Search Results (540)

When artificial satellites disintegrate in orbit due to collisions, explosions, or anti-satellite tests, they generate a large amount of space debris. These debris move in orbit through formation-like flight patterns, which we refer to as debris clusters. Compared to constellation satellites in formation flight, debris clusters have closer mutual distances and higher spatial density. To address this phenomenon, this paper proposes a detection method for satellite breakup events through a two-layer clustering strategy using the DBSCAN algorithm in three-dimensional spatiotemporal domains. The method provides a systematic approach for identifying and characterizing breakup debris through orbital backpropagation and density analysis, serving as a screening tool for potential breakup events. Using the 2009 Cosmos 2251-Iridium 33 collision breakup event as a validation case, experimental results demonstrate that our two-layer clustering approach achieves good clustering accuracy (98.92%) and a positive recall rate (93.20%), with a temporal resolution of 5 s and spatial precision of 21.68 km. The methodology was further applied to analyze the 2024 Intelsat 33E breakup event, successfully identifying sparse debris clusters from Intelsat 33E. The detected breakup time showed an 8 min deviation from the Space-track.org official report. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

Relative satellite navigation is critical for formation flying, rendezvous, and docking. This study augments a vision-based relative navigation framework with a robust multiplicative extended Kalman filter (RMEKF) that adaptively scales the measurement covariance using innovation-based covariance matching and a chi-square fault-detection test. A two-spacecraft scenario is simulated in which a deputy monocular camera observes six active beacons on a chief spacecraft. To evaluate fault tolerance, constant line-of-sight (LOS) errors are injected on two beacon measurements during a fixed interval. Over the fault-centered evaluation window, the RMEKF reduces attitude root mean square error (RMSE) by approximately 71–73% compared to the conventional multiplicative extended Kalman filter (MEKF), while also improving relative/orbital state accuracy by 19–93%. These results indicate improved robustness to LOS measurement faults without degrading overall estimation stability. Full article

The Qinghai–Tibet Plateau is characterized by highly complex terrain, and civil aviation serves as a primary mode of transportation for regional mobility. A comprehensive understanding of wind field characteristics within the terminal areas of plateau mountain airports, as well as the formation mechanisms of wind shear during different flight phases, is of considerable importance for flight risk assessment, improvement of transport efficiency, and refined meteorological support services. However, studies focusing on wind field structures within the terminal areas of plateau mountain airports remain limited. In this study, dry-season observations from Coherent Doppler Wind Lidars at two critical locations in the terminal area of Lhasa Airport are analyzed. A comparative analysis is conducted on the vertical structure, diurnal variation, and the characteristics of turbulence and wind shear under different terrain conditions. The results show that above the valley height, both sites are dominated by stable westerly winds. Below the valley height, the wind field is strongly influenced by terrain complexity. At the Lhasa Airport site (LS), the valley is regular in shape and has a stable orientation. The prevailing wind direction is aligned with the valley, and easterly winds dominate the entire valley, especially in the middle and lower layers. In contrast, the Qushui site (QS) is located at the confluence of two valleys, where the terrain is more open and complex. The prevailing wind shifts clockwise with height, from northeasterly in the lower layers to easterly aloft. The wind direction is less concentrated than at LS. In terms of diurnal variation, a stable easterly layer forms within the valley at LS in the morning. A transition layer of about 200–300 m exists between this layer and the westerlies aloft. Within the transition layer, wind speed is relatively weak and wind direction stability is low. At QS, morning winds are weaker and more variable within the valley. Wind direction stability increases with height. In the afternoon, both sites are influenced by the downward transport of westerly momentum. However, the effect is more pronounced at QS, where low-level wind speed is higher and wind direction is more stable. Turbulence at both sites peaks between 14:00 and 17:00 and is mainly driven by thermally induced updrafts. Turbulence intensity at QS is stronger, with a vertical extent exceeding 1500 m, indicating a stronger response to thermal forcing. Wind shear at both sites mainly occurs between 12:00 and 18:00, with peak frequency from 13:00 to 17:00. This period is consistent with peak turbulence activity. Wind shear at LS occurs more frequently and lasts longer. At QS, momentum transport from above 1500 m enhances wind shear occurrence at 800–1000 m. The causes of wind shear differ under different prevailing wind conditions. Under prevailing westerlies, wind shear is mainly caused by rapid changes in wind direction with height. Under prevailing easterlies, it is primarily associated with an enhanced vertical gradient of wind speed. These results reveal the significant influence of complex terrain on low-level wind structures and causes of wind shear. The findings provide a scientific basis for operational decision-making at plateau mountain airports. Full article

