Search Results (5,398)

In mountainous canyon regions, BeiDou Navigation Satellite System (BDS)/Global Navigation Satellite System (GNSS) receivers are susceptible to multireflection and tropospheric factors, which frequently reduce the accuracy in monitoring vertical deformation monitoring under short-baseline methods. This limitation hinders the application of BDS/GNSS in high-precision monitoring scenarios in those cases. To address this issue, this study proposes a three-dimensional (3D) deformation measurement method that integrates BDS/GNSS positioning with dihedral corner reflectors (CRs). By incorporating high-precision horizontal positioning results obtained from BDS/GNSS into the radar line-of-sight (LOS) correction process and utilizing ascending and descending Synthetic Aperture Radar (SAR) data for joint monitoring, the method achieves millimeter-level- accuracy in measuring vertical deformation at corner reflector sites. At the same time, it enhances the 3D positioning accuracy of BDS/GNSS to the 1 mm level under short-baseline configurations. Based on monitoring stations deployed at the Jinsha River dam site, the proposed deformation fusion monitoring method was validated using high-resolution SAR imagery from Germany's TerraSAR-X (TSX) satellite. Simulated horizontal and vertical displacements were introduced at the stations. The results demonstrate that BDS/GNSS achieves better than 1 mm horizontal monitoring accuracy and a vertical accuracy of around 5 mm. Interferometric SAR (InSAR) CRs achieve approximately 2 mm in horizontal accuracy and 1 mm in vertical accuracy. The integrated method yields a 3D deformation monitoring accuracy better than 1 mm. This paper’s results show high potential for achieving high-precision deformation observations by fusing BDS/GNSS and dihedral CRs, offering promising prospects for deformation monitoring in reservoir canyon regions. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article

Small object detection in UAV-based remote sensing imagery is crucial for applications such as traffic monitoring, emergency response, and urban management. However, aerial images often suffer from low object resolution, complex backgrounds, and varying lighting conditions, leading to missed or false detections. To address these challenges, we propose DCS-YOLOv8, an enhanced object detection framework tailored for small target detection in UAV scenarios. The proposed model integrates a Dynamic Convolution Attention Mixture (DCAM) module to improve global feature representation and combines it with the C2f module to form the C2f-DCAM block. The C2f-DCAM block, together with a lightweight SCDown module for efficient downsampling, constitutes the backbone DCS-Net. In addition, a dedicated P2 detection layer is introduced to better capture high-resolution spatial features of small objects. To further enhance detection accuracy and robustness, we replace the conventional CIoU loss with a novel Scale-based Dynamic Balanced IoU (SDBIoU) loss, which dynamically adjusts loss weights based on object scale. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed DCS-YOLOv8 significantly improves small object detection performance while maintaining efficiency. Compared to the baseline YOLOv8s, our model increases precision from 51.8% to 54.2%, recall from 39.4% to 42.1%, $mA{P}_{0.5}$ from 40.6% to 44.5%, and $mA{P}_{0.5:0.95}$ from 24.3% to 26.9%, while reducing parameters from 11.1 M to 9.9 M. Moreover, real-time inference on RK3588 embedded hardware validates the model’s suitability for onboard UAV deployment in remote sensing applications. Full article

The European energy transition, marked by the increasing share of renewable sources in the production mix, brings to the fore the issue of maintaining power quality under conditions of high variability. This study proposes an adaptive monitoring model based on a zonal classification of electrical networks according to the volatility of net renewable production (wind and photovoltaic). The approach relies on a proprietary Renewable Variability Index (RVI), developed using publicly available European datasets, to assess the mismatch between electricity consumption and renewable generation in six representative countries: Germany, Denmark, Spain, Poland, Romania, and Sweden. Based on this index, the model defines three zonal risk levels and recommends differentiated power quality monitoring strategies: continuous high-resolution observation in critical areas, adaptive monitoring in medium-risk zones, and conditional event-based activation in stable regions. The results demonstrate a significant reduction in data acquisition requirements, without compromising the capacity to detect disruptive events. By incorporating adaptability, risk sensitivity, and selective allocation of monitoring resources, the proposed framework enhances operational efficiency in smart grid environments. It aligns with current trends in smart grid digitalization, enabling scalable, context-aware control and protection mechanisms that support Europe’s sustainability and energy security objectives while contributing to the broader goals of sustainable energy transition and long-term grid resilience. Full article

