Abstract The process of transferring fuel from a tanker aircraft to a receiving aircraft is called air-to-air refueling (AAR). This study examines the probe-and-drogue method of AAR, in which a tanker aircraft flies ahead of a receiving aircraft and extends a flexible hose with an attached drogue midflight, allowing the receiver to manually couple its refueling probe with the drogue. In previous research the German Aerospace Center developed two visual, mixed-reality assistance systems to support receiver pilots during contact approach and contact hold by visualizing important, previously unavailable information. One system visualizes the relative speed difference between the probe and the drogue, while the other highlights the drogue’s rim and displays fuel offload information. The second assistance system was only tested in a preliminary state focusing on drogue highlighting without fuel offload and status information. This study examines these assistance systems, though the primary aim is not to evaluate pilot usability and experience, but to assess the effectiveness of the methodology used for evaluating Human Factors (HF) aspects in visual, mixed-reality assistance systems during simulated AAR contact approaches. Conducted in a fighter aircraft simulator, the study involved six experienced fighter aircraft squadron pilots performing multiple refueling contact approaches with and without visual assistance. Alongside simulator flight data, HF aspects such as workload, situation awareness, performance, usability and user experience were recorded and analyzed. The applied methodology proved to be effective for evaluating HF aspects of visual mixed-reality assistance systems for pilots. However, the employed methods did not yield robust statistical findings, as expected given the small sample size. Nevertheless, the findings suggest that visual mixed-reality assistance systems may influence pilot behavior and may facilitate pilot support during manual AAR.

The effect of aircraft cockpit rudder pedal shape on lower limb muscle activation, plantar pressure, and comfort.

This paper is devoted to the issue of smooth transition control synthesis of multi-equilibrium switched systems (MESS), which exhibits a pair of unique trim states and control inputs in each mode and treats conventional common-equilibrium switched systems as special cases. Unlike the existing mode-dependent Lyapunov function (MLF) with common-equilibrium assumption, a partly increasable equilibrium-related mode-dependent Lyapunov function (piEMLF) is proposed for stability analysis of MESS, where the increase allows flexible control design or complex dynamics, while the descending ensures states move toward a limited set, i.e. stability region. Considering the difference of switched controller in equilibria, trim control inputs, and gains, a multi-step smooth transition control (MS-STC) approach is proposed via piEMLF, where a series of variable interpolation parameters between them are utilised to gradually transfer the control inputs in adjacent modes, in contrast to the existing studies that only consider the gains-arising bump alleviation or utilise the one-step transition approach. By a numerical example and a flight control simulation of a multiple heavy payloads airdrop of a fixed-wing aircraft for forestry fire fighting with the parameter and equilibrium changes therein, the advantages and effectiveness of the proposed MESS modelling and MS-STC approach are demonstrated.

