Search Results (321)

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Dust aerosols significantly affect climate and air quality in Northwest China (30–50° N, 70–110° E), where frequent dust storms complicate accurate aerosol classification when using CALIPSO satellite data. This study introduces an Enhanced 1D U-Net model to enhance dust aerosol retrieval, incorporating Inception modules for multi-scale feature extraction, Transformer blocks for global contextual modeling, CBAM attention mechanisms for improved feature selection, and residual connections for training stability. Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. The model achieved a precision of 94.11%, recall of 99.88%, and F1 score of 96.91% for dust aerosols, outperforming baseline models. Dust aerosols were predominantly detected between 0.44 and 4 km, consistent with observations from CALIPSO. These results highlight the model’s potential to improve climate modeling and air quality monitoring, providing a scalable framework for future atmospheric research. Full article

In remote sensing, object classification often suffers from severe degradation caused by atmospheric turbulence and low-signal conditions. Traditional image reconstruction approaches are computationally expensive and fragile under such conditions. In this work, we propose a novel image-free classification framework using single-pixel imaging (SPI), which directly classifies targets from 1D measurements without reconstructing the image. A learnable sampling matrix is introduced for structured light modulation, and a hybrid CNN-Transformer network (Hybrid-CTNet) is employed for robust feature extraction. To enhance resilience against turbulence and enable efficient deployment, we design a $(N+1)\times L$ hybrid strategy that integrates convolutional and Transformer blocks in every stage. Extensive simulations and optical experiments validate the effectiveness of our approach under various turbulence intensities and sampling rates as low as 1%. Compared with existing image-based and image-free methods, our model achieves superior performance in classification accuracy, computational efficiency, and robustness, which is important for potential low-resource real-time remote sensing applications. Full article

Blocking anticyclones are important atmospheric phenomena generally associated with extreme weather (e.g., droughts and cold air surges). Blockings also constitute large-scale indicators of climate change. The study of blockings in the Southern Hemisphere (SH) has been traditionally carried out utilizing reanalysis products. This paper is aimed at presenting an updated, comprehensive climatology of blockings in the SH as extracted from the ERA5 and the NCEP/NCAR reanalysis datasets in the 1940–2022 and 1948–2022 periods, respectively. Blockings were located by means of a unidimensional index at 500 hPa. The results were stratified by season, longitude, region, persistence, and intensity, and the climatology from both datasets was compared. The primary location of blockings was close to the Date Line in every season. Additionally, depending on the season, up to fourth-rank maxima could be located. Generally, the secondary maxima were found in the south Atlantic; lower-order maxima were located in the south-eastern Pacific, west of South America, and in the south-western Indian Ocean east of South Africa. The most intense blockings were concentrated in the Pacific and in the south Atlantic in both datasets, and they were also located in the Indian Ocean, but in the ERA5 reanalysis only. The longest-lived blockings occurred in the south Pacific and in the south Atlantic during southern winter. Full article

Polymer membranes often face challenges of oil fouling and rapid water flux decline during the separation of oil-in-water emulsions, making them a focal point of ongoing research and development efforts. Coating PVDF membranes with a hydrogel layer equips the developed membranes with robust potential to mitigate oil fouling. However, developing a controllable thickness of a stable hydrogel layer to prevent the blocking of membrane pores remains a critical issue. In this work, atmospheric pressure low-temperature plasma was used to prepare the surface of a PVDF membrane to improve its wettability and adhesion properties for coating with a thin hydrophilic film of an AM-NaA copolymer hydrogel. The AM-NaA/PVDF membrane exhibited superhydrophilic and underwater superoleophobic properties, along with exceptional anti-crude oil-fouling characteristics and a self-cleaning function. The AM-NaA/PVDF membrane achieved high separation efficiency, exceeding 99% for various oil-in-water emulsions, with residual oil content in the permeate of less than 10 mg/L after a single-step separation. Additionally, it showed a high-water flux of 5874 L/m²·h for crude oil-in-water emulsions. The AM-NaA/PVDF membrane showed good stability and easy cleaning by water washing over multiple crude oil-in-water emulsion separation and regeneration cycles. Adding CaCl₂ destabilized emulsions by promoting oil droplet coalescence, further boosting flux. This strategy provides a practical pathway for the development of highly reusable and oil-fouling-resistant membranes for the efficient separation of emulsified oily water. Full article

