Abstract
On a CPU-FPGA system, C/C++ applications are typically accelerated by offloading specific code regions onto the FPGA using High-level Synthesis (HLS). Although modern FPGAs can implement increasingly large and complex designs, the size and variety of potential offloading code regions remain constrained by the limitations of HLS tools (e.g., no support for dynamic memory allocation and system calls). This paper proposes automated C/C++ source-to-source transformations that tackle these limitations in two steps. Firstly, transformations reduce the entropy of an input C/C++ application by converting it into a subset of C, e.g., by flattening arrays and structs. Secondly, additional transformations make a selected code region synthesizable, e.g., by moving dynamic memory allocations out of the region, converting them to static memory, and offloading non-synthesizable C standard library calls, such as printf(), to the CPU. We evaluate the impact of these transformations showing results obtained through Vitis HLS for four real-world examples: the disparity and texture-synthesis benchmarks from CortexSuite, which contain dynamic memory allocations and indirect pointers in their hotspots; llama2, a Large Language Model that calls printf() every time it predicts a new word; and the spam-filter benchmark from Rosetta, as a debugging showcase.

Abstract
The aggressive increase in the pervasiveness of Internet of Things (IoT) technologies has introduced substantial security challenges, particularly regarding device authentication. In this scenario, covert channels are increasingly being used to support malware with stealthy behaviors in IoT deployments, aiming at exfiltrating data or secretly orchestrating botnets. Although concerning, such covert communication strategies can be harnessed in a beneficial way, supporting lightweight message authentication mechanisms which can not only remain undetected to the prying eye, but also mitigate any kind of ill intended exploitation of such covert techniques. In parallel, these techniques can become an effective alternative, particularly where traditional cryptographic approaches often struggle due to resource constraints, protocol diversity, and the risk of protocol-level observation or manipulation. In this work, we present a novel authentication architecture that leverages multi-layer covert channels to embed authentication techniques within seemingly benign IoT communications. Our approach dynamically injects and modifies network traffic at runtime using Frida, a powerful dynamic instrumentation toolkit, enabling covert embedding of authentication data without requiring changes to the IoT application's codebase.

Abstract
Soil moisture plays a central role in agricultural sustainability and water-resource management under climate change and increasing water scarcity. Remote-sensing technologies have transformed soil-moisture estimation by enabling large-scale, high-resolution, and continuous monitoring. Following the PRISMA framework, this systematic review analyzes 64 studies published between 2016 and 2024, selected from 379 screened articles, focusing on agricultural applications. Remote-sensing data span optical, thermal, and microwave observations from satellites and unmanned aerial vehicles (UAVs), with estimation approaches classified as empirical, semi-empirical, physical, or learning-based. Satellite observations dominate the literature (73% of studies), while UAVs are increasingly used for high-resolution, site-specific assessments. Multi-sensor fusion, combining optical, thermal, and microwave data, is a growing strategy to overcome the limitations of individual sensors. Active SAR systems provide weather-independent measurements with high spatial resolution, whereas optical and thermal sensors offer valuable spectral indices but are limited by cloud cover and shallow penetration depth. Learning-based methods are the most frequent approach (54% of studies), using machine and deep learning to model complex relationships between soil moisture and remote-sensing variables. Principal challenges include vegetation interference, surface roughness, and limited in-situ calibration data. Mitigation strategies involve longer-wavelength SAR (L-and P-bands), multi-sensor fusion, downscaling, and integration of auxiliary datasets (soil texture, elevation, meteorology). By synthesizing recent advances and emerging trends, this review provides practical guidance for accurate, scalable, and operational soil-moisture monitoring in precision agriculture and environmental management.

Abstract
This paper introduces a comprehensive methodology for day-ahead planning of renewable energy systems geared toward green hydrogen and ammonia production. This approach is a forecasting algorithm that uses synthetic data, which feeds a short-term load forecasting (STLF) algorithm to predict the 24-hour hydrogen demand. This capability enables the optimization of hourly system operations, with the goal of maximizing profitability while maintaining system efficiency. The case study presented includes a renewable energy source - photovoltaic power plant (PV) - and a grid connection, which supply power to an electrolyser. Essential supporting infrastructure such as the auxiliary system of the electrolyser is incorporated into the model. Additionally, an electrochemical battery - a battery energy storage system (BESS) - is incorporated, which helps to keep a high electrolyser load factor and creates smoother operating profiles. This BESS also allows the system to contribute to the energy reserves market, enhancing its economic and operational viability.

Abstract
This paper explores the main challenges and barriers to VR/AR adoption and categorizes common activities performed with these technologies, explaining each specific factor affecting them. After reviewing literature on metaverse retailing, channel strategies, VR/AR technologies, and user experiences, a conceptual framework was developed. Data from the Voice of the Consumer: Digital Survey (20202024) in over 20 countries was analyzed, using Pearson's correlation, factor analysis, and multiple linear regressions. The results point that key challenges for VR/AR adoption include security, privacy, content, price, headset-free experiences, digital fatigue, and poor experiences. Gaming is the most common VR/AR activity, while metaverse retailing activities like shopping and virtual try-ons have fewer users. Practical considerations drive metaverse retailing, unlike gaming, which is mainly hedonic. Privacy concerns, safety risks, poor experiences, and lack of knowledge surprisingly increase VR/AR usage for metaverse retailing, indicating informed consumers or threshold characterization of these variables. Additional insights were found for tourism, hospitality, and gaming activities. Theoretical implications, insights, and potential actions for retailers and tech companies are discussed, along with limitations and suggestions for further research.

Abstract
This work proposes a robust methodology for the location and sizing of grid forming (GFM) converters that simultaneously considers the solution costs and the security gains while accounting for the TSO nonlinear cost-security sensitivity. Such methodology, which includes a collection of techniques to reduce the problem dimensionality, formulates the placement problem as a non-linear multi-criteria decision support problem and uses a solution-seeking algorithm based on Bayesian Optimisation to determine the solution. To ease comprehension, a modified version of the IEEE 39 Test System is used as a case study throughout the method's detailed explanation and application example. A sensitivity analysis of the GFM converter's over-current capacity in the solution of the formulated placement problem is also performed. The results show that the proposed method is successful in finding solutions with physical meaning and that respect the decision agent preferences.