Combining Fleetwide AviTeam Aviation Emission Modeling with LCA Perspectives for an Alternative Fuel Impact Assessment.

Reducing aviation emissions is important as they contribute to air pollution and climate change. Several alternative aviation fuels that may reduce life cycle emissions have been proposed. Comparative life cycle assessments (LCAs) of fuels are useful for inspecting individual fuels, but systemwide analysis remains difficult. Thus, systematic properties like fleet composition, performance, or emissions and changes to them under alternative fuels can only be partially addressed in LCAs. By integrating the geospatial fuel and emission model, AviTeam, with LCA, we can assess the mitigation potential of a fleetwide use of alternative aviation fuels on 210 000 shorter haul flights. In an optimistic case, liquid hydrogen (LH2) and power-to-liquid fuels, when produced with renewable electricity, may reduce emissions by about 950 GgCO2eq when assessed with the GWP100 metric and including non-CO2 impacts for all flights considered. Mitigation potentials range from 44% on shorter flights to 56% on longer flights. Alternative aviation fuels' mitigation potential is limited because of short-lived climate forcings and additional fuel demand to accommodate LH2 fuel. Our results highlight the importance of integrating system models into LCAs and are of value to researchers and decision-makers engaged in climate change mitigation in the aviation and transport sectors.

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management.

Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

Original and Low-Cost ADS-B System to Fulfill Air Traffic Safety Obligations during High Power LIDAR Operation.

LIDAR is an atmospheric sounding instrument based on the use of high-power lasers. The use of these lasers involves fulfilling obligations with respect to air safety. In this article, we present a low-cost air traffic surveillance solution integrated into an automated operating system for the Rayleigh-Mie-Raman LIDAR of Clermont Ferrand and the statistical elements of its application over more than two years of operation from September 2019 to March 2022. Air traffic surveillance that includes the possibility of shutting off lasers is required by international regulations because LIDAR is equipped with a class four laser that presents potential dangers to aircraft flying overhead. The original system presented in this article is based on software-defined radio. ADS-B transponder frames are analyzed in real-time, and laser emission is stopped during LIDAR operation when an aircraft is detected within a 2 km radius around the LIDAR. The system was accredited in 2019 by the French air traffic authorities. Laser shutdowns due to the detection of aircraft near the Clermont Ferrand LIDAR caused a data loss rate of less than 2% during the period of application.

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data.

This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously theoretical assertions about LA, which have remained largely unexamined due to LA's intricate design. Our methodology encompasses the evaluation of prospective technologies across all levels of LA, the examination of layer-specific design limitations, and the implementation of a uniform software development framework across multiple layers. Specifically, our methodology employs a unique set of metrics, including data latency and processing accuracy under various conditions, which serve as critical indicators of LA's accurate data-processing performance. Our findings compellingly illustrate LA's "eventual consistency". Despite potential transient inconsistencies during real-time processing in the Speed Layer (SL), the system ultimately converges to deliver precise and reliable results, as informed by the comprehensive computations of the Batch Layer (BL). This empirical validation not only confirms but also quantifies the claims posited by previous theoretical discourse, with our results indicating a 100% accuracy rate under various severe data-ingestion scenarios. We applied this methodology in a practical case study involving air/ground surveillance, a domain where data accuracy is paramount. This application demonstrates the effectiveness of the methodology using real-world data-intake scenarios, therefore distinguishing this study from hardware-centric evaluations. This study not only contributes to the existing body of knowledge on LA but also addresses a significant literature gap. By offering a novel, empirically supported methodology for testing LA, a methodology with potential applicability to other big-data architectures, this study sets a precedent for future research in this area, advancing beyond previous work that lacked empirical validation.

Using aircraft tracking data to estimate the geographic scope of noise impacts from low-level overflights above parks and protected areas.

Sightseeing air tours have proven to be a challenging management issue for many tourist destinations around the world, especially at locations meant to protect natural and cultural resources and wilderness character. Two of the primary challenges with managing air tours are a lack of information about their travel patterns and how such patterns result in a measurable noise impact to listeners. Recent studies have highlighted the usefulness of newer technology for tracking aircraft travel patterns, particularly over national parks. In this synthesis, we pair aircraft tracks with acoustic data using a quantitative observer-based audibility modelling software toolkit. The findings delimit the long-term geographic scope of audibility for specific aircraft noise sources above landscapes of Hawai'i Volcanoes and Denali National Parks, U.S. and identify practical, 3-dimensional offset distances that can be used to reduce the functional effects of air tour noise in terms of sound level.

Developing a High-Resolution Emission Inventory of China's Aviation Sector Using Real-World Flight Trajectory Data.

Economic growth and globalization have led to a surge in civil aviation transportation demand. Among the major economies in the world, China has experienced a 12-fold increase in terms of total passenger aviation traffic volume since 2000 and is expected to be the largest aviation market soon. To better understand the environmental impacts of China's aviation sector, this study developed a real-world flight trajectory-based emission inventory, which enabled the fine-grained characterization of four-dimensional (time, longitude, latitude, and altitude) emissions of various flight stages. Our results indicated that fuel consumption and CO2 emissions showed two peaks in altitude distribution: below 1,000 m and between 8,000 and 12,000 m. Various pollutants depicted different vertical distributions; for example, nitrogen oxides (NOX) had a higher fraction during the high-altitude cruise stage due to the thermal NOX mechanism, while hydrocarbons had a dominant fraction at the low-altitude stages due to the incomplete combustion under low-load conditions. This improved aviation emission inventory approach identified that total emissions of CO2 and air pollutants from short-distance domestic flights would be significantly underestimated by the conventional great-circle-based approach due to underrepresented calculation parameters (particularly, flight distance, duration, and cruise altitude). Therefore, we suggest that more real-world aviation flight information, especially actual trajectory records, should be utilized to improve assessments of the environmental impacts of aviation.

