Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2 by N. Thomas, A. P. Pertiwi, D. Traganos, D. Lagomasino, D. Poursanidis, S. Moreno, L. Fatoyinbo

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USING THE KARHUNEN-LOÈVE TRANSFORM TO SUPPRESS GROUND ROLL IN SEISMIC DATA by Kazmierczak Thaís de Souza, Castillo López Luís, Gómez Londoño Ernesto

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Remote Sensing of Multitemporal Functional Lake‐To‐Channel Connectivity and Implications for Water Movement Through the Mackenzie River Delta, Canada by W. Dolan, T. M. Pavelsky, A. Piliouras

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Analysis of a Four-Legged Robot Kinematics during Rotational Movements of Its Body by M. J. Fernando, G. R. Saypulaev, M. R. Saypulaev

“…The process of constructing a kinematic model of the robot was based on the use of formulas for the geometry of spatial motion of solids. To test the efficiency of the proposed algorithms for moving the robot legs when performing rotational movements of its body, numerical modeling of the robot kinematics was used. …”
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Assimilating satellite‐based canopy height within an ecosystem model to estimate aboveground forest biomass by E. Joetzjer, M. Pillet, P. Ciais, N. Barbier, J. Chave, M. Schlund, F. Maignan, J. Barichivich, S. Luyssaert, B. Hérault, F. vonPoncet, B. Poulter

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EMIRS Observations of the Aphelion‐Season Mars Atmosphere by Michael D. Smith, Khalid Badri, Samuel A. Atwood, Christopher S. Edwards, Philip R. Christensen, Michael J. Wolff, Tanguy Bertrand, François Forget, Eman Al Tunaiji, Christopher Wolfe, Nathan Smith, Saadat Anwar

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A Distributed Machine Learning Model for Blue and Green Water Resources With Transferable Applications in Similar Climatic Zones by Zhibin Li, Haroon Sahotra, Sajjad Ahmad, Wei Wang, Zhe Yang, Pute Wu, Eakalak Khan, La Zhuo

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Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System by Sean W. Fleming, Karl Rittger, Catalina M. Oaida Taglialatela, Indrani Graczyk

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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…By integrating InSAR with advanced machine learning algorithms, this research provides valuable insights into the spatial distribution and key drivers of subsidence susceptibility. …”
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Application of the Double Approximation Method for Constructing Stiffness Matrices of Volumetric Finite Elements by P. P. Gaidzhurov, N. A. Saveleva

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The Role of PLIN3 in Prognosis and Tumor-Associated Macrophage Infiltration: A Pan-Cancer Analysis by Yang S, Liu H, Zheng Y, Chu H, Lu Z, Yuan J, Xu S

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Probabilistic approach to robust wearable gaze tracking by Miika Toivanen, Kristian Lukander, Kai Puolamäki

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Using an Explainable Machine Learning Approach to Produce High‐Resolution Hourly Precipitation Estimates for a Typical Data‐Deficiency Basin by Yi Lyu, Bin Yong

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Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports by Alexandre Naprous, Marta Avalos-Fernandez, Catherine Pradeau, Emmanuel Lagarde, Cedric Gil-Jardine

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On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed by Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander

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Prospecting for pluripotency in metamaterial design by Ryan van Mastrigt, Marjolein Dijkstra, Martin van Hecke, Corentin Coulais

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Comparing Xenium 5K and Visium HD data from identical tissue slide at a pathological perspective by Mengping Long, Taobo Hu, Weixin Wang, Junshun Gao, Nan Wang, Mats Nilsson

“…Abstract Recent advancements in spatial transcriptomics have been largely triggered by two high-resolution technologies: Visium-HD and Xenium in-situ. …”
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RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection by Aaron Alex Philip, Ziad Kobti

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High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Rongzi Chai, Ting Zhang, Kaixin Wang, Chaoyu Wang

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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…To advance DL‐based hydrogeological modeling, future research should focus on enhancing data availability through data fusion and public databases, improving model interpretability using physics‐informed and explainable DL techniques, and developing more efficient algorithms for training large‐scale models. …”
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