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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Field Line Resonances estimated using Machine Learning methods

Authors: Raffaello, Foldes; Alfredo, Del Corpo; Ermanno, Pietropaolo; Massimo, Vellante;

Field Line Resonances estimated using Machine Learning methods

Abstract

This data set contains the machine learning input matrix (composed by 1D Fourier cross-spectra) + additional information, for the Classification algorithm implemented in Foldes et al. (Automatic Detection of Field Line Resonance Frequencies in the Earth’s Plasmasphere, 2023) for the pair of station Tartu-Birzai (TAR-BRZ). Each file contains the following header at line 1. Columns are: - P(f0)-P(f211): Cross-phase value per frequency bin - YEAR - DOY (Day Of Year) - HOUR - ToD_flag: "Umbra", "Penumbra", 'Light' - L: McIllwain parameter - stat_tag: "tarbrz" - Kp - Kp_w_05d: Kp index weighted on a 12hrs time window - Kp_w_10d: Kp index weighted on a 24hrs time window - Kp_w_15d: Kp index weighted on a 36hrs time window - Kp_w_20d: Kp index weighted on a 2-day time window - Kp_w_25d: Kp index weighted on a 2.5-day time window - Kp_w_30d: Kp index weighted on a 3-day time window - Kp_m_05d: Kp index max on a 12hrs time window - Kp_m_10d: Kp index max on a 24hrs time window - Kp_m_15d: Kp index max on a 36hrs time window - Kp_m_20d: Kp index max on a 2-day time window - Kp_m_25d: Kp index max on a 2.5-day time window - Kp_m_30d: Kp index max on a 3-day time window - DST - DST_m_05d: DST index min on a 12hrs time window - DST_m_10d: DST index min on a 24hrs time window - DST_m_15d: DST index min on a 36hrs time window - DST_m_20d: DST index min on a 2-day time window - DST_m_25d: DST index min on a 2.5-day time window - DST_m_30d: DST index min on a 3-day time window - F107: F10.7 solar activity proxy - EField: Earth Electric co-rotation field - f(mHz): FLR frequency in mHz - df(mHz): Uncertainty on the validated frequency - class: 0 for "NoFreq", 1 for "Freq" and 2 for "PBL"

Related Organizations
Keywords

machine learning, plasmasphere, FLR frequencies, field line resonance, supervised learning, regression problem, plasmaspheric density

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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