Universidad Politécnica de Madrid

Benefiting from the progresses of sensing and sustainable computing technologies, recent years have witnessed the dramatic progresses of artificial intelligence of things (AIoT). As a typical AIoT application, WiFi-based human activity recognition has increasing popularities in smart homes. However, WiFibased action recognition often has unstable performance due to environmental interference. To this end, a robust deep learning framework called MSF-Net is proposed for coarse and fine activity recognition using channel state information (CSI) information. First, a dual-stream structure incorporating short-time Fourier transform and discrete wavelet transform is developed to highlight abnormal information in the CSI data. Then, a Transformer is employed as the backbone to effectively extract high-level features. In addition, an attention-based fusion branch is designed to enhance cross-model fusion. Experimental results show that MSF-Net achieves Cohens Kappa scores of 91.82%, 69.76%, 85.91%, and 75.66% on the SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively. These performance records demonstrate the advantages of MSF-Net over existing methods for coarse and fine activity recognition based on WiFi data.

The research examines the evolution of urbanisation and population density in China between 2000 and 2020, with three key objectives. Firstly, it analyses how state policies have influenced the configuration of the real estate market and urban sprawl in the country. Secondly, it studies the variation of population density through annual census data, focusing on how policies have affected demographic distribution. Thirdly, it investigates the impact of national policies on population density in relation to community policies. The inquiry employs data from over 5000 urban entities to assess the influence on the spatial distribution of population. Historically, the relationship with Chinese community policies is manifested in the promotion of a balanced regional development attempt, the reform of the hukou system to facilitate mobility and rural–urban integration, and, more recently, the implementation of strategies for sustainable urbanisation. These policies are designed to enhance the living standards and urban infrastructure in Chinese cities by addressing the challenges of urban concentration while promoting more inclusive and balanced development across the country. However, the outcomes of these policies are yet to be determined. The chapter comprises the following sections. Section 6.1 outlines the methodology employed in the research, which culminates in an examination of urban development policies in China and their impact at the national level (Sect. 6.2). The research then proceeds to examine the impact of state interventions on urbanisation patterns (Sect. 6.3), before summarising the key findings on the challenges China faces in density distribution for the creation of effective community policies (Sect. 6.4). The implications of these issues are discussed in Sect. 6.5, which leads to a final section that highlights the main lessons learned and provides recommendations for future policies (Sect. 6.6).

A detailed analysis of the low energy (0.01-10 meV) integral reaction cross section has been carried out for the F + HD(v=0, 1; j=1) → HF(DF) + D(H) reaction using...

Polar biaxial crystals with extreme anisotropy hold promise for the spatial control and the manipulation of polaritons, as they can undergo topological transitions. However, taking advantage of these unique properties for nanophotonic devices requires to find mechanisms to modulate dynamically the material response. Here, we present a study on the propagation of surface phonon polaritons (SPhPs) in a photonic architecture based on a thin layer of α-MoO 3 deposited on a semiconducting 4H-SiC substrate, whose carrier density can be tuned through photoinduction. By employing this system, we establish a comprehensive polaritonic platform where the propagation of the hybridized SPhPs can be manipulated dynamically due to their coupling with the electron plasma. Specifically, we demonstrate that increasing the doping of the 4H-SiC substrate allows for modulating the on/off switch behavior of SPhP propagation or its controlled canalization. Furthermore, this modulation leads to a notable increase in the Purcell factor, primarily attributed to the doping-induced flat dispersion curve creating ultra-slow light. These findings have significant implications for the development of nanophotonic and quantum technologies, as they enable the utilization of polaritonic materials exclusively.

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The recovery of Co(II), Mn(II), Ni(II), and Cu(II) from black mass e-waste solutions through cellulose nanofibers (CNFs) and nanocrystals (CNCs) was investigated. These materials were synthetized by TEMPO-oxidation followed by high-pressure homogenization, and acid hydrolysis, respectively. The NC characterization included the measurement of consistency, cationic demand, carboxylic content, dissolved amorphous cellulose, and transmittance at λ = 600 nm. These parameters revealed a high transmittance of the NC solutions and a large presence of anionic groups on the surface. The high surface area and charge of the NC justify their high interaction with the cationic metals. Results indicate that short contact times (even 1 min) and low sorbent doses (10 mg/L) at acidic pHs (2 to 4) implied remarkable sorption capacities in most cases with more than 1 g/g of sorption capacity of Co(II), Mn(II), and Cu(II) in single-step sorption tests. Such levels of sorption capacities exceed by at least one order of magnitude most of the literature values of metal recovery applying cellulosic materials. Isotherm modeling through a combination of Langmuir and Freundlich models suggested that both sorption and surface precipitation occurred. A novel procedure following multiple-step batch operation was applied for Mn(II) sorption. This new method was applied as a five-step process, leading to a fourfold and 18-fold increase of sorption capacity onto CNCs and CNFs, respectively, compared to the single-step process. Therefore, this process shows an innovative way to implement the multiple-step batch sorption with NC as an efficient and environmentally friendly solution for critical metal recovery from e-waste leachates. Graphical Abstract