Abstract-The rapid expansion of the global gig economy has fundamentally changed the structure of personal finance management. Unlike salaried professionals who operate within predictable monthly income cycles, freelancers and independent contractors face highly volatile cashflow patterns characterized by delayed client payments, irregular project pipelines, seasonal fluctuations, and unstable liquidity reserves. Traditional Personal Financial Management (PFM) systems primarily focus on historical transaction tracking and static budgeting, making them ineffective for proactive financial survival planning in modern freelance ecosystems. This project introduces Prophet AI v1.1, an AI-driven financial intelligence platform engineered specifically to simulate, forecast, and analyze unstable freelance cashflow environments using distributed cloud infrastructure, cryptographic verification, and real-time neural intelligence. The proposed system functions as a Financial Flight Simulator that allows freelancers to model financial risk before it becomes catastrophic in real life. The platform combines machine learning-based forecasting, stochastic risk simulation, cryptographic integrity validation, asynchronous AI orchestration, and multilingual neural voice synthesis within a single integrated ecosystem. The system architecture follows a distributed deployment model consisting of a Next.js 14 frontend hosted on Vercel, a FastAPI Intelligence Gateway hosted on Render, and a Supabase PostgreSQL secure transaction vault. This decoupled architecture ensures scalability, modularity, low frontend latency, and reliable handling of long-running AI inference tasks. The financial forecasting engine utilizes a hybrid intelligence pipeline combining statistical forecasting principles and ensemble-based analytical logic. The platform generates 30-day rolling liquidity forecasts, safe spending corridors, and stress-based runway simulations that help users evaluate financial survival scenarios under varying burn conditions. Unlike conventional financial dashboards, Prophet AI introduces dynamic What-If simulation controls, allowing users to manipulate variables such as liquidity lag, expense escalation, and delayed client payments in real time. To establish institutional-grade trust and forensic-grade auditability, the system implements an Integrity Shield powered by the SHA-256 cryptographic hashing algorithm. Every transaction entered into the system generates a unique digital fingerprint using transaction attributes including amount, date, category, and user identification. This verification mechanism ensures that tampered or manipulated financial records cannot enter the intelligence pipeline, thereby maintaining a Verified Ledger architecture. The project additionally documents real-world deployment challenges involving decimal precision mismatches between JavaScript and Python environments and explains the implementation of strategic normalization bypass mechanisms for stable production deployment. The intelligence layer of Prophet AI is powered using Llama 3.3-70B via Groq infrastructure, enabling high-speed financial reasoning and structured JSON-based strategy generation. The platform utilizes a carefully engineered Ruthless Financial Strategist system prompt designed to deliver direct, survival-oriented financial recommendations rather than emotionally comforting advice. This design philosophy reflects the real-world operational needs of freelancers who require accurate liquidity warnings and actionable strategic insights during financial instability. The generated intelligence is converted into multilingual audio briefings using the edge-tts neural voice synthesis engine, supporting both English and Hindi voice outputs. To avoid cloud timeout failures and synchronous processing bottlenecks, the platform implements an asynchronous polling architecture using UUID-based job orchestration. The frontend submits a /briefing request and continuously polls a /briefing-status/{job_id} endpoint until the AI-generated strategy and MP3 briefing become available. This architecture enables the system to safely execute computationally expensive large language model inference and neural voice generation workflows even on limited-resource cloud infrastructure. The completed system demonstrates the practical integration of distributed AI infrastructure, cryptographic verification, asynchronous backend engineering, financial forecasting, and multimodal intelligence synthesis within a real-world production environment. Prophet AI v1.1 represents a transition from passive financial recordkeeping to proactive survival-oriented financial intelligence. The project establishes a scalable blueprint for next-generation AI-powered fintech systems capable of delivering real-time strategic decision support for the rapidly growing global freelance economy.Keywords-Freelance finance; cashflow forecasting; stochastic simulation

ObjectiveTo identify eye movement patterns that are correlated with spatial disorientation (SD) events during flights in a flight simulator that induces SD.BackgroundSpatial Disorientation is one of the main causes for aviation mishaps. It can result from illusions caused by misinterpreted vestibular or visual sensory cues, leading to an incorrect perception of an aircraft's position, attitude, or motion. SD prevention is of great importance, as there is currently no objective tool to identify its occurrence.MethodEye movements of 45 participants (30 aircrew members, 15 cadets) were recorded using Tobii Pro Glasses 2 in a Gyro-IPT SD flight simulator. Illusions were either vestibular or visual. Gaze metrics such as fixations, saccades (rapid gaze shift between two points), and visits were compared between subjects who experienced SD and those who did not. Statistical analyses were conducted to identify significant differences.ResultsAmong 284 flight profiles, 136 SD occurrences were recorded (48%). During visual illusions the participants who more frequently checked the instrument panel had a higher chance of avoiding SD. In contrast, during vestibular illusions, participants who examined the head-up display (HUD) more frequently had a lower probability of SD occurrence.ConclusionMitigating SD requires distinct eye-movement strategies tailored to the illusion type. Our results suggest that to mitigate visual illusions, there is a need for greater instrument panel focus, whereas to mitigate vestibular illusions, increased HUD engagement is needed, as opposed to the current instructions.ApplicationOur findings may inform training programs to enhance performance in high-risk SD flight profiles. Additionally, results support the potential development of a real-time SD alert system for aircraft, aiming to mitigate or prevent SD-related incidents.

Do carbon dioxide, volatile organic compounds and atmospheric pressure affect the cognitive performance of occupants in indoor environments? Results of a large-scale experiment with simulated flights.

