Search Results (658)

As network environments become increasingly interconnected, ensuring robust cyber-security has become critical, particularly with the growing sophistication of modern cyber threats. Intrusion detection systems (IDSs) play a vital role in identifying and mitigating unauthorized or malicious activities; however, conventional machine learning-based IDSs often rely on handcrafted features and are limited in their ability to detect diverse attack types across disparate network domains. To address these limitations, this paper introduces a novel unified intrusion detection framework that implements “Structural Dualism” to integrate three heterogeneous benchmark datasets (CSE-CIC-IDS2018, NF-BoT-IoT-v2, and IoT-23) into a harmonized, protocol-agnostic representation. The framework employs a shared autoencoder architecture with dataset-specific projection layers to learn a unified latent manifold. This 15-dimensional space captures the underlying semantics of attack patterns (e.g., volumetric vs. signaling) across multiple domains, while dataset-specific decoders preserve reconstruction fidelity through alternating multi-domain training. To identify complex micro-signatures within this manifold, the framework utilizes a synergistic hybrid convolutional neural network–deep neural network (CNN–DNN) classifier, where the CNN extracts spatial latent patterns and the DNN performs global classification across twenty-five distinct classes. Class imbalance is addressed through resampling strategies such as adaptive synthetic sampling (ADASYN) and edited nearest neighbors (ENN). Experimental results demonstrate remarkable performance, achieving 99.76% accuracy for binary classification and 99.54% accuracy for multi-class classification on the merged dataset, with strong generalization confirmed on individual datasets. These findings indicate that the shared autoencoder-based CNN–DNN framework, through its unique feature alignment and spatial extraction capabilities, significantly strengthens intrusion detection across diverse and heterogeneous environments. Full article

Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and availability. To address these limitations, a rice mapping framework suitable for different geographical environments was developed based on a random forest (RF) by combining time-series harmonic analysis (HANTS) with Sentinel-1 and Sentinel-2 multi-source data. To address these limitations, a rice mapping classification algorithm for different geographical environments was developed by combining Harmonic Analysis of Time Series (HANTS) with Sentinel-1/2 multi-source data. The research obtained annual maps of single-season and double-season rice in the research area from 2019 to 2024, with a spatial resolution of 10 m. The results indicated that the Sentinel-1, Sentinel-2, GEE, and HANTS algorithm can effectively support the yearly mapping of single- and double-season paddy rice in Anhui Province, China. The resultant paddy rice map has a high accuracy with overall accuracies exceeding 92% and Kappa coefficients above 0.84. HANTS effectively captures key phenological features of paddy rice, and it can especially enhance the discrimination between single- and double-season rice; compared to existing rice mapping products, the proposed approach reduces classification errors by an average of 3.92% in six major rice-producing cities, each with cultivation areas exceeding 1 million hectares; spatial correlation analysis indicates substantial heterogeneity in rice cultivation patterns across northern, central, and southern Anhui, associated with both biophysical and anthropogenic factors. These results indicate that integrating phenological data with machine learning can enhance the accuracy of long-term, high-resolution crop monitoring, and annual rice maps will offer valuable support for food security assessment, water resource management, and policy planning. Full article

In recent years, improving the energy efficiency of induction machines (IM) has become a key research focus, with particular attention to loss reduction. Losses in IM are significantly influenced by two design-related factors: the nonlinear magnetization characteristic and the non-sinusoidal distribution of the magnetomotive force (MMF) in stator slots. These effects lead to harmonic distortions in stator and rotor currents as well as pulsations of the electromagnetic torque. This paper presents a comprehensive harmonic analysis of the interaction between the nonlinear magnetization curve and the non-sinusoidal MMF distribution in induction machines. A mathematical model in phase coordinates was developed, incorporating both effects through the introduction of harmonic components into the magnetizing inductance. The proposed model enables the evaluation of the impact of these phenomena on stator and rotor currents, as well as on the electromagnetic torque. The validity of the model is verified by experimental results, which show close agreement with simulations. The analysis demonstrates that the nonlinearity of the magnetization curve results in the appearance of the third harmonic in stator currents and the second harmonic in torque, while the non-sinusoidal MMF distribution produces the fifth and seventh harmonics in stator currents and the sixth harmonic in torque. Additionally, the study reveals that in no-load conditions, the third harmonics are dominant, whereas with increasing load, their magnitudes decrease, and the amplitudes of the fifth and seventh harmonics increase due to the interaction between stator and rotor currents. The proposed modeling approach provides an effective tool for accurate performance evaluation and design optimization of induction motor drives Full article

