Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
214
datasets available to search
ShareScore release 0.9.0
Dataset results
214 results for “spacecrafts”
Synthetic Collision Dataset for Spacecraft Collision Avoidance
<p>This dataset is intended to be used as a banchmark for testing collision avoidance strategies.</p> <p>It is made of 21000000 relative geometries between LEO space objects, 1000 of which are true collision (miss-distance smaller than combined hard body radius).<br>These relative geometries are expressed as target and chaser 6-dimensional state vectors (cartesian coordinates) at time of closest approach.</p> <p>The relative geometry of the encounters are statistically matched to the ESA's Kelvins dataset for the collision avoidance challenge through statistical fitting methods.</p> <p>The collision proportion is tuned to reflect a 1year mission in LEO orbit with an a-priori collision probability of 1e-3 (yearly) and a 21 collision warnings per year.</p> <p>*<em><strong> Implementation Description *</strong></em></p> <p>The dataset is made of a series of .mat files storing the following variables:</p> <div> <ul> <li>'rv_t', target's cartesian state at TCA (km, km/s, in ECI) 6xN vector</li> <li>'rv_c', chaser's cartesian state at TCA (km, km/s, in ECI) 6xN vector </li> <li>'Ct', target's position covariance matrix at TCA (km^2, in target's RTN at TCA) 3x3xN </li> <li>'Cc', chaser's position covariance matrix at TCA (km^2, in chaser's RTN at TCA) 3x3xN </li> <li>'Rc', combined hard body radius (m) 1xN</li> <li>'CollFlag', logic value of collision 1xN (0: no-collision, 1: collision)</li> <li>'missDistance', miss distance at TCA (km) Nx1</li> </ul> <p>The name of the .mat file is formatted as:</p> <p>batch_<batch start index>.mat</p> <p>Each batch file has a maximum dimension of N = 1e5.</p> </div>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Catalog of Coronal Mass Ejections Observed in Conjunction between Radially Aligned Spacecraft in the Inner Heliosphere
<p>This catalog lists 47 CME events observed in a longitudinal conjunction between MESSENGER, Venus Express, STEREO, and Wind/ACE. We list the onset date and time of the probable CME candidate. If the CME was observed by LASCO onboard the SOHO<br> spacecraft, we report the average CME onset time as calculated in the CDAW catalog (average between first-order-constant speed and second-order-constant acceleration onset times). Otherwise, we report the time of the first STEREO/COR image containing the<br> CME. We then list the arrival times of the shock/discontinuity, magnetic ejecta leading edge and trailing edge at spacecraft 1 and 2. Arrival times at MESSENGER are listed from Winslow et al. (2015, 2017), Venus Express from Good and Forsyth (2016), STEREO from Jian, Russell, Luhmann, and Galvin (2018), and L1 from Richardson and Cane (2010). We also list the heliocentric distances of the spacecraft at the CME onset time, the longitudinal separation between the spacecraft when the discontinuity/ejecta arrives<br> at spacecraft 1, and the maximum magnetic field strength observed in the CME (including both the sheath and the ejecta) at each spacecraft. The maximum magnetic field strength measured in the CME at MESSENGER are listed from Winslow et al. (2015, 2017), the maximum magnetic field strength measured in the ejecta at Venus Express are listed from Good and Forsyth (2016). The longitudinal separations are in Heliographic Inertial (HGI) coordinates. We also list the initial CME speed. For the speed, we select the coronagraph which observed the CME closest to a limb event. Limb views signicantly minimize projection effects as compared to halo views and provide a better estimate of CME speeds. When LASCO observed the CME as a limb event, we report the second-order CME speed at 20 Rs (solar radius) listed in the CDAW catalog. For STEREO observations, we report the maximum<br> speed as listed in the CACTus catalog. We also list the average impact speeds at spacecraft 1 from the DBM (Vrsnak et al., 2013) and either the average impact speeds (when spacecraft 2 is Venus Express) or the maximum CME speed (when spacecraft 2 is<br> STEREO/Wind/ACE) measured at spacecraft 2. The maximum CME speeds measured at STEREO are listed from Jian et al. (2018) and at L1 listed from Richardson and Cane. (2010). We list the average transit speeds as well between the Sun and spacecraft 1,<br> spacecraft 1 and spacecraft 2, and the Sun and spacecraft 2.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
