Search Results (16)

With the requirements of government data protection regulations and industrial concerns regarding data protection and privacy, the security level required for data privacy and protection has increased. This has led researchers to investigate techniques that can train cybersecurity machine learning (ML) models without sharing personal data. Federated Learning (FL) is a newly developed decentralized and distributed ML mechanism that emphasize privacy. In this technique, a learning algorithm is trained without collecting or exchanging sensitive data from distributed client models running at different locations. With the rapid increase in the number of cybersecurity attacks reported in the aviation industry in the last two decades, strong, dynamic, and effective countermeasures are required to protect the aviation industry and air passengers against such attacks, which can most of the time lead to catastrophic situations. This paper proposes and implements an FL model for identifying cyberattacks on a Software Defined Network (SDN)-based aeronautical communication networks. The machine learning model used in the FL architecture is a Deep Neural Network (DNN) model. The publicly available National Security Laboratory–Knowledge Discovery and Datamining (NSL-KDD) dataset was employed to train and validate the proposed FL model. The simulation results illustrated that the FL-based system can accurately and effectively identify potential cybersecurity attacks and minimize the risk of data and service exposure without degrading model performance. A comparison was also made between the FL and non-FL machine learning models. Preliminary results demonstrated that the FL model outperformed the non-FL machine learning approaches. FL reached an accuracy of 96%, compared to 76% and 83% for NFL. Full article

This paper presents a new design for a multimode fusion communication system, aimed at tackling the complexities of modern aeronautical communication. The system integrates multiple communication technologies, such as ad hoc networking, 5G, BeiDou satellite, RTK positioning, and ADS-B broadcasting. This integration effectively solves the problem of increasing the size and weight of aviation communication equipment while also improving the efficiency and security of data communication. The study demonstrates that the implementation of this fusion communication system can lead to the development of more efficient and intelligent avionics equipment in the future, thereby offering robust technical support for flight safety. Full article

Satellite communication (satcom) is experiencing increased interest to cover the connectivity gaps of terrestrial networks. To ensure high performance and throughput for the user—and even more so in Communications-On-The-Move(COTM) systems, e.g., in aeronautics—steerable antennas such as phased arrays are required to adjust the beam so as to follow the satellite’s trajectory. The mutual movement of terminals and satellite in COTM systems calls for a broad Field of View (FoV) and, hence, poses a challenge to common planar systems. For improving the FoV, common solutions require ad hoc designs, such as multi-mode antennas, wide half-power-beamwidth antennas or metasurfaces. By contrast, 3D arrays are able to cover a wider angular region by the 3D allocation of the antennas. In this paper, the benefits and drawbacks of moving from 2D (planar) arrays to 3D phased arrays are investigated. Multiple geometrical configurations are analyzed, keeping in mind the size requirements of aeronautic terminals. The best configuration is, hence, an array capable of enhancing the FoV of the terminal. The proposed antenna architecture offers a good trade-off between design complexity and performance, and it could be further developed to become an aeronautic-grade terminal aperture. Full article

Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according to the real-time position of the aircraft based on the known trajectory. Firstly, the route and trajectory models are used to obtain geographical coordinates by the aircraft positioning algorithm. On this basis, the geographical cluster and cluster head region are divided in order to complete the cluster setting. Considering the aircraft maintenance cluster generation time updates, we use the communication sub-cluster generation algorithm to control the size of the cluster, and also, the flexibility of cluster hops is guaranteed by the subsidiary cluster members. The continuity of communication and the scalability of the cluster are maintained by the gateway node, thereby forming a network structure and increasing the stability of clusters. Finally, the actual route data are used to simulate the performance of the algorithm. The experimental and analytical results show that clustering and maintenance of the algorithm have zero overhead. Additionally, compared with the traditional algorithm, our proposed method can maintain a reasonable number of clusters, reduce the frequency of cluster head replacement, reduce the number of cluster members entering and leaving the cluster and avoid the loss of control of cluster heads to cluster members. So, it has important application value in the field of civil aviation. Full article

Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%. Full article

Automatic modulation classification is an important component in many modern aeronautical communication systems to achieve efficient spectrum usage in congested wireless environments and other communications systems applications. In recent years, numerous convolutional deep learning architectures have been proposed for automatically classifying the modulation used on observed signal bursts. However, a comprehensive analysis of these differing architectures and the importance of each design element has not been carried out. Thus, it is unclear what trade-offs the differing designs of these convolutional neural networks might have. In this research, we investigate numerous architectures for automatic modulation classification and perform a comprehensive ablation study to investigate the impacts of varying hyperparameters and design elements on automatic modulation classification accuracy. We show that a new state-of-the-art accuracy can be achieved using a subset of the studied design elements, particularly as applied to modulation classification over intercepted bursts of varying time duration. In particular, we show that a combination of dilated convolutions, statistics pooling, and squeeze-and-excitation units results in the strongest performing classifier achieving 98.9% peak accuracy and 63.7% overall accuracy on the RadioML 2018.01A dataset. We further investigate this best performer according to various other criteria, including short signal bursts of varying length, common misclassifications, and performance across differing modulation categories and modes. Full article

