Naudts, DriesDriesNaudtsMaglogiannis, VasilisVasilisMaglogiannisGavrielides, AndreasAndreasGavrielidesMiranda, GilsonGilsonMirandaCheng, JianqiaoJianqiaoChengGomez, Carlos TianaCarlos TianaGomezMarquez-Barja, JohannJohannMarquez-BarjaMoerman, IngridIngridMoerman2026-04-142026-04-142025979-8-3503-9181-72475-6490https://imec-publications.be/handle/20.500.12860/59078This paper presents a comprehensive assessment of 5G SA networks deployed in a factory setting and an offshore wind farm at the North Sea. The study focuses on critical use cases such as teleoperated Automatic Guided Vehicles (AGVs) in factories and teleoperated Unmanned Surface Vessels (USVs) in offshore settings. Key performance indicators (KPIs) such as network latency, data rates, and signal quality were measured using advanced network evaluation tools. The results demonstrate that 5G SA networks can meet the stringent requirements of industrial applications. The paper also discusses the challenges of deploying 5G networks in metal-dense factories and dynamic offshore environments, highlighting the importance of practical evaluations in these settings. Furthermore, the study introduces a machine learning-driven framework for signal and Quality of Service (QoS) prediction, leveraging diverse datasets collected in the factory environment. This framework aims to optimize network performance and lays the groundwork for creating a digital twin for factory communication systems.engProceedings paper10.1109/eucnc/6gsummit63408.2025.11037210WOS:001550976800079