UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

Airspace capacity artificial intelligence model in UAM environment based on the airspace complexity.

Quando consideramos o tráfego aéreo convencional, onde os pousos ocorrem normalmente em aeroportos ou helipontos, encontramos todos os critérios necessários para a realização dos voos (regras de tráfego aéreo), para os procedimentos de pouso e decolagem, estrutura do espaço aéreo e para os aeroportos. Estes critérios são desenvolvidos por órgãos como a ICAO e a FAA. Considerando qualquer porção de espaço aéreo, visando manter os níveis de segurança aceitáveis (safety), caberá à autoridade aeronáutica a definição da capacidade deste espaço aéreo, normalmente apresentada em quantidade máxima de tráfegos que poderão voam simultaneamente. Para isso, utilizam modelagem matemática adequada, normalmente baseada na carga de trabalho do controlador de Tráfego Aéreo (ATCo). No entanto, consenso entre os pesquisadores, é que a complexidade do espaço aéreo deverá ser considerada, pois impacta diretamente a carga de trabalho do ATCo e consequentemente a capacidade do espaço aéreo. Tem sido um constante desafio estabelecer a relação entre a complexidade do espaço aéreo e a capacidade do espaço aéreo. No entanto, nos deparamos com um desafio maior ainda que é pensarmos em estratégias para viabilizamos a realização e o crescimento de uma nova demanda: UAM (Urban Air Mobility) . Em muitas partes do mundo, a cada ano, o tráfego terrestre aumenta, resultando em tempos de deslocamento mais longos, com custos econômicos significativos. Além de diversas estratégias para resolver o problema de congestionamento de tráfego (criação de viadutos, novas vias ou restrições de tráfego em determinados horários e locais), um conceito que começou com o uso de helicópteros e com amplo desenvolvimento tecnológico, é a Mobilidade Aérea Urbana (UAM) , definido como operações de tráfego aéreo seguras e eficientes em uma área metropolitana para aeronaves tripuladas e não tripuladas. Nesta pesquisa, o eVTOL (electric vertical takeoff and landing) será a aeronave considerada no ambiente UAM que poderá realizar pousos e decolagens nos mais diferentes lugares, que serão chamados de TOLA (takeoff and landing área). Uma das principais preocupações de pesquisadores sobre o assunto é considerar que atual estrutura de controle do espaço aéreo, de estrutura espaço aéreo, assim como as regras de tráfego aéreo utilizadas atualmente poderão ser fatores que impeçam o crescimento do UAM. Este trabalho tem o objetivo de apresentar um Modelo de Inteligência Artificial de Capacidade do Espaço Aéreo no Ambiente UAM com Base na Complexidade do Espaço Aéreo. No entanto, nesta ambiente não foi considerada a presença do ATCo, sendo proposta a utilização de ferramenta computacional para as instruções de controle de tráfego aéreo. Para que o objetivo fosse alcançado, foram apresentados critérios para a criação de espaço aéreo controlado em ambiente UAM e regras de tráfego aéreo específicas para o ambiente UAM. Foram apresentados também novos conceitos, como por exemplo, Capacidade Dinâmica do Espaço Aéreo e um índice de limite de complexidade aceitável (complexity Treshold).When we consider conventional air traffic, where landings normally occur at airports or helipads, we find all the necessary criteria for carrying out flights (air traffic rules), for landing and take-off procedures, and airspace structure. These criteria are developed by International Entities such as ICAO and FAA. Aeronautical authorities must define the airspace capacity for any portion of the airspace to maintain acceptable safety levels. This normally involves determining the maximum amount of traffic that can fly simultaneously. For this, they use adequate mathematical modeling, usually based on the workload of the Air Traffic Controller (ATCo). However, researchers agree that airspace complexity must also be considered, as it directly impacts the ATCo workload and, consequently, the airspace capacity. Establishing the relationship between airspace complexity and airspace capacity is a challenging task. On top of that, we face the even greater challenge of envisioning strategies that enable the consolidation and growth of a new demand: UAM (Urban Air Mobility). Every year increases in ground traffic worldwide have resulted in longer commute times with high economic costs. In addition to several strategies to solve the problem of traffic congestion (creation of viaducts, new lanes, or traffic restrictions at certain times and locations), the concept of Urban Air Mobility (UAM) has emerged to encompass safe and efficient air traffic operations in a metropolitan area for manned and unmanned aircraft. This research examines eVTOL (electric vertical takeoff and landing) aircraft in the UAM environment. These aircraft can perform landings and takeoffs in a wide range of places, which will be called TOLA (takeoff and landing area). However, researchers worry that the current airspace structure and air traffic rules may not meet the demands of the UAM.This work aims to present an Artificial Intelligence Model of Airspace Capacity in the UAM Environment Based on Airspace Complexity. The presence of ATCo was not considered in this environment, with a computational tool being used for air traffic control instructions instead. Criteria for creating the controlled airspace in a UAM environment were presented, as well as specific air traffic rules. This work also introduces new concepts, such as Dynamic Airspace Capacity and an Acceptable Complexity Threshold Index

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in several domains, and Deep Learning (DL) has attracted attention due to its impressive results and disruptive capabilities. The adoption of DL models in ATM solutions enables new cognitive services that have never been considered before. The main goal of this research is to present a comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM). This review focuses on describing applications, identifying opportunities, and highlighting open challenges to foster the evolution of ATM systems. To accomplish this, we discuss the fundamental topics of DL and ATM and categorize the contributions based on different approaches. First, works are grouped based on the DL approach adopted. Then, future directions are identified based on the ATM solution area. Finally, open challenges are listed for both DL applications and ATM solutions. This article aims to support the community by identifying research problems to be faced in the future

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field