Abstract
Sensing will be a key technology in 6G networks, enabling a plethora of new sensing-enabled use cases. Some of the use cases require deployments over a wide physical area that needs to be sensed by multiple sensing sources at different locations. The efficient management of the sensing resources is pivotal for sustainable sensing-enabled mobile network designs. In this paper, we provide an example of such use case, and argue the energy consumption due to sensing has potential to scale to prohibitive levels. We then propose architectural enhancements to solve this problem, and discuss energy saving and energy efficient strategies in sensing, that can only be properly quantified and applied with the proposed architectural enhancements.

Abstract
Cybercrime is growing rapidly, and it is increasingly important to use advanced tools to combat it and support investigations. One of the battlefronts is the forensic investigation of mobile devices to analyze their misuse and recover information. Mobile devices present numerous challenges, including a rapidly changing environment, increasing diversity, and integration with the cloud/IoT. Therefore, it is essential to have a secure and reliable toolbox that allows an investigator to thwart, discover, and solve all problems related to mobile forensics while deciphering investigations, whether criminal, civil, corporate, or other. In this work, we propose an original and innovative instantiation of a structure in a forensic toolbox for mobile devices, corresponding to a set of different applications, methods, and best practice information aimed at improving and perfecting the investigative process of a digital investigator. To ensure scientific support for the construction of the toolbox, the Design Science Research (DSR) methodology was applied, which seeks to create new and unique artifacts, drawing on the strength and knowledge of science and context. The toolbox will help the forensic investigator overcome some of the challenges related to mobile devices, namely the lack of guidance, documentation, knowledge, and the ability to keep up with the fast-paced environment that characterizes the mobile industry and market. © 2024 by the authors. Licensee ESJ, Italy.

Abstract
Demand Driven Material Requirements Planning argues that production replenishment orders should be scheduled on the shop floor according to the buffers' on-hand inventory. However, the actual performance impact of this remains largely unknown. Using discrete event simulation, this study compares scheduling based on the on-hand inventory, with scheduling based on the inventory net flow position. Results of our study show that scheduling based on the former performs best, particularly when multiple production orders are simultaneously generated and progress independently on the shop floor. Our finds give hints that are important to both, industrial practice and software development for production planning and control.

Abstract
Cyber-resilience is a relatively recent concept that, in essence, adds risk management to the cybersecurity concept and extends the scope of its action to processes and people, in addition to the technological component. It aims to ensure that organizations, systems, and especially critical infrastructures of our society function properly regardless of their dependence on cybernetic resources that may be affected by adverse events. Considering that the ongoing digital transition increases the exposure of such infrastructures to physical and cyberspace threats, this article reports on an exploratory study supported by bibliographical research, which aimed to analyze recent scientific publications to determine the relevance of cyber-resilience in the context of national security and defense. Although the number of publications focused on cyber-resilience is still relatively reduced when compared to the number of publications related to cybersecurity, there is a growing interest in exploring cyber-resilience in areas such as international relations, internal security, and national defense, which are fundamental pillars of the security and defense of States. © 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Radiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis. © The Author(s) 2024.

Abstract
Frequency stability in inverter-based renewable energy sources (RES)-dominated, low-inertia, power systems is a timely challenge. This paper employs a systematic approach, utilizing an artificial neural network (ANN) and dynamic simulation, to infer two key frequency stability indicators: nadir and rate of change of frequency (RoCoF). By reformulating the ANN mathematical model, these indicators are then integrated as mixed-integer non-linear constraints into a classical AC security-constrained optimal power flow (AC SCOPF), resulting in the proposed AC-F-SCOPF problem. The results of the proposed AC-F-SCOPF on the IEEE 39-bus system show that the problem identifies accurately the synchronous condensers which must run to ensure the frequency stability.