Air Traffic Control Research Articles

Role of Law in Combatting Air Pollution

Air pollution in urban areas arises from multiple sources, which may vary with location to location and activity to activity of development. Anthropogenic activities such as rampant industrialization, exploitation and over consumption of natural resources, population explosion are major contributors of air pollution. Regulations and legislations adopted are enumerated. The Clean Air Act envisaged guidelines for industries regarding emission. It has been seen and observed that in spite several rules and regulations, not much has been achieved in controlling air pollution particularly in countries like India. India needs to make tough legislations The present paper is an effort to discuss various aspects of air pollution and control legislation in India emphasizing on the history, present scenario, gaps and drawbacks. It also presents legislative controls with judicial response to certain landmark judgments related to air pollution. The down sides related to enforcement mechanism for the effective implementation of environmental laws for air pollution control have been highlighted.

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology's applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

Challenges and possibilities in compiling Aeronautical English corpora: The case of the Aerocorpus

Authentic materials are an important resource for teaching and assessment, especially when it comes to English for Specific Purposes. To this end, corpus linguistics has developed a supporting role as it provides a wide range of possibilities for extracting and exploring authentic data. With regard to Aeronautical English (AE), which refers to oral communications between pilots and air traffic controllers (ATCOs), corpora offer substantial benefits in the development of teaching and testing materials. However, putting together corpora in the context of AE faces challenges in availability, selection, compilation, and copyright issues as some countries limit access to radiotelephony communications due to their sensitive content. This paper reports the experience of compiling a collaborative corpus called “Aerocorpus” proposed during two webinars held for AE teachers and testers from all over the world. The compilation methodology aims to address the aforementioned challenges and offer alternative solutions. Despite existing limitations, we see considerable gains in using authentic materials and are looking into ways of protecting the “black box” containing sensitive information while continuing to compile the corpus, which currently features 38,135 words from 45 situations, and to make it available to a wider community of AE professionals.

Characteristics of Ice Super Saturated Regions in Washington, D.C. Airspace (2019–2023)

Contrails are estimated to contribute 2% of the Earth’s anthropogenic global warming. Contrails are ice crystal clouds formed by the emission of soot and water vapor from jet engines in atmospheric conditions known as Ice Super Saturated (ISS) regions. The formation of contrails can be avoided by flying over or under the ISS regions. Aircraft operators/dispatchers and air traffic control need to know the location of ISS regions in a given airspace to flightplan to avoid contrails. This paper describes the statistics for the presence of ISS regions in the airspace over metropolitan Washington, D.C. These statistics can be used to better understand the operational implications for contrail avoidance. Based on the measurements taken from the twice-daily launch of an aerosonde from Sterling, Virginia (adjacent to Washington, D.C.), analysis of five years of data (2019–2023) indicated that this airspace experiences ISS regions 40% of the days. ISS regions were equally likely during daylight hours (26%) than nighttime (27%). The vertical depth of the ISS region averaged 3000 feet but with a median of 2000 feet. The ISS region floor and ceiling varied by season, with an annual average floor of FL330 and ceiling of FL360. The implications of these results on the operations to avoid contrails, limitations, and future work are discussed.

Microbiological air control of food industry enterprises: Relevance, regulatory documents and research methods

Microbiological air control of food industry enterprises: Relevance, regulatory documents and research methods

Air Traffic Control Speech Enhancement Method Based on Improved DNN-IRM

The quality of air traffic control speech is crucial. However, internal and external noise can impact air traffic control speech quality. Clear speech instructions and feedback help optimize flight processes and responses to emergencies. The traditional speech enhancement method based on a deep neural network and ideal ratio mask (DNN-IRM) is prone to distortion of the target speech in a strong noise environment. This paper introduces an air traffic control speech enhancement method based on an improved DNN-IRM. It employs LeakyReLU as an activation function to alleviate the gradient vanishing problem, improves the DNN network structure to enhance the IRM estimation capability, and adjusts the IRM weights to reduce noise interference in the target speech. The experimental results show that, compared with other methods, this method improves the perceptual evaluation of speech quality (PESQ), short-term objective intelligibility (STOI), scale-invariant signal-to-noise ratio (SI-SNR), and speech spectrogram clarity. In addition, we use this method to enhance real air traffic control speech, and the speech quality is also improved.

Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors.

Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.

Impact of an Integrated Human Performance Support Group: Evaluation of Air Force Special Warfare Candidate Training and Musculoskeletal Injury Outcomes Over Eight Fiscal Years.

