Deutsche Telekom (Germany)

The Copernicus Data Space Ecosystem is the official data platform for the Copernicus Programme’s satellites. CDSE combines instant access to satellite imagery with Application Programming Interfaces and virtual machine processing. Instead of downloading satellite imagery for local computation, CDSE utilizes cloud-optimized files to provide data according to the filtering and processing request of the user, facilitating large-scale scientific analysis. Cloud computing on CDSE eliminates the need for users to rely on their own data infrastructure. The incorporated standards support both Open Science and commercialization of scientific tools and algorithms. CDSE serves all users from beginners to professionals, from the interactive visualization of imagery to custom ML algorithms. Acquiring the skills required to process Earth Observation data is facilitated by the open-source codebase and tutorials. Access to public cloud processing is expected to foster the uptake of Earth Observation across new domains. CDSE now provides the critical mass to serve as a tool for knowledge exchange and to influence commercial and public providers alike to support cloud processing.

As next-generation mobile networks evolve toward 6G and beyond, intelligent semantic-aware networking across heterogeneous environments becomes paramount for real-time, mission-critical analytics. This work introduces a trust-centric networking framework for the future mobile core, built around a novel, multi-dimensional Quality of Trust (QoT) profile. This profile enables applications to specify dynamic trust requirements, allowing for adaptive and trustworthy service orchestration. The framework is also designed with quantum-ready capabilities to ensure robust data protection in future infrastructures. We validate the core principles of our framework on a testbed emulating an Uncrewed Aerial Vehicle (UAV)-assisted disaster-response mission. Using real-world measurements from classical nodes and analytical models for more secure scenarios, we demonstrate that our trust-aware orchestration preserves the critical latency and bandwidth gains of edge offloading while virtually eliminating processing errors from untrusted nodes.

6G aims to provide significant advantages compared to 5G while being designed to be energy-efficient and quantum-safe from the start. Goals for 6G include superior radio performance, ubiquitous connectivity using Satellite (Non-Terrestrial Networks, NTN) and Wi-Fi, native artificial intelligence (AI) support, a modular design enabling delivery of new features in a modular manner and scalable networks for diverse device types. In this paper, we present a modular Non-Access Stratum (NAS) approach allowing new NAS features to be upgraded and deployable without impacting other NAS features that are already deployed. We include a high-level view of the 6G System architecture, and a detailed view of modular NAS protocol stack, including NAS message routing, User equipment (UE) identifiers and NAS security architecture. We conclude with an outlook for future work.

<strong class="journal-contentHeaderColor">Abstract.</strong> Multi-hazard events pose increasingly complex challenges to societies worldwide, as natural hazards interact in cascading and compounding ways that amplify risks beyond individual hazards. Understanding these complex interactions is critical for effective disaster risk management, preparedness, and response strategies. National and international frameworks have increasingly recognised these risk dynamics, most notably the Sendai Framework for Disaster Risk Reduction 2015&ndash;2030. With the Sendai Framework approaching its conclusion, there is a pressing need to address current shortcomings and contribute meaningfully to shaping the next generation of global disaster risk reduction (DRR) frameworks. Acknowledging this need, the 3rd International Conference on Natural Hazards and Risks in a Changing World took place on June 12&ndash;13, 2024, with the objective of strengthening the integration of multi-hazard risk into scientific research and policy practice in support of the Sendai Framework for Disaster Risk Reduction. Here, we document the arc of the scientific discussions held at the conference, synthesise the main findings from sessions, and set forth expert knowledge on how state-of-the-art science can fill gaps outlined by the Sendai Framework Mid Term Review by identifying four perspective themes: (1) assessments and tools for risk understanding and decision-making; (2) complex risk landscapes; (3) emerging technologies for risk and resilience; and (4) multi-level governance for coordinated risk management. Ultimately, there was a strong call from the conference for moving beyond siloed thinking toward greater integration of multi-hazards, vulnerability dynamics, multi-level governance, stakeholder engagement, and scientific disciplines across spatial and temporal dimensions, while recognising that the challenge ahead lies in finding the optimal balance between sufficient integration and manageable complexity. This perspective emphasises that effective DRR must initiate transformative processes to build resilience against increasing global challenges while informing the development of post-2030 frameworks and supporting broader Sustainable Development Goals.

