Abstract

Abstract
Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

Abstract
The increasing number of vehicle to grid (V2G) charging stations connected to the electrical grid changes the characteristics of electrical distribution grids. Active power electronics introduces additional capacitance and inductance to the electrical grid and affects the frequency dependent grid impedance. This study shows the impact of a V2G charging station to the frequency dependent grid impedance up to 500 kHz. The LCL filter, the DC link capacitor and inductors cause parallel and series resonances. Resonance frequencies appear in a wide frequency range starting from 500 Hz up to 30 kHz. It is shown that the V2G charger can represent a source of supraharmonic emissions and the importance to consider supraharmonic emissions and the frequency dependent grid impedance to determine the impact of V2G chargers (active power electronics) to the electrical grid is outlined.

Abstract
This paper describes the shared task on Automatic Classification of Literary Epochs (CoLiE) held as a part of the 1st International Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT’23) held at SIGIR 2023. The competition aimed to enhance the capabilities of large-scale analysis and cross-comparative studies of literary texts by automating their classification into the respective epochs. We believe that the competition contributed to the field of information retrieval by exposing the first large benchmark dataset and the first study’s results with various methods applied to this dataset. This paper presents the details of the contest, the dataset used, the evaluation procedure, and an overview of participating methods.

Abstract
The recent increasing interest in the application of radiology contrasting agents to create transparency in biological tissues implies that the diffusion properties of those agents need evaluation. The comparison of those properties with the ones obtained for other optical clearing agents allows to perform an optimized agent selection to create optimized transparency in clinical applications. In this study, the evaluation and comparison of the diffusion properties of gadobutrol and glycerol in skeletal muscle was made, showing that although gadobutrol has a higher molar mass than glycerol, its low viscosity allows for a faster diffusion in the muscle. The characterization of the tissue dehydration and refractive index matching mechanisms of optical clearing was made in skeletal muscle, namely by the estimation of the diffusion coefficients for water, glycerol and gadobutrol. The estimated tortuosity values of glycerol (2.2) and of gadobutrol (1.7) showed a longer path-length for glycerol in the muscle.

Abstract
We propose Soteria, a system for distributed privacy-preserving Machine Learning (ML) that leverages Trusted Execution Environments (e.g. Intel SGX) to run code in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The conducted experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41%, when compared to previous related work. Our protocol is accompanied by a security proof, as well as a discussion regarding resilience against a wide spectrum of ML attacks.