Our dependency on the telecommunication infrastructure is continuously increasing, as different infrastructures – such as energy and telecommunication – now have mutual dependencies. This calls for increased monitoring of the fibre network, which is a highly critical part of the infrastructure. State of Polarisation (SoP) of light propagating through fibre transmission systems is impacted by any vibrations and mechanical impacts on the fibre. By continuously monitoring the SoP, any unexpected movements of a fibre along a fibre-path may be traced. Movements may be caused by e.g. work in node-rooms impacting patch-cords, trawlers or other types of sub-sea equipment touching or hooking into sub-sea fibre cables, digging close to a fibre-cable, or geophysical phenomena like earthquakes. In this paper, we describe a low-cost, scalable system for SoP monitoring and give examples of patterns monitored in different types of fibre infrastructures. The monitoring system consists of single-unit rack-mount instruments connected to taps from live optical transmission signals. Each instrument has local storage for 1-2 years of data, and is periodically automatically uploading data to a server for backup and data-access purposes. Examples of observed patterns are impact from a thunderstorm on a Fibre-To-The-Home (FTTH) cable, 50 Hz on a fibre-cable spun around a high-voltage power air-cable, as well as animal impact on a patch-cord.

High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs’ potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients’ privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.