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To meet the comprehensive requirements of drivers for Human-Computer Interaction (HCI) during operation and to enhance situation awareness (SA), this paper proposes a design and evaluation method for flight simulator HCI interfaces grounded in SA. Firstly, the widely adopted Endsley's SA model, which comprehensively covers processes relevant to individual drivers was selected as the foundational framework. The study concentrates on flight, cruise, and landing scenarios within the simulator, analyzing and restructuring the information architecture of the HCI interface. Secondly, a self-assessment scale was developed based on the three-level model of information processing for SA to measure drivers' SA cognitive levels. The core scenarios of the simulation training were analyzed to establish a design-making process. Thirdly, invite participants were invited to engage in simulated driving sessions on the flight simulator, where their SA cognitive levels were assessed. An analysis of detail features was performed on samples with high SA cognitive levels. Subsequently, drivers' requirements for the control layout features of the HCI interface design were gathered through surveys. The design detail features of the HCI interface associated with high SA cognitive levels were identified and synthesized into four distinct dimensions to guide the detailed design process. Finally, participants engaged in simulated driving using the finalized interface design, and their SA cognitive levels were reassessed. The results demonstrated that the HCI interface design derived from this method significantly improved SA cognitive levels compared to the original design. [ABSTRACT FROM AUTHOR]

To solve the aircraft dynamics modeling problem in the entire envelope range, this work proposes a closed-loop system identification method based on deep learning. A closed-loop flight test was designed, under the framework of the closed-loop flight test, the motion mode of the aircraft was fully stimulated by the input signal of the control rudder surface and the airspeed and position commands. The lateral and longitudinal aerodynamic coefficients were solved from the flight test data, and the black box relationship between the aerodynamic coefficients and their influencing factors was established based on the deep network technology. The aerodynamic coefficient black box model was combined with the dynamics and kinematic equations of the aircraft to form a deep network dynamic model of the aircraft, which belongs to a gray box dynamics model. The deep network can easily and uniformly process different batches of flight test data, thus combining the flight test data at different flight state points, and finally building a complete aerodynamic model within the entire envelope range. Three groups of flight tests were performed: the first group of tests was used for model set training, the second group of test data was used for the selection of the best model, and the third group of flight tests was used for model validation. The model verification was completed from two aspects: the prediction of the aerodynamic coefficient and the prediction of the flight state variables. The results show that the deep network model can complete high-precision modeling of aerodynamic coefficients; and the gray box dynamic model can complete the modeling of aircraft dynamics within the entire envelope, and can be used as a long-term, high-precision flight simulator. [ABSTRACT FROM AUTHOR]

The objective of pilot training is to equip trainees with the knowledge, judgment, and skills to maintain control of an aircraft and respond to critical flight tasks. The present research aims to investigate changes in trainees' cognitive control levels during a pilot training process while they underwent basic flight maneuvers. EEG microstate analysis was applied together with spectral power features to quantitatively monitor trainees' cognitive control under varied flight tasks during different training sessions on a flight simulator. Not only could EEG data provide an objective measure of cognitive control to complement the current subjective assessments, but the application of EEG microstate analysis is particularly well-suited for capturing rapid dynamic changes in cognitive states that may happen under complex human activities in conducting flight maneuvers. Comparisons were conducted between two types of tasks and across different training stages to monitor how pilot trainees' cognitive control responds to varied flight task types and training stages. The present research provides insights into the changes in trainees' cognitive control during a pilot training process and highlights the potential of EEG microstate analysis for monitoring cognitive control. [ABSTRACT FROM AUTHOR]

The profession of airline pilots is characterized by high levels of stress and a significant risk of burnout. The health emergency period has exacerbated these challenges to health and well-being, with long COVID adding further strain to aviation professionals even in the post-pandemic scenario. In this context, it is essential to promote positive organizational behaviors to reconcile individual well-being with work performance. This study aimed to explore the mechanisms linking burnout to work performance behaviors (i.e., proficiency, adaptivity, and proactivity) among pilots, hypothesizing the mediating role of continuous learning behaviors. Based on the Conservation of Resources theory, we posited that burnout depletes pilots' resources, thereby hindering continuous learning and reducing performance behaviors. Moreover, this study examined the work-related stress factors that could affect burnout and the consequences of performance behaviors on actual performance measured by a flight simulator. Data were collected from 123 pilots through an online survey and analyzed using path analysis. The results revealed that continuous learning mediated the relationship between burnout and work performance behaviors. Furthermore, work-related stress factors were significantly related to burnout, and work performance behaviors were linked to flight simulator performance. Our findings underscored the critical role of continuous learning in explaining the adverse effects of burnout on performance. These insights could inform targeted interventions to promote continuous learning and stress management among aviation professionals, ensuring sustained performance and well-being in the long term. [ABSTRACT FROM AUTHOR]

Objective: This paper aims at identifying the factors influencing performance in basic flight simulators in the selection of ab initio pilot candidates and to determine the incremental value of these flight simulators in predicting performance in flight training. Background: Several factors such as cognitive ability, personality, and demographic information are discussed to be associated with performance in basic flight simulators. Many commercial flight operations tend to refrain from using flight simulators in the selection of ab initio pilot candidates in favor of less expensive test batteries. Method: Study 1 uses 4,340 candidates in the selection of major European airlines to investigate the factors influencing performance in the basic flight simulator. Study 2 then uses 1,753 candidates to evaluate the incremental validity of the basic flight simulator in predicting issues in flight training. Results: Multiple task ability, gender, and previous flight experience are the strongest predictors of performance in the basic flight simulator. Still, the flight simulator shows significant incremental validity in predicting problems in flight training. Conclusion: These findings seem to justify the additional use of a basic flight simulator in the selection of ab initio pilot candidates. Despite a strong correlation with more economical multiple task ability tests, the flight simulator still shows incremental validity in predicting success in flight training. [ABSTRACT FROM AUTHOR]

