bluuebunny/crossref_metadata_2025 huggingface dataset:
Highly Permeable, Electrically Switchable Filter for Multidimensional Sorting of Suspended Particles
Authors: Weirauch Laura , Giesler Jasper , Pesch Georg R. , Baune Michael , Thöming Jorg
Published: November 2024

The creation of highly specific particle systems in the nano- and micrometer size range is a challenging task. The demand for particle systems with narrowly distributed properties is increasing in many applications, especially for use in high-tech products. Conventional separation techniques often reach their limits in the micrometer size range or become (labor-)intensive, which makes them economically or ecologically unsustainable. In addition, sorting based on several properties is rarely feasible in just one separator. Dielectrophoretic processes can be a viable option for complex sorting tasks like this, given their ability to address several particle properties and their high degree of selectivity. In this paper, we summarize the progress of a project in which the capability of dielectrophoretic methods for multidimensional sorting of microparticles was investigated. We were able to develop an operation mode for multidimensional sorting of microparticles using dielectrophoresis as well as a scalable electrically switchable filter. This creates a basis for high-throughput and multi-target sorting of technical microparticles using dielectrophoretic processes.
Comprehensive Analysis and Optimization of Peach (Prunus persica) Vacuum Drying: Employing Principal Component Analysis, Artificial Neural Networks and the Standard Score Approach
Authors: Šumić Zdravko , Tepić Horecki Aleksandra , Pezo Lato , Pavlić Branimir , Nastić Nataša , Milić Anita
Published: November 2024

Dried peaches are widely consumed as a snack food product and used as an ingredient in cereals as well in chocolate and energy bars. Accordingly, the main objective of this investigation was to optimize the vacuum-drying process for peaches using a combination of three different statistical methods: principal component analysis, the standard score method and an artificial neural network approach. Applied input drying parameters were temperature (50–70 °C), pressure (20–120 mbar) and time (6–10 h), while the investigated output parameters were moisture content, water activity, total color change, phenolic and flavonoid contents and antioxidant activity. It was noted that all investigated output parameters constantly decreased (moisture content, water activity) and increased (total color change, total phenolic and flavonoid contents and antioxidant activity (FRAP, DPPH and ABTS assays)) in accordance with the applied drying temperature. The key variables accounted for 86.33% of data variance based on the PCA results, while the SS and ANN method resulted in the same optimal drying conditions: 60 °C, 70 mbar and 6 h, which indicated the effectiveness of the applied statistical methods.
Research on Vibration Reduction, Energy Enhancement, and Speed Increase Methods for Drilling String in Deep Heterogeneous Strata
Authors: Chen Changchang , Bi Chenchao , Ji Guodong , Wang Hong , Huo Yunru , Yu Puwei
Published: November 2024

There are a series of problems in the drilling process of deep heterogeneous formations, such as severe drilling string vibration, slow rock-breaking drilling speed, and the short practical working life of drill bits. It is urgent to develop supporting technical methods for breakthroughs. Based on the main characteristics of the drilling environment in deep formations and the results of drilling string dynamics research in recent years, a technical equipment design concept was proposed to use the vibration of the drill string during the drilling process in heterogeneous formations to improve the hydraulic energy of the bottom hole drilling fluid and thus improve the drilling speed. A technical equipment research and development design was carried out to use the vibration energy of the drill string to enhance the injection energy of the bottom hole drilling medium, and a vibration reduction and energy enhancement device for the bottom hole drill string was developed; we conducted on-site acceleration effect testing, and the research results showed that the vibration of the drill string contains enormous energy, which can be converted into rock-breaking acceleration energy; The designed vibration reduction and energy enhancement device for the bottom hole drilling string can enhance the injection energy of drilling fluid, and reduce the harm of preparing a string vibration; The vibration reduction and energy enhancement device of the bottom hole drill string can significantly improve the drilling speed. The research results have opened up new directions for deep well acceleration technology and provided equipment support for accelerating the exploration and development of deep oil and gas resources. It is recommended to strengthen further the research on the basic theory of downhole drill string vibration, the acceleration technology equipment based on drill string vibration, and vibration acceleration methods.
A Study of Flexible Job-Shop Scheduling with Dual Constraints
Authors: Gao Zhenhua , Qiao Hengyun
Published: November 2024

