Abstract
Depositing and sharing research data is at the core of open science practices. However, institutions in the long tail of science are struggling to properly manage large amounts of data. Support for research data management is still fragile, and most existing solutions adopt generic metadata schemas for data description. These might be unable to capture the production contexts of many datasets, making them harder to interpret. EUDAT is a large ongoing EU-funded project that aims to provide a platform to help researchers manage their datasets and share them when they are ready to be published. Data-Publication@U. Porto is an EUDAT Data Pilot proposing the integration between Dendro, a prototype research data management platform, and the EUDAT B2Share module. The goal is to offer researchers a streamlined workflow: they organize and describe their data in Dendro as soon as they are available, and decide when to deposit in a data repository. Dendro integrates with the API of B2Share, automatically filling the standard metadata descriptors and complementing the data package with additional files for domain-specific descriptors. Our integration offers researchers a simple but complete workflow, from data preparation and description to data deposit.

Abstract
We develop a theoretical framework to study the location-price competition in a Hotelling-type network game, extending the Hotelling model, with linear transportation costs, from a line (city) to a network (town). We show the existence of a pure Nash equilibrium price if, and only if, some explicit conditions on the production costs and on the network structure hold. Furthermore, we prove that the local optimal localization of the firms are at the cross-roads of the town.

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.

Abstract

Abstract
In this work we identify a seminal design guideline that prevents current Full-Duplex (FD) MAC protocols to scale the FD capacity gain (i.e. 2x the half-duplex throughput) in single-cell Wi-Fi networks. Under such guideline (referred to as 1:1), a MAC protocol attempts to initiate up to two simultaneous transmissions in the FD bandwidth. Since in single-cell Wi-Fi networks MAC performance is bounded by the PHY layer capacity, this implies gains strictly less than 2x over half-duplex at the MAC layer. To face this limitation, we argue for the 1:N design guideline. Under 1:N, FD MAC protocols 'see' the FD bandwidth through N > 1 orthogonal narrow-channel PHY layers. Based on theoretical results and software defined radio experiments, we show the 1:N design can leverage the Wi-Fi capacity gain more than 2x at and below the MAC layer. This translates the denser modulation scheme incurred by channel narrowing and the increase in the spatial reuse factor enabled by channel orthogonality. With these results, we believe our design guideline can inspire a new generation of Wi-Fi MAC protocols that fully embody and scale the FD capacity gain.

Abstract
Cyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internet-of-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research efforts on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks.