Stochastic Modeling Of Physical Drag Coefficient – Its Impact On Orbit Prediction And Space Traffic Management

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 better space domain awareness (SDA). Correct modeling of uncertainties in force models and orbital states, among other things, is an essential part of SDA. For objects in the low-Earth orbit (LEO) region, the uncertainty in the orbital dynamics mainly emanate from limited knowledge of the atmospheric drag-related parameters and variables. In this paper, which extends the work by Paul et al. (2021), we develop a feed-forward deep neural network model for the prediction of the satellite drag coefficient for the full range of satellite attitude (i.e., satellite pitch ∈ (-90°, +90°) and satellite yaw ∈ (0°, +360°)). The model simultaneously predicts the mean and the standard deviation and is well-calibrated. We use numerically simulated physical drag coefficient data for training our neural network. The numerical simulations are carried out using the test particle Monte Carlo method using the diffuse reflection with incomplete accommodation gas-surface interaction model. Modeling is carried out for the well-known Challenging Minisatellite Payload (CHAMP) satellite. Finally, we use the Monte Carlo approach to propagate CHAMP over a three-day period under various modeling scenarios to investigate the distribution of radial, along-track, and cross-track orbital errors caused by drag coefficient uncertainty. The key takeaways of this paper are - (a) a constant drag coefficient cannot be used for reliable SDA purposes, and (b) stochastic machine learning models allow for the computation of drag coefficients in a timely manner while providing reliable uncertainty estimates

Updates And Improvements To The Satellite Drag Coefficient Response Surface Modeling Toolkit

For satellites in the Low Earth Orbit (LEO) region, the drag coefficient is a primary source of uncertainty for orbit determination and prediction. Researchers at the Los Alamos National Laboratory (LANL) have created the so-called Response Surface Modeling (RSM) toolkit to provide the community with a resource for simulating and modeling satellite drag coefficients for satellites with complex geometries (modeled using triangulated facets) in the free molecular flow (FMF) regime. The toolkit fits an interpolation surface using non-parametric Gaussian Process Regression (GPR) over drag coefficient data computed using the numerical Test Particle Monte Carlo (TPMC) method. The fitted response surface provides a substantial computational benefit over numerical approaches for calculating drag coefficients. In this work, the RSM toolkit is further developed into a versatile software with extended capabilities. The capabilities are specifically expanded to include uncertainty quantification and adaptation for automatic development of regression models for satellites with non-stationary components (e.g., rotating solar panels). Furthermore, the toolkit uses Python 3.x and C programming languages to provide an open-source software package with a OSI approved GPL license. To assist the end user, the new RSM toolkit has been developed to have a user-friendly installation process and is provided with extensive documentation. The analysis of two different conceptual satellites is performed during this work: a simple cube and a CubeSat consisting of a simple cube body with 2 rotating solar panels. During the creation of the regression model for each satellite for different atmospheric species, it is found that the cube\u27s minimum Root Mean Squared Error (RMSE) is 0.00211 and the maximum RMSE is 0.00350. The CubeSat has a minimum RMSE of 0.00304 and the maximum is 0.00498. These results are overall conducive of a well performing regression model

SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant

Updates and Improvements to the Satellite Drag Coefficient Response Surface Modeling Toolkit

For satellites in Low Earth Orbit, the drag coefficient is a major area of uncertainty. Researchers at the Los Alamos National Laboratory have created a Response Surface Modeling (RSM) toolkit to provide the community with a resource for simulating and modeling satellite drag coefficients in Free Molecular Flow. The toolkit combines the high fidelity of numerical simulation techniques with the speed of regression modeling. Specifically, it uses a training sample of drag coefficients simulated with the Test Particle Monte Carlo method with the robust Gaussian Process Regression approach. The RSM toolkit is the prime process to become a toolkit of other expanded capabilities. In this work, the capabilities are specifically expanded to include uncertainty quantification, utilizing Python, to create a free and open source software, and adapting the toolkit for automatic development of regression models of satellites with rotating components. The new RSM toolkit has been developed to have user friendly installation along with extensive documentation of operation

Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 7

SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

AbstractVaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant.</jats:p