scholarly journals Metrics analysis of the coupled Block Adaptive-Tree Solar Wind Roe-Type Upwind Scheme and Fok ring current model performance

Space Weather ◽  
2007 ◽  
Vol 5 (11) ◽  
pp. n/a-n/a ◽  
Author(s):  
A. Taktakishvili ◽  
M. Kuznetsova ◽  
M. Hesse ◽  
L. Rastätter ◽  
A. Chulaki ◽  
...  
Keyword(s):  
Solar Wind ◽  
Ring Current ◽  
Current Model ◽  
Model Performance ◽  
Upwind Scheme

Saturn’s ring current observed during Cassini’s Grand Finale

2020 ◽  
Author(s):  
Gabrielle Provan ◽  
Tom Bradley ◽  
Emma Bunce ◽  
Stan Cowley ◽  
Michele Dougherty ◽  
...  
Keyword(s):  
Solar Wind ◽  
Current Density ◽  
Ring Current ◽  
Current System ◽  
Current Model ◽  
Internal Field ◽  
Total Current ◽  
Magnetometer Data ◽  
Proton Data ◽  
Azimuthal Current

<p>The presence of a substantial azimuthal current sheet in Saturn’s magnetosphere was identified in Voyager and Pioneer magnetometer data.  Data from these spacecraft showed depressions in the strength of the field below that expected for the internal field of the planet alone.  This ring current was  modelled  as a simple axisymmetric current system by Connerney et al. [1980, 1983].  In this study we utilise the Connerney ring current model to look at the size, shape, current density and total current of Saturn’s ring current as observed during the Cassini proximal orbits.  We compare the variations in these parameters with the phases of the planetary period oscillations and with the occurrence of magnetospheric storms as determined from propagated solar wind data and LEMMS electron and proton data. Overall, we find that Saturn’s ring current is a dynamical environment which varies in size and magnitude due to  both  planetary period oscillations and solar-driven storms.  </p>

Ring current model of the naphthalene molecule

1997 ◽  
Vol 92 (3) ◽  
pp. 609-617 ◽  
Author(s):  
RICCARDO ZANASI ◽  
PAOLO LAZZERETTI
Keyword(s):  
Ring Current ◽  
Current Model ◽  
Naphthalene Molecule

scholarly journals Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

2010 ◽  
Vol 28 (2) ◽  
pp. 381-393 ◽  
Author(s):  
L. Cai ◽  
S. Y. Ma ◽  
Y. L. Zhou
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Correlation Coefficient ◽  
Characteristic Time ◽  
Ring Current ◽  
Wind Data ◽  
Distinct Advantage ◽  
Elman Network ◽  
H Index ◽  
Narx Neural Network

Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

scholarly journals Magnetospheric Response to Solar Wind Forcing: ULF Wave – Particle Interaction Perspective

2021 ◽  
Author(s):  
Qiugang Zong
Keyword(s):  
Solar Wind ◽  
Radiation Belt ◽  
Ring Current ◽  
Dynamic Pressure ◽  
Plasma Heating ◽  
Interplanetary Shock ◽  
Wind Forcing ◽  
Ulf Waves ◽  
Radiation Belt Electrons ◽  
The Impact

Abstract. Solar wind forcing, e.g. interplanetary shock and/or solar wind dynamic pressure pulses impact on the Earth’s magnetosphere manifests many fundamental important space physics phenomena including producing electromagnetic waves, plasma heating and energetic particle acceleration. This paper summarizes our present understanding of the magnetospheric response to solar wind forcing in the aspects of radiation belt electrons, ring current ions and plasmaspheric plasma physics based on in situ spacecraft measurements, ground-based magnetometer data, MHD and kinetic simulations. Magnetosphere response to solar wind forcing, is not just a “one-kick” scenario. It is found that after the impact of solar wind forcing on the Earth’s magnetosphere, plasma heating and energetic particle acceleration started nearly immediately and could last for a few hours. Even a small dynamic pressure change of interplanetary shock or solar wind pressure pulse can play a non-negligible role in magnetospheric physics. The impact leads to generate series kind of waves including poloidal mode ultra-low frequency (ULF) waves. The fast acceleration of energetic electrons in the radiation belt and energetic ions in the ring current region response to the impact usually contains two contributing steps: (1) the initial adiabatic acceleration due to the magnetospheric compression; (2) followed by the wave-particle resonant acceleration dominated by global or localized poloidal ULF waves excited at various L-shells. Generalized theory of drift and drift-bounce resonance with growth or decay localized ULF waves has been developed to explain in situ spacecraft observations. The wave related observational features like distorted energy spectrum, boomerang and fishbone pitch angle distributions of radiation belt electrons, ring current ions and plasmaspheric plasma can be explained in the frame work of this generalized theory. It is worthy to point out here that poloidal ULF waves are much more efficient to accelerate and modulate electrons (fundamental mode) in the radiation belt and charged ions (second harmonic) in the ring current region. The results presented in this paper can be widely used in solar wind interacting with other planets such as Mercury, Jupiter, Saturn, Uranus and Neptune, and other astrophysical objects with magnetic fields.

Graph Convolutional Networks for Exercise Motion Classification

2021 ◽  
Vol 65 (1) ◽  
pp. 685-689
Author(s):  
Parian Haghighat ◽  
Aden Prince ◽  
Heejin Jeong
Keyword(s):  
Nearest Neighbor ◽  
Current Model ◽  
Model Performance ◽  
Classification Performance ◽  
Great Promise ◽  
Graph Classification ◽  
Convolutional Network ◽  
Motion Data ◽  
Convolutional Networks ◽  
Personal Fitness

The growth in self-fitness mobile applications has encouraged people to turn to personal fitness, which entails integrating self-tracking applications with exercise motion data to reduce fatigue and mitigate the risk of injury. The advancements in computer vision and motion capture technologies hold great promise to improve exercise classification performance. This study investigates a supervised deep learning model performance, Graph Convolutional Network (GCN) to classify three workouts using the Azure Kinect device’s motion data. The model defines the skeleton as a graph and combines GCN layers, a readout layer, and multi-layer perceptrons to build an end-to-end framework for graph classification. The model achieves an accuracy of 95.86% in classifying 19,442 frames. The current model exchanges feature information between each joint and its 1-nearest neighbor, which impact fades in graph-level classification. Therefore, a future study on improved feature utilization can enhance the model performance in classifying inter-user exercise variation.

Spatial Ring Current Model for the Prismane Molecule

2008 ◽  
Vol 112 (23) ◽  
pp. 5175-5186 ◽  
Author(s):  
Stefano Pelloni ◽  
Paolo Lazzeretti
Keyword(s):  
Ring Current ◽  
Current Model
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