A bstract The rates of flavor-changing weak processes are crucial in determining the conditions of beta equilibrium in neutron stars and mergers, influencing the damping of oscillations, the stability of rotating pulsars, and the emission of gravitational waves. We derive a formula for these rates at nonzero temperature, to leading order in the Fermi coupling and exact in the QCD coupling. Utilizing a simple phenomenological holographic model dual to QCD, we study massless unpaired quark matter at high densities. We numerically compute the rate for small deviations from beta equilibrium and derive an analytic approximation for small temperatures. Our findings reveal that, compared to the perturbative result, the rate is suppressed by logarithmic factors of the temperature.

Physical activity measured by accelerometry (PA-accelerometry) is used as an indicator of physical capacity in chronic diseases. Currently, only fragmented age ranges of reference percentile curves are available for European children and adolescents. This study aimed to provide age- and sex-specific percentiles for physical activity measured by hip-worn accelerometry derived throughout the full age range of European children and adolescents. Individual-level population-based PA data measured by accelerometry from HELENA and IDEFICS/I.Family studies were pooled and harmonized. Together these studies involved children and adolescents aged 2–18 years from 12 European countries. Primary outcomes included averaged counts per minute (CPM), sedentary time (SED), light PA (LPA) and moderate-to-vigorous PA (MVPA). Generalized Additive Models for Location, Scale and Shape were used to derive age- and sex-specific reference percentile curves for these outcomes. The combined cohort consisted of 11,645 children and adolescents aged 2 to 18 years who contributed 14,610 valid accelerometry recordings, with a median accelerometer wear time of 6 days. This dataset allowed for the construction of age- and sex-specific reference percentile curves for CPM, SED, LPA, and MVPA. The curves demonstrated varying trends and variability across age groups. Conclusions: This study provides age- and sex-specific percentile curves for PA-accelerometry in European children and adolescents, addressing a current gap in the availability of full-age range reference data. These curves based on healthy children and adolescents can be used by clinicians, researchers, and policymakers to interpret PA-accelerometry measurements, track physical activity trends, and evaluate treatment responses and health interventions. What is Known: • Daily physical activity (PA) is considered an important measure in various paediatric conditions. Existing reference data for PA in European children based on hip-worn accelerometers are limited to specific age ranges, and comprehensive data covering the full age range are lacking. What is New: • The study provides age- and sex-specific reference curves for PA derived by hip-worn accelerometers in European children and adolescents. These curves aid clinicians, researchers, and policymakers in interpreting PA measurements and tracking trends over time in European children.

This paper investigates a critical aspect of wind energy research—the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on the exploration and comparison of two mesh refinement strategies, h- and p-refinement, in their ability to accurately compute the development of wind turbine wake. The h-refinement method refines the mesh by reducing the size of the elements, while the p-refinement method increases the polynomial degree of the elements, potentially reducing the error exponentially for smooth flows. A comprehensive comparison of these methods is presented that evaluates their effectiveness, computational efficiency, and suitability for various scenarios in wind energy. The findings of this research could potentially guide future studies and applications in wind turbine wake modeling, thus contributing to the optimization of wind farms using high-order h/p methods. This study fills a gap in the literature by thoroughly investigating the application of these methods in the context of wind turbine wake development.

This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence, and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, LLMs can capture the nuanced meanings of multi-word expressions. We systematically evaluated GPT-4o's ability to predict concreteness, valence, and arousal. In Study 1, GPT-4o showed strong correlations with human concreteness ratings (r = .8) for multi-word expressions. In Study 2, these findings were repeated for valence and arousal ratings of individual words, matching or outperforming previous AI models. Studies 3–5 extended the valence and arousal analysis to multi-word expressions and showed good validity of the LLM-generated estimates for these stimuli as well. To help researchers with stimulus selection, we provide datasets with LLM-generated norms of concreteness, valence, and arousal for 126,397 English single words and 63,680 multi-word expressions.

Laser–Plasma ion acceleration is acquiring importance on a daily basis due to incipient applicability in certain research fields. However, the energy and divergence control of these brilliant sources can be considered a bottleneck in the development of some applications. In this work, we present the commissioning of a compact proton beamline based on a triplet of quadrupoles dedicated to focus and collect short and energetic pulses, open to the user community. The focused proton beam characterization has been carried out by imaging of scintillation detectors with different particle filters. Experimental results have been compared with numerical simulations performed with Monte Carlo code (MCNP6) and TSTEP that have been used to retrieve the deposited energy, the particle tracking, and the particle distribution in different focal configurations, respectively. Charges of nC ($\sim$$10^{10}$ protons with energies up to 17.25 MeV) have been measured at the focal planes reducing the beam to spot sizes of a few millimetres in RMS (root mean square). The percentage fluctuation of the transported charges values has been studied. Finally, the beam rigidity has been measured by transverse moving of the quadrupoles and subsequent beam centroid shift, allowing to cross correlate the deflected energy with the energy ranges resulting from the filtering process.

