Abstract
Geophysical studies on the lunar surface have, in the past, used various methods that contribute not only towards the knowledge of the lunar subsurface but also contribute towards the design of future lunar missions, namely those that will, in the near future, take humans to the Moon’s surface. This work analyzes a specific set of ground penetrating radar (GPR) data, collected during the Chang’E-4 mission of the Chinese Space Agency, using theYutu-2 rover within the von Kármán crater, on the far-side of the Moon. From this dataset two electrical parameters were estimated. The approach uses the backscatter of the electromagnetic wavefield in order to obtain estimates of the real component of the complex relative permittivity as well as the electrical resistivity. © 2024 International Multidisciplinary Scientific Geoconference. All rights reserved.

Abstract
The Ambient Assisted Living (AAL) systems are human-centered and designed to prioritize the needs of elderly individuals, providing them with assistance in case of emergencies or unexpected situations. These systems involve caregivers or selected individuals who can be alerted and provide the necessary help when needed. To ensure effective assistance, it is crucial for caregivers to understand the reasons behind alarm triggers and the nature of the danger. This is where an explainability module comes into play. In this paper, we introduce an explainability module that offers visual explanations for the fall detection module. Our framework involves generating anchor boxes using the K-means algorithm to optimize object detection and using YOLOv8 for image inference. Additionally, we employ two well-known XAI (Explainable Artificial Intelligence) algorithms, LIME (Local Interpretable Model) and Grad-CAM (Gradient-weighted Class Activation Mapping), to provide visual explanations.

Abstract
This paper explores the internationalisation of small and medium-sized firms (SMEs) in Africa and Latin America. A total of 97 papers covering the period between 1995 and 2017 were analysed, providing a unique comparative perspective of the internationalisation of SMEs. The analysis of the papers revealed the following six main topics: international networking; financing, export promotion; internationalisation strategies; resources and business environment/context; e-business, e-commerce; and barriers to internationalisation. The topic 'internationalisation strategies' is the most researched topic both regarding the internationalisation of both African and Latin American SMEs. However, while the studies on Latin American SMEs focus on rapid internationalisation, international entrepreneurship orientation and export performance, the studies on African SMEs focus on supply performance, international behaviour, internationalisation process, knowledge and key-selection of foreign markets. This provides a clear perspective on how SMEs of those two emerging continents deal with the intricacies of internationalisation.

Abstract
The Czech Republic is home to the Hranice Abyss, the world's deepest natural underwater cave, a site extensively explored by a dedicated team of divers from a speleology group. Over the years, numerous studies have been conducted to unravel the cave's mysteries, delving into fields such as biology, hydrogeology, and geology. Mapping a cave of such vast dimensions and staggering depth poses formidable challenges, making the task hazardous, demanding, and timeintensive for a limited team of divers. In July 2022, the UNEXUP project was invited to explore and map the cave with its robot (UX1-neo), which contains many acoustic and optical sensors, used for navigation, localization, and mapping. Its unique control and dynamics allow the robot to successfully navigate through caves and flooded mines. This paper delves into the specifics of the six days of mission dives, offering insights into the mapping process, and presenting some of the results obtained from the entire cave.

Abstract
Raw materials are essential for Europe’s industrial base, as they are used to produce vital goods and technologies. The European Comission’s assessment identifies lithium and tin as critical and strategic raw materials due to rising demand. A PhD thesis aims to create a 3D geological model of the Argemela District in Central Portugal to provide vital information about the genesis of the hydrothermal tin and lithium mineralizations founded in the region. The study places emphasis on the very-low-frequency (VLF) electromagnetic method as a tool to provide information about the mineralization, lithologic contacts, and structural features that can be related to the mineralizations. Argemela district has two main areas, the Argemela Tin and Lithium Mine and the Argemela Hill Top. VLF data was collected and analyzed, showing that low resistivity may be associated with mineralization in the Argemela Mine, while high resistivity may be linked to the Argemela microgranite in the Argemela Hill Top. This geophysical method is effective in non-invasively mapping subsurface features, assisting in the development of a comprehensive 3D geological model and enhancing resource evaluation.

Abstract
Chest X-Ray (CXR), plays a vital role in diagnosing lung and heart conditions, but the high demand for CXR examinations poses challenges for radiologists. Automatic support systems can ease this burden by assisting radiologists in the image analysis process. While Deep Learning models have shown promise in this task, concerns persist regarding their complexity and decision-making opacity. To address this, various visual explanation techniques have been developed to elucidate the model reasoning, some of which have received significant attention in literature and are widely used such as GradCAM. However, it is unclear how different explanations methods perform and how to quantitatively measure their performance, as well as how that performance may be dependent on the model architecture used and the dataset characteristics. In this work, two widely used deep classification networks - DenseNet121 and ResNet50 - are trained for multi-pathology classification on CXR and visual explanations are then generated using GradCAM, GradCAM++, EigenGrad-CAM, Saliency maps, LRP and DeepLift. These explanations methods are then compared with radiologist annotations using previously proposed explainability evaluations metrics - intersection over union and hit rate. Furthermore, a novel method to convey visual explanations in the form of radiological written reports is proposed, allowing for a clinically-oriented explainability evaluation metric - zones score. It is shown that Grad-CAM++ and Saliency methods offer the most accurate explanations and that the effectiveness of visual explanations is found to vary based on the model and corresponding input size. Additionally, the explainability performance across different CXR datasets is evaluated, highlighting that the explanation quality depends on the dataset's characteristics and annotations.