cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4463682
title: Collision Avoidance Challenge dataset

description: The Collision Avoidance Challenge dataset is the official dataset used during the <strong>ESA's Kelvins</strong> <strong>competition for "Collision Avoidance Challenge"</strong>. The dataset is a collection of Conjunction Data Messages (CDMs) received by ESA from 2015 to 2019. The CDMs have been anonymised for distribution. The initial raw data, as well as the labels that were kept private during the competition, are also released.<br> ESA thanks the US Space Surveillance Network for the provision of surveillance data supporting safe operations of ESA’s spacecraft. In addition, we are grateful to the agreement which allows to publicly release the current dataset. The dataset is represented as a table, where each row corresponds to a single CDM, and each CDM contains 103 recorded characteristics/features. There are thus 103 columns, which are described in the competition pages. The dataset is made of several unique collision/close approach events, which are identified in the <code>event_id</code> column. In turn, each collision event is made of several CDMs recorded over time. Therefore, a single collision event can be thought of as a times series of CDMs. From these CDMs, for every collision event, we are interested in predicting the final risk which is computed in the last CDM of the time series (i.e. the risk value in the last row of each collision event). For a detailed description on the challenge and this dataset, visit kelvins.esa.int/collision-avoidance-challenge/data/. The paper describing the competition setup and result can be found at arxiv.org/pdf/2008.03069.pdf.
DOI: 10.15468/ajn3uw
title: Eleven new species of Amblypsilopus Bigot (Diptera: Dolichopodidae: Sciapodinae) and a key to the species of Madagascar and adjacent islands

description: This dataset contains the digitized treatments in Plazi based on the original journal article Grichanov, Igor Ya. (2021): Eleven new species of Amblypsilopus Bigot (Diptera: Dolichopodidae: Sciapodinae) and a key to the species of Madagascar and adjacent islands. European Journal of Taxonomy 755 (1): 47-87, DOI: https://doi.org/10.5852/ejt.2021.755.1399, URL: http://dx.doi.org/10.5852/ejt.2021.755.1399
DOI: 10.15468/hjw4pa
title: Morphology and biological notes of Macrosiagon octomaculata (Gerstaecker) triungulinid and a summary of the knowledge on immature stages of the genus (Ripiphoridae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ulysséa, Mônica Antunes, Albertoni, Fabiano Fabian (2020): Morphology and biological notes of Macrosiagon octomaculata (Gerstaecker) triungulinid and a summary of the knowledge on immature stages of the genus (Ripiphoridae). Papéis Avulsos de Zoologia 60 (25): 1-26, DOI: 10.11606/1807-0205/2020.60.special-issue.25, URL: http://dx.doi.org/10.11606/1807-0205/2020.60.special-issue.25
DOI: 10.5517/ccdc.csd.cc26xfvf
title: CCDC 2053125: Experimental Crystal Structure Determination

description: Related Article: Mohammad Taghi Nazeri, Ali Beygzade Nowee, Siamak Javanbakht, Hassan Farhid, Ahmad Shaabani, Behrouz Notash|2021|Tetrahedron|91|132243|doi:10.1016/j.tet.2021.132243
DOI: 10.21232/yq8hczah
title: 2009 to 2016 Cedar Creek Enemy Removal Experiment Canopy Reflectance (MSR)

description: null
DOI: 10.5281/zenodo.3618974
title: Earth - Venus Low-Thrust Optimal Transfers / Database G

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 999,985 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3618226
title: Earth - Venus Low-Thrust Optimal Transfers / Database F

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3618973
title: Earth - Venus Low-Thrust Optimal Transfers / Database G

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 999,985 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3617555
title: Earth - Venus Low-Thrust Optimal Transfers / Database D

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 5.0 and contains 265,603 trajectories with 128 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3617964
title: Earth - Venus Low-Thrust Optimal Transfers / Database E

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 20.0 and contains 409,076 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3618225
title: Earth - Venus Low-Thrust Optimal Transfers / Database F

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3614872
title: Earth - Venus Low-Thrust Optimal Transfers / Database C

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.4 and contains 764,479 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3614805
title: Earth - Venus Low-Thrust Optimal Transfers / Database B

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.4 and contains 382,193 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3617963
title: Earth - Venus Low-Thrust Optimal Transfers / Database E

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 20.0 and contains 409,076 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3614871
title: Earth - Venus Low-Thrust Optimal Transfers / Database C

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.4 and contains 764,479 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3617554
title: Earth - Venus Low-Thrust Optimal Transfers / Database D

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 5.0 and contains 265,603 trajectories with 128 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3614804
title: Earth - Venus Low-Thrust Optimal Transfers / Database B

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.4 and contains 382,193 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3613772
title: Earth - Venus Low-Thrust Optimal Transfers / Database A

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.5281/zenodo.3613771
title: Earth - Venus Low-Thrust Optimal Transfers / Database A

description: A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.
DOI: 10.6084/m9.figshare.14807667.v1
title: Huffmanetal2021_Powder_River_geomorphic_mapping

description: Geomorphic mapping of the Powder River, Montana between Moorhead and Broadus USGS gaging stations. Files are supplementary to "Floodplain Sediment Storage Timescales of the Meandering Powder River, U.S.A." by Huffman et al., 2021.<br>Lateral accretion and alluvial terraces maps were created by Sheila Trampush. Channel positions were delineated by Derek Schook.