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Soil moisture plays a crucial role in hydrological processes and serves as a key driver of rainfall-induced landslides, especially in regions with steep terrain and intense precipitation. Traditional landslide risk models often oversimplify soil moisture and infiltration dynamics, which limits their predictive accuracy. This study presents a deep learning-based framework for generating high-resolution, spatiotemporal Surface Soil Moisture (SSM) maps for Prince George’s County, Maryland—a region highly susceptible to rainfall-triggered landslides—aimed at improving infiltration modeling and landslide prediction. A Convolutional Long Short-Term Memory (ConvLSTM) network integrates static spatial features (elevation, slope, soil type) with multi-temporal meteorological variables (precipitation, temperature, humidity, wind speed, evapotranspiration) and vegetation indices. The model is trained using dense SSM maps derived from Sentinel-1 SAR data processed through a change detection algorithm, providing a physically meaningful alternative to sparse in-situ observations. To address data imbalance, a two-pass patch extraction strategy was implemented to enhance representation of high-SSM conditions. The framework leverages high-performance computing resources to process large-scale, multi-temporal raster datasets efficiently. Evaluation results show strong predictive performance, with the two-day model achieving R2 = 0.72, correlation = 0.85, RMSE = 0.154, and MAE = 0.103. The results demonstrate the model’s capability to produce fine-resolution, wall-to-wall SSM maps that capture the spatial and temporal dynamics of surface soil moisture, supporting the development of early warning systems and landslide hazard mitigation strategies. 

This paper addresses the problem of monitoring structures with potential emergent damage through adaptive sensing provided by teams of mobile robots. Advantages of mobile robot teams for structural health monitoring include: 1. Multiple views of a given structure, 2. Adaptive movements that focus attention in response to observed conditions,3. Heterogeneous sensing and movement, and 4. Federated health monitoring and prognosis assessment through networked sharing and processing of information. Towards this end three cases of the use of mobile robot teams will be presented: 1. Heterogeneous robot teams for home and small building maintenance – Identifying, diagnosing and mitigating damage to homes and small buildings is a vexing set of problems for the owners. As an aid small controlled bristlebots and quadruped robot dogs (QRDs) carry sensors throughout a small building, assess conditions, provide prognoses and networked links to repair options; 2. Culverts are primary components of stormwater and flood prevention infrastructure. Inspecting small culverts is difficult for humans and large culverts are accessible but dangerous due to issues of confined spaces. Low-cost mobile robots have emerged as a competitive inspection option for accessible culverts with straight or short runs that permit wireless telemetry. Longer culverts and those with bends, branches and drop inlets pose challenges to the telemetry. Teams of robots extend the range of inspection through multi-hop video and control telemetry; 3. Ground penetrating radar (GPR) is a method of sensing subsurface infrastructure conditions with high-frequency electromagnetic waves. Conventional GPRs operate in a suboptimal monostatic or bistatic mode, are tedious to operate and have limitations in sensing congested utility subsurface conditions. Coordinated multistatic ground penetrating radar operated with mobile robot teams alleviates some of these concerns and provide better subsurface assessments with automated methods that focus attention on subsurface features of interest. Results from laboratory and field tests of these robot teams, as well as organizing principles of control and automated information processing are presented. 

Abstract Electron-only magnetic reconnection was first detected by the Magnetospheric Multiscale (MMS) mission in Earth’s turbulent magnetosheath. Its prevalence in kinetic-scale turbulence has attracted great interest in heliophysics, but also revealed a great challenge in identifying it in turbulence, where electron flows are often complex. The magnetic flux transport (MFT) method is an innovative method to identify active reconnection in numerical simulations and in situ observations of turbulent plasmas. Here we extend this method to distinguish between electron-only and ion-coupled reconnection. The coupling of magnetic field motion with plasma flows in the diffusion regions sets distinct scales in the MFT velocity. While both forms of reconnection satisfy the MFT signature for active reconnection as MFT inflows and outflows at an X-line, the specific electron-only MFT signature is only an electron-scale MFT outflow along the current sheet normal direction, whereas the specific ion-coupled signature is a two-scale, outer-ion-and-inner-electron-scale MFT outflow in the electron diffusion region, which evolves into a single ion-scale in the ion diffusion region. These signatures are verified in a simulation of gyrokinetic turbulence. The dependence of the MFT outflow on the distance downstream from the X-lines also agrees well with the framework of magnetic field–plasma flow coupling. The new MFT signatures provide a clear and reliable tool for investigating electron-only reconnection in turbulence, independent of the development of electron outflows. They are directly applicable to kinetic and fluid simulations, and have potential application to observations of diffusion region crossings by spacecraft missions such as MMS. 

We present the first ambient mechanosynthesis of 16 flexible covalent organic frameworks (COFs) within an hour. Notably, one representative COF exhibited a high iodine uptake capacity of ∼4.3 g g⁻¹from aqueous solutions and 5.97 g g⁻¹from vapor.