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Large deployable mesh reflectors play a critical role in satellite communications, Earth observation, and deep-space exploration, offering high-gain antenna performance through precisely shaped reflective surfaces. Traditional dynamic modeling approaches—such as wave-based and finite element methods—often struggle to accurately capture the complex behavior of three-dimensional reflectors due to oversimplifications of cable members. To address these challenges, this paper proposes a novel spatial discretization framework that systematically decomposes cable member displacements into boundary-induced and internal components in a global Cartesian coordinate system. The framework derives a system of ordinary differential equations for each cable member by enforcing the Lagrange’s equations, capturing both longitudinal and transverse internal displacement of the cable member. Numerical simulations of a two-dimensional cable-network structure and a center-feed parabolic deployable mesh reflector with 101 nodes illustrate the improved accuracy of the proposed method in predicting vibration characteristics across a broad frequency range. Compared to standard finite element analysis, the proposed method more effectively identifies both low- and high-frequency modes and offers robust convergence and accurate prediction for both frequency and transient responses of the structure. This enhanced predictive capability underscores the significance of incorporating internal cable member displacements for reliable dynamic modeling of large deployable mesh reflectors, ultimately informing better design, control, and on-orbit performance of future space-based reflector systems. 

Operators of web archives have two options for how to crawl pages from the web. Browser-based dynamic crawlers capture all of the resources on every page, but incur high compute overheads. Static browserless crawlers are more lightweight, but miss page resources which are fetched only when scripts are executed. In this paper, we make the case that a web archive does not have to make a binary choice between dynamic or static crawling. Instead, by using a browser for a carefully chosen small subset of crawls, an archive can significantly improve its ability to serve statically crawled pages with high fidelity. First, we show how to reuse crawled resources, both across pages and across multiple crawls of the same page over time. Second, by leveraging a dynamic crawl of a page, we show that subsequent static crawls of the page can be augmented to fetch resources without executing the scripts which request them. We estimate that, as long as 8.9% of page crawls use a browser, an archive can serve roughly 99% of the remaining statically crawled pages without any loss in fidelity, up from 55% without our techniques. 

This work presents a systematic study of the relationship between structural stochasticity and the crush energy absorption capability of lattice structures, with controlled stiffness and weight. We develop a Voronoi tessellation-based approach to generate multiple series of lattice structures with either equal weight or equal stiffness, smoothly transitioning from periodic to stochastic configurations for crush energy absorption analysis. The generated lattice series fall into two categories, originating from periodic honeycomb and diamond lattice structures. A new stochasticity metric is proposed for quantifying the structural stochasticity and is compared with the state-of-the-art stochasticity metrics to ensure a consistent measurement. The crush energy absorption properties are obtained using explicit finite element analysis and we observe similar stochasticity-property trends in simulations using both elastic-plastic and hyperelastic materials. We report a new observation that an intermediate level of stochasticity between periodic and high randomness leads to the best crush energy absorption performance. Our analysis reveals that this optimal performance arises from enhanced activation of deformation hinges, promoting efficient energy absorption. 

In geographical image segmentation, performance is often constrained by the limited availability of training data and a lack of generalizability, particularly for segmenting mobility infrastructure such as roads, sidewalks, and crosswalks. Vision foundation models like the Segment Anything Model (SAM), pre-trained on millions of natural images, have demonstrated impressive zero-shot segmentation performance, providing a potential solution. However, SAM struggles with geographical images, such as aerial and satellite imagery, due to its training being confined to natural images and the narrow features and textures of these objects blending into their surroundings. To address these challenges, we propose Geographical SAM (GeoSAM), a SAM-based framework that fine-tunes SAM using automatically generated multi-modal prompts. Specifically, GeoSAM integrates point prompts from a pre-trained task-specific model as primary visual guidance, and text prompts generated by a large language model as secondary semantic guidance, enabling the model to better capture both spatial structure and contextual meaning. GeoSAM outperforms existing approaches for mobility infrastructure segmentation in both familiar and completely unseen regions by at least 5% in mIoU, representing a significant leap in leveraging foundation models to segment mobility infrastructure, including both road and pedestrian infrastructure in geographical images. The source code is publicly available. 

Abstract This study examines December-January-February (DJF) soil moisture responses to multi-year (MY) and single-year (SY) La Niñas using a 2200-year CESM1 simulation, AGCM experiments, and observational data. Four regions where MY La Niñas amplify SY La Niñas’ impacts on soil moisture were identified: North America, Australia, the Middle East, and the Sahel. SY La Niñas typically cause soil moisture drying in the Middle East and North America and wetting in Australia and the Sahel. MY La Niñas enhance these effects in the second DJF due to the strengthening of precipitation anomalies or the accumulation of precipitation-induced soil moisture anomalies, except in the Sahel where wetting is driven in part by evapotranspiration anomalies. Soil moisture variations are linked to La Niña-induced sea surface temperature changes in the Indian Ocean (for Australia and the Middle East) and the Pacific Ocean (for North America). These amplified effects are largely supported by the observed MY La Niña events from 1948 to 2022. These findings emphasize the need to integrate MY La Niñas into regional agriculture and water resource management strategies to better anticipate and mitigate their impacts.