The operational weather radar network is highly suitable for large-scale bird monitoring and early warning systems. This study discusses, compares and contrasts current state-of-the-art technologies for large-scale bird surveillance – including manual observation, satellite tracking, dedicated avian radar and the operational weather radar network – evaluating their respective strengths, limitations and applicability for different monitoring scenarios. Key research questions focus on how to extract reliable bird movement information from weather radar data and how such data can support ecological studies and aviation safety. From the perspective of radar signal analysis, we examine the characteristics of bird echoes in terms of reflectivity, altitude distribution, flight speed and direction. We further review and compare methodologies for distinguishing bird echoes from weather and ground clutter, including clutter suppression, meteorological echo removal, feature extraction, machine learning classification and cross-validation with auxiliary data. Our synthesis demonstrates that weather radar can effectively identify and track bird movements over wide areas, providing valuable data on migration patterns and flight behaviours. These findings have practical significance for applications in avian ecology research and bird strike prevention in aviation.

Unmanned aerial vehicles (UAVs) are increasingly employed in environmental monitoring due to their operational flexibility, cost-effectiveness and high spatial resolution. UAV-based water sampling has emerged as a vital approach for the biological and physicochemical assessment of aquatic ecosystems. Although previous reviews have examined water-sampling mechanisms or propulsion technologies independently, a unified and integrated assessment remains absent. This paper presents a systematic review of long-range UAV platforms for water-sampling applications, with particular emphasis on the combined evaluation of water-sampling mechanisms, in-flight physicochemical sensing technologies and hybrid propulsion architectures. The reviewed literature is categorised into three principal areas: UAV-based water-sampling systems, in-flight physicochemical analysis technologies and hybrid UAV configurations, including fuel-cell-powered and other hybrid-propulsion systems. These advances pave the way for sustainable and efficient aerial platforms for water-sampling applications. Finally, the paper identifies key research gaps and outlines potential directions for future innovation in UAV-based water sampling.

This study presents a generalised theory that describes the thermodynamical processes during mixing of a moist aircraft exhaust with the ambient air and allows one to decide whether or not a contrail forms. Usage of alternative fuels like hydrogen or ammonia increases the moisture content in aircraft plumes compared to current kerosene combustion. Our analysis compares the thermodynamic plume evolution for the classical mixing line and a novel generalised formulation. Additionally, both formulations are used to evaluate the limiting ambient temperature, above which an aircraft does not produce a contrail. We find that the inaccuracies introduced by the classical mixing line cancel each other out, leading to negligible differences between both formulations. Furthermore, the impact of potential heat and water vapour recuperation systems on contrail formation behind fuel-cell-propelled aircraft is investigated. Reducing the exhaust’s thermal energy by technical means increases the contrail formation propensity. Especially, if fuels with high hydrogen content are used, plumes with reduced heat content could reach supersaturation values above $ 500{\%}$. This can trigger liquid water droplet formation directly from the gas phase, a process absent in conventional contrail scenarios, and may increase the number of formed ice crystals drastically. A concurrent increase in ice crystal numbers and contrail formation propensity would increase the contrail-cirrus climate impact. To mitigate this scenario, our analysis identifies requirements on the reduction of exhaust water vapour to suppress contrail formation by technical means and reduce the potential contrail climate impact by fuel-cell-propelled aircraft.

The maximum take-off weight (MTOW) affects many aspects of aircraft design, such as performance, stability and control. It is the first and most important design variable that affects numerous aircraft design decisions. Understanding how parametric changes to the major design variables would likely affect the MTOW in the early stages of the preliminary design phase is crucial. This research introduces a ground-breaking approach to precision weight estimation in light aircraft design. Employing a statistical approach, specifically multilinear regression alone and coupled with p-value analysis, the study focuses on predicting the MTOW using datasets from aircraft still ‘in production’ and/or ‘in service’. The aircraft are categorised by landing gear arrangement and number of engines. Eight crucial design parameters that influence aircraft weight are used to determine the empirical models. The developed models successfully predict the MTOW with an error of less than 5% and outperform existing weight estimation techniques, empowering designers to conduct parametric studies that include essential design parameters at the early stages of aircraft design. The proposed methodology represents a paradigm shift in the field, offering a reliable and practical means of achieving precision in light aircraft weight predictions.

The air data system (ADS) consists of multiple sensors designed to measure atmospheric parameters during an aircraft’s flight. These measurements are then processed by the air data computer to compute the relevant flight parameters required for guidance and control. However, due to the system’s inherent complexity and the harsh flight environment, internal sensors are highly susceptible to failures, including scenarios where multiple components fail simultaneously. To address the issue of complex fault diagnosis in the aircraft air data system, this paper investigates a fault diagnosis method based on the unscented Kalman filter (UKF) and capsule network (CapsNet). First, a UKF model for atmospheric parameters is established based on the computational principles of the air data system, and parameters that can effectively reflect fault information are selected. Then, feature mode decomposition is applied to enhance fault characteristics, which are used as inputs for the neural network. Next, the convolutional layers of the CapsNet are improved using the InceptionV3 network to enhance feature extraction capabilities, and a multi-label classifier is designed to achieve fault location of compound faults. Finally, the proposed algorithm is validated on the simulation platform. Through ablation and comparative experiments, the proposed algorithm achieves a fault diagnosis accuracy of over 95%, demonstrating a more effective solution to the compound fault problem in the aircraft air data system. In our study, compared to convolutional neural networks (CNN), fully convolutional network (FCN), temporal convolutional network (TCN), Inception V3 and the original CapsNet, the improved CapsNet achieved the best performance in compound fault diagnosis.