Modelling and Simulation of Collaborative Surveillance for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems rely on collaborative position reporting to track unmanned aerial system (UAS) operations over wide unsurveilled (with counter-UAS systems) areas. Many different technologies, such as Remote-ID, ADS-B, FLARM, or MLAT might be used for this purpose, in addition to the direct exploitation of C2 telemetry, relayed though cellular networks. This paper provides an overview of the most used collaborative sensors and surveillance systems in this context, analyzing their main technical parameters and performance effects. In addition, this paper proposes an abstracted general statistical simulation model covering message encoding, network capacity and access, sensors coverage and distribution, message transmission and decoding. Making use of this abstracted model, this paper proposes a particularized set of simulation models for ADS-B, FLARM and Remote-Id; it is thus useful to test their potential integration in UTM systems. Finally, a comparative analysis, based on simulation, of these systems, is performed. It is shown that the most relevant effects are those related with quantification and the potential saturation of the communication channels leading to collisions and delays.

Cyberattack on Flight Safety: Detection and Mitigation Using LoRa.

Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B's vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber-vulnerabilities. Finally, a set of simulations and field experiments is presented to test the expected performance of the suggested IoT flight safety system. We conjecture that the presented system can be implemented in a wide range of civilian airplanes, leading to an improvement in flight safety in cases of cyberattacks or the absence of reliable ADS-B communication.

ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection.

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages' injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.

Real-time identification of aircraft sound events.

Metropolitan airports constitute an environmental nuisance, mainly due to noise pollution originating from aircraft landings and takeoffs, affecting the wellbeing of the airports' neighboring populations. Noise measurement is considered the fundamental means to evaluate, enforce, validate, and control noise abatement. Noise measurements performed by sound monitors located close to urban airports are often disrupted by urban background noise that interferes with aircraft sounds. Detecting aircraft noise, classifying, identifying, and separating it from the residual background noise is a challenge for unattended aircraft noise monitors. This paper suggests a simple and inexpensive methodology, based on ADS-B (Automatic Dependent Surveillance-Broadcast), which can facilitate isolating aircraft noise from background noise. Experiments showed that using ADS-B driven noise monitors is at least as accurate as the commonly used radar-driven noise monitors, in terms of true positive, false positive, or false negative detection during the examined periods.

Integrated Display and Simulation for Automatic Dependent Surveillance-Broadcast and Traffic Collision Avoidance System Data Fusion.

Automatic Dependent Surveillance-Broadcast (ADS-B) is the direction of airspace surveillance development. Research analyzing the benefits of Traffic Collision Avoidance System (TCAS) and ADS-B data fusion is almost absent. The paper proposes an ADS-B minimum system from ADS-B In and ADS-B Out. In ADS-B In, a fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is proposed for integrated display and an airspace traffic situation display is developed by using ADS-B information. ADS-B Out includes ADS-B Out transmission based on a simulator platform and an Unmanned Aerial Vehicle (UAV) platform. This paper describes the overall implementation of ADS-B minimum system, including theoretical model design, experimental simulation verification, engineering implementation, results analysis, etc. Simulation and implementation results show that the fused system has better performance than each independent subsystem and it can work well in engineering applications.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology.

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency.

Impacts of COVID-19 on aircraft usage and fuel consumption: A case study on four Chinese international airports.

COVID-19 pandemic starting in early 2020 has greatly impacted human and industrial activities. Air transport in China shrank abruptly in February 2020, following a year-long gradual recovery. The airline companies reacted to this unprecedented event by dramatically reducing the flight volume and rearranging the aircraft types. As the first major economy that successfully controls the spread of COVID-19, China can provide a unique opportunity to quantify the medium-long impacts on the air transport industry. To quantify the corresponding changes and to elucidate the effects of COVID-19 in the wake of two major outbreaks centered in Wuhan and Beijing, we analyze twelve flight routes formed by four selected airports, using the Automatic Dependent Surveillance-Broadcast (ADS-B) data in 2019 and 2020. Our results show that the total flight volume in 2020 reduced to 67.8% of 2019 in China. The recovering time of flight volume was about 2-6 months, dependent on the severity. In order to unwind the severe challenge, airlines mainly relied on aircraft B738 and A321 between February and June in 2020 because the fuel consumption per seat of these two aircraft types is the lowest. Besides, fuel consumption and aircraft emissions are calculated according to the Base of Aircraft Data (BADA) and the International Civil Aviation Organization's Engine Emissions Databank (ICAO's EEDB). At the end of 2020, the ratios of daily fuel consumption and aircraft emissions of 2020 to 2019 rebounded to about 0.875, suggesting the domestic commercial flights were nearly fully recovered. Our results may provide practical guidance and meaningful expectation for commercial aircraft management for other countries.