[Objective] Recently, underground space disasters have occurred with increasing frequency, highlighting the urgent need for a comprehensive, multidimensional support system for emergency communication. A key task in emergency communication for underground spaces is the rapid reorganization of communication networks to relay disaster information in real time. Unmanned aerial vehicles(UAVs), with their flexible deployment, unmanned operation, and all-terrain mobility, can play a vital role in emergency rescue efforts. In the event of disasters such as fires, collapses, or floods, UAVs can quickly access affected areas and establish stable ad-hoc networks using their onboard communication devices. UAVs can promptly transmit the collected disaster information back to the rescue center, thereby enhancing the efficiency of rescue operations. However, while extensive research has been conducted on UAV applications in low-altitude areas, further development is necessary for their use in underground spaces. Particularly, there is a lack of research on experimental platforms that integrate scientific research and teaching. This gap has resulted in a disconnect between the educational content and real-world scenarios. Therefore, establishing a scenario-based teaching and practice platform for emergency communication networks in underground spaces is essential. By enhancing practical teaching content, we can effectively guide students in learning modern emergency communication network technologies, ensuring a strong connection between theoretical instruction and real-world applications. [Methods] In this paper, we employ hypergraph theory to abstract the rapidly changing network topology into a dynamically evolving hypergraph, modeling the evolution of interference relations through the insertion and deletion of superedges. Specifically, we reformulate the dynamic channel selection problem in UAV communication networks as a hypergraph coloring problem. To adapt to the dynamic network topology, we leverage reinforcement learning theory to enhance the traditional hypergraph coloring algorithm and develop a dynamic hypergraph coloring algorithm. Additionally, when the topology of the UAV communication network changes, the traditional greedy algorithm requires recalculation at each time slot, whereas the Q-learning algorithm can quickly converge based on prior learning. Building on this discussion, we propose a dynamic channel selection algorithm based on the Q-learning framework, which can adjust the learning rate parameters according to the rate of topological changes. [Results] To validate the effectiveness of our proposed algorithm, we conduct extensive simulation experiments to observe how the proportion of link conflicts in the network changes with varying algorithm iterations and UAV flight speeds. Additionally, we analyze the changes in average throughput across different network sizes, comparing the Q-learning algorithm to the greedy algorithm. The simulation results demonstrate that our algorithm can dynamically adjust the learning rate parameters based on the topological change rate of the UAV communication networks, achieving an optimal trade-off between algorithm performance and convergence speed. And the network performance of this algorithm is better than that of the greedy algorithm. [Conclusions] In conclusion, we utilize hypergraph theory to design an adaptive channel selection algorithm based on the Q-learning framework and establish a simulation experiment platform using ns-3 simulation software. Specifically, we employ the ns-3 platform to create a network simulation experiment for an underground space emergency scenario, helping students apply their theoretical knowledge of communication networks to practical engineering contexts. This work lays a solid foundation for developing a simulation teaching platform for UAV emergency communication networks within the interdisciplinary integration of electronic information science and emergency management. [ABSTRACT FROM AUTHOR]

Objective: We examined whether active head aiming with a Helmet Mounted Display (HMD) can draw the pilot's attention away from a primary flight task. Furthermore, we examined whether visual clutter increases this effect. Background: Head up display symbology can result in attentional tunneling, and clutter makes it difficult to identify objects. Method: Eighteen military pilots had to simultaneously perform an attitude control task while flying in clouds and a head aiming task in a fixed-base flight simulator. The former consisted of manual compensation for roll disturbances of the aircraft, while the latter consisted of keeping a moving visual target inside a small or large head-referenced circle. A "no head aiming" condition served as a baseline. Furthermore, all conditions were performed with or without visual clutter. Results: Head aiming led to deterioration of the attitude control task performance and an increase of the amount of roll-reversal errors (RREs). This was even the case when head aiming required minimal effort. Head aiming accuracy was significantly lower when the roll disturbances in the attitude control task were large compared to when they were small. Visual clutter had no effect on both tasks. Conclusion: We suggest that active head aiming of HMD symbology can cause attentional tunneling, as expressed by an increased number of RREs and less accuracy on a simultaneously performed attitude control task. Application: This study improves our understanding in the perceptual and cognitive effects of (military) HMDs, and has implications for operational use and possibly (re)design of HMDs. [ABSTRACT FROM AUTHOR]

In 2023, the leading US aerospace corporation Lockheed Martin announced the simultaneous development of several extended/augmented reality (XR/AR) simulators for pilots of TF-50, F-16, F-22, and F-35 without being a pioneer in this area of focus, in 2022 similar projects were launched by Boeing and the leading British aeronautical equipment manufacturer BAE Systems. In January 2024 the US Air Force invested in the development of pilot AR simulators based on Microsoft Hololens augmented reality smart glasses. At the same time, Apple began bulk sales of the Apple Vision Pro AR headset. It is difficult to doubt that in 2024 a variety of new aviation simulators will appear using this device. The rapid development of a new generation of aerospace simulator technology, i.e., XR/AR simulators, is accompanied by a boom in research in the field of visual coherence (VC) of augmented reality scenes: virtual objects in these scenes should be virtually identical with real ones. It is VC that provides new capabilities of AR simulators, which fundamentally distinguish from conventional flight simulators with virtual reality. Recently, VC has been increasingly provided by neural network methods, thereby, the most important aspects of VC are lighting conditions, so the major share of research is focused on transferring these conditions (location of light sources and their color tone) from the real world to the virtual one, but the great body of the known approaches are characterized by the lack of versatility and the need to perform manual procedures. These disadvantages are not found in the spectral transplantation method based on twodimensional spectral image conversions, which, however, requires determining the size of the spectrum part being transplanted from the real picture of the world to a virtual object. This article is devoted to the development of a neural network model for the mechanism of selecting the optimal size of a spectral transplant.

