Search Results (3,605)

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R², reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R² of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R² of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R² of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change. Full article

To address the persistent challenges of the “disconnect between macro-level spatial zoning and micro-level land allocation” and the paradox of “localized intensification accompanied by overall inefficiency” in territorial spatial governance, this study adopts a multi-scale coupling perspective to explore an optimized spatial pattern that promotes the coordinated development of production, living, and ecological functions (PLEFs), thereby enhancing the systematic and scientific basis of spatial governance. Taking the Central Yunnan Urban Agglomeration (CYUA) as a case study, a coupled optimization framework integrating macro-scale spatial zoning and micro-scale land allocation was established. First, using multi-period land use data (2000–2020) in conjunction with multi-source geographic and socio-economic datasets, the correspondence between land use types and PLEFs was constructed, and the spatiotemporal evolution characteristics of these functions were systematically analyzed. Second, the GMOP-PLUS model was employed to simulate and optimize land use patterns for 2035 under multiple development scenarios, and dominant spatial types were further refined based on grid-scale spatial suitability evaluation results. Third, the NRCA model was applied to identify comparative functional advantages at the county level. These advantages were comprehensively integrated with the revised dominant spatial types following the principle of “seeking common ground while preserving differences,” ultimately enabling the delineation of optimized territorial spatial development zones. The results indicate the following: (1) Throughout the study period, ecological space remained the dominant land use type (exceeding 75%), although its proportion gradually declined. Living space expanded markedly, while the internal structure of production space shifted toward industrial and mining land uses. (2) The planning control scenario was identified as the most suitable development pathway, exhibiting a spatial configuration characterized by a “central core with stronger development in the southeast than in the northwest.” Under this scenario, production and living spaces continued to expand, whereas ecological space maintained its dominant status. (3) Spatial suitability evaluation revealed a high degree of functional compatibility across the study area (79.01%), with ecological suitability prevailing. The revised dominant spatial types were predominantly ecological (78.94%), forming a hierarchical structure described as a “living core–production composite ring–ecological periphery.” (4) By integrating dominant spatial types with comparative functional advantages, the study area was classified into five major categories of territorial spatial development zones, for which differentiated governance strategies were proposed. This study provides methodological insights and practical guidance for improving refined territorial spatial management and advancing regional sustainable development. Full article

In this paper, the performance of a conventional distance protection relay employing a single ground compensation factor (k₀) per protection zone is investigated for non-uniform transmission lines consisting of mixed overhead line and underground cable sections. In such composite lines, the use of a single k₀ value may lead to inaccurate apparent impedance calculation during phase-to-ground faults due to significant differences in zero- and positive-sequence parameters among line sections. To address this limitation, a novel ground distance protection algorithm is proposed, which applies separate ground compensation factors corresponding to individual line sections within the same distance protection zone. The proposed algorithm dynamically identifies the faulted line section based on the measured reactance and selects the appropriate compensation factor accordingly. A three-section composite transmission line model is developed in the ATP–EMTP environment, including overhead and cable segments with different electrical characteristics. Phase-to-ground faults are simulated at various locations along each line section, and the apparent impedances calculated using the proposed algorithm are quantitatively compared with those obtained from the classical ground distance protection algorithm. Simulation results demonstrate that, under resistive fault conditions (Rarc = 1 Ω), the proposed method reduces impedance magnitude estimation errors from over 23% to below 7%, while maintaining comparable or improved angle estimation accuracy across the protected zone. Although the proposed algorithm introduces an additional computational step due to the selection of appropriate ground compensation factors for individual line sections, this aspect has not been evaluated under real-time conditions and is left for future implementation-oriented studies. Overall, the proposed approach offers a practical and effective solution for improving ground distance protection performance in non-uniform transmission lines. Full article