Spacecraft Pose Estimation Dataset (SPEED)
<p>The SPEED dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>"Pose Estimation challenge" </strong>in collaboration<strong> with Stanford Universitiy's Space Rendezvous Lab (SLAB)</strong>. It features images and poses of the Tango spacecraft (PRISMA mission), 12000 of them generated by SLAB's Optical Simulator using a high fidelity texture model and 300 images from the TRON facility, using a physical mock-up model of Tango.</p> <p>The goal of the competition was estimate the relative pose (distance and orientation) from pixel images only.</p> <ul> <li>Detailed information about the original competition can be found at <a href="https://kelvins.esa.int/satellite-pose-estimation-challenge/">https://kelvins.esa.int/satellite-pose-estimation-challenge/</a></li> <li>A follow-up competition with a larger and improved dataset <strong>(SPEED+)</strong> is available on Zenodo as well: <a href="https://zenodo.org/record/5588480">https://zenodo.org/record/5588480</a></li> </ul> <p>A publication about the results of the pose estimation challenge has been published as</p> <ul> <li>Kisantal, Mate, et al. "Satellite pose estimation challenge: Dataset, competition design, and results." <em>IEEE Transactions on Aerospace and Electronic Systems</em> 56.5 (2020): 4083-4098.</li> </ul>
Location of ground stations, targets and spacecraft for Spire Global case study
<p>Datasets for case study in article:<br> "Data sink selection using consensus leadership: improving target connectivity for a spacecraft constellation"</p>
Processed EUVM and NGIMS data from the MAVEN spacecraft
<p>The .csv files contain EUVM and NGIMS data that have been interpolated onto a uniform altitude grid and time scale. For every orbit, the data are interpolated onto a uniform altitude scale. The time at for the average of these points is used to interpolate the EUVM data. the density is recorded in the 'density' variable and the EUVM data are recorded for four spectral bands: (0-7 nm = da, 17-22 nm = db, 0-45 nm = dc, 117-125 nm = dd). Auxiliary parameters of local solar time (LST), longitude (lon), latitude (lat), solar zenith angle (SZA), and the distance between maven and the sun (Rs), are also provided.</p> <p>The .p file contains the same data, but with additional fields for the relative changes in both the EUVM and NGIMS data as well as the slope and r value for a linear fit inside of a rolling 27 day bin. The relative change is defined as the 5 day running mean of the 27-day residuals. Additionally, days with high flares are removed as these data may be affected. The .p file is a pickle file, made with python. </p>
SEENIC: dataset for Spacecraft posE Estimation with NeuromorphIC vision
<p>Dataset used in the paper "Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing" (<a href="https://doi.org/10.48550/arXiv.2209.11945">arXiv</a>, <a href="https://ieeexplore.ieee.org/document/10160531">IEEE Xplore</a>), for the purpose of satellite pose estimation with an event camera.</p> <p>Both events and ground truth camera poses were captured across the 20 scenes in total. There are two trajectories, five lighting configurations and two camera speeds. All combinations of trajectory type, speed and lighting configuration were enumerated for capture. Sample event frames and dataset statistics are available in the paper linked above, along with our pose estimation method used on this dataset.