English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ambitious satellite constellation projects by commercial entities and theease of access to space in recent times have led to a dramatic proliferation oflow-Earth space traffic. It jeopardizes space safety and long-termsustainability, necessitating better space traffic management (STM). Correctmodeling of uncertainties in force models and orbital states, among otherthings, is an essential part of STM. For objects in the low-Earth orbit (LEO)region, the uncertainty in the orbital dynamics mainly emanate from limitedknowledge of the atmospheric drag-related parameters and variables. In thispaper, which extends the work by Paul et al. [2021], we develop a feed-forwarddeep neural network model for the prediction of the satellite drag coefficientfor the full range of satellite attitude (i.e., satellite pitch $\in$ ($-90^0$,$+90^0$) and satellite yaw $\in$ ($0^0$, $+360^0$)). The model simultaneouslypredicts the mean and the standard deviation and is well-calibrated. We usenumerically simulated physical drag coefficient data for training our neuralnetwork. The numerical simulations are carried out using the test particleMonte Carlo method using the diffuse reflection with incomplete accommodationgas-surface interaction model. Modeling is carried out for the well-knownCHAllenging Minisatellite Payload (CHAMP) satellite. Finally, we use the MonteCarlo approach to propagate CHAMP over a three-day period under variousmodeling scenarios to investigate the distribution of radial, in-track, andcross-track orbital errors caused by drag coefficient uncertainty.

Stochastic modeling of physical drag coefficient -- its impact on orbit prediction and space traffic management