Search Results (6,885)

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

Ultrasonic Lamb waves are promising for the nondestructive evaluation of silicon wafers; however, their dispersive, multimode, and orientation-dependent propagation complicates multidirectional measurements in anisotropic media. This study presents a measurement system for wedge-free, multidirectional, A0-dominant Lamb-wave interrogation in a silicon wafer. The distinctive feature of the system is the integration of a fixed normal-incidence PZT source with non-contact scanning laser Doppler vibrometry, enabling wavefield acquisition along arbitrary in-plane directions without repeated wedge coupling or directional source reconfiguration. Frequency–wavenumber analysis demonstrates an A0-dominant wavefield, with the S0-associated power virtually indistinguishable from the baseline spectrum. A noise-adaptive Hilbert-envelope time-of-flight method and locally weighted scatterplot smoothing (LOWESS) reconstruct the orientation-dependent A0 group-velocity profile. The reconstruction shows a root-mean-square percentage deviation of 1.39% relative to the theoretical group velocities obtained from numerical simulations. This capability is practically important for wafer inspection as it reduces setup complexity, thereby improving measurement consistency. Full article

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage restricts large-scale applications. Conversely, regional airborne laser scanning (ALS) offers broad spatial coverage, but its relatively low point cloud density makes individual-tree level analysis unreliable. To bridge this scale and data gap, this study develops a scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation. High-density UAV-LiDAR data were first used to construct individual-tree AGB models, and the predicted tree-level biomass was aggregated to generate spatially representative “agent plots” for stand-scale modeling. A three-dimensional (3D) radiative transfer simulation framework was further employed to reproduce airborne LiDAR observations under different point densities, enabling the evaluation of structural information loss caused by LiDAR sparsity. Structural features derived from simulated LiDAR and spectral information from Sentinel-2 imagery were integrated using the Tabular Prior-data Fitted Network (TabPFN). Model reliability was assessed through 10-fold spatial block cross-validation and Monte Carlo simulations, which quantified spatial generalization and uncertainty propagation from individual-tree estimation to stand-level prediction. Feature interpretation using SHapley Additive exPlanations (SHAP) revealed that the LiDAR-derived vertical canopy structure provided the primary constraints for biomass estimation, whereas Sentinel-2 shortwave infrared features supplied complementary information related to canopy conditions. The optimal TabPFN model achieved a stand-level accuracy of R² = 0.88 and RMSE = 9.23 Mg·ha⁻¹ using LiDAR combined with Sentinel-2 data. Uncertainty analysis further demonstrated the robustness of the proposed framework under propagated errors, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropical ecosystems. Full article

When a shipborne helicopter performs vertical replenishment with external cargo, changes in flight attitude and the combined airflow field may cause the suspended load to rotate, generating torque at the connection assembly. This torque cannot be measured directly during flight, and the interactions among the boom, swivel eye, lifting eye, and cargo frame produce coupled torque components that are difficult to evaluate by simulation alone. Therefore, a real-time torque measurement test system is developed in this study. The system emulates the multi-component connection used in vertical replenishment and can adjust the external load, rotational speed, rotational direction, and offset angle. The tensile force, torque, and rotational speed borne by the swivel eye are measured by a load cell, torque transducer, and tachometer with wireless data transmission. By measuring the torques of a single swivel eye and a double series-connected swivel eye after rotation decoupling, the main torque patterns at the top of the swivel eye are obtained. The results show that the proposed system can measure the torque responses of swivel-eye connections under different loads, offset angles, rotational speeds, and rotation directions. This provides an experimental basis for evaluating the torque transmission behavior of single and double series-connected swivel eyes in shipborne helicopter vertical replenishment. Full article

The rapid evolution of Urban Air Mobility (UAM) necessitates high-performance morphing structures capable of seamless transitions between flight and ground modes. This research presents a rigorous structural optimization framework for a wing deployment mechanism, addressing the critical challenge of minimizing stress concentrations in cantilevered revolute joints. To overcome the computational prohibitive cost of traditional multibody dynamics, a Generalized Spectral Surrogate Model (GSSM) is introduced. This novel approach maps the mechanism’s geometric parameters to its kinetic response using polynomial-modulated Fourier series, reducing the evaluation time of ${10}^5$ design configurations from 4.2 h to merely 0.8 s while maintaining a determination coefficient $R^2>0.995$. Comparative analysis demonstrates that the GSSM outperforms Artificial Neural Networks and Kriging models in capturing periodic kinematic boundaries without spurious local minima. Through a weighted topological analysis, the study identifies a global optimum ($L_2=0.5$ m, ${\theta }_2=64.2^{\circ }$) that effectively shunts 70.3% of the aerodynamic load to the robust vehicle chassis. The proposed solution deviates from the theoretical unconstrained minimum by only 0.24%, providing a validated mathematical basis for the rapid synthesis of reliable aerospace mechanisms. Full article

