Search Results (9,572)

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

The success of wind energy projects depends on reliable site selection and cost-effective operation. Existing studies largely focus on either resource potential or standalone economic feasibility, while a unified wind power rating framework for site prioritization across India remains lacking. This study proposes a multi-criteria wind power assessment framework and investigates the spatial and scale-dependent variability of wind speed (WS) and wind power density (WPD) over six major regions of India. Hourly WS data were at diurnal, monthly and annual scales to capture atmospheric and seasonal influences. The results reveal significant temporal variabilities in WS and WPD, especially over the southern and western coastal and high-altitude regions during the monsoon months (June–August). The spatial analysis revealed a non-linearly increasing trend for WS with altitude, contrary to the simplifying assumptions. Regions such as the Southern Peninsular States (SPSs) and western middle states (WMSs) show high suitability for large-scale deployment, whereas the Northeastern States (NESs) and parts of northern border states (NBS) exhibit lower potential. The site suitability is further evaluated using wind variability indices such as the wind variability index (WVI) and Windy Site Identifier (WSI), along with the plant capacity factor (PCF), cost of energy (COE), and greenhouse gas (GHG) emissions, enabling a comprehensive and decision-oriented framework for wind energy planning. Full article

The recovery of low- and medium-temperature waste heat using Organic Rankine Cycles (ORCs) is increasingly important for improving the efficiency and sustainability of industrial and energy systems. In compact ORC turboexpanders, high specific power output and large pressure ratios often require single- or two-stage turbines operating in transonic or supersonic regimes. Under these conditions, stator blade design is complicated by strong compressible-flow effects and, for organic working fluids, by real-gas thermodynamic behavior. Conventional supersonic stator design methods, such as the method of characteristics, are mainly applicable to the diverging supersonic portion of the blade passage, while the converging region is typically defined using empirical or heuristic prescriptions. This paper presents a physics-informed neural-network-based design method for supersonic turbine stator blades. The proposed framework generates the complete inter-blade passage, including both the converging and diverging regions, starting from a prescribed mean-line geometry and Mach number distribution. The velocity field is obtained by solving the governing equations of steady, inviscid, adiabatic, irrotational compressible flow within a PINN formulation. A hard boundary-condition strategy is used to impose the specified mean-line velocity distribution exactly, while real-fluid thermodynamic effects are incorporated through lookup tables for the speed of sound and density. The blade contours are then reconstructed from stream-function isolines predicted from the computed velocity field. The method is demonstrated for two working fluids: air, treated as a perfect gas, and toluene undergoing transcritical expansion. The resulting blade passages are first validated using inviscid CFD simulations, which show close agreement between the prescribed and computed mean-line Mach number distributions. Turbulent CFD simulations of the final blade cascades confirm smooth acceleration through the inter-blade passage, with no strong internal shocks and only weak fishtail shocks downstream of the trailing edge. For both fluids, the post-expansion ratio is approximately unity and the exit flow angle remains close to the prescribed blade metal angle, indicating well-matched supersonic stator designs. The results demonstrate that the proposed PINN-based design method provides a physically consistent approach for generating supersonic stator blade profiles for both ideal-gas and real-gas turbine applications. Full article

High-frequency ultrasound-assisted activation of peracetic acid (PAA) was investigated as a tunable advanced oxidation process for the removal of organic pollutants from water. Sunset Yellow FCF (SSY), a representative anionic azo dye, was used as a probe contaminant in a 425 kHz sonoreactor to clarify the roles of PAA speciation, acoustic cavitation, dissolved gases, oxidant dose, acoustic power, and initial pH. UV spectroscopic analysis showed that PAA exhibits pH-dependent far-UV absorbance associated with acid-base speciation and peroxide equilibria, while ultrasonication promoted simultaneous PAA activation and H₂O₂ accumulation. Compared with PAA alone and ultrasound alone, the combined US/PAA process markedly enhanced SSY decolorization. Under natural conditions, 5 mg/L SSY and 5 mM PAA were completely decolorized within 210 min, with an initial rate of 0.116 mg/L·min, compared with 0.078 and 0.0086 mg/L·min for ultrasound and PAA alone, respectively. The corresponding synergy ratio and synergy index were 1.5 and 1.34. The process exhibited tunable reaction-pathway control, with two favorable pH windows: a strongly acidic region, where interfacial HO^•-driven sonochemistry and PAA stability are favored, and a mildly alkaline region, where PAA deprotonation promotes peracetate-driven acyl/peroxyl radical-chain propagation. Oxygen saturation improved performance, whereas CO₂ suppressed cavitation-driven activation. Increasing PAA concentration and acoustic power enhanced removal up to practical limits, beyond which radical scavenging and diminishing sonochemical returns became evident. Beyond demonstrating enhanced decolorization, this study distinguishes US/PAA from previously reported UV/PAA, transition-metal/PAA, and ultrasound-only systems by showing how 425 kHz cavitation converts PAA into a tunable hybrid HO^•/acyl–peroxyl radical network. The main contribution is a mechanistic operating map that links PAA speciation, sonochemical peroxide accumulation, dissolved gas chemistry, acoustic power, oxidant dose, and pH to pollutant-removal performance, thereby defining practical windows for sono-activated PAA treatment of anionic dyes and related recalcitrant contaminants. Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