Haze caused by atmospheric scattering often leads to color distortion, reduced contrast, and diminished clarity, which significantly degrade the quality of remote sensing images. To address these issues, we propose a novel network called DWTMA-Net that integrates discrete wavelet transform with multi-dimensional attention, aiming to restore image information in both the frequency and spatial domains to enhance overall image quality. Specifically, we design a wavelet transform-based downsampling module that effectively fuses frequency and spatial features. The input first passes through a discrete wavelet block to extract frequency-domain information. These features are then fed into a multi-dimensional attention block, which incorporates pixel attention, Fourier frequency-domain attention, and channel attention. This combination allows the network to capture both global and local characteristics while enhancing deep feature representations through dimensional expansion, thereby improving spatial-domain feature extraction. Experimental results on the SateHaze1k, HRSD, and HazyDet datasets demonstrate the effectiveness of the proposed method in handling remote sensing images with varying haze levels and drone-view scenarios. By recovering both frequency and spatial details, our model achieves significant improvements in dehazing performance compared to existing state-of-the-art approaches. Full article

Nitrous oxide is a potent greenhouse gas due to its long atmospheric lifespan (121 years) that results in a high global warming potential (GWP). Research has shown that no-tillage may be implemented as a mitigation strategy to reduce N₂O emissions. The objective of the was to evaluate how conventional tillage (CT) and no-tillage (NT) can potential influence N₂O emissions in soybean rotation in a semi-arid region of the central Free State of South Africa. The effect of conventional and no-till tillage practices on N₂O emissions under soybean rotation was evaluated in the 3rd year of a 5-year rotation system, in a semi-arid region of the Free State of South Africa, from December 2022 to December 2023. The experimental area was divided into three blocks and there were two plots in each block: in total there were six plots. The treatments were planted in a soybean rotation system under no-tillage and conventional tillage. The monthly averages of N₂O emissions were significantly different from each other during the soybean growing season; the highest emissions were recorded in August/September 2023 from both the NT and CT treatments after harvest. During this time, there were crop residues in the soil that increased soil carbon. There was a positive correlation between N₂O emissions and soil carbon content (p = 0.21) and between N₂O emissions and soil organic matter (p = 0.43). Emissions were significantly higher in CT (LSD = 0.3) than in NT. The lowest N₂O emissions were recorded in December 2023 (LSD = 0.05) and were significantly reduced in the no-till plots compared to those of the conventional tillage plots. Furthermore, the lowest cumulative N₂O emissions of 0.26 ± 0.22 kg N₂O-N ha⁻¹ were recorded during NT in the winter season and were significantly different from CT (LSD = 0.19). The results from our study indicate that the no-till practices in soybean rotation can decrease N₂O emissions. Full article

Brazil is increasingly affected by extreme weather events due to climate change, with pronounced regional differences in temperature and precipitation patterns. The southeast region is particularly vulnerable, frequently experiencing severe droughts and extreme heatwaves linked to atmospheric blocking events and intense rainfall episodes driven by the South Atlantic Convergence Zone (SACZ). These phenomena contribute to recurring climate-related disasters. The country’s heavy reliance on hydropower heightens its susceptibility to droughts, while growing evidence points to intensifying dry spells and wildfires across multiple regions, threatening agricultural output and food security. Urban areas, particularly, are experiencing more frequent and severe heatwaves, posing serious health risks to vulnerable populations. This study investigates the links between global teleconnection indices and synoptic-scale systems, specifically blocking events and SACZ activity, and their influence on Brazil’s extreme heat, drought conditions, and river flow variability over the past 30 to 40 years. By clarifying these interactions, the research aims to enhance understanding of how large-scale atmospheric dynamics shape climate extremes and to assess their broader implications for water resource management, energy production, and regional climate variability. Full article

Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article

Global warming is driven by the increasing atmospheric emissions of greenhouse gases. Soils are highly sensitive to climate change and can shift from being carbon reservoirs to carbon sources under warmer and wetter conditions. This study is the first to simultaneously measure trace gas fluxes in Euterpe oleracea (açaí) plantations in upland areas, contrasting them with floodplain areas managed for açaí production in the eastern Amazon. Flux measurements were conducted during both the rainy and dry seasons using the closed dynamic chamber technique. In upland areas, CO₂ fluxes exhibited spatial (plateau vs. lowland) and temporal (hourly, daily, and seasonal) variations. During both the rainy and dry months, CH₄ uptake in upland soils was higher in lowland areas compared to the plateau. When comparing the two ecosystems, upland areas emitted more CO₂ during the rainy season, while floodplain areas released more CH₄ into the atmosphere. Unexpectedly, during the dry season, floodplain soils produced more CO₂ and captured more CH₄ from the atmosphere compared to upland soils. In upland areas, CO₂-equivalent production reached 59.1 Mg CO₂-eq ha⁻¹ yr⁻¹, while in floodplain areas, it reached 49.3 Mg CO₂-eq ha⁻¹ yr⁻¹. Soil organic matter plays a vital role in preserving water and microorganisms, enhancing ecosystem productivity in uniform açaí plantations and intensifying the transfer of CH₄ from the atmosphere to the soil. However, excessive soil moisture can create anoxic conditions, block gas diffusion, reduce soil respiration, and potentially turn the soil from a sink into a source of CH₄. Full article

