Adaptive Forecasting of High-Energy Electron Flux at Geostationary Orbit Using ADALINE Neural Network

2009 ◽  
pp. 797-804 ◽  
Author(s):  
Masahiro Tokumitsu ◽  
Yoshiteru Ishida ◽  
Shinichi Watari ◽  
Kentarou Kitamura
Keyword(s):  
Neural Network ◽  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
Geostationary Orbit ◽  
High Energy Electron ◽  
Adaline Neural Network

scholarly journals Forecast of High-energy Electron Flux at Geostationary Orbit Using Neural Network

2009 ◽  
Vol 7 (ists26) ◽  
pp. Tr_2_47-Tr_2_51 ◽  
Author(s):  
Shinichi WATARI ◽  
Masahiro TOKUMITSU ◽  
Kentarou KITAMURA ◽  
Yoshiteru ISHIDA
Keyword(s):  
Neural Network ◽  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
Geostationary Orbit ◽  
High Energy Electron

Formation of Luminescing High-Temperature Ceramics upon Exposure to Powerful High-Energy Electron Flux

2021 ◽  
Vol 63 (9) ◽  
pp. 1615-1621
Author(s):  
V. M. Lisitsyn ◽  
L. A. Lisitsyna ◽  
M. G. Golkovskii ◽  
D. A. Musakhanov ◽  
A. V. Ermolaev
Keyword(s):  
High Temperature ◽  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
High Energy Electron

scholarly journals Pc5 modulation of high energy electron precipitation: particle interaction regions and scattering efficiency

2005 ◽  
Vol 23 (5) ◽  
pp. 1533-1542 ◽  
Author(s):  
E. Spanswick ◽  
E. Donovan ◽  
G. Baker
Keyword(s):  
Field Line ◽  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
Electron Precipitation ◽  
Necessary Condition ◽  
High Energy Electron ◽  
Magnetic Pulsation ◽  
Field Line Resonances ◽  
Morning Sector

Abstract. Using the NORSTAR riometer and CANOPUS magnetometer arrays we have investigated the modulation of high energy electron precipitation by ULF waves in the Pc5 frequency band. We conducted two separate studies of Pc5 activity in the riometers. The first is an independent survey of three riometer stations in the Churchill line (one at each sub-auroral, auroral, and typical polar cap boundary latitudes) in which we identified all riometer Pc5-band pulsations over 11 years. All had a corresponding magnetometer pulsation implying that a magnetic pulsation, is a necessary condition for a riometer pulsation (in the Pc5 Band). We find seasonal and latitude dependencies in the occurrence of riometer pulsations. By a factor of two, there are more riometer pulsations occurring in the fall-winter than the spring-summer. At higher latitudes there is a tendency towards noon pulsations during the spring-summer, suggesting that the criteria for riometer pulsations is affected by the dipole tilt. Our second study was based on the previous magnetometer study of Baker et al. (2003). Using the database of Pc5 activity from that study we were able to select the riometer Pc5 pulsations which adhere to the strict Pc5 definition in the magnetometer. We find that roughly 95% of the riometer pulsations occurred in the morning sector compared to 70% in the magnetometer. Given a magnetometer pulsation at Gillam in the morning sector, there is a 70% chance of there being a corresponding riometer pulsation. The morning sector probabilities at Rankin (geomagnetic (PACE) latitude 74°) and Pinawa (61°) are 3% and 5%, respectively. These statistics suggest there is a localized region in the pre-noon magnetosphere where Pc5 band ULF activity can modulate high energy electron precipitation. We also find that riometer pulsations display a Kp selection towards mid (i.e. 3–4) activity levels which mimics the product of the Kp dependence of high-energy electron fluxes on the dawn side (from CRRES) and all magnetic Pc5 activity. A superposed epoch analysis revealed that the elevated electron flux needed to produce a riometer pulsation is most likely provided by substorm injections on the nightside. We also find that the amplitude of modulated precipitation correlates well with the product of the background absorption and the magnetic pulsation amplitude, again leading to the idea that a riometer pulsation needs both favorable magnetospheric electron flux conditions and large enough magnetic Pc5 wave activity. We further separate our pulsations into field line resonances (FLRs), and non-field line resonances (non-FLRs), as identified in the Baker et al. (2003) survey. We find that FLRs are more efficient at modulating particle precipitation, and non-FLRs display an amplitude cutoff below which they do not interact with the high energy electron population. We conclude that the high energy electron precipitation associated with Pc5 pulsations is caused by pitch angle scattering (diffusion) rather than parallel acceleration. We suggest two future studies that are natural extensions of this one. Keywords. Energetic Particles/Precipitating; Wave-Particle Interactions; Auroral Phenomena

Double Probe Characteristics in High-Energy Electron Flux Presence

2008 ◽  
Vol 47 (9) ◽  
pp. 7334-7338 ◽  
Author(s):  
Ján Husárik ◽  
Milan Širý ◽  
Masashi Kando
Keyword(s):  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
High Energy Electron ◽  
Double Probe

Mid-latitude OI green emission and high-energy electron flux

1968 ◽  
Vol 16 (11) ◽  
pp. 1418-1419 ◽  
Author(s):  
R.O. Waddoups ◽  
D.J. Baker
Keyword(s):  
Electron Flux ◽  
Green Emission ◽  
High Energy ◽  
Energy Electron ◽  
High Energy Electron

scholarly journals Machine Learning for the Relationship of High-Energy Electron Flux between GEO and MEO with Application to Missing Values Imputation for Beidou MEO Data

2021 ◽  
Vol 30 (1) ◽  
pp. 62-72
Author(s):  
Ruifei Cui ◽  
Yu Jiang ◽  
Chao Tian ◽  
Riwei Zhang ◽  
Sihui Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Electron Flux ◽  
Original Data ◽  
High Energy ◽  
Energy Electron ◽  
Earth Orbit ◽  
High Energy Electron ◽  
Relationship Of ◽  
The Relationship

Abstract We consider the problem of building the relationship of high-energy electron flux between Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO). A time-series decomposition technique is first applied to the original data, resulting in trend and detrended part for both GEO and MEO data. Then we predict MEO trend with GEO data using three machine learning models: Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Experiment shows that RF gains best performance in all scenarios. Feature extraction analysis demonstrates that the inclusion of lagged features and (possible) ahead features is substantially helpful to the prediction. At last, an application of imputing missing values for MEO data is presented, in which RF model with selected features is used to handle the trend part while a moving block method is for the detrended part.

Variations of the High Energy Electron Flux Along the Ulysses Trajectory

1995 ◽  
pp. 409-414
Author(s):  
A. Raviart ◽  
R. Ducros ◽  
P. Ferrando ◽  
C. Rastoin ◽  
B. Heber ◽  
...  
Keyword(s):  
Electron Flux ◽  
High Energy ◽  
Energy Electron ◽  
High Energy Electron

scholarly journals Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning

Space Weather ◽  
2018 ◽  
Vol 16 (7) ◽  
pp. 903-916 ◽  
Author(s):  
Lihang Wei ◽  
Qiuzhen Zhong ◽  
Ruilin Lin ◽  
Jingjing Wang ◽  
Siqing Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Energy ◽  
Energy Electron ◽  
Geostationary Orbit ◽  
Quantitative Prediction ◽  
High Energy Electron ◽  
Integral Flux
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