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214 results for “spacecrafts”
Applicability of Model Checking for Verifying Spacecraft Operational Designs - Artifact
<p>This artifact contains the accompanying experiment data for the submission "Applicability of Model Checking for Verifying Spacecraft Operational Designs".</p>
The Evolution of a Spacecraft-Generated Lunar Exosphere
<p>Simulated spacecraft trajectory parameters, and data required to reproduce Figures 1–5 from Prem et al. (2020), The Evolution of a Spacecraft-Generated Lunar Exosphere, J. Geophys. Res. (<a href="https://pubmed.ncbi.nlm.nih.gov/33959468">https://pubmed.ncbi.nlm.nih.gov/33959468</a>).</p> <p>The file spacecraft_trajectory.dat contains a descriptive header, and the simulated descent profile in Cartesian coordinates. The .dat ASCII files contain the data shown in Figures 1–5, and the .lay files are <a href="https://www.tecplot.com/products/tecplot-focus">Tecplot Focus</a> layouts that were used to visualize the data. Please feel free to contact lead author Dr. Parvathy Prem (parvathy.prem@jhuapl.edu) with any questions. </p>
POES N15 spacecraft L* values 1998-2014 determined by the TS05 external magnetic field model
<p>L* values for the POES N15 spacecraft from 1998-2014 determined by the TS05 external magnetic field model. L* calculations were performed by the SpacePy package in Python (<a href="https://spacepy.github.io/">https://spacepy.github.io/</a>).</p> <p>Each year has been broken up into multiple files in chronological number order (beginning at 1) . The number of files for each year are as follows:</p> <p>1998 - 5</p> <p>1999 - 10</p> <p>2000-2014 - 15</p> <p>Each file is a gzipped csv file named via the following: poes_n15_YEAR_FILENUM.csv.gz.</p> <p> </p> <p>References:</p> <p>Tsyganenko, N. A., and Sitnov, M. I. (2005), Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <em>J. Geophys. Res.</em>, 110, A03208, doi:<a href="https://doi.org/10.1029/2004JA010798">10.1029/2004JA010798</a>.</p>
Data for "Spacecraft charging simulations of probe B1 of Comet Interceptor during the cometary flyby"
<p>Simulation results of the spacecraft-plasma interactions of probe B1 of Comet Interceptor obtained with the Spacecraft Plasma Interaction Software (SPIS) and the ElectroMagnetic Spacecraft Environment Simulator (EMSES). Detailed descriptions of the simulations and the results are given in the paper "Spacecraft charging simulations of probe B1 of Comet Interceptor during the cometary flyby" by Bergman et al.</p><p>Contact email: sofiabergmanphd@gmail.com</p>
A dataset of proton kinetic-scale current sheets selected at 1 AU using Wind spacecraft measurements
<p>This is a dataset of proton kinetic-scale current sheets selected at 1 AU using 11 Samples/s magnetic field measurements aboard Wind spacecraft. The current sheets were selected using Partial Variance Increments method. The detailed analysis of this dataset can be found at https://arxiv.org/abs/2112.15256v1</p> <p>The first column gives a CS index number, the second panel gives a date in the year/month/day format, the last two columns give <br> temporal positions of the left and right boundaries of a CS. These moments of times are in seconds from the beginning of the day indicated in the second column. </p>
Tango Spacecraft Wireframe Dataset Model for Line Segments Detection
<p><strong>Reference Paper:</strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" dataset here published should be used for line detection and segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the only publicly available dataset of synthetic space-borne images tailored to line detection tasks (up to our knowledge). The label of each image gives the reprojection of a simplified wireframe model of Tango on the image plane split into lines. The labels are written following the Wireframe Model format. The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" is also the largest dataset with wireframe annotations available up to date. More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels in the Wireframe dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> width : 98 # width in pixels (int) of the current image</li> <li> height : 176 # height in pixels (int) of the current image</li> <li> lines : [[line1], [line2], ..., [lineN]] # list of lines in each image</li> <li> filename : tango_img_866.png # string with image name and format</li> </ul> <p>Per each line (line1, ... , lineN) in lines, the format is [x0, y0, x1, y1].