The flexible production job shop was selected as the research object, with the goal of minimizing the maximum completion time. A novel production scheduling model was devised by taking into account the sequence of occurrence of two distinct perturbations, namely preventive maintenance and emergency order insertion, within the same production scheduling plan. This was solved by an improved three-body crossover operator genetic algorithm. Finally, the superiority and effectiveness of the new production scheduling were demonstrated through an illustrative analysis.
Pressure Transient Analysis for Fractured Shale Gas Wells Using Trilinear Flow Model
Authors: Liu Li , Xue Liang , Han Jiangxia
Published: November 2024

Shale gas, a low-permeability, unconventional resource, requires horizontal drilling and multi-stage fracturing for commercial production. This study develops a trilinear flow model for fractured horizontal wells in shale gas formations, incorporating key mechanisms such as adsorption, desorption, diffusion, wellbore storage, and skin effects. The model delineates seven distinct flow regimes, providing insights into gas migration processes and the factors controlling production. Sensitivity analyses reveal that desorption plays a critical role under low-pressure and low-production conditions, significantly enhancing gas transfer rates from the matrix to the fracture network and contributing to overall production. Monte Carlo simulations further highlight the variability in pressure responses under different input conditions, offering a comprehensive understanding of the model’s behavior in complex reservoir environments. These findings advance the characterization of shale gas flow dynamics and inform the optimization of production strategies.
Research on Gas Emission Prediction Based on KPCA-ICSA-SVR
Authors: Liu Li , Dai Linchao , Mao Xinyi , Chen Yutao , Jing Yongheng
Published: November 2024

In the context of deep mining, the uncertainty of gas emission levels presents significant safety challenges for mines. This study proposes a gas emission prediction model based on Kernel Principal Component Analysis (KPCA), an Improved Crow Search Algorithm (ICSA) incorporating adaptive neighborhood search, and Support Vector Regression (SVR). Initially, data preprocessing is conducted to ensure a clean and complete dataset. Subsequently, KPCA is applied to reduce dimensionality by extracting key nonlinear features from the gas emission influencing factors, thereby enhancing computational efficiency. The ICSA is then employed to optimize SVR hyperparameters, improving the model’s optimization capabilities and generalization performance, leading to the development of a robust KPCA-ICSA-SVR prediction model. The results indicate that the KPCA-ICSA-SVR model achieves the best performance, with RMSE values of 0.17898 and 0.3071 for the training and testing sets, respectively, demonstrating superior robustness and generalization capability.
Effects of the COVID-19 Pandemic on Early Childhood Development and Mental Health: A Systematic Review and Meta-Analysis of Comparative Studies
Authors: Alcon Sasha , Shen Sa , Wong Hong-nei , Rovnaghi Cynthia R. , Truong Leni , Vedelli Jordan K. H. , Anand Kanwaljeet J. S.
Published: November 2024

From 2020 to 2023, the coronavirus-19 (COVID-19) pandemic exposed children to a variety of adverse childhood events, including parental loss, abuse, and disruption in services, and it exacerbated societal inequities. Studies evaluating the mental health of older children and adolescents reported increases in depression and anxiety symptoms, but no reviews have addressed the effects of the pandemic on preschool children. This systematic review and meta-analysis is the first to explore these effects. The goal was to analyze and synthesize longitudinal cohort studies to determine impact of the COVID-19 pandemic on the development and mental health of young children. Searches of multiple databases were performed for studies published between 2018 and 2023 with pre- and post-pandemic evaluations of the mental health or development of preschool children (aged 0–6 years) using objective measures and according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Cohen’s d effect sizes were calculated for each study that utilized the Strengths and Difficulties Questionnaire (SDQ), Ages and Stages Questionnaire (ASQ), or the Child Behavior Checklist (CBCL). Random-effects mixed models combined the estimates of effect sizes to calculate the overall mean effect size. The meta-analyses included 22,348 children from 16 countries. The analyses showed small increases in emotional symptoms and conduct problems, as well as increases in emotional reactivity, anxiety/depression, withdrawal symptoms, attention problems, and aggressive behaviors. A decrease in fine motor and personal–social skills was noted. Studies not included in these meta-analyses also showed negative effects on language and executive function. This systematic review characterizes the detrimental effects of the COVID-19 pandemic on the mental health and development of preschool children across the world. Our results suggest the vulnerability of early childhood to pandemic-related disruptions, although the heterogeneity in study design and child characteristics may limit some of these conclusions.
Thermodynamic Properties of an Electron Gas in a Two-Dimensional Quantum Dot: An Approach Using Density of States
Authors: Pereira Luís Fernando C. , Silva Edilberto O.
Published: November 2024