Objective: The aim of our study was to investigate whether faecal concentrations of eosinophil cationic protein (fECP) and human β-defensins (HBD2s) are significantly elevated in children with cow’s milk-protein-induced allergic colitis (MPIAP) and whether a monthly milk-free diet reduces these markers. Materials and methods: This was a single-centre, prospective, observational cohort study involving 70 infants with MPIAP, aged 1–3 months, and 30 healthy controls of the same age. The concentrations of fECP and HBD2 were measured using the ELISA method (IDK^® Eosinophil Cationic Protein and β-Defensins ELISA Kit, Immunodiagnostik AG, Germany). Diagnosis of MPIAP was confirmed with an open milk challenge test. Results: The concentrations of fECP and HBD2 proved useful in evaluating MPIAP treatment with a milk-free diet, where the resolution of allergy symptoms and a significant (p = 0.0000) decrease in the concentrations of both biomarkers were observed after 4 weeks of following the diet. The concentrations of fECP and HBD2 were still higher than those in the control group. High concentrations of fECP can be helpful in diagnosing MPIAP (100% sensitivity), but the low specificity of the assay means that there is a risk of diagnosing MPIAP in one in six children who do not have the disease. The concentrations of HBD2 have low sensitivity, so one in four children with MPIAP will not be confirmed to have the disease using this indicator. Conclusions: fECP and HBD2 can be used to monitor the resolution of colitis in infants with MPIAP treated with a milk diet, indicating a slower resolution of allergic inflammation than the resolution of allergic symptoms. Therefore, neither of the parameters are useful for the diagnosis of MPIAP. Full article

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Japanese knotweed (Fallopia japonica) is a noxious invasive plant species that requires scalable and precise monitoring methods. Current visually based ground surveys are resource-intensive and inefficient for detecting Japanese knotweed in landscapes. This study presents a transformer-based semantic segmentation framework for the automated detection of Japanese knotweed patches using high-resolution RGB imagery acquired with unmanned aerial vehicles (UAVs). We used the Twins Spatially Separable Vision Transformer (Twins-SVT), which utilizes a hierarchical architecture with spatially separable self-attention to effectively model long-range dependencies and multiscale contextual features. The model was trained on 6945 annotated aerial images collected in three sites infested with Japanese knotweed in West Virginia, USA. The results of this study showed that the proposed framework achieved superior performance compared to other transformer-based baselines. The Twins-SVT model achieved a mean Intersection over Union (mIoU) of 94.94% and an Average Accuracy (AAcc) of 97.50%, outperforming SegFormer, Swin-T, and ViT. These findings highlight the model’s ability to accurately distinguish Japanese knotweed patches from surrounding vegetation. The method and protocol presented in this research provide a robust, scalable solution for mapping Japanese knotweed through aerial imagery and highlight the successful use of advanced vision transformers in ecological and geospatial information analysis. Full article

The on-orbit geometric calibration accuracy of high-resolution space cameras directly affects the application value of Earth observation data. Conventional on-orbit geometric calibration methods primarily rely on ground calibration fields, making it difficult to simultaneously achieve high precision and real-time monitoring. To address this limitation, we, in collaboration with Tsinghua University, propose a high-precision, real-time, on-orbit geometric calibration system based on active optical monitoring. The proposed system employs reference lasers to integrate the space camera and the star tracker into a unified optical system, enabling real-time monitoring and correction of the camera’s exterior orientation parameters. However, during on-orbit operation, the space camera is subjected to a complex thermal environment, which induces thermal deformation of optical elements and their supporting structures, thereby degrading the measurement accuracy of the geometric calibration system. To address this issue, this article analyzes the impact of temperature fluctuations on the focal plane, the reference laser unit, and the laser relay folding unit and proposes athermalization design optimization schemes. Through the implementation of a thermal-compensated design for the collimation optical system, the pointing stability and divergence angle control of the reference laser are effectively enhanced. To address the thermal sensitivity of the laser relay folding unit, a right-angle cone mirror scheme is proposed, and its structural materials are optimized through thermo–mechanical–optical coupling analysis. Finite element analysis is conducted to evaluate the thermal stability of the on-orbit geometric calibration system, and the impact of temperature variations on measurement accuracy is quantified using an optical error assessment method. The results show that, under temperature fluctuations of 5 °C for the focal plane and the reference laser unit, 1 °C for the laser relay folding unit, and 2 °C for the star tracker, the maximum deviation of the system’s measurement reference does not exceed 0.57″ (3σ). This enables long-term, stable, high-precision monitoring of exterior orientation parameter variations and improves image positioning accuracy. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