Whereas environmental effects on driver cognition have been extensively studied in ground transportation, research linking low-altitude visual environment characteristics to pilot cognitive load (CL) in urban air mobility (UAM) remains scarce. This study combines multimodal physiological data with explainable machine learning to elucidate how low-altitude visual environments influence pilots’ CL. First, a CL quantification framework integrating electroencephalography (EEG) and eye-tracking data is developed to capture real-time cognitive dynamics during flight. Second, multidimensional visual environment indicators are extracted from low-altitude urban landscape images captured during simulated flights using computer vision techniques. These indicators, combined with flight dynamics features, serve as input variables for constructing pilot CL prediction models via machine learning approaches. The results demonstrate that a Bayesian-optimized XGBoost model achieves superior predictive performance. Further interpretability analysis based on SHAP reveals that environmental contrast and the visibility of buildings and water bodies are key factors influencing pilot CL. Additionally, significant interaction effects are also identified among spatial morphology, color characteristics, and landscape typology, with certain landscape elements exhibiting marked variations in both importance and directional influence across different low-altitude flight scenarios. These findings inform low-altitude route optimization, urban morphological regulation, and blue-green infrastructure configuration, advancing an air-ground synergistic planning paradigm. Full article

Under high-Mach-number flight conditions, the combustion chambers of hypersonic vehicles encounter extreme thermal environments marked by unilateral heating, high-heat-flux density, and supercritical pressure. Traditional hydrocarbon fuel cooling often suffers from insufficient heat sinks, high-temperature cracking and coking blockages, making it difficult to meet long-endurance thermal protection requirements. Liquid metal, due to its extremely high thermal conductivity, wide liquid phase temperature range, low Prandtl number and electromagnetic pump driving capability, has become a key technology for breaking through the bottleneck of high-heat-flux thermal protection. Apart from the magnitude of heat flux, the heat-transfer time scale (such as the characteristic thermal response time of the wall and the fluid) is also crucial. During hypersonic flight, transient thermal loads can change within milliseconds, requiring rapid thermal response. Liquid metals, due to their high thermal diffusivity, have a shorter thermal diffusion time compared to hydrocarbon fuels. This review employs a systematic literature review of approaches using gallium-indium-tin alloy, GaInSn, focusing on three core directions: the flow and heat-transfer characteristics of liquid metals, the optimization of cooling micro-channels, and the application of thermal protection systems. It summarizes the research progress at home and abroad, compares and analyzes the performance differences and applicable scenarios of typical liquid-metal working fluids, and summarizes the advantages and disadvantages of existing models, structural designs, and system schemes. The research shows that liquid metals can significantly alleviate thermal stratification and eliminate coking, and deep, narrow, tree-shaped, and biomimetic micro-channels can effectively enhance heat transfer. The liquid-metal-fuel dual-channel waste heat recovery and thermoelectric power generation system has demonstrated engineering application potential. Currently, the field still faces key challenges, such as unclear heat-transfer mechanisms under extreme conditions, the lack of general heat-transfer correlation formulas, insufficient compatibility with high-temperature materials, poor miniaturization and vibration resistance of electromagnetic pumps, and low system integration. In the future, efforts should be focused on developing multi-field coupled heat-transfer models under extreme thermal environments using engineered micro-channel structures, corrosion-resistant materials, and lightweight electromagnetic pumps, promoting the research and development of integrated thermal protection, heating and power generation systems, and providing support for the development of advanced thermal management systems for hypersonic aircraft and aviation engines. Full article