The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome. Full article

Embedded systems used in critical systems, such as aeronautics, have undergone continuous evolution in recent years. In this evolution, many of the functionalities offered by these systems have been adapted through the introduction of network services that achieve high levels of interconnectivity. The high availability of access to communications networks has enabled the development of new applications that introduce control functions with higher levels of intelligence and adaptation. In these applications, it is necessary to manage different components of an application according to their levels of criticality. The concept of “Industry 4.0” has recently emerged to describe high levels of automation and flexibility in production. The digitization and extensive use of information technologies has become the key to industrial systems. Due to their growing importance and social impact, industrial systems have become part of the systems that are considered critical. This evolution of industrial systems forces the appearance of new technical requirements for software architectures that enable the consolidation of multiple applications in common hardware platforms—including those of different criticality levels. These enabling technologies, together with use of reference models and standardization facilitate the effective transition to this approach. This article analyses the structure of Industry 4.0 systems providing a comprehensive review of existing techniques. The levels and mechanisms of interaction between components are analyzed while considering the impact that the handling of multiple levels of criticality has on the architecture itself—and on the functionalities of the support middleware. Finally, this paper outcomes some of the challenges from a technological and research point of view that the authors identify as crucial for the successful development of these technologies. Full article

The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, 5G also presents itself as a key enabler to fulfill Industry 4.0 requirements. In this article, the authors first propose a 5G-enabled architecture for Industry 4.0. Smart Networks for Industry (SN4I) is introduced, an experimental facility based on two 5G key-enabling technologies—Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)—which connects the University of the Basque Country’s Aeronautics Advanced Manufacturing Center and Faculty of Engineering in Bilbao. Then, the authors present the deployment of a Wireless Sensor Network (WSN) with strong access control mechanisms into such architecture, enabling secure and flexible Industrial Internet of Things (IIoT) applications. Additionally, the authors demonstrate the implementation of a use case consisting in the monitoring of a broaching process that makes use of machine tools located in the manufacturing center, and of services from the proposed architecture. The authors finally highlight the benefits achieved regarding flexibility, efficiency, and security within the presented scenario and to the manufacturing industry overall. Full article

One of the praxeological problems of safe and sustainable air transport (airfreight transport/air cargo, and air passenger transport) is the prevention and management of risks by competent staff, with the support of modern information and communication technologies. This paper presents an educational information model and software for the airport network and information systems risk assessment, primarily intended for aviation education and training of professionals for ensuring safe and sustainable air transport. The solution to the problem is based on the application of the fuzzy logic method in the air transport environment. Based on a fuzzy expert model, the selected scenario, and the input data established separately for airport assets by a group of 23 experts from aviation practice and a university, the following three assessments of airport network information system assets were constructed: Asset A₂ (meteorological information systems) has an insignificant risk with an estimated 0.1162, and assets A₁ (air traffic control and management (ATM), navigational aids and approach) and A₃ (runway monitoring system) received a low risk of airport network and information systems (NIS) security with ratings of 0.2623 and 0.2915, respectively. An airport NIS risk assessment was aggregated (0.2288), indicating a low degree of security risk to the airport’s network and information systems. The aggregated risk assessment of airport NIS, including financial loss data, was calculated as 0.1438, representing a low degree of security risk to the airport’s network and information systems. Scenarios for evaluating airport assets are changing for students during education. The results of the developed model and its software will be part of the Simulation Center of the Faculty of Aeronautics. Full article

