN2+uWLHCw8OjKpMSGhrau3dv88wLgUB4RfSgz4Xc6i26EaBCKNSDngXWDdwmw+TaI9Cvgru7+xtvvHHq1Cmz2txLvn1wcPC237Z16tipVatW3G4nxjg1NXXVqlVCodCYLaAs2dnZ//77L03TXMKvBw8esCxrdGy3s7Nbvny5i4vLnj17tm/f7uzsvHz58vHjx1dxrAaDQa/X83g8kmubQKj9ZEDGV/BVERRVb0eUBVYGslcxuNdCWJbVaDTmWbyXI+52dnbvvffe1KlThw4d2rNnzwYNGlAUlZGRce7cuaSkpMWLFwcHB1fU3fDhwwcNGlT6PXjP38Xb2/v777/Pyc1Rq9ROTk4ODg5VVGqKom7cuLFhw4YxY8ZUw1eHQCCYGQMYsiFbDnIA4Ep0Gz3KS2ZoKfurEQooBhgzpGExGwkJCXv37o2Li7OMzR0A+vfvv23btl9+WXn8+PE///wTY2xraxsWFrZy5cqxY8dWMiwej1dSyitq4+X50sFQLMu2aNFi3LhxEkldeDojEOoDFFAIkD3Y+4FfuTbxin4teTAQAs2Q/dw8+Pn5vfXWW/Hx8Wb1likJRVF9+vTp2LFjUlJSXl4ey7IODg7+/v6lYlbNjK2tLckjRiBYFyywjaDRp/ApD3jVOJ1LAMCHOpKJRCAQuLi4CAQCi4k7h1gsbtSokaVn4zncdGCMic2dUCuo699DLWgLoKB6foQUUMbdVBpoG7CpnrjXMTDGDMOYQdmhpLjn5+cXFxc7OTnZ2dnl5ORoNJpyT6Bp2sPD44W2F5ODELp+/fr69etHjhxp2QcIgvWBMSiVYDCYrEOEoKjIlB3WSh7Bo+/gOz3oK0/GUpEBXQ96Fajg/+7qBABITEzct29ffHy8WW3u33777c6dO7/55puxY8e+8847165dK5v9gGVZDw+Pffv2+fn5mXlSMMYREREDBw4kNnfCS6PTwdat8OiRKRNyMQxkZZk4JWQtQwvaLMjSgY7bDn3Z0zkbOgtsXdoRfUU8PT2HDRsWExNjVrNMeHh4jx49fHx8EEItW7a0sbEpV9wdHR25qkzmRyqVktpMhOqAMWRkQHw80DRgbJoMX1wmSJaFOh1Yxwm0K7i6w/O4xXLjhirJbB4EQWYoGWoV2NjY+Pn52djYmFXcJ0+ePHnyZO7njz/+2NKTQCCYFC43L58Prq7A45kyYbq3d91ev2PAHaDDTJhZ7XAkHvDqjLuLFfEC0znGWKvVYoyFQqEZjESVo1AocnNzHR0dzW/xJ9QFMAaJBObNAw8PU4o7jwd1OtkRC6wAC8SIVJ83AVqtltvRNINXSIUqqVAoTp06dfLkyeTkZIZhPDw8OnXq9MYbb1QlVUBNgBC6d+/en3/+OXbsWBLERKgmFAVSKRCH2peBWFRMSGpq6u49e1JSUiwm7rm5ufPnz9+9ezefz3d1deXxeLdu3dq1a9euXbtWr15tEf9IjHGHDh3eeecd8781oe5Ayte9DFwIkqVHUacIDg6e98EHaamplsktAwDr1q3bu3fvpEmTJk2a5OPjQ1FUQUHB4SNHfvzhhy+++OL333+3tbVAyVeKooiHO4FQddIh/RE8qraj+lN4ygADxJfRdHA1i8yjY+WIe35+/oEDB4YOHfrjjz8aRdzLy6tx48Y2ItHy5ctjY2MjIyMtPUsEAuEF3IJbP8FPL5XRpdRBY0tLXwrhpSlnjzQ/P18mk3Xr1q3s8rxjx440TWdlZVl62AQC4cVwpSpYYBlgDGDQg974f9lfuX860JU8yABTx1J31R/KWbmLRCIej5ednV32pYKCAoZhLOLnjhCKiYk5cuRIly5dSBwTgVBFECAW2EiIbASNqu3L2AQ1sfR11BEyMzMvXryYkZFhmayQbm5ujRo1+u2331577bXXXnuN8zvEGCcmJq5cuVIqlValKl5NIBKJpFKpxT0yCQQrggsubQftRsEoS4+FAHw+XyqV8vnmyINW/sr9nXfemThx4ogRI3r27BkeHk7TdFpa2tmzZ9PS0r799ltfX1/zTwrGOCgoqFOnTuZ/awLBeiHm8lqFi4tLz549Dx06ZDFvme7du