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Multi-Media applications are amongst the most demanding services, requiring high amounts of network capacity and computational resources for synchronous audio-visual streaming with high quality. Gaming streaming is one of the most demanding of these media applications. Recent technological advances in the 5G domain, precisely Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC), unlock media industries’ potential by offering high-quality media services through dynamic and efficient resource allocation with low latency. This work presents a multi-site gaming streaming use case implemented over an end-to-end 5G-enabled platform provided by the EU H2020 5G-PPP 5G EVE project. Furthermore, we present the executed use case experiment scenarios to validate the use case performance following a set of defined Quality of Experience(QoE)-related Key Performance Indicators (KPIs). In particular, this paper discusses the design workflow and orchestration of the multi-site gaming streaming use case across two 5G EVE sites (i.e., Spain and Greece), providing a detailed description of the network function applications and resources utilized for the use case. Subsequently, leveraging the 5G EVE monitoring platform, this paper elaborates on the executed experiment scenarios that provide the defined KPI metrics data. Finally, this paper presents, discusses, and analyzes the obtained KPI metrics data results and provides recommendations on future works for these kinds of use cases. [ABSTRACT FROM AUTHOR]

This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders. [ABSTRACT FROM AUTHOR]

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. [ABSTRACT FROM AUTHOR]

5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE). [ABSTRACT FROM AUTHOR]

The use of protons and heavy ions for the treatment of tumors is increasing since it provides a better relative biological effectiveness (RBE) than traditional radiotherapy. Accurate knowledge of the energy deposition at submicrometric scales is paramount for RBE characterization. This paper shows the latest version of the silicon cylindrical microdosimeter array developed by the Instituto de Microelectrónica de Barcelona, Centro Nacional de Microelectrónica (IMB-CNM, Spain). The detector consists of a matrix of $11 \times 11$ cylindrical sensitive unit cells with individual readout etched within the silicon substrate available in different diameters and pitches between detectors. The detector employed in this paper had a diameter of $15~\mu \text{m}$ , a pitch of $200~\mu \text{m}$ , and a thickness of $5.5~\mu \text{m}$. The detectors were tested in the clinical facilities of Fondazione Centro Nazionale di Adronterapia Oncologica (CNAO) (Pavia, Italy) employing a 12C pencil beam at a therapeutic beam fluence rate. Microdosimetric spectra of lineal energy were measured in different depths of polymethyl methacrylate (PMMA) up to the Bragg peak. Results were then compared with Monte Carlo simulations using the FLUKA particle transport code, showing an excellent agreement between experimental and simulated microdosimetric distributions even at the high fluence rates associated with clinical beams. [ABSTRACT FROM AUTHOR]

Banks are the backbone of the financial sector and catalysts in the economic development of any country. Current changes in their global business ecosystem make knowledge of about the fund-supplying and fund-demanding parties of the society a key resource for the fulfillment of banks’ investment and saving functions. This paper addresses the relationship between the performance and a learning culture that is supported by knowledge processes within the organization. Given the relevance of this subject for organizations from most knowledge-intensive domains, this research has focused on the Spanish banking sector. Using a structural equation model, feedback received from 215 employees from 142 individual branches from a major banking institution in Spain is studied. The results of the data analysis show that in addition to maximizing on what is already known about the customer base, employees’ learning about the potential new stakeholders and also about the internal strategies, tools, and techniques is directly related to the bank's performance, which, in turn, influences the economic recovery and socio-economic development. Results support that an active pursuit of learning within the context of the organization is required for banks to remain competitive in the dynamic, global business ecosystem, where international, national, and local banking sectors operate. [ABSTRACT FROM AUTHOR]

The large penetration of renewable energy resources is demanding additional flexibility for the operation of power systems. In this sense, real world applications are proving that wind power units are able to provide part of the required flexibility. Therefore, these new sources of flexibility need to be considered by power system planners in order to make appropriate generation capacity expansion decisions. This paper proposes a generation and storage capacity expansion formulation considering that wind power units are able to participate in the reserve provision service. The proposed model is a stochastic linear program that is solved using a commercial solver. A case study based on the isolated power system of Lanzarote and Fuerteventura in Spain is solved to quantify the impact of considering the reserve provision of wind power in capacity investment decisions. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain. [ABSTRACT FROM AUTHOR]

The recently approved regulation about self-consumption in Spain will dramatically change the level of penetration of photovoltaic distributed generation combined with storage devices at residential level in short term. In this paper, we will study the impact of the massive installation of such technologies under the constraints imposed by the new regulation, over the terminal distribution network. As it will be demonstrated, this impact will be drastic in terms of energy demand, voltage profile, and load curves. [ABSTRACT FROM AUTHOR]

Tunnels represent strong multipath environments for communications, where wireless propagation differs from regular indoor or outdoor scenarios. Moreover, with an adequate operating frequency choice, a tunnel behaves as an oversized waveguide, extending the communication range but affecting the signal with strong fadings. These fadings have been closely studied in the literature in the longitudinal dimension (tunnel length). Nevertheless, they also occur in the cross section, where they have only been analyzed statistically and on a small scale. In this paper, we present the results from an extensive measuring campaign carried out at the Somport tunnel, in Spain. We have adopted the modal theory approach to analyze the large-scale fadings (also known as slow fadings) in the longitudinal, transversal, and vertical dimensions. More specifically, the modes’ geometry allowed for the adequate placement of transmitters to create different periodic fading structures and to virtually divide the tunnel cross section into sectors, where the fadings presented a phase shift of 180°. This study enabled the development of large-scale spatial diversity schemes and navigation techniques to improve communication and localization inside these scenarios. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption in buildings is one of the priorities of the European Union energy strategy. The European legislation has defined a legal framework to determine the energy qualification of a building that is used by each of the member states to define its own energy qualification procedure. This paper presents a methodology to evaluate the effect of climate conditions in the energy qualification procedure. It evaluates the effectiveness of the qualification in representing the yearly energy demand and \CO2 emissions. Moreover, it determines the effects of improvements in different parameters of the procedure (e.g., the construction materials of the enclosure, thermal installations, etc.) in order to improve qualifications. It has been applied to the Spanish legislation using a series of simulations of a typical domestic building, with climatic data from different zones of the Spanish territory. The most significant results are the similarity in the energy certification values obtained in all scenarios and the lack of concordance between the energy rating scale, energy consumption, and \CO2 emissions. [ABSTRACT FROM PUBLISHER]