Potential applications of quantum dots in the nanotechnology industry make these systems an important field of study in various areas of physics. In particular, thermodynamics has a significant role in technological innovations. With this in mind, we studied some thermodynamic properties in quantum dots, such as entropy and heat capacity, as a function of the magnetic field over a wide range of temperatures. The density of states plays an important role in our analyses. At low temperatures, the variation in the magnetic field induces an oscillatory behavior in all thermodynamic properties. The depopulation of subbands is the trigger for the appearance of the oscillations.
Postcolonial Typology: A Pedagogical Note on the Field of Ecotheology
Authors: Aruan Abel K.
Published: November 2024

In the burgeoning field of ecotheology, scholars have been compelled to propose typologies to introduce and categorize existing but diffuse ecotheological insights. Taking ecotheology as a particular epistemic theology, I argue for an alternative way of typologizing that entails meticulously examining the extent to which ecotheologians engage with competing epistemes, namely “postcolonial typology”. To illustrate, I will examine a range of ecotheological works from a postcolonial nation, Indonesia. I present three groups of ecotheologies: the “expansionist”, the “tribalist”, and the “essentialist” approaches (or strategies). The expansionist group extends or expands the systematic theology formerly introduced by European missionaries during the colonial period as a way to ecologize their theology. The tribalist approach prioritizes retrieving and incorporating local or tribal wisdom. Finally, the essentialist group focuses on identifying categorical frameworks that may signify “Indonesianness”, which involves a “strategic” choice of essentialism that yields to a national or transtribal theological cohesion. In the end, I will also note one pedagogical implication of employing postcolonial typology.
Three Type-Scene Murder Stories in 2 Samuel 1, 3, and 4: David’s Accession Apology
Authors: Lee-Sak Yitzhak
Published: November 2024

This article discusses how the affinitive elements in 2 Samuel 1, 3, and 4 can be well-understood using type-scene concepts based on the concept of family resemblance and prototype theory. Applying the “type-scene” concept flexibly enables various arrangements of similar motifs and scenes, or even their absence, in the three murder stories, conveying associated messages. The repetitive motifs and scenes employed are the key characters “going out and coming back”; a background of death; killing a Saulide; mourning; judging or executing the killer; a “blood” curse; and elegy. The successive stylistic variations and modifications in conversation, narration, and elegy, in terms of motif alternations and changes in order, imply that, while David refrains from taking advantage and defends the Saulides’ honor, he treats Saul, Abner, and Ishbosheth differently according to their high and low political values. The increasingly fast pace of the narratives’ logical flow—variously determined by the length of conversation (in both dialogue and soliloquy), mourning, lamentation, and their changes in order and even their absence—indicates that, as the Saulide demise becomes gradually fatal, the narrator’s focus on their remaining members diminishes. Consequently, the establishment of David’s kingship based on his innocence is intensified.
ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing
Authors: Zheng Xiangyue , Qiu Yijuan , Zhang Gang , Lei Tao , Jiang Ping
Published: November 2024

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications.
Real-Time Data Collection and Trajectory Scheduling Using a DRL–Lagrangian Framework in Multiple UAVs Collaborative Communication Systems
Authors: Wang Shanshan , Luo Zhiyong
Published: November 2024

