In air traffic flow management (ATFM), the EUROCONTROL Network Manager regulates flights in the case of unexpected events that temporarily reduce airport capacity. In this case, regulated flights receive a new target time of arrival (TTA) following a first-planned, first-served approach. These regulations typically result in flight delays and additional delay costs for airspace users (AUs) and the arrival airport. The HARMONIC project, which is funded by the SESAR Joint Undertaking within the EU Horizon Europe program, develops, among others, a Target Time Management System (TTMS) to enable collaborative flight prioritization and optimization based on preferences of the AUs and the airport. The TTMS was successfully tested in live trials at Zurich Airport, with results indicating increased arrival punctuality and fewer missed connections. Although short-term inequity is acceptable to improve overall efficiency in a given situation, the TTMS shall ensure long-term equity, i.e., no AU is favored or disfavored compared to the others over time. In this regard, one challenge is to design an Equity Mechanism as part of the TTMS to ensure long-term equity across AUs. In this paper, we introduce an Equity Mechanism that modifies the preferences of AUs. We investigate various configurations of three different strategies for adjusting the preferences of AUs with the Equity Mechanism. We use the Theil index to measure inequity and preferences from 51 regulations obtained during the live trials at Zurich Airport for re-running each optimization in a controlled lab environment. Our results show that some configurations of the Equity Mechanism can reduce the Theil index over time, i.e., successfully improve long-term equity, while having only a minor impact on the quality of the flight lists found by the TTMS.

The L-band Digital Aeronautical Communications System (LDACS) is a broadband air–ground data link designed to overcome VHF band saturation and support the aviation sector’s transition toward the ATN/IPS infrastructure. While LDACS has reached a high level of technical maturity, interoperability across vendors and compatibility with the existing ATN/OSI environment had not previously been demonstrated under realistic conditions. This paper presents results from two complementary demonstration campaigns conducted within the CyMoDACs and PaWaDACS projects. First, LDACS vendor interoperability was validated across all protocol layers using radios from two manufacturers in the operational network of the German air navigation service provider, including mobility support and coordinated handovers. Second, interoperability with ATN/OSI was achieved by extending an LDACS ground radio with legacy-network interfaces and demonstrating transport of CPDLC messages over LDACS in an ATN/OSI network. The results establish an interoperable LDACS reference implementation, confirm LDACS as a viable broadband bearer for current ATN/OSI systems, and provide key contributions toward operational deployment and industry-driven standardization.

In air traffic flow management (ATFM), unexpected events such as bad weather conditions regularly reduce airport capacity. In such cases of temporarily reduced capacity, the EUROCONTROL Network Manager issues a regulation, which results in arriving flights being assigned a new target time of arrival (TTA), usually on a first-planned, first-served (FPFS) basis. These TTA assignments, however, may be inefficient due to the varying delay costs of individual flights. Therefore, one of the solutions of the SESAR project HARMONIC aims to develop a platform for collaborative optimization of arrival flight lists - a Target Time Management System (TTMS). The TTMS allows airspace users (AUs) and the airport to provide their preferences as input for an optimization of the arrival flight list. In this regard, one challenge is to define conformance criteria applying to the preferences submitted by the AUs in order to reduce the possibility that the preferences of individual AUs intentionally manipulate the optimization process, thus ensuring fairness among AUs. These conformance criteria should be easy to understand and have little or no negative impact on the optimization results. In this paper, we investigate the impact of different configurations of easy-to-understand conformance criteria on the quality of the optimization results. The experimental evaluation shows that the application of conformance criteria has only a minor impact on the quality of the flight lists. Our results will inform the choice of conformance criteria for the live trials of the TTMS developed in the course of the HARMONIC project.

In Air Traffic Flow Management (ATFM), flights are issued arrival times during unexpected events, such as adverse weather conditions, in order to ensure smooth and safe airport operations. These target times are traditionally allocated on a first-planned, first-served basis, which often leads to inefficiencies due to the different delay costs of individual flights. The HAR-MONIsed network through smart technology and Collaboration (HARMONIC) project, funded by the Single European Sky ATM Research (SESAR) Joint Undertaking within the EU Horizon Europe program, therefore, explores a collaborative optimization platform to enable flight prioritization across airspace users. In this paper, we describe initial results of the development and validation of a Target-Time Management System (TTMS) in the HARMONIC project. In particular, we present different architecture options, one assuming a trusted platform provider, another option for the assumption of an honest-but-curious plat-form provider, using secure multi-party computation to protect the private inputs. We also present experimental results for a genetic algorithm for the optimization of assignment of target times to flights.