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study. Full article

The Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD) are the two main European policy instruments for assessing eutrophication in coastal waters, yet their differing assessment architectures often lead to inconsistent classification outcomes. This study provides a scientific comparison of WFD Ecological Status and MSFD Good Environmental Status (GES) classifications for Portuguese coastal waters across three assessment cycles. This is achieved by quantifying the coherence between Eutrophication assessments, by identifying the main drivers of divergence beyond chance, and evaluating where harmonization improved agreement, providing an evidence-based guidance to decision-making and policy regulation. Using officially validated national classifications, we analyzed the methodological drivers of divergence (without reprocessing raw monitoring data) and harmonized both outcomes into a common three-class scheme. Coherence was evaluated using a Discordance Index and Cohen’s kappa coefficient. Results showed that divergence was systematic rather than random, primarily driven by structural (spatial and temporal) misalignment, methodological differences in indicator integration, and contrasting statistical metrics. Both Directives consistently identify eutrophication hotspots associated with major river plumes but differ in how these signals are aggregated and translated into status classes. The study demonstrated that WFD and MSFD provide complementary but only partially aligned assessments, and that coherence improved with methodological harmonization. Full article

Pakistan’s forests cover only 4.2% of the national territory yet deliver critical ecosystem services that remain largely unaccounted for in policy and planning. This study provides the first harmonized, country-wide assessment of timber production and carbon sequestration services using Sentinel 2 imagery and standardized valuation frameworks. A cloud-free Sentinel 2 composite for 2024 was processed at 20 m resolution to map forest cover, revealing an extent of 40,784 km² concentrated below 2500 m a.s.l. Timber production was valued under two perspectives: forest-derived harvests (289,000 m³ yr⁻¹; ~140 million USD yr⁻¹) and total national supply (15 million m³ yr⁻¹; ~7.3 billion USD yr⁻¹), highlighting the marginal role of natural forests in Pakistan’s wood economy. Conversely, carbon sequestration emerges as a high magnitude regulating service: forests remove 2.53 million Mg CO₂ yr⁻¹, corresponding to 78 million USD yr⁻¹ at a carbon price of 31 USD t⁻¹ CO₂. Sensitivity analysis across canopy thresholds (30%, 50%, 75%) confirms the robustness of this pattern. Despite their limited spatial footprint, Pakistan’s forests provide ecosystem services whose economic and ecological significance far exceeds their area. Findings underscore the need for integrated forest-landscape governance, improved monitoring systems, and inclusion of regulating services in national planning and carbon-finance mechanisms. Full article

Remote sensing has revolutionized monitoring landscapes that are inaccessible or impractical to survey on the ground. Satellite platforms such as Sentinel-2 enable assessment of ecosystem changes over extensive areas with high temporal frequency, while Unmanned Aerial Systems (UAS) offer flexible, ultra-high-resolution observations ideal for site-specific analysis and sensitive environments. This study compares the performance of Sentinel-2 and Phantom 4 multispectral RTK data for monitoring vegetation dynamics in Mediterranean shrubland ecosystems, focusing on the Normalized Difference Vegetation Index (NDVI). Both platforms produced broadly consistent patterns in seasonal and interannual vegetation dynamics. However, UAS outperformed satellite data in capturing fine-scale heterogeneity, regeneration patches, and subtle disturbance responses, particularly in sparsely vegetated or heterogeneous terrain where satellite metrics may be insensitive. The comparison of NDVI across platforms accounted for standardized processing, harmonization, radiometric and atmospheric correction, and spatial resolution differences. Results show platform selection can be optimized according to monitoring objectives: satellite data are well suited for long-term monitoring of landscape-level vegetation dynamics, as both platforms capture consistent patterns when evaluated at comparable, spatially aggregated scales, while UAS data provide critical detail for localized management, early stress detection, and restoration prioritization by resolving fine-scale features. A combined approach enhances ecosystem disturbance assessments and resource management by binding the strengths of both wide-area coverage and precise spatial detail. Full article

