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The utilization of remote operated vehicles (ROVs) has become essential across various subsea industries, such as oil and gas exploration and offshore wind energy, yet significant challenges remain in achieving effective human-ROV interaction. Despite advancements, ROV operations are hindered by complex control systems, high physical and cognitive demands on pilots, and a lack of sensory feedback mechanisms that fully convey the underwater environment’s dynamics. This study addresses these gaps by surveying ROV pilots and industry stakeholders to identify prevalent operational challenges, essential skills, and perspectives on integrating novel teleoperation technologies, including mixed reality and haptic feedback. Findings reveal a strong industry interest in technologies that enhance situational awareness and ease control demands, although concerns remain regarding practical integration and operator fatigue. By highlighting the critical skills required and potential benefits of human-centered augmentation systems, this study provides insights to inform future ergonomic designs, training frameworks, and technology development aimed at advancing safe and effective ROV teleoperation. 

This paper presents findings from an extensive 28 GHz mmWave measurement campaign conducted in New York City. The study includes over 20 million power measurements collected from two key scenarios: around-corner (non-line-ofsight due to building blockages) and same-street (nominally lineof-sight without obstructions from street furniture or foliage), covering over 1,300 unique links. For urban macro-cell (UMa) rooftop base stations above local clutter, the dominant angle of arrival (AoA) deviates by only 2 to 3.5 degrees from the direct transmitter/receiver direction. This small deviation allows for effective spatial separation between users, facilitating the future development of Multi-User MIMO algorithms for Beyond5G networks. In the urban micro-cell (UMi) dataset, with base stations below local clutter, a path gain drop of over 20 dB was observed in around-corner segments just 20 meters into a corner. Our Street-Clutter-NLOS path loss model achieves an RMSE of 6.4 dB, compared to 11.9 dB from NLOS 3GPP models. Using the best path loss model to estimate coverage for 90% of users traveling around corners, downlink rates could drop by over 10 times after 50 meters, highlighting the challenges in maintaining consistent user experience over mmWave networks in urban street canyons. 

Backscatter power measurements are collected to characterize indoor radar clutter in monostatic sensing applications. A narrowband 28 GHz sounder used a quasi-monostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth. Power variation in azimuth around the local average is found to be within 1 dB of a lognormal distribution with a standard deviation of 6.8 dB. Backscatter azimuth spectra are found to be highly variable with location, with cross-correlation coefficients on the order of 0.3 at separations as small as 0.1 m. These statistics are needed for system-level evaluation of RF sensing performance. 

Backscatter power measurements are collected to characterize indoor radar clutter in monostatic sensing applications. A narrowband 28 GHz sounder used a quasimonostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth. Power variation in azimuth around the local average is found to be within 1 dB of a lognormal distribution with a standard deviation of 6.8 dB. Backscatter azimuth spectra are found to be highly variable with location, with cross-correlation coefficients on the order of 0.3 at separations as small as 0.1 m. These statistics are needed for system-level evaluation of RF sensing performance. 

Abstract Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine‐learning was employed to re‐construct global satellite microwave vegetation optical depth (VOD) retrievals to 500‐m resolution. The re‐constructed results were able to delineate diverse vegetation conditions undetectable from the original 25‐km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro‐climate variability over diverse sub‐regions. The re‐constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil‐vegetation‐atmosphere continuum in heterogeneous drylands.