Flying-wing aircraft are more susceptible to wind disturbance due to their smaller wing loading, making gust alleviation critical for flight performance and safety. Conventional control surfaces may exhibit insufficient manipulation efficiency on such configurations, motivating the adoption of active flow control, particularly circulation control (CC) due to its favorable control efficiency. This paper presents an Active Disturbance Rejection Control (ADRC) framework for gust load alleviation (GLA) of flying-wing aircraft equipped with CC actuators, which enables real-time estimation and compensation of both gust disturbance and practical uncertainties and is validated through closed-loop wind-tunnel experiments under various sinusoidal gust conditions. An unsteady aerodynamic model with experimental data is established and simulations are performed for further investigation of alleviation performance and response characteristics under a wide range of gust conditions. Results show that both ADRC and PID exhibit degraded performance at higher gust frequencies and larger gust ratios, but ADRC achieves higher alleviation efficiency across the tested conditions. Furthermore, ADRC maintains satisfactory performance with actuator delays up to 0.04 s and outperforms PID under measurement noise and Dryden turbulence. These findings validate the effectiveness and robustness of ADRC for GLA, underscoring its practical potential for active flow control systems. Full article

Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS^® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction. Full article

Urban low-altitude bus operations require coordinated eVTOL route decisions under station time windows, dynamic passenger boarding and alighting, load-dependent energy consumption, opportunity charging, and battery safety requirements. We formulate a mixed-integer programming model for reservation-based shared services that lexicographically minimizes the number of deployed aircraft first and total flight distance second while enforcing passenger-capacity, time-window, charging, and battery-safety constraints. To solve large instances efficiently, an Elite-Pool guided Load-Coupled Energy-aware Adaptive Large Neighborhood Search algorithm (EP-LCE-ALNS) is developed by combining forward load-energy-coupled decoding, multi-start construction, elite-route guidance, and adaptive neighborhood search. Benchmark comparisons show that EP-LCE-ALNS consistently achieves strong solution quality across instances of different scales and outperforms the comparison algorithms on large-scale problems. Sensitivity analyses further show that increasing passenger capacity reduces fleet requirements and flight distance, whereas a larger battery safety margin increases both, providing decision support for fleet configuration, route organization, and safety settings. Full article

Accurate assessment and forecasting of air traffic conditions in multi-airport terminal areas are essential for improving early-warning capabilities, mitigating flight conflicts, and alleviating air-route congestion. Accordingly, this study develops an air traffic situation prediction approach that combines an air route–flight state interdependent network with an Optimal Training Sample Online Fuzzy Least-Squares Support Vector Machine (OTSOF-LSSVM). An interdependent network model is first established. The Analytic Hierarchy Process (AHP) is then employed to combine three network indicators, namely node degree, weighted clustering coefficient, and node strength, thereby producing a comprehensive air traffic situation value and its corresponding evolutionary time series. Considering the time-varying and long-periodic properties of this series, an OTSOF-LSSVM-based prediction method is developed. Training samples are selected according to their temporal and spatial proximity to the prediction moment. In addition, block-matrix operations are introduced during model updating to streamline the computational procedure and improve algorithmic efficiency. The proposed approach is validated using actual flight data from the multi-airport terminal area of the Guangdong–Hong Kong–Macao Greater Bay Area. The results demonstrate that the proposed assessment method can effectively characterize the prevailing air traffic situation. Moreover, in comparison with several existing prediction techniques, the proposed method achieves the best overall performance, yielding a mean absolute error of only 0.0111. Full article

To addressthe challenge of attitude control in quad-ducted fan unmanned aerial vehicles (UAVs) under coupled disturbances comprising thrust lag, ground effect and a composite wind field during near-ground flight and to mitigate the inherent trade-off between disturbance rejection and noise suppression in fixed-bandwidth extended state observers (ESOs), this paper proposes a robust attitude control method based on a Sigmoid law adaptive-bandwidth extended state observer (AB-ESO). An attitude dynamic model covering the above multi-source disturbances is established, with all uncertainties uniformly treated as lumped disturbances. An adaptive-bandwidth mechanism with filtering and rate-limiting modules is designed for smooth continuous bandwidth tuning. A composite control framework integrating disturbance feedforward, lag compensation and attitude feedback is constructed, and the uniform ultimate boundedness of the closed-loop system is proved. Comparative simulations are conducted against six baseline controllers, including a cascade proportional–integral–derivative (PID) controller, fixed-bandwidth ESOs, incremental nonlinear dynamic inversion (INDI), fast terminal sliding mode control (FTSMC) and a time-varying bandwidth ESO, in a near-ground composite wind scenario. Results show that the proposed method achieves improved comprehensive performance: the three-axis average tracking root mean square error (RMSE) is approximately 72% lower than of the PID controller and 15.8% lower than that of the high-bandwidth ESO, and the control output total variation is reduced by about 27.8%. Monte Carlo verification with 100 random turbulence groups further validates the strong statistical robustness of the proposed method. All validations in this work are based on numerical simulations. This study provides a technical reference for high-precision control of ducted fan UAVs in near-ground environments. Full article