The paper discusses a case study of obtaining an airline pilot license in integrated training—the so-called "from zero to Airline Transport Pilot License". The environmental implications of simulator-based training were examined across multiple dimensions. Key areas of research include the reduction of harmful exhaust gases pollution associated with traditional flight training activities. Based on our analysis, it can be stated that increasing the use of Flight Simulation Training Devices in pilot training should be significant consideration. This approach brings many benefits, especially ecological ones. Changing the training program and increasing the use of flight simulators can result in a reduction of CO2 emissions by up to 70%. Based on country specific electricity factors, CO2 emissions during flight training in each EU country were calculated. Using Levelized Cost of Electricity average value to calculate training costs in EU countries depends on the mix of energy sources (wind, photovoltaics, carbon and gas). The findings highlight the significant ecological advantages of simulator-based training methods in mitigating the environmental footprint of aviation operations. By seeking to minimize environmental disruption and increase training efficiency, the adoption of simulators is a sustainable approach to pilot training that is consistent with global efforts to mitigate climate change and protect natural ecosystems. [ABSTRACT FROM AUTHOR]

AbstractWith the continuous maturation of technology and the deepening of human-centered design philosophy, eye tracking technology has gradually been applied in research studies for the optimization of flight deck design in commercial aviation. While these studies have focused solely on Pilot Flying (PF), without considering the visual attention distribution of Pilot Monitoring (PM). The purpose of this study was to analyze and compare the visual attention distribution of PF and PM during different flight phases, providing more comprehensive guidance and optimization suggestion to the flight deck design in commercial aviation. This study was conducted in the A320 full-motion flight simulator, with eye tracking technology recording eye movements of PF and PM in the same flight crew simultaneously. The result showed significant differences in three eye movement metrics between PF and PM, not only throughout the whole common manual circuit but also within specific flight phases. Additionally, the results indicated that in certain flight phases, PF and PM exhibited statistically significant correlations in certain eye movement metrics for specific areas of the flight deck. However, in most other instances, no statistically significant correlations were observed between the eye movement metrics of PF and PM. Therefore, the future design of flight decks may benefit from considering differentiation between PF and PM sides in certain flight phases or areas. Additionally, it can be beneficial to make dynamic adjustments to the current flight deck display and control interface while maintaining the existing layout design. [ABSTRACT FROM AUTHOR]

Human-centered artificial intelligence (HCAI) needs to be able to adapt to anticipated user behavior. We argue that the anticipation capabilities required for HCAI adaptation can be modeled best with the help of a cognitive architecture. This paper introduces an ACT-R cognitive model that uses instance-based learning to observe and learn situations and actions in the form of mental models. These mental models enable the anticipation of the behavior of individual users. The model is applied to a use case of automation surprise in commercial aviation to test how anticipation can best be modeled for cockpit applications. Empirical data from a flight simulator study including behavioral, neurophysiological and eye-tracking measures from 13 pilots were used to evaluate the model. Results show that the accuracy of the model is significantly higher than chance, demonstrating that combining context information, user state data and a cognitive model can enable HCAI adaptation based on anticipated user behavior. [ABSTRACT FROM AUTHOR]