This study develops a reproducible regional screening workflow to assess geothermal potential in the Cambrian reservoir system of Western Lithuania under conditions of sparse and heterogeneous legacy subsurface data. The approach integrates data compilation, cleaning, and harmonization from archival well materials, ordinary kriging spatialization of key reservoir properties with uncertainty multipliers, standardized doublet simulations to derive comparative thermal performance indicators, and a neural network surrogate to accelerate regional inference. The workflow integrates 12 compiled reservoir control points into a gridded regional representation (25 × 30 cells; ~6750 km²) and evaluates uncertainty through low, mid and high scenarios (±10%). Physics-based simulations were executed for 303 representative grid locations per scenario, yielding cumulative extracted-energy indicators on the order of 10⁵–10⁷ MWh across cases (reported as comparative indicators). The neural network surrogate reproduced simulation outputs with a high predictive agreement (test R² = 0.996; cross-validation mean R² ≈ 0.99), enabling swift prediction across the remaining grid cells after training. Relative potential maps highlight spatially coherent zones of higher prospectivity and provide a transparent basis for prioritizing follow-up investigations and data acquisition. The proposed framework is modular and can be refined as improved geological constraints, thermophysical properties, and operational assumptions become available. Full article

Spatial analyses of built-up areas based on aggregated land cover data are inherently affected by the Modifiable Areal Unit Problem (MAUP). This study quantifies the influence of the data scale and the areal unit configuration on Lorenz-based measures of the concentration of the built area. Using Łódź Metropolitan Area (Poland) as a case study, harmonized land cover datasets at scales of 1:10,000 and 1:100,000 were analysed with regular square and hexagonal grids of varying sizes, as well as irregular cadastral units. Concentration was measured using a Lorenz curve-based coefficient and sensitivity to zonation was assessed using the coefficient of variation. The results show that the data scale is the primary determinant of the concentration values, with coarser-scale data consistently producing higher and more variable coefficients. Increasing the size of the areal unit leads to a systematic decrease in the concentration measured, while differences in unit geometry and location exert a comparatively minor influence. Irregular cadastral units improve spatial interpretability, but do not reduce susceptibility to MAUP. The findings confirm the strong scale dependency of concentration measures and highlight the necessity of multiscale approaches in quantitative analyses of built-up areas. Full article

Cyclic freezing and thawing (FT) are a primary cause of cracking in concrete, yet current assessment procedures in Germany rely heavily on qualitative estimation using the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM) capillary suction, internal damage and freeze-thaw (CIF) and Capillary de-icing freeze-thaw (CDF) tests. Although these standard tests provide a general overview of the condition of concrete damage in specimens through the estimation of water saturation through capillary suction, mass of surface delamination, qualitative open surface damage, and relative dynamic modulus of elasticity, they do not take quantitative analysis of voids, including cracks and air pores, directly into account. To address this, we propose a novel workflow utilizing deep learning-based semantic segmentation with Fourier-based slice-to-volume registration by combining 2D light microscopy (LM) of petrographic sections and 3D micro-computed tomography ($\mu$CT). We segment cracks, air pores, and aggregates in both modalities and employ feature matching alongside spatial harmonics analysis for 3D shape description. The best proposed 3D registration framework through feature matching demonstrated a success rate of 89.75%, achieving a dissimilarity of 5.21% in relative root mean square error (RRMSE) terms and thereby significantly surpassing the performance of compared 2D-only methods adapted from the body of research. Our approach enables precise, automated, and verifiable quantification of voids across CT and LM modalities and paves the way for advanced computational modeling-based methods to investigate moisture transfer mechanisms for more accurate assessments of frost damage in concrete, service life prediction models, deep learning applications for multimodal data fusion, and more comprehensive FT damage simulations. Full article

KANECS is a 3D multigroup neutronics code based on the Simplified Spherical Harmonics (SP_N) approximation and the Continuous Galerkin Finite Element Method (CGFEM). In this work, the code is extended to solve the time-dependent neutron kinetics by implementing a fully implicit backward Euler scheme for the neutron transport equation and an implicit exponential integration for delayed neutron precursors. These schemes ensure unconditional stability and minimize temporal discretization errors, making the method suitable for fast transients. The new formulation transforms each time step into a transient fixed-source problem, which is solved efficiently using the GMRES solver with ILU preconditioning. The kinetics module is validated against established benchmark problems, including TWIGL, the C5G2 MOX benchmark, and both 2D and 3D mini-core rod-ejection transients. KANECS shows close agreement with the reference solutions from well-known neutron transport codes, with consistent accuracy in normalized power evolution, spatial power distributions, and steady-state eigenvalues. The results confirm that KANECS provides a reliable and accurate framework for solving neutron kinetics problems. Full article

