Denizer, BirkanBirkanDenizerDohse, NilsNilsDohseLandsiedel, OlafOlafLandsiedel2025-10-292025-10-292025-06101st IEEE Vehicular Technology Conference, VTC 2025979-8-3315-3148-5979-8-3315-3147-8https://hdl.handle.net/11420/58351In recent years, the use of machine learning (ML) in cellular networking has increased significantly, enabled by the availability of new ML algorithms and cellular datasets. However, existing 5G datasets focus mainly on land-based vehicular networks, which do not capture the unique challenges of the coastal maritime domain. This includes large distances from base stations, dynamic sea states, such as waves and tides, and varying interference from water surface reflections and nearby vessels. This paper introduces the Fjord5G¹¹https://github.com/ds-kiel/Fjord5G, a 5G dataset for coastal maritime connectivity research. We conduct an extensive measurement campaign aboard research and public ferries in the Kiel Fjord, Germany, collecting GPS-located cellular data along maritime routes. These measurements cover the network conditions encountered in coastal and near-shore regions and provide insights into metrics such as signal strength, modulation, and bandwidth. The resulting dataset includes cellular measurements at a sampling rate of 1 Hz from two mobile network operators, four 5G routers, and two ferries for up to 12 months per router. Initial data analysis reveals key challenges for ML, such as dealing with varying bandwidth and handover events, while highlighting potential features, such as signal strength metrics, that can be exploited to improve coastal maritime connectivity.en5Gautonomous ferrycoastalDatasetLTEmachine learningmaritimeQoS predictionremote controlTechnology::600: TechnologyConference Paper10.1109/VTC2025-Spring65109.2025.11174898Conference Paper