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Traffic management in aviation, shipping, and rail transport shows similarities and dissimilarities in the work process. For example, they share the temporal aspect, but different levels of urgency in the control work set different requirements on monitoring, decisions, and actions. However, few studies have been presented that model and compare the different domains in terms of temporal decision-making. The Joint Control Framework (JCF) is an approach to analyse and temporally model operators' control processes from a cognitive systems engineering perspective. In this study, we have used JCF to map, and compare, cognitive joints, such as perceptions, decisions, and actions, in temporally challenging control situations in air traffic control, maritime vessel traffic service, and train traffic management. Data was collected collaboratively with traffic operators, focusing on (1) identifying challenging traffic situations and (2) jointly modelling the temporal decision-making patterns of these situations using simplified JCF. Post-analysis was done by breaking down the results into different processes and comparing domains to ascertain how operators maintain control. An intermediate level of activity—between general monitoring and work with specific vehicles—was identified: processes-in-focus. A shared problem arises in the shift between general monitoring and the processes-in-focus. All processes-in-focus comprise cognitive joint cycles of perceptions, decisions, and actions. However, depending on the framing of processes-in-focus, the patterns of joints, such as temporal extension and complexity, differ. In the remainder of the article, implications for the interaction design, in particular the potential for human–AI/automation teaming with higher levels of automation and cognitive autonomy, are discussed. [ABSTRACT FROM AUTHOR]

This paper examines the learning and performance of the air traffic control (ATC) work domain. This domain was chosen because it embodies features that represent future work for many other industries (e.g., information service provision mediated by information technologies; a high reliance on communication skills and collaborative work; increasing complexity and intensity of the work activity), within an organisational context undergoing considerable change. In ATC work learning occurs formally as part of accredited training and informally, as part of everyday practice. In this way learning and experience of ATC work shapes – and is shaped by – the way work is organised, which includes the divisions of labour and technological artefacts. ATC work is experienced temporally, complexly, through affect and socially. Controllers learn to use a variety of socio-material resources which include embodiment and artefacts to undertake the work. In so doing the article demonstrates the ways in which learning and performance is not just a cognitive undertaking, but one that draws on embodiment to achieve what here is called "interdependent sentience". This involves using all their senses to gain an awareness of problems as they emerge to collectively accomplish a seamless air traffic flow in a complex interdependent socio-technical system. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control. Background: Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects. Method: Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation. Results: For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance. Conclusions: RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust. Applications: Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements. [ABSTRACT FROM AUTHOR]

There is a significant surge in unmanned aerial vehicles' (UAVs) popularity with an ongoing increase in demand due to their multi-functional uses. The capacity of the UAVs to respond to society and industry needs account for their pervasiveness. UAVs can be strategically deployed to enhance network efficiency and support critical missions, including vital tasks like infrastructure monitoring operations. To be effective, a UAV must interact securely with its network's entities, such as ground control stations, other UAVs, air traffic control systems, and navigation satellite systems. UAVs are exposed to a dangerous and costly world of cyber dangers as a result of cyber technology and connections. The UAV and the Ground Control Station (GCS) exchange information using communication lines, which are vulnerable to cyber-attacks. As a result, detective, defensive, and preventative countermeasures are critical. The Micro Air Vehicle Link (MAVLink) protocol is a widely used lightweight communication protocol for enabling communication between UAVs and GCSs. It outlines communication exchanges between a GCS and a UAV. It carries information about the UAV's condition as well as commands for control from the GCS. Although widely used, the MAVLink protocol lacks sufficient security measures and is susceptible to various types of attacks, which can pose significant risks to public safety. UAV data should be encrypted because they are very sensitive. To give a high level of security, complex operation encryption techniques are developed. These mathematical operations bring forth issues with efficiency and power usage, though. One of these methods, the ChaCha cipher, recently gained popularity after Google used it in several applications. In the current study, a new stream cipher method with low-duty cycles is proposed for protecting data in UAVs and is compared with existing security algorithms on the basis of various factors. The research findings show that by including the suggested method in MAVLink, it is possible to maintain both message secrecy and battery life for a resource-constrained UAV. This can be done while using comparable amounts of memory and CPU and without affecting MAVLink's performance. [ABSTRACT FROM AUTHOR]

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR. [ABSTRACT FROM AUTHOR]

Objective: We investigated the extent to which a voluntary-use range and bearing line (RBL) tool improves return-to-manual performance when supervising high-degree conflict detection automation in simulated air traffic control. Background: High-degree automation typically benefits routine performance and reduces workload, but can degrade return-to-manual performance if automation fails. We reasoned that providing a voluntary checking tool (RBL) would support automation failure detection, but also that automation induced complacency could extend to nonoptimal use of such tools. Method: Participants were assigned to one of three conditions, where conflict detection was either performed: manually, with RBLs available to use (Manual + RBL), automatically with RBLs (Auto + RBL), or automatically without RBLs (Auto). Voluntary-use RBLs allowed participants to reliably check aircraft conflict status. Automation failed once. Results: RBLs improved automation failure detection – with participants intervening faster and making fewer false alarms when provided RBLs compared to not (Auto + RBL vs Auto). However, a cost of high-degree automation remained, with participants slower to intervene to the automation failure than to an identical manual conflict event (Auto + RBL vs Manual + RBL). There was no difference in RBL engagement time between Auto + RBL and Manual + RBL conditions, suggesting participants noticed the conflict event at the same time. Conclusions: The cost of automation may have arisen from participants' reconciling which information to trust: the automation (which indicated no conflict and had been perfectly reliable prior to failing) or the RBL (which indicated a conflict). Applications: Providing a mechanism for checking the validity of high-degree automation may facilitate human supervision of automation. [ABSTRACT FROM AUTHOR]

Recently, shift work, including night shift, has been widely implemented in modern society. These include workplaces that have to work 24 hours a day for production processes, such as petrochemicals, power plants, and steel industries, as well as industries necessary to secure public services such as gas and electricity. In particular, workers in the aviation sector are mostly engaged in shift work due to the nature of their 24 hours of continuous work. Most shift workplaces comply with legal work regulations, but they fail to take into account workers’ fatigue and focus only on compliance with regulatory requirements. Specifically, shift work needs to provide minimum guidelines for workers’ safety and health. Therefore, this study analyzed and reviewed the current status of shift work in the air traffic control field, where shift work is essential, and presented guidelines for the safety and health care of shift workers. First of all, the international shift work guidelines and domestic laws related to shift work were reviewed, and the characteristics of the shift work system in the air traffic control field were reviewed. For the guidelines, we discussed the shift method, shift speed (direction), shift work hours and hours, night work, and other considerations. [ABSTRACT FROM AUTHOR]

"A Short History of Transport in Japan: From Ancient Times to the Present" by John Andrew Black is a comprehensive study of transportation in Japan. The book covers various aspects of transport history, including ports and shipping, canals, roads, railroads, civil aviation, and urban planning. Black applies the theory of new institutional economics to analyze the organizations and institutions governing transportation in Japan. While the book provides a detailed examination of Japanese transportation history, it does not delve into topics such as motorization, militaristic states, or the competition between Shinkansen and airlines. Overall, the book offers valuable insights into the state of mobility in an aging Japan and is recommended for those interested in transportation history and smart cities. [Extracted from the article]

This article provides an update on the trends in North Atlantic winter jet stream wind speeds and turbulence. The authors used data from the Global Aircraft Data Set (GADS) and European Centre for Medium-range Weather Forecasts Reanalysis v5 (ERA5) to analyze the wind speeds over a 22-year period. The results show that the previously observed increase in wind speeds is no longer statistically significant when three additional years of data are included. The authors also discuss the impact of the COVID-19 pandemic on aircraft observations and the need for more years of data to reach a conclusive result. [Extracted from the article]

The article takes a look at the privatization of space exploration and the need for global regulations of privatized space travel. Topics discussed include existing international treaties and agreements on commercial space travel, global privatized space projects, domestic space programs such as SpaceX, Blue Original and Virgin Galactic, foreign space travel programs of Russia and China, and modeling of space travel regulations after global air traffic regulations.

Safety is the lifeline of civil aviation. With the advancement of technology, human factors have become the primary factor affecting aviation safety. Among these, human errors in air traffic control (ATC) account for a significant proportion of aviation accidents. In order to investigate human errors in ATC, this paper introduces two common human factors conceptual models – the Reason model and the HFACS model, and analyzes the specific application of the HFACS model in aviation. It is found that the HFACS model can effectively establish a classification system for human errors in ATC. Moreover, this paper combines the HFACS model with AHP (Analytic Hierarchy Process) and uses the HFACS model to establish a first and second level indicator system for human errors in ATC. We calculated the weights of each specific factor using the Analytic Hierarchy Process. The results show that the most significant influencing factor in the first level indicators is organizational factors, while the most significant influencing factor in the second level indicators is Air Traffic Resource Management. Among the 13 second level indicators, the smallest weight is the controller's violation. Based on the calculated weight results, in order to improve the safety level of ATC, the first step should be to improve the level of ATC organizational management. This is also of great significance for improving the safety level of the entire aviation system. The findings of this study suggest that the HFACS model, combined with AHP, can be an effective tool for identifying and analyzing human factors in ATC and ultimately improving the safety of the aviation industry. [ABSTRACT FROM AUTHOR]

The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport's changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions. [ABSTRACT FROM AUTHOR]

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. [ABSTRACT FROM AUTHOR]

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B's signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft. [ABSTRACT FROM AUTHOR]

This paper proposes a novel aviation CO 2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO 2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO 2 emissions and mitigate the effects of global warming. • An aviation CO 2 emissions prediction model is proposed considering both microscopic flight dynamics and macroscopic traffic demand. • A more efficient market-based CO 2 emission reduction framework is developed based on a two-phase carbon trading model by means of taxation and cap-and-trade. • Case study shows that the proposed method has a more optimal incentive for CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term 'modal' refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode. [ABSTRACT FROM AUTHOR]

The utilisation of Aviation English is paramount in ensuring the safety and efficiency of the aviation industry, and it serves as a preventative measure against incidents and accidents related to communication. This study examines the challenges in radiotelephony communication faced by Korean pilots and air traffic controllers, who are English as a second language (L2) speakers. The study aims to identify the causes of communication difficulties and to explore effective English educational materials to mitigate these difficulties. The research employed a questionnaire to gather the perspectives of pilots and controllers, who are experts in the aviation industry, regarding the issues. The results indicate that communication difficulties are caused by a lack of English language proficiency, excessive use of plain language, rapid rate of speech, and accents that deviate from standard English. Moreover, the results suggest that practical training, using materials such as real transmission voice recordings and diverse vocabulary resources, can enhance speaking and listening comprehension skills in non-routine situations. This study provides valuable insights for the development of effective training programs and materials, which could help reduce communication difficulties and improve the safety and efficiency of aviation operations. • Communication challenges vary depending on an English L1 and L2 speaker. • Some vocabulary for emergency situations can be included in standard phraseology. • Voice files recording actual transmissions can be used as educational materials. • English L2 pilots' and controllers' views should be reflected in aviation education. [ABSTRACT FROM AUTHOR]