Aircraft arresting systems (AAS) are critical safety components required for the operation of fighter aircraft, particularly during aborted take-off or emergency landing scenarios. The most significant challenge in the development and certification of these systems is the high-cost full-scale dead-load testing required to verify their performance under various aircraft mass and entry-speed conditions. This study presents a novel and fully domestic test system that reproduces the kinetic energy input defined in the MIL-STD-3036 standard by using high-inertia flywheels driven by electric servomotors. The proposed system stores the required energy in dual flywheels and transfers it to the arresting barrier through a controlled release mechanism, thereby replicating real aircraft-entry conditions without the need for jet engines, runways or physical dead-load vehicles. The study focuses on the conceptual design and analytical modelling of the proposed system. Dynamic analyses were conducted to determine flywheel geometry, material selection, allowable stresses, rotational speeds and energy absorption capacity. The system was shown to meet the highest energy level defined in the MIL-STD-3036 test matrix, while being designed to enable accurate and controlled evaluation of arresting force, torque response, angular deceleration and stopping time. Experimental validation has not yet been conducted and is planned as part of future work. The results demonstrate that the proposed test system provides an economical, repeatable and safe alternative to conventional dead-load tests and enables performance evaluation, maintenance verification and certification of AAS. Furthermore, the system offers a scalable platform for future arresting technologies, including those designed for unmanned aircraft and next-generation military platforms.

This study aims to identify physiological features sensitive to human–automation trust (HAT) and to distinguish between high HAT (HHAT) and low HAT (LHAT) levels. A simulated aviation monitoring and takeover task was conducted under a 2 (reliability) × 2 (transparency) experimental design, in which HHAT and LHAT levels were defined based on their Jian scale scores. Paired t-tests were then employed to analyse HAT-sensitive features derived from five physiological modalities: electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), eye tracking, facial expression and electrocardiography (ECG). Finally, HAT discrimination models with varying interpretability were constructed using the identified sensitive features. The results indicated that: (1) compared with HHAT, LHAT exhibited significantly higher EEG $\alpha$ relative power and lower $\delta$ relative power, shorter eye-tracking metrics including the average duration of whole fixations and the average and total durations of fixations in takeover and flight areas, as well as higher ECG metrics such as the standard deviation of normal-to-normal intervals, the low-frequency to high-frequency ratio, and the low-frequency ratio; (2) no significant differences were found in fNIRS oxygenated haemoglobin and deoxygenated haemoglobin concentrations or in facial micro-expression and action units across HAT levels and (3) the highly interpretable linear support vector machine model constructed based on HAT-sensitive features achieved optimal performance, yielding a fivefold cross-validation accuracy of 0.8065 and an F1-score of 0.7942. These findings can provide empirical evidence for identifying HAT-sensitive physiological markers and developing real-time HAT assessment methods, thereby supporting the monitoring of trust degradation that may compromise flight safety.

This study investigates the effects of continuous atmospheric turbulence on the longitudinal and lateral-directional flight dynamics of a tailless unmanned combat aerial vehicle (UCAV) with a lambda wing planform. The absence of vertical tail surfaces reduces inherent stability and introduces strong coupling between flight modes, making disturbance rejection a critical challenge. The UCAV dynamics were linearised using small perturbation theory and represented in state-space form, with turbulence disturbances modelled using the Von Karman spectrum under light, moderate and severe intensities. The dynamic flight modes were evaluated with reference to the MIL-F-8785C flying quality requirements. State feedback and state observer-based controllers were initially designed using pole placement techniques to evaluate the performance of linear systems and gain insight into their flying qualities. A model predictive controller (MPC) was subsequently developed and compared with these controllers to assess their turbulence rejection and attitude tracking capabilities. Simulation results demonstrate that MPC provides superior robustness, effectively rejecting turbulence across all intensities while ensuring accurate pitch and roll attitude tracking. The findings provide new insights into the dynamics and control of tailless UCAVs under realistic turbulence conditions, supporting the viability of such configurations for mission-oriented applications.

