Remote Sensing Solutions (United States)

This paper proposes a stochastic adaptive robust optimization approach to build the bidding curves of an aggregator managing a fleet of electric vehicles (EVs) participating in the day-ahead and intraday electricity markets. These bidding decisions are made hourly, one day in advance, within an uncertain environment. In this context, uncertainties comprise market prices, as well as driving requirements of EV users. These uncertainties are accounted for by using a set of scenarios and confidence bounds, respectively. In this way, this paper combines classic stochastic optimization techniques with adaptive robust optimization, realistically modeling multiple sources of uncertainty. EVs are equipped with vehicle-to-grid technology so that they can both buy and sell energy to the market. The resulting stochastic adaptive robust optimization problem is solved by using the column-and-constraint generation algorithm, which ensures the attainment of the optimal solution in a finite number of steps. Simulations are run by applying CPLEX under GAMS. A case study demonstrates the effectiveness of the proposed approach. Results show that the bidding decisions of the EV aggregator are sensitive to the uncertainty in driving requirements of EVs, which can be controlled through the uncertainty budget. This highlights the usefulness of the proposed approach to prevent the attainment of suboptimal bidding decisions. Moreover, the good performance of the algorithm in terms of obtaining the optimal solution with computational times lower than 6 min suggests potential for model expansion and increased complexity in future works.

Citrus fruits are crucial globally, impacting economies and livelihoods. However, climate change is affecting water availability and usage, posing challenges for managing water in citrus crops. This paper introduces CARP-flux, a new model designed to assess water needs in citrus orchards, specifically addressing the limitations of traditional methods under changing climates. The study was conducted in El Hundido, an irrigation village located in the Vega del Guadalquivir Valley near Cantillana, Seville (Spain), acknowledged as a significant citrus trade area at both the European and national levels. To adjust the model to contemporary climate conditions, time-series data from the Sentinel-1 Interferometric Wide (IW) satellite (VV and VH polarizations) from 2021 and 2022 were employed. Employing genetic algorithms and the Weibull distribution, the study incorporated 3D radiation models from backscatter data, a spatial interpretation of water and radiation dynamics, which, in turn, validated CARP-flux’s performance. This model proved effective in identifying land-use changes and evaluating radiation intensity, which are critical factors for understanding crop water needs in climate change scenarios. CARP-flux offers a precise alternative to the conventional Hargreaves technique for citrus groves, which underestimates water requirements in irrigated and humid regions with ample soil moisture, such as the Vega del Guadalquivir.

Typhoons cause significant damage to coastal and inland areas, making the accurate classification of typhoon cloud images essential for effective monitoring and forecasting. While integrating multimodal data from different satellites can improve classification accuracy, existing methods often rely on aligned images and fail to account for radiometric and structural differences, leading to performance degradation during image fusion. In registration methods designed to address this issue, two-stage approaches inaccurately estimate deformation fields, while one-stage methods typically overlook radiometric differences between typhoon cloud images. Additionally, fusion methods suffer from inherent noise accumulation and insufficient cross-modal feature utilization due to cascaded structures. To address these issues, this study proposed a coarse-to-fine registration and fusion network (CoReFuNet) that integrated a one-stage registration module with a cross-modal fusion module for multimodal typhoon cloud image classification. The registration module adopted a one-stage coarse-to-fine strategy, using cross-modal style alignment to address radiometric difference and global spatial registration by affine transformations to resolve positional differences. Bidirectional local feature refinement (BLFR) then ensured precise adjustments, facilitating fine registration by evaluating feature points in each image. Following registration, the fusion module employed a dual-branch alternating enhancement (DAE) approach, which reduced noise by learning cross-modal mapping relationships and applying feedback-based adjustments. Additionally, a cross-modal feature interaction (CMFI) module merged low-level, high-level, intra-modal, and intermodal features through a residual structure, minimizing modality differences and maximizing feature utilization. Experiments on the FY-HMW (Feng Yun-Himawari) dataset, constructed using data from the Feng Yun and Himawari satellites, showed that the CoReFuNet outperformed existing registration methods (VoxelMorph and SIFT) and fusion methods (IFCNN and DenseFuse), achieving 84.34% accuracy and 87.16% G-mean on the FY test dataset, and 82.88% accuracy and 85.54% G-mean on the HMW test dataset. These results showed significant improvements, particularly in unaligned data scenarios, highlighting the potential for real-world typhoon monitoring and forecasting.