Aerospace embodied intelligence aims to empower unmanned aerial vehicles (UAVs) and other aerospace platforms to achieve autonomous perception, cognition, and action, as well as egocentric active interaction with humans and the environment. The aerospace embodied foundation model serves as an effective means to realize the autonomous intelligence of UAVs and represents a necessary pathway toward aerospace embodied intelligence. [Background] However, existing embodied foundation models primarily focus on ground-level intelligent agents in indoor scenarios, while research on UAV intelligent agents remains unexplored, lacking systematic and standardized benchmark suites. [Aim] To address this gap, this study aims to construct a comprehensive benchmark suite, AeroVerse, to facilitate the simulation, pre-training, finetuning, and evaluation of aerospace embodied foundation models. [Innovations] We develop AeroSimulator, a simulation platform that encompasses four realistic urban scenes for UAV flight simulation. Additionally, we construct the first large-scale real-world image-text pre-training dataset from a first-person UAV perspective, AerialAgent-Ego15k, and create a virtual image-text-pose alignment dataset, CyberAgent-Ego500k, to facilitate the pre-training of the aerospace embodied foundation model. We clearly define five downstream tasks for the first time, i.e., aerospace embodied scene awareness, spatial reasoning, navigational exploration, task planning, and motion decision, and have constructed corresponding instruction datasets for fine-tuning. We also develop SkyAgent-Eval, a downstream task evaluation system based on GPT-4. Furthermore, we propose SkyAgent, the first UAV-agent large model integrating "perception-reasoning-navigating-planning", which incorporates an aerospace embodied chain-of-thought mechanism and a multitask curriculum learning strategy. [Results] By benchmarking ten mainstream models, our results reveal the significant limitations of existing 2D/3D visual-language models in complex aerospace embodied tasks and demonstrate the superior performance of SkyAgent, which outperforms existing methods by an average of 8.52% across four core tasks, underscoring the necessity and contribution of our work. The AeroVerse benchmark suite will be released to the community to promote exploration and development of aerospace embodied intelligence.

This study examines how cockpit interface complexity shapes pilot cognitive regulation and task performance in general aviation. A high-fidelity flight simulation experiment manipulated three dimensions of interface complexity: visual crowding, modal complexity, and structural depth. Multivariate analysis of variance showed that increased interface complexity significantly impaired attentional deployment, elevated cognitive load, reduced residual cognitive capacity, and degraded situation awareness. Artificial neural network models further revealed nonlinear regulatory patterns that were not fully captured by linear analysis. Specifically, attentional deployment strongly predicted residual cognitive capacity, while residual cognitive capacity emerged as the dominant predictor of situation awareness. These results indicate a dual-pathway mechanism in which interface complexity affects performance through both attentional regulation and resource-preservation processes. The findings extend cognitive load and multiple resource perspectives by identifying threshold-like cognitive degradation under complex cockpit conditions, and provide design implications for adaptive cockpit interfaces and real-time cognitive state monitoring.

Full-discharge simulations of the TCV tokamak using the Fenix flight simulator

Action video game playing has been linked with enhanced cognitive and perceptual abilities measured via a variety of basic psychological lab tests. However, much less work has examined such relations in the context of measures with direct real-world workplace relevance. Here we examined the performance of male United States military personnel on a psychomotor task battery, part of an official selection test designed to assess aptitude for military aviation. We found that action video game playing was significantly and positively associated with performance on this battery. More specifically, increased action video game experience among Naval Flight Students was linked to both better initial performance as well as more rapid learning within the psychomotor task. In contrast, when examining the same associations in a control group for whom the psychomotor task was not occupationally relevant (i.e., not Naval Flight Students), action video game playing was not reliably associated with psychomotor task learning. Time spent playing recreational video games with mechanics more similar to the screening task, flight simulator games, was not related to psychomotor task learning speed in either group. This pattern suggests that recreational action video game playing may work in concert with explicit occupational training to support effective learning and successful workplace outcomes in high-stakes professions.