</p> <p>(x0, y0) are the coordinates (float) of the line starting point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).<br> (X1, y1) are the coordinates (float) of the line ending point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).</p> <p>Note that the starting point is assumed to be the left-most endpoint (lower x coordinate in image reference frame) of each line. In the case of vertical lines, the starting point is the upper-most endpoint (lower y coordinate in image reference frame) of each line.</p> <p><strong>VERSION CONTROL</strong></p> <ul> <li><strong>v1.0</strong>: All the images (both for train and test) have different resolutions, with Tango always centered in the image. The height of the images is in the range 19 - 352 pixels, while the width is in the range 16 - 336 pixels. The height over width ratio spans from 0.34 to 3.25.</li> <li><strong>v2.0</strong>: This version contains all the images of v1.0 in the .zip folder named <em>Tango_WF.zip</em>, while in the .zip folder named <em>Tango_WF_fullscale.zip</em> there is the dataset (both train and test) of full scale images. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. The labels for the wireframe are in the same format of v1.0.</li> </ul> <p>Note: the dataset in v1.0 is obtained by cropping the fullscale images in v2.0 and by properly rescaling the wireframe annotations.</p> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em><em>"</em> v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6507863">https://doi.org/10.5281/zenodo.6507863</a>) and also "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6499007">https://doi.org/10.5281/zenodo.6499007</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Tango Spacecraft Dataset for Monocular Pose Estimation
<p><strong>Reference Paper:</strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" dataset here published should be used for relative pose estimation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to pose estimation tasks (up to our knowledge). The label of each image gives relative quaternion (in scalar-last format) between Tango and the camera (hence the relative position of the target with respect to the camera in camera reference frame) and the relative position of Tango with respect to the camera in camera reference frame. More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints. The dataset contains also a .txt file with the parameters of the camera used to generate the images.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels in the SPEED and SPEED+ dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> filename : tango_img_1 # name of the image to which the data are referred</li> <li> q_TRG2CAM : [qx qy qz qw] # relative quaternion from Target to Camera reference frame</li> <li> t_CAM2TRG : [x, y, z] # relative position of Tango with respect to the camera expressed in meters</li> </ul> <p>Notice that for making the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.</p> <p><strong>VERSION CONTROL</strong></p> <ul> <li>v1.0: This version contains the dataset (both train and test) of full scale images with relative pose annotations. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. </li> </ul> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Wireframe Dataset Model for Line Segments Detection"</em> v2.0 full-scale (DOI: <a href="https://doi.org/10.5281/zenodo.6372848">https://doi.org/10.5281/zenodo.6372848</a>) and also "<em>Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6507863">https://doi.org/10.5281/zenodo.6507863</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation
<p><strong>Reference Paper: </strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em>" dataset here published should be used for Region of Interest (ROI) and/or semantic segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to ROI extraction and Semantic Segmentation tasks (up to our knowledge). The label of each image gives, for the Bounding Box annotations, the filename of the image, the ROI top-left corner (minimum x, minimum y) in pixels, the ROI bottom-right corner (maximum x, maximum y) in pixels, and the center point of the ROI in pixels. The annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward. Concerning the Semantic Segmentation, RGB masks are provided. Each RGB mask correspond to a single image in both train and test dataset. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels for the bounding box extraction are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> filename : tango_img_1 # name of the image to which the data are referred</li> <li> rol_tl : [x, y] # ROI top-left corner (minimum x, minimum y) in pixels</li> <li> roi_br : [x, y] # ROI bottom-right corner (maximum x, maximum y) in pixels</li> <li> roi_cc : [x, y] # center point of the ROI in pixels</li> </ul> <p>Notice that the annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward.To make the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.</p> <p>Concerning the Semantic Segmentation Labels, they are provided as RGB masks named as "filename_mask.png" where "filename" is the filename of the image of the training set or the test set to which a specific mask is referred. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). </p> <p><strong>VERSION CONTROL</strong></p> <ul> <li>v1.0: This version contains the dataset (both train and test) of full scale images with ROI annotations and RGB masks for Semantic Segmentation tasks. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. </li> </ul> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Wireframe Dataset Model for Line Segments Detection"</em> v2.0 full-scale (DOI: <a href="https://doi.org/10.5281/zenodo.6372848">https://doi.org/10.5281/zenodo.6372848</a>) and also "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6499007">https://doi.org/10.5281/zenodo.6499007</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Datasets for "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations"
<p>Files used to build Figures 3, 4, 5, 7, 9, 10, 13 in manuscript entitled "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations" submitted with JGR.</p>
Multi-Spacecraft Cycle 25 Interplanetary Shock List
<p>This dataset contains a list of interplenatary shocks observed by multiple spacecraft in conjunction with multi-spacecraft SEP events reported by WP2 during the SERPENTINE project. Please find documentation below</p> <p><a href="https://data.serpentine-h2020.eu/static/doc/Shock_Cycle25_Documentation.pdf">https://data.serpentine-h2020.eu/static/doc/Shock_Cycle25_Documentation.pdf</a></p> <p>Please refer to the SERPENTINE data centre for the latest (and best) version of the catalog.</p> <p><a href="https://data.serpentine-h2020.eu/catalogs/shock-sc25/">https://data.serpentine-h2020.eu/catalogs/shock-sc25/</a></p> <p> </p>
CRESENT: a CRatEr-baSed pose estimation datasEt for cisluNarlocated spacecrafT
<h3>CRESENT is first introduced in the paper “Robust Perspective-n-Crater for Crater-based Camera Pose Estimation” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.</h3> <p><strong>Overview:</strong></p> <p>This dataset contains images produced by The University of Adelaide using PANGU Planet Surface Simulation Software developed by the Space Technology Centre at the University of Dundee, Scotland.</p> <p>High-resolution lunar DEMs from the PDS data node were rendered in PANGU, and images were taken above the lunar surface at an altitude of 100km with varying angles off nadir to mimic the expected conditions of a lunar orbiter surface surveillance mission.</p> <p>The dataset contains images taken above four different surface regions on the Moon, each within a region of 45 degrees latitude and 45 degrees longitude.