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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,...

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Autores principales: da Silva, Daniel, Barrie, A., Gershman, D., Elkington, S., Dorelli, J., Giles, B., Patterson, W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
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)
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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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