2/duvXHH3/8559/9uzZUyqfu6Xnh0AgVBXi6FJvqTCIqXfv3h06dIiPj8/NzeXyuQcHB5PoIYI50GhMnHBRowGGsfRVVYcrcCUKoqpXag4Biod4snKvt5Qj7jk5OcuWLWvTps3YsWObNWtm6RES6hksC3v2wO3bpszgyLKQkWGNGWCiIXov7OUSs5RM1/VC10ZO07lKF5a+CIJlKEfc5XL533//bRHDOoEAAJCR8Sw9r6kyOAIARZmyN3OBANFA00BXL1iUU3kKKCLxtQfzVOqAcsXdxcXF09OzXFdIC4IQioqK2rJly9ChQx0dHS09HEJNghBQFNA0ODiACf0KMAZHR1M+EJhtPgCNhtFNoWn1fBkpoPzB39IXQQAASEpKOnjwYEJCgmVcIR0dHSdPnrxu3brz58936tSJrh1/DJy3TLdu3cRikp2uHoAx2NjA3LkQGGjiDI7WGSTREBq2g3aWHgXhVeG8ZW7dumWZGqp6vV6r1RYWFo4ZM6Zr165+fn7GmwzGWCqVTp8+3SKF7lxcXIKCgsz/vgTLgBA4OgLZwyfUIezs7EJDQ+3s7Cwm7idPniwqKkII/fPPPyVz3LAs6+XlNXbsWFLFlEAgEGoz5QcxrVy5UqlUcrJe6g7D5/M9PT0tPWwCgUAgVEY54k5RVGBgoKUHRiAQAEiIKaG6VBjExDDMo0ePbt26lZmZ2alTpw4dOhgMhqdPn0okEh8fH0sPm0CoF5StQ00gVJHyxV2pVP7000/r1q2TyWQajebLL7/s0KEDy7LfffddcXHxtm3bLJIYkkCwLjDgFEgpgIJqq3MWZJXKt04gVJHyxf3XrVtXrFgxePDgoUOHLlu2jGEYABAIBB06dFi6dGlaWppFEkNybjx8Pp8khiRYBSywf8AfJ+EkF2JaDRhgyLK9LsEwjFKpZMySDKMclZQVynbu2NG3T9+1a9e+/vrrUqnUuKcaHh7OsExaWpr5J4WiqOvXr69bt66goMD8704gVA+u/IUe9BrQcP/UoDb+X+rXsgf1oOdKbZDFe92AS5weFxdnhvihclbuebl5OTk5kydPtrOz0+v1JV8Si8UIUHFxsfknhWXZNm3aTJs2jViECFYEAoQA8YDXDto5giMLbOVl7Urlh+H+p4H2Ai9LXwrBBAQEBMyePTvVUgWyeTweRVEajabsS7m5uXq93s7OziLzIhQKSXgqwbrgamUIQTgOxjWGxtWuhUSMM3UDHo9nb2/P4/HMEMRUjlnGzd09ICDg6NGjBQUFJSOY1Gr1nj17JBJJcHCwpaeIQLAmMGAudReXw+tl/xFlJ1SDcsRdbGs7adKkGzduzJw58+TJk8XFxQUFBZcuXXrvvfd27do1ZswYkjCSQHgpiDoTzE/53jLDhw/PyMj44YcfDh06ZDAY7t67t2XLFgCYMGHC3LlzSy7nCQTTU4e+YETWCZaifHEXiUTz5s3r0aPH6dOnnz59qtPpPDw8unXr1qVLF1tbW0uPmVCbUKtBqzWlHDMM6HSWvqpnYMB5kKfF2uqdziJWCUpLXwShnlKOuGOM1Wo1j8eLjIyMjIws+ZJery8uLra1tSWe5oRnHD4MFy6YuMhRbm4tybquBvVP8FM0iq52sQslKGmgiSMjwfyUI+5arXbhwoXt2rUbN25cqZfOnz+/fv361atXk9xhhGfk5UF8vOmrJgEAy1q8cBIGXARF+ZBPAVU9RxcOFlii7wQzU464Mwxz9+7dcuU7Jyfn2rVrarXa0sMm1BqMVZOcnUEkMmXPdnYgFFr68p65IQpBGAIhQhC+rEZzNncxiO3AMg7EhHpLOeKO/k/Zl2QymUAg4PF4L+6YUK/g8WDiRGjeHEwVV83dM2pH1SQM2BEcF8ACL/Cq3gKcAooPpqsXSCBUgf/I9IkTJ27cuMEwTEpKyrlz5zDGJcOoFIriv//eHxgY6ODgYOlhE2ofEgnU6S+GCEQiMOmjCYFQk/xH3E+fPr127VqaptVqdXZ29o0bN0q+SlFUUFDQvHnz7O3tzT9QhNCDBw/+/vvvHj16WGQAhPoM8WgkmIT09PRz586lpaWZu0D23LlzR48