</p> <p> </p> <p>Live-capture scene names use the following encoding: {satellite model}-{trajectory}-{speed}-{lighting configuration}</p> <p>The calibration scene (calibration.tar.gz) includes multiple views of a chessboard used to calibrate the camera intrinsics and extrinsics for the live-capture scenes. Camera parameters calibrated using this scene can be found in the <strong>calib.txt</strong> file, with the format: fx fy cx cy k1 k2 p1 p2 k3.</p> <p> </p> <p>All <strong>live-capture</strong> scenes have the same data format:</p> <p>scene/</p> <p> poses/ -- Raw timestamped robot gripper to base transforms</p> <p> cam-poses.csv -- Ground truth camera poses with the format {timestamp, Rx, Ry, Rz, x, y, z}</p> <p> events.csv -- Event stream with the format {timestamp, x, y, polarity (0=off, 1=on)}</p> <p> meta.json -- Metadata file with camera frame dimensions</p> <p>Note: all timestamps are in microseconds.</p> <p> </p> <p>The <strong>synthetic</strong> scene (synthetic.tar.gz) has the following data format:</p> <p>synthetic/</p> <p> poses/ -- Sequential poses captured at a constant time interval</p> <p> events.txt -- Event stream with the format: time (float s), x, y, polarity (0=off, 1=on) as specified at <a href="https://rpg.ifi.uzh.ch/davis_data.html">https://rpg.ifi.uzh.ch/davis_data.html</a></p> <p> camera_intrinsics.txt -- The camera intrinsic matrix (space separated)</p> <p>Note: please refer to the paper referenced below for further details on using this synthetic scene.</p> <p> </p> <p><strong>When using the data in an academic context, please cite the following paper.</strong></p> <pre>@INPROCEEDINGS{10160531, author={Jawaid, Mohsi and Elms, Ethan and Latif, Yasir and Chin, Tat-Jun}, booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, title={Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing}, year={2023}, volume={}, number={}, pages={11866-11873}, keywords={Adaptation models;Satellites;Pose estimation;Lighting;Robot sensing systems;Robustness;Data models}, doi={10.1109/ICRA48891.2023.10160531} }</pre>
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft
<p>The dataset titled “Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft” employs the measurements from the MESSENGER spacecraft’s Magnetometer (MAG) and Fast Imaging Plasma Spectrometer (FPIS) instruments to identify magnetopause and bow shock crossings during MESSENGER's orbit of Mercury. MESSENGER's data orbiting Mercury were collected between 23-03-2011 and 30-04-2015 and are available from the Planetary Data System’s Planetary Plasma Interactions (PDS/PPI) Node at https://pds-ppi.igpp.ucla.edu.</p> <p> </p> <p>The dataset includes four lists:</p> <p>a, Bow_Shock_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p>b, Bow_Shock_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>c, MagPause_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>d, MagPause_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p> </p> <p>Here are examples for the time in the list:</p> <p>Example A</p> <p>2011 03 23 15 39 10.5 2011 03 23 16 24 02.4 BSO m </p> <p>This entry represents multiple bow shock crossings. The first six columns indicate the time of the first boundary crossing, while the next six columns indicate the time of the last boundary crossing. “BSO” stands for outbound crossing of the bow shock, and “m” indicates that this is a multiple bow shock crossing made by MESSENGER. Only the first and last boundaries were selected out, we did not identify the boundary crossings in between.</p> <p> </p> <p> </p> <p>Example B</p> <p>2011 03 25 13 04 24.2 2011 03 25 13 04 24.0 BSI s </p> <p>This entry represents a single bow shock crossing. The first six columns and the next six columns are identical, indicating that this is a single event. “BSI” stands for inbound crossing of the bow shock, and “s” indicates that this is a single bow shock crossing made by MESSENGER.</p> <p><br> </p> <p>The dataset does not include magnetopause and bow shock crossings during the following time intervals:</p> <p>a. From 03:02 to 20:00 on 05-04-2011</p> <p>b. From 24-05-2011 to 03-06-2011</p> <p>c. From 17:50 to 22:53 on 16-04-2012</p> <p>d. From 09-06-2012 to 13-06-2012</p> <p>e. From 07:30 on 08-01-2013 to 16:00 on 09-01-2013</p> <p>f. From 07:55 to 18:33 on 28-02-2013</p> <p>g. From 14:22 to 17:38 on 26-12-2014</p> <p> </p> <p>The current version is updated on 29 August 2023.</p> <p> </p> <p>This work was supported by NASA Discovery Data Analysis Program (DDAP) Grant #80NSSC22K1061 (PI Weijie Sun).</p>