The hierarchical unmanned aerial systems (UAS) traffic management (UTM) is proposed for UAS operation in Taiwan. The proposed UTM is constructed using the similar concept of ATM from the transport category aviation system. Based on the airspace being divided by 400 feet of altitude, the RUTM (regional UTM) is managed by the local government and the NUTM (national UTM) by the Civil Aeronautical Administration (CAA). Under construction of the UTM system infrastructure, this trial tests examine the effectiveness of UAV surveillance under 400 feet using automatic dependent surveillance-broadcast (ADS-B)-like on-board units (OBU). The ground transceiver station (GTS) is designed with the adoptable systems. In these implementation tests, five long-range wide area network (LoRa) gateways and one automatic packet reporting system (APRS) I-Gate are deployed to cover the Tainan Metropolitan area. The data rates are set in different systems from 8 to 12 s to prevent from data conflict or congestion. The signal coverage, time delay, data distribution, and data variance in communication are recorded and analyzed for RUTM operation. Data streaming and Internet manipulation are verified with cloud system stability and availability. Simple operational procedures are defined with priority for detect and avoid (DAA) for unmanned aerial vehicles (UAVs). Mobile communication and Zello broadcasts are introduced and applied to establish controller-to-pilot communication (CPC) for DAA. The UAV flight tests are generally beyond visual line-of-sight (BVLOS) near suburban areas with flight distances to 8 km. On the GTS deployment, six test locations examine communication coverage and effectiveness using ADS-B like OBUs. In system verification, the proposed ADS-B like OBU works well in the UTM infrastructure. The system feasibility is proven with support of receiving data analysis and transceiver efficiency. The trial test supports RUTM in Taiwan for UAV operations. Full article

High Frequency (HF) communications through ionospheric reflection is a widely used technique specifically for maritime, aeronautical, and emergency services communication with remote areas due to economic and management reasons, and also as backup system. Although long distance radio links can be established beyond line-of-sight, the availability, the usable frequencies and the capacity of the channel depends on the state of the ionosphere. The main factors that affect the ionosphere are day-night, season, sunspot number, polar aurora and earth magnetic field. These effects impair the transmitted wave, which suffers attenuation, time and frequency dispersion. In order to increase the knowledge of this channel, the ionosphere has been sounded by means of narrowband and wideband waveforms by the research community all over the world in several research initiatives. This work intends to be a review of remarkable projects for vertical sounding with a world wide network and for oblique sounding for high latitude, mid latitude, and trans-equatorial latitude. Full article

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION). Full article

The latest Version-7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products were released by the National Aeronautics and Space Administration (NASA) in December of 2012. Their performance on different climatology, locations, and precipitation types is of great interest to the satellite-based precipitation community. This paper presents a study of TMPA precipitation products (3B42RT and 3B42V7) for an extreme precipitation event in Beijing and its adjacent regions (from 00:00 UTC 21 July 2012 to 00:00 UTC 22 July 2012). Measurements from a dense rain gauge network were used as the ground truth to evaluate the latest TMPA products. Results are summarized as follows. Compared to rain gauge measurements, both 3B42RT and 3B42V7 generally captured the rainfall spatial and temporal pattern, having a moderate spatial correlation coefficient (CC, 0.6) and high CC values (0.88) over the broader Hebei, Beijing and Tianjin (HBT) regions, but the rainfall peak is 6 h ahead of gauge observations. Overall, 3B42RT showed higher estimation than 3B42V7 over both HBT and Beijing. At the storm center, both 3B42RT and 3B42V7 presented a relatively large deviation from the temporal variation of rainfall and underestimated the storm by 29.02% and 36.07%, respectively. The current study suggests that the latest TMPA products still have limitations in terms of resolution and accuracy, especially for this type of extreme event within a latitude area on the edge of coverage of TRMM precipitation radar and microwave imager. Therefore, TMPA users should be cautious when 3B42RT and 3B42V7 are used to model, monitor, and forecast both flooding hazards in the Beijing urban area and landslides in the mountainous west and north of Beijing. Full article

The Next Generation Air Traffic Management system (NextGen) is a blueprint of the future National Airspace System. Supporting NextGen is a nation-wide Aviation Simulation Network (ASN), which allows integration of a variety of real-time simulations to facilitate development and validation of the NextGen software by simulating a wide range of operational scenarios. The ASN system is an environment, including both simulated and human-in-the-loop real-life components (pilots and air traffic controllers). Real Time Distributed Simulation (RTDS) developed at Embry Riddle Aeronautical University, a suite of applications providing low and medium fidelity en-route simulation capabilities, is one of the simulations contributing to the ASN. To support the interconnectivity with the ASN, we designed and implemented a dedicated gateway acting as an intermediary, providing logic for two-way communication and transfer messages between RTDS and ASN and storage for the exchanged data. It has been necessary to develop and analyze safety/security requirements for the gateway software based on analysis of system assets, hazards, threats and attacks related to ultimate real-life future implementation. Due to the nature of the system, the focus was placed on communication security and the related safety of the impacted aircraft in the simulation scenario. To support development of safety/security requirements, a well-established fault tree analysis technique was used. This fault tree model-based analysis, supported by a commercial tool, was a foundation to propose mitigations assuring the gateway system safety and security. Full article