Stable interactions among electricity, carbon allowance, and fossil fuel markets are essential for sustainable energy transition, because excessive cross-market risk transmission may affect energy affordability, carbon-price credibility, and low-carbon investment signals. This study provides comparative evidence on dynamic connectedness, tail-state shock responses, and return-based complexity in representative Chinese and European benchmark markets. Using daily market data from the Wind database for November 2021–January 2026, the empirical framework combines time-varying parameter vector autoregression (TVP-VAR), quantile vector autoregression and quantile impulse response functions (QVAR/QIRFs), and rolling multifractal detrended fluctuation analysis (MFDFA). The results show that the European benchmark system has a higher absolute connectedness level than the Chinese benchmark system: the full-sample mean total connectedness index (TCI) is 18.75 in Europe and 5.63 in China, while the crisis-period mean TCIs are 25.19 and 12.12, respectively. Post-peak adjustment depends on the reversion metric used: China shows a faster initial half-life decline from the crisis peak, whereas reversion to lower region-specific connectedness thresholds depends on the selected benchmark. Natural-gas-shock QIRFs indicate stronger upper-tail persistence in Europe, whereas China is characterized mainly by short-run directional divergence; supplementary coal-, oil-, and carbon-shock checks show that response patterns are shock-source-dependent. Electricity-return multifractal spectrum width (MFW) does not show stable full-sample explanatory power for TCI, but it provides stage-dependent auxiliary diagnostic information. These findings provide a comparative diagnostic framework for monitoring cross-market systemic risk and supporting sustainability-oriented energy-market governance under low-carbon transition. Full article

Optimizing the carrier concentration across the entire operating temperature range is crucial for maximizing the power factor in n-type PbTe. However, conventional shallow donors produce a nearly temperature-invariant electron concentration, leading to an increasingly large deviation from the optimal carrier concentration at elevated temperatures. Herein, we implement a dynamic deep-shallow co-doping strategy by combining iodine (a shallow donor) with gallium (a deep-level donor) in PbTe. The Ga-related deep impurity states thermally ionize at elevated temperatures, providing additional electrons and driving the Hall carrier concentration above ~563 K toward its temperature-dependent optimum. Concurrently, our optimized synthesis preserves a high carrier mobility, which synergistically sustains a remarkable peak power factor of 30 μW·cm⁻¹·K⁻² for the optimal composition, Ga_0.02Pb_0.98Te_0.996I_0.004. Combined with a strongly suppressed lattice thermal conductivity, this results in a maximum figure of merit (ZT) of 1.41 at 803 K and an average ZT of 1.00 within 400–773 K for Ga_0.02Pb_0.97Te_0.996I_0.004—a 25% improvement over the I-only doped baseline. These findings establish deep-shallow co-doping as a robust and broadly applicable carrier-engineering paradigm for thermoelectric optimization. Full article

Resource-constrained electromagnetic interference (EMI) management requires a frequency-domain feedback path, while FFT-based full-spectrum processing introduces redundant computation, storage, and data movement for decision tasks. This paper proposes a limit-aware sparse frequency-domain decision engine for internal EMI risk feedback. The engine redefines EMI analysis from spectrum reconstruction to selective exceedance verification and uses randomized spectral reordering, flat-window bucket aggregation, and folded sampling to compress the length-N spectral search into bucket-level observations. Then, by comparing bucket-level amplitude envelopes with local limit envelopes, the method excludes risk-negative buckets, and only uncertain buckets are further refined through phase localization and sequential verification. Degradation experiments involving continuous background uplift, main-harmonic sidebands, and parasitic resonance clusters clarify the applicability boundary of the proposed method, and measured GaN power-converter spectra acquired through an in situ EMI sensing chain remain inside the empirical usable region. RTL evaluation at 100 MHz shows that the proposed design achieves an average decision latency of 6.031 ms. Compared with two FFT baseline implementations, it reduces BRAM usage by 95.17% and 97.59%, dynamic power by 54.0% and 83.0%, and per-decision dynamic energy by 46.3× and 33.3×, respectively. The results show that the proposed decision engine reduces hardware overhead for frequency-domain EMI risk feedback in resource-constrained systems. Full article

