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. Doi: 10.28991/ESJ-2024-08-03-011 Full Text: PDF

Abstract
The growing demand for sustainable and environment-friendly energy sources resulted in extensive research in the field of renewable energy. Biomass, derived from organic materials such as agricultural waste, forestry products, and wastewater treatment plant (WWTP) sludge, holds great potential as a renewable energy resource that can reduce greenhouse gas emissions and offer sustainable solutions for energy production. This study focused on diverse biomass materials, including sludge from WWTPs, forest biomass, swine waste, cork powder, and biochar. Chemical and physicochemical characterizations were performed to understand their energy potential, highlighting their elemental composition, proximate analysis, and calorific values. Results showed that different biomasses have varying energy content, with biochar and cork powder emerging as high-energy materials with net heating values of 32.56 MJ/kg and 25.73 MJ/kg, respectively. WWTP sludge also demonstrated considerable potential with net heating values of around 14.87 MJ/kg to 17.44 MJ/kg. The relationships between biomass compositions and their heating values were explored, indicating the significance of low nitrogen and sulphur content and favourable carbon, hydrogen, and moisture balances for energy production. Additionally, this study looked into the possibility of mixing different biomasses to optimize their use and overcome limitations like high ash and moisture contents. Mixtures, such as 75% Santo Emiliao WWTP Sludge + 25% Biochar, showed impressive net heating values of approximately 21.032 MJ/kg and demonstrated reduced emissions during combustion. The study's findings contribute to renewable energy research, offering insights into efficient and sustainable energy production processes and emphasizing the environmental benefits of biomass energy sources with low nitrogen and sulphur content.

Abstract
In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. © 2024 by the authors.

Abstract
Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://git hub.com/ipleiria-robotics/maritime-metadata-adaptation.

Abstract
This work addresses the contemporary challenges in computing, caused by the stagnation of Moore's Law and Dennard scaling. The shift towards heterogeneous architectures necessitates innovative compilation strategies, prompting initiatives like the Multi-Level Intermediate Representation (MLIR) project, where progressive code lowering can be achieved through the use of dialects. Our work focuses on developing an MLIR dialect capable of representing streaming data accesses to memory, and Single Instruction Multiple Data (SIMD) vector operations. We also propose our own Structured Representation Language (SRL), a Design Specific Language (DSL) to serve as a precursor into the MLIR layer and subsequent inter-operation between new and existing dialects. The SRL exposes the streaming and vector computational concepts to a higher-level, and serves as intermediate step to supporting code generation containing our proposed dialect from arbitrary input code, which we leave as future work. This paper presents the syntaxes of the SRL DSL and of the dialect, and illustrates how we aim to employ them to target both General-Purpose Processors (GPPs) with SIMD co-processors and custom hardware options such as Field-Programmable Gate Arrayss (FPGAs) and Coarse-Grained Re-configurable Arrays (CGRAs).

Abstract
Hardware specialization is seen as a promising venue for improving computing efficiency, with reconfigurable devices as excellent deployment platforms for application-specific architectures. One approach to hardware specialization is via the popular RISC-V, where Instruction Set Architecture (ISA) extensions for domains such as Edge Artifical Intelligence (AI) are already appearing. However, to use the custom instructions while maintaining a high (e.g., C/C++) abstraction level, the assembler and compiler must be modified. Alternatively, inline assembly can be manually introduced by a software developer with expert knowledge of the hardware modifications in the RISC-V core. In this paper, we consider a RISC-V core with a vectorization and streaming engine to support the Unlimited Vector Extension (UVE), and propose an approach to automatically transform annotated C loops into UVE compatible code, via automatic insertion of inline assembly. We rely on a source-to-source transformation tool, Clava, to perform sophisticated code analysis and transformations via scripts. We use pragmas to identify code sections amenable for vectorization and/or streaming, and use Clava to automatically insert inline UVE instructions, avoiding extensive modifications of existing compiler projects. We produce UVE binaries which are functionally correct, when compared to handwritten versions with inline assembly, and achieve equal and sometimes improved number of executed instructions, for a set of six benchmarks from the Polybench suite. These initial results are evidence towards that this kind of translation is feasible, and we consider that it is possible in future work to target more complex transformations or other ISA extensions, accelerating the adoption of hardware/software co-design flows for generic application cases.