UAV-assisted communication facilitates efficient data collection from IoT nodes by exploiting UAVs’ flexible deployment and wide coverage capabilities. In this paper, we consider a scenario in which UAVs equipped with high-precision sensors collect sensing data from ground terminals (GTs) in real-time over a wide geographic area and transmit the collected data to a ground base station (BS). Our research aims to jointly optimize the trajectory scheduling and the allocation of collection time slots for multiple UAVs, to maximize the system’s data collection rates and fairness while minimizing energy consumption within the task deadline. Due to UAVs’ limited sensing distance and battery energy, ensuring timely data processing in target areas presents a challenge. To address this issue, we propose a novel constraint optimization-based deep reinforcement learning–Lagrangian UAV real-time data collection management (CDRLL—RDCM) framework utilizing centralized training and distributed execution. In this framework, a CNN–GRU network units extract spatial and temporal features of the environmental information. We then introduce the PPO–Lagrangian algorithm to iteratively update the policy network and Lagrange multipliers at different time scales, enabling the learning of more effective collaborative policies for real-time UAV decision-making. Extensive simulations show that our proposed framework significantly improves the efficiency of multi-UAV collaboration and substantially reduces data staleness.
Climatological Trends and Effects of Aerosols and Clouds on Large Solar Parks: Application Examples in Benban (Egypt) and Al Dhafrah (UAE)
Authors: Dhake Harshal , Kosmopoulos Panagiotis , Mantakas Antonis , Kashyap Yashwant , El-Askary Hesham , Elbadawy Omar
Published: November 2024

Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting impact over the years. The trends were quantified into the actualised drop in energy production (Wh/m2/year) in order to obtain the impact of each factor. Aerosols displayed a falling trend of −17.78 Wh/m2/year for Benban and −44.88 Wh/m2/year for Al Dhafrah. Similarly, clouds also portrayed a largely falling trend for both stations, −36.29 Wh/m2/year (Benban) and −70.27 Wh/m2/year (Al Dhafrah). The aerosol and cloud trends were also observed on a monthly basis to study their seasonal variation. The trends were further translated into net increases/decreases in the energy produced and the resulting emissions released. The analysis was extended to quantify the economic impact of the trends. Owing to the falling aerosol and cloud trends, the annual production was foreseen to increase by nearly 1 GWh/year (Benban) and 1.65 GWh/year (Al Dhafrah). These increases in annual production estimated reductions in emission released of 705.2 tonne/year (Benban) and 1153.7 tonne/year (Al Dhafrah). Following these estimations, the projected revenue was foreseen to increase by 62,000 USD/year (Benban) and 100,000 USD/year (Al Dhafrah). Considering the geographical location of both stations, aerosols evidently imparted a larger impact compared with clouds. Severe dust storm events were also analysed at both stations to examine the worst-case scenario of aerosol impact. The results show that the realized losses during these events amounted to 2.86 GWh for Benban and 5.91 GWh for Al Dhafrah. Thus, this study showcases the benefits of Earth observation technology and offers key insights into climatological trends for solar energy planning purposes.
Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications
Authors: Lu Tianxin , Han Peng , Gong Wei , Li Shuangshuang , Mo Shuangling , Hu Kaiyan , Zhang Yihua , Mo Chunyu , Li Yuyan , An Ning , Li Fangjun , Han BingBing , Wan Baofeng , Li Ruidong
Published: November 2024

Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning.
An Automatic Solution for Registration Between Single-Image and Point Cloud in Manhattan World Using Line Primitives
Authors: He Yifeng , Zou Jingui , Zhai Ruoming , Meng Liyuan , Zhao Yinzhi , Yang Dingliang , Wang Na
Published: November 2024

