Abstract
Geospatial data acquisition methods like airborne LiDAR allow for obtaining large volumes of data, such as aerial and satellite imagery, which are increasingly being used in archaeology. As in other subjects, the ability to produce raw datasets far exceeds the capacity of domain experts to process and analyze them, but recent developments in image processing, Geographic Information Systems (GIS), Machine Learning (ML) and related technologies enable the transformation of large volumes of data into useful information. However, these technologies are challenging to use and not designed to interact with each other. Hence, tools are needed to efficiently manage, share, document, and reuse archaeological data. This article presents the Odyssey SDI platform, a spatial data infrastructure for annotating, validating, and visualizing data about archaeological sites. This platform is built upon GeoNode, and special-purpose modules were developed for dealing with archaeological information. The main contribution is the integration of remote sensing, GIS features and ML algorithms in a single framework. © 2024 The Author(s).

Abstract
We live in an era where robotics and IoT represent a significant transition towards a unified and automated world. Nonetheless, this convergence faces challenges, including system compatibility and device interoperability. The lack of flexibility of conventional robotic architectures amplifies these obstacles, highlighting the urgency for solutions. Furthermore, the complexity of adopting new technologies can be overwhelming. To address these challenges, this article features a Robot Operating System (ROS2)-centered middleware, referred to as Gateway since it applies the concept of a gateway, designed to ease the robot integration. Focusing on the payload module and fostering several types of external communication, it enhances modularity and interoperability. Developers can select payloads and communication modes through a console, which the middleware subsequently configures, guaranteeing flexibility. The goal is to highlight this middleware's potential to overcome robotics limitations, allowing a flexible integration of robots. This work contributes to the Internet of Robotic Things (IoRT) matter, underscoring the importance of modular payload engineering and interoperable communication in robotics and IoT.

Abstract
Continuous monitoring of hydrogen (H2) concentration is critical for safer use, which can be done using optical sensors. Palladium (Pd) is the most commonly used transducer material for this monitoring. This material absorbs H2 leading to an isotropic expansion. This process is reversible but is affected by the interaction with interferents, and the lifetime of Pd thin films is a recurring issue. Fiber Bragg Grating (FBG) sensors are used to follow the strain induced by H2 on Pd thin films. In this work, it is studied the stability of Pd-coated FBGs, protected with a thin Polytetrafluoroethylene (PTFE) layer, 10 years after their deposition to assess their viability to be used as H2 sensors for long periods of time. It was found that Pd coatings that were PTFE-protected after deposition had a longer lifetime than unprotected films, with the same sensitivities that they had immediately after their deposition, namely 23 and 10 pm/vol% for the sensors with 150 and 100 nm of Pd, respectively, and a saturation point around 2 kPa. Furthermore, the Pd expansion was analyzed in the presence of H2, nitrogen (N2), carbon dioxide (CO2), methane (CH4) and water vapor (H2O), finding that H2O is the main interferent. Finally, an exhaustive test for 90 h is also done to analyze the long-term stability of Pd films in dry and humid environments, with only the protected sensor maintaining the long-term response. As a result, this study emphasizes the importance of using protective polymeric layers in Pd films to achieve the five-year lifetime required for a real H2 monitoring application.

Abstract
Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.

Abstract
In an era characterized by extensive use of and reliance on information and communications technology (ICT), cyber-physical power systems (CPPSs) have emerged as a critical integral of modern power infrastructures, providing vital energy sources to consumers, communities, and industries worldwide. The integration of ICT in these systems, while beneficial, introduces a rapidly evolving range of cybersecurity challenges that significantly threaten their confidentiality, integrity, and availability. To address this, our article offers a comprehensive and timely survey of the current landscape of cyber vulnerabilities in CPPS, reflecting the latest developments in the field up to the present. This includes an in-depth analysis of the diverse types of cyber threats to CPPS and their potential consequences, underscoring the necessity for a broad, multidisciplinary approach. Our review is distinguished by its thoroughness and timeliness, covering recent research to offer one of the most current overviews of cybersecurity in CPPSs. We adopt a holistic perspective, integrating technical, societal, environmental, and policy implications, thereby providing a more comprehensive understanding of cybersecurity in CPPSs. We delve into the complexities of cyberattacks, exploring sophisticated, targeted attacks alongside common threats, and emphasize the dynamic nature of cyber threats, providing insights into their evolution and future trends. Additionally, our review highlights critical yet often overlooked challenges, such as system visibility and standardization in security protocols, arguing their significance in enhancing CPPS resilience. Furthermore, our work gives special attention to the aspects of restoration and recovery postcyberattack, an area less emphasized in the existing literature. Through this comprehensive overview of the current state and evolving challenges of CPPS security, our article serves as an indispensable resource for research, practice, and policymaking dedicated to safeguarding the safety, reliability, and resilience of ICT-empowered energy systems.

Abstract
The early and accurate detection and the grading characterization of brain cancer will generate a positive impact on the treatment plan of those patients. AI-based models can help analyze the Magnetic Resonance Imaging (MRI) to make an initial assessment of the tumor grading. The objective of this work was to develop an Al-based model to classify the grading of the tumor using the MRI. Two regions of interest were explored, with several levels of complexity for the neural network architecture, and Iwo strategies to deal with Unbalanced data. The best results were obtained for the most complex architecture (Resnet50) with a combination of weighted random sampler and data augmentation achieving a balanced accuracy of 62.26%. This work confirmed that complex problems required a more dense neural network and strategies to deal with the unbalanced data.