LINKS Foundation

High-speed rail (HSR) is often considered a promising and sustainable alternative for long-distance travel in the European context, aligned with Europe’s ambitious mobility and climate goals for 2050. However, a cohesive European HSR network is yet to be realised. Critically, the planning of a European HSR network requires considering how the network is to gradually evolve from its current fragmented state. We introduce an Evolutionary Network Growth model with Infrastructure and Network Effects considerations for European Rail (ENGINEER). This novel iterative network growth model selects the HSR infrastructure with the highest economic potential, continuously updating network configurations and demand patterns, subject to budget feasibility constraints. ENGINEER integrates cost estimates based on a microscopic representation and benefits estimated based on a macroscopic travel demand representation and is applied across 28 European countries. Our findings highlight the importance of path dependency and the benefits of an integrated decision-making in infrastructure planning. Model results demonstrate that ENGINEER can effectively identify promising HSR investments, yielding a cohesive and well-integrated European HSR network which leads to an increase in rail mode share per trip from 13% in 2023 to 27% by 2065.

Achieving ubiquitous global connectivity requires integrating satellite and terrestrial networks, particularly to serve remote and underserved regions. In this work, we investigate the design and optimization of Space-Terrestrial Integrated Networks (STINs) using a hybrid quantum-classical approach. We formalize three key combinatorial optimization problems: the Satellite Selection Problem (SSP), the Gateway Selection Problem (GSP), and the Spectrum Assignment Problem (SAP), each capturing critical aspects of network deployment and operation. Leveraging neutral-atom quantum processors, we map the SSP onto a Maximum Weight Independent Set problem, embedding it onto the Aquila platform and solving it via the Quantum Adiabatic Algorithm (QAA). Postprocessing ensures feasible solutions that guide downstream GSP and SAP optimization. Benchmarking across 165 realistic remote regions shows that QAA solutions closely match classical exact solvers and outperform greedy heuristics, while subsequent GSP and SAP outcomes remain largely robust to differences in initial satellite selection. These results demonstrate that quantum optimization achieves performance broadly comparable to classical approaches for end-to-end STIN design, with rare instances where it can even surpass state-of-the-art solvers. This suggests that, while not yet consistently superior, quantum methods may offer competitive advantages for larger or more complex instances of the underlying combinatorial subproblems.

The increasing complexity and capacity demands of modern optical communication networks require advanced tools for predictive maintenance and fault prevention. This study presents a systematic methodology for generating a comprehensive network failure database using Digital Twins (DTs) based on the open-source optical network simulation framework GNPy (Gaussian Noise model in Python). GNPy enables the generation of synthetic datasets to support the development of Machine Learning (ML) models for proactive fault detection and network performance optimization. By modeling a wide range of failure scenarios such as amplifier degradation, span loss variation, and component misconfigurations, GNPy enables detailed analysis of their impact on critical transmission parameters including Generalized Signal-to-Noise Ratio (GSNR), Optical Signal-to-Noise Ratio (OSNR), and channel power. The proposed methodology allows the generation of a diverse and scalable database that reflects both normal and degraded network operating conditions. This synthetic dataset provides valuable input for training ML algorithms to detect subtle performance degradations and predict potential failures before they impact running services. By leveraging this simulation-based data generation approach, network operators can significantly enhance the reliability and resilience of their transport networks. Furthermore, the integration of GNPy with data-driven ML models lays the foundation for intelligent network management systems capable of automated fault prediction and proactive maintenance strategies in future high-capacity optical networks.

Around 30%-40% of intra-datacenter connections still rely on vertical cavity surface emitting lasers (VCSELs) and multi-mode fibers (MMFs), generally legacy OM3 and OM4. However, the next-generation systems will require transmission throughputs above 100G/lane, with targets towards 200 G/lane. An open discussion is undergoing whether VCSEL-MMF technology is still suitable, so in this paper we discuss the challenges and the solutions for high-speed intra-datacenter connections.

Abstract This paper introduces a stateless orchestration architecture and mechanism for managing distributed data pipelines in multi-agent systems. Workflows are decomposed into independent subworkflows by a splitting module. A caching mechanism preserves execution context without relying on local state, supporting true statelessness. Communication is event-driven via Apache Kafka, and a unified data model ensures consistent interaction among components. The architecture is validated on a Kubernetes environment, showing scaling and low-latency features. This approach combines the control of orchestration with the scalability of event-driven systems, offering a robust solution for modular workflow execution.

We present the design and numerical analysis of an asymmetric bow-tie vertical-cavity surface-emitting laser (ABT-VCSEL) that enables PAM-2 direct modulation up to 100 GHz. The device concept relies on photon-photon resonance (PPR) between laterally coupled modes, triggered by a structural asymmetry introduced via a hollowed dielectric mirror. This approach avoids the need for asymmetric pumping and probing while ensuring strong modal coupling under uniform carrier injection. A dynamical in-house suite, combining field-carrier interaction, electromagnetic, and thermal problems, is developed and discussed to evaluate device performance under realistic conditions. The simulation results predict a 3-dB bandwidth up to 70 GHz with low modulation swing, and open eye diagrams at 100 GHz PAM-2 modulation and 70 GHz PAM-4 modulation. The proposed ABT-VCSEL concept shows robustness against geometrical variations and ambient temperature when proper cavity detuning is applied, making it a promising candidate for next-generation ultra-fast short-reach interconnects.

The secure and trustworthy exchange of interoperable data assets is a key enabler for the development of IoT-based data spaces. This paper presents a decentralised framework for offering management and asset sharing within trusted and interoperable data spaces, leveraging Distributed Ledger Technologies (DLTs) and the Self-Sovereign Identity (SSI) paradigm to ensure transparency, integrity, and participant self-sovereignty. The proposed architecture integrates identity management and tamper-resistant smart contracts leveraging the IOTA Tangle to support decentralised offering discovery, access control and verifiable transactions. By addressing critical challenges related to trust, interoperability, and decentralisation, the framework contributes to the technological foundations required for scalable, secure, and resilient data ecosystems.

Decentralised Autonomous Organisations (DAOs) face a recurring challenge in maintaining active, high-quality participation among their members. Voter apathy and strategic abstentions undermine the collective intelligence that DAOs aim to harness. This paper proposes a game-theoretic incentive model, specifically tailored to encourage sustained participation in DAO governance. By combining a reputation-based mechanism with monetary rewards (e.g., token incentives), the model aligns individual actions with the collective good, penalising prolonged inactivity and rewarding long-term collaboration. We describe how key parameters, such as reputation thresholds and rewards for voting, can be calibrated to preserve a DAO’s vitality and avert excessive concentration of power. Simulations in a community-energy scenario demonstrate the feasibility of the approach and provide insights into balancing tolerance for occasional abstentions with the need for consistent governance engagement. Ultimately, our results indicate that with carefully chosen parameters, DAOs can maintain high participation rates and secure more robust decision-making over time.