Three unsteady aerodynamic tools at different levels of fidelity and computational cost were used to investigate the unsteady aerodynamic behavior of a delta kite applied to airborne wind energy. The first tool is an in‐house unsteady panel method that is fast but delivers low to mid fidelity predictions. The second tool uses the open‐source CFD code SU2 to solve the unsteady Reynolds‐averaged Navier–Stokes equations with the k−ω$$ k-\omega $$ SST turbulence model. At an intermediate level of fidelity, a semiempirical dynamic stall model that combines the panel method with a phenomenological dynamic stall module is proposed. The latter has free parameters that are fine‐tuned with CFD results from the second tool. The research on the dynamic stall model has been inspired by two flight test campaigns suggesting dynamic stall phenomena possibly driven by the periodic variation of the angle of attack (aerodynamic pitching motion) during crosswind maneuvers. The recorded inflow along the flight path was prescribed in the three aerodynamic tools. As expected, the price to pay for the low computational cost of the panel method is its inability to capture the dynamic stall phenomenon. The results from unsteady CFD qualitatively matched the experimental data identifying a leading‐edge vortex that forms and detaches cyclically during the pitching motion. Using RANS data, the semiempirical tool was fined‐tuned to reproduce the dynamic stall behavior, becoming an accurate and fast aerodynamic tool for coupling with any kite flight simulator. Further discussions on the effects of kite aerostructural deflections are included. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The benefits of using head-up displays (HUDs) include reducing head-down time during critical flight phases, enhancing awareness of the external environment, and improving in-flight crew performance. However, the monochromatic nature of HUDs, increased head rotation, and longer gaze movement paths might affect pilots' reactions to different types of alerts. METHODS: Pilot workload and behavior differences were examined between HUD and head-down display (HDD) configurations in three alert scenarios. The study was carried out in an A320 flight simulator and 12 pilots participated. RESULTS: Except for one engine-on-fire scenario, pilot workload when using a HUD was significantly lower than using an HDD. In one engine-on-fire (3.98 s vs. 3.57 s) and one gear-disagree (5.42 s vs. 4.69 s) scenario, pilot response time to alerts using HUD was significantly longer than using an HDD. The angle deviations were significantly smaller when using HUDs in both go-around-under-crosswind (2.67° vs. 3.37°) and one engine-on-fire scenario (1.22° vs. 1.89°). DISCUSSION: The HUD is suitable for a lengthy process of manual flight control inputs, which not only reduces workload but also promotes control accuracy. For tasks that rely on automation, the benefits for workload become less obvious. In addition, head rotation and reorientation of attention adversely affected the response time to non-time-critical warnings and cautions. For instantaneous control with high precision requirements, HUDs did not demonstrate a significant advantage. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly significant in modern warfare due to their versatility and capacity to perform high-risk missions without risking human lives. Beyond surveillance and reconnaissance, UAVs with jet propulsion and engagement capabilities are set to play roles similar to conventional jets. In various scenarios, military aircraft, drones, and UAVs face multiple threats while ground radar systems continuously monitor their positions. The interaction between these aerial platforms and radars causes temporal fluctuations in scattered echo power due to changes in aspect angle, impacting radar tracking accuracy. This study utilizes the potential radar cross-section (RCS) dynamics of an aircraft throughout its flight, using ground radar as a reference. Key factors influencing RCS include time, frequency, polarization, incident angle, physical geometry, and surface material, with a focus on the complex scattering geometry of the aircraft. The research evaluates the monostatic RCS case and examines the impact of attitude variations on RCS scintillation. Here, we present dynamic RCS modeling by examining the influence of flight dynamics on the RCS fluctuations of a UAV-sized aircraft. Dynamic RCS modeling is essential in creating a robust framework for operational analysis and developing effective countermeasure strategies, such as advanced active decoys. Especially in the cognitive radar concept, aircraft will desperately need more dynamic and adaptive active decoys. A methodology for calculating target aspect angles is proposed, using the aircraft's attitude and spherical position relative to the radar system. A realistic 6DoF (6 degrees of freedom) flight data time series generated by a commercial flight simulator is used to derive aircraft-to-radar aspect angles. By estimating aspect angles for a simulated complex flight trajectory, RCS scintillation throughout the flight is characterized. The study highlights the importance of maneuver parameters such as roll and pitch on the RCS measured at the radar by comparing datasets with and without these parameters. Significant differences were found, with a 32.44% difference in RCS data between full maneuver and no roll and pitch changes. Finally, proposed future research directions and insights are discussed. [ABSTRACT FROM AUTHOR]

Applications of Unmanned Aerial Systems (UAS) have become increasingly diverse and advanced over the years. While initial applications targeted strictly military applications, with a rapid decrease in cost and increase in availability to consumers, applications such as surveillance, journalism, agriculture, film-making, entertainment, etc. have begun to flourish. One of these applications is an immersive drone-piloting modality known as First-person view (FPV) piloting. This method of flying leads to the challenge of streaming video and transmitting control information at very low latencies to enable proper real-time control. FPV technology most commonly relies on implementing video streaming through analog technology due to strict latency requirements. In this paper, we present our research directed toward developing a digital alternative to this technology. We report on two user studies designed to assess the Quality of Experience (QoE) of an FPV system, coupled with a cloud-based drone flight simulator, with the objective to evaluate the impact of various video encoding parameters, namely bitrate, resolution, and frame rate. Two user studies with the same core test methodology were conducted: the first involved University students and staff at the Univ. of Zagreb, while the second study involved Spanish Air Force Academy flight students and instructors with (unlike the first study group) significant prior experience in piloting manned aircraft and experience in the use of a flight simulator for military pilot training. The primary motivation of conducting the second user study was thus to investigate quality perception involving users with different prior experience as compared to our first study. Obtained results indicate that video encoding parameters have a significant impact on user perceived QoE, with only a slight impact of prior piloting experience on quality ratings. Furthermore, our findings highlight the relevance of simulator sickness in this kind of system. [ABSTRACT FROM AUTHOR]

Flight simulators have an essential role in aircrew training. Occasionally, symptoms of motion sickness, defined as simulator sickness, develop during training sessions. The reported incidence of simulator sickness ranged widely in different studies.The aims of this study were to calculate the incidence of and to define a threshold value for simulator sickness among rotary-wing pilots using the validated Simulator Sickness Questionnaire (SSQ).CH-53 and UH-60 helicopter pilots, who trained in helicopter simulators in the Israeli Air Force, were asked to fulfill SSQ. A score of 20 in the SSQ was defined as the threshold for simulator sickness. Simulator sickness incidence and average SSQ were calculated. Correlations between age and simulator training hours to SSQ scores were analyzed.A total of 207 rotary-wing aircrew participated in the study. Simulator sickness was experienced by 51.7% of trainees. The average SSQ score was 32.7. A significant negative correlation was found between age and SSQ score.Simulator sickness was experienced by more than half of helicopter pilots. A score of 20 in the SSQ was found to be suitable as the threshold for this condition. [ABSTRACT FROM AUTHOR]

