Search Results (15,360)

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

As critical assets for polar development and global strategy, nuclear-powered icebreakers necessitate rigorous safety research under extreme meteorological conditions. Evaluating their reliability under tornado loads is essential to ensure sustainable Arctic operations. This study employed numerical methods to solve tornado loads and assess the safety performance of an icebreaker subjected to tornado-induced loads. Tornado loads at varying azimuth angles were solved using a modified Ward-type simulator, while wave loads under tornado conditions were determined by a numerical wave model. The results demonstrated that the tornado applied the maximum wind load on the structure at a 0° azimuth angle. The total wind load was reduced by approximately 39% at a 60° azimuth angle. The tornado-induced moment on the ship exhibited a strongly nonlinear relationship with the azimuth angle. The maximum total moment occurred at a 15° azimuth angle, whereas the minimum total moment was observed at a 90° azimuth angle, where the hull experienced minimal wind loads. Full article

This paper proposes a dual−loop model predictive control (MPC) scheme based on grid−side current for modular multilevel converter−based high−voltage direct current (MMC−HVDC) systems. The proposed hybrid control structure combines an MPC−based inner current loop with a PI−based outer voltage loop, designed to enhance dynamic response and steady−state accuracy in HVDC transmission. With the advancement of flexible HVDC technology, modular multilevel converters (MMCs) have been widely adopted due to their excellent scalability and operational flexibility. Model predictive control (MPC), as an advanced control strategy, has demonstrated significant advantages in MMC−HVDC applications. In this study, a dual−loop control system is designed, with MPC as the inner current loop and PI control as the outer voltage loop. This structure effectively enhances control accuracy and ensures system reliability. To validate the effectiveness of the proposed control strategy, a 1000 MW wind power integration MMC−HVDC simulation model was built in Simulink. Simulation results show that the proposed dual−loop MPC strategy can significantly improve control precision and maintain the reliability of the MMC−HVDC system. The proposed strategy is validated through detailed simulations of a 1000 MW wind−integrated MMC−HVDC system, demonstrating superior performance over conventional PI control in terms of overshoot reduction and disturbance rejection. Full article

In this research, a novel hybrid methodology is proposed for predicting the structural response of high concrete arch dams, combining the Discrete Element Method (DEM) with the Locally Estimated Scatterplot Smoothing (LOESS) technique. A structured calibration strategy is employed during the numerical model preparation to enable the generation of a wide range of reliable output variables for training and prediction. The methodology is then applied to the El Atazar arch dam to demonstrate its capability to forecast displacement and stress responses. The study reveals that using the current air temperature as an input variable is not adequate for representing the thermal behavior of the dam body; instead, the mean air temperature over a specified period yields significantly better results. Additionally, the findings highlight the importance of the loading path and the dam’s initial state in determining its structural response. The developed model shows a strong agreement between predicted and observed data, demonstrating its effectiveness in capturing the nonlinear behavior of high concrete arch dams. Compared to traditional parametric models commonly used for dam deformation analysis, the proposed framework offers greater flexibility in representing nonlinearity while requiring less training data, making it ideal for dams with limited monitoring records, such as older dams or newly operated ones. Full article

Traditional reliability indicators, such as SAIDI, SAIFI, DEC, and FEC, remain essential benchmarks, but they have proven insufficient to capture recovery capacity and vulnerability under extreme events. This bibliometric review clarifies these limitations while mapping how advanced control solutions—such as deep reinforcement learning (DRL), model predictive control (MPC), and graph neural networks (GNNs)—are being employed to enhance network restoration, voltage regulation, and outage management. By integrating discussions of conventional indices with the emerging role of artificial intelligence and storage technologies, this study provides a dual contribution: (i) identifying how resilience and reliability are being redefined in the literature, and (ii) highlighting research gaps in the standardization of event-based metrics, such as restoration time and customer minutes lost. The results aim to support regulators and operators in adopting intelligent, secure, and sustainable strategies for distribution networks, ensuring that technological advances are aligned with energy justice and real operational challenges. Full article

The high penetration rate of renewable energy poses significant challenges to the planning and operation of power systems in regions with scarce data. In these regions, it is impossible to accurately simulate the complex nonlinear dependencies among hydro–wind–solar energy resources, which leads to huge operational risks and investment uncertainties. To bridge this gap, this study proposes a new data-driven framework that embeds the natural climate cycle (24 solar terms) into a physically consistent scenario generation process, surpassing the traditional linear approach. This framework introduces the Comprehensive Similarity Distance (CSD) indicator to quantify the curve similarity of power amplitude, pattern trend, and fluctuation position, thereby improving the K-means clustering. Compared with the K-means algorithm based on the standard Euclidean distance, the accuracy of the improved clustering pattern extraction is increased by 3.8%. By embedding the natural climate cycle and employing a two-stage dimensionality reduction architecture: time compression via improved clustering and feature fusion via Kernel PCA, the framework effectively captures cross-source dependencies and preserves climatic periodicity. Finally, combined with the simplified Vine Copula model, high-fidelity joint scenarios with a normalized root mean square error (NRMSE) of less than 3% can be generated. This study provides a reliable and computationally feasible tool for stochastic optimization and reliability analysis in the planning and operation of future power systems with high renewable energy grid integration. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

This article analyzes the effectiveness of Poland’s central government administration in implementing the 2030 Agenda for Sustainable Development, addressing the context of high-level strategic declarations versus actual policy outcomes. The study employs a qualitative critical document analysis, conducted as comprehensive desk research. This method involves a comparative analysis of official strategic and policy documents (e.g., “Strategy for Responsible Development”) against the empirical findings of external audits from the Supreme Audit Office (NIK), supplemented by national (GUS) and international statistical data. The analysis reveals a fundamental “implementation gap.” While Poland has successfully created a robust strategic and institutional framework, reflected in high international SDG rankings, this success masks deep deficits and stagnation in key areas, particularly in the environmental dimension. Audits consistently confirm systemic problems with inter-ministerial coordination, ensuring adequate financing, and the lack of reliable evaluation for key programs, such as “Clean Air” or the circular economy roadmap. Considering these findings, the study concludes that operational effectiveness does not match strategic declarations. The analysis identifies systemic weaknesses and recommends urgent, targeted strategic actions to bridge the gap between policy and practice, particularly by strengthening coordination and evaluation mechanisms. Full article

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of $R^2=0.697$ and $R^2=0.794$ for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

Forensic statisticians have long debated whether searching large DNA databases undermines the evidential value of matches. Modern surveillance faces an exponentially harder problem: screening populations across thousands of attributes using threshold rules. Intuition suggests that requiring many coincidental matches should make false alerts astronomically unlikely. This intuition fails. Consider a system monitoring 1000 attributes, each with a 0.5 percent innocent match rate. Matching 15 pre-specified attributes has probability ${10}^{-35}$, 1 in 30 decillion, effectively impossible. But operational systems may flag anyone matching any 15 of the 1000. In a city of one million innocents, this produces about 226 false alerts. A seemingly impossible event becomes guaranteed. This is a mathematical consequence of high-dimensional screening, not implementation failure. We identify fundamental probabilistic limits on screening reliability. Systems undergo sharp transitions from reliable to unreliable with small data scale increases, a fragility worsened by data growth and correlations. As data accumulate and correlation collapses effective dimensionality, systems enter regimes where alerts lose evidential value even when individual coincidences remain vanishingly rare. This framework reframes the DNA database controversy as a regime shift. Unequal surveillance exposures magnify failure, making “structural bias’’ mathematically inevitable. Beyond a critical scale, failure cannot be prevented through threshold adjustment or algorithmic refinement. Full article

This study presents an enhanced hybrid TLBO–ANN model for daily photovoltaic (PV) power generation prediction. By combining the strong nonlinear modeling capacity of Artificial Neural Networks (ANN) with the robust optimization capability of the Teaching–Learning-Based Optimization (TLBO) algorithm, the proposed framework effectively improves prediction accuracy and generalization performance. The model was trained using real meteorological and power generation data and validated on a grid-connected PV power plant in Türkiye. Results indicate that the hybrid TLBO–ANN approach outperforms the conventional ANN by achieving 39.97% and 37.46% improvements on the test subset and overall dataset, respectively. The improved convergence behavior and avoidance of local minima by TLBO contribute to this enhanced accuracy. Overall, the proposed hybrid model provides a powerful and practical tool for reliable PV power forecasting, which can facilitate better grid integration, operational planning, and energy management in renewable energy systems. Full article

Accurate terrain perception is essential for safe rover operations and reliable geotechnical interpretation of Martian surfaces. The heterogeneous scales, colors, and textures of Martian terrain present significant challenges for semantic segmentation. We present MarsTerrNet, a dual-backbone segmentation framework that combines Progressive Residual Blocks (PRB) with a Swin Transformer to jointly capture fine-grained local details and global contextual dependencies. To further enhance discrimination among geologically correlated classes, we design a feature-guided loss that aligns representative features across terrain categories and reduces confusion between visually similar but physically distinct types. For comprehensive evaluation, we establish MarsTerr2024, an extended dataset derived from the Curiosity rover, providing diverse geological scenes for terrain understanding. Experimental results show that MarsTerrNet achieves state-of-the-art performance and produces geologically consistent segmentation results, supporting automated mapping and geotechnical assessment for future Mars exploration missions. Full article

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article