arXiv subjects

Yu-Qiang Li

Publications and source records attributed to Yu-Qiang Li.

3 recordsLinked to original sources

arXiv2026

Distribution and Evolution of the Debris Cloud from the Fragmentation of Intelsat 33E

The breakup of Intelsat 33E on 19 October 2024 posed a potential risk to satellites in the Geostationary Earth Orbit (GEO). This study analyzes the evolution and distribution of these fragments using a probabilistic approach. The initial distribution of the fragments, derived from the NASA Standard Breakup Model, indicates the generation of 4,393 fragments larger than 1 centimeter. The spatial propagation of these fragments is modeled analytically in the Earth-Centered Earth-Fixed reference frame, showing the formation of high-density ring structures in the equatorial plane from 24 hours to 28 days after the breakup. The orbits of 36 cataloged fragments are retrieved and compared with the probability density. Furthermore, Monte Carlo simulations validate the probabilistic model and highlight its efficiency in capturing low-probability events. Collision risks to other GEO satellites are assessed, showing that the top 10\% of satellites encounter a collision probability of up to $10^{-8}$ after 28 days. Satellites near the equatorial plane are at higher risk, whereas those with higher inclinations are less affected. These findings underscore the need for enhanced monitoring and mitigation strategies for GEO breakup events, given the challenges in detecting small fragments.

astro-ph.EP↗

arXiv2024

Short-Term Evolution and Risks of Debris Cloud Stemming from Collisions in Geostationary Orbit

The increasing population of objects in geostationary orbit has raised concerns about the potential risks posed by debris clouds resulting from fragmentation. The short-term evolution and associated hazards of debris generated by collisions in the geostationary region is investigated in this study. The initial distribution of two debris clouds is modeled using a single probability density function. The combined distribution of the evolved clouds is determined by solving boundary value problems. The risks associated with these debris clouds are evaluated by calculating the instantaneous impact rate and cumulative collision probability. The probability of collisions with millimeter-sized fragments may increase to 1% within 36 hours, while the probability of collisions with fragments 5 cm or larger is approximately $10^{-5}$. These findings underscore the vulnerability of the geostationary region to space traffic accidents.

astro-ph.EP↗

arXiv2024

Machine Learning-Based Identification of Contaminated Images in Light Curves Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these outliers to obtain high quality light curves. Through statistical analysis, the reasons leading to outliers can be categorized into two main types: first, the brightness of the object significantly increases due to the passage of a star nearby, referred to as "stellar contamination," and second, the brightness markedly decreases due to cloudy cover, referred to as "cloudy contamination." Traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive. However, We propose the utilization of machine learning methods as a substitute. Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) are employed to identify cases of stellar contamination and cloudy contamination, achieving F1 scores of 1.00 and 0.98 on test set, respectively. We also explored other machine learning methods such as Residual Network-18 (ResNet-18) and Light Gradient Boosting Machine (lightGBM), then conducted comparative analyses of the results.

astro-ph.IM↗