Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Licata, Richard"'
Autor:
Licata, Richard J., Mehta, Piyush M.
The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that
Externí odkaz:
http://arxiv.org/abs/2211.04392
For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space situational awareness activities such as the satellite conjunction analysis. This p
Externí odkaz:
http://arxiv.org/abs/2210.16992
Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating
Externí odkaz:
http://arxiv.org/abs/2210.08364
Autor:
Tobiska, W. Kent, Pilinski, Marcin D., Mutschler, Shaylah, Wahl, Kaiya, Yoshii, Jean, Bouwer, Dave, Mehta, Piyush, Licata, Richard
With more commercial constellations planned, the number of Low Earth Orbit (LEO) objects is set to TRIPLE in two years. The growth in LEO objects directly increases the probability of unintentional collisions between objects due to accumulating space
Externí odkaz:
http://arxiv.org/abs/2209.05597
The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 pr
Externí odkaz:
http://arxiv.org/abs/2208.11619
Autor:
Licata, Richard J., Mehta, Piyush M., Weimer, Daniel R., Drob, Douglas P., Tobiska, W. Kent, Yoshii, Jean
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related t
Externí odkaz:
http://arxiv.org/abs/2206.05824
Autor:
Licata, Richard J., Mehta, Piyush M.
Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and Earth. These sy
Externí odkaz:
http://arxiv.org/abs/2201.02067
Autor:
Oliveira, Denny M., Zesta, Eftyhia, Mehta, Piyush M., Licata, Richard J., Pilinski, Marcin D., Tobiska, W. Kent, Hayakawa, Hisashi
Publikováno v:
Published in Frontiers in Astronomy and Space Sciences (2021)
Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of
Externí odkaz:
http://arxiv.org/abs/2110.04360
The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Space Environment Technologies (SET), contains 20 years of outputs from
Externí odkaz:
http://arxiv.org/abs/2109.07651
Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serve as proxies for various processes that cause energy flow and depositi
Externí odkaz:
http://arxiv.org/abs/2003.04743