A two-dimensional lithospheric magnetic anomaly field model of Egypt using the measurements from Swarm satellites

We use magnetic field data observed by the Swarm mission from 2014 to 2020 to construct,for the first time,a two-dimensional(2 D)lithospheric magnetic anomaly model of Egypt and its surrounding area.Nighttime data during quiet geomagnetic conditions has been expanded in terms of the Legendre polynomial in harmonic terms N=6-50.The damped least square method has been used to estimate the model coefficients based on the lithospheric magnetic data.Modeled data at two different altitudes(438-448 km and 503-511 km)were compared with the CHAOS model.Results exhibit that the 2 D model is superior to the CHAOS model in the capability of extracting more information about small-scale crustal anomaly field.At low altitudes(438-448 km),the strength of the anomaly field increases,but the noise of the external fields has greatly reduced at high altitudes(503-511 km).Besides,the magnetic anomaly field at low altitudes has illuminated short-scale anomalies that didn’t appear at high altitudes.Both the total and vertical magnetic anomaly vectors showed their ability to reveal tectonic structures compared with Moho depth map and the geological maps.

Full-State-Constrained Non-Certainty-Equivalent Adaptive Control for Satellite Swarm Subject to Input Fault

Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.

PARTICLE SWARM OPTIMIZATION BASED ON PYRAMID MODEL FOR SATELLITE MODULE LAYOUT

To improve the global search ability of particle swarm optimization （PSO）, a multi-population PSO based on pyramid model （PPSO） is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite （INTELSAT-Ⅲ） module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO （ring and Neumann PSO）. The results show that PPSO has higher computational accuracy, efficiency and success ratio.

Automatic Anomaly Detection for Swarm Observations

The Swarm satellite mission was launched on 22 November 2013,it is the first European Space Agency’s constellation of three satellites,dedicated to monitoring geomagnetic field changes.The measurements delivered by the three satellites are very valuable for a range of applications,including the earthquake prediction study.However,for more than 5 years,relatively little advancement has been achieved in establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes.This paper presents the challenges of developing a pragmatic framework for automatic anomaly detection and highlights innovative features of functional components developed.Through a case study we demonstrate a functionality pipeline of the system in detecting anomalies,and present our solutions to coping with data sparsity and parameter tuning as well as insights into the differences between discovering seismic anomalies from periodic and non-periodic data observed by the Swarm satellites.