Taking the coastal area of Binhai County, Jiangsu Province, as an example, this study first investigated the basic natural geography and the regional geological and hydrogeological conditions of the study area, and then carried out in-depth geophysical prospecting, hydrogeological tests, geothermal temperature monitoring, hydrochemistry and isotope analyses, and other studies based on the results to comprehensively and systematically reveal the genesis mechanism of the geothermal water resources of this coastal area from multiple perspectives. The results showed the following: the geothermal water in this area mainly comes from atmospheric precipitation; the deep east–northwest interlaced fracture is the recharge and transportation channel; the Cambrian–Ordovician carbonate rock layer, enriched by the development of cavernous fissures, forms the thermal storage layer; the underground heat mainly comes from the upward heat flow along the deep fracture and the natural warming of the strata; and the thermal reservoir cover comprises Paleozoic and Mesozoic clastic rocks that have a high mud content and form a thick layer. The genesis mode of this area is as follows: the atmospheric precipitation infiltrates and is recharged through the exposed alpine carbonate fissures in the Lianyungang area, and then it is transported to the south along the large deep fracture under the action of a high hydraulic pressure head; meanwhile, it is heated by the heat flow in the deep part of the fracture and water–rock interactions with the strata occur. Geothermal water with a calculated thermal storage temperature of 83.6 °C is formed at a depth of 2.9 km, which is blocked by the intersection of the northeast and northwest fractures to form a stagnant zone in the coastal area. Full article

Securing the power grid is of extreme concern to many nations as power infrastructure has become integral to modern life and society. A high-altitude electromagnetic pulse (HEMP) is generated by a nuclear detonation high in the atmosphere, producing a powerful electromagnetic field that can damage or destroy electronic devices over a wide area. Protecting against HEMP attacks (insults) requires knowledge of the problem’s bounds before the problem can be appropriately solved. This paper presents a collection of analyses to determine the basic requirements for controller placements on a power grid. Two primary analyses are conducted. The first is an inverted controllability analysis in which the HEMP event is treated as an unbounded control input to the system. Considering the HEMP insult as a controller, we can break down controllability to reduce its influence on the system. The analysis indicates that either all but one neutral path to ground must be protected or that all transmission lines should be secured. However, further exploration of the controllability definition suggests that fewer blocking devices are sufficient for effective HEMP mitigation. The second analysis involves observability to identify the minimum number of sensors needed for full-state feedback. The results show that only one state sensor is required to achieve full-state feedback for the system. These requirements suggest that there is room to optimize controller design and placement to minimize total controller count on a power grid to ensure HEMP mitigation. As an example, the Horton et al. system model with 15 transformers and 15 transmission lines is used to provide a baseline comparison for future optimization studies by running all permutations of neutral and transmission line blocking cases. The minimum number of neutral controllers is 8, which is approximately half of the bounding solution of 14. The minimum number of transmission line controllers is 3, which is one-fifth of the bounding solution of 15 and less than half of the required neutral controllers. Full article

Lithium-ion batteries risk failing when subjected to different abuse tests, resulting in gas and flames. In this study, 5 Ah nickel manganese cobalt oxide (NMC) pouch cells were subjected to external heating; overcharge at rates of 2.5, 5 and 10 A; and nail penetration. Tests were conducted in air and N₂ atmospheres. Additional external heat tests were performed on cells at 5, 25, 50, and 75% SoC and on two, three, and four cell blocks. Gas volumes were calculated, and the gas composition was given for H₂, CO, CO₂, C₂H₄, C₂H₆, CH₄, C₃H₆, and C₃H₈. For tests under an air atmosphere at 100% SoC, the volume of gas varied between abuse methods: 3.9 L (external heat), 6.4 L (overcharge), and 8.9 L (nail penetration). The gas composition was found to predominantly contain H₂, CO₂, and CO for all abuse methods; however, higher concentrations of H₂ and CO were present in tests performed under N₂. External heat tests at different SoCs showed that the gas volume decreased with SoC. Overall, the type of abuse method can have a large effect on the gas volume and composition produced by cell failure. Full article

