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Asymptotic analysis of a planar reaction front in gasless combustion: Higher order effects and the influence of the large but finite Lewis number on the propagation velocity
Get PDFAn asymptotic analysis of a planar combustion front in a condensed phase with an arbitrary temperature dependence of thermal conductivity has been carried out by the method of matched asymptotic expansions. The Zel’dovich number is used as a large parameter, and three expansion terms are obtained for the flame velocity. The asymptotic results are compared with direct numerical calculations, and very good agreement is obtained even at low Zel’dovich numbers.
The effect of a large but finite Lewis number on the flame propagation velocity are also examined. It is shown that even at Lewis numbers of the order of the Zel’dovich number, the effect of fuel diffusion becomes significant. The presented asymptotic analysis allows us to write a uniformly valid expression for the propagation velocity that can be used both for large Lewis numbers and for Lewis numbers of order unity.
Novelty and significance statement
For the first time an analytical expression for the velocity of a planar combustion front in the condensed (gasless) phase was obtained up to third-order terms in the inverse Zel’dovich number, taking into account the arbitrary dependence of the thermal conductivity coefficient on temperature. For the first time, the influence of a small but finite reactant diffusion coefficient on the propagation velocity of a planar combustion front was analytically studied. The result allows us to write a uniformly valid formula for the flame velocity applicable in both the solid and gaseous phases.VK thanks MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1) as well by grants #TED2021-129446-C42/MCIN /AEI/10.13039/501100011033 /NextGeneration EU/PRTR and #PID 2022-139082NB-C52 MCIN/ AEI /10.13039/501100011033/ and FEDER. CH gratefully acknowledges the support of projects PID2022-139082NB-C51 and TED2021-129446B-C41, funded by MCIN/AEI, Spain
Segment, Compare, and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration
Get PDFMotion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to be learned. To address this challenge, this work presents an algorithm for acquiring robotic skills through automatic and unsupervised segmentation. The algorithm divides tasks into simpler subtasks and generates motion primitive libraries that group common subtasks for use in subsequent learning processes. Our algorithm is based on an initial segmentation step using a heuristic method, followed by probabilistic clustering with Gaussian Mixture Models. Once the segments are obtained, they are grouped using Gaussian Optimal Transport on the Gaussian Processes (GPs) of each segment group, comparing their similarities through the energy cost of transforming one GP into another. This process requires no prior knowledge, it is entirely autonomous, and supports multimodal information. The algorithm enables generating trajectories suitable for robotic tasks, establishing simple primitives that encapsulate the structure of the movements to be performed. Its effectiveness has been validated in manipulation tasks with a real robot, as well as through comparisons with state-of-the-art algorithms.The Advanced Mobile dual-arm manipulator for Elderly People Attendance (AMME) project research was funded by the Ministerio de Ciencia e Innovacion with grant number PID2022-139227OB-I00
Desarrollo de las funcionalidades de la Ficha Electrónica y las Comunicaciones CPDLC para Simulador de Formación de Controladores de Tráfico Aéreo
Get PDFEl control de tráfico aéreo es una disciplina donde la seguridad es crítica. En este contexto, la formación de controladores aéreos desempeña un papel fundamental para garantizar la seguridad y eficiencia de las operaciones. Los controladores deben formarse en entornos que reflejen fielmente las herramientas y situaciones a las que se enfrentarán en una Torre de Control real.
Una de las herramientas principales para dotar de realismo a la formación de controladores son los simuladores. Los simuladores permiten a los futuros controladores entrenarse en escenarios de Torres de Control y acostumbrarse a las operaciones que realizarán en el futuro. Debido a esto, es esencial que los simuladores están actualizados con las últimas tecnologías que se utilizan en las Torres de Control, ofreciendo escenarios realistas y actualizados para la formación.
En este trabajo se actualizan las funcionalidades del simulador Altius, un simulador de Torre de Control portátil dedicado a la formación. El objetivo del trabajo es incluir en el simulador los módulos de fichas de progresión de vuelo electrónicas y comunicación por enlace de datos entre controlador y piloto. Estas tecnologías se utilizan actualmente en Torres de Control de todo el mundo, por lo que se debe asegurar que los futuros controladores las conocen y saben operar con ellas.