Nonlinear loads are widely applied, making the generation mechanism of grid harmonics increasingly intricate. However, high-precision monitoring devices suffer from high deployment costs and limited coverage. This poses a major challenge to directly acquiring harmonic voltages at some nodes. To solve this problem, this paper proposes a harmonic state estimation method based on a Dual-Stream Adaptive Fusion Generative Adversarial Network (DSAF-GAN), with an innovative design in its generator architecture. A dual-path generator is developed to extract multi-scale features through heterogeneous network branches collaboratively. The ResNet-GRU path integrates convolutional residual modules with Bidirectional Gated Recurrent Units (Bi-GRUs). It effectively captures local spatial patterns and temporal dynamic characteristics of time-series data. The multi-layer perceptron (MLP) path focuses on mining global nonlinear correlations, thereby enhancing the overall feature-expressing capability. An adaptive weight fusion module (Attention Weight Net) fuses the outputs of the two paths. It dynamically allocates contribution weights, improving the model’s flexibility and generalization performance. Experimental results show that the proposed DSAF-GAN can accurately reconstruct the harmonic voltage component content rate of missing nodes. Full article

The monitoring of perennial and annual crops requires different analytical approaches due to their contrasting phenological dynamics and management practices. This study investigates the temporal behavior of the Normalized Difference Vegetation Index (NDVI) derived from Harmonized Landsat and Sentinel-2 (HLS) imagery to characterize apple, grape, soybean, and maize crops in Vacaria, Southern Brazil, between January 2024 and April 2025. NDVI time series were extracted from cloud-free HLS observations and analyzed using raw, interpolated, and Savitzky–Golay, smoothed data, supported by field reference points collected with the AgroTag application. Distinct NDVI temporal patterns were observed, with apple and grape showing higher stability and soybean and maize exhibiting stronger seasonal variability. Descriptive statistics derived from 112 observation dates confirmed these differences, highlighting the ability of HLS-based NDVI time series to capture crop-specific phenological patterns at the municipal scale. Complementary analysis using the SATVeg platform demonstrated consistency in long-term vegetation trends while evidencing scale limitations of coarse-resolution data for small perennial plots. Overall, the findings demonstrate that the NDVI enables robust monitoring of mixed agricultural landscapes, with complementary spatial resolutions and analytical tools enhancing crop-specific phenological analysis. Full article

Almonds are a highly valued nut due to their rich protein and nutritional content. However, they are vulnerable to aflatoxin B1 (AFB1) contamination in warm and humid environments. Consumption of AFB1-contaminated almonds can pose serious health risks, including kidney damage, and may lead to significant economic losses. Consequently, a rapid and non-destructive detection method is essential to ensure food safety by identifying and removing contaminated almonds from the supply chain. Hyperspectral imaging (HSI) and 3D deep learning provide a non-destructive, efficient alternative to current AFB1 detection methods. This study presents an attention-guided Inception ResNet 3D Network (AGIR-3DNet) for fast and precise detection of AFB1 contamination in almonds utilizing HSI. The proposed model integrates multi-scale feature extraction, residual learning, and attention mechanisms to enhance spatial-spectral feature representation, enabling more precise classification. The proposed 3D model was rigorously tested, and its performance was compared against 3D Inception and various conventional machine learning models. Compared to conventional machine learning models and deep learning architectures, AGIR-3DNet outperformed and achieved superior validation accuracy of 93.30%, an F1-score (harmonic mean of precision and recall) of 0.94, and an area under the receiver operating characteristic curve (AUC) value of 0.98. Furthermore, the model enhances processing efficiency, making it faster and more suitable for real-time industrial applications. Full article

Wildfires represent a growing environmental and socio-economic threat across Mediterranean landscapes, where prolonged summer droughts and human activity increasingly shape ignition susceptibility. This study presents an open and reproducible modelling framework for comparing the relative influence of anthropogenic and biophysical drivers of wildfire ignition susceptibility across selected Mediterranean regions. Using harmonized 500 m predictors derived from global remote-sensing datasets, we integrate vegetation condition, topography, climatic context, and human pressure indicators within a cloud-based Google Earth Engine workflow. Two tree-based machine-learning models (Random Forest and Extreme Gradient Boosting) are trained and evaluated using spatial cross-validation and cross-region transfer experiments. Results consistently highlight the dominant role of anthropogenic pressure in shaping ignition susceptibility across all study areas, with night-time lights and human modification indices contributing to the largest share of model importance. Both models achieve high predictive performance (AUC > 0.90) and retain stable accuracy under cross-region transfer (mean transfer AUC ≈ 0.85), indicating partial generalization of human-driven ignition patterns across Mediterranean landscapes. Beyond predictive performance, the principal contribution of this work lies in its harmonized cross-regional comparison and explicit evaluation of model transferability using open data and scalable cloud processing. The resulting susceptibility maps provide a transparent and operational basis for comparative wildfire risk assessment and prevention planning within comparable Mediterranean contexts. Full article