This study presents the design and modeling of a first-of-its-kind experimental prototype integrating a high-temperature air-based Concentrated Solar Power (CSP) receiver with a diabatic Compressed Air Energy Storage (CAES) system. The prototype architecture and operating modes are defined, and a detailed thermal model of an Open Volumetric Air Receiver (OVAR) is developed and optimized, with emphasis on passive mass flow regulation under non-uniform solar flux. At nominal conditions (800 °C), the receiver achieves a predicted thermal efficiency of 81.6%. Transient simulations assess off-design dynamic behavior under realistic conditions, showing sensitivity to solar fluctuations and need for heliostat aiming strategies to reduce thermal non-uniformities and ensure stable outlet temperatures. For the CAES subsystem, a techno-economic analysis identifies high-pressure (300 bar) commercial gas cylinders as the most cost-effective aboveground storage solution, while discharge simulations yield a required storage volume of 4.8 m³. Finally, the complete piping and instrumentation diagram (P&ID) of the integrated system is presented, defining the experimental configuration. Overall, this work establishes the design basis for the future experimental demonstration of hybrid CAES-CSP operation for dispatchable renewable power generation and supports subsequent control development and scale-up analyses. Full article

Laurel (Laurus nobilis L.) is a characteristic species of the Mediterranean flora, valued for its medicinal, aromatic, and culinary uses. Many of these properties are attributed to its volatile constituents. In this study, the effect of Atmospheric Room Temperature Plasma (ARTP) pretreatment on laurel powder was evaluated, with emphasis on volatile recovery and food application. Volatile extraction was optimized using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS), investigating key parameters such as salt concentration, extraction temperature, equilibration time, extraction time, and fiber type. Subsequently, critical ARTP variables (nitrogen flow, treatment duration, treatment distance, substrate thickness, and plasma power) were optimized. Response Surface Methodology was applied in both optimization processes. The results demonstrated that fiber type was the most influential factor for volatile recovery, with extraction temperature also exerting a significant effect. A more nuanced pattern emerged during ARTP pretreatment, where moderate plasma intensities enhanced the recovery of several key volatiles. This trend was not uniform across compounds, indicating that plasma-induced microstructural changes interact with the physicochemical properties of individual analytes. To demonstrate food relevance, a ready-to-use broth model prepared with laurel powder confirmed improved headspace transfer of characteristic volatiles following ARTP treatment. Taken together, these findings suggest that ARTP can serve as a practical, non-thermal pretreatment for improving volatile release, supporting its potential use in the food, pharmaceutical, and cosmetic industries. Full article

Oscillatory neural networks (ONNs) provide a neuromorphic computing framework that exploits the phase dynamics of coupled oscillators for parallel and energy-efficient pattern recognition. In this study, we design a single-layer, fully connected ONN to classify handwritten digits from the MNIST dataset. Input images were downsampled to 6 × 6 binary patterns, which were optimized using a genetic algorithm to evolve effective templates, as experiments with higher-resolution inputs showed only marginal accuracy improvements at significantly increased computational and energy costs. Coupling weights were determined using Hebbian learning, and the network dynamics were simulated using the Kuramoto model to encode information via phase relationships. To the best of our knowledge, this is the first work to apply genetic algorithm optimization to design the templates used by an ONN and to combine evolutionary template generation with Hebbian-based ONN training for image classification. The results show that the ONN achieves 75–76% accuracy in the full 10-class MNIST task, with outputs exhibiting stable sinusoidal behavior and resilience to moderate noise. These findings highlight the potential of ONNs as a practical, low-power alternative to conventional deep learning models, particularly for real-time edge-level applications where energy efficiency and robustness are critical. Full article

Accurate healthy-state performance modeling under multiple operating conditions is essential for the condition assessment of industrial centrifugal compressors. However, conventional healthy-state baselines often struggle to meet the requirements of real-time condition assessment for centrifugal compressors operating under complex real-gas and multi-condition environments. To address this issue, this study proposes a two-layer framework combining a CFD-based physical baseline with data-driven residual correction using limited factory data. A three-dimensional full-machine CFD model was reconstructed and validated under real-gas conditions, then used to generate 1440 healthy-state operating points. XGBoost, LightGBM, Random Forest, and multilayer perceptron were evaluated as baseline surrogate models. A residual-correction model was subsequently trained to compensate for systematic discrepancies between CFD predictions and actual machine performance. Ablation tests compared the CFD baseline, a factory-data-only model, and the proposed hybrid model. Online computation requires only surrogate inference and residual correction, achieving an inference latency of 0.6585 ms per operating point on Intel64 Family, compatible with the 60 s SCADA sampling interval. After correction, the maximum errors in power, polytropic head, and polytropic efficiency were 0.611%, 0.481%, and 0.899%, respectively. Post-overhaul field validation yielded maximum errors of 1.650%, 3.048%, and 1.708% for outlet pressure, power, and polytropic efficiency. The framework provides a physically grounded and computationally efficient healthy-state reference, although its transferability requires validation using additional station-specific data. Full article