Laser measurement technology is widely used for deformation or pose monitoring of the dual-reflector antenna systems. However, conventional models of surface temperature variation with altitude fail to accurately characterise the temperature gradients between the main reflector and the subreflector of the large-aperture antennas, due to the complex near-ground environment, the antenna’s dual-reflector structural properties, and the antenna’s own rotation changes. This temperature modelling discrepancy significantly influences the laser atmospheric propagation deflection characteristics, ultimately leading to a decrease in the accuracy of antenna attitude measurements. To address these issues, this paper proposes a theory of air stratification within large-aperture antennas and utilizes this theory to optimize the temperature gradient between the antenna’s dual reflectors. Secondly, a coupled heat-fluid dynamics model for the dual-reflector surfaces is established using Computational Fluid Dynamics to simulate the atmospheric stratification under different rotational positions of the antenna. Finally, the effectiveness and feasibility of the proposed theory were verified through experiments in the antenna model and the China Nanshan 25 m non-rotatable antenna. This research provides an original theoretical and practical basis for precision environmental modelling in antenna measurements, offering prior assurance for improving the accuracy of laser-based antenna attitude measurement. Full article

Air pollution from industrial point sources remains a major concern in urban environments, highlighting the need for accessible tools that support both education and preliminary environmental assessment. This study presents the development and intercomparison of an open-source, QGIS-based geospatial model for simulating atmospheric pollutant dispersion from fixed point sources using the Gaussian plume formulation. The model integrates emission parameters, meteorological conditions, and terrain data within a fully spatial workflow implemented through the QGIS graphical modeler, enabling the generation of ground-level concentration fields without advanced programming expertise. Dispersion is simulated with and without inclusion of a ground reflection term, allowing comparative analysis of boundary condition effects. The model was applied to a representative urban industrial source at the National University of Science and Technology POLITEHNICA Bucharest, using CO₂ emissions treated as a passive tracer. Model outputs were evaluated through descriptive statistics and quantitative comparison with two established open-source Gaussian plume implementations developed in Python. Ground reflection leads to an increase of approximately 60% in modeled near-surface concentrations, particularly in the upper tail of the distribution, underscoring its importance for screening-level exposure assessment. The proposed model provides a transparent, reproducible, and user-friendly framework suitable for teaching activities, rapid screening analyses, and exploratory air quality assessments. Full article

Flying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain vulnerable under device capture, while existing lightweight and Physical Unclonable Function (PUF)-assisted proposals commonly assume stable connectivity, lack formal adversarial verification, or are evaluated only through simulation. This paper presents a lightweight PUF-assisted authentication protocol designed for dynamic multi-hop FANET operation. The scheme provides mutual UAV–Ground Station (GS) authentication and session key establishment and further enables secure UAV–UAV communication using an off-path ticket mechanism that eliminates continuous infrastructure dependence. The protocol is constructed through verification-driven refinement and formally analysed under the Dolev–Yao model, establishing authentication and session key secrecy and resistance to replay and impersonation attacks. Implementation-oriented latency measurements on Raspberry-Pi-class embedded platforms demonstrate that cryptographic processing time can be further reduced with hardware improvements, while the overall end-to-end delay is still largely determined by channel conditions and connection behaviour. Comparative evaluation shows reduced communication cost and broader security coverage relative to existing UAV authentication schemes, indicating practical deployability in large-scale FANET environments. Full article

UAV communication, leveraging high mobility and flexible deployment, is gradually becoming an important component of 6G integrated air–ground networks. With the expansion of aerial services, air–ground spectrum resources are increasingly scarce, and spectrum sharing and opportunistic access have become key technologies for improving spectrum utilization. Spectrum sensing is the prerequisite for UAVs to perform dynamic access and avoid causing interference to primary users. However, in air–ground links, the channel time variability caused by Doppler effects, carrier frequency offset, and Rician fading can weaken feature separability, making it difficult for deep learning-based spectrum sensing methods to maintain reliable detection in complex environments. In this paper, a dual-stream hybrid-attention spectrum sensing method (DSHA) is proposed, which represents the received signal simultaneously as a time-domain I/Q sequence and an STFT time-frequency map to extract complementary features and employs a hybrid attention mechanism to model key intra-branch dependencies and achieve inter-branch interaction and fusion. Furthermore, a noise-consistent paired training strategy is introduced to mitigate the bias induced by noise randomness, thereby enhancing weak-signal discrimination capability. Simulation results show that under different false-alarm constraints, the proposed method achieves higher detection probability in low-SNR scenarios as well as under fading and CFO perturbations. In addition, compared with multiple typical baselines, DSHA exhibits better robustness and generalization; under Rician channels, its detection probability is improved by about 28.6% over the best baseline. Full article