Background: Remote monitoring for cardiac electronic implantable devices (CIEDs) has become standard of care, resulting in an abundance of remote transmission data. While this data is valuable, the number of clinically irrelevant non-actionable alerts (NAA) can lead to alert fatigue and reduce device clinic efficiency. Objective: We aimed to propose clinically reasonable alert parameters to reduce the number of NAA and explore its impact on clinical outcomes. Methods: We included 412 participants with CIEDs indicated for atrial fibrillation (AF) or non-sustained ventricular tachycardia (NSVT) from a single academic medical center. Reprogramming changes set as no alerts for AF episodes < 5.5 hours, persistent AF episodes > 1 month when a prior alert has already been issued, AF episodes < 24 hours in patients with paroxysmal AF on anticoagulation or with left atrial appendage occlusion, NSVT in patients with defibrillators. The number of NAAs were tracked 90 days before and after reprogramming. Incident ischemic stroke and sudden cardiac death (SCD) was ascertained over a median follow-up of 20.9 months. Linear regression was used to compare differences in alarms between pre- and post-periods, and multivariable linear regression was applied to identify factors associated with alerts. Results: Among participants, indication for reprogramming included 69% (n=285) patients with AF and 30.7% (n=127) with NSVT. Compared to pre-programming, the number of NAAs was significantly lower post reprogramming implementation (median [IQR]: 3 [6] vs. 1 [2], P < 0.001) respectively. During follow up, the incidence of primary outcomes was 3.6% (n:15) for ischemic stroke and 0.7% (n=3) for SCD. (Table) Conclusion: Our findings demonstrate that implementing guidelines-derived alert parameters in patients with CIEDs significantly reduces the number of clinically irrelevant NAA. This reduction in NAA appears to be safe, with incidence of adverse clinical outcomes comparable to the general population of patients with CIED. This approach has the potential toreduce clinician alert fatigue and safely improve efficiency of device clinics.

Many patients are unable to receive organ transplantation as there is an expanding gap between the number of patients waiting for an organ and the number who receive it. Organ procurement from the brain-dead can address this expanding gap, especially because one brain-dead patient can potentially donate multiple organs to several recipients. Here, we describe a rare case of a previously healthy 26-year-old male who was declared brain dead after a motor vehicle accident but underwent hemodialysis to treat his acute kidney injury and hyperkalemia before successfully donating his heart and left kidney.

Abstract The future Surface Water and Ocean Topography (SWOT) mission aims to map sea surface height (SSH) in wide swaths with an unprecedented spatial resolution and subcentimeter accuracy. The instrument performance needs to be verified using independent measurements in a process known as calibration and validation (Cal/Val). The SWOT Cal/Val needs in situ measurements that can make synoptic observations of SSH field over an O(100) km distance with an accuracy matching the SWOT requirements specified in terms of the along-track wavenumber spectrum of SSH error. No existing in situ observing system has been demonstrated to meet this challenge. A field campaign was conducted during September 2019–January 2020 to assess the potential of various instruments and platforms to meet the SWOT Cal/Val requirement. These instruments include two GPS buoys, two bottom pressure recorders (BPR), three moorings with fixed conductivity–temperature–depth (CTD) and CTD profilers, and a glider. The observations demonstrated that 1) the SSH (hydrostatic) equation can be closed with 1–3 cm RMS residual using BPR, CTD mooring and GPS SSH, and 2) using the upper-ocean steric height derived from CTD moorings enable subcentimeter accuracy in the California Current region during the 2019/20 winter. Given that the three moorings are separated at 10–20–30 km distance, the observations provide valuable information about the small-scale SSH variability associated with the ocean circulation at frequencies ranging from hourly to monthly in the region. The combined analysis sheds light on the design of the SWOT mission postlaunch Cal/Val field campaign.

During the 2020 hurricane season, scientists at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) Ocean Surface Winds Team (OSWT) in collaboration with Remote Sensing Solutions (RSS) operated the RSS Ka-band Interferometric Altimeter (KaIA) from the NOAA WP-3D aircraft, making the first comprehensive set of airborne Ka-band radar altimeter measurements of a tropical cyclone. KaIA is a nadir-looking Ka-band interferometric radar altimeter with a real-time tracker/retracker and is capable of centimetric radar altimetry. This paper shows significant wave height (SWH) retrievals from the 2020 hurricane season and compares them to wave models, and satellite and buoy observations.

In order to solve the problem of repeated collection of ownership information in the process of real estate integration confirmation and registration, a collection system applet was specially developed, which changed from the original technical staff from door to door collecting ownership information to government-led and farmer cooperation. According to the simple and clear system interface, the farmers enter the ownership information, upload the photos of the right source materials, the homestead and the house photos, which solves the big problem that the ownership investigation must be repeated and the ownership information must be entered one by one, which has changed to a certain extent. The operation mode of traditional ownership investigation has greatly reduced the pressure of field work, reduced the cost of manpower and material resources, improved work efficiency, and protected the legitimate rights and interests of farmers.