Addressing the real-time monitoring challenges posed by high dynamics, strong nonlinearity, and observational uncertainty in flight test envelope boundary missions, traditional threshold methods and pure data-driven models struggle to balance identification accuracy, physical consistency, and predictive warning capability. To this end, this paper proposes a CPSSMF aimed at providing interpretable and calibratable unified probabilistic evidence for safety-critical decision-making. Based on sequential Bayesian inference, the framework achieves closed-loop monitoring through three coupled modules: (1) PSA module, which injects physical constraints into posterior distribution shaping through endogenous potential functions, combined with output-side safety masking and multi-source anomaly detection to suppress non-physical state jumps; (2) HPRF module, which performs multi-step rolling prediction based on corrected posteriors, outputting trend risks within a fixed time horizon to achieve quantitative trade-off between lead time and false alarm rate; (3) EGUAQ module, which dynamically evaluates data value using posterior entropy to support active verification. Evaluation results based on simulated flight stall test data demonstrate that CPSSMF maintains high phase identification accuracy (Acc 0.969, F1 0.785) while achieving an average warning lead time of approximately 3.34s under the same false alarm constraints, significantly outperforming baseline methods. Furthermore, under sensor contradiction injection and out-of-distribution disturbance scenarios, the method exhibits excellent robustness and stability. This study establishes an interpretable analysis chain integrating situational awareness, risk quantification, and anomaly diagnosis, effectively enhancing the engineering applicability of flight test safety monitoring.

This study analysed the effects of four parameters (current intensity, rising time, maintenance duration, and falling time) of ramped galvanic vestibular stimulation (GVS) on vestibular perception. Based on these findings, an optimal waveform for inducing roll sensation was designed and applied to a virtual reality (VR) flight simulator. Current intensity and rising time significantly affected perceived strength and annoyance, whereas maintenance duration affected only annoyance, and falling time showed no significant effects. Application of the recommended waveform in a VR flight simulator significantly increased presence and reduced simulator sickness compared with the no-GVS condition.

Bistable perception, where the brain alternates between two interpretations of ambiguous stimuli, has individual-specific switching rates. Although it is related to neural activities, no prior research has investigated its correlation with external behavioral performance. This study aimed to explore the relationship between the stability of bistable perception and behavioral performance, and the corresponding application prospects in the selection of special talents, such as pilots. We chose to use simulated flight operations in this research. Two experiments were conducted on ab initio pilot cadets. In Experiment 1, 38 cadets completed a simulated flight and then observed a bistable point-light rotating sphere (with or without rhythmic information) and reported its rotation direction. Experiment 2 involved 54 cadets divided into two groups observing either rhythmic or nonrhythmic spheres, with pre- and postflight tests. The perceptual switch rate of the rotating sphere was negatively correlated with simulated flight scores (rhythmic: r = -0.547; nonrhythmic: r = -0.484). After observing three sessions of 3-min trials of rhythmic stimuli, the perceptual switch rate increased, which led to an increase in the dominant frequency of flight altitude fluctuations (t = 2.440). Bistable perception stability can predict the performance of external behaviors in simulated flight. The change of perception stability will change operational stability. This research provides support for the application of bistable rhythmic rotating spheres in assessing the perceptual stability of flight candidates in pilot selection, and for enhancing the perceptual stability of pilot cadets in flight training. Zhang X, Wei L, Li X, Luo Y, Yuan J, Tan Q, Mu H. The association between bistable perception stability and performance in simulated flight operations. Aerosp Med Hum Perform. 2026; 97(5):337-343.

To safely accommodate significantly higher air traffic demands, the future air traffic management (ATM) concept will make use of trajectory-based operations (TBOs) in combination with reduced separation criteria, where the intended flight trajectories will be better communicated to the air traffic control system on the ground, and an improved ground capability for monitoring the realization of or deviation from the communicated flight plan will be available. Current ATM makes use of ground target tracking systems that account for basic mode switching between level flight, climb, and descent. However, future ATM is in need of a ground system that also considers onboard flight guidance modes. In a prior contribution, we developed an interacting multiple model (IMM) filter that takes onboard flight guidance modes into account and demonstrated that such an approach performs well under nominal conditions. But the IMM was developed under the unrealistic assumption that, for each guidance mode, the actual control set point is known. The objective of the current contribution is to extend our earlier work by dropping such an unrealistic assumption. In nonlinear filtering, a simultaneous switching of a guidance mode and a jump in the control set point is referred to as a hybrid jump. To cope with such hybrid jumps, the standard IMM has been extended to a generalized IMM (GIMM). The current article develops this GIMM approach, considering ADS-B and enhanced mode S surveillance data, for the joint estimation of simultaneous jumps in aircraft guidance modes and control set points and shows its performance on both simulated and real flight data.

