Abstract
The increasing number of IoT devices and digital services offers cross-domain sensing and control opportunities to a growing set of stakeholders. The provision of cross-domain digital services requires interoperability as a key enabler to bridge domain specifics, while inferring knowledge and allowing new data-driven services. This work addresses H2020 InterConnect project's Interoperability Framework, highlighting the use of semantic web technologies. The interoperability framework layering is presented, particularly addressing the Semantic Interoperability layer as its cornerstone to build an interoperable ecosystem of cross-domain digital services via a federation of distributed knowledge bases. Departing from a generic, ontology-agnostic approach that can fit any cross-domain use case, it validates the approach by considering the SAREF family of ontologies, showcasing an IoT and energy cross-domain use case.

Abstract
The success of forest management towards achieving desired outcomes depends on various factors and can be improved through forest planning based on optimization approaches. Regardless of the owner type (state, private or common land) and/or governance model, the number of owners or management bodies considered in most studies is low, typically involving one owner/management body or a very small group. This study extends the approach of formulating a Forest Management Plan (FMP) to a large forest area, consisting of areas with different management bodies. The FMP model returns the harvest schedule that maximizes the volume of wood harvested during the planning horizon, while ensuring (1) sustainability and environmental constraints at the overall scale and (2) independent revenues for each management body. The FMP is tested in a real forested area, consisting of 22 common lands, governed by local communities for a planning period of 30 years. The results show that our approach is appropriate for several management bodies. When evaluating the impact of grouping areas (and their owner bodies) on the total volume removed, a comparison of the FMP model with an alternative model that allows for independent management (FMP-IND) showed significant differences, in terms of total volume removed at the end of the horizon. Global management leads to a reduction of about 8.6% in the total removed volume; however, it will ensure a heritage of well-diversified stands, in terms of age classes. The results highlight the importance of managing multi-stakeholder forest areas as a whole, instead of being managed independently, if the aim is to assure more sustainable management of forest resources in the mid and long term.

Abstract
Social networks are the main marketing entity of the hotel industry of the 21st century. To understand the impact that social networks have on the hotel sector, both from the point of view of the hotel and the consumer is becoming essential. The aim of this research is to find out how 5 stars Slovakian hotels present themselves in 3 different social networks Instagram, Facebook and Twitter and to compare them with each other. For that purpose a descriptive analyses of the most relevant features of this social networks was conducted. The study offers relevant insight for marketing practitioners in the hospitality industry

Abstract
Municipal Solid Waste Management Systems (MSWMS) worldwide are currently facing pressure due to the rapid growth of the population in cities. One of the biggest challenges in this system is the inefficient expenditure of time and fuel in waste collection. In this regard, cities/municipalities in charge of MSWMS could take advantage of information and communication technologies to improve the overall quality of their infrastructure. One particular strategy that has been explored and is showing interesting results is using a Wireless Sensors Network (WSN) to monitor waste levels in real-time and help decision-making regarding the need for collection. The WSN is equipped with sensing devices that should be carefully chosen considering the real scenario in which they will work. Therefore, in this work, three sets of sensors were studied to evaluate which is the best to be used in the future WSN assembled in Braganca, Portugal. Sets tested were HC-SR04 (S1), HC-SR04 + DHT11 (S2), and US-100 (S3). Tests considered for this work were air temperature and several distances. In the first, the performance of each set to measure a fixed target (metal and plastic box) was evaluated under different temperatures (1.7-37 degrees C). From these results, two best sets were further used to assess distance measurement at a fixed temperature. This test revealed low absolute errors measuring the distances of interest in this work, ranging from 0.18% to 1.27%.

Abstract
Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.

Abstract
Environment understanding in real-world scenarios has gained an increased interest in research and industry. The advances in data capture and processing allow a high-detailed reconstruction from a set of multi-view images by generating meshes and point clouds. Likewise, deep learning architectures along with the broad availability of image datasets bring new opportunities for the segmentation of 3D models into several classes. Among the areas that can benefit from 3D semantic segmentation is the automotive industry. However, there is a lack of labeled 3D models that can be useful for training and use as ground truth in deep learning-based methods. In this work, we propose an automatic procedure for the generation and semantic segmentation of 3D cars that were obtained from the photogrammetric processing of UAV-based imagery. Therefore, sixteen car parts are identified in the point cloud. To this end, a convolutional neural network based on the U-Net architecture combined with an Inception V3 encoder was trained in a publicly available dataset of car parts. Then, the trained model is applied to the UAV-based images and these are mapped on the photogrammetric point clouds. According to the preliminary image-based segmentation, an optimization method is developed to get a full labeled point cloud, taking advantage of the geometric and spatial features of the 3D model. The results demonstrate the method's capabilities for the semantic segmentation of car models. Moreover, the proposed methodology has the potential to be extended or adapted to other applications that benefit from 3D segmented models.