As drones and unmanned aerial vehicles (UAVs) are used in different scenarios, a variety of potential risks and safety challenges have arisen. One of the threats is that an increasing number of UAV encounter events are found near the airport in recent years, which pose significant dangers to manned aircraft and result in accidents. However, only a few studies examine the impacts of these events and propose effective countermeasures to enhance safety. To unveil the risks of UAV risk events (incidents or accidents) and examine the mechanism with risk factors, this study uses a tree-augmented naive Bayes network (TAN-BN). This method analyses the relationships among risk factors and UAV accidents/incidents to assess the efficacy of risk mitigation measures. Environmental, technological and human factors are simultaneously considered in constructing the Bayesian network. The analysis results reveal 12 specific risk factors that are significantly associated with UAV accidents/incidents in UAV operation scenarios, among which flight control system failure (the most critical factor), remote communication failure, other aircraft approaching, loss of electrical power, adverse weather, electromagnetic interference, operational errors, violations and risk factors leading to loss-of-control in flight are recognised as the most prominent factors. Based on these, five targeted risk mitigation measures are comprehensively implemented and evaluated. Moreover, a case study using UAV operation data near the Guanghan airport is introduced to justify the generalisability of the proposed TAN-BN model and the effectiveness of risk mitigation measures.

The structural optimisation of aircraft wings remains a critical task in modern, lightweight aeronautical design, where computational efficiency must be balanced against modeling fidelity. To that end, this paper presents an automated framework for the parametric finite element modeling (FEM) and surrogate-based optimisation (SBO) of an aircraft wing using MSC Patran and Nastran. The geometry and structural layout of the wing are generated through Patran Command Language (PCL) scripting, enabling fully parametric control of ribs, spars, stringers and thickness distributions. The automated model is then linked to MSC Nastran for static, global linear buckling and dynamic aeroelastic (flutter) analyses. To reduce the computational cost associated with repeated commercial FEM-based evaluations, surrogate models are constructed and used to drive the structural optimisation process. The framework is demonstrated on a representative high aspect-ratio wing structure, showing significant reductions in design cycle time while maintaining accuracy in predicting mass and performance metrics. The results highlight the potential of integrating surrogate modeling with commercial FEM software in an automated workflow, offering a practical and scalable approach for aerospace structural design and optimisation.

Sled helicopters, equipped with sleds on landing gear, can land on thick snow-covered pavement. However, existing research focuses on hard-surface landings, with little investigation into snow-covered pavement, hampering safety assessments. This study analyses sled helicopter landing characteristics on snow-covered pavement. Using the capped Drucker–Prager snow constitutive model and the coupled Eulerian–Lagrangian method, a multi-field finite-element model of the helicopter-snow system is established. Symmetrical and asymmetrical landing characteristics are compared, and the effects of initial landing velocity on landing and sliding safety boundaries are simulated. Results show that under identical symmetrical landing parameters, the maximum vertical overload at the centre of mass is 1.11 g, 25% lower than for wheeled helicopters. After an asymmetrical landing, the helicopter remains tilted, increasing load on a single landing gear. At a 5° roll angle, the peak load on the right landing gear is 22.88% higher than at 0°. Without rotor pitch moment, a heading velocity exceeding 3.8 m/s risks overturning during landing. Based on these findings, the velocity-moment safety boundary for the safe landing of sled helicopters is presented.

Modern aviation supports an ever-broader range of civil and military missions, and the airframes designed for these missions must satisfy stringent safety and performance requirements. The takeoff and landing phases are the most accident-prone portions of a flight despite representing only a short interval of the total block time, which makes the accurate prediction of takeoff speed a safety-relevant problem. A previous machine learning study addressed the takeoff-speed prediction problem of the Boeing 737-300 with classical regressors using pressure altitude, outside air temperature, gross weight and flap angle as the predictors. In the present work, the same regression problem is revisited under the deep learning paradigm. Four neural architectures are trained on an identical pre-processing pipeline and train-validation partition, namely a multilayer perceptron, a one-dimensional convolutional network, a long short-term memory network and a wide-and-deep architecture incorporating multi-head self-attention. Among the four candidates, the long short-term memory network attains the lowest root mean square error and mean square error on the unseen test file and is subsequently subjected to Bayesian hyperparameter optimisation through the Keras Tuner library. The predicted and the measured takeoff speeds are reported side by side for the first time in the deep learning literature for this airframe, and the simulation results indicate that the developed networks constitute an effective alternative tool for takeoff-speed prediction.

With the development of the low-altitude economy, challenges and opportunities are gradually emerging. In potential crash accidents, it is necessary to enhance the crashworthiness of low-altitude aircraft to ensure the safety of the occupants. This paper studies different types of honeycomb energy-absorbing structures located at the bottom of the fuselage. Two types of honeycombs, hexagonal honeycomb (HEX) and re-entrant honeycomb (REH), are selected to construct an integrated energy-absorbing structure to absorb the impact kinetic energy during a crash accident. The effects of cellular type, unit size and wall thickness on the deformation mode and specific energy absorption (SEA) were systematically investigated. The results indicate that REH exhibits more stable hierarchical folding and a higher SEA. Furthermore, the study identifies an optimal geometric configuration, characterised by a specific combination of cellular size and wall thickness, that achieves the most effective balance between lightweight requirements and energy-absorbing capabilities.