arXiv subjects

Xiaoli Bai

Publications and source records attributed to Xiaoli Bai.

7 recordsLinked to original sources

arXiv2026

AETHER-P3 Nowcast v1.0: Model Description, Training-Data Construction, and Validation Technical report prepared in support of CCMC onboarding

AETHER-P\textsuperscript{3} Nowcast v1.0 is a machine-learning-based global thermospheric neutral-density model developed for low-Earth-orbit applications and prepared for onboarding to NASA's Community Coordinated Modeling Center (CCMC). The model provides pointwise neutral-density estimates together with predictive uncertainty using a deep evidential regression framework driven by causal solar, solar-wind, geomagnetic, spatial, temporal, and empirical-model inputs. This report documents the released model configuration, training-data construction, software traceability, output products, validation strategy, benchmark performance, and known limitations. The training archive combines accelerometer- and mission-derived density observations from CHAMP, GRACE-A, GOCE, Swarm-C, and GRACE-FO spanning 2000--2023. More than 40 million eligible 30-s observations are available, but the archive is strongly dominated by consecutive quiet-time measurements. To preserve coverage of physically important regimes, the final 1.67-million-sample training set is constructed using deterministic regime-aware sampling that progressively subsamples quiet conditions while retaining all available extreme-condition observations. Validation uses temporally disjoint chronological blocks with exclusion guards to reduce information leakage. Evaluation across quiet, moderate, and extreme conditions shows competitive performance relative to HASDM, JB2008, NRLMSISE-00, and available WAM-IPE cases, while also identifying limitations associated with sparse training coverage, mission-dependent density products, and condition-dependent uncertainty calibration. The report provides a reproducible technical description of the AETHER-P\textsuperscript{3} Nowcast v1.0 research release and its current CCMC onboarding configuration.

physics.space-ph↗

arXiv2026

A Machine-Learning-Based Global Thermospheric Density Forecasting Model

Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER-P3 (Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER-P3 formulates thermospheric density forecasting as a sequence-to-sequence regression task conditioned on recent space weather evolution and a user-specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER-P3 incorporates JB2008 and NRLMSISE-00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar-wind drivers. The network employs dual recurrent encoders and an evidential Normal-Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER-P3 achieves high forecast skill (R=0.95). Under moderate activity, strong skill is retained (R=0.93), with reduced physical-domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust (R=0.89-0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER-P3 as a practical, low-latency, uncertainty-aware capability for thermospheric density forecasting that supports orbit prediction, drag-risk assessment, and operational decision-making over its validated altitude range of approximately 300-520 km, with highest confidence in the data-rich 400-520 km region.

physics.space-ph↗

arXiv2026

Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning

The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensitive and could improve the reaction of thermospheric density to solar stimulation. In this study, we present a new deep learning model, named the Solar Index Network, or SINet for short, to predict daily values of the F10.7 and F30 solar indices. The SINet model is designed to make medium-term predictions of the index values (1-60 days in advance). The observed data used for SINet training were taken from the National Oceanic and Atmospheric Administration (NOAA) as well as Toyokawa and Nobeyama facilities. Our experimental results show that SINet performs better than five closely related statistical and deep learning methods for the prediction of F10.7. Furthermore, to our knowledge, this is the first time deep learning has been used to predict the F30 solar index.

astro-ph.SR↗

arXiv2025

Imitation Learning for Satellite Attitude Control under Unknown Perturbations

This paper presents a novel satellite attitude control framework that integrates Soft Actor-Critic (SAC) reinforcement learning with Generative Adversarial Imitation Learning (GAIL) to achieve robust performance under various unknown perturbations. Traditional control techniques often rely on precise system models and are sensitive to parameter uncertainties and external perturbations. To overcome these limitations, we first develop a SAC-based expert controller that demonstrates improved resilience against actuator failures, sensor noise, and attitude misalignments, outperforming our previous results in several challenging scenarios. We then use GAIL to train a learner policy that imitates the expert's trajectories, thereby reducing training costs and improving generalization through expert demonstrations. Preliminary experiments under single and combined perturbations show that the SAC expert can rotate the antenna to a specified direction and keep the antenna orientation reliably stable in most of the listed perturbations. Additionally, the GAIL learner can imitate most of the features from the trajectories generated by the SAC expert. Comparative evaluations and ablation studies confirm the effectiveness of the SAC algorithm and reward shaping. The integration of GAIL further reduces sample complexity and demonstrates promising imitation capabilities, paving the way for more intelligent and autonomous spacecraft control systems.

eess.SY↗

arXiv2020

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

astro-ph.SR↗

arXiv2019

Modeling and Simulation of UAV Carrier Landings

With UAVs promising capabilities to increase operation flexibility and reduce mission cost, we are exploiting the automated carrier-landing performance advancement that can be achieved by fixed-wing UAVs. To demonstrate such potentials, in this paper, we investigate two key metrics, namely, flight path control performance, and reduced approach speeds for UAVs based on the F/A-18 High Angle of Attack (HARV) model. The landing control architecture consists of an auto-throttle, a stability augmentation system, glideslope and approach track controllers. The performance of the control model is tested using Monte Carlo simulations under a range of environmental uncertainties including atmospheric turbulence consisting of wind shear, discrete and continuous wind gusts, and carrier airwakes. Realistic deck motion is considered where the standard deck motion time histories under the Systematic Characterization of the Naval Environment (SCONE) program released by the Office of Naval Research (ONR) are used. We numerically demonstrate the limiting approach conditions which allow for successful carrier landings and factors affecting it's performance.

eess.SY↗

arXiv2018

Improving Orbit Prediction Accuracy through Supervised Machine Learning

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: 1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; 2) the ML model can be used to improve predictions of the same RSO at future epochs; and 3) the ML model based on a RSO can be applied to other RSOs that share some common features.

astro-ph.EP↗