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing radio interference sources play a crucial role in ensuring the safe operation of civil aviation navigation, communication, airport management, and air traffic control. Traditional ground monitoring methods are slow and inadequate for tracking aerial and mobile interference sources effectively. Although flight methods such as helicopters and airships can effectively monitor aerial interference, the flight approval process is time-consuming and expensive. This paper investigates a novel approach to locating civil aviation radio interference sources using four unmanned aerial vehicles (UAVs) to address this issue. It establishes a model for aerial positioning of radio interference sources with the four UAVs and proposes a method for time synchronization and data communication among them. The paper conducts simulations of the four-UAV time–frequency difference positioning method, analyzing the geometric accuracy dilution with different deployment configurations of the UAVs, positioning biases, and root mean square errors (RMSEs) under varying interference source movement speeds. The simulation results provide crucial data to support subsequent experiments. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has demonstrated its ability to complete complex tasks in various fields. In urban studies, AI technology has been utilized in some limited domains, such as control of traffic and air quality. This study uses AI to better understand diverse urban studies data through a novel approach that uses a convolutional neural network (CNN). In this study, a building outline in the form of a two-dimensional image is used with its corresponding metadata to test the applicability of CNN in reading urban data. MobileNet, a high-efficiency CNN model, is trained to predict the location of restaurants in each building in Seoul, Korea. Consequently, using only 2D image data, the model satisfactorily predicts the locations of restaurants (AUC = 0.732); the model with 2D images and their metadata has higher performance but has an overfitting problem. In addition, the model using only 2D image data accurately predicts the regional distribution of restaurants and shows some typical urban forms with restaurants. The proposed model has several technical limitations but shows the potential to provide a further understanding of urban settings. [ABSTRACT FROM AUTHOR]

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. [ABSTRACT FROM AUTHOR]

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios. [ABSTRACT FROM AUTHOR]

Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed. [ABSTRACT FROM AUTHOR]

The widely-used ATC simulator laboratory in air traffic control education lacks research on its development. This study aims to develop a computer-based aerodrome control tower simulator for ATC using the research and development method. The simulator offers a realistic training environment, covering simulation, technicalities, facilities, configuration, equipment, specifications, software, operations, and development. It is a crucial facility in ATC courses at Indonesian aviation universities. Findings demonstrate its effectiveness in enhancing student proficiency and reducing reliance on external suppliers. The resulting design concept, developed through research and development, can be utilized by local experts to promote advanced technology mastery and cost efficiency in ATC training systems. It provides opportunities for local experts and serves as a model for other countries, advancing ATC education. [ABSTRACT FROM AUTHOR]

The broadcast automatic dependent surveillance (ADS-B) system is a new-generation air traffic control system designed to avoid the waste of resources in secondary radars. The establishment of the spaceborne ADS-B system provides a broad prospect for ionospheric tomography. In this paper, the external observation information of the ionosphere is obtained by measuring the Faraday rotation angle, that is, the total electron content (TEC). Tomography research can be carried out all over the world to conduct large-scale ionospheric electron density research. The experiment selected two different regions and had a time resolution of two hours, a height resolution of 200 km, a latitude resolution of 2°, and a longitude resolution of 5°. Based on the simulated spaceborne ADS-B signal to invert the regional ionospheric electron density, the latitude, longitude, and height distributions of inversion result are basically consistent with those of the actual ionospheric electron density. [ABSTRACT FROM AUTHOR]

There is frequent discussion on the shortage of aviation maintenance technicians, but less frequent is the discussion on the instructors who will train these technicians. Most aviation maintenance technicians are trained at FAAcertified Part 147 Aviation Maintenance Technician Schools (AMTS) at the college or university level. What is less understood are the challenges faced by aviation technician instructors. What are some of the factors that make teaching difficult? How did those challenges change the instructor's approach to their career? The purpose of this study is to identify challenges faced by current aviation maintenance instructors and identify how those challenges affected the instructors' approach to their careers. This study surveyed instructors from FAA Part 147 AMTS on their background, the challenges they face, and their effect on the approach to their careers. The survey was sent to 172 programs with n = 44 respondents. Participants identified COVID, instructor shortage, incoming experience and knowledge of students, and lack of qualified instructors as common issues. [ABSTRACT FROM AUTHOR]