</p> <p><strong>Data organisation:</strong></p> <p>There are four root folders, each containing images produced under one of the four lunar regions:</p> <p><em>LDEM_x_yE_l_mN/</em></p> <p>where x and y are the latitude bounds (degrees) of the lunar region and l and m are the longitude bounds (degrees) of the lunar region, rendered in PANGU. Note that due to the high resolution of the DEMs, any region of the Moon that was outside these latitude and longitude bounds was not rendered (and the surface will appear cut off/black at the boundaries of these regions).</p> <p>Within each of the lunar region folders, there are seven subfolders:</p> <p><em>LDEM_x_yE_l_mN_float_60fov_1024_1024_ideg_off_nadir/</em></p> <p>where i is the viewing angle (degree) off nadir the image was taken at, where i is either 0, 10, 20, 30, 40, 50, or 60 degrees. Each subfolder contains a folder of images, a poses.csv file of ground truth poses, a calibration file and a file detailing the specifics of the rendered LDEM. Note that each image taken within each sub-directory of each lunar region folder will have the same number of files, each located at the same position in the Selenographic reference frame, but at different angles off nadir. For example,<em> LDEM_-90_-45E_0_45N/LDEM_-90_-45E_0_45N_float_60fov_1024_1024_0deg_off_nadir/</em> and <em>LDEM_-90_-45E_0_45N/LDEM_-90_-45E_0_45N_float_60fov_1024_1024_60deg_off_nadir/</em> will both have the same number of images in their <em>images/</em> subdirectory, and each image file number in these directories was taken at the same camera position, but at viewing angles of 0 degrees and 50 degrees off nadir, respectively.</p> <p>Each line in the poses file contains the X, Y, Z position (m) and the yaw, pitch, roll angles (degrees) of the camera in the selenographic reference frame. There are the same number of lines in the poses file as images in each subfolder, e.g., the first line of the poses file corresponds to the XYZ yaw pitch roll pose of the camera that generated <em>images/0.png</em>, the second line of the poses file corresponds to the pose of the camera of <em>images/1.png</em>, etc.</p> <p><strong>How to cite:</strong></p> <p>Users of this dataset are requested to cite the following paper.</p> <p>Reference String</p> <p><em>McLeod, S. et al. Robust Perspective-n-Crater for Crater-based Camera Pose Estimation in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2024)</em></p> <p>Bibtex</p> <p><code>@InProceedings{Mcleod_2024_CVPR,</code></p> <p><code>author = {Mcleod, Sofia and Chng, Chee Kheng and Ono, Tatsuharu and Shimizu, Yuta and Hemmi, Ryodo and Holden, Lachlan and Rodda, Matthew and Dayoub, Feras and Miyamoto, Hirdy and Takahashi, Yukihiro and Kasai, Yasuko and Chin, Tat-Jun}, </code></p> <p><code>title = {Robust Perspective-n-Crater for Crater-based Camera Pose Estimation}, </code></p> <p><code>booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, </code></p> <p><code>month = {June}, </code></p> <p><code>year = {2024}, </code></p> <p><code>pages = {6760-6769} </code></p> <p><code>}</code></p> <p><strong>DISCLAIMER</strong>:</p> <p><em>The dataset provided is a lower resolution version of the original and is intended for academic research purposes only, consistent with the terms of the Open Access License that applies to the usage of this dataset. Please contact <a href="mailto:tat-jun.chin@adelaide.edu.au">tat-jun.chin@adelaide.edu.au</a> if you require the higher resolution versions of the dataset.</em></p>
List of Mercury's Bow Shock and Magnetopause Crossings by the MESSENGER Spacecraft
