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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>

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