2020
DOI: 10.1038/s41467-020-18545-y
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Very-Low-Frequency transmitters bifurcate energetic electron belt in near-earth space

Abstract: Very-Low-Frequency (VLF) transmitters operate worldwide mostly at frequencies of 10–30 kilohertz for submarine communications. While it has been of intense scientific interest and practical importance to understand whether VLF transmitters can affect the natural environment of charged energetic particles, for decades there remained little direct observational evidence that revealed the effects of these VLF transmitters in geospace. Here we report a radially bifurcated electron belt formation at energies of ten… Show more

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Cited by 47 publications
(68 citation statements)
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“…Figure 1h indicates the flux ratio, which is the smaller one of aforementioned ratio1 and ratio2. Overall, the flux ratio is above ∼0.8, which agrees with previous studies that the electron loss due to VLF scattering effects is a relatively slow process (Claudepierre et al, 2020a;Hua et al, 2020;Ross et al, 2019).…”
Section: Identification Criteria Of Bifurcated Inner Electron Beltsupporting
confidence: 91%
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“…Figure 1h indicates the flux ratio, which is the smaller one of aforementioned ratio1 and ratio2. Overall, the flux ratio is above ∼0.8, which agrees with previous studies that the electron loss due to VLF scattering effects is a relatively slow process (Claudepierre et al, 2020a;Hua et al, 2020;Ross et al, 2019).…”
Section: Identification Criteria Of Bifurcated Inner Electron Beltsupporting
confidence: 91%
“…4. The L-shell distribution of the bifurcation dip, energy, and seasonal dependences of the occurrence of bifurcated inner electron belt supports the important role of VLF transmitter signals in electron flux decay in the inner belt in addition to other naturally occurring waves (Hua et al, 2020). The present statistics is therefore crucial to understand when and where the VLF transmitters can efficiently scatter energetic electrons (at tens of keV) in near-Earth space.…”
Section: Conclusion and Discussionmentioning
confidence: 58%
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“…Nowadays artificial neural networks (ANN) (e.g., Hebb, 1949;Marr & Poggio, 1976;McCulloch & Pitts, 1943) are extensively used in the approximation of functions, classification, pattern recognition, and clustering. Neural networks are capable of mapping complicated nonlinear functions between inputs and outputs (Zhelavskaya et al, 2016).…”
Section: Neural Network and Input Selectionmentioning
confidence: 99%