<p>This dataset contains a list of Mercury’s bow shock and magnetopause boundary crossings by the MESSENGER spacecraft during its entire orbital mission, from March 23, 2011, to April 30, 2015. A total of 4,054 orbits were identified.</p> <p>The boundary crossings were visually identified by abrupt changes in the magnetic field strength and direction as the spacecraft traversed the current layer, assisted by a sharp boundary in the ion flux spectrogram between different regions. Due to the inward and outward motions of these boundaries, multiple crossings were typically observed. The dataset provides the innermost and outermost crossings for each pass. </p> <p>File descriptions:</p> <p>*MP_in.dat*: Inbound magnetopause crossings.</p> <p>*MP_out.dat*: Outbound magnetopause crossings.</p> <p>*BS_in.dat*: Inbound bow shock crossings.</p> <p>*BS_out.dat*: Outbound bow shock crossings.</p> <p>Each file includes two columns: the time strings for the first and last crossings during each pass. For passes with only one crossing, the second time string is labeled as “NAN.” In a few orbits where the bow shock was not detected, both columns are labeled as “NAN.”</p> <p>The MESSENGER MAG and FIPS data used for the identification is publicly available via NASA’s Planetary Data System (https://pds-ppi.igpp.ucla.edu/).</p> <p>This dataset is linked to the following publications:</p> <p>Zhong et al. (2015), Mercury’s three-dimensional asymmetric magnetopause, <em>J. Geophy. Res. Space Physics</em>, 120, 7658–7671, <a href="https://doi.org/10.1002/2015JA021425">https://doi.org/10.1002/2015JA021425</a>.</p> <p>Zhong et al. (2015), Compressibility of Mercury’s dayside magnetosphere, <em>Geophy. Res. Lett.</em>, 42(23), 10135-10139. <a href="https://doi.org/10.1002/2015GL067063">https://doi.org/10.1002/2015GL067063</a>.</p> <p>Zhong et al. (2020), Effects of Orbital Eccentricity and IMF Cone Angle on the Dimensions of Mercury’s Magnetosphere, <em>Astrophys. J.</em>, 892(1), 2. 10135-10139. <a href="https://doi.org/10.3847/1538-4357/ab7819">https://doi.org/10.3847/1538-4357/ab7819</a>.</p> <p>Zhong & Wang, Global magnetic field properties in Mercury’s solar wind interaction from MESSENGER measurements [submitted, A&A, 2024].</p>
Location of ground stations, targets and spacecraft for Spire case study 2021
<p>Locations of ground stations, targets, and spacecraft for the analysis presented in article: "Dynamical influence driven space system design"</p>
Dataset for "The VLF Transmitter, Narrowband Receiver, and Tuner Investigation on the DSX Spacecraft" manuscript
<p>Dataset for "The VLF Transmitter, Narrowband Receiver, and Tuner Investigation on the DSX Spacecraft" publication submitted to JGR Space Physics</p>
Energetic Particle Injection during Short Isolated Bubble as seen in RCM Simulation and Spacecraft Observations in the Flow Braking Region
<p>The names of the RCM simulation output files specify the simulation time in a format of "hhmmss". The stored quantities are, "I","J","COLAT","ALOCT","MLT","BNDLOC","XMIN","YMIN","FTV","BMIN","V", "BIRK_fromV(NH)","P(RCM),nPa", "PV_gamma","Vtotx", "Vtoty","Vx_exb","Vy_exb","vel_x","vel_y","Ey","PEDLAM", "PEDPSI", "HALL", "RCM_T_p","RCM_T_e","RCM_N_e","EFLUX","EAVG","f_i_50-75","f_i_75-125","f_i_125-200","f_i_200-300","f_e_50-75","f_e_75-125","f_e_125-200","f_e_200-300","Vm","dbxdz","dbydz","dbrdz","dbzdx","dbzdy".<br> For the first hour of substorm-growth-phase-like quasi-steady convection, we uploaded the RCM simulation output files at 1-minute intervals. Then, the RCM output files were uploaded at 20-second intervals for the next 7 minutes of bubble injection, and at 1-minute intervals for the rest of the simulation.</p>
Dataset corresponding to the SIRs observed by the Wind spacecraft during 2007, 2008, 2018, and 2019
<p>Dataset corresponding to the Stream Interaction Regions (SIRs) observed by the Wind spacecraft during 2007, 2008, 2018, and 2019. The table consists of several columns detailing the solar wind (SW) properties and related parameters.</p> <p>In addition, we show the coronal holes (CHs) locations from which the high-speed streams originate using synoptic maps. CHs can be near the solar equator, at midlatitudes, or as low-latitude extensions of polar CH.</p>
Current sheet intervals for "Solar wind current sheets: MVA inaccuracy and recommended single-spacecraft methodology"
Open the record for dataset details and reuse information.
Online tree-based planning for active spacecraft fault estimation and collision avoidance
Open the record for dataset details and reuse information.
