Propellant–free station–keeping design of a solar sail around the Sun–Earth collinear equilibrium points

In this paper, we focus on the propellant–free station-keeping design of a solar sail spacecraft around the unstable Sun–Earth collinear equilibrium point L2. The dynamical model considered is the Sun–Earth restricted three-body problem, including the solar radiation pressure on a sail that depends on three parameters: its reflectivity and its attitude with respect to the Sun, expressed by means of two angles. In the libration zone, the solar sail maneuvers performed by means of changing the values of the sail parameters, can be understood as ”jumps” in position instead of in velocity inside the phase space. The paper uses this fact to systematically analyze the impact of a maneuver (an instantaneous sail reorientation) on a spacecraft moving along a libration point Lissajous orbit. The station–keeping strategy periodically performs maneuvers to prevent the spacecraft to escape from a certain Lissajous orbit following its unstable manifold. Random errors in the execution of the maneuvers are also considered.D.X. special thanks to all the colleagues from the News and Publicity Center of CNSA and the support of the Chinese Scholarship Council. G. G. thanks the Ministerio de Ciencia e Innovación for the grant PID2019-104851GBI00. J.J.M. thanks the Ministerio de Ciencia e Innovación- FEDER for the grant PID2021-123968NB-I00 and the Catalan government for the grant 2017SGR-1049.Peer ReviewedPostprint (published version

UNIDEAL: Curriculum Knowledge Distillation Federated Learning

Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.Comment: Submitted to ICASSP 202