Periodic orbits in the Earth-Moon three-body system hold significant theoreticaland engineering values for deep space exploration. Solar sail spacecraft, which do not require propellant, show great potential for long-term space missions. Introducing the solar sail propulsion technology in the Earth-Moon three-body system can create more valuable orbits. In previous studies, the state transition matrices of dynamical systems are not found to include the solar sail acceleration term. In this paper, a new solar sail pointing law is proposed based on a simplified non-autonomous dynamical model for the solar sail Earth-Moon circular restricted three-body problem, making acceleration of the solar sail dependent on the sail state vector. On this basis, the Jacobian matrix of the system is derived. This pointing law is applied to the near rectilinear Halo orbit. Using an improved differential correction method in combination with a continuation on solar sail acceleration, new orbit families are generated. Numerical simulations verify the effectiveness of the pointing lawand numerical computation method. Analysis shows that the newly established solar sail periodic orbit families can be used for lunar illuminationand observation of high-latitude lunar regions. Finally, periodic orbits are migrated to a non-coplanar bi-elliptical perturbation model, and the quasi-periodic orbits under perturbation are obtained using the second-order differential correction method. It is shown through analysis that the orbits’ illuminationand coverage performance can be maintained under perturbation. Further calculations of the quasi-periodic orbits corresponding to different initial moments indicate that the closer the Earth-Moon Barycenter (EMB) and the Moon are to the perigee of their orbits, the greater the impact of perturbative factors on the orbit is.

Traveling Wave Tube (TWT) is a key component in communication satellites, typically operating in high-temperature and high-pressure environments for extended periods, making its degradation evaluation particularly important. Traditional mechanism models and threshold rule methods struggle to effectively represent degradation levels under complex electro-thermal coupling effects. To address this challenge, we propose a deep neural network degradation evaluation method based on a Multi-Scale Attention Encoder (MSA-Encoder). First, the complex characteristics of telemetry data are analyzed in detail, exploring the difficulties these characteristics pose for training deep learning models and providing solutions. Second, the MSA-Encoder network is designed, which precisely captures the temporal and channel dependencies in multi-dimensional telemetry data through auto-correlation and Global-Local Channel Attention (GLCA) mechanisms, respectively, achieving accurate data reconstruction. Third, we propose a TWT degradation index calculation method based on Mahalanobis distance and moving average seasonal decomposition. This method calculates data drift index through reconstruction difference theory, followed by moving average seasonal decomposition to compute the degradation index. Finally, using the full life cycle TWT data from a certain communication satellite, a TWT degradation dataset is constructed, and numerical experiments are conducted on this dataset. The experimental results show that the MSA-Encoder surpasses benchmark models of the same parameter scale in telemetry data extraction capability, proving the reliability of the degradation index calculation.

An attitude-orbit integrated controller with prescribed performance based on fully actuated system parameterization approach and adaptive neural network is proposed for close flying-around of the space target in the presence of inertia parameter uncertainty and orbit perturbation. The six degree of freedom motion of rigid spacecraft is derived under the framework of Lie group SE (3), and an accurate and concise attitude-orbit integrated error dynamic model is established. An exponential-form based performance function is introduced to perform prior and quantitative constraints on the dynamic and stable processes of attitude error and position error. Considering the nonlinear characteristics of the dynamic model, a feedback control term based on the fully actuated system approach is designed to obtain a constant linear closed-loop system with an arbitrarily assignable eigenstructure, thus reducing the difficulty of the subsequent controller design process. An adaptive neural network based integral sliding mode controller is further designed to compensate for the loss of control accuracy caused by inertia parameter uncertainty and orbit perturbation. In addition, to further enhance the engineering utility, an improved particle swarm optimization algorithm is developed to optimize the controller parameters. Numerical simulation results indicate that the high-precision attitude-orbit integrated flying-around is achieved without significant control chatter while satisfying the predesigned performance constraints, thus verifying the effectiveness and feasibility of the proposed controller.