2D-3D registration is increasingly being applied in various scientific and engineering scenarios. However, due to appearance differences and cross-modal discrepancies, it is demanding for image and point cloud registration methods to establish correspondences, making 2D-3D registration highly challenging. To handle these problems, we propose a novel and automatic solution for 2D-3D registration in Manhattan world based on line primitives, which we denote as VPPnL. Firstly, we derive the rotation matrix candidates by establishing the vanishing point coordinate system as the link of point cloud principal directions to camera coordinate system. Subsequently, the RANSAC algorithm, which accounts for the clustering of parallel lines, is employed in conjunction with the least-squares method for translation vectors estimation and optimization. Finally, a nonlinear least-squares graph optimization method is carried out to optimize the camera pose and realize the 2D-3D registration and point colorization. Experiments on synthetic data and real-world data illustrate that our proposed algorithm can address the problem of 2D-3D direct registration in the case of Manhattan scenes where images are limited and sparse.
Discriminating Seagrasses from Green Macroalgae in European Intertidal Areas Using High-Resolution Multispectral Drone Imagery
Authors: Oiry Simon , Davies Bede Ffinian Rowe , Sousa Ana I. , Rosa Philippe , Zoffoli Maria Laura , Brunier Guillaume , Gernez Pierre , Barillé Laurent
Published: November 2024

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m2. The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales.
An Improved 2D Pose Estimation Algorithm for Extracting Phenotypic Parameters of Tomato Plants in Complex Backgrounds
Authors: Cheng Yawen , Ren Ni , Hu Anqi , Zhou Lingli , Qi Chao , Zhang Shuo , Wu Qian
Published: November 2024

Phenotypic traits, such as plant height, internode length, and node count, are essential indicators of the growth status of tomato plants, carrying significant implications for research on genetic breeding and cultivation management. Deep learning algorithms such as object detection and segmentation have been widely utilized to extract plant phenotypic parameters. However, segmentation-based methods are labor-intensive due to their requirement for extensive annotation during training, while object detection approaches exhibit limitations in capturing intricate structural features. To achieve real-time, efficient, and precise extraction of phenotypic traits of seedling tomatoes, a novel plant phenotyping approach based on 2D pose estimation was proposed. We enhanced a novel heatmap-free method, YOLOv8s-pose, by integrating the Convolutional Block Attention Module (CBAM) and Content-Aware ReAssembly of FEatures (CARAFE), to develop an improved YOLOv8s-pose (IYOLOv8s-pose) model, which efficiently focuses on salient image features with minimal parameter overhead while achieving a superior recognition performance in complex backgrounds. IYOLOv8s-pose manifested a considerable enhancement in detecting bending points and stem nodes. Particularly for internode detection, IYOLOv8s-pose attained a Precision of 99.8%, exhibiting a significant improvement over RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose by 2.9%, 5.4%, 3.5%, and 5.4%, respectively. Regarding plant height estimation, IYOLOv8s-pose achieved an RMSE of 0.48 cm and an rRMSE of 2%, and manifested a 65.1%, 68.1%, 65.6%, and 51.1% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose, respectively. When confronted with the more intricate extraction of internode length, IYOLOv8s-pose also exhibited a 15.5%, 23.9%, 27.2%, and 12.5% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose. IYOLOv8s-pose achieves high precision while simultaneously enhancing efficiency and convenience, rendering it particularly well suited for extracting phenotypic parameters of tomato plants grown naturally within greenhouse environments. This innovative approach provides a new means for the rapid, intelligent, and real-time acquisition of plant phenotypic parameters in complex backgrounds.
The Coupling Characteristics of Vapor Pressure Deficit and Soil Moisture in China
Authors: Nie Chong , Hao Chunxu , Chen Xingan , Zhou Juan , Xu Rui , Deng Chenning , Zhang Zeqian , Zhu Yanzhong , Wang Lijing
Published: November 2024

