Reference Center for Research, Development and Innovation in ATM

Abstract Climate extremes and their occurrences are the main factors that determine climate risk, especially for the resource-poor farmers with limited adaptive capacities. This study examines the trends, variability, and return periods of climate extremes across the northeastern hill (NEH) region of India using long-term climatic data and standardized indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results reveal a consistent increase in temperature-related extremes, including summer days, maximum temperature, and diurnal temperature range, particularly pronounced at stations in Meghalaya (Umiam) and Mizoram (Kolasib). Precipitation extremes, such as consecutive dry days, very heavy rainfall events, and high-intensity precipitation, also exhibited increasing trends at several locations, most notably in Kolasib and Manipur (Imphal). Shortened return periods for both prolonged dry spells and extreme precipitation events suggest an increasing frequency and intensity of compound climate extremes. Seasonal analysis indicates that the most pronounced changes occur during the monsoon and pre-monsoon periods, directly impacting cropping calendars and agricultural systems. These changes pose significant challenges to rainfed agriculture, water security, and disaster vulnerability in the region’s steep and fragile landscapes. The findings underscore the urgent need for climate-resilient agricultural planning, improved watershed and disaster risk management, and the development of region-specific adaptation strategies. Timely advisories, robust institutional support, and targeted policy interventions are essential to reduce vulnerability and enhance resilience. This research provides critical baseline information for informed regional climate policy, sustainable development planning, and adaptive capacity building in one of India’s most ecologically and culturally significant regions.

The study of human performance of air traffic controllers (ATCOs) is an interesting line of research to improve operational safety. In recent years, there has been an increase in the number of techniques available to develop this research based on massive data analysis. This study presents the use of certain electroencephalography (EEG) parameters to study the human performance of ATCOs. Although software and applications are now available to calculate these parameters, there are often problems in understanding the detailed process used to calculate them. The parameters presented in this study are intended to overcome this limitation and are applicable in real air traffic control (ATC) situations. Six parameters are analysed: excitement, stress, relaxation, boredom, engagement, and attention. As an application case for the parameters, a total of 50 data samples obtained during the development of real-time simulations on a highly realistic ATC platform are analysed. From these data, the above-mentioned EEG parameters and their trends are calculated. In addition, the evolution of these parameters is studied in relation to two other variables that characterise the operational situation of the sector during the simulations: the taskload based on ATC events and the number of simultaneous aircraft in the sector per minute.

The use of electroencephalography (EEG) techniques has many advantages in the study of human performance in air traffic control (ATC). At present, these are non-intrusive techniques that allow large volumes of data to be recorded on a continuous basis using wireless equipment. To achieve the most with these techniques, it is essential to establish appropriate EEG parameters with a clear understanding of the process followed to obtain them and their practical application. This study explains, step by step, the approach adopted to obtain six EEG parameters: excitement, stress, boredom, relaxation, engagement, and attention. It then explains all the steps involved in analysing the relationship between these parameters and two other parameters that characterise the state of the air traffic control sector during the development of real-time simulations (RTS): taskload and number of simultaneous aircraft. For this case study, the results showed the highest relationships for the engagement and attention parameters. In general, the results confirmed the potential of using these EEG parameters.

Due to the increasing complexity of airspace, the ATC system does not have sufficient capacity to cope with aircraft demand. For this reason, the ATFCM system needs to implement more and more measures to balance capacity and demand. These measures are the ATFCM regulations. In this paper, a methodology to predict ATFCM capacity regulations based on a machine learning model is proposed. This model will try to predict whether an ATC sector will be regulated or not at a specific time based on the time of prediction and certain operating variables in the sector. A test has been carried out in the LECMPAU sector of Spanish airspace. With results of 91% accuracy in predicting whether the sector will be regulated or not and a logical explainability, it can be concluded that with the sufficient historical operation and certain operational variables, it is possible to predict when an ATFCM regulation capacity will appear in the airspace. It can also be concluded that without a detailed knowledge of the operation in a sector, it is possible to make this prediction because patterns can be found in historical behaviour.

The ITACA project aimed to shed light on the drivers and barriers for the adoption of new technologies in ATM, with the ultimate goal of supporting the identification, formulation and implementation of policies and regulations that accelerate ATM modernisation. To this end, the project has brought together the wide body of theory on technology adoption developed in the field of industrial organisation with the state-ofthe-art in computational behavioural economics and participatory simulation, in an attempt to provide a rich, multifaceted analysis able to capture the complexity of the ATM R&I lifecycle. The paper presents an overview of the insights gained through different analysis and modelling techniques (economic modelling, agent-based simulation, participatory experiments with industry experts) on the mechanisms that drive technology adoption in ATM, as well as a set of key takeaways, policy recommendations and a look to the future.

Os organismos planctônicos são pequenas espécies marinhas constituídas por microalgas marinhas, animais, protistas e organismos autótrofos e heterótrofos.Este estudo objetivou estudar as principais espécies de zooplâncton, fitoplâncton e bacterioplâncton, destacando sua distribuição e sazonalidade no litoral do Nordeste brasileiro,

The ATC service has the objective of controlling airspace operations safely and efficiently. This control is becoming more and more difficult due to the increasing complexity of airspace. With the objective of collaborating and facilitating the provision of the control service, FLUJOS project aims to develop a methodology to characterise ATC sectors according to their complexity. This paper shows the first results obtained in this project. A methodology is proposed that first performs a statistical analysis of the data present in the flight plans of individual aircraft. The statistical analysis will be used to estimate the impact of air traffic flows. With this, the complexity of ATC sectors will finally be determined. In addition, a machine learning tool will be added to develop a dynamic methodology. After evaluating the methodology with data from Spanish airspace in 2019, it can be said that the results obtained are logical from an operational point of view, and that they allow a fairly accurate classification of the sectors based on their complexity. However, the proposed methodology is still a preliminary version, so more work will have to be done to add variables to achieve the objective of obtaining an even more accurate and realistic classification.