Sebastian Raubitzek

Sebastian Raubitzek, Werner Strasser, Sebastian Ramacher et al.

The European Union is developing the European Quantum Communication Infrastructure (EuroQCI) as a pan-European network to provide secure communication capabilities across Member States, including governmental and critical-infrastructure domains. While the strategic objective is defined at EU level, the required scale and structure of national quantum key distribution (QKD) networks remain largely unspecified. This work addresses the question of how to plan and size national terrestrial QKD networks to support critical infrastructure and public authorities. We propose a reproducible planning methodology that estimates network size, total fiber length, and the number of required QKD components based on a small set of explicit assumptions. The approach is demonstrated for Austria, where a synthetic but structured network model is constructed and evaluated using Monte Carlo simulation. The model focuses on terrestrial QKD infrastructure and explicitly excludes space-based segments. It estimates endpoint counts, trusted repeater node requirements, and hop-length distributions under realistic operational constraints. The Austrian case is then used as a baseline to derive scaling rules for other EU Member States based on population and geographic extent. The results provide first-order planning estimates for national QKD backbone sizes across Europe. These estimates are not intended as deployment designs but as planning-level references that support early-stage cost assessment and infrastructure dimensioning under the EuroQCI framework.

Georg Goldenits, Philip Koenig, Sebastian Raubitzek et al.

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper investigates the feasibility of small language models (SLMs) for detecting phishing websites using only their raw HTML code. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used Small Language Models (SLMs), ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption, demonstrating that while SLMs underperform compared to state-of-the-art proprietary LLMs, they can still provide a viable and scalable alternative to external LLM services. By presenting a comparative analysis of costs and benefits, this work lays the foundation for future research on the adaptation, fine-tuning, and deployment of SLMs in phishing detection systems, aiming to balance security effectiveness and economic practicality.

Georg Goldenits, Kevin Mallinger, Sebastian Raubitzek et al.

Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited compared to other industries. Meanwhile, machine learning, particularly reinforcement learning, has shown potential in agricultural applications like optimizing decision-making, task automation, and resource management. A key aspect of Digital Twins is representing physical assets or systems in a virtual environment, which aligns well with reinforcement learning's need for environment representations to learn the best policy for a task. Reinforcement learning in agriculture can thus enable various Digital Twin applications in agricultural domains. This review aims to categorize existing research employing reinforcement learning in agricultural settings by application domains like robotics, greenhouse management, irrigation systems, and crop management, identifying potential future areas for reinforcement learning-based Digital Twins. It also categorizes the reinforcement learning techniques used, including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic algorithms, to overview currently employed models. The review seeks to provide insights into the state-of-the-art in integrating Digital Twins and reinforcement learning in agriculture, identifying gaps and opportunities for future research, and exploring synergies to tackle agricultural challenges and optimize farming, paving the way for more efficient and sustainable farming methodologies.