Abstract With the continuous development of space and sensor technologies during last 40 years, ocean remote sensing has entered into big-data era with typical five-V (volume, variety, value, velocity veracity) characteristics. Ocean remote-sensing data archives reach several tens petabytes massive satellite are acquired worldwide daily. To precisely, efficiently intelligently mine useful information submerged in such sets is a big challenge. Deep learning—a powerful technology recently...

We developed a deep learning model to forecast the sea surface temperature evolution associated with tropical instability waves.

Abstract Background Although traditional diagnostic techniques of infection are mature and price favorable at present, most them time-consuming with a low positivity. Metagenomic next⁃generation sequencing (mNGS) was studied widely because identification typing all pathogens not rely on culture retrieving DNA without bias. Based this background, we aim to detect the difference between mNGS method, explore relationship results severity, prognosis infectious patients. Methods 109 adult...

This study develops a deep learning (DL) model to classify the sea ice and open water from synthetic aperture radar (SAR) images. We use U-Net, well-known fully convolutional network (FCN) for pixel-level segmentation, as backbone. employ DL-based feature extracting model, ResNet-34, encoder of U-Net. To achieve high accuracy classifications, we integrate dual-attention mechanism into original U-Net improve representations, forming (DAU-Net). The SAR images are obtained Sentinel-1A....

The preparation of self-supporting anion exchange membranes with enhanced mechanical strength and ultrathin thickness is still a challenge to improve the performance fuel cells.

With the development of deep learning, impressive progress has been made in field image restoration. The existing methods mainly rely on CNN and Transformer to obtain multi-scale feature information. However, these rarely integrate frequency domain information effectively during extraction, limiting their performance Additionally, few have combined Mamba with Fourier for restoration, which limits Mamba's ability perceive global degradation domain. Therefore, we propose a new restoration...

We demonstrate the unique capability of MODIS instruments in detecting oil slicks an open ocean environment. On 13 May 2006, NW Gulf Mexico where water depth ranges from 50 to 2500 m, one 250‐m resolution image showed at least 164 surface under sun glint (glint reflectance, L g , ranged between 0.0001 and 0.06 sr −1 ). After discounting other possible causes, we believe these are result natural seeps. Our analysis total coverage ∼1900 km 2 with individual varying area (11.7 ± 14.8 ) length...

[1] Polarimetric SAR decomposition parameters, average alpha angle () and entropy (H) are estimated for oil-slick contaminated sea surfaces slick-free conditions using a RADARSAT-2 quad-polarization image. The values of H within oil slick areas significantly higher than those the ambient surface, indicating dominance Bragg scattering ocean non-Bragg area. In land classification, conformity coefficient (μ) is often used to discriminate surface with double-bounce or volume scattering. Based on...

In 2008, the Canadian Space Agency sponsored Radarsat Hurricane Applications Project (RHAP), for researching new developments in application of Radarsat-1 synthetic aperture radar (SAR) data and innovative mapping approaches to better understand dynamics tropical cyclone genesis, morphology, movement. Although cyclones can be detected by many remote sensors, SAR yield high-resolution (subkilometer) low-level storm information that cannot seen below clouds other sensors. addition wind field...

In this paper, we perform a comparison of wind speed measurements from the ENVISAT Advanced Synthetic Aperture Radar (ASAR), MetOp-A Scatterometer (ASCAT), U.S. National Data Buoy Center's moored buoys, and Navy Operational Global Atmospheric Prediction System (NOGAPS) model. These comparisons were made in near coast regions over 17-month period March 2009 to July 2010. The ASAR retrieval agreed well with scatterometer model estimates, mean differences ranging -0.69 0.85 m/s standard...

We present an efficient algorithm for retrieving the ocean-surface wind vector from C-band Radar Satellite RADARSAT-2 fully polarimetric synthetic aperture radar (SAR) measurements based upon copolarized geophysical model function, i.e., CMOD5.N, and cross-polarized ocean backscatter model, C-2PO. The analysis of fine quad-polarization mode single-look complex SAR data collocated in situ moored buoy observations reveals that correlation coefficient between co- cross-polarization channels has...

Satellite-borne synthetic aperture radar (SAR) data are widely used for detection of hydrocarbon resources, pollution, and oil spills. These applications require recognition particular spatial patterns in SAR data. We developed a texture-classifying neural network algorithm (TCNNA), which processes from wide selection beam modes, to extract these imagery semisupervised procedure. Our approach uses combination edge-detection filters, descriptors texture, collection information (e.g., mode),...

We developed a Textural Classifier Neural Network Algorithm (TCNNA) to process Synthetic Aperture Radar (SAR) data map oil spills. The algorithm processes SAR and wind model outputs (CMOD5) using combination of two neural networks. first network filters out areas the image that do not need be processed by flagging pixels as candidates; second performs statistical textural analysis differentiate between sea surface with or without floating oil. By combining networks, we are able full...

Betaine (BET) is a native compound widely studied as an antioxidant in agriculture and human health. However, the mechanism of BET remains unclear. In this research, radical scavenging assays showed that had little free activity. activity was confirmed by cellular (CAA) erythrocyte hemolysis assays. The results quantitative PCR (qPCR) enzyme determination kits not due to gene expression antioxidases. High-pressure liquid chromatography (HPLC) assessment effect on sulfur-containing amino acid...

A hybrid backscattering model is built to provide a consistent description for C-band VH- and VV-polarized normalized radar cross sections (NRCSs). Ocean surface coand cross-polarized NRCS are both treated as sum of Bragg non-Bragg scattering components. To better understand the synthetic aperture (SAR) observed signals under high-wind conditions, five RADARSAT-2 dual-polarization SAR hurricane images collocated wind vectors measured by airborne stepped-frequency microwave radiometer (SFMR)...

Abstract This study develops an effective and robust method to mine bitemporal dual‐polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored network‐based SAR flooding mapping network (SARCFMNet) leverages two modifications improve the accuracy robustness: physics‐aware input design regularization. proposed SARCFMNet is applied impact analysis of caused by 2017 Hurricane Harvey near...

Abstract Recent satellite sea surface height (SSH) and temperature (SST) observations have shown that abnormal eddies, is, warm cyclonic eddies cold anticyclonic occur sporadically in some regions, which triggers an essential question on the spatiotemporal distribution of global ocean. In this study, a deep learning framework was developed to systematically mine information from synergy satellite‐sensed SSH SST data over 1996–2015, 20‐year period. Abnormal account for surprising one‐third...

In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity tropical cyclones (TCs) over Northwest Pacific Ocean from brightness temperature data observed by Advanced Himawari Imager onboard Himawari-8 geostationary satellite. We used 97 TC cases 2015 to 2018 train CNN models. Several models with different inputs and parameters are designed. A comparative study showed that selection infrared (IR) channels has significant impact on performance...