This work presents the first design of an active frequency quadrupler based on GaN HEMTs. It is based on two cascaded frequency doubler stages that allow up-conversion from the S band to the X band, obtaining conversion gain in narrowband without the need for any additional buffer amplifier. A hybrid prototype of the quadrupler provides, at the input fundamental frequency of 2.5 GHz, a measuredfourthharmonic maximum conversion gain of 13.7 dB and an output power of 23.5 dBm, with suppression of both fundamental andsecondharmonic above 14 dBc. To obtain these results, due to its impact in the final quadrupler behavior, a prototype of the higher frequency second stage doubler was designed and manufactured separately to assess its RF performance. The experimental results of this standalone prototype are highly competitive with the current state of the art in frequency doublers at the C band. Full article

Direct renewable-to-load systems have emerged as a promising pathway for improving local renewable energy utilization for large electricity consumers under constrained grid interaction and device operating limits. This paper investigates a park-level direct renewable-to-load system integrating battery energy storage and power-to-hydrogen facilities and formulates its operation problem as a sequential continuous-control task. A Deep Deterministic Policy Gradient (DDPG)-based scheduling framework is developed to explicitly model renewable generation, load demand, battery dynamics, hydrogen conversion and inventory evolution, renewable curtailment, storage-related operating cost, and the no-power-export operating boundary. Case studies based on measured wind/PV and load time-series data demonstrate that coordinated heterogeneous storage can effectively enhance system flexibility. Compared with single-type storage configurations, the coordinated battery–hydrogen scheme achieved the best overall storage performance, reducing the daily operating cost to CNY $25.28\times {10}^4$/day while increasing renewable energy utilization to 54.79%. Further benchmarking against MILP, GA, and a without-storage baseline shows that, although MILP remains the best offline benchmark, the proposed DDPG method provides a favorable trade-off between solution quality and online efficiency. Specifically, DDPG achieved an operating cost of CNY $20.93\times {10}^4$/day and a renewable energy utilization of 514.38 MWh, while requiring only 0.25 s/step for online inference. Typical-day analysis further reveals a clear functional complementarity between the two storage types; battery storage mainly provides fast short-term regulation, whereas the hydrogen subsystem mainly supports longer-duration energy shifting. These results indicate that the proposed framework offers a practical and efficient solution for the operation optimization of direct renewable-to-load systems under no-power-export constraints. Full article

This paper investigates the coordinated deployment and routing of communication equipment by a Sub-mother UAV swarm in power-grid emergency communication scenarios. Considering mission timeliness and payload constraints, a heterogeneous MUAV–SUAV coordinated deployment-and-routing model is established to minimize the total system cost, including platform flight cost, SUAV activation cost, and penalty cost caused by delayed deployment. To solve this problem, a two-stage optimization framework is proposed. In the first stage, an improved K-means clustering algorithm with neighborhood search (K-means-NS) is developed to divide deployment points into feasible sub-regions while satisfying SUAV endurance constraints and maintaining the deployment–retrieval payload balance required by the MUAV. In the second stage, the MUAV inter-region visiting sequence is treated as a routing subproblem, and an improved adaptive genetic algorithm (IAGA) is designed to optimize the coordinated routes of the MUAV and SUAVs within each sub-region. The IAGA adopts hybrid encoding, feasible-solution adjustment, elitist selection, and adaptive crossover–mutation operations to improve search efficiency under complex constraints. Numerical experiments on small-, medium-, and large-scale scenarios show that the proposed method can generate feasible sub-region divisions and coordinated routing schemes. Compared with GA and G-PSHA, IAGA reduces the total flight cost by approximately 21.2%, 10.5%, and 23.2% relative to GA and by approximately 0.2%, 2.5%, and 8.1% relative to G-PSHA in the three scenarios, respectively. Sensitivity analysis further indicates that stricter mission-timeliness requirements increase penalty costs, highlighting the importance of timely communication-device deployment in emergency restoration. Full article