Abstract
In this paper, it is presented an integration between a finishing device and a collaborative robot in order to automate the sanding process of a ceramic industry in Portugal. The finishing device and the collaborative robot are described as well as the communication between the devices. It was used a computer responsible for decision making and control of all the system. The system was able to control the position of the finishing device according to the force done in the sponge by the ceramic. The final system behavior was presented and discussed, which was satisfactory and performed well. The presented experimental setup is not intended for industrial use, but it is suitable for concept prove, in laboratory. The outputs that will emerge here will be applied in a future industrial application, with requisites compatible with the application environment, regarding robustness and repeatability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
This paper presents an approach for a multi-agent-based cyber-physical system dedicated to operating the warehouse plant with a distributed approach. The recent technological evolution has improved the quality and robustness of the services for current warehouses. However, systems that operate warehouses do not follow this evolution, presenting predominantly central monolithic or hierarchical approaches, resulting in fragility related to flexibility, scalability, and robustness in the face of disturbances. In the proposed approach, each warehouse physical component has a computational unit associated, i.e. a cyber agent, with communication, negotiation, and data analysis capabilities. Agents contain all the information, algorithms, and functions necessary to operate the physical component, and instead of receiving orders from higher-layer agents, they negotiate and collaborate to perform the tasks. The proposed system was tested in a laboratory testbed, composed of six racks and up to eight robots for transporting products. Extensive experiments show the feasibility of the approach. Copyright (c) 2023 The Authors.

Abstract
[No abstract available]

Abstract
The scoping review reported by this article aimed to analyze and synthesize state-of-the-art studies focused on the integration of cyber resilience in the implementation of smart cities. An electronic search was conducted, and 11 studies were included in this review after the selection process. According to the findings, cyber resilience represents a gap of the current research related to smart cities and, therefore, additional efforts are required to guarantee that smart cities are resilient to challenging events such as cyber-attacks or natural disasters. © 2023 ITMA.

Abstract
Several systems that employ machine learning models are subject to strict latency requirements. Fraud detection systems, transportation control systems, network traffic analysis and footwear manufacturing processes are a few examples. These requirements are imposed at inference time, when the model is queried. However, it is not trivial how to adjust model architecture and hyperparameters in order to obtain a good trade-off between predictive ability and inference time. This paper provides a contribution in this direction by presenting a study of how different architectural and hyperparameter choices affect the inference time of a Convolutional Neural Network for network traffic analysis. Our case study focus on a model for traffic correlation attacks to the Tor network, that requires the correlation of a large volume of network flows in a short amount of time. Our findings suggest that hyperparameters related to convolution operations-such as stride, and the number of filters-and the reduction of convolution and max-pooling layers can substantially reduce inference time, often with a relatively small cost in predictive performance.

Abstract
Public lighting plays a very important role for society's safety and quality of life. The identification of faults in public lighting is essential for the maintenance and prevention of safety. Traditionally, this task depends on human action, through checking during the day, representing expenditure and waste of energy. Automatic detection with deep learning is an innovative solution that can be explored for locating and identifying of this kind of problem. In this study, we present a first approach, composed of several steps, intending to obtain the segmentation of public lighting, using Seville (Spain) as case study. A dataset called NLight was created from a nighttime image taken by the JL1-3B satellite, and four U-Net and FPN architectures were trained with different backbones to segment part of the NLight. The U-Net with InceptionResNetv2 proved to be the model with the best performance, obtained 761 of 815, correct locations (93.4%). This model was used to predict the segmentation of the remaining dataset. This study provides the location of lamps so that we can identify patterns and possible lighting failures in the future.