Background: Heart rate variability (HRV) is a valuable indicator for assessing vascular health, but keeping clinical decision support systems (CDSSs) aligned with the rapidly evolving literature remains challenging. This study aimed to develop an LLM-assisted literature synthesis framework to support transparent HRV-based risk stratification, enabling systematic extraction and organization of HRV evidence from published studies. Methods: An LLM-driven framework was developed to extract HRV parameters from 140 medical abstracts. The system simulated step-by-step human reasoning to identify key HRV indicators and group patient data using predefined statistical thresholds derived from the literature. System performance was evaluated using ECG-derived HRV features as a feasibility evaluation of literature-guided HRV classification. Results: The proposed framework demonstrated an accuracy of 86% in literature-guided HRV classification, with a sensitivity of 81% and a specificity of 87%. Compared with traditional machine learning approaches, the LLM-assisted system provided transparent, literature-grounded reasoning and could be readily updated as new studies became available. Conclusions: Large language models can support evidence-guided parameter selection and feasibility-level HRV-based risk stratification, rather than serving as predictive classifiers. This approach reduces manual effort, enhances transparency, and addresses common “black box” concerns associated with AI-assisted CDSS development in clinical practice. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

This paper develops a reliability-based Decision Support System (DSS) for logistics networks, grounded in the Eurocode EN 1990 and Recommendations for Maritime Works ROM 0.0 framework. The DSS defines logistics-specific limit states (i.e., operational failure thresholds for the overall network) and computes annual exceedance probabilities through a multi-hazard fault-tree model. Its contribution is conceptual and regulatory: it transfers structural reliability principles to system-level assessment, generating auditable, norm-referenced indicators aligned with the EU Critical Entities Resilience Directive (CER) and the Network and Information Security Directive (NIS2). A central result is the Criticality Flip: Systemic vulnerability does not decline monotonically with hub density. Instead, risk shifts non-linearly between gateways and inland integrators, yielding a narrow operating range where the reliability margin (β) is maximized and annual limit-state exceedance is minimized. Beyond this range, additional hubs may provide limited—or even adverse—reliability improvement. The system operates as a compliance audit tool rather than a simulation engine: it evaluates whether a given network configuration meets declared reliability thresholds under multi-hazard scenarios, using standardized input formats and static topology. To support strategic decision-making, the DSS provides normalized and reproducible compliance indicators—such as annual limit-state exceedance probabilities and the associated reliability margin ($\mathsf{\beta }$) referenced to declared thresholds—supporting cross-network benchmarking under CER and NIS2 constraints within an engineering reliability framework. Full article

This study presents a hybrid modeling framework integrating a deep learning-based black-box model of a CO₂ heat pump with a physics-based white-box system model developed in Modelica. The approach reduces the complexity of thermodynamic modeling while maintaining system-level accuracy. A deep neural network (DNN) trained on measured data predicts outlet temperatures and compressor power, coupled with the Modelica model through the Functional Mock-up Unit (FMU) interface. The framework was applied to a ground-source CO₂ heat pump system in Oslo, Norway, to evaluate hysteresis-based control strategies with different return temperature ranges (20–50 °C, 20–55 °C, 20–70 °C) and flow rates (1.3–1.5 kg/s). Results showed similar total heating but 25% lower compressor energy use for the 20–50 °C, 1.5 kg/s case compared to 20–70 °C. Temperature-based control improved coefficient of performance (COP) of the heat pump, while narrower temperature ranges and lower flow rates enhanced tank stratification and heat utilization. The findings demonstrate the effectiveness of the hybrid model for dynamic simulation and control optimization of CO₂ heat pump systems. Full article

