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Researchers from the Italian National Institute for Astrophysics and collaborators developed a deep neural network to reconstruct planetary surface elemental composition from exospheric measurements. The model achieved over 91% Euclidean similarity in composition prediction at optimal altitudes, demonstrating the feasibility of using data-driven approaches for remote sensing in planetary science.