El trabajo abarca todo el proceso de desarrollo de estas funcionalidades, desde el análisis de requisitos y el diseño, hasta el desarrollo e integración de las funcionalidades en el simulador. Tras el desarrollo, se realizaron pruebas para validar el funcionamiento de los dos nuevos módulos, obteniendo resultados satisfactorios en todas las pruebas. Esto indica que las nuevas funcionalidades contribuyen de forma efectiva a la formación de alumnos en entornos realistas.Grado en Ingeniería Informátic
Reglamento de Administración Electrónica de la Universidad Carlos III de Madrid, aprobado por el Consejo de Gobierno en sesión de 10 de junio de 2021 y modificada en sesión de 14 de julio de 2025
No full textEste Reglamento modifica el Reglamento de Administración Electrónica de la Universidad Carlos III de Madrid, aprobado por el Consejo de Gobierno en sesión de 10 de Junio de 2021
From LOW-COST Spectrum Monitoring to 5G Networks: algorithms and systems for localizing and identifying wireless transmissions
No full textMención Internacional en el título de doctorElectromagnetic Radiation is the effect that Electromagnetic fields can exert at a distance from their source. Depending on the frequency of this radiation, we can divide it into categories such as: Radio-Frequency (RF), Infrared, Visible Light, X-rays or Gamma rays. In particular, the RF spectrum is the foundation for all modern wireless communications. Our lives would be difficult to imagine should we not have access to cellular, GPS, WiFi, Bluetooth, IoT and many other technologies. Although the RF spectrum is heavily regulated, current advances and the availability of hardware make it easier than ever to disrupt legitimate communications. In fact, at the beginning of the decade 2020, attacks on the spectrum are becoming more and more common. Currently, spectrum regulators must deploy bulky and expensive hardware to monitor these threats, which does not scale due to the amount of manual labor required. Software-Defined Radio (SDR) devices are a flexible alternative for monitoring the RF spectrum due to how easy it is to reconfigure their measuring parameters like sampling rate or center frequency in real-time. This flexibility has allowed researchers and engineers to apply them in numerous applications. It is possible to perform low-level tasks like signal identification, localization or even self-positioning, up to building car tracking networks via tire transmissions or 5G standard compliant core networks. Not all SDR receivers can perform all tasks, as lower cost ones tend to have limitations in their hardware, but each category of device can enable a wide range of applications nonetheless. In this thesis, we present four applications for localizing and identifying wireless transmissions that can be achieved taking advantage of the flexibility offered by SDR receivers. The first three demonstrate that with low-cost receivers it is possible to perform a broad range of activities. The first application is a transmitter localization system that can be deployed at the city level and achieves notable accuracy without the need for additional hardware for synchronization. The second application shows that it is possible to build a self-positioning architecture that employs aircraft broadcast signals and the metadata contained within. We show that by careful modeling of the hardware and precise selection of signals, it is possible to provide results comparable to Global Positioning System (GPS) or other commercial solutions, without the need for it. The last of this set explores a higher-level application, where we build a car tracking infrastructure based on capturing the signals transmitted by the pressure sensors installed on wheels. We then show that with careful processing it is possible to robustly track cars over long periods of time and infer driver patterns from this tracking. Our results not only show the feasibility of building monitoring networks with low-cost receivers but also highlight the urge to redesign less privacy-aware protocols in this and many other areas. The fourth application makes use of higher-end SDR receivers that support better synchronization hardware to build a custom 5G compliant network. With this network, we are able to explore the localization performance that these higher-end platforms can offer, after doing several modification to the underlying platform. Our extensive experiments with different geometric configurations, number of sensors, optimization routines and processing techniques show that it is possible to achieve highly accurate positions. This thesis makes several contributions to the proposed architectures for all applications, as well as the techniques employed. In order to validate our proposals, we perform an extensive analysis with data coming from real-world scenarios.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Henk Wymeers.- Secretaria: Matilde Pilar Sánchez Fernández.- Vocal: Vincenzo Sciancalepor
A quadrature formula on triangular domains via an interpolation-regression approach
Get PDFIn this paper, we present a quadrature formula on triangular domains based on a set of simplex points. This formula is defined via the constrained mock-Waldron least squares approximation. Numerical experiments validate the effectiveness of the proposed method.This research has been achieved as part of RITA “Research ITalian network on Approximation” and as part of the UMI group “Teoria dell’Approssimazione e Applicazioni”. The research was supported by GNCS-INdAM 2024 project “Metodi kernel e polinomiali per l’approssimazione e l’integrazione: teoria e software applicativo”. Project funded by the EuropeanUnion – NextGenerationEU under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.1 - Call PRIN 2022 No. 104 of February 2, 2022 of Italian Ministry of University and Research; Project 2022FHCNY3 (subject area: PE - Physical Sciences and Engineering) “Computational mEthods for Medical Imaging (CEMI)”. The work of F. Marcellán has been supported by the research project [PID2021- 122154NB-I00], Ortogonalidad Aproximación con Aplicaciones en Machine Learning y Teoría de la Probabilidad funded by MICIU/AEI/10.13039/501100011033 and by “ERDF A Way of making Europe”