Precise orbit determination for multi-spacecraft deep space missions faces challenges including long communication delays, sparse tracking, dynamic model uncertainties, and inefficient data fusion. Presenting a hybrid estimation architecture, this study integrates onboard autonomous navigation with ground-based batch processing of delayed measurements. The framework makes three key contributions: (1) a delay-aware fusion paradigm that dynamically weights space- and ground-based observations according to real-time Earth–Mars latency (4–22 min); (2) a model-informed online calibration framework that jointly estimates and compensates dominant dynamic error sources, reducing model uncertainty by 60%; (3) a lightweight hierarchical architecture that balances accuracy and efficiency for resource-constrained “one-master-multiple-slave” formations. Validated through Tianwen-1 mission data replay and simulated Mars sample return scenarios, the method achieves absolute and relative orbit determination accuracies of 14.2 cm and 9.8 cm, respectively—an improvement of >50% over traditional centralized filters and a 30% enhancement over existing federated approaches. It maintains 20.3 cm accuracy during 10 min ground-link outages and shows robustness to initial errors >1000 m and significant model uncertainties. This study presents a robust framework applicable to future multi-agent deep space missions such as Mars sample return, asteroid reconnaissance, and cislunar navigation constellations. Full article

To address the challenge that traditional dam model materials are difficult to simultaneously meet the requirements of microstructural similarity, dynamic damage simulation, and environmental friendliness, a novel microparticle mortar simulated concrete was developed. This new material consists of cement, sand, gypsum, mineral oil, water, and baryte sand. Through systematic material mechanical tests, the effects of each component on the material’s strength, density, and elastic modulus were revealed, and the optimal mix ratio was determined. This enabled precise control of low elastic modulus and had a high density, while the material is environmentally friendly, non-toxic, and compatible with direct contact with natural water. Its mechanical properties are highly similar to those of the prototype concrete. Based on a 1:70 geometric scale, a shaking table model test of the concrete gravity dam-reservoir system was conducted. The dynamic response and damage evolution under empty and full reservoir conditions were compared and analyzed. The study shows that this material can accurately simulate the stress-strain relationship and failure mode of prototype concrete. Under the full reservoir condition, the dam’s fundamental frequency showed only a 2.72% deviation from the numerical simulation, and as the seismic excitation amplitude increased, the changes in the fundamental frequency effectively reflected the accumulation of damage. Under the design seismic motion, the measured accelerations and stress responses for both empty and full reservoir conditions were in good agreement with numerical calculations. Under overload conditions, the acceleration amplification factor at the dam crest decreased with damage accumulation, and the dam neck was identified as the seismic weak zone. As the peak ground acceleration (PGA) increased from 0.15 g to 0.70 g, the fundamental frequency changes effectively reflected the damage accumulation process in the dam, while the hydrodynamic pressure at the dam heel showed a linear increase (457% increase). The experimentally measured hydrodynamic pressure distribution was between the rigid dam and elastic dam hydrodynamic pressures, reflecting the real fluid-structure interaction effect. This study provides a reliable material solution and data support for dam seismic physical model testing. Full article

The maritime industry is experiencing significant growth due to globalized trade, but this expansion has led to increasing environmental concerns. Studies project that shipping emissions could reach 90–130% of 2008 levels by 2050 without intervention potentially contributing up to 17% of global CO₂ emissions by 2050, thereby posing a major environmental challenge. Stringent environmental regulations from international organizations and government agencies necessitate the maritime industry to find effective solutions to reduce its greenhouse gas (GHG) emissions and improve energy efficiency. This research proposes a methodology for dynamically calculating optimal ship speed to enhance energy efficiency and reduce GHG emissions. By leveraging real-time environmental data (e.g., weather forecasts, sea state information) and operational parameters (e.g., ship characteristics, cargo load), the study utilizes an Adaptive Particle Swarm Optimization based on Velocity Information (APSO-VI) to predict optimal speed over ground (SOG) in real time. The study utilizes the Energy Efficiency Operational Index (EEOI) as a performance metric. EEOI is a widely employed measure in the maritime industry that quantifies the grams of CO₂ emitted per tonne-nautical mile (g CO₂/t nm) of transport work. The effectiveness of the proposed dynamic optimization model (APSO-VI) is assessed by comparing its performance with constant velocity models through extensive simulations, showing a 5–12% reduction in EEOI with the optimized speed model. The results demonstrate significant reductions in fuel consumption and emissions, supporting the adoption of such technologies for a more sustainable maritime industry. Future research may explore integrating machine learning techniques and advanced weather forecasting models for even more robust optimization strategies. Full article