Dataset for "Deorbiting spacecraft with passively stabilised attitude using a simplified quasi-rhombic-pyramid sail"
<p>Dataset of the paper "Deorbiting spacecraft with passively stabilised attitude using a simplified quasi-rhombic-pyramid sail" by Miguel, N. and Colombo, C.</p> <p>This paper has been published in the journal Advances in Space Research (ASR), in a special issue entitled "Solar Sailing: Concepts, Technology, and Missions II".</p>
SERPENTINE catalog of solar cycle 25 multi-spacecraft solar energetic particle events
<p>This catalog contains multi-spacecraft solar energetic particle (SEP) events, which were observed with the new heliospheric spacecraft fleet in solar cycle 25. It has been created within the European Union’s Horizon 2020 project <em>SERPENTINE</em> (Solar energetic particle analysis platform for the inner heliosphere). The catalog comprises key SEP characteristics observed by five different observer locations as provided by <em>Solar Orbiter, Parker Solar Probe, STEREO A, Wind</em> and <em>SOHO</em> (at the Lagrangian point 1), and <em>BepiColombo</em>. It focuses on large events, which show energetic proton increases above 25 MeV observed at least at two spacecraft. The catalog provides not only key parameters of the proton event but also the same parameters for 1 MeV and 100 keV electrons, respectively.</p> <p>For more information, and if you use this catalog, please refer to the corresponding publication:</p> <blockquote> <p>The solar cycle 25 multi-spacecraft solar energetic particle event catalog of the SERPENTINE project<br>N. Dresing, A. Yli-Laurila, S. Valkila, J. Gieseler, D. E. Morosan, G. U. Farwa, Y. Kartavykh, C. Palmroos, I. Jebaraj, S. Jensen, P. Kühl, B. Heber, F. Espinosa, R. Gómez-Herrero, E. Kilpua, V.-V. Linho, P. Oleynik, L. A. Hayes, A. Warmuth, F. Schuller, H. Collier, H. Xiao, E. Asvestari, D. Trotta, J. G. Mitchell, C. M. S. Cohen, A. W. Labrador, M. E. Hill and R. Vainio<br>A&A, 687 (2024) A72<br>DOI: <a href="https://doi.org/10.1051/0004-6361/202449831">10.1051/0004-6361/202449831</a>, arXiv:<a href="https://arxiv.org/abs/2403.00658">2403.00658</a>. </p> </blockquote> <p>The csv files provided here are archived versions of the dynamic catalog available at <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/">https://data.serpentine-h2020.eu/catalogs/sep-sc25/</a></p> <p>Provided are two semicolon-separated csv files containing the same data. The only difference is that the file <em>sep-sc25_extended_header.csv </em>includes an extensive header.</p> <ul> <li>Field descriptions <ul> <li>id: ID</li> <li>science_case: Science case</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>decametric_type2_freq_range: Decametric type II frequency range [MHz]</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>radio_type2_freq_range: Frequency range of metric type II burst [MHz]</li> <li>radio_imaging_available: Metric radio imaging available</li> <li>radio_comments: Radio comments</li> <li>cme_id: Associated CMEs</li> <li>solar_mach_link: Solar-Mach link</li> </ul> </li> </ul> <ul> <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> </ul> <ul> <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}_ip_shock_id: Associated IP shocks</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 peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV 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_pathlength: S/C protons 25 MeV path length used for inferred injection time [au]</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 peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV 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_pathlength: S/C electrons 100 keV path length used for inferred injection time [au]</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 peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV 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_pathlength: S/C electrons 1 MeV path length used for inferred injection time [au]</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> </ul> <div> </div> <div><strong>CHANGELOG:</strong></div> <div> <ul> <li>2025-06-05 <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> <li>2024-09-06 <ul> <li>Flare information has been updated</li> <li>Updated cme_id info corresponding to updated CME catalog</li> <li>(Zenodo version only: change file format from "semicolon separated" to "comma separated, with fields containing commas surrounded by quotes")</li> </ul> </li> <li>2024-05-24<br> <ul> <li>Events 35, 36: flare times (GOES) were incorrectly provided as peak times. They have been changed to start times (like for all flares).</li> <li>All 8 events where SOLO/STIX has been used for flare determination had been time shifted to the Sun. Now they are shifted to 1 AU to be consistent with the GOES times.</li> </ul> </li> </ul> </div>
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