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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202408062000+TO+202408122000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, physics.data-an, cs.AI staritng 202408062000 and ending 202408122000</h1>Feed last updated: 2024-08-12T00:00:00-04:00<a href="http://arxiv.org/pdf/2408.03691v1"><h2>Generative Design of Periodic Orbits in the Restricted Three-Body
-  Problem</h2></a>Authors:  Alvaro Francisco Gil, Walther Litteri, Victor Rodriguez-Fernandez, David Camacho, Massimiliano Vasile</br>Comments: SPAICE Conference 2024 (7 pages)</br>Primary Category: cs.LG</br>All Categories: cs.LG, astro-ph.EP, cs.AI</br><p>The Three-Body Problem has fascinated scientists for centuries and it has
-been crucial in the design of modern space missions. Recent developments in
-Generative Artificial Intelligence hold transformative promise for addressing
-this longstanding problem. This work investigates the use of Variational
-Autoencoder (VAE) and its internal representation to generate periodic orbits.
-We utilize a comprehensive dataset of periodic orbits in the Circular
-Restricted Three-Body Problem (CR3BP) to train deep-learning architectures that
-capture key orbital characteristics, and we set up physical evaluation metrics
-for the generated trajectories. Through this investigation, we seek to enhance
-the understanding of how Generative AI can improve space mission planning and
-astrodynamics research, leading to novel, data-driven approaches in the field.</p></br><a href="http://arxiv.org/pdf/2408.03445v1"><h2>Spacecraft inertial parameters estimation using time series clustering
-  and reinforcement learning</h2></a>Authors:  Konstantinos Platanitis, Miguel Arana-Catania, Leonardo Capicchiano, Saurabh Upadhyay, Leonard Felicetti</br>Comments: 6 pages, 3 figures, 1 table. To be presented in ESA - AI for Space
-  (SPAICE)</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, cs.LG, cs.RO</br><p>This paper presents a machine learning approach to estimate the inertial
-parameters of a spacecraft in cases when those change during operations, e.g.
-multiple deployments of payloads, unfolding of appendages and booms, propellant
-consumption as well as during in-orbit servicing and active debris removal
-operations. The machine learning approach uses time series clustering together
-with an optimised actuation sequence generated by reinforcement learning to
-facilitate distinguishing among different inertial parameter sets. The
-performance of the proposed strategy is assessed against the case of a
-multi-satellite deployment system showing that the algorithm is resilient
-towards common disturbances in such kinds of operations.</p></br>
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202408072000+TO+202408132000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on stat.*, cs.AI, physics.data-an, cs.LG staritng 202408072000 and ending 202408132000</h1>Feed last updated: 2024-08-13T00:00:00-04:00<a href="http://arxiv.org/pdf/2408.05387v1"><h2>EclipseNETs: a differentiable description of irregular eclipse
+  conditions</h2></a>Authors:  Giacomo Acciarini, Francesco Biscani, Dario Izzo</br>Comments: No comment found</br>Primary Category: cs.LG</br>All Categories: cs.LG, astro-ph.IM, physics.space-ph</br><p>In the field of spaceflight mechanics and astrodynamics, determining eclipse
+regions is a frequent and critical challenge. This determination impacts
+various factors, including the acceleration induced by solar radiation
+pressure, the spacecraft power input, and its thermal state all of which must
+be accounted for in various phases of the mission design. This study leverages
+recent advances in neural image processing to develop fully differentiable
+models of eclipse regions for highly irregular celestial bodies. By utilizing
+test cases involving Solar System bodies previously visited by spacecraft, such
+as 433 Eros, 25143 Itokawa, 67P/Churyumov--Gerasimenko, and 101955 Bennu, we
+propose and study an implicit neural architecture defining the shape of the
+eclipse cone based on the Sun's direction. Employing periodic activation
+functions, we achieve high precision in modeling eclipse conditions.
+Furthermore, we discuss the potential applications of these differentiable
+models in spaceflight mechanics computations.</p></br>