Search Results (1,765)

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

This paper addresses distributed formation control for multiple unmanned aerial vehicles (UAVs) operating in obstacle-dense environments under directed switching communication topologies. A leader–follower architecture is adopted, wherein the leader performs online trajectory replanning while followers rely on delayed and intermittently available neighbor information. To simultaneously tackle collision avoidance, formation feasibility under narrow passages, and communication intermittency, we propose an integrated deformable formation navigation framework. The framework couples Safe Flight Corridor (SFC)-constrained Bézier trajectory planning with a dynamic formation scaling mechanism, allowing the swarm to adaptively shrink or expand its geometric configuration when traversing constricted spaces, thereby ensuring all agents remain within certified collision-free corridors. A nonlinear distributed consensus-based estimator is designed to propagate leader reference states under directed switching graphs with bounded delays. Using a max-min contraction analytical approach, we establish guaranteed practical convergence for both leader tracking and inter-follower agreement without requiring persistent connectivity. Extensive simulations in complex cluttered environments demonstrate that the proposed approach enables flexible and real-time formation reshaping, enhancing navigational safety and robustness while maintaining cohesive swarm behavior under challenging communication and spatial constraints. Full article

Ultrasonic testing is a prevalent method for non-destructive evaluation of railway rails; however, conventional Time-of-Flight (ToF) approaches applied in practical dry-coupled inspections often rely on simplified assumptions regarding wave propagation velocity and neglect complex waveform characteristics. This paper presents a robust depth estimation framework for surface-breaking cracks that enhances sizing accuracy through effective velocity calibration and Hilbert envelope extraction. Unlike standard methods that assume the free-space speed of sound in air (343 m/s) for wave propagation within the air-filled gap of a surface-breaking crack, we propose an effective velocity model derived from in situ calibration to account for the boundary layer viscosity and thermal conduction effects within narrow crack geometries. The signal processing chain incorporates spectral analysis, band-pass filtering, and Hilbert Transform-based envelope detection to mitigate noise and resolve phase ambiguities. Experimental validation on steel specimens with controlled defects (0.2–10.0 mm) demonstrates that the proposed method achieves an exceptional linear correlation (R² ≈ 0.9976). The calibrated effective velocity was determined to be 289.3 m/s, approximately 15.6% lower than the speed of sound in air, confirming the significant influence of confinement effects. Furthermore, excitation parameters were optimized, identifying that high-voltage excitation (≥110 V) and a tuned pulse width (≈150 ns) are critical for maximizing the signal-to-noise ratio. The results confirm that combining physical model calibration with advanced signal analysis significantly reduces systematic errors, paving the way for portable, high-precision rail inspection systems. Full article

Accurate assessment of cotton emergence rates is essential for precision agriculture management, and unmanned aerial vehicle (UAV) imagery provides a scalable means for field-level monitoring. However, cotton seedling detection from UAV images faces persistent challenges: individual seedlings appear as small targets with diverse morphologies across varying flight altitudes; strong plastic film reflections, weeds, and soil cracks introduce substantial background interference; and “missing seedling” targets, which manifest as negative space features, exhibit high similarity to background noise. Existing CNN–Transformer hybrid detection architectures are limited by fixed convolutional receptive fields that cannot adapt to multi-scale target variations, attention mechanisms that lack explicit directional geometric modeling, and interpolation-based upsampling that attenuates high-frequency edge details of small targets. To address these issues, this paper proposes DDF-DETR (Dynamic-Direction-Frequency Detection Transformer), a multi-scale spatial context detection method based on RT-DETR. The method incorporates three components: a Dynamic Gated Mixer Block (DGMB) for adaptive multi-scale feature extraction with background noise suppression, a Direction-Aware Adaptive Transformer Encoder (DAATE) for directional geometric feature modeling at linear computational complexity, and a Frequency-Aware Sub-pixel Upsampling Network (FASN) for high-frequency detail recovery in the feature pyramid. On the self-constructed Xinjiang cotton field dataset, DDF-DETR achieves 83.72% mAP@0.5 and 63.46% mAP@0.5:0.95, representing improvements of 2.38% and 5.28% over the baseline RT-DETR-R18, while reducing the parameter count by 30.6% and computational cost to 42.8 GFLOPs. Generalization experiments on the VisDrone2019 and TinyPerson datasets further validate the robustness of the proposed method for small target detection across different scenarios. Full article