Air traffic flow management (ATFM) regulations issued by the EUROCONTROL Network Manager (NM) during periods of reduced capacity in the European air traffic network typically result in flight delays and additional costs for airspace users (AUs). However, not all flights are equally impacted by these regulations, and AUs would like to prioritize flights based on their preferences while protecting the confidentiality of such information. Thus, in the SlotMachine project, we proposed a privacy-preserving marketplace for collaborative optimization of flight lists during ATFM regulations An auction mechanism incentivizes AUs to participate in the SlotMachine's optimization runs. The proposed implementation of the auction mechanism in a privacypreserving manner employs a genetic algorithm in combination with multi-party computation (MPC), since a privacy-preserving implementation of a deterministic algorithm would not finish within the time constraints. Experiments using realistic synthetic datasets based on real-world samples demonstrate feasibility of the proposed implementation.

Today's digital landscape is characterized by diverse encodings, syntaxes, and semantics, which forces stakeholders to dedicate considerable resources to ensure smooth data exchange and interoperability. While the W3C-recommended Resource Description Framework (RDF) offers a solid solution to these challenges, many developers find i t d aunting t han conventional data structures like JSON or YML. There have been alternative solutions suggested for data interoperability, including DLite and Layered Schemas. However, these do not fully leverage existing open standards, which diminishes their overall effectiveness. To help address this issue, we propose the Semantic Overlay Architecture (SOyA), a lightweight RDF-centric approach for data interoperability and exchange. SOyA facilitates core functionalities for data interoperability and exchange while reducing the barrier of RDF adoption. Our preliminary evaluations, focusing on roles within Knowledge Graph engineering, show encouraging outcomes.

Current air traffic communication systems are mainly based on voice communication, with a newer digital communication technology called L-band digital aeronautical communication system (LDACS) being investigated in the single European sky air traffic management research project. An essential feature of this communication infrastructure is the encoding of data that guarantees reliable transport. While the encoding in the transmission path is straightforward, the decoding is computationally expensive due to the peculiarities of the convolutional codes used. As the target platform for the communication equipment is an embedded system, a proper system design is essential for the receiving path to ensure real-time processing. This article therefore focuses on the hardware/software codesign of the functional system parts needed for decoding in the LDACS radio receiver. We describe the fundamental design considerations, followed by the actual implementation in form of software and field-programmable gate array based hardware modules. Subsequently, the decoding solution was verified to prove a standard-compliant system. In an experimental validation, the actual system was fed with test data from a reference system. This allows conclusions to be drawn about system characteristics like data throughput, latency, and error correction. The resulting system demonstrates high-performance decoding that can exceed the desired requirements for quality and speed for use in the LDACS communication system.

In many industries, competitors are required to cooperate in order to conduct optimizations, e.g., to solve an assignment problem. For example, in air traffic flow management (ATFM), flight prioritization in case of temporarily reduced capacity of the air traffic network is an instance of the assignment problem. Participants, however, are typically reluctant to share sensitive information regarding their preferences for the optimization, which renders conventional approaches to optimization inadequate. This paper proposes a method for combining genetic algorithms with multi-party computation (MPC) as the basis for building a platform for optimizing the assignment of resources to different agents under the assumption of an honest-but-curious platform provider; the method is illustrated on the ATFM use case. In the proposed method a genetic algorithm iteratively generates a population of candidate solutions to the assignment problem while a Privacy Engine component evaluates the population in each iteration step. The participants’ private inputs are kept from competitors and not even the platform provider knows those inputs, receiving only encrypted input which is processed by MPC nodes in a way that preserves the secrecy of the inputs.

In case of a congestion in the European air traffic network, the Network Manager issues a regulation which causes flights to be allocated a new slot. The current operating procedure is to use a first-planned first-served (FPFS) policy to allocate new slots to the flights. Since different flights have different cost structures, the FPFS policy may not be optimal in terms of overall economic utility for the airspace users. In the SlotMachine project we aim to build a privacy-preserving online marketplace where airspace users can participate in optimization sessions regarding the allocation of ATFM slots to flights. In this paper, we present different options regarding the deployment of the SlotMachine system,and we discuss the question of the market mechanism that the SlotMachine project must tackle.

In case of reduced capacity and congestion at an airport, flights are delayed, which means additional costs for the airlines. The amount of costs incurred by an airline differ between flights and depend on various factors, e.g., passenger compensation and costs for crew replacements. Some flights can wait longer than others before the delay causes significant additional costs. From a global perspective, it would be beneficial to prioritize the flights based on the incurred costs. Airlines, however, will be reluctant to share those costs. Therefore, we propose the SlotMachine system for flight prioritization that keeps confidential inputs from airlines private in an encrypted form that not even the system can read the costs. Using multiparty computation in combination with a heuristic optimization algorithm, the SlotMachine system finds an optimal flight list. A flexible credit system may ensure fairness and equity over time: Airlines may earn credits by accepting additional delay, which can be spent for prioritizing flights in the future.

Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance.

