Search

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]

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]

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]

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 COVID-19 pandemic presents unprecedented challenges to the healthcare systems around the world. In 2020, Spain was among the countries with the highest Intensive Care Unit (ICU) hospitalization and mortality rates. This work analyzes data of COVID-19 patients admitted to a Spanish ICU during the first wave of the pandemic. The patients in our study either died (deceased patients) or were discharged from the ICU (non-deceased patients) and underwent the following landmarks: beginning of symptoms; arrival at the emergency department; beginning of the hospital stay; and ICU admission. Our goal is to create a graph-based data-science methodology to find associations among patients’ comorbidities, previous medication, symptoms, and the COVID-19 treatment, and to analyze their evolution across landmarks. Towards that end, we first perform a hypothesis test based on bootstrap to identify discriminative features among deceased and non-deceased patients. Then, we leverage graph-based representations and network analytics to determine pairwise associations and complex relations among clinical features. The descriptive statistical analysis confirms that deceased patients exhibit multiple comorbidities with stronger levels of association and are treated with a wider range of drugs during the ICU stay. We also observe that the most common treatment was the simultaneous administration of lopinavir/ritonavir with hydroxychloroquine, regardless of the patients’ outcome. Our results illustrate how graph tools and representations yield insights on the relations among comorbidities, drug treatments, and patients’ evolution. All in all, the approach puts forth a new data-analysis tool for clinicians that can be applied to analyze (post-COVID) symptom/patient evolution. [ABSTRACT FROM AUTHOR]

Presents information on the ICDL 2022 Conference. [ABSTRACT FROM AUTHOR]

Remote spectral imaging of coastal areas can provide valuable information for their sustainable management and conservation of their biodiversity. Unfortunately, such areas are very sensitive to changes due to human activity, natural phenomenon, introduction of non-native species, and climate change. Thus, the main objective of this research is the implementation of a robust image processing methodology to produce accurate bathymetry maps in shallow coastal waters using high-resolution multispectral WorldView-2/3 satellite imagery for the monitoring at the maximum spatial and spectral resolutions. Two different island ecosystems have been selected for the assessment, since they stand out for their richness in endemic species and they are more vulnerable to climate change: Cabrera National Park and Maspalomas Natural Protected area, located in the Balearic and Canary Islands, Spain, respectively. In addition, a third example to show the applicability of the mapping methodology to monitor the construction of a new port in Granadilla (Canary Islands) is presented. Contributions of this work focus on improving the preprocessing methodology and, mainly, on the proposal and assessment of new satellite-derived regression and machine learning bathymetric models, which have been validated and compared with respect to measured reference bathymetry. After a thorough analysis of nine techniques, using visual and quantitative statistical parameters, ensemble learning approaches have demonstrated excellent performance, even in challenging scenarios up to 35-m depth, with mean RMSE values around 2 m. [ABSTRACT FROM AUTHOR]

Contribution: This article explores the factors favoring the enrollment in engineering studies in Spain, analyzing gender differences. Background: Understanding the motivation of choosing engineering studies is very important in order to strengthen this choice in future promotions in Spain, considering that it is well below the average of the European Union at the present time. Research Questions: What are the main factors that influence students to choose engineering studies? Are there any noticeable differences between those affecting male and female students? Methodology: Data were collected from a survey completed by 624 students and an exploratory study was performed extracting five components. Independent sample $T$ -tests were used to determine significant differences between the answers of males and females. Findings: The factors found in the exploratory analysis were “Interest and development,” “Career advice and previous contact,” “Outcome expectations,” “Social influences” and “Not preferred option.” Significant differences between the answers given by males and females were also found. The outcome of this study is important for the development of new interventions aimed to increase interest, enrollment, and retention of engineering students, especially among females. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic (CSEM) measurements are complementary data for magnetotelluric (MT) characterization although its methodology on land is not sufficiently developed and tested as in marine environments. Acquiring expertise in CSEM is crucial for surveys in places where MT cannot be performed due to high levels of cultural noise. To acquire that expertise, we perform CSEM experiments in the Vallès fault [Northeast (NE), Spain], where MT results have been satisfactory and allow us to verify the CSEM results. The Vallès basin is relevant for potential heat generation because of the presence of several geothermal anomalies and its nearby location in urban areas. In this article, we present the experimental setup for that region, a 2-D joint MT+CSEM inverse model, several 3-D CSEM simulations in the presence of metallic casing, and its comparison with real data measurements. We employ a parallel and high-order vector finite element algorithm to discretize the governing equations. By using an adapted meshing strategy, different scenarios are simulated to study the influence of the source position/direction and the conductivity model in a metallic casing presence. An excellent agreement between the simulated data and analytical/real field data demonstrates the feasibility of study metallic structures in realistic configurations. Our numerical results confirm that metallic casing strongly influences electromagnetic (EM) responses, making surface measurements more sensitive to resistivity variations near the metallic structure. It could be beneficial getting higher signal-to-noise ratios and sensitivity to deep targets. However, such a casing effect depends on the input model (e.g., conductivity contrasts, frequency, and geometry). [ABSTRACT FROM AUTHOR]

The 2021 Nations League Cup Final was a soccer/football match played between France and Spain. It was settled by a goal scored by a player who was in an offside position when a ball was passed toward him by his teammate, but was not penalized because in its trajectory, the ball scraped the studs of an opponent trying to intercept the ball. According to FIFA’s laws of the game “A player in an offside position receiving the ball from an opponent who deliberately plays the ball, $\ldots $ is not considered to have gained an advantage” and therefore has not caused an offside offense. The goal was allowed to stand, and France duly won the match, and the cup, with this the winning goal. [ABSTRACT FROM AUTHOR]

This article presents the Schumann resonance (SR) variations, obtained in the extreme low frequency (ELF) signal observatory of Sierra de Filabres belonging to the University of Almería Spain, in operation since 2011. This station has two large and sensitive orthogonal sensors operating between 1 and 100 Hz. A 24 bit low-noise, high-resolution system processes the signal. The Sierra de Filabres ELF station is operating to offer valuable information on SRs’ activity in this area. This system allows us to have a broader range of comparison patterns to study correlations with other phenomena. The information presented in this work deals with the diurnal and seasonal variations of the first six modes of the SRs in 2016 and 2017. The graphs have been obtained through Lorentzian functions. We explain the diurnal variation based on the position and distance to the most important lightning hotspots and demonstrate its relation with the first mode captured in our observatory. We introduce a new yearly evolution method based on the resonance frequency through the most representative lightning activity hours in one year. This article is supported by the comparison between our signals and the global thunderstorm center’s activity. [ABSTRACT FROM AUTHOR]