Abstract
We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

Abstract
This study investigates the fabrication of plasmonic optical fiber sensors for glyphosate detection, employing silver thin film coatings deposited via the Tollens' reaction and further enhanced with protective gold plating. Silver films were produced through electroless deposition, forming rough plasmonic surfaces with localized hotspots that amplify the electromagnetic field. Surface roughness effects on the creation of hotspots were first evaluated numerically using the finite element method (FEM) and later experimentally assessed the impact on optical response. Furthermore, to address the inherent susceptibility of silver to oxidation and corrosion, a gold plating was applied using the Kirkendall effect, selectively replacing surface silver atoms with gold. This approach significantly improved the chemical stability of the sensors while preserving their plasmonic properties. This configuration was applied in developing a biosensor, using aptamers, for detecting glyphosate in concentrations ranging from 10(-1) to 10(4) mu g/L. The results demonstrated a sensitivity of 25.08 +/- 0.22 nm/(mu g/L) and a limit of detection (LOD) of 0.04 mu g/L, nearly ten times lower than the European Union's safety limit for glyphosate. Experimental results highlight the potential of this fabrication approach for developing sensitive, stable, and scalable plasmonic sensors tailored for environmental and agricultural monitoring applications.

Abstract
Underwater wireless communications face significant challenges due to propagation constraints, limiting the effectiveness of traditional radio and optical technologies. Long-range acoustic communications support distances up to a few kilometers, but suffer from low bandwidth, high error ratios, and multipath interference. Semantic communications, which focus on transmitting extracted semantic features rather than raw data, present a promising solution by significantly reducing the volume of data transmitted over the wireless link. This paper evaluates the resilience of SAGE, a semantic-oriented communications framework that combines semantic processing with Generative Artificial Intelligence (GenAI) to compress and transmit image data as textual descriptions over acoustic links. To assess robustness, we use a custom-tailored simulator that introduces character errors observed in underwater acoustic channels. Evaluation results show that SAGE can successfully reconstruct meaningful image content even under varying error conditions, highlighting its potential for robust and efficient underwater wireless communication in harsh environments.

Abstract
Robotic competitions have been popularly applied in the educational context, proving to be an excellent method for fostering student engagement and interest in science, technology, engineering, and math (STEM). In this context, this paper presents the application of mobile robots in a classroom competition, in order to encourage students to enhance mobile robotics concepts learning in a dynamic and collaborative environment. The mobile robot prototyping is presented, and the methodology, including the Hardware-in-the-loop approach applied in the classrooms, is also described, together with the competition rules and challenges proposed for the students. The results indicated an improvement in students’ motivation, teamwork, communication, and the development of technical skills, computational thinking, and problem-solving.

Abstract

Abstract
Graphs are present in various fields of knowledge, representing a wide range of structures and systems, such as transportation networks, pathways, electrical circuits, among others. They are mathematical structures that model relationships between objects through vertices (points of interest) and edges, which represent possible paths between these vertices and may carry weights associated with distances, times, costs, or other relevant metrics. Graphs are widely used in the modeling and effective resolution of route optimization problems, aiming to find the best possible solution by minimizing or maximizing a specific metric. Although end users often do not interact directly with or need to understand their complexity, the benefits produced lead to positive outcomes in the user experience. In urban mobility, especially with electric vehicles, optimization enables the structuring of efficient routes, benefiting both users and the environment by reducing travel time, noise, and pollutant emissions. Recent applications include route planning for agricultural drones and autonomous vehicles, as well as the management of school and industrial transportation, consistently promoting sustainability, efficiency, and cost-effectiveness. Additionally, graphs are used in the Internet of Things, smart networks, and even in aerial systems, optimizing complex operations and enhancing safety and reliability. This paper presents a study aimed at developing a solution capable of addressing the needs of route optimization and providing effective responses to the challenges of urban mobility, considering the specific application context of electric motorbikes used in service delivery.