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Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set
During the September 2015 to March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission, the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380320/ https://www.ncbi.nlm.nih.gov/pubmed/32728509 http://dx.doi.org/10.1029/2019JA027181 |
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author | da Silva, Daniel Barrie, A. Gershman, D. Elkington, S. Dorelli, J. Giles, B. Patterson, W. |
author_facet | da Silva, Daniel Barrie, A. Gershman, D. Elkington, S. Dorelli, J. Giles, B. Patterson, W. |
author_sort | da Silva, Daniel |
collection | PubMed |
description | During the September 2015 to March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission, the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked, it came at the expense of lost information content for a fraction of the frames. Following this period of lossy compression, the DES was reconfigured in a way that allowed for 95% of the frames to arrive to the ground without loss. Using this high‐quality set of frames from on‐orbit observations, we compressed and decompressed the frames on the ground to create a side‐by‐side record of the compression effect. This record was used to drive an optimization method that (a) derived basis functions capable of approximating the lossless sample space and with nonnegative coefficients and (b) fitted a function which maps the lossy frames to basis weights that recreate the frame without compression artifacts. This method is introduced and evaluated in this paper. Data users should expect a higher level of confidence in the absolute scale of density/temperature measurements and notice less sinusoidal bias in the velocity X and Y components (GSE) |
format | Online Article Text |
id | pubmed-7380320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73803202020-07-27 Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set da Silva, Daniel Barrie, A. Gershman, D. Elkington, S. Dorelli, J. Giles, B. Patterson, W. J Geophys Res Space Phys Technical Reports: Methods During the September 2015 to March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission, the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked, it came at the expense of lost information content for a fraction of the frames. Following this period of lossy compression, the DES was reconfigured in a way that allowed for 95% of the frames to arrive to the ground without loss. Using this high‐quality set of frames from on‐orbit observations, we compressed and decompressed the frames on the ground to create a side‐by‐side record of the compression effect. This record was used to drive an optimization method that (a) derived basis functions capable of approximating the lossless sample space and with nonnegative coefficients and (b) fitted a function which maps the lossy frames to basis weights that recreate the frame without compression artifacts. This method is introduced and evaluated in this paper. Data users should expect a higher level of confidence in the absolute scale of density/temperature measurements and notice less sinusoidal bias in the velocity X and Y components (GSE) John Wiley and Sons Inc. 2020-04-24 2020-04 /pmc/articles/PMC7380320/ /pubmed/32728509 http://dx.doi.org/10.1029/2019JA027181 Text en ©2020. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Technical Reports: Methods da Silva, Daniel Barrie, A. Gershman, D. Elkington, S. Dorelli, J. Giles, B. Patterson, W. Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title | Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title_full | Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title_fullStr | Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title_full_unstemmed | Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title_short | Neural Network Repair of Lossy Compression Artifacts in the September 2015 to March 2016 Duration of the MMS/FPI Data Set |
title_sort | neural network repair of lossy compression artifacts in the september 2015 to march 2016 duration of the mms/fpi data set |
topic | Technical Reports: Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380320/ https://www.ncbi.nlm.nih.gov/pubmed/32728509 http://dx.doi.org/10.1029/2019JA027181 |
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