La problemática jurídica de la inferencia algorítmica en la era del big data en la UE; su protección como dato personal. Una perspectiva desde la tríada técnica, ética, y jurídica
No full textPrograma de Doctorado en Derecho por la Universidad Carlos III de MadridPresidente: Tomás de la Quadra-Salcedo Fernández del Castillo.- Secretario: Moisés Barrio Andrés.- Vocal: Wilma Arellano Toled
Generative networks for spatio-temporal gap filling of Sentinel-2 reflectances
No full textEarth observation from satellite sensors offers the possibility to monitor natural ecosystems by deriving spatially explicit and temporally resolved biogeophysical parameters. Optical remote sensing, however, suffers from missing data mainly due to the presence of clouds, sensor malfunctioning, and atmospheric conditions. This study proposes a novel deep learning architecture to address gap filling of satellite reflectances, more precisely the visible and near-infrared bands, and illustrates its performance at high-resolution Sentinel-2 data. We introduce GANFilling, a generative adversarial network capable of sequence-to-sequence translation, which comprises convolutional long short-term memory layers to effectively exploit complete dependencies in space time series data. We focus on Europe and evaluate the method"s performance quantitatively (through distortion and perceptual metrics) and qualitatively (via visual inspection and visual quality metrics). Quantitatively, our model offers the best trade-off between denoising corrupted data and preserving noise-free information, underscoring the importance of considering multiple metrics jointly when assessing gap filling tasks. Qualitatively, it successfully deals with various noise sources, such as clouds and missing data, constituting a robust solution to multiple scenarios and settings. We also illustrate and quantify the quality of the generated product in the relevant downstream application of vegetation greenness forecasting, where using GANFilling enhances forecasting in approximately 70% of the considered regions in Europe. This research contributes to underlining the utility of deep learning for Earth observation data, which allows for improved spatially and temporally resolved monitoring of the Earth surface.Authors acknowledge the support from the European Research Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187), the European Union’s Horizon 2020 research and innovation program within the projects ‘XAIDA: Extreme Events - Artificial Intelligence for Detection and Attribution’, (grant agreement 101003469), ‘DeepCube: Explainable AI pipelines for big Copernicus data’ (grant agreement 101004188), the ESA AI4Science project “Multi-Hazards, Compounds and Cascade events: DeepExtremes”, 2022–2024, the computer resources provided by the Jülich Supercomputing Centre (JSC) (Project No. PRACE-DEV-2022D01-048), the computer resources provided by Artemisa (funded by the European Union ERDF and Comunitat Valenciana), as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV)
Labor reallocation effects of furlough schemes: Evidence from two recessions in Spain
No full textWe examine the impact of furlough schemes in scenarios where aggregate risk has a large sector-specific component and workers accumulate sector-specific human capital. In particular, we investigate the different dynamic responses of the Spanish labor market during the Great Recession and the Great Contagion as both downturns were triggered by such shocks. A big difference between these recessions is that job losses were much lower during the pandemic crisis, possibly due to firms' widespread use of furlough schemes (ERTEs), which had been seldom activated during the Great Recession. In line with the consensus view, we find that this policy helps stabilize the unemployment rate by keeping matches alive in those industries hardest hit by a crisis. However, under their current design, we argue both empirically and theoretically that ERTEs: (i) crowd out labor hoarding by employers in the absence of those schemes, (ii) increase the volatility of effective working rates and output, and (iii) hinder worker reallocation, especially in short recessions.Financial support by MCIU/AEI (grants PID2019-107161GB-C1, PRE2019-088620, CEX2021-001181-M, PID2020-117354GB-I00), and Comunidad de Madrid, Spain (grants REACT-Predcov-CM, EPUC3M11-V PRICIT) is gratefully acknowledged
Generation of shear flows induced by AE / EPM in LHD plasma
No full textThe generation of shear flows (SFs) by Alfven Eigenmodes (AEs) and energetic particle modes (EPMs) have important effects on the operation of future nuclear fusion reactors, because SFs regulate the saturation of the AEs/EPMs, the transport of EPs and thermal plasma, as well as the formation of transport barriers among other consequences. The aim of this study is the analysis of SFs generation during the saturation phase of AEs and EPMs in LHD plasma. Experiments performed in the 23rd and 24th LHD experimental campaigns are dedicated to explore the destabilization of AEs/EPMs in discharges with different heating patterns, thermal plasma and magnetic field configurations. In particular, the shots 176490 and 179697 show the destabilization of MHD bursts and energetic-ion-driven resistive interchange modes (EIC), respectively. Charge exchange spectroscopy measurements in both discharges indicate that the generation of SFs by AE/EPM is uncorrelated with the perturbation induced by the neutral beam injector (NBI). Nonlinear simulations performed using the gyro-fluid code FAR3d show the generation of zonal structures, especially SFs, induced during the saturation phase of Toroidal Alfven Eigenmodes (TAEs) triggered in the MHD burst as well as by the 1 / 1 EIC in the bursting phase. The simulations indicate that SFs are caused by the radial electric fields powered by energy transfers from the unstable AE/EPM towards the thermal plasma. The strongest SFs are measured during the EIC bursting phase once the 1 / 1 EPM overlaps with nearby resonances at the plasma periphery. Likewise, the largest SFs during the MHD burst are observed once TAEs radially overlap in the inner-middle plasma region.This work was supported by US DOE under Grant DE-FG02-04ER54742, the Comunidad de Madrid under the Projects 2019-T1/AMB-13648 and NIFS07KLPH004