Jianqiao Xu | Searcharxiv

arXiv2026

Analysis of Tidal Perturbations Due to Asymmetric Response of LARES 2 and LAGEOS

Earth tidal perturbations affecting laser-ranged satellites are critical for refining satellite orbital dynamics modeling, and their accurate computation represents a prerequisite for high-precision fundamental physical effects and geodetic investigations based on satellite orbit analysis. This study focuses on the tidal perturbations induced by the asymmetric responses of LARES 2 and LAGEOS on their orbital nodes and inclinations. Perturbations induced by a total of 402 (392 2nd and 10 3rd-order) earth tide constituents on the two satellites were calculated, based on Kaula's orbital perturbation theory and Lagrange's planetary equations for satellites, considering the frequency dependence of Love numbers. The asymmetric characteristics of tidal perturbations between the two satellites were quantitatively analyzed. The minimum resolutions of orbital inclinations and nodes, used as screening thresholds for significant constituents, were derived from the RMS of overlapping orbit differences using orbital geometry and error propagation law. With these thresholds, 83 significant constituents were identified from the 402. The cumulative effect of the 319 minor constituents was further evaluated, and it was found that their total impact, from coherent superposition, noticeably exceeds the thresholds, thus becoming non-negligible. The results of this study provide accurate tidal perturbation parameters for LARES 2 and LAGEOS, and offer methodological references for the screening of Earth tide constituents in high-precision satellite orbital dynamics research, laying a foundation for subsequent studies on inverting geophysical parameters from satellite orbits and verifying fundamental physical effects, particularly the relativistic Lense-Thirring effect.

gr-qc↗

arXiv2023

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN.

cs.NI↗