## 2 An Overview of the Published Contributions
It presents a comprehensive framework that integrates CPS, service-oriented architectures (SOA) [5], and real-time data acquisition for logistics and asset tracking. The proposed architecture highlights the potential of CPS to transform manufacturing by enabling fully interconnected, autonomous systems capable of real-time decision making. In a detailed review, [PERSON] et al. examine the emerging technologies that are transforming automation in environmental sensing. The authors cover a range of applications, including unmanned aerial systems (UAS) [6], smart agriculture, and environmental robotics, providing a comprehensive analysis of how automation technologies are enhancing data collection and monitoring. Despite significant advancements, the review highlights several challenges, including interoperability, system scalability, and cost, suggesting the need for continued research in these areas to fully realize the potential of automation in environmental sensing. [PERSON] et al. contribute a context-aware system for managing information flow in smart factories. Their work introduces the \"Context Engine\", a microservices-based application designed to enhance decision-making and alert systems within factories of the future. The system enables real-time monitoring and contextualized responses to production changes, offering a flexible and scalable solution that can integrate with various factory applications and devices. The case studies provided in this work demonstrate the practical application of the context engine in optimizing factory operations and supporting worker decision making. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Contribution** & **Research Area** & **Focus** & **Research Type** & **Organization/Industry** \\ \hline \multirow{6}{*}{Contribution 5: [PERSON] et al. (2024)} & & Review of techniques & & \\  & & such as EEG and & & \\  & & reinforcement learning & & \\  & & to enhance & Review & \\  & & human–robot & & Industrial and Medical \\  & & collaboration and & & \\  & & safety & & \\ \hline \multirow{6}{*}{Contribution 6: [PERSON] et al. (2024)} & & Federated & & \\  & & reinforcement learning & & \\  & & for optimizing accuracy & & \\  & & and energy & & \\  & & consumption in & & \\  & & heterogeneous & & \\  & & industrial IoT networks & & \\ \hline \multirow{6}{*}{Contribution 7: [PERSON] and [PERSON] (2024)} & & Comprehensive review & & \\  & & of traffic modeling & & \\  & & approaches relevant to & & \\  & & autonomous vehicles, & & \\  & & including AV–human & & \\  & & interaction and mixed & & \\  & & traffic modeling & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._[PERSON] et al. provide a comprehensive review of techniques for enhancing robot behavior in collaborative robotics, focusing on the integration of EEG data and reinforcement learning. The paper highlights the growing importance of human-robot collaboration (HRC) in industrial settings and examines how brain activity monitoring and reinforcement learning can be applied to improve robot decision making and safety in real-time interactions with humans. This review also outlines the future research directions necessary to advance HRC, particularly in terms of ensuring intuitive and emotionally aware interactions between robots and human operators. [PERSON] et al. address the optimization of energy consumption and accuracy in industrial IoT systems through an innovative Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) approach. Their work proposes a predictive weighted average aggregation technique that improves the performance of federated learning models in heterogeneous IoT environments. The ability to dynamically manage resource-constrained devices and maintain high levels of accuracy under non-ideal conditions is a significant step toward making federated learning feasible for real-world industrial applications. Finally, [PERSON] and [PERSON] present a comprehensive review of traffic modeling techniques relevant to autonomous vehicles (AVs).