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A Python-based tool for constructing observables from the DSN's closed-loop archival tracking data files - Bundle Order Volume Toggle viewing into brief mode
verma-python-2022      UNCERTIFIED
This bundle contains the code presented in Verma, A. K., A Python-based tool for constructing observables from the DSN?s closed-loop archival tracking data files, SoftwareX, Volume 19, 2022, 101190, ISSN 2352-7110,
This bundle contains the code presented in Verma, A. K., A Python-based tool for constructing observables from the DSN's closed-loop archival tracking data files, SoftwareX, Volume 19, 2022, 101190, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2022.101190 Radio science data collected from NASA's Deep Space Networks (DSNs) are made available in various formats through NASA's Planetary Data System (PDS). The majority of these data are packed in complex formats, making them inaccessible to users without specialized knowledge. This bundle includes the Python-based tool presented in the paper that can preprocess the closed-loop archival tracking data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along with ancillary information. A copy of the paper is also included in this bundle. This research was funded by NASA Cooperative Agreement Notice (CAN) Award 80NSSC22M0023.     less ...
Citiation Information
Author Verma, A. K.
TitleA Python-based tool for constructing observables from the DSN's closed-loop archival tracking data files - Bundle
Description This bundle contains the code presented in Verma, A. K., A Python-based tool for constructing observables from the DSN?s closed-loop archival tracking data files, SoftwareX, Volume 19, 2022, 101190, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2022.101190 Radio science data collected from NASA?s Deep Space Networks (DSNs) are made available in various formats through NASA's Planetary Data System (PDS). The majority of these data are packed in complex formats, making them inaccessible to users without specialized knowledge. This bundle includes the Python-based tool presented in the paper that can preprocess the closed-loop archival tracking data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along with ancillary information. A copy of the paper is also included in this bundle. This research was funded by NASA Cooperative Agreement Notice (CAN) Award 80NSSC22M0023.
PublisherNASA Planetary Data System
Publication Year 2022
doi https://doi.org/10.1016/j.softx.2022.101190
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DescriptionThis bundle contains the code presented in Verma, A. K., A Python-based tool for constructing observables from the DSN?s closed-loop archival tracking data files, SoftwareX, Volume 19, 2022, 101190, ISSN 2352-7110,
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