arXiv subjects

Zhihao Shen

Publications and source records attributed to Zhihao Shen.

4 recordsLinked to original sources

arXiv2026

Topographic Modulation of Martian Near-Surface Winds: Insights from Perseverance Measurements and CFD Modeling in Jezero Crater

Near-surface wind fields on Mars are profoundly modulated by complex topography, yet fine-scale wind field characteristics remain poorly resolved for key geomorphological units such as deltas, valleys, and impact craters, due to the spatial constraints of lander-based wind observations. To address this, we identified three dominant wind directions using measured near-surface wind data from the Perseverance rover at Jezero Crater and then integrated in-situ wind measurements with high-resolution numerical modeling. We established a high-resolution three-dimensional (3D) terrain model encompassing key local geomorphic units, including the delta, an impact crater, and nearby mesas, and performed Computational Fluid Dynamics (CFD) simulations under the above-mentioned three dominant wind directions. The results reveal a robust coupling mechanism between local topography and near-surface wind field structures. We demonstrate that wind speed is significantly enhanced over windward slopes but evidently attenuated within depressions and crater floors. Crucially, significant wind direction deflection angles were particularly evident in areas characterized by steeper slopes. For instance, wind flow exhibited a symmetrical deflection pattern along the opposing inner walls of the modeled impact crater, but stabilizing on the crater floor. Spatial comparisons indicate that wind deflection is most pronounced over steeper slopes, while sector-based distributions within the impact crater reveal a consistent symmetry between opposing wall and floor regions. These findings offer new and critical insights into the intimate connection between Martian surface aeolian erosion/deposition processes and local topographic evolution, which is vital for interpreting the sedimentary history of Jezero Crater.

physics.geo-ph↗

arXiv2025

Probing Structural Dynamics in Photocatalytic Water Splitting: X-ray vs. Neutron Scattering

Photocatalytic water splitting represents a pivotal pathway for converting solar energy into chemical energy, with the core challenge lying in the design and optimization of photocatalysts [1] . TiO2, as a quintessential photocatalytic material, undergoes significant alterations in its electronic and crystalline structures under intense light irradiation, which may directly impacts its photocatalytic efficiency [2] . To gain a profound understanding of these transformations, in situ characterization techniques such as X-ray scattering and neutron scattering have emerged as crucial tools. This paper, from a combined perspective of theoretical computation and experimental characterization, explores the differential capabilities of X-ray scattering and neutron scattering in characterizing the pair distribution function (PDF) of materials during photocatalytic water splitting. Furthermore, through simulation calculations, it aims to unveil the changes in the electronic and crystalline structures under intense light irradiation. This initial draft of the paper is subject to subsequent revisions.

cond-mat.mtrl-sci↗

arXiv2024

Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection

In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilities of Multimodal Large Language Models (MLLMs). Recognizing the inherent limitations of current COD methodologies, which predominantly rely on supervised learning models demanding extensive and accurately annotated datasets, resulting in weak generalization, our research proposes a zero-shot MMCPF that circumvents these challenges. Although MLLMs hold significant potential for broad applications, their effectiveness in COD is hindered and they would make misinterpretations of camouflaged objects. To address this challenge, we further propose a strategic enhancement called the Chain of Visual Perception (CoVP), which significantly improves the perceptual capabilities of MLLMs in camouflaged scenes by leveraging both linguistic and visual cues more effectively. We validate the effectiveness of MMCPF on five widely used COD datasets, containing CAMO, COD10K, NC4K, MoCA-Mask and OVCamo. Experiments show that MMCPF can outperform all existing state-of-the-art zero-shot COD methods, and achieve competitive performance compared to weakly-supervised and fully-supervised methods, which demonstrates the potential of MMCPF. The Github link of this paper is \url{https://github.com/luckybird1994/MMCPF}.

cs.CV↗

arXiv2019

Retrieving Similar Trajectories from Cellular Data at City Scale

Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajectories, this paper investigates the feasibility of identifying similar trajectories from cellular data observed by mobile infrastructure, which provide more comprehensive coverage. To handle the large localization errors and low sample rates of cellular data, we develop a holistic system, cellSim, which seamlessly integrates map matching and similar trajectory search. A set of map matching techniques are proposed to transform cell tower sequences into moving trajectories on a road map by considering the unique features of cellular data, like the dynamic density of cell towers and bidirectional roads. To further improve the accuracy of similarity search, map matching outputs M trajectory candidates of different confidence, and a new similarity measure scheme is developed to process the map matching results. Meanwhile, M is dynamically adapted to maintain a low false positive rate of the similarity search, and two pruning schemes are proposed to minimize the computation overhead. Extensive experiments on a large-scale dataset and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4% and recall of 89.8%).

eess.SP↗