Additive manufacturing (AM) is increasingly recognized as a critical enabler for sustainable space exploration, offering on-demand fabrication, reduced reliance on Earth-based resupply, and enhanced mission autonomy. Over the past decade, in-space AM has progressed from early polymer extrusion experiments aboard the International Space Station (ISS) to the demonstration of multi-material capabilities involving polymers, metals, ceramics, recycling systems, and in situ resource utilization (ISRU) concepts. This review provides a comprehensive synthesis of AM technologies developed for space applications, with emphasis on demonstrated flight heritage, process behavior under microgravity and vacuum conditions, and materials validated in orbit. The paper surveys major AM process families relevant to space, including fused filament fabrication, directed energy deposition, ceramic stereolithography, bioprinting, and closed-loop recycling systems. Key ISS-based platforms—such as the Additive Manufacturing Facility, Ceramic Manufacturing Module, and Refabricator—are reviewed to assess technological maturity and system-level integration. Materials performance across polymers, metals, ceramics, and regolith-based feedstocks is discussed, highlighting the influence of microgravity, thermal transport, and environmental exposure. By comparing in-space results with terrestrial and reduced-gravity studies, this review identifies consistent trends, critical limitations, and remaining knowledge gaps, providing a structured perspective on the readiness of in-space additive manufacturing for future orbital and deep-space missions. Full article

With the increasing complexity of the space electromagnetic environment, traditional omnidirectional antenna-aided communication and networking techniques can no longer meet the collaboration requirements of aircraft clusters. To achieve goals such as anti-jamming, anti-interception, and enhanced spatial multiplexing, an increasing number of aircraft are being equipped with high-gain directional antennas. However, modeling of directional antenna-constrained Flying Ad Hoc Networks (FANETs) is far more complex than modeling of omnidirectional antenna-aided networks. The former task is highly dependent on the real-time flight state and the spatial topology of the network. In response to the communication challenges posed by directional networking of highly-dynamic aircraft clusters, this study proposes an antenna selection and beam pointing control algorithm, which is deeply integrated with the aircraft’s Guidance, Navigation, and Control (GNC) system. By introducing parameters that characterize dynamic flight state, such as position and attitude information, and combining them with high-precision multi-coordinate system transformations and spatial geometric analysis methods, the proposed algorithm enables the real-time optimization of antenna selection and beam pointing under the relative motion trends of aircraft. It effectively maintains high-quality connections between flying nodes. Digital simulation and physical experiment results demonstrate that the proposed method can accurately calculate the appropriate antenna selection and determine precise beam pointing directions based on the position data of flying nodes. This provides an important reference for the design of optimized communication strategies used in directional networking of highly-dynamic aircraft clusters. Full article

Metaheuristic methods for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning often suffer from premature convergence and reduced accuracy in complex high-dimensional spaces, in which waypoint-based decision variables exhibit structured dependencies and segment-level regularities. In a symmetry-inspired operational sense, these regularities can be interpreted as exploitable dependency patterns across path segments and permutation invariance among homogeneous UAVs, which are often overlooked by standard algorithms. The paper proposes an enhanced dung beetle optimizer (LEDBO) that integrates interaction-aware variable handling, adaptive role regulation, and a fitness-state-driven hybrid search mechanism. Correlation-based variable grouping clusters dependent waypoints into segments to exploit statistical dependency patterns among waypoint-coordinate variables and enhance local refinement. A three-level adaptive role-regulation scheme adjusts search behaviors according to convergence status and population diversity, thereby mitigating stagnation. Meanwhile, a fitness-state-driven hybrid engine combines Nelder–Mead local refinement with Lévy-flight global exploration to balance exploitation and exploration across stages. Experiments on the CEC2017 benchmark suite and complex 3D UAV path-planning simulations demonstrate that LEDBO achieves better solution quality, convergence behavior, and robustness than representative metaheuristics, producing smoother, shorter, and safer trajectories. The results suggest that incorporating interaction-aware variable grouping and adaptive search regulation can improve UAV path planning and related high-dimensional continuous optimization tasks. Full article