This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. During flight, each Raspberry Pi establishes inter-UAV communication via a Wi-Fi network using the UDP protocol, enabling real-time data exchange and attitude adjustments. An outer-loop proportional–integral control design implemented on the Raspberry Pi generates corrective commands to the corresponding autopilot to reduce the followers’ position errors. Under the tested conditions, the framework achieves formation tracking with horizontal and vertical errors of approximately 60 and 20 cm, respectively, providing initial experimental validation in a small-scale setting. In addition, a simulation environment based on pre-recorded UAV and environmental data with 3D visualization is developed to support behavior prediction, performance evaluation, and control tuning prior to real-world deployment, although its applicability beyond the tested scenarios remains to be established. Full article

To address cooperative path planning for multiple UAVs in complex environments, this paper proposes an improved multi-agent deep deterministic policy gradient algorithm, named Prioritized Experience Multi-Agent Deep Deterministic Policy Gradient (PE-MADDPG). An urban low-altitude inspection environment is first constructed within a reinforcement-learning framework, in which dynamic constraints, safety-separation requirements, and formation-cooperation objectives are incorporated into a partially observable Markov decision process. To improve training effectiveness, prioritized experience replay is introduced to increase the utilization of informative samples, an adaptive exploration-noise strategy is designed to regulate exploration intensity, and a multi-head attention mechanism is embedded in the Critic network to enhance the representation of inter-agent interactions. Simulation results in a three-dimensional urban inspection scenario show that PE-MADDPG outperforms the selected benchmark methods in task completion rate, formation maintenance, flight efficiency, and energy consumption. These results provide an effective solution for urban low-altitude inspection tasks. Full article

Rapid expansion of the plant-based milk market has increased the need to understand how the aroma profiles of these alternatives differ from that of dairy milk and how raw material selection and processing influence volatile formation. This study compared the volatile profiles of dairy milk, commercial plant-based milks, and laboratory-prepared cereal and pseudocereal milk prototypes to identify promising materials for plant-based milk development. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) combined with chemometric analysis was used to characterize volatile compounds in bovine milk, four commercial plant milks, and five laboratory-prepared plant milks. Dairy milk was characterized by fatty acids, esters, and other lipid-derived volatiles, whereas plant-based samples were associated with hydrocarbons, pyrazines, ketones, and phenols. Within the plant-based group, volatile differences were influenced by raw material type and processing history. Commercial products showed more evident processing-related features, whereas laboratory-prepared cereal samples exhibited a simpler volatile background. Among them, barley milk displayed a distinctive toasted and cereal-like signature. Overall, the selected cereal and pseudocereal matrices showed distinct volatile characteristics, as well as relatively uniform raw material backgrounds, implying greater flexibility in aroma expression. These features make them promising candidates for dairy alternatives and may help guide future plant-based milk formulation. Full article

In this work, an adaptive event-triggered-based consensus control strategy is proposed for the quadrotor unmanned aerial vehicle (QUAV) formation system in the presence of external disturbances and state constraints. Firstly, the disturbed QUAV formation system dynamic model is established. Then, to address the initial peaking explosion problem in the traditional active disturbance rejection control method, a time-varying gain extended state observer (TGESO) is designed to suppress external disturbances. Meanwhile, a novel barrier Lyapunov function (BLF) is constructed to cope with the adverse effects caused by state constraints. Furthermore, aiming to alleviate network congestion and reduce communication burden, the adaptive event-triggered mechanism (AETM) is adopted to design the formation flight controller. Finally, the stability of the developed consensus controller and the boundedness of all error signals are proved via Lyapunov theory. Comparative simulation results demonstrate the practicality of the presented control algorithm. Full article

Unmanned aerial vehicles (UAVs) are increasingly being used as diagnostic platforms in electricity transmission and distribution, enabling safer and faster inspections compared to manual climbing operations or manned aerial support. This article presents an implementation-oriented inspection process that integrates RGB imaging, infrared (IR) thermography and (optionally) LiDAR documentation for critical energy infrastructure and photovoltaic (PV) installations. The survey consists of two stages: a preliminary stage under controlled conditions and an operational stage in a real-world environment, limited only by UAV flight restrictions. Thermal measurements are recorded in radiometric formats and analyzed using polygon- and profile-based tools to identify temperature anomalies (hot spots) and support maintenance escalation decisions. This manuscript presents standardized sample templates for mission logs, QA/QC activities, and anomaly lists, intended to support reproducible data collection in future studies. The proposed process supports predictive maintenance by enabling repeatable inspections, archive-based trend analysis, and integration with asset management processes, while minimizing operational risk and avoiding power outages when technically feasible. Full article

Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. 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