Vapor pressure deficit (VPD) and soil moisture (SM) are the two main parameters related to ecosystem water stresses. They are tightly coupled through land–atmosphere interactions and have large impacts on terrestrial ecosystems and global water and carbon cycles. However, the coupling characteristics between the two have not been thoroughly studied, particularly in the context of climate warming. In this study, based on remote sensing and reanalysis datasets, spatial and temporal variations in the VPD and SM and their correlation coefficients in the growing season in China from 1982 to 2018 were evaluated. Then, the probabilities of compound water stress (high VPD and low SM) were investigated under three scenarios based on a copula analysis. The results show that, over the 37 years, the VPD significantly increased during the growing season, while the SM significantly decreased. The coupling relationship between the VPD and SM was relatively weak in extreme arid and arid regions. In contrast, this relationship was stronger in semi-arid and semi-humid regions, where the probabilities of compound water stress were significantly higher (p < 0.05). The probabilities of compound water stress (high VPD and low SM) were significantly higher than the probabilities when the VPD and SM were independent, and this difference increased with the severity of the water stress in the same region. The obtained results can be further applied to improve Earth system models and formulate agricultural irrigation schemes.
Weather Radars Reveal Environmental Conditions for High Altitude Insect Movement Through the Aerosphere
Authors: Hodges Samuel , Hassall Christopher , Neely Ryan
Published: November 2024

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity.
HyperGAN: A Hyperspectral Image Fusion Approach Based on Generative Adversarial Networks
Authors: Wang Jing , Zhu Xu , Jing Linhai , Tang Yunwei , Li Hui , Xiao Zhengqing , Ding Haifeng
Published: November 2024

The objective of hyperspectral pansharpening is to fuse low-resolution hyperspectral images (LR-HSI) with corresponding panchromatic (PAN) images to generate high-resolution hyperspectral images (HR-HSI). Despite advancements in hyperspectral (HS) pansharpening using deep learning, the rich spectral details and large data volume of HS images place higher demands on models for effective spectral extraction and processing. In this paper, we present HyperGAN, a hyperspectral image fusion approach based on Generative Adversarial Networks. Unlike previous methods that deepen the network to capture spectral information, HyperGAN widens the structure with a Wide Block for multi-scale learning, effectively capturing global and local details from upsampled HSI and PAN images. While LR-HSI provides rich spectral data, PAN images offer spatial information. We introduce the Efficient Spatial and Channel Attention Module (ESCA) to integrate these features and add an energy-based discriminator to enhance model performance by learning directly from the Ground Truth (GT), improving fused image quality. We validated our method on various scenes, including the Pavia Center, Eastern Tianshan, and Chikusei. Results show that HyperGAN outperforms state-of-the-art methods in visual and quantitative evaluations.
Improvement of Space-Observation of Aerosol Chemical Composition by Synergizing a Chemical Transport Model and Ground-Based Network Data
Authors: Li Zhengqiang , Li Zhiyu , Ji Zhe , Xie Yisong , Zhang Ying , Yang Zhuolin , Shi Zheng , Qie Lili , Zhang Luo , Zhang Zihan , Gu Haoran
Published: November 2024

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM2) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events.
Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China
Authors: Wu Shuangshuang , Tao Zhigang , Zhang Li , Chen Song
Published: November 2024

The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases—stop, start, and acceleration—aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk’s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation’s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model’s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation’s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework.
AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images
Authors: Li Jiarui , Cheng Shuli
Published: November 2024

The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. Building upon this foundation, we develop a global multi-scale attention mechanism (GMAM) in the decoder that effectively synthesizes spatial information across multiple scales by learning comprehensive global–local relationships. The architecture is further enhanced by an efficient feature-weighted fusion module (FWFM) that systematically integrates remote spatial features with local semantic information to improve segmentation accuracy. Experimental results across diverse scenarios demonstrate that AFENet achieves superior performance in building structure detection, exhibiting enhanced segmentation connectivity and completeness compared to state-of-the-art methods.
A Universal Method for Quantitatively Measuring Land Surface Anomaly Intensity Using Multiscale Remote Sensing Features
Authors: Gao Shiying , Zhang Jinshui , Duan Yaming , Wang Qiao
Published: November 2024