AimTo calculate growth charts for pontocerebellar hypoplasia (PCH) type 2A (PCH2A) and compare them to German reference charts, especially with regard to nutritional aspects.MethodData were gathered from a cohort of patients with genetically confirmed PCH2A, who were predominantly recruited through the German PCH patient network (65 patients [33 female, 32 male] at a mean age of 8 years 7 months). We collected data retrospectively using a parent questionnaire, and from medical records (December 2020–September 2022). Disease‐specific growth charts were prepared from predominantly longitudinal data using the gamlss package implemented in R. Sex‐disaggregated growth charts for PCH2A were compared to German reference data from the Kinder‐ und Jugendgesundheitssurvey (German Health Interview and Examination Survey for Children and Adolescents).ResultsThe height and weight of participants with PCH2A were within the reference range at birth. Mean height was significantly lower at 6 months of age, weight at 3 months, and body mass index at 4 months. These deviations were also mostly significant later on. Head circumference in individuals with PCH2A was significantly below average at birth; all participants showed severe and progressive microcephaly in the further course. Caloric intake was within or above reference values.InterpretationParticipants with PCH2A exhibited progressive microcephaly and frequently failed to thrive. Disease‐specific growth charts are a helpful tool to monitor those with PCH2A.

Deploying Machine Learning (ML) models in wireless networks comes with challenges beyond standard performance evaluation, particularly in their ability to remain reliable under changing network conditions. This study highlights some of these challenges by analyzing how models perform when tested on new radio links after training was completed, revealing significant performance degradation. A testbed was developed to study these effects, capturing real-world variations in wireless environments. Using this setup, the study based on link quality estimation (LQE), that is a critical aspect of network performance, demonstrates how data distribution shifts affect model performance. The findings from this first step emphasize the need for continuous monitoring and adaptation strategies, as well as further research on effectively implementing these methods in wireless networks.

Modern 5G+ network terminals are equipped with multiple access interfaces (e.g. cellular and WiFi), enabling the implementation of multipath protocols, providing means for better network resource utilisation and better Quality of Service (QoS). This paper addresses a critical challenge in multipath network environments: the degradation of application performance and increase in out-of-order packet delivery due to the volatility of wireless paths. The paper presents a new scheduling algorithm, Best Path First (BPF), which leverages a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model. By analysing historical path data, the model predicts the path state, enabling selection of the optimal path in a multipath framework. BPF uses the path time-throughput characteristics (rolling standard variation, path bottleneck queue, path throughput) in real-time to prioritise the path with the highest expected quality, predicting with a 2% Mean Absolute Error (MAE). A Mininet testbed with real mobility traces is used to demonstrate the solution and evaluate its performance. BPF can reduce UDP end-to-end delays and jitter by 20% and 21% respectively when compared to the standard Cheapest-path-first (CPF) multipath scheduler. Tests on TCP traffic demonstrate that BPF improves by 7% and 6% TCP end-to-end delay and jitter and reduces FTP download times by 7% compared to CPF and up to 30% compared to Peekaboo.

5G-and-beyond mobile communication systems can benefit from Virtualized Network Functions (VNFs) when deployed on cloud infrastructures to achieve elasticity and scalability to provide novel services, such as massive MTC for IoT use cases. 3GPP has developed a transition from point-to-point architecture between network entities to service-based architecture. However, new challenges arise regarding managing Network Functions (NFs) states. Especially control plane VNFs, mainly found in cellular core networks like the 5G Core (5GC), received little attention since the shift towards virtualizing NFs. Most existing solutions for these core networks are often complex and intrusive and must comply more with the standard. These devices communicate more deterministically, bringing new opportunities for elaborated state management. This work presents a feasibility study for measurements of state access patterns rigorously. Measurement results provide a solid base for designing an efficient, lightweight state management service toward realizing stateless design for 5G Core’s network functions.

Many state-of-the-art Segment Routing (SR) Traffic Engineering (TE) algorithms rely on Linear Program (LP)-based optimization. However, the poor scalability of the latter and the resulting high computation times impose severe restrictions on the practical usability of such approaches for many use cases. A promising way to address these issues is to preemptively limit the number of SR paths considered during optimization by employing certain preprocessing strategies. In the first part of this paper, we conduct an extensive literature review of such preprocessing approaches together with a large-scale comparative performance study on a plethora of real-world topologies, including recent data from a Tier-1 Internet Service Provider (ISP). In the second part, we then use the insights gained from the former study to develop a novel combined preprocessing approach which also guarantees to not interfere with the satisfiability of practically important latency bound constraints. Our approach is able to reduce the number of SR paths to consider during optimization by as much as 97%–99%, while still allowing to achieve close to optimal solutions. This facilitates an around 10 × speedup for different LP-based SR TE algorithms, which is more than twice as good as what is achievable with any of the previously existing methods. Finally, we also study the applicability of the path preprocessing paradigm to the use case of tactical TE, showing that it facilitates an around 37% speedup in this context as well. All in all, this constitutes a major improvement over the current state-of-the-art and further facilitates the reliable use of LP-based TE optimization for large segment-routed networks.