Background: In this study, we aimed to compare metabolomic profiles, biodistribution, and detoxification patterns of the novel SN-38 derivative NMe with irinotecan (IR), and to identify NMe-specific metabolites to evaluate its preclinical pharmacokinetic advantages. Methods: In vivo ADME studies were conducted for NMe, a 9-aminomethyl SN-38 derivative, and IR following a single intraperitoneal dose of 40 mg/kg in mice. Additionally, ADMET properties were predicted using ADMETlab and SwissADME tools for comparison. Levels of NMe and irinotecan absorbed into plasma, distributed to tissues, and metabolized were monitored in liver, lung, spleen, kidney, and stool samples at 15, 30, and 60 min post-administration. Tissue extracts were analysed using high-performance liquid chromatography (HPLC), liquid chromatography–electrospray ionization quadrupole time-of-flight-tandem mass spectrometry (LC-ESI-QTOF-MS), and nuclear magnetic resonance (NMR) techniques after lyophilization and reconstitution. We compared the metabolomic profiles of irinotecan and NMe. Results: We identified and confirmed NMe-specific metabolites, including 9-CH₂-S-cysteine conjugate, 9-CH₂OH, and NMe-formyl. Notably, novel irinotecan metabolites (IR-OH and IR-ΔE) were detected in small amounts in kidney samples. In some cases, two literature-known photodegradation products of irinotecan were present. NMe was found to quickly metabolize with different distribution to tissues, significantly greater to kidney and liver. Two SN-38 glucuronides, SN-38G(α) and SN-38G(β), were detected corresponding to α- and β-anomers. Where it was possible, NMe, IR and SN-38 were quantified using external calibration curves. In IR group, controlled and prolonged release of SN-38 was confirmed in all samples, yet SN-38G was observed in minority only in plasma, kidney, or lungs. In NMe groups, great relative amounts of SN-38 and SN-38G were detected. Greater content of SN-38G in NMe group than in irinotecan is expected to contribute to modulation and alleviation of some side effects in irinotecan-involved therapies, such as gastrointestinal toxicities (GIT). Conclusions: NMe shows a distinct metabolic profile characterized by rapid biotransformation, higher systemic glucuronidation of SN-38, and formation of unique metabolites, suggesting a potentially wider therapeutic window and reduced toxicity compared with IR. Full article

Biopolymers are increasingly explored as safer and more sustainable food packaging materials. This study evaluated the migration behavior of intentionally and non-intentionally added substances (IAS and NIAS), as well as the safety of gelatin and xanthan gum blends reinforced with microcrystalline cellulose, with and without oxygen plasma treatment, incorporating glycerol and limonene as plasticizers. Migration tests were conducted according to European Union (EU) Regulation No. 10/2011 using simulants of different polarities, and IAS/NIAS were analyzed by gas chromatography–mass spectrometry and ultra-high-pressure liquid chromatography–quadrupole time-of-flight mass spectrometry (GC–MS and UPLC-QTOF-MS). Films containing limonene were also evaluated for antioxidant activity. Results showed that plasticizer migration is strongly influenced by simulant polarity, glycerol predominantly migrated into hydrophilic media, whereas limonene and its derivatives exhibited higher migration in fatty simulants. Ethanol 95% acted as a conservative worst-case simulant, promoting extensive migration, while substantially lower migration levels were observed in isooctane and tenax plasma treatment resulted in modest changes in volatile compound migration, while significantly enhancing the antioxidant activity of limonene-containing films. Although overall migration levels were low under most of the tested conditions, NIAS formation, particularly from limonene degradation, highlights the need to account for chemical stability and simulant type when assessing bio-based films. Overall, the study demonstrates that film composition, surface modification, and simulant characteristics jointly influence migration behavior and functional performance under the evaluated conditions reinforcing the need to adapt current regulatory frameworks to the specific behavior of biopolymeric packaging materials. Full article

This paper investigates the flight kinematics and unsteady aerodynamics of butterfly flight using high-speed schlieren imaging. Butterfly trajectories are reconstructed to examine flight control mechanisms, with particular emphasis on thorax-driven manoeuvring and body reorientation. By reconstructing free-flight trajectories utilizing image recognition algorithms, we isolate the mechanisms of flight control, with particular emphasis on how thoracic oscillation drives manoeuvring and body reorientation. Phase-resolved analysis reveals distinct wingbeat modes, including clap-and-fling motions associated with hovering and low-speed ascent. Schlieren visualization further captures a detailed view of the wake topology, displaying the formation and evolution of wingtip vortices during the downstroke, as well as attached and entrained flow structures during cupped wing configurations. The results demonstrate the strong coupling between body dynamics, wing kinematics, and wake structure, highlighting how butterflies combine aerodynamic and inertial mechanisms to achieve efficient lift generation and control. These findings provide biomimetic insights relevant to the design of flapping wing micro air vehicles, particularly for low-speed flight, hover efficiency, and passive stability and control through body–wing coupling. Full article