The Special Warfare Training Wing and Special Warfare Human Performance Support Group (HPSG; all-co-authors) were established in fiscal year (FY) 2019 to consolidate and oversee all Air Force Special Warfare (AFSPECWAR) training and provide embedded medical and human performance support to candidates with the goal of improving graduation rates and the longevity of the AFSPECWAR operator. The purpose of this manuscript is to assess the impact of the HPSG on AFSPECWAR graduation rates, musculoskeletal injury (MSKI) incidence, and cost. Graduation rates, MSKI incidence (including incidence density and cumulative incidence probability), and MSKI-related health care costs were assessed across all AFSPECWAR training pipelines spanning 8 FYs 2015-22, including Indoctrination, Assessment, and Selection Courses (Selection); Tactical Air Control Party (TACP); Special Tactics (ST), and Guardian Angel (GA). A total of 5,728 distinct candidates were assessed over the time frame. There were significant decreases in attrition when comparing the HPSG era (FY 19-22) with the prior 4-year period for the ST (47% vs. 82% graduates) and TACP (34% vs. 41% graduates) training pipelines. The corresponding Selection (36% graduates) and GA (80% graduates) pipeline attrition rates remained stable. MSKI incidence rates (both incidence density and cumulative incidence probability) overall were not significantly different when comparing the pre-HPSG and HPSG time frames; however, they varied between the two time frames by course. GA candidates had a decrease in MSKI during the apprentice course in the HPSG era compared with the pre-HPSG era (2.4-1.0 cases per 100 trainee weeks; P < .001), which corresponds to a nearly 20% reduction in the cumulative incidence probability (i.e., proportion of trainees injured) after 10 weeks of course. For ST and TACP courses, however, significantly increased incidence of MSKI was observed when comparing the two time frames (P < .001), while for Selection courses, the rates remained stable. A significant reduction in the cost of MSKI-related management (62% total relative value units, 83% total costs) was observed. The impact of the HPSG on attrition, MSKI incidence, and cost-of-care was not consistent across all training pipelines; however, taken together, there were no increases in attrition, and the cost of MSKI management was significantly lower.

A resource prediction method for air traffic cyber-physical-social system

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

A corpus-based multi-dimensional analysis of the linguistic features of Aviation English

Aviation English (AE), a specialized register of English, prioritizes precision, brevity and clarity to maximize aviation safety. While there has been a growing focus within the linguistics community on AE training and assessment since the release of a set of standards and recommended practices, its linguistic properties remain comparatively underexplored. Drawing upon Biber's (1988) multi-dimensional (MD) analysis framework, the present study conducted a corpus-based comparative MD analysis to investigate the multi-dimensional linguistic profile of AE vs. casual conversational English (CE) and exploratory factor analysis (EFA) to extract and interpret the co-occurring linguistic features of routine AE and non-routine AE. The comparative MD analysis shows that AE exhibits more information condensation, less authorial stance and technicality, and fewer features of online information elaboration compared to CE. The EFA shows variations in the linguistic and textual composition of the two sub-registers of AE across two dimensions. Non-routine AE demonstrates a present-focused, viewpoint/intention-oriented approach, involving higher levels of integrative information flow compared to routine AE. Routine AE is characterized by a higher degree of information condensation and is marked by a planned, procedural, and intensive use of standard phraseology. Some pedagogical implications are then proposed for enhancing AE training to cultivate pilots' and air traffic controllers' language competence for precise, unambiguous communication tailored to both routine and non-routine operational contexts.

Fatigue crack initiation in lead-bismuth eutectic and its effect on the cyclic stress behaviour of austenitic stainless steel 316L

Active media such as liquid metals have an effect on the fatigue resistance of steels when compared to vacuum or inert environments. This effect might impact the initiation of fatigue cracks, their propagation, or both. This paper focusses on the initiation of fatigue cracks in an austenitic stainless steel when in contact with three environments: vacuum, air, and lead-bismuth eutectic (LBE), and the impact of such cracks on cyclic loading behaviour.Solid cylindrical samples of a 316L-type steel were fatigued under strain control in vacuum, air, and LBE in order to induce the initiation of cracks on their surface. The characteristics of fatigue damage in the form of surface microcracks were analysed with microscopy techniques. It was found that the nucleation of fatigue microcracks is enhanced by LBE, but the majority of these cracks do not propagate beyond the grain size of the steel (50 μm). An analysis with finite element methods showed that the large number of small (<10 μm) microcracks nucleated in LBE environments has a negligible impact on the sample's stiffness, as opposed to the fewer but deeper (100–500 μm) microcracks that initiate in air, which produce a loss of stiffness, observable on the mechanical stress response of the fatigue sample.A model based on the adsorption of LBE atoms along surface slip bands is proposed to explain the phenomenon of enhanced fatigue crack nucleation in LBE.

Understanding the human factors challenge of handover between levels of automation for uncrewed air systems: a systematic literature review

ABSTRACT Using uncrewed air systems (UASs) as part of the logistics chain is anticipated to reduce overall operating costs and speed up delivery. One particular challenge faced by UAS operators is in handover between high levels of autonomous control and piloted control. This review presents the findings from the published literature based on the human factors of handover between levels of automation within different domains. A total of 188 full publications were reviewed of which 52 publications were included. The literature outlines several challenges in transition of handover in publications focussing on UASs, crewed flight, healthcare, maritime, robotics and air traffic control. Interventions included interface design, training strategies, the design of the automation itself to support the operator and organisational design opportunities. There is currently only a small literature base, understanding the application of human factors interventions and their benefits to this unique application needs to be better understood to support effective operational safety.

Proposals for designing the airspace management of the ATM sectors of the Ho Chi Minh City Area Control Center to increase its capacity

According to the International Air Transport Association (IATA), Vietnam is among the countries with the rapid development of civil aviation (CA). This development is a positive sign to enhance the role and position of Vietnam civil aviation in the civil aviation community. But at the same time, the increase in flight operations poses challenges to flight safety, increases the load on airspace and airports, and pollutes the environment. The Ho Chi Minh City (HCM) airspace is one of the largest and busiest airspace in Southeast Asia. Every year, thousands of flights depart, arrive, and cross the HCM (VVTS) airspace. In addition, aviation operations in the HCM airspace are becoming increasingly complex (they include activity of state aviation, civil and general aviation), which requires the continuous improvement of airspace management. Airspace management, in turn, requires a certain flexibility and a quick response to difficulties arising in the airspace. One of the important components of the HCM airspace is the airspace of the HCM Area Control Centre (ACC1 airspace). At present, the structure of the HCM ACC airspace, which is divided into 6 sectors, shows signs of congestion, which leads to an increase in the workload of air traffic controllers (ATC). Therefore, the HCM ACC airspace redesign is imperative. In this regard, the authors of the article consider the issue of dividing the HCM ACC airspace into 8 sectors and the proposals which are necessary for its implementation. According to the authors, the HCM airspace redesign will contribute to improving air traffic management (ATM) quality, increasing airspace capacity, and reducing ATC workload.

Aerosol exposure at air-liquid-interface (AE-ALI) in vitro toxicity system characterisation: Particle deposition and the importance of air control responses

Experimental systems allowing aerosol exposure (AE) of cell cultures at the air-liquid-interface (ALI) are increasingly being used to assess the toxicity of inhaled contaminants as they are more biomimetic than standard methods using submerged cultures, however, they require detailed characterisation before use. An AE-ALI system combining aerosol generation with a CULTEX® exposure chamber was characterised with respect to particle deposition and the cellular effects of filtered air (typical control) exposures. The effect of system parameters (electrostatic precipitator voltage, air flowrate to cells and insert size) on deposition efficiency and spatial distribution were investigated using ICP-MS and laser ablation ICP-MS, for an aerosol of CeO2 nanoparticles. Deposition varied with conditions, but appropriate choice of operating parameters produced broadly uniform deposition at suitable levels. The impact of air exposure duration on alveolar cells (A549) and primary small airway epithelial cells (SAECs) was explored with respect to LDH release and expression of selected genes. Results indicated that air exposures could have a significant impact on cells (e.g., cytotoxicity and expression of genes, including CXCL1, HMOX1, and SPP1) at relatively short durations (from 10 mins) and that SAECs were more sensitive. These findings indicate that detailed system characterisation is essential to ensure meaningful results.

Impact of airtaxi operations in the control zone on air traffic controllers

AbstractUrban air mobility is one approach to reduce congested road traffic and door-to-door travel times. Introducing these vehicles into the urban areas challenges air traffic control once the designated corridors interfere with the control zone of an airport. In this paper, it is evaluated how the performance of air traffic controllers is affected by airtaxi operations in the control zone given the example of Hamburg airport. Moreover, it is assessed whether an enhanced airside situation display showing routes and labels is able to counteract adverse impacts on workload and situation awareness. As a method, eight controllers participated in three simulation runs: first with conventional air traffic, second with additional airtaxis, and third with airtaxis, and the enhanced airside situation display. It revealed that workload increases by more than 40% without reaching overload states in the simulation. The enhanced airside situation display is able to reduce some, but not all adverse impacts. Based on the results, further ways to handle UAM vehicles in the control zone are suggested.

Novel nanocomposite based microstrip patch antenna for C and X band applications

Microstrip patch antennas (MPAs) are essential components in modern wireless communication systems, and their design is significantly influenced by the properties of the materials used. In this study, we investigate the impact of incorporating composites of fumed silica within RT-Duroid dielectric material on the performance of microstrip patch antennas. The effective permittivity of the composite is calculated using the Maxwell-Garnett model. Through systematic theoretical studies, we analyze the performance of composite antennas with varying composite volumes and dielectric substrate thicknesses. Utilizing HFSS 13.0 software, we optimize the substrate thickness and antenna dimensions to design MPAs resonating at 10 GHz. The proposed antenna, with dimensions of 11.85 mm by 9.044 mm for the patch and 50 mm by 50 mm for the substrate, exhibits superior performance compared to conventional antennas, achieving a higher gain of 10 dB, good efficiency of 96 % along with favorable return loss characteristics. The frequency ranges of the C and X bands are better suited for military applications, weather radar, satellite communication, air traffic control, maritime radar, and remote sensing. This research underscores the significance of incorporating composite materials into MPA design to enhance performance and broaden application potential in modern communication technologies.

Magnetoelastic resonators functionalized with metal–organic framework water harvesters as wireless humidity sensors

Relative humidity is a crucial parameter in several fields, such as air quality monitoring, food storage, or control of industrial processes. In this study, we propose a wireless humidity sensor based on magnetoelastic resonance sensors functionalized with Metal–Organic Frameworks (MOFs). Different MOF water harvesters were tested as sensor active coatings: MOF-801, MOF-808, UiO-66-NH2, Al-Fumarate, and CAU-23. Their water absorption capacity and overall performance, when integrated into the sensors, were evaluated. As expected, the selected MOFs showed promising water harvesting capacity, enabling a successful sensor response to humidity in a wide range of relative humidity: 3%–85%. These humidity sensors showed a great sensitivity, stability, and selectivity to water molecules. The response time of the devices was around 15 s, with stabilization and recovery times in adsorption and desorption processes of less than 1 min for certain ranges of operation. In addition to the satisfactory behavior as humidity sensors, magnetoelastic resonators have emerged as a promising tool for the characterization of the dynamic adsorption capacity of MOF materials, as they easily provide a quantitative measure of the water mass adsorbed by the material.

Improved Data-Driven Trajectory Optimization Method Utilizing Deep Trajectory Generation

The imbalance between air traffic capacity and demand, especially in the terminal maneuvering area, constrains the development of the civil aviation industry. To enhance the capacity, providing aircraft with optimal trajectories is the way forward. The key challenge of building a trajectory optimization model lies in embedding air traffic controllers’ operation experience into the optimization process. Previous efforts proposed a data-driven trajectory optimization method to learn operation experience from historical data for trajectory optimization use, which is limited by the issues of insufficient quantity and diversity of trajectory data. To solve those issues, an improved model is proposed to further improve the trajectory optimization performance. Firstly, this paper exploited a connecting-based trajectory generation model to generate massive synthetic trajectories. Then, this paper used a data-mining-based valuable trajectory identification technique to find valuable trajectories that can be used to enrich its operation experience. A case study on Guangzhou Baiyun International Airport was conducted to verify the proposed method. The results show that the proposed model successfully incorporates more operation experience during the optimization process. Compared with the benchmark model, the proposed model can adapt to more complex air traffic and provide optimal trajectories for more incoming aircraft.

A Proposal for Model‐Based Systems Engineering Method for Creating Secure Cyber‐Physical Systems

AbstractRising levels of risk as cyber‐attackers look to exploit system vulnerabilities threatens the Air Traffic Control industry. Attacks on Air Navigation Service Providers' communications systems may lead to airspace closure and even cause safety issues. This paper presents a novel Model‐Based Systems Engineering method that enables systems engineers, in collaboration with system security and software engineers, to perform threat‐modeling analysis of cyber‐physical systems early in the system development process and incorporate mitigation strategies into the system design. The proposed model‐based method covers few security concepts, including misuse cases, system assets, threats, risks, vulnerabilities, and security control identification. The study found that the proposed method is suitable for conducting security analysis for complex cyber‐physical systems early in the system development process.

Predicted Trajectory Accuracy Requirements to Reduce Aviation Impact of Space Launch Operations

Thousands of aircraft flight plans are affected by space launch and reentry operations each year, increasing the distances flown, causing flight delays, and increasing air traffic controllers workload. Due to regulations and procedures, predefined Aircraft Hazard Areas (AHAs) are used to protect aircraft from the risks of space launch and reentry debris, thus constraining the available airspace for other airspace users. While disruptive, there is no less impactful approach at present that adequately protects the flying public. In this paper, we explore the application of trajectory-based operations to evaluate the impact of an AHA on commercial aircraft, with the aim of reducing the number of flights that must be rerouted or rescheduled. This approach relies on precise trajectory predictions to the AHA boundary to determine which flights are expected to clear the AHA before its activation or remain clear of the AHA until after its deactivation. This paper derives the required predicted trajectory accuracy for air traffic automation systems to effectively predict flights impacted by an AHA. The required accuracy is derived based on a model for managing flights relative to the AHA using speed changes alone (as opposed to reroutes or holding) in the context of operational uncertainties like departure time delays and flight characteristics. Additionally, we derived a model to relate scheduling buffers to the AHA activation time, delivery accuracy at the AHA boundary, and AHA violation probability.