The Architecture Analysis and Design Language (AADL) is a SAE standard for modeling both hardware and software architecture of embedded systems. Widely embraced by stakeholders in critical real-time embedded systems, the AADL standard is used to address a large set of concerns including performances (latency, schedulability), safety, and security. The ADEPT workshop aims to present and report on current projects in the field of design, implementation, and verification of critical real-time embedded systems where AADL is a first-citizen technology. This article is a summary of the second edition of the workshop in 2023.

New Space has been revolutionizing how space software is developed. While in the past the development of systems lasted years to minimize errors, nowadays, with the reduction in manufacturing costs of micro and nanosatellites, companies are capitalizing by focusing on launching frequently, focusing on rapid iterations and innovations. As a result, software development of space systems is also adapting to meet the demands from platform specific code to code that can be reused across different platforms. As a result, there has been a shift from developing tightly coupled client-server systems to loosely coupled publisher subscriber systems. In this article, we propose a solution focused on integrating cFS, a Publisher Subscriber runtime made by NASA, inside TASTE, a model-based toolset developed by ESA, to build space systems focusing on a publishersubscriber methodology, allowing the user to develop platform agnostic components, allowing for faster iterations, reducing the development time and increasing portability and reusability.

The complexity of machine learning (ML) systems increases each year. As these systems are widely utilized, ensuring their reliable operation is becoming a design requirement. Traditional error detection mechanisms introduce circuit or time redundancy that significantly impacts system performance. An alternative is the use of concurrent error detection (CED) schemes that operate in parallel with the system and exploit their properties to detect errors. CED is attractive for large ML systems because it can potentially reduce the cost of error detection. In this article, we introduce concurrent classifier error detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors. CCED identifies a set of check signals in the main ML system and feed them to the concurrent ML classifier that is trained to detect errors. The proposed CCED scheme has been implemented and evaluated on two widely used large-scale ML models: Contrastive language-image pretraining (CLIP) used for image classification and bidirectional encoder representations from transformers (BERT) used for natural language applications. The results show that more than 95% of the errors are detected when using a simple Random Forest classifier that is orders of magnitude simpler than CLIP or BERT.

In binocular vision, the visual system combines images in the retina to generate a single perception, which triggers a sensorimotor process that forces the eyes to point to the same target. Thus, following a moving target, both eyes are expected to move synchronously following identical motor triggers but, in practise, significant differences between eyes are found due to the presence of certain artifacts and effects. Thus, a better indirect characterisation of the underlying neurological behaviour during eyes motion would require new automatic preprocessing methods applied to the eye tracking sequences for rendering the common and most significant movements of both eyes. To address this need, the present study proposes an automatic method for extracting the common components of the left and right eye motions from a set of Smooth Pursuit tests by applying an Independent Component Analysis. To do so, both sequences are decomposed into two independent latent components: the first, presumably correlated with the common motor triggering at the brain, while the second collects artifacts introduced during the recording process and small effects due to convergence deficits and eye dominance biases. The evaluations were carried out using data corresponding to 12 different smooth pursuit eye movements tests, which were collected using an infrared high-speed video-based eye tracking device from 41 parkinsonian patients and 47 controls. The results show that the automatic method can separate the aforementioned components in 99.50% of cases, extracting a latent component correlated with the common motor triggering at the brain, which we hypothesise is characterising the movements of the cyclopean eye. The estimated component could be used to simplify any other potential automatic analysis.</p

Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones, due to their need for heavy sensors and high computational resources. To address these challenges, we introduce ExploreBug , a novel hybrid frontier range-bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments, involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests prove that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones.

Phase change memory (PCM) is a scalable, reliable, and robust technology for embedded and stand-alone memory device. PCM has also been extensively demonstrated for analog in-memory computing (IMC), which allows energy-efficient acceleration of AI workloads. High-temperature data retention requirements in PCM devices are met by Ge-rich GeSbTe (GST), which allows to satisfy consumer-and automotive-grade reliability specifications. However, Ge-rich GST suffers from set state drift, which affects the stability of the multilevel cell (MLC), hence the accuracy of IMC. This work presents: 1) a novel multilevel programming algorithm from weak reset state to prevent conductance instabilities; 2) a drift compensation scheme through a differential weight approach, validated on matrix-vector multiplication (MVM) after high-temperature annealing; and 3) a detailed study of the impact of PCM variability and weight quantization on hardware implementation of neural networks.