Results 271 to 280 of about 246,893 (375)
Geospace environment modeling 2008–2009 challenge: Dst index
L. Rastätter +17 more
semanticscholar +1 more source
Applying Hpo Indices to Empirical Thermospheric Density Models During Geomagnetic Storms
Abstract Accurate atmospheric drag modeling is essential for precise orbit determination and prediction of Low Earth Orbit satellites. A key component is the thermospheric density, typically estimated using empirical models driven by geomagnetic activity indices such as the 3‐hr Kp or ap.
Kemin Zhu +5 more
wiley +1 more source
The Anemomilos prediction methodology for Dst
W. Tobiska +8 more
semanticscholar +1 more source
Abstract The May 2024 superstorm, as the most intense geomagnetic storm since 2003, caused a variety of disturbances in the magnetosphere‐ionosphere‐thermosphere system. This study investigates the long‐lasting electron density depletion in the polar region and the underlying ionosphere‐thermosphere coupling, based on a comprehensive set of ...
Lei Cai +8 more
wiley +1 more source
The Relationship Between Bone Health Status of Post-Menopausal Women with Non-Functional Adrenal Tumours/Mild Autonomous Cortisol Secretion and Their Baseline Morning Adrenocorticotropic Level. [PDF]
Trandafir AI +5 more
europepmc +1 more source
Abstract Whistler‐mode chorus waves play a key role in driving radiation belt dynamics by enabling both acceleration of electrons to relativistic energies as well as their loss into the atmosphere via pitch‐angle scattering. The ratio between the electron plasma frequency (fpe ${f}_{pe}$) and the electron gyrofrequency (fce ${f}_{ce}$) significantly ...
K. A. Bunting +5 more
wiley +1 more source
Computational modeling of the temporal influences between cues, craving and use in addiction: a dynamical system analysis based on ecological momentary assessment data. [PDF]
Gauld C +3 more
europepmc +1 more source
Requirements for Diluting & Flushing Double Shell Tank (DST) Systems
D.P. FASSETT
openalex +2 more sources
Forecasting Local Ionospheric Parameters Using Transformers
Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago +4 more
wiley +1 more source