The present study shows the development of computational algorithms to represent aircraft instruments such as the attitude indicator or the turn-and-slip indicator; moreover, the algorithms represent a magnetic compass and other instruments that function according to other physical theories. These instruments work by using the gyroscope and magnetic principles and help the pilot in navigation. These are considered to be the basic instruments required to provide location-related and positional information about the actual aircraft attitude. The algorithms developed in this study are capable of working in concordance with other instruments and the physical conditions established. The programming language used was C++ and the algorithms were compiled in independent files and subroutines for computational efficiency, eliminating unnecessary code. The display options were successfully tested. Additionally, an analysis that evaluated the error and approached flight simulation as a function of step time (Δt) is also described. [ABSTRACT FROM AUTHOR]

Multi-Attribute Task Battery (MATB) is a computerized flight simulator for aviation-related tasks, suitable for non-pilots and available in many versions, including open source. MATB requires the individual or simultaneous execution of 4 sub-tasks: systemmonitoring (SYSMON), tracking (TRACK), communications (COMM), and resourcemanagement (RESMAN). Fully customizable, the design of test duration, number of sub-tasks used, event rates, response times and overlap, create different levels of mental load. MATB can be combined with an additional auditory attention (Oddball) task, or with physiological constraints (i.e., sleep loss, exercise, hypoxia). We aimed to assess the main characteristics of MATB design for assessing the response to different workload levels. We identified and reviewed 19 articles for which the effects of low and high workload were analyzed. Although MATB has shown promise in detecting performance degradation due to increase workload, studies have yielded conflicting or unclear results regarding MATB configurations. Increased event rates, number of sub-tasks (multitasking), and overlap are associated with increased perceived workload score (ex. NASA-TLX), decreased performance (especially tracking), and neurophysiological responses, while no effect of time-on-task is observed. The median duration used for the test is 20 min (range 12--60) with a level duration of 10 min (range 4--15). To assess mental workload, the median number of stimuli is respectively 3 events/min (range 0.6--17.2) for low, and 23.5 events/min (range 9--65) for high workload level. In this review, we give some recommendations for standardization of MATB design, configuration, description and training, in order to improve reproducibility and comparison between studies, a challenge for the future researches, as human-machine interaction and digital influx increase for pilots. We also open the discussion on the possible use of MATB in the context of aeronautical/operational constraints in order to assess the effects combined with changes in mental workload levels. Thus, with appropriate levels of difficulty, MATB can be used as a suitable simulation tool to study the effects of changes on the mental workload of aircraft pilots, during different operational and physiological constraints. [ABSTRACT FROM AUTHOR]

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training. [ABSTRACT FROM AUTHOR]

While the current practice of pilot training relies on flight instructors' subjective assessment, computational cognitive modeling may be used to support future objective assessment and diagnosis of pilot performance. We built two models in a cognitive architecture to simulate pilot flight performance during pre-flight and take-off tasks. Modeling results were compared with human results collected from the same tasks using X-Plane 11 flight simulator. The models were able to capture human pilot performance and workload results from both tasks with good levels of fitness (percentage errors ranging from 0.8% to 13.2%). This work demonstrated the capability and advantage of this theory-driven modeling approach for supporting general aviation pilot training. We expect that this type of cognitive model will be complementary to data-driven machine learning models, and the current work provides the foundation for future work to expand the modeling capability and test practical applications in general aviation. [ABSTRACT FROM AUTHOR]

Currently, researches on the comfort of flight simulators primarily focus on the objective and rational comfort aspects of drivers' physiology and psychology, neglecting the deeper subjective and perceptual comfort experiences of drivers. To better meet the holistic comfort needs of drivers, this paper introduces a concept of comfortable field design and proposes a comfortable field design model for flight simulators. Firstly, the model uses product morphology analysis to determine the morphological design elements of flight simulator representative samples, and uses principal component analysis and factor analysis to determine its representative imagery words. Secondly, utilizing quantitative class I theory, a psychological comfortable field evaluation method is devised, establishing a regression model and matching degree correlating flight simulator design elements with perceptual imagery word pairs. This approach facilitates a swift and precise determination of the direction for imagery design. Ergonomic data are used to construct a physiological expression of the comfortable field. Finally, taking a flight simulator as an example, the industrial design method and JACK are used for design practice and evaluation. The results show that the optimized flight simulator can effectively meet the comfortable field requirements of the drivers, which verifies the validity and feasibility of the model. [ABSTRACT FROM AUTHOR]

The article "Non-Technical Skills Proficiency in Aviation Pilots: A Systematic Review" delves into the significance of non-technical skills in aviation safety, focusing on communication and decision-making. Research indicates that deficiencies in these skills can lead to accidents, underscoring the importance of crew resource management training. The findings stress the critical role of effective communication and decision-making in enhancing team cooperation and ensuring safe flight operations, ultimately contributing to improved flight safety culture and operational success in the aviation industry. The document also discusses assessment techniques, recommends continuous improvement in training programs, and suggests future research directions to address cultural and contextual differences in applying non-technical skills in aviation. [Extracted from the article]