The Authors investigated the Aviation English proficiency levels of Vilnius Tech Aircraft Piloting and Air Traffic Control (ATC) students by considering general English examination scores, marks for Aviation English modules and proficiency levels of a specialised speaking task. The aim was to assess how well students acquire the skills and abilities not only to pass an Aviation English proficiency examination (level 4), but also to obtain a higher level 5 necessary for a longer endorsement of their language certificate. Finally, it was determined which areas of language are the easiest and most difficult to master with regards to either a level 4 or 5 according to the ICAO (International Civil Aviation Organisation) Rating scale. It was found that four out of five students have the background skills to reach at least an ICAO level 4 upon graduating, however only a fifth are able to achieve a higher level 5. The proportion of level 5 students does not match the proportion of students with good initial general English results, so there should be a greater focus on improving the language training programme by making it more challenging and broadening its scope in terms of vocabulary and grammar in particular. [ABSTRACT FROM AUTHOR]

With the continuous increase in flight flows, the flight conflict risk in the airspace has increased. Aiming at the problem of conflict resolution in actual operation, this paper proposes a tactical conflict resolution strategy based on Deep Reinforcement Learning. The process of the controllers resolving conflicts is modelled as the Markov Decision Process. The Deep Q Network algorithm trains the agent and obtains the resolution strategy. The agent uses the command of altitude adjustment, speed adjustment, or heading adjustment to resolve a conflict, and the design of the reward function fully considers the air traffic control regulations. Finally, simulation experiments were performed to verify the feasibility of the strategy given by the conflict resolution model, and the experimental results were statistically analyzed. The results show that the conflict resolution strategy based on Deep Reinforcement Learning closely reflected actual operations regarding flight safety and conflict resolution rules. [ABSTRACT FROM AUTHOR]

Malaysia Airlines Flight 370, which was flying from Kuala Lumpur, Malaysia, to Beijing, China, lost communication with air traffic control shortly after take-off on March 8, 2014, and vanished. While the rest of the world awaited news of the missing plane and the 239 people on board, officials and experts began to look into the limited information available concerning the airliner's real activities. The disappearance of a Malaysia Airline flight, carrying 239 passengers and crew, is one of the world's biggest aviation mysteries. Despite all the efforts and evidence obtained so far, the crash site of Malaysia Airlines Flight MH370 could not be determined exactly. In this study, which we have done, the crash zone of the aircraft has been determined approximately by using the data in the previously prepared reports. In addition, the average ocean current speeds, directions and the parts of the debris of the aircraft were investigated on the satellite images, and the estimation of the area where the plane crashed was tried to be strengthened. [ABSTRACT FROM AUTHOR]

Space-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven. [ABSTRACT FROM AUTHOR]

The primary concern of an air traffic controller is to ensure the safety and fluidity of ever-increasing air traffic. This requires effective training through practical work supervised by instructors. Based on certain rules called separation rules, the trainee must find a solution to a traffic configuration defined by flight plans (FPL) initially containing a number of conflicts. This solution will then be compared to the one proposed by the instructor. The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made. The application will use the GAMA platform, very suitable for this and a set of tests composed of actual exercises will be performed to validate the work. [ABSTRACT FROM AUTHOR]

Minimization of approaching time and maximization of the utilization of air route and airport resources are the two ultimate goals of air traffic control. This research considers arrival sequence and the uncertain arrival times at entry waypoints of flights and investigates the impact of air traffic controller specific parameters in the model. The two-stage optimization framework will first determine a deterministic schedule and optimize the approaching time while considering additional longitudinal separation, efficiency loss from deterministic schedule, and cruise speed adjustment in the second-stage optimization model via sample average approximation. The numerical experiments suggested an efficiency loss of 20%, which can absorb the empirical probabilistic lateness at entry waypoints with the real-world data. The proposed method could determine the estimated average delay time at runways with different settings of additional buffer for longitudinal separation and trade-off parameters between the estimated average delay time and estimated penalty cost of cruise speed adjustment. [ABSTRACT FROM AUTHOR]