Land surface anomalies refer to various activities on the Earth’s surface that consist of short-term and sudden changes due to external disturbances. These anomalies are closely related to the safety of human life and property. Remote sensing offers irreplaceable advantages such as broad coverage, high temporal dynamics, and comprehensive observations, so it is the most effective tool for monitoring land surface anomalies and measuring their intensities. However, existing studies have limitations such as unclear sensitivity features, uncertain applicability, and a lack of quantitative expression at different scales. Therefore, this study develops a quantitative assessment framework for land surface anomaly intensity across four scales: the pixel scale, structure scale, object scale, and scene scale. This framework enables an adaptive and flexible weight determination of the intensity of land surface anomalies from a satellite perspective. Using the Chongqing fire as an example of a land surface anomaly, this study evaluates its land surface anomaly intensity. Moreover, we demonstrate the method’s applicability to other land surface anomaly events, such as floods and earthquakes. The experiments reveal that the land surface anomaly intensity evaluation framework, which is constructed based on pixel-scale, structure-scale, object-scale, and scene-scale features, can quantitatively express the land surface anomaly intensity with an accuracy of 75.25% and more effectively represent severely affected areas. The weights of the features at the four scales sequentially decrease: structure scale (0.2974), pixel scale (0.3225), object scale (0.1867), and scene scale (0.1932). The extensive application of this method to other land surface anomaly events provides accurate quantitative expressions of the land surface anomaly intensity. This remote sensing-based multiscale feature assessment method is adaptable and applicable to various land surface anomalies and offers critical decision support for land surface anomaly intensity warning systems.
Improving Flood Streamflow Estimation of Ungauged Small Reservoir Basins Using Remote Sensing and Hydrological Modeling
Authors: Zhou Fangrong , Wu Nan , Luo Yuning , Wang Yuhao , Ma Yi , Wang Yifan , Zhang Ke
Published: November 2024

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment.
Clutter-Sensing-Driven Space-Time Adaptive Processing Approach for Airborne Sub-Array-Level Digital Array
Authors: Wu Youai , Jiu Bo , Pu Wenqiang , Zheng Hao , Li Kang , Liu Hongwei
Published: November 2024

Sub-array-level digital arrays effectively diminish the computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive applications in many airborne platforms. Nonetheless, airborne sub-array-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing-driven STAP approach for airborne sub-array-level digital arrays is proposed in this paper. Firstly, we derive a signal model of sub-array-level clutter sensing in detail and then further analyze the influence of the sidelobe characteristics of the conventional sub-array joint beam on clutter sensing. Secondly, a sub-array joint beam optimization model is proposed, which optimizes the sub-array joint beam into a wide beam with flat-top characteristics to improve the clutter-sensing performance in the beam sidelobe region. Finally, we decompose the complex optimization problem into two subproblems and then relax them into the low sidelobe-shaped beam pattern synthesisproblem and second-order cone programming problem, which can be effectively solved. The effectiveness of the proposed approach is validated in a real clutter environment through numerical experiments.
Sensitivity Analysis of Sentinel-2 Imagery to Assess Urban Tree Functional Traits: A Physical Approach Based on Local Climate Zones
Authors: Le Saint Théo , Nabucet Jean , Lefebvre Sidonie , Gastellu-Etchegorry Jean-Philippe , Hubert-Moy Laurence , Adeline Karine
Published: November 2024

Urban trees contribute to urban well-being but face challenging environments that can reduce their lifespan and increase young tree mortality. Although many studies have used remote sensing data to monitor the functional status of trees in rural areas, few have done so in urban areas to assess the health or estimate the biomass of large green areas. This study assessed the suitability of using Sentinel-2 images to characterize two urban tree functional traits—leaf chlorophyll content (Cab) and leaf area density (LAD)—in isolated trees and tree rows. Simulated Sentinel-2 images were generated using the DART radiative transfer model, considering 16 tree-endogenous and 14 tree-exogenous parameters, with 15 vegetation indices (VIs) analyzed. Sensitivity analysis was performed in four contrasting urban environments using local climate zone taxonomy. The accuracy of the simulated images was validated with real Sentinel-2 images, field measurements, and ancillary data collected for four tree species in Rennes, France. The results showed that the tree parameters significantly influenced Sentinel-2 spectral bands, with NGBDI and OSAVI VIs being most sensitive to Cab and LAD. The model showed high accuracy, with a mean RMSE of 0.016 for key spectral bands. The results also highlighted the importance of considering ancillary data to capture specific urban characteristics.