This study explores potential disparities between flight instructor evaluations and pilot self-assessments in the context of full flight simulator training. Evaluated performance was based on the Competency-based Training and Assessment framework, a recent development of competency-based education within aviation. Self-assessed performance is derived from survey responses and debriefing interviews. The simulator session involves eight multi-crew pilot training graduates and eight experienced flight captains, encompassing two tasks featuring sudden technical malfucntions during flight. The flight instructor's evaluations reveal no significant differences in pilot performance. However, disparities become apparent when pilots engaged in reflecting their performance. Novice pilots, despite perceiving both tasks as easy, exhibited an overconfidence that led them to underestimate the inherent risks. Conversely, experienced pilots demonstrated greater caution towards the risks and engaged in discussing possible hazards. Furthermore, this study highlights the challenge of designing flight simulator training that incorporates surprise elements. Pilots tend to anticipate anomalies more readily in simulator training than during actual flights. Thus, this study underscores the importance of examining how pilots reflect on their performance, complementing the assessment of observable indicators and predefined competencies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Spatial disorientation (SD) remains the leading contributor to Class A mishaps in the U.S. Navy, consistent with historical trends. Despite this, SD training for military aircrew is largely confined to the classroom and experiential training replicating SD illusions is limited and infrequent. Static flight simulators are most commonly used for training but offer no vestibular stimulation to the flight crew, omitting the source of vestibular-mediated SD. BACKGROUND: We first cover vestibular-mediated SD illusions which may be replicated through galvanic vestibular stimulation (GVS) in a static environment. GVS is a safe, reliable, low-cost avenue for providing vestibular sensory stimulation. We review the underlying mechanisms of GVS such as the excitement and inhibition of the afferent neurons innervating the vestibular system, particularly in the binaural bipolar electrode montage. APPLICATIONS: Two approaches for how GVS may be used to enhance SD training are examined. The first is a means for providing unreliable vestibular sensory perceptions to pilots, and the second details how GVS can be leveraged for replicating vestibular-mediated SD illusions. DISCUSSION: We recommend GVS be pursued as an enhancement to existing SD training. The ability to disorient aircrew in the safe training environment of a static flight simulator would allow for aircrew familiarization to SD, serving as an opportunity to practice life-saving checklist items to recover from SD. A repeatable training profile that could be worn by military aircrew in a static flight simulator may afford a low-cost training solution to the number one cause of fatalities in military aviation. [ABSTRACT FROM AUTHOR]

Focusing on the tasks collaboration requirements of Unmanned Aerial Vehicle (UAV) swarm in complex battlefield environment, it is especially important to ensure the reliability of routing communication within the formation. Wireless ultraviolet (UV) secret communication can meet the reliable and secret communication needs of UAV swarm in the complex atmospheric environment. Moreover, considering the limited energy carried by UAVs, in order to maximize the flight time of UAVs, this paper proposes an unequal clustering energy consumption balancing routing algorithm (UCEBRA) of UAV swarm based on UV secret communication. Combined the advantages of wireless UV scattering communication, this algorithm improves the selection threshold of candidate cluster heads (CHs) and the clustering conditions of nodes according to the performance of UAV nodes. Furthermore, a function, which can comprehensively reflect both distance and remaining energy of UAVs, is built in the process of inter-cluster routing, to balance the energy consumption of the CHs. The simulation results show that compared with low-energy adaptive clustering hierarchy (LEACH) and energy-efficient unequal clustering (EEUC), the energy efficiency of the UAV network is increased by 21.7% and 4.6% respectively, and the lifetime of the network prolongs by 26.2% and 7.8% under UCEBRA algorithm. Therefore, the algorithm proposed in this paper can efficiently balance the energy consumption of nodes and thus prolong the lifetime of UAV swarm. [ABSTRACT FROM AUTHOR]

A novel combination of three control systems is presented in this paper: an adaptive control system, a type-two fuzzy logic system, and a super-twisting sliding mode control (STSMC) system. This combination was developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE). This controller incorporates two methods to calculate the gains of the switching term in the STSMC utilizing the particle swarm optimization algorithm: (1) adaptive gains and (2) optimized gains. This methodology was applied to a nonlinear model of the Cessna Citation X business jet aircraft generated by the simulation platform developed at the LARCASE in Simulink/MATLAB (R2022b) for aircraft lateral motion. The platform was validated with flight data obtained from a Level-D research aircraft flight simulator manufactured by the CAE (Montreal, Canada). Level D denotes the highest qualification that the FAA issues for research flight simulators. The performances of controllers were evaluated using the turbulence generated by the Dryden model. The simulation results show that this controller can address both turbulence and existing uncertainties. Finally, the controller was validated for 925 flight conditions over the whole flight envelope for a single configuration using both adaptive and optimized gains in switching terms of the STSMC. [ABSTRACT FROM AUTHOR]

This work presents the first step in the development of the VISION toolkit, a set of python tools that allows for easy, efficient and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources to be used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, is sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing and handling a large amount of highly temporally resolved model files, resulting in a process which is data, labour, and time intensive. In this paper we focus on comparison between model data and in-situ observations (from aircrafts, ships, buoys, sondes etc.). A stand-alone code, In-Situ Observation simulator, or ISO_simulator in short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. This model data is then written to NetCDF files that can be efficiently archived, due to their small sizes, and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in-situ data. By interpolating global, gridded, hourly files onto observations locations, we reduce data output for a typical climate resolution run, from ~3 Gb per model variable per month to ~15 Mb per model variable per month (a 200 times reduction in data volume). The VISION toolkit is fast and easy to use, therefore enabling the exploitation of large observational datasets spanning decades, to be used for large scale model evaluation. Although this code has been initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models. [ABSTRACT FROM AUTHOR]

Investigating aircraft flight dynamics often requires dynamic wind tunnel testing. This paper proposes a non-contact, off-board instrumentation method using vision-based techniques. The method utilises a sequential process of Harris corner detection, Kanade–Lucas–Tomasi tracking, and quaternions to identify the Euler angles from a pair of cameras, one with a side view and the other with a top view. The method validation involves simulating a 3D CAD model for rotational motion with a single degree-of-freedom. The numerical analysis quantifies the results, while the proposed approach is analysed analytically. This approach results in a 45.41% enhancement in accuracy over an earlier direction cosine matrix method. Specifically, the quaternion-based method achieves root mean square errors of 0.0101 rad/s, 0.0361 rad/s, and 0.0036 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. Notably, the method exhibits a 98.08% accuracy for the pitch rate. These results highlight the performance of quaternion-based attitude estimation in dynamic wind tunnel testing. Furthermore, an extended Kalman filter is applied to integrate the generated on-board instrumentation data (inertial measurement unit, potentiometer gimbal) and the results of the proposed vision-based method. The extended Kalman filter state estimation achieves root mean square errors of 0.0090 rad/s, 0.0262 rad/s, and 0.0034 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. This method exhibits an improved accuracy of 98.61% for the estimation of pitch rate, indicating its higher efficiency over the standalone implementation of the direction cosine method for dynamic wind tunnel testing. [ABSTRACT FROM AUTHOR]

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches. [ABSTRACT FROM AUTHOR]

Background: Globally, there is growing attention towards health system strengthening, and the importance of quality in health systems. However, fragile and conflict-affected states present particular challenges. The aim of this study was to explore health system strengthening in fragile and conflict-affected states by synthesising the evidence from published literature. Methods: We conducted a review of systematic reviews (Prospero Registration Number: CRD42022371955) by searching Ovid (Medline, Embase, and Global Health), Scopus, Web of Science, and the Cochrane Library databases. Only English-language publications were considered. The Joanna Briggs Institute (JBI) Critical Appraisal Tool was employed to assess methodological quality of the included studies. The findings were narratively synthesised and presented in line with the Lancet's 'high-quality health system framework'. Results: Twenty-seven systematic reviews, out of 2,704 identified records, considered key dimensions of health systems in fragile and conflict-affected states, with the 'foundations' domain having most evidence. Significant challenges to health system strengthening, including the flight of human capital due to safety concerns and difficult working conditions, as well as limited training capacities and resources, were identified. Facilitators included community involvement, support systems and innovative financing mechanisms. The importance of coordinated and integrated responses tailored to the context and stage of the crisis situation was emphasised in order to strengthen fragile health systems. Overall, health system strengthening initiatives included policies encouraging the return and integration of displaced healthcare workers, building local healthcare workers capacity, strengthening education and training, integrating healthcare services, trust-building, supportive supervision, and e-Health utilisation. Conclusion: The emerging body of evidence on health system strengthening in fragile and conflict-affected states highlights its complexity. The findings underscore the significance of adopting a comprehensive approach and engaging various stakeholders in a coordinated manner considering the stage and context of the situation. [ABSTRACT FROM AUTHOR]

Visual information is important in final approach and landing phases for an approaching aircraft, it presents supplementary source for navigation system, and provides backup guidance when radio navigation fails, or even supports a complete vision‐based landing. Relative position and attitude can be solved from the runway features in the image. Traditional runway detection methods have high latency and low accuracy, which is unable to satisfy the requirements for a safe landing. This paper proposes a real‐time runway detection model, efficient runway feature extractor (ERFE), based on deep convolutional neural network, generating semantic segmentation and feature lines output. In order to evaluate the model's effectiveness, a benchmark is proposed to calculate the actual error between predicted feature line and ground truth one. A novel runway dataset which is based on pictures from Microsoft Flight Simulator 2020 (FS2020), is also proposed in this paper to train and test the model. The dataset will be released at https://www.kaggle.com/datasets/relufrank/fs2020‐runway‐dataset. ERFE shows excellent performance in FS2020 dataset, it gives satisfactory results even for real runway images excluded from our dataset. [ABSTRACT FROM AUTHOR]

Previous research indicated a need to improve pilot training with regard to understanding of autopilot logic and behavior, especially in non-routine situations. Therefore, we tested the effect of problem-based exploratory training on pilots' understanding of autopilot functions. Using a moving-base flight simulator, general aviation pilots (n = 45) were trained to diagnose failures either without foreknowledge and guidance (exploratory group), without foreknowledge but with some guidance (exploratory-guidance group) or with foreknowledge and full guidance (control group). They subsequently performed six test scenarios in which their understanding of the effects of failures was tested by requiring them to deduce the failures and select autopilot modes that were still functioning. Those who received exploratory training with guidance were significantly more likely than the other groups to diagnose failures correctly. The exploratory training group also selected the most appropriate functioning autopilot modes significantly faster than the control group. The results suggest that exploratory training with an appropriate level of guidance is useful for gaining a practical understanding of autopilot logic and behavior. Exploratory training may help to improve transfer of training to operational practice, and prevent automation surprises and accidents. [ABSTRACT FROM AUTHOR]

In this paper, a fixed wing, tail fin controlled gliding munition has been designed. A guidance and control system is developed for the designed model and the glide towards the target is tested in the XPLANE 11 flight simulator environment. The simulation was carried out in two stages, both in the software loop and in the hardware loop. After the munition system is released from the aircraft, it measures the angles that will enable gliding to the target coordinates with the guidance system and starts to control fin movements. It compares the instantaneous position data received from GPS with the target position data. With the LOS method, the pitch angle is found by comparing the heading angle required to reach the target and the instantaneous altitude information with the target altitude information. The angles found are compared with the information from the IMU sensor and the errors are processed in the PID controller. The output of the PID controller is converted as a PWM signal to the tail fins in accordance with the munition dynamics. The system was transferred to XPLANE 11 and scenarios with different initial conditions were tested. [ABSTRACT FROM AUTHOR]