To enable more efficient management of airport departure and landing slots, SlotMachine envisions a new kind of marketplace in air traffic management. The platform will enable a more flexible, fast and scalable semi-automated flight prioritisation process for airlines in a fair and trustworthy way. Built with a privacy-first approach it will protect sensitive airline data from competitors and airport operators but fully unleash the potential of inter-airline slot swapping.

This paper describes how a Service Specification Guideline has been used to define a new Service Specification in the field of 5G drones. Frequentis began developing such a guide back in 2010 during the development of system-wide information management (SWIM) as part of the first joint research project on European air traffic management (SESAR phase 1).The paper discusses how the guideline, which has been evolved over time, was used in the recently published NetworkCoverage Service [1], a general architecture comprising stakeholders, services, interfaces and data models for the automated data exchange between Mobile Network Operators (MNO) and the Unmanned Aircraft System Traffic Management (UTM) ecosystem.A separate section deals with the NetworkCoverage service itself, which provides four-dimensional information about data link conditions along a flight route or in a specific area. It provides information about whether or not the connectivity conditions are good enough for secure and reliable data connectivity that meets a certain level of service and is provided by individual connectivity providers.

A reduction of delay costs can be achieved through more adaptable flight planning, which hinges on accurate prediction of delays. In order to counteract the expected delay of flights, air traffic control may adapt flight plans through slot swapping, opening another runway, or changing the runway configuration, for example. Environmental conditions and external events such as runway and airspace closures may render a flight plan obsolete, which must be taken into account when aiming to reduce delay. Air traffic control must recognize changes in the environment and external events such as runway and airspace closures as early as possible in order to adapt flight plans accordingly and avoid delays. Current systems employed by air traffic control do not sufficiently leverage the multitude of available data for the detection of upcoming congestion and, consequently, flight delays. Therefore, flight plans are not adapted fast enough in air traffic scenarios with potentially high delay. In this paper, we aim to predict the risk class of an air traffic scenario based on the expected cost of the delays, and considering information about environmental conditions and external events. In particular, we present a random forest classifier for Atlanta International Airport, which achieves an accuracy of 82.5% for the highest and thus most important risk classes. The development of similar classifiers for other airports may help air traffic control to more accurately predict scenarios with high congestion, and counteract accordingly in the future.

ATM information providers publish reports and notifications of different types using standardized information exchange models. For a typical information user, e.g., an aircraft pilot, only a fraction of the published information is relevant for a particular task. Filtering out irrelevant information from different information sources is in itself a challenging task, yet it is only a first step in providing relevant information, the challenges concerning maintenance, auditability, availability, integration, comprehensibility, and traceability. This paper presents the Semantic Container approach, which employs ontology-based faceted information filtering and allows for the packaging of filtered information and associated metadata in semantic containers, thus facilitating reuse of filtered information at different levels. The paper formally defines an abstract model of ontology-based information filtering and the structure of semantic containers, their composition, versioning, discovery, and replicated physical allocation. The paper further discusses different usage scenarios, the role of semantic containers in SWIM, an architecture for a semantic container management system, as well as a proof-of-concept prototype. Finally the paper discusses a blockchain-based notary service to realize tamper-proof version histories for semantic containers.

ABSTRACTPilot briefings, in their traditional form, drown pilots in a sea of information. Rather than unfocused swathes of air traffic management (ATM) information, pilots require only the information for their specific flight, preferably with an emphasis on the most important information. In this paper, we introduce the notion of ATM information cubes – in analogy to the well-established concept of Online analytical processing (OLAP) cubes in data warehousing. We propose a framework with merge and abstraction operations for the combination and summarization of the information in ATM information cubes to obtain management summaries of relevant information. To this end, we adopt the concept of semantic data container – a package of data items with a semantic description of the contents. The semantic descriptions then serve to hierarchically organise semantic containers along the dimensions of an ATM information cube. Leveraging this hierarchical organisation, a merge operation combines ATM information from individual semantic containers and collects the data items into composite containers. An abstraction operation summarises the data items within a semantic container, replacing individual data items with more abstract data items with summary information.

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

An enormous wealth of data is being created in our increasingly digitized life and economies, but the majority of this data is either being monopolized or shared for questionable purposes rather than facilitating innovation. One of the main issues that inhibits data reuse and the emergence of vibrant data markets is the lack of standard mechanisms for the execution of controlled transactions between data providers and consumers, including efficient provisioning of data and associated services. Consequently, many potential data providers find it difficult to turn data into viable business models. At the same time, data consumers face challenges accessing heterogeneous data provided via various mechanisms and have limited means to trace data provenance and ensure data quality. Semantic Containers aim to tackle both issues by facilitating controlled transactions through an integrated set of methods and capabilities. Specifically, they package data, semantic descriptions, and processing capabilities into executable and shareable containers. In this paper, we illustrate the concept by means of a pilot project in collaboration with the largest meteorological institute in Austria that illustrates how Semantic Containers can be used to provide Seismic activity data.