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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407032000+TO+202407092000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.AI, cs.LG staritng 202407032000 and ending 202407092000</h1>Feed last updated: 2024-07-09T00:00:00-04:00<a href="http://arxiv.org/pdf/2407.05832v1"><h2>A Data-Driven Machine Learning Approach for Detecting Albedo Anomalies
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407042000+TO+202407102000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.LG, physics.data-an, stat.*, cs.AI staritng 202407042000 and ending 202407102000</h1>Feed last updated: 2024-07-10T00:00:00-04:00<a href="http://arxiv.org/pdf/2407.05832v1"><h2>A Data-Driven Machine Learning Approach for Detecting Albedo Anomalies
   on the Lunar Surface</h2></a>Authors:  Sofia Strukova, Sergei Gleyzer, Patrick Peplowski, Jason P. Terry</br>Comments: No comment found</br>Primary Category: astro-ph.EP</br>All Categories: astro-ph.EP, cs.LG</br><p>This study introduces a data-driven approach using machine learning (ML)
 techniques to explore and predict albedo anomalies on the Moon's surface. The
 research leverages diverse planetary datasets, including