scholarly journals The dynamics of Van Allen belts revisited

2018 ◽  
Vol 14 (2) ◽  
pp. 102-103 ◽  
Author(s):  
Yuri Y. Shprits ◽  
Richard B. Horne ◽  
Adam C. Kellerman ◽  
Alexander Y. Drozdov
Keyword(s):  
Van Allen Belts

Investigation of the van allen belts in the region of the brazilian magnetic anomaly at altitudes of 235–345 km

1965 ◽  
Vol 13 (4) ◽  
pp. 347-351
Author(s):  
S.N. Vernov ◽  
V.E. Nestrov ◽  
N.F. Pisarenko ◽  
I.A. Savenko ◽  
O.I. Savun ◽  
...  
Keyword(s):  
Magnetic Anomaly ◽  
Van Allen Belts

The HEPD particle detector of the CSES satellite mission for investigating seismo-associated perturbations of the Van Allen belts

2018 ◽  
Vol 61 (5) ◽  
pp. 643-652 ◽  
Author(s):  
Giovanni Ambrosi ◽  
Simona Bartocci ◽  
Laurent Basara ◽  
Roberto Battiston ◽  
William J. Burger ◽  
...  
Keyword(s):  
Particle Detector ◽  
Satellite Mission ◽  
Van Allen Belts

Boundaries and enhancements: ULF wave-driven dynamics of energetic particles in the Van Allen belts

2021 ◽  
Author(s):  
Allison N. Javnes ◽  
Jayasri Joseph ◽  
Joshua Doucette ◽  
Daniel N. Baker ◽  
Xinlin Li ◽  
...  
Keyword(s):  
Energetic Particles ◽  
Ulf Wave ◽  
Van Allen Belts

scholarly journals Data-driven modelling of the Van Allen Belts: The 5DRBM model for trapped electrons

2019 ◽  
Vol 64 (9) ◽  
pp. 1701-1711
Author(s):  
Lionel Métrailler ◽  
Guillaume Bélanger ◽  
Peter Kretschmar ◽  
Erik Kuulkers ◽  
Ricardo Pérez Martínez ◽  
...  
Keyword(s):  
Data Driven ◽  
Trapped Electrons ◽  
Van Allen Belts

X-rays from van allen belts around a magnetic neutron star

1965 ◽  
Vol 39 (1) ◽  
pp. 151-156
Author(s):  
V. de Sabbata ◽  
C. Gualdi
Keyword(s):  
Neutron Star ◽  
X Rays ◽  
Van Allen Belts

scholarly journals How Earth’s Outer Radiation Belts Lose Their Electrons

Eos ◽  
2017 ◽  
Author(s):  
Emily Underwood
Keyword(s):  
Radiation Belts ◽  
Particle Loss ◽  
Van Allen Belts ◽  
Three Space

A new analysis of three space storms reveals the mechanisms of particle loss from the Van Allen belts.

High-Voltage Space Tether for Enhanced Particle Scattering in van Allen Belts

1999 ◽  
Vol 35 (1T) ◽  
pp. 312-314 ◽  
Author(s):  
Valentin V. Danilov ◽  
Vladimir V. Mirnov ◽  
Defne Üçer
Keyword(s):  
High Voltage ◽  
Particle Scattering ◽  
Space Tether ◽  
Van Allen Belts

On the possibility of detecting synchrotron radiation from electrons in the Van Allen belts

1959 ◽  
Vol 64 (9) ◽  
pp. 1163-1168 ◽  
Author(s):  
R. B. Dyce ◽  
M. P. Nakada
Keyword(s):  
Synchrotron Radiation ◽  
Van Allen Belts

scholarly journals An hybrid neuro-wavelet approach for long-term prediction of solar wind

2010 ◽  
Vol 6 (S274) ◽  
pp. 153-155 ◽  
Author(s):  
Christian Napoli ◽  
Francesco Bonanno ◽  
Giacomo Capizzi
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Wavelet Decomposition ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Correct Prediction ◽  
Data Series ◽  
Intrinsic Structure ◽  
Data Prediction ◽  
Van Allen Belts

AbstractNowadays the interest for space weather and solar wind forecasting is increasing to become a main relevance problem especially for telecommunication industry, military, and for scientific research. At present the goal for weather forecasting reach the ultimate high ground of the cosmos where the environment can affect the technological instrumentation. Some interests then rise about the correct prediction of space events, like ionized turbulence in the ionosphere or impacts from the energetic particles in the Van Allen belts, then of the intensity and features of the solar wind and magnetospheric response. The problem of data prediction can be faced using hybrid computation methods so as wavelet decomposition and recurrent neural networks (RNNs). Wavelet analysis was used in order to reduce the data redundancies so obtaining representation which can express their intrinsic structure. The main advantage of the wavelet use is the ability to pack the energy of a signal, and in turn the relevant carried informations, in few significant uncoupled coefficients. Neural networks (NNs) are a promising technique to exploit the complexity of non-linear data correlation. To obtain a correct prediction of solar wind an RNN was designed starting on the data series. As reported in literature, because of the temporal memory of the data an Adaptative Amplitude Real Time Recurrent Learning algorithm was used for a full connected RNN with temporal delays. The inputs for the RNN were given by the set of coefficients coming from the biorthogonal wavelet decomposition of the solar wind velocity time series. The experimental data were collected during the NASA mission WIND. It is a spin stabilized spacecraft launched in 1994 in a halo orbit around the L1 point. The data are provided by the SWE, a subsystem of the main craft designed to measure the flux of thermal protons and positive ions.

Sign in / Sign up

Export Citation Format

Share Document