Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

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Advancing the Held of research in Immersive Learning Environments requires avoiding the pitfalls of previous educational technologies. Studies must consider the actual use of these environments and the context where it occurs, not simply the technocentric perspectives on these environments. This paper provides an overview and analysis of surveys on this topic, in order to map the field and find out which information on actual uses of Immersive Learning Environments are reported, and hence which gaps need to be covered towards a robust, encompassing knowledge on their relationship with learning. Collected accounts of use were clustered via thematic analysis and contrasted with research areas in learning and technology, highlighting the gaps in the field and serving as a blueprint for research agendas on uses of immersive learning environments.

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The use of Autonomous Underwater Vehicles (AUVs) is increasingly seen as a cost-effective way to carry out underwater missions. Due to their long endurance and set of sensors onboard, AUVs may collect large amounts of data, in the order of Gbytes, which need to be transferred to shore. State of the art wireless technologies suffer either from low bitrates or limited range. Since surfacing may be unpractical, especially for deep sea operations, long-range underwater data transfer is limited to the use of low bitrate acoustic communications, precluding the timely transmission of large amounts of data. The use of data mules combined with short-range, high bitrate RF or optical communications has been proposed as a solution to overcome the problem.In this paper we describe the implementation and validation of UDMSim, a simulation platform for underwater data muling oriented systems that combines an AUV simulator and the Network Simulator 3 (ns-3). The results presented in this paper show a good match between UDMSim, a theoretical model, and the experimental results obtained by using an underwater testbed when no localization errors exist. When these errors are present, the simulator is able to reproduce the navigation of AUVs that act as data mules, adjust the throughput, and simulate the signal and connection losses that the theoretical model can not predict, but that will occur in reality. UDMSim is made available to the community to support easy and faster evaluation of data muling oriented underwater communications solutions, and enable offline replication of real world experiments. © 2020 IEEE.

Abstract
With the collapse of communism, some former communist States of Eastern Europe managed to muddle through their way to a market economy and entered the European Union. This brought about the acceleration of Foreign Direct Investment (FDI) among the European economies and accelerated the globalization process. Although there is plenty of research on FDI and trade among countries, the aim of this chapter is to analyze how trade between Portugal and the Czech Republic have evolved over form 2000 until 2015. The chapter seeks to complement previous studies on FDI and trade as Portugal and the Czech Republic are part of the European Union, but have had different historical, cultural, and economic paths. The main conclusion of the chapter is that trade between both countries has grown significantly. The main reason affecting trade between both countries is the economic unrest Portugal has been through since 2008.

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