Wind spacecraft floating potential measurements
<p><strong>Quick Summary:</strong></p> <p>The ASCII files herein are a dataset of spacecraft electric potential for the <em>Wind</em> spacecraft between January 1, 2005 and January 1, 2022. The data is thoroughly described in the publication "Spacecraft floating potential measurements for the <em>Wind</em> spacecraft," <em>The Astrophysical Journal Supplement Series</em>.</p> <p><strong><em>Wind</em> Spacecraft:</strong></p> <p>The <em>Wind</em> spacecraft (<a href="https://wind.nasa.gov">https://wind.nasa.gov </a>and <a href="https://doi.org/10.1029/2020RG000714">https://doi.org/10.1029/2020RG000714</a>) was launched on November 1, 1994 and currently is in a halo orbit about the first Sun-Earth Lagrange point. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP). The MFI measures 3-vector <strong>B</strong><sub>o</sub> at ~11 samples per second (sps); the SWE measures reduced velocity distribution functions (VDFs) of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein.</p> <p><strong>Brief Method Description:</strong></p> <p>The spacecraft potential, <span class="math-tex">\(\phi_{sc}\)</span>, was found using four methods. Three of these methods return a range of values while the fourth returns a single value. The methods rely on examining the shape of the electron energy distribution function (EDF), f(E) versus energy, E, for three different pitch-angles (parallel, perpendicular, and anti-parallel with respect to the quasi-static magnetic field, <strong>B</strong><sub>o</sub>). The instrument has a physical lower energy threshold, E<sub>min</sub>, below which no data are measured. We impose an upper energy threshold, E<sub>max</sub>, allowed when searching for <span class="math-tex">\(\phi_{sc}\)</span> based on empirical evidence. The methods are as follows:</p> <p>Method 1: find the range of energies where d<sup>2</sup>f/dE<sup>2</sup> > 0, also referred to as the positive curvature region;<br> Method 2: find the range of energies where df/dE transitions from negative to positive, i.e., the local minimum point of f(E);<br> Method 3: find the range of energies bounding the minimum and maximum values of d<sup>2</sup>f/dE<sup>2</sup>, i.e., region of minimum to maximum curvature; and<br> Method 4: find the local minimum between E<sub>min</sub> and E<sub>max</sub></p> <p>There are some additional constraints imposed in the software, available at <a href="https://github.com/lynnbwilsoniii/wind_3dp_pros">https://github.com/lynnbwilsoniii/wind_3dp_pros</a> (<a href="https://doi.org/10.5281/zenodo.6141586">https://doi.org/10.5281/zenodo.6141586</a>). We found four basic shapes for the EDFs (see paper for example figures), two (i.e., Types A and B) of which satisfy E<sub>min</sub> < <span class="math-tex">\(\phi_{sc}\)</span> and thus are good. The other two shapes (i.e., Types C1 and C2) satisfy E<sub>min</sub> > <span class="math-tex">\(\phi_{sc}\)</span>, and thus we cannot determine <span class="math-tex">\(\phi_{sc}\)</span> from the EDF. We can only know that it has an upper bound of E<sub>min</sub>. All Type A EDFs are given a quality flag (QF) of 4 (i.e., the best), all Type Bs are given a QF of 2 (i.e., still okay and useable), and all Type Cs are given a QF of 0 (i.e., do not use these).</p> <p><strong>ASCII File Description:</strong></p> <p>Each ASCII file contains one year of data. There is summary information contained in the header of each file. The first two columns are the start and end times (UTC) of the EDF (format 'YYYY-MM-DD/hh:mm:ss.xxx'). After the times, Methods 1-3 have six columns and Method 4 has three columns. The first(second) three columns for Methods 1-3 correspond to the lower(upper) bound on the range of <span class="math-tex">\(\phi_{sc}\)</span> [eV] solutions. Method 4 only has one set of three-column solutions. Each three-column set corresponds to the parallel, perpendicular, and anti-parallel pitch-angle solutions. All of these in total comprise 21 columns. The <span class="math-tex">\(\phi_{sc}\)</span> solutions are followed by a column for E<sub>min</sub> [eV] and E<sub>max</sub> [eV]. The last two columns are the EDF label or type (i.e., A, B, C1, or C2) and the quality flag (i.e., 4, 2, or 0).</p> <p>Note that NaNs have been replaced with -10<sup>30</sup> fill values</p>
Distribution of interplanetary dust detected by the Juno spacecraft and its contribution to the Zodiacal Light
<p>The Zodiacal light is sunlight reflected by dust in the inner solar system. Variations in the Zodiacal light with ecliptic latitude reveal discrete bands of dust orbiting near the ecliptic plane. The Juno spacecraft, in transit from earth to Jupiter, recorded a sufficient number of impacts with this dust to characterize their distribution in space for the first time. </p> <p>This dataset (filename IDP_List.txt) contains a time-ordered list of all IDP impact detections along with supplementary engineering and ephemeris information. The file is an ASCII text file and the file format is described in the word document (IDP_List_Format.docx).</p>
Supporting Information for "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25"
<p>Table of parameters employed in the study "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25". The table contains the original peak intensities directly taken from the <em>SERPENTINE SEP event catalog</em>, without any scaling or inter-calibration factors that are applied in the study. All information provided in the table is based on the <em>SERPENTINE SEP event catalog</em> and <em>SERPENTINE CME and coronal shocks catalog</em><strong>,</strong> only limiting the variables to those used in this study. The dataset is in CSV format.</p> <p>For more information, and if you use this table, please refer to the corresponding publication:</p> <blockquote> <div> <p>Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25<br>G. U. Farwa, N. Dresing, J. Gieseler, L. Vuorinen, I. G. Richardson, C. Palmroos, S. Valkila, B. Heber, S. Jensen, P. Kühl, L. Rodríguez-García and R. Vainio<br>A&A, 693 (2025) A198<br>DOI: <a href="https://doi.org/10.1051/0004-6361/202450945">10.1051/0004-6361/202450945</a></p> </div> </blockquote> <div> <div> </div> </div> <p><strong>Field descriptions</strong></p> <ul> <li>id: ID</li> <li>date: Event date [UTC]</li> <li>flare_time: Flare time [UTC]</li> <li>flare_lat: Flare Carrington latitude [deg]</li> <li>flare_lon: Flare Carrington longitude [deg]</li> <li>flare_class: Flare class (GOES)</li> <li>flare_comments: Flare Comments</li> <li>radio_type2: Radio type II bursts</li> <li>decametric_type2_start: Decametric type II burst start time [UT]</li> <li>decametric_type2_stop: Decametric type II burst end time [UT]</li> <li>radio_type2_start: Metric radio type II burst start time [UT]</li> <li>radio_type2_stop: Metric radio type II burst end time [UT]</li> <li>solar_mach_link: Solar-Mach link</li> <li>S/C codes <ul> <li>BepiC: BepiColombo</li> <li>L1: L1 (SOHO/Wind)</li> <li>PSP: Parker Solar Probe</li> <li>STA: STEREO A</li> <li>SolO: Solar Orbiter</li> </ul> </li> <li>S/C related field descriptions <ul> <li>{sc}_sc_lat: S/C Carrington latitude [deg]</li> <li>{sc}_sc_lon: S/C Carrington longitude [deg]</li> <li>{sc}_dist: S/C radial distance [au]</li> <li>{sc}_p25MeV_onset_date: S/C protons 25 MeV onset date [UTC]</li> <li>{sc}_p25MeV_onset_time: S/C protons 25 MeV onset time [UTC]</li> <li>{sc}_p25MeV_onset_time_formatted: S/C protons 25 MeV onset time [UTC] (Formatted)</li> <li>{sc}_p25MeV_onset_averaging: S/C protons 25 MeV averaging used for onset [min]</li> <li>{sc}_p25MeV_onset_sector: S/C protons 25 MeV sector used for onset</li> <li>{sc}_p25MeV_peak_date: S/C protons 25 MeV peak date [UTC]</li> <li>{sc}_p25MeV_peak_time: S/C protons 25 MeV peak time [UTC]</li> <li>{sc}_p25MeV_peak_time_formatted: S/C protons 25 MeV peak time [UTC] (Formatted)</li> <li>{sc}_p25MeV_peak_flux: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_p25MeV_peak_averaging: S/C protons 25 MeV averaging used for peak [min]</li> <li>{sc}_p25MeV_peak_sector: S/C protons 25 MeV sector used for peak</li> <li>{sc}_p25MeV_injection_date: S/C protons 25 MeV inferred injection date [UTC]</li> <li>{sc}_p25MeV_injection_time: S/C protons 25 MeV inferred injection time [UTC]</li> <li>{sc}_p25MeV_sw_speed: S/C protons 25 MeV onset solar wind speed [km/s]</li> <li>{sc}_p25MeV_comments: S/C protons 25 MeV comments</li> <li>{sc}_e100keV_onset_date: S/C electrons 100 keV onset date [UTC]</li> <li>{sc}_e100keV_onset_time: S/C electrons 100 keV onset time [UTC]</li> <li>{sc}_e100keV_onset_time_formatted: S/C electrons 100 keV onset time [UTC] (Formatted)</li> <li>{sc}_e100keV_onset_averaging: S/C electrons 100 keV averaging used for onset [min]</li> <li>{sc}_e100keV_onset_sector: S/C electrons 100 keV sector used for onset</li> <li>{sc}_e100keV_peak_date: S/C electrons 100 keV peak date [UTC]</li> <li>{sc}_e100keV_peak_time: S/C electrons 100 keV peak time [UTC]</li> <li>{sc}_e100keV_peak_time_formatted: S/C electrons 100 keV peak time [UTC] (Formatted)</li> <li>{sc}_e100keV_peak_flux: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e100keV_peak_averaging: S/C electrons 100 keV averaging used for peak [min]</li> <li>{sc}_e100keV_peak_sector: S/C electrons 100 keV sector used for peak</li> <li>{sc}_e100keV_injection_date: S/C electrons 100 keV inferred injection date [UTC]</li> <li>{sc}_e100keV_injection_time: S/C electrons 100 keV inferred injection time [UTC]</li> <li>{sc}_e100keV_sw_speed: S/C electrons 100 keV onset solar wind speed [km/s]</li> <li>{sc}_e100keV_comments: S/C electrons 100 keV comments</li> <li>{sc}_e1MeV_onset_date: S/C electrons 1 MeV onset date [UTC]</li> <li>{sc}_e1MeV_onset_time: S/C electrons 1 MeV onset time [UTC]</li> <li>{sc}_e1MeV_onset_time_formatted: S/C electrons 1 MeV onset time [UTC] (Formatted)</li> <li>{sc}_e1MeV_onset_averaging: S/C electrons 1 MeV averaging used for onset [min]</li> <li>{sc}_e1MeV_onset_sector: S/C electrons 1 MeV sector used for onset</li> <li>{sc}_e1MeV_peak_date: S/C electrons 1 MeV peak date [UTC]</li> <li>{sc}_e1MeV_peak_time: S/C electrons 1 MeV peak time [UTC]</li> <li>{sc}_e1MeV_peak_time_formatted: S/C electrons 1 MeV peak time [UTC] (Formatted)</li> <li>{sc}_e1MeV_peak_flux: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e1MeV_peak_averaging: S/C electrons 1 MeV averaging used for peak [min]</li> <li>{sc}_e1MeV_peak_sector: S/C electrons 1 MeV sector used for peak</li> <li>{sc}_e1MeV_injection_date: S/C electrons 1 MeV inferred injection date [UTC]</li> <li>{sc}_e1MeV_injection_time: S/C electrons 1 MeV inferred injection time [UTC]</li> <li>{sc}_e1MeV_sw_speed: S/C electrons 1 MeV onset solar wind speed [km/s]</li> <li>{sc}_e1MeV_comments: S/C electrons 1 MeV comments</li> <li>{sc}_ep_ratio: Ratio of Electrons (~1MeV) / Protons (25-40 MeV)</li> </ul> </li> <li>CME related descriptions <ul> <li>cme_id: CME ID</li> <li>L1_date: Date of CME identification at L1</li> <li>L1_time: Time of CME identification at L1</li> <li>L1_pos_speed: Plane of sky speed of CME measured at L1</li> </ul> </li> </ul> <div><strong>CHANGELOG:</strong></div> <div> <ul> <li>2025-06-12 <ul> <li>Updated peak fluxes and peak times of PSP 1 MeV electrons, as well as PSP's e/p ratios (the previous flux values are erroneous!)</li> </ul> </li> </ul> </div>
Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected by spacecraft 1 of the Cluster mission in the foreshock of Earth
<p>Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.</p> <p>An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field. </p> <p>More details on the creation of the database are given in the following publication:</p> <p><span><span lang="EN-US">Bergman, S.</span></span><span lang="EN-US">, Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large</span><span lang="EN-US">‐</span><span lang="EN-US">Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. <em>Journal of Geophysical Research: Space Physics</em>, 130. </span><a href="https://doi.org/10.1029/2024JA033568"><span lang="EN-US">https://doi.org/10.1029/2024JA033568</span></a></p> <p>Contact: S. Bergman, sofiabergmanphd@gmail.com </p>
Radio Science observations of Mars Express and Tianwen-1 spacecraft from the 2021 Maritan Solar Conjunction
<p>Phase scintillation dataset from the 2021 Martian solar conjunction is contained in the DATA.zip file. The Read Me pdf file contiants information useful to understanding the file naming conventions and their contents.</p>
List of Saturn magnetopause and bow shock crossings by the Cassini spacecraft
<p>This is an updated list of magnetopause (MP) and bow shock (BS) crossings from Cassini, based on the list presented in Jackman et al. [2019] <a href="https://doi.org/10.1029/2019JA026628">https://doi.org/10.1029/2019JA026628</a>. This uploaded list has the following changes from the list first presented in that paper (and included in that supplementary material):</p> <p> </p> <p>I = inbound</p> <p>O = outbound</p> <p>Amendments to Cassini MP/BS crossing list<br> Changes made 26/01/2021</p> <p>Total #MP crossings: 2118 (some times changed)<br> Total #BS crossings: 1247 (3 pairs added, 1 pair removed, some times changed)</p> <p>Change list below:</p> <p>Change #1:<br> Note the following crossings come from a list on MAPSView which was originally collated based on MAG and CAPS data. <br> MAG data gap spans these dates so crossings are based on other instruments at these times.<br> ELS and SNG data available 2004 181-185 and 195-196 (except 196 13-23 UT).</p> <p>2004 181 02 43 MP I <br> 2004 186 04 42 MP O <br> 2004 186 10 24 MP I <br> 2004 186 17 57 MP O <br> 2004 186 20 17 MP I <br> 2004 188 07 06 MP O <br> 2004 188 08 11 MP I <br> 2004 188 13 14 MP O <br> 2004 188 15 13 MP I <br> 2004 188 19 39 MP O <br> 2004 189 14 05 BS O <br> 2004 189 16 15 BS I <br> 2004 189 21 46 BS O <br> 2004 190 00 30 BS I <br> 2004 190 04 13 BS O <br> 2004 190 10 56 BS I <br> 2004 190 11 54 BS O <br> 2004 190 11 54 DG S_SW <br> 2004 194 05 12 DG E_SH <br> 2004 194 05 12 BS O <br> 2004 196 03 11 BS I <br> 2004 196 06 59 BS O </p> <p>Change #2:<br> Change<br> 2004 348 05 27 MP I <br> to<br> 2004 348 05 00 MP I </p> <p>Change #3:<br> Added some crossings on 2004 day 345<br> 2004 345 11 52 BS I<br> 2004 345 11 58 BS O<br> 2004 345 12 20 BS I<br> 2004 345 12 26 BS O<br> 2004 345 23 06 BS I<br> 2004 345 23 13 BS O</p> <p>Change #4: <br> Changed 2004 day 345 13 24 BS I <br> to<br> 2004 day 345 12 57 BS I</p> <p>Change #5: <br> Remove<br> 2014 322 6 8 BS I <br> 2014 326 1 23 BS O </p> <p>_________________________________________________________________________________________<br> Additional changes made through discussion with Matthew Cheng, August 13th 2021<br> Original list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_published<br> 1st Revised list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_revised<br> 2nd Revised list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_revised_120821</p> <p>Since 1st revised list:<br> Total #MP crossings: 2122 (2 new pairs, some times changed)<br> Total #BS crossings: 1249 (1 new pair, some times changed)</p> <p>Change #6<br> Changed typo<br> 2016 068 12 19 DG E_MP <br> to<br> 2016 068 12 19 DG E_SP </p> <p><br> Change #7<br> Change <br> 2016 242 12 00 MP O <br> To:<br> 2016 242 11 15 MP O </p> <p>Change #8<br> Change <br> 2016 152 16 33 BS O<br> to <br> 2016 152 15 22 BS O</p> <p><br> Change #9<br> Change <br> 2016 153 02 24 BS O <br> to <br> 2016 153 02 08 BS O </p> <p><br> Change #10<br> Change <br> 2016 153 12 00 BS I <br> to<br> 2016 153 11 45 BS I </p> <p>Change #11<br> Add new MP pair<br> 2016 155 11 05 MP O <br> 2016 155 12 52 MP I </p> <p><br> Change #12<br> Change<br> 2011 2 03 14 MP O <br> to<br> 2011 2 01 48 MP O </p> <p><br> Change #13<br> Change:<br> 2012 145 15 11 MP O <br> To<br> 2012 145 14 36 MP O</p> <p>Change #14<br> Changed<br> 2007 33 16 25 MP I <br> to <br> 2007 33 15 25 MP I </p> <p>Change #15<br> Add new MP pair<br> 2005 45 11 55 MP O <br> 2005 45 12 36 MP I </p> <p>Change #16<br> Add new BS pair<br> 2012 277 00 15 BS I <br> 2012 277 01 36 BS O</p> <p>Change #17<br> Add new MP pair<br> 2008 85 14 31 MP I <br> 2008 85 15 38 MP O </p>