Accurate monitoring of snow and ice cover is essential for climate research and disaster management, but conventional remote sensing methods often struggle in complex terrain and fog-contaminated conditions. To address the challenges of high-resolution UAV-based snow and ice segmentation—including visual similarity, fragmented spatial distributions, and terrain shadow interference—we introduce IceSnow-Net, a U-Net-based architecture enhanced with three key components: (1) a ResNet50 backbone with atrous convolutions to expand the receptive field, (2) an Atrous Spatial Pyramid Pooling (ASPP) module for multi-scale context aggregation, and (3) an auxiliary path loss for deep supervision to enhance boundary delineation and training stability. The model was trained and validated on UAV-captured orthoimagery from Ganzi Prefecture, Sichuan, China. The experimental results demonstrate that IceSnow-Net achieved excellent performance compared to other models, attaining a mean Intersection over Union (mIoU) of 98.74%, while delivering 27% higher computational efficiency than U-Mamba. Ablation studies further validated the individual contributions of each module. Overall, IceSnow-Net provides an effective and accurate solution for cryosphere monitoring in topographically complex environments using UAV imagery. Full article

Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m², N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices. Full article

This study proposes a rigid-flexible neural optrode integrated with anti-bending SU-8 optical waveguides and locally soft peptide-functionalized microelectrodes to address the challenges of precise implantation and long-term biocompatibility in traditional neural interfaces. Fabricated via microelectromechanical systems (MEMS) technology, the optrode features a PBK/PPS/(PHE)₂ trilayer electrochemical modification that suppresses photoelectrochemical (PEC) noise by 63% and enhances charge storage capacity by 51 times. A polyethylene glycol (PEG)-enabled temporary rigid layer ensures precise implantation while allowing post-implantation restoration of flexibility and enabling positioning adjustment. In vitro tests demonstrate efficient light transmission through SU-8 waveguides in agar gel and a 63% reduction in PEC noise peaks. Biocompatibility analysis reveals that peptide-coated PI substrates improve cell viability by 32.5–37.1% compared to rigid silicon controls. In vivo validation in crucian carp midbrain successfully records local field potential (LFP) signals (60–80 μV), thereby confirming the optrode’s sensitivity and stability. This design provides a low-damage and high-resolution tool for neural circuit analysis. It also lays a technical foundation for future applications in monitoring neuronal activity and researching neurodegenerative diseases with high spatiotemporal resolution. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article

Low-cost ocean monitoring systems are increasingly needed to address data gaps in coastal environments, particularly in regions where traditional research infrastructure is limited. This paper presents the design, development, and field deployment of a biophysical ocean buoy (BOB)—a compact, solar-powered autonomous buoy system capable of measuring sea surface temperature, salinity (via electrical conductivity), total dissolved solids, pH, and GPS position. The system features real-time data transmission via the Iridium satellite, local data logging, and modular sensor integration. The BOB was deployed for three missions in the Seto Inland Sea, Japan, ranging from 26–56 h in duration. The system successfully recorded high-resolution environmental data, revealing coastal gradients, diurnal heating cycles, and tidal current reversals. Over 95% of the measurements were successfully recovered, and the Iridium communications exceeded 90% reliability. The temperature and salinity data captured fine-scale variations consistent with freshwater plume interactions and tidal forcing. With a total system cost under USD 2000 and minimal deployment requirements, the BOB offers a scalable solution for distributed ocean monitoring. Its performance suggests strong potential for use in aquaculture monitoring, coastal hazard detection, and climate change research, especially in data-sparse regions. This work contributes to the growing field of democratized ocean observation, combining affordability with operational reliability. Full article