Science, Technology, Engineering, and Mathematics (STEM) are key disciplines towards tackling the challenges related to the Sustainable Development Goals. However, evidence shows that women are enrolling in these disciplines in a smaller percentage than men, especially in Engineering related fields. As stated by the United Nations Women section, increasing the number of women studying and working in STEM fields is fundamental towards achieving better solutions to the global challenges, since the potential for innovation is larger. In this paper, we present the Girls4STEM project, which started in 2019 at the Escola Tècnica Superior d’Enginyeria de la Universitat de València, Spain. This project works towards breaking the stereotypes linked to STEM fields, addressing both boys and girls aged from 6 to 18, but especially trying to open the range of career options for young girls through interaction with female STEM experts. The goal is to spark girls’ interest in STEM disciplines from childhood, so that they become more self-confident in these areas. To achieve this goal, the project is built over three main actions: the Girls4STEM Family Talks, where students, families, and teachers participate; the Girls4STEM Professional Talks, where the target is a general audience; and the Initial Training Seminars for teachers. Short-term results are here presented, showing that aspects related to self-perception and perception from others (family, teachers) play a significant role. Moreover, these results also indicate that there may not be a general understanding of which disciplines are included in STEM.

As the standardization of network-assisted device-to-device (D2D) communications by the Third Generation Partnership Project progresses, the research community has started to explore the technology potential of new advanced features that will largely impact the performance of 5G networks. For 5G, D2D is becoming an integrative term of emerging technologies that take an advantage of the proximity of communicating entities in licensed and unlicensed spectra. The European 5G research project Mobile and Wireless Communication Enablers for the 2020 Information Society (METIS) has identified advanced D2D as a key enabler for a variety of 5G services, including cellular coverage extension, social proximity, and communicating vehicles. In this paper, we review the METIS D2D technology components in three key areas of proximal communications—network-assisted multi-hop, full-duplex, and multi-antenna D2D communications—and argue that the advantages of properly combining cellular and ad hoc technologies help to meet the challenges of the information society beyond 2020.

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Radio environment map (REM) reconstruction based on large-scale channel measurements is a promising technology for future mobility services involving vehicle-to-everything (V2X) communications. REMs provide contextual information which can be exploited to reduce V2X communication latency and control signaling, for instance, through a fast access to channel state information. However, the accuracy of radio mapping techniques is limited by the availability of measurements, which require for collection significant signaling overhead. Moreover, mobility scenarios impose strict latency constraints that render fast channel acquisition a challenging problem. This paper presents a low-complexity deep-learning-based approach based on long-short term memory (LSTM) cells for REM reconstruction on roads, addressed as a data-filling problem. To improve model generalization, the network is trained on a virtually infinite dataset generated according to a 3GPP-compliant freeway scenario, considering different correlation properties and missing point configurations. The results show that the proposed approach provides a performance closer to the theoretical lower bound than the classical Ordinary Kriging spatial interpolation method, without increasing the complexity order. Experiments performed in realistic scenarios using a 3D city model confirm the generalization capability of the proposed solution.

Multicore and graphic processing units (GPUs) can be combined to efficiently implement signal-processing algorithms for communication systems, due to their parallel processing capabilities. This paper proposes a fully parallel fixed-complexity soft-output detector, which is suitable for GPU implementation and allows a considerable decrease in the computational time required for the data detection stage in multiple-input-multiple-output (MIMO) systems. A novel channel matrix preprocessing stage, based on column-norm ordering, is developed to efficiently match the multicore architecture. The throughput of the implementation is shown to outperform other recent implementations and to support some of the configurations in the long-term evolution (LTE) standard.

The concept of sustainability has been recently extended to cover economic and social factors besides the traditional environmental ones. This paper reflects on the potential of mobile communication standards towards achieving sustainable mobility, with focus on vehicular communications and use cases in smart cities scenarios. In this context, intelligent transportation systems, including connected and autonomous vehicles, will be key for developing affordable and sustainable infrastructures and services. We start by identifying three current technology trends, namely, towards climate neutral; cloudification and edge computing; and big data and artificial intelligence, and then we examine their capability to enable sustainable Vehicle-to-Everything (V2X) communication systems in beyond 5G and 6G networks. In the second part of the paper, a set of selected use case categories involving connected and autonomous vehicles is presented, showcasing the potential impact of the selected technology trends. Finally, a review of the estimates of the quantitative savings that could be achieved in environmental-related parameters such as energy/fuel consumption and greenhouse gas emissions is provided.