Abstract
This paper proposes a novel approach for the provision of non-frequency ancillary service (AS) by consumers connected to low-voltage distribution networks. The proposed approach considers an asymmetric pool-based local market for AS negotiation, allowing consumers to set a flexibility quantity and desired price to trade. A case study with 98 consumers illustrates the proposed market-based non-frequency AS provision approach. Also, three different strategies of consumers' participation are implemented and tested in a real low-voltage distribution network with radial topology. It is shown that consumers can make a profit from the sale of their flexibility while contributing to keeping the network power losses, voltage, and current within pre-defined limits. Ultimately, the results demonstrate the value of AS coming directly from end-users.

Abstract
Real Estate Agents perform the tedious job of selecting and filtering pictures of houses manually on a daily basis, in order to choose the most suitable ones for their websites and provide a better description of the properties they are selling. However, this process consumes a lot of time, causing delays in the advertisement of homes and reception of proposals. In order to expedite and automate this task, Computer Vision solutions can be employed. Deep Learning, which is a subfield of Machine Learning, has been highly successful in solving image recognition problems, making it a promising solution for this particular context. Therefore, this paper proposes the application of Vision Transformers to indoor room classification. The study compares various image classification architectures, ranging from traditional Convolutional Neural Networks to the latest Vision Transformer architecture. Using a dataset based on well-known scene classification datasets, their performance is analyzed. The results demonstrate that Vision Transformers are one of the most effective architectures for indoor classification, with highly favorable outcomes in automating image recognition and selection in the Real Estate industry.

Abstract

Abstract

Abstract
The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of indus-tries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capa-ble of identifying certain patterns which can represent a mal-function or deterioration in the monitored machines. There-fore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This pa-per introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and wash-ing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home ap-pliances at a repair center and included readings of elec-trical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An ex-tracted features dataset, corresponding to the collected work-ing cycles is also made available. This dataset could bene- fit research and development of AI systems for home ap-pliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption pat-terns of such home appliances.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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.