The two most severe cosmological tensions in the Hubble constant $H_0$ and the matter clustering amplitude $S_8$ have the same relative discrepancy of 8.3%, which suggests that they may have a common origin. Modifications of gravity and exotic dark fields with numerous free parameters introduced in the Einstein field equations often struggle to simultaneously alleviate both tensions; thus, we need to look for a common cause within the standard $\Lambda$CDM framework. At the same time, linear perturbation analyses of matter in the expanding $\Lambda$CDM universe have always neglected the impact of comoving peculiar velocities $\mathbf{v}$ (generally thought to be a second-order effect), the same velocities that, in physical space, cannot be fully accounted for in the observed late-time universe when the cosmic distance ladder is used to determine the local value of $H_0$. We have reworked the linear density perturbation equations in the conformal Newtonian gauge (sub-horizon limit) by introducing an additional drag force per unit mass $-\Gamma \left(t\right)\mathbf{v}$ in the Euler equation with $\Gamma \equiv \gamma \left(2H\right)$, where $\gamma \ll 1$ is a positive dimensionless constant and $2H\left(t\right)$ is the time-dependent Hubble friction. We find that a damping parameter of $\gamma =0.083$ is sufficient to resolve the $S_8$ tension by suppressing the growth of structure at low redshifts, starting at $z_{★}\simeq 3.5–6.5$ to achieve $S_8\simeq 0.78–0.76$, respectively. Furthermore, we argue that the physical source causing this additional friction (a tidal field generated by nonlinear structures in the late-time universe) is also responsible for a systematic error in the local determinations of $H_0$—the inability to subtract peculiar tidal velocities along the lines of sight when determining the Hubble flow via the cosmic distance ladder. Finally, the dual action of the tidal field on the expanding background—reducing both the matter and the dark energy sources of the squared Hubble rate $H^2$, thereby holding back the cosmic acceleration $\ddot{a}$—is of fundamental importance in resolving cosmological tensions and can also substantially alleviate the density coincidence problem. Full article

This paper proposes a power architecture for a Compound-Wing Unmanned VTOL to supply power during the hovering state. To enhance the hovering efficiency of the UAV while considering the cruising efficiency, the layout structure of a traditional Compound-Wing Unmanned VTOL is optimized. A high-power-density hybrid-power range-extender using an ICE (internal combustion engine) suitable for the Compound-Wing Unmanned VTOL is designed. The engine electronic control unit (ECU) suitable for the range-extender is presented by using a locally linearized state-space equation and LQR (Linear-Quadratic Regulator). Simulation experiments, ground running tests, and flight tests have been conducted to verify the performance of the Compound-Wing Unmanned VTOL and its power architecture. Full article

Precision irrigation is crucial for sustainable agriculture, yet conventional single-objective optimization methods struggle to balance conflicting demands such as crop yield, operational cost, and environmental sustainability. This study introduces an Adaptive Multi-Strategy Grey Wolf Optimizer with Reinforcement Learning for Multi-Objective Optimization (AMSGWO-RL-MO) to enhance precision irrigation decision-making. AMSGWO-RL-MO integrates four strategies: standard GWO exploitation, Lévy flight exploration, differential evolution-based diversity enhancement, and Stochastic Elite Opposition-Based Learning. A Q-learning mechanism dynamically adjusts these strategies, adapting to real-time search conditions to select the optimal approach. We constructed a comprehensive three-objective framework incorporating soil moisture dynamics, crop growth models, and environmental impact assessments. Experimental simulations over a 40-day growth cycle demonstrate AMSGWO-RL-MO’s rapid convergence by the sixth generation, consistently achieving a high-quality Pareto front across 30 independent runs. The knee-point solution yielded a mean crop yield of 96.96%, outperforming standard GWO and multi-strategy variants by approximately 3.8%. Statistical analysis confirms its superior robustness and well-distributed solutions along the Pareto front. These results indicate that the RL-driven adaptive mechanism effectively balances exploration and exploitation. The proposed method offers a more diverse array of Pareto-optimal solutions, presenting a broader trade-off space for balancing crop yield and environmental sustainability compared to traditional weighted-sum approaches. This enhancement facilitates scientific agricultural decision-making under various operational constraints. Full article