Climate-change-driven elevation of atmospheric CO₂ (e[CO₂]) disrupts rice physiology by impairing nitrogen use efficiency (NUE) and leaf carbon balance. This study investigated how sodium chloride (NaCl) amendment modulates these processes in upland rice (Oryza sativa L. cv. CMG 2085) under current (400 μmol mol⁻¹) and elevated (700 μmol mol⁻¹) CO₂ concentrations. Using a randomized block design with factorial treatments (CO₂ × NaCl), we analyzed leaf nutrients, gas exchange, chlorophyll fluorescence, and yield parameters. Our findings revealed that 3 mmol L⁻¹ NaCl under ambient CO₂ (1) reduced photorespiration by half, (2) increased grain yield, and (3) enhanced leaf area despite lower leaf N content, indicating improved NUE. Conversely, under e[CO₂], NaCl supplementation decreased rice yield by 15%, demonstrating CO₂-dependent reversal of sodium benefits. Photosynthetic modeling showed higher Vcmax and J values at ambient CO₂, while e[CO₂] increased J/Vcmax, suggesting altered nitrogen allocation to photosynthetic reactions. These results demonstrate that applying low-dose NaCl (3 mmol L⁻¹) can optimize carbon and nitrogen economy under current CO₂ concentrations, although its efficacy diminishes under e[CO₂]. These findings support climate-resilient cultivation strategies for upland rice in tropical and subtropical regions where mild salinity can be used to enhance nitrogen use efficiency and yield under present-day atmospheric conditions. Full article

The quality of medicinal plants is closely related to the ecological factors of their growing environment, as their efficacy is reflected in the content of key medicinal components, which in turn indicates the quality of the plants. This study measured the daily variations in major constituents, including lobetyolin, polysaccharides, and total flavonoids, in Codonopsis pilosula (Franch.) Nannf., which in the Changzhi and Jincheng regions of Shanxi Province, China is known as Lu Tangshen. Throughout its growth cycle. Additionally, the study explored the effects of 11 ecological factors (both climatic and soil variables) on the primary medicinal components of C. pilosula. Through block experiments and comparisons between future data predictions and actual measurements, the reliability of the model and the consistency of block experimental data were ultimately confirmed. Principal component analysis (PCA), stepwise multiple linear regression analysis, and nonlinear polynomial modeling were employed to investigate the relationships between ecological factors and quality-related constituents (polysaccharides, total flavonoids, and lobetyolin). The results showed that linear models effectively explained daily temperature (DT) with an adjusted R² exceeding 0.8, but due to the inherently nonlinear nature of the data, it is evident that linear models are fundamentally inadequate for accurately capturing the underlying relationships. Therefore, their fit for total flavonoids and lobetyolin was suboptimal. The introduction of nonlinear polynomial models (second-, fourth-, and fifth-order) significantly improved the model fit, indicating the existence of complex nonlinear relationships between ecological factors and medicinal components. For polysaccharides, the fourth-order model demonstrated the best performance, while fifth-order models were required to adequately describe the relationships for total flavonoids and lobetyolin. Based on the best models, the optimal ranges for key ecological factors were identified: polysaccharides were best influenced by atmospheric pressure (AP) between 9.1 and 9.3 kPa, air relative humidity (ARH) between 30% and 60%, 40 cm soil mean annual temperature (40cmMAT) between 27.5 °C and 28.5 °C, soil pH between 9.68 and 9.72, and soil nitrogen (N) content between 7 and 9 mg/kg. For total flavonoids, narrow optimal ranges were observed for temperature, humidity, and pH (MAT between 10 °C and 15 °C, 40cmMAT between 27.5 °C and 28.5 °C, and pH between 9.68 and 9.72). Lobetyolin showed optimal conditions at AP of 9.1 to 9.3 kPa, 40cmMAT of 28.0 °C to 28.5 °C, ARH of 65% to 75%, pH near 9.70, and days after planting (DAP) between 10 and 50. The adoption of higher-order polynomial models clarified critical nonlinear inflection points and optimal ecological ranges, providing a refined reference for enhancing the content of medicinal components. These findings offer valuable insights for precision cultivation strategies aimed at improving the quality of C. pilosula. Full article