Spacecraft Thruster Firing Test Dataset
<p> </p> <p><strong>WARNING</strong></p> <p>This version of the dataset is not recommended for anomaly detection use case. We discovered discrepancies in the anomalous sequences. A new version will be released. In the meantime, please ignore all sequence marked as anomalous.</p> <p><strong>CONTEXT</strong></p> <p>Testing hardware to qualify it for Spaceflight is critical to model and verify performances. Hot fire tests (also known as life-tests) are typically run during the qualification campaigns of satellite thrusters, but results remain proprietary data, hence making it difficult for the machine learning community to develop suitable data-driven predictive models. This synthetic dataset was generated partially based on the real-world physics of monopropellant chemical thrusters, to foster the development and benchmarking of new data-driven analytical methods (machine learning, deep-learning, etc.).</p> <p>The PDF document "STFT Dataset Description" describes in much details the structure, context, use cases and domain-knowledge about thruster in order for ML practitioners to use the dataset.</p> <p><strong>PROPOSED TASKS</strong></p> <p><strong>Supervised</strong>:</p> <ul> <li><strong>Performance Modelling:</strong> Prediction of the thruster performances (target can be thrust, mass flow rate, and/or the average specific impulse)</li> <li><strong>Acceptance Test for Individualised Performance Model refinement: </strong>Taking into account the acceptance test of individual thruster might be helpful to generate individualised thruster predictive model</li> <li><strong>Uncertainty Quantification</strong> <strong>for </strong><strong>Thruster-to-thruster reproducibility verification</strong>, i.e. to evaluate the prediction variability between several thrusters in order to construct uncertainty bounds around the prediction (predictive intervals) of the thrust and mass flow rate of future thrusters that may be used during an actual space mission</li> </ul> <p><strong>Unsupervised / Anomaly Detection</strong></p> <ul> <li><strong>Anomaly Detection</strong>: Anomalies can be detected in an unsupervised setting (outlier detection) or in a semi-supervised setting (novelty detection). The dataset includes a total of 270 anomalies. A simple approach is to predict if a firing test sequence is anomalous or nominal. A more advanced approach is trying to predict which portion of a time series is anomalous. The dataset also provide a detailed information about each time point being anomalous or nominal. In case of an anomaly, a code is provided which allows to diagnosis the detection system performance on the different types of anomalies contained in the dataset.</li> </ul> <p> </p>
URSO: Unreal Rendered Spacecrafts On-Orbit Datasets
<p>Synthetic image datasets of Soyuz and Dragon spacecraft models orbiting the Earth, rendered using our simulator, to learn and evaluate pose estimation algorithms. For more information, please refer to our preprint: <a href="https://arxiv.org/abs/1907.04298">https://arxiv.org/abs/1907.04298</a></p> <p>Each dataset contains:</p> <ul> <li>5000 RGB images with a resolution of 1280x960 px</li> <li>Train/val/test set splits as a list: <strong><set>_images.csv</strong></li> <li>Pose ground truth as: <strong><set>_poses_gt.csv</strong> with <ul> <li>Row format:<strong> <x y z qx qy qz qw></strong></li> </ul> </li> </ul> <p>The camera calibration matrix is:</p> <p><span class="math-tex">\(K = \begin{bmatrix} 640.00 & 0 & 640 \\ 0 & 640.46 & 480\\ 0 & 0 & 1 \end{bmatrix}\)</span></p> <p>And the horizontal and vertical field-of-view are respectively 90.0° and 73.7º.</p> <p> </p> <p> </p>
Model Checking of Spacecraft Operational Designs: A Scalability Analysis - Artifact
<p>This artifact contains the accompanying experiment data for the publication "Model Checking of Spacecraft Operational Designs: A Scalability Analysis".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.