Jing Guan Fang (JGF) is a traditional multi-herb formula used for hyperinflammatory conditions associated with severe viral infections; however, its protein-level effects in macrophage-mediated inflammation remain incompletely characterised. In this study, we combined ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS)-based phytochemical profiling, functional anti-inflammatory assays, and discovery-level proteomic and secretome analyses to investigate the anti-inflammatory effects of JGF in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. Phytochemical profiling revealed predominantly flavonoid- and phenolic-related annotated features, together with selected terpenoid- and alkaloid-related annotations. Functionally, JGF treatment was associated with reduced nitric oxide production and differential effects on pro-inflammatory cytokine release, with a clearer concentration-dependent reduction in interleukin-6 (IL-6) and a more variable tumour necrosis factor-α (TNF-α) response, without reducing cell viability. Label-free quantitative proteomics suggested that JGF treatment was associated with modulation of LPS-induced protein changes, including reduced abundance of inflammation-associated proteins such as myristoylated alanine-rich C-kinase substrate (MARCKS) and inducible nitric oxide synthase (NOS2), together with partial restoration of selected regulatory proteins such as AIMP1 and AIMP2. Secretome analysis further suggested that JGF reduced extracellular inflammatory and tissue-remodelling-related mediators, including SERPINE1/PAI-1. Exploratory pathway analysis indicated that JGF treatment was associated with changes in inflammation-related and oxidative stress-related signalling networks. Collectively, these findings provide discovery-level molecular insights into the anti-inflammatory effects of JGF in LPS-stimulated macrophages and support further targeted validation of its pharmacological activity as a candidate multi-component anti-inflammatory formula. Full article

Bamboo leaves contain structurally diverse flavonoid glycosides; whereas, compound-level information for Bambusa textilis remains limited. In this study, a flavonoid-enriched 40% ethanol fraction prepared from B. textilis leaves by petroleum-ether partitioning and AB-8 resin adsorption was analyzed by ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UHPLC-ESI-Q-TOF-MS/MS) in negative-ion mode. Retention times and MS/MS spectra were compared with seven in-house reference standards, while the remaining constituents were annotated from accurate-mass measurements, diagnostic product ions, fragmentation behavior, and data from the literature. According to Metabolomics Standards Initiative criteria, seven compounds were assigned at Level 1 and twenty-four were reported as Level 3 annotations because their exact positional structures were not confirmed by additional standards or NMR. Thirty-one flavonoid-related constituents were annotated in the analyzed fraction, encompassing six glycoside classes; most annotations, by count, were apigenin- or luteolin-derived. These annotations add compound-level information for the analyzed B. textilis fraction and allow qualitative comparison with other bamboo flavonoid profiles. Because the sample was selectively enriched, the results do not describe the complete leaf metabolome or compound concentrations. Additional standards or NMR analysis are needed to confirm the glycosylation positions of tentatively annotated compounds. Full article

Hierarchical multi-UAV planning can produce scheduled paths that become unsafe during execution because tracking dynamics, actuator limits, sampling, and communication are not fully represented upstream. We introduce a certificate-guided safe-tracking framework that treats each planned path as a versioned execution contract with explicit tube, separation, timing, uncertainty, communication, and input bounds. A rigid-body-derived translational interface supports a command-producing control Lyapunov function–control barrier function quadratic program with hard safety constraints. When a candidate becomes infeasible, a separate minimum-slack program localizes the conflict without passing its command to the plant, and an explicit repair map converts the resulting witness into timing or vertical-spacing updates, with escalation when local repair fails. Randomized comparisons show that certificate feedback removes the observed tube and separation failures of a plain CBF-QP while maintaining reliable completion and competitive tracking relative to a tracking-error-bound comparator. Disturbance and sensor-noise sweeps characterize robustness, and separate indoor flights confirm single-reference trackability. The framework therefore turns execution infeasibility into actionable planning feedback rather than a terminal controller failure. Full article