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

We examine the transition from the Fowler–Nordheim (FN) field emission regime to the space-charge-limited (SCLC) regime in the case of a pointed nanometric emitter with radius of curvature R ≥ 5 nm, for which the traditional FN equations do not hold. To accomplish this, we use the generalized FN equation for the emission law and the “time of flight” methodology to solve the equations of motion. Taking advantage of the fact that emission from emitters with R = a few nm takes place primarily along the emitter axis, we approximate the paths as linear, provided that the anode is at a far distant position from the cathode. In the approach to the SCLC regime, the calculated currents for emitters with R = a few nm may differ by orders of magnitude, compared to the currents of planar emitters, for the a given fixed anode–cathode separation D. Differences of even greater orders of magnitude are obtained for these currents when R is fixed and D is varied. Furthermore, the variation of currents with D is heavily dependent on R. However, for emitters with R ≥ 25 nm, no appreciable differences in current are observed, compared to the results obtained using the planar theory. An explanation for the observed trends is given. Full article

Angular Distribution Models (ADMs) are essential for converting observed radiances from satellite sensors to the energy-budget–relevant quantity of irradiance. In preparation for the NASA Libera mission, this study presents a data-driven framework to identify optimal groupings of International Geosphere–Biosphere Programme (IGBP) surface types for Libera’s split-shortwave ADMs, in an effort to minimize the uncertainty associated with radiance-to-irradiance conversions while maintaining operational feasibility. Using data from the Clouds and the Earth’s Radiant Energy System (CERES) Flight Model 5 (FM-5), K-means clustering is applied within angular bins to capture viewing-geometry-dependent radiometric behavior. These angular clustering solutions are then assessed via hierarchical consensus clustering to derive consistent surface groups. The analysis suggests seven surface groups (K = 7) optimize the surface clustering space. The resulting classifications are broadly consistent with historical CERES–TRMM ADM surface definitions, preserving radiometrically distinct surfaces such as water bodies and snowy surfaces while highlighting opportunities to consolidate vegetative IGBP surface classes. This study provides an objective and physically grounded basis for defining Libera ADM surface groups, ensuring a robust balance between model accuracy and operational simplicity. Full article

The rapid expansion of commercial human spaceflight is forcing a re-examination of how we decide who is “fit to fly” in space. For more than six decades, astronaut selection has been dominated by government programmes employing stringent medical and psychological criteria designed to minimise risk for small cohorts undertaking long-duration, high-consequence missions. Contemporary standards such as NASA-STD-3001 reflect this paradigm, treating astronauts as highly trained national assets expected to perform reliably under extreme physiological and psychological stress. In contrast, commercial operators aim to fly large numbers of spaceflight participants with highly heterogeneous medical and psychological profiles, within regulatory frameworks that emphasise informed consent and currently impose very limited prescriptive health requirements on passengers. This review examines the evolution and structure of traditional astronaut selection, outlines emerging approaches to screening and certifying commercial spaceflight customers, and explores the conceptual and practical gap between “selection” and “screening”. Particular attention is given to the increasing relevance of behavioural and psychological risk in short-duration but high-stress commercial missions, where acute responses, passenger–crew interaction, and behavioural variability can influence safety, especially in mixed-capability crews. Drawing on agency standards, psychological selection research, and recent proposals for commercial medical guidelines, this paper proposes a risk-informed, mission- and role-specific framework that adapts lessons from government astronaut corps to the needs of commercial spaceflight. We argue that future practice must balance safety, inclusion, and commercial viability through proportionate, evidence-based risk management, supported by systematic data collection across government and commercial flights. Full article