Drones have emerged as a cost‐effective solution to detect and map plant invasions, offering researchers and land managers flexibility in flight design, sensors and data collection schedules. A systematic review of trends in drone‐based image collection, data processing and analytical approaches is needed to advance the science of invasive species monitoring and management and improve scalability and replicability.We systematically reviewed studies using drones for plant invasion research to identify knowledge gaps, best practices and a path toward advancing the science of invasive plant monitoring and management. We devised a database of 33 standardized reporting parameters, coded each study to those parameters, calculated descriptive statistics and synthesized how these technologies are being implemented and used.Trends show a general increase in studies since 2009 with a bias toward temperate regions in North America and Europe. Most studies have focused on testing the validity of a machine learning or deep learning image classification technique with fewer studies focused on monitoring or modelling spread. Very few studies used drones for assessing ecosystem dynamics and impacts such as determining environmental drivers or tracking re‐emergence after disturbance. Overall, we noted a lack of standardized reporting on field survey design, flight design, drone systems, image processing and analyses, which hinders replicability and scalability of approaches. Based on these findings, we develop a standard framework for drone applications in invasive species monitoring to foster cross‐study comparability and reproducibility.We suggest several areas for advancing the use of drones in invasive plant studies including (1) utilizing standardized reporting frameworks to facilitate scientific research practices, (2) integrating drone data with satellite imagery to scale up relationships over larger areas, (3) using drones as an alternative to in‐person ground surveys and (4) leveraging drones to assess community trait shifts tied to plant fitness and reproduction. [ABSTRACT FROM AUTHOR]

Gateway represents a key element of the Artemis program for the upcoming lunar exploration aimed at establishing a sustainable presence by the mid-2030s. This paper investigates minimum-fuel bi-impulsive orbit transfers from Gateway to low lunar orbits (LLOs) with a maximum time of flight of 48 h. Two distinct scenarios are analyzed: (i) target orbits with free right ascension of the ascending node (RAAN), and (ii) target orbits with specified RAAN. For case (i), a global optimization technique based on a heuristic algorithm is exploited to obtain the minimum-fuel transfer. Several inclinations of the target orbit are considered. For case (ii), two distinct techniques are proposed: (a) a purely heuristic approach, and (b) a semi-analytical method based on local refinement of a Lambert-based solution. Numerical propagations are conducted in all scenarios in a high-fidelity framework that includes all relevant perturbations. A comparison between the different strategies and the related numerical results is provided. [ABSTRACT FROM AUTHOR]

Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value. [ABSTRACT FROM AUTHOR]

A deterioration cycle model is presented, designed to consider the turbomachinery efficiency losses that are expected during real engine in-service operations. The cycle model was developed using information from practical experience found in the literature to account for both short- and long-term deterioration effects; the former occurring during the first flight cycles, the latter due to regular in-service operation. This paper highlights the importance of analyzing the inter-turbine temperature margin ( I T T M ) to track engine deterioration to determine the extent of an in-service engine life. The proposed model was used to assess the I T T M and fuel consumption impact in the CRJ-700 regional aircraft (powered by two CF34-8C5B1 engines) for three representative missions: short (0.4 h), average (1.4 h), and long (2.5 h), considering different levels of engine deterioration, from the new engine level up to fully deteriorated. The fuel consumption at the new engine level (zero deterioration) was validated against a real-time engine model embedded in a Level-D flight simulator, the so-called Virtual Research Flight Simulator located at the Laboratory of Applied Research in Active Control, Avionics, and AeroServoElasticity. The errors found in this validation for the trip mission fuel consumption in the short, average, and long missions were −3.6, +0.9, and +0.6%, respectively. The cycle model predictions suggest the I T T M for a new engine is 55.2 °C, whereas for a fully deteriorated engine, it is 26.4 °C. Finally, in terms of fuel consumption, the results presented here show that an average CF34-8C5B1 engine shows an increase in the cumulative fuel consumption of 2.25% during its life in service, which translates to a 4.5% impact in aircraft fuel consumption. [ABSTRACT FROM AUTHOR]

The illusory experience of self-motion known as vection, is a multisensory phenomenon relevant to self-motion processes. While some studies have shown that neck muscle vibrations can improve self-motion parameter estimation, the influence on vection remains unknown. Further, few studies measured cybersickness (CS), presence, and vection concurrently and have shown conflicting results. It was hypothesized that 1) neck vibrations would enhance vection and presence, and 2) CS to negatively relate to presence and vection, whereas presence and vection to positively relate to one another. Thirty-two participants were visually and audibly immersed in a virtual reality flight simulator and occasional neck muscle vibrations were presented. Vection onset and duration were reported through button presses. Turning angle estimations and ratings of vection quality, presence, and CS were obtained after completion of the flights. Results showed no influence of vibrations on turning angle estimation errors, but a medium positive effect of vibrations on vection quality was found. Presence and vection quality were positively related, and no strong association between CS and presence or vection was found. It is concluded that neck vibrations may enhance vection and presence, however, from the current study it is unclear whether this is due to proprioceptive or tactile stimulation. [ABSTRACT FROM AUTHOR]