erdVq33333Q4dOkyePLlk9jI+n+/h4eHq6mqpebG3t/f09CRGIQKBYKXY2Nh4eXnZ2NiYu4aqn5+fn58fY2BWrFjh4ekRER5h6al4DsbY39+/XTtSAJ5gAYivC8EkODk5de7c+a+//rJMgWyaR7dp00alUjEMw+WlzMnJOXHiRHZ2dpvWbTq81oHPJ1tDBAKBUKspR9wNBsOnn3wqL5KvXbvWxsZGoVB88MEHf/75J0LI3t7+u+++mzx5sqWHTSCYFWJzJ1gd5Rj15XL5+QvnOcMQAFy8ePHgwYOTJk06evRohw4dfv7558zMTEsPm0AwH8QmQ7BGylm5FxYWKpVKYyG98+fPSySS+fPnh4WFIYQmTZr09OlTEqFKqD+QKqYEa6T8CFWGYbgvs0ql+vfffxs1auTn5wcAnp6efD5fLpdbetgEQpXQgz4FUnSgq55dRQ1qNaiBmGUIVkg54u7g4CAWi2/dvjVx0sSHDx9GR0dPmTKFSwZZVFTEGBhS6I5gLchBvhyWp0Fa9TJ/YcAa0FQ7axiBYEHKEXcXF5fevXtv2bIlJycnLi4Os7hfv37cSw8ePGBYxsuLlHMkWAcssCpQKUCBAFU78xcGbACDpS+FUHsxaAyMhqnk6c6gMWhlWs790WAw6Iv1ZqiKUY64UxT1/vvvKxSKc+fO2drafrH4i/bt2wOAVquNiopq3ry5t7e3BeeRQKgGUpA2gkY8qGYEnBM42QB5YK11aAo0epW++kYzBAaNQZmpZBm2ok5Yli1KKtIr9ZW8Sc7tnPzY/Ar1GoGuWKfKUnE7NwzLpEvSEW0JcQcAHx+f1atXFxQU8Hg8JycnLlKWx+N99tlnQqFQKpXW9LAIBBOCAfuAz0fwkQQk1c78RYwzZobRGlIvpOmKdJWo6qNdj7JuZSGq+kLJGlhdkQ6zFX8rMOjVepZ5wWNf1b9XBjAYBAYzbOJUuJARCAQeHh4lj9A0HRAQUKOjUalUOTk5LMu6u7uLxeIav3pC/YD7w+MBj4ZaUQOEAAC6Il3B44KKVBUhpM5XnZp1uii5qBLtxizmbB3mcWeq5BGBe8nGyYZvy69oLIhCIicR35EfJYsyw3grFHeMsVwuz8jIKC4uLnlQIBBEREQIayDR9p07d5YuXVpYWIgxlkqlH330Udu2bWt8AgiviDXUOyW+LiYHM7iSAHqEUNbNrKybWRVNPKKQ7Kksdlcsq69gRYwAs1hXpAMMmOEKwVT4dhRNOYU4CSSCaismQkggEdi42lT4VUHAF/OlwVJaQJf/LghoAe3RykPsJa5wc4cCvi0fCdHFhRdZQ/Vrv1SR8sVdq9Vu37590+bNyUlJer2eYRiMMZfk3d3d/cSJEzWxhKcoavjw4W3atKEoavny5V9++eWePXvs7EiJA9Px6BE8emRiOU5IqP36zukC8VU3FYyW+feHf3Pu5ZS7pkaAAEHug9zch7lVua1WLtwAIHYThw4L5dnwyr2dIIT4Yn7D4Q0lPpLKrCuVg4AW0Hwxv/I2JtkFNRgMZjC4Q0XivnPnznnz5rVo0aJnz54nTpwYM2YMwzBnzpwpLi4eNWqUo6NjJT3q9Xq5XF5QUCAUCn18fOgyxTALCwtjY2MVCoWXl1fDhg0FAgF3vFmzZs2aNeN+HjRo0GeffVZUVETE3ZTcugU7d5q+PGmtL6jLSQxZv1cRzGC9Ul+hUCLQK/WJpxKTzydXbqbgXn2hdjsEOLg1cYOKO3IMdWz/eXuBncDSE2NllCPuCoXit99+a9Wq1e7du2NjY6OiombNmtWkSZOYmBjOi4bzeS8LxnjXrl379u2Li4vLz8+PjIzcvn17qd3XO3fufPTRR/fv3+cy9w4fPvyzzz5zcnIq2cZgMJw+fTosLMzFxcXS81NHsXRtUnNfLlm5l4A1sIy2woJZiEKFcYVn3zurzlNXJN2YxUVJRTTQgIBnw6tsPUtBgyEN3Jq6VWhbp5BrM1ffrr4VfjgIEELmWerWMcoR99zc3PT09Dlz5ri4uHDFmLis7hEREbNnz543f/6UKVMaNWpU9kSWZW/dupWZmdmqVauzZ89mZWWVLOTE9fzBBx+kp6evWbMmNDT00OFDy79dbm9vv3jxYuP3g2XZrVu33rhxY/Xq1cZFPcGUIARdu0LDhsCayOqHEPD54Otr6QureIBk5V6C5FPJN769gRlc/oQg0Cv1OdE5jK6yiolcSgYbB5ueq3tKA6TlazcCxEeOIY42TsSL1AKUn34AALjVtEgk0mq1CoWCe8nf31+v0yUnJ5cr7hRFffrppwKBgKbpgQMHymSyUg3OnDkTFRX1yy+/DB06FAAaNGjw4P6DHTt2TJo0KTAwEP6/9v/999+//vrr5s2bW3py6igIQatW0KWLpcdBqBHkSXJ1rrqi1TSiUcb1jNTLqZWbpzmjCt+Gb+dlV5GzCsZY7C72au8lDSS+0bWRcsTd0dHRTmKXkZEBAG5ubkKhMCoqqlOnTgAQGxtbXFxc0YIaIcTdEjQaTbkNLl26JJVKua4AQCAQ9OrV6++//75//35gYCDG+I8//ti8efPSpUu7EOmpUazHLKMHfSEUssBWY92NAOVBHgPPVqD1xCzz7w//Ptz6kKIr2AhBwOgZxCIESGAvENpX4PaGAWPs2daz8/LOtLDCTRqaT9u4klV5LaX83DIR4RFXrlzRaDQ+Pj6dO3f+/vvvc3NzBQLBzp07vb29w8PDq/FOGo0mIT7B1dW1pCU9MDCQpumEhAQAuHTp0rx589q0aXPv3r3bt2/b29sPHz6cBEzVc9Ig7Vv4thiKq2dUMYAhF3LrlVnGoDZolVoKqMrTLWDAEW9GRL4XWcnGqcBOIPYSmyFQnlATlCPuPB5v7ty59+/f12q1IpFo4cKF2dnZ69ev1+v1ISEhS5curV76Ab1eXygvtLW1LZl3zM7OjqbpwsJCABCJRCNHjuS0HmPs5ORkMDxP6IEQio6O/uOPP1iWbd68ebl2IULdQwe6NEgrgiIAqF5yGE7mWGDrycqd02JEI582PmIPcUXe6Agh366+jg0dX653QnXBGF+/fj0hIYFl2fT0dHMXyDbSvn17Lp8MAISFhe3atSsuLk6r1QYEBLxKJneMcalL4r6I3PevTZs2bdq0qeR0pVLJbdIqlcqanhdC7YEL/bcDO0/wNK6+MZSzH1jqIKfm3NZfEATVgfBU1sAWPCowqCvLYqbKVQEAT8hr90m7wL6BFQbLIKjQdEOoGQoLCzMzMzHGFRmuTUuV8ihJpdLIyMhXfSceT+ogzc7K1mg0RmfK4uJihmEcHBxeeDrGuE2bNu+//74ZJoVQq0CAGGCaQJOFsJAP1azfywOeCESWvpRXRVekOzX9VO7D3Eoi8g0qA5cGh+JTiEa0ycMaCNUCIdSvX79+/foZDIbExETWVL5qFVPNJHnVwMbGJigw6MH9B3l5eUbH9sTERIZhOFeZFyKTydLS0tzd3UmF7nqFMTOMBCTVFneroDi9+PGexxWttRFCumJdYUKhtkgLL9ofZoGtH1YoK0OlUqWnp6tUKsuk/H11nln9yoy+Y8eOv//++5WrVxo2bAgAOp3u9OnTnp6eTZs2rUqfsbGxhw8fHjVqVKmgJwKhbiBPlF/85GLlVhfOSVEgEXh18OKJKvz75Yv5th62QKhlZGVlHThwID0jw/rEPTY29ubNmwaDITMzU6lU7tmzx9bWtmnTplxegV69ekVGRi7/drmzk3NoaOjhw4ePHj06e/bsoKCgF/aMMe7QocPMmTNrekYIBAtSMmq/3AZcNL+dj13vDb3tvO0qjB4iUZ21kqCgoPfee88qzTKXLl369NNPaZrm9kgXL17MMMy8efM4cXdzc/vxxx8//PDDmTNnCkVCvU4/bty49957j/haEeoDrIFldExFHpmIhwxaAwBgwF5tvCLGR5RvWMeAEbZ1t7VxtaF4ZEfU+jCb3D0X98OHD2s0mmHDhiGEUlNTxWKxs7Pzy3Y3cuTI7t27lzpYMtFYq1at/vrrr4cPH8rlcl9f37CwsCpmD0YI3b9/f9++fb169bK3tzfP7BBqD3XASz3+cNzddfcq2gtFCGlkGkbHYMBOYU7NZzYnS++6R3p6+pkzZ1JTU83qCvnXX3/JZLKhQ4dqtdr33nuvW7duc+bMednuHBwcXuj64ujoaAxSfdnOfX19uYxjhPpGLfdSxxgXxBRUkmyLolHymeSEUwmV36We2WSsJ4SY8FLY2Nj4+fnZ2tqa4SN+LpR8Pl+hUOh0OoxxTk6OMZ9MLQFj7OfnV7kjPKEOU8tX7tiAry29FncgrhJTCaNnOF97vh2fJ6ysTo5QavpiOITagJOTU8eOHffs2WNWcW/SpMn+/fs/++wzf3//7OzsqKioTZs2cUnESiIWiwcNGkQMIwSzURtkXVuojdkRo5Vpyx8LAszg3Hu5eo0eAXphJG2rea3CRoVhpsLYUaGjkNhkCK/Ic3EfPnz4pUuXNm3apNfrtVptcnLyyZMnS7XGGHt5eXXq1Mn84s65Qh49erRz584SicTS80aoKsVQnAEZ1S5LnQzJBni2zVhzg9TINPJ4eYWR+hRSpCquL7uuyFS8sDwFLaADuweKHEUVlQ1CNPLr5ucc/tIbWoQ6QGZm5qVLlzIyMsxqc/f29v71118fP36clZX10UcftWnTZty4cWVX7kKh0N3d3SLzIhAIJBKJGSalzmIJr6QYiFkGy/Sgr97pLLBqUNf04j39Svo/k/+pJIM5ZrFeqeciPyuusokwxgJ7QcevO7q1cKtkYQ7kK1xf4fN59vYS82wc/uc97O3tW7durdVqz50716ZNmx49elh6Kp6DMQ4ODu7cubOlB2IucnMhOdmUHVIUZGSYX98ZYBSgMICh2qm7uPQDNbpyZ/WsRq7hxL2SN8KAnYKdIt+NrKRKMl/MlwZIEUKIR+wq9R09Wzq7ttTJtVvPXocOHbaMn7tQKPzmm2+4lBQ6nU6hULAsa2dnJxKJiEO6+bh9GzZsMLEWM4xF6p1y624/8AuCoJLSyaX0euGv3OmNoBFVkyte7l0cgx1dmrhUch9xaezSbHozWkASttQ7VHq4lQlqPVTlMRIBqPVwIgEKNaWbMwwkZYEZkraV/3QgFAqzs7P37t37zz//pKWlsSzr4uLSqVOnsWPHcpkDLALLspwds17cY1gWdDpACFjWlPXwuJ7N6GnHZWpkgW0P7afD9GoOnDNnVzetI2ZxJYWHKB7FmVAw4KABQV1/6FqRRQUQIAqR0KHaiUwD97OBqZmvNgWQpYSfbkC+qqorLoxBywD3vcMljmKDXqzEZpCw8sU9OTl5xowZp0+fdnNz41zLk5OTL126tH///g0bNhizAZsThND169fXrVs3atSoakRXWTERERAYaEo5pmmoVkb+6sEtwDFgCihLpf16+NvDp/ufVhg9RKHi9GIuXRfFoygeZcZ8eoTKqPq3HiGIzYM5J0BjqCnTI4uB25TB+O
<p><strong>Introduction</strong></p> <p>In the last ten years, stellar occultations by Trans-Neptunian Objects (TNOs) have become one of the best techniques to gather information about the main physical characteristics (size, shape, density and albedo) of these objects, as well as revealing possible atmospheres, satellites or rings [1,2].&#160;</p> <p>Due to their large orbital periods and the lack of observations, it is still very challenging to predict and observe positive occultations by TNOs, requiring worldwide campaigns. Here we present the results of the multi-chord stellar occultation produced on 2019 October 22nd by (84922) 2003 VS<sub>2</sub>, a plutino object with a double-peaked rotational light-curve and no secondary features discovered so far. A total of twelve positive chords were obtained, reaching the largest number of positive chords by a TNO published thus far [3].</p> <p><strong>Observations</strong></p> <p>As part of the systematic search for stellar occultations by TNOs carried out by the Lucky Star project collaboration [4], we predicted, observed and analyzed the occultation of the GAIA source 3449076721168026624 (m<sub>V</sub>=14.1 mag) by 2003 VS<sub>2</sub> on 2019 October 22nd. Astrometric updates were performed prior to the occultation with the 1.5-m telescope at Sierra Nevada Observatory in Granada, Spain, and the Liverpool 2-m Telescope in Roque de los Muchachos Observatory (La Palma, Spain). Out of the thirty-nine participating observing sites distributed along the predicted shadow path in Europe, twelve sites, located in Bulgaria (1), Romania (10) and Serbia (1), reported a positive detection (see Figure 1).</p> <p><br /><img src="data:image/png;base64, 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
Recent numerical simulations (Greenstreet et al. 2012, Granvik et al. 2018 predicted the existence of a population of small bodies that is orbiting entirely inside Venus orbit. They could represent about 0.22 % of the steady-state near-Earth asteroids (NEAs). These asteroids are called Vatiras (by analog with the Atira-class NEAs) or Interior to Venus Orbit Objects. However, only at the beginning of this year (January 4, 2020) the first one was discovered by Zwicky Transient Facility (Bolin et al. 2020). It is called 2020 AV 2 and has the aphelion at 0.654 AU, and the perihelion at 0.457 AU.The dynamical history of this object has been explored using N-body simulations (de la Fuente Marcos & de la Fuente Marcos 2020). It has been shown that 2020 AV 2 was a former Atira-class, and perhaps a former Aten-class asteroid, which reached the Vatira orbit relatively recently in astronomical terms, ∼10 5 yr (within 9σ confidence level). Similar results have also been reported by Greenstreet (2020).
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