The aviation industry faces increasing pilot training demands, and reliance on conventional Full Flight Simulators (FFS) limits training capacity. Virtual Reality (VR) offers scalable, remote training opportunities, but its role as a complement to FFS requires empirical validation. In collaboration with Singapore Airlines (SIA) instructor pilots, we developed a VR training prototype for a visual approach into Gimhae International Airport, emphasizing Competency-Based Training and Assessment (CBTA)-based Threat and Error Management (TEM). An empirical study with 39 SIA Boeing 737-MAX 8 type-rated first officers evaluated VR against FFS. An equivalence analysis showed that VR achieved performance outcomes comparable to FFS in 13 of the 16 Observable Behaviors (OBs) and across 4 Competencies. In addition, a comparative analysis indicated measurable performance improvements when VR was used to supplement FFS training. Our results suggest VR can meaningfully complement FFS in targeted competency areas, with future work required to assess its broader integration across additional scenarios and performance metrics.

The Republic of Singapore Air Force uses the Human Training Centrifuge (HTC) to train aircrew in effective anti-G straining maneuvers and improve G tolerance. Training comprises computer-controlled (open-loop) profiles for trainees and dynamic flight simulation (closed-loop) profiles for trained aircrew, where simulated aerial combat maneuvers allow for pilot-controlled G onset rates. This study investigated medical incidents related to HTC usage in aviation physiology training. A retrospective audit was conducted of 8013 HTC runs over 10 yr (2014-2023), during which 103 medical incident reports were documented. Data was collected through incident report forms and analyzed using descriptive statistics and statistical tests to compare training profile outcomes. Musculoskeletal injuries were the most prevalent (76.70%), with back pain being the predominant condition within this category (41.77%). Other conditions included cardiovascular (13.59%) and ophthalmological (3.88%) events. There were 3 medical incidents out of 955 (0.314%) closed-loop profiles, compared to 100 in 7058 (1.42%) open-loop profiles. Among reported incidents, 35 aircrew (33.98%) had significantly related past medical history. Post-incident, 73 aircrew (70.87%) completed centrifuge training eventually and returned to flying. HTC training demonstrates a favorable safety profile with low complication rates, and most affected aircrew completed their training requirements successfully. The yearly reported incidents of medical events post-HTC have shown a general downward trend. Compared with open-loop training profiles, closed-loop training provides dual benefits of providing realistic scenarios while allowing aircrew to control G exposure parameters, resulting in significantly lower medical complications while achieving training objectives. To YH, Woo JHA, Low JW, See B, Kwong J. Medical events during centrifuge training in the Republic of Singapore Air Force: a 10-year analysis. Aerosp Med Hum Perform. 2026; 97(5):308-315.

Application and analysis of a monte carlo-based improved turbulent wind field flight simulation platform

Dynamic aero-structural coupled circular flight simulations of soft kites

A multi-task network for runway lines and runway markings based on deep learning was designed to address the issue of prior information dependence on runway width in unmanned aerial vehicle visual autonomous landing application scenarios. By detecting runway images captured at different positions during flight, the parameters of the runway start line, left and right boundary lines, and runway markings were obtained. On this basis, a runway width estimation model and visual positioning algorithm based on line features were designed. In standard runway scenarios, the recognition of runway signs provides valuable prior information about the runway width. For simplified runways or cases where signs are missing, we have devised a width estimation model based on the left/right boundary lines. Furthermore, considering the variation in pitch angle during the UAV’s landing process, we have analyzed and refined the width estimation model to ensure its applicability throughout the entire landing process. Additionally, we have developed a visual positioning algorithm that utilizes the runway width and runway line parameters to calculate the relative position between the UAV and the runway. Considering the limitations of a single visual positioning algorithm, we adopt a visual and inertial navigation fusion positioning algorithm to enhance the reliability of landing positioning. To validate our algorithms, we have constructed a visual simulation platform and flight test. These tests confirm the effectiveness and accuracy of our detection algorithm and width estimation model. Furthermore, by utilizing the estimated runway width and the detected runway line parameters, we have successfully calculated the relative position, further validating the effectiveness of our positioning algorithm.

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots' flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the "OODA loop" theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots' flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities.