1,860 research outputs found

    Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

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    In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

    Novel Acoustic Methods for Directly Monitoring Seabed Sediment Transport, Geohazards & Scour

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    In the natural environment, sediment transport processes can pose significant hazards to marine infrastructure, such as offshore wind turbines or seabed cables that carry both power onshore as well as carrying over 99% of global data. These processes are often extremely challenging to measure directly because sensors can be easily damaged by the processes themselves. It would, therefore, be highly advantageous to remotely sense and quantify sediment transport via sensors that are located outside the region of sediment transport. One way to do this is via sensors higher in the water column that detect acoustic signals emitted by sediment transport processes closer to the bed. Previous work such as Wren et al. (2015), Marineau et al. (2016), and Le Guern et al. (2021) have started to develop passive acoustic methods to record signals from sediment transport, using tools such as hydrophones and acoustic Doppler current profilers (ADCPs). Normally, ADCPs actively emit their own acoustic pulses, and their reflections are used to monitor flow velocities and concentrations. However, with modification to extend their listening times, ADCP’s can also be used to passively record acoustic signals emitted by sediment transport processes. Thus far, the potential of these passive acoustic methods have not been fully developed, and the fundamental controls that determine the type of acoustic signals produced are not yet fully understood. This PhD sought to understand what controls the nature (frequencies, strength etc) of these signals and, thus, what they can tell us about sediment transport processes (Thorne, 1985,1986,1990,2014; Rigby et al. 2016). It aims to do this using a combination of laboratory experiments (Chapter 2) and detailed fieldwork (Chapters 3 and 4) using acoustic signals passively emitted by sediment flows. In addition, the thesis includes work testing the use of active acoustic methods to monitor sediment transport processes within the natural environment, specifically seabed sediment flows (called turbidity currents) (Chapter 5). Results from this thesis found a general relationship between the strength of self-generated noise and flow speed in some types of sediment flows (Chapters 2, 3 and 4). However, the strength of this relationship changes depending on the frequency and details of the environment investigated. Field data from the Río Paraná (Chapter 3) suggested no relationship between bedload flux and acoustic signal strength, nor between acoustic signal strength and friction velocity. This is unexpected because previous research by Sime et al. (2007), Hossein and Rennie (2009), Hatcher (2017), Hay et al. (2021) and Le Guern et al. (2021) proposed links between flow speed (and bed shear stress and bedload transport) and passively detected noise strength. Passive acoustic signals generated by turbidity currents were used to monitor these flows in a set of submarine canyons, which were Bute Inlet (Canada), Monterey Canyon (offshore California), and the Congo Canyon (offshore West Africa) (Chapter 4). Noticeable variations in the level of passively detected noise between these three field sites were observed. These variations are thought to be related to the main sediment grain size present within each canyon, with lower noise being detected with an increasing mud content of the seabed. In addition, differences in noise down submarine canyons suggest that flow processes and concentration could be controlling the level of sediment-generated noise, with implications of flow field dynamics. Chapter 5 uses one of the most detailed (near-daily) series of multibeam swath bathymetry surveys yet collected, which come from within Bute Inlet, Canada, in September 2022. This unusual set of field observations is used to understand the relationship between flow evolution and the initiation mechanism of turbidity currents. For example, the Bute Inlet study supports the findings from Hizzett et al. (2018) that there is no link between the initiation mechanism and runout distance of a turbidity current. Further research is needed to improve understanding of the controls on acoustic signals in the natural environment, and to also improve our ability to use acoustic signals to monitor sediment transport in a wider range of environments, such as around offshore wind farms

    Marchenko-Lippmann-Schwinger inversion

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    Seismic wave reflections recorded at the Earth’s surface provide a rich source of information about the structure of the subsurface. These reflections occur due to changes in the material properties of the Earth; in the acoustic approximation, these are the density of the Earth and the velocity of seismic waves travelling through it. Therefore, there is a physical relationship between the material properties of the Earth and the reflected seismic waves that we observe at the surface. This relationship is non-linear, due to the highly scattering nature of the Earth, and to our inability to accurately reproduce these scattered waves with the low resolution velocity models that are usually available to us. Typically, we linearize the scattering problem by assuming that the waves are singly-scattered, requiring multiple reflections to be removed from recorded data at great effort and with varying degrees of success. This assumption is called the Born approximation. The equation that describes the relationship between the Earth’s properties and the fully-scattering reflection data is called the Lippmann-Schwinger equation, and this equation is linear if the full scattering wavefield inside the Earth could be known. The development of Marchenko methods makes such wavefields possible to estimate using only the surface reflection data and an estimate of the direct wave from the surface to each point in the Earth. Substituting the results from a Marchenko method into the Lippmann-Schwinger equation results in a linear equation that includes all orders of scattering. The aim of this thesis is to determine whether higher orders of scattering improve the linear inverse problem from data to velocities, by comparing linearized inversion under the Born approximation to the inversion of the linear Lippmann-Schwinger equation. This thesis begins by deriving the linear Lippmann-Schwinger and Born inverse problems, and reviewing the theoretical basis for Marchenko methods. By deriving the derivative of the full scattering Green’s function with respect to the model parameters of the Earth, the gradient direction for a new type of least-squares full waveform inversion called Marchenko-Lippmann-Schwinger full waveform inversion is defined that uses all orders of scattering. By recreating the analytical 1D Born inversion of a boxcar perturbation by Beydoun and Tarantola (1988), it is shown that high frequency-sampling density is required to correctly estimate the amplitude of the velocity perturbation. More importantly, even when the scattered wavefield is defined to be singly-scattering and the velocity model perturbation can be found without matrix inversion, Born inversion cannot reproduce the true velocity structure exactly. When the results of analytical inversion are compared to inversions where the inverse matrices have been explicitly calculated, the analytical inversion is found to be superior. All three matrix inversion methods are found to be extremely ill-posed. With regularisation, it is possible to accurately determine the edges of the perturbation, but not the amplitude. Moving from a boxcar perturbation with a homogeneous starting velocity to a many-layered 1D model and a smooth representation of this model as the starting point, it is found that the inversion solution is highly dependent on the starting model. By optimising an iterative inversion in both the model and data domains, it is found that optimising the velocity model misfit does not guarantee improvement in the resulting data misfit, and vice versa. Comparing unregularised inversion to inversions with Tikhonov damping or smoothing applied to the kernel matrix, it is found that strong Tikhonov damping results in the most accurate velocity models. From the consistent under-performance of Lippmann-Schwinger inversion when using Marchenko-derived Green’s functions compared to inversions carried out with true Green’s functions, it is concluded that the fallibility of Marchenko methods results in inferior inversion results. Born and Lippmann-Schwinger inversion are tested on a 2D syncline model. Due to computational limitations, using all sources and receivers in the inversion required limiting the number of frequencies to 5. Without regularisation, the model update is uninterpretable due to the presence of strong oscillations across the model. With strong Tikhonov damping, the model updates obtained are poorly scaled, have low resolution, and low amplitude oscillatory noise remains. By replacing the inversion of all sources simultaneously with single source inversions, it is possible to reinstate all frequencies within our limited computational resources. These single source model updates can be stacked similarly to migration images to improve the overall model update. As predicted by the 1D analytical inversion, restoring the full frequency bandwidth eliminates the oscillatory noise from the inverse solution. With or without regularisation, Born and Lippmann-Schwinger inversion results are found to be nearly identical. When Marchenko-derived Green’s functions are introduced, the inversion results are worse than either the Born inversion or the Lippmann-Schwinger inversion without Marchenko methods. On this basis, one concludes that the inclusion of higher order scattering does not improve the outcome of solving the linear inverse scattering problem using currently available methods. Nevertheless, some recent developments in the methods used to solve the Marchenko equation hold some promise for improving solutions in future

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Geological controls on megathrust slip: the 2014 Pisagua, Chile, earthquake sequence as a natural laboratory

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    Most subduction zones remain poorly resolved when it comes to the study of the precise location of the far oïŹ€shore located updip limit of coseismic slip and its controlling parameters, which is an essential component of earthquake hazard assessment. The relative lack of seismicity on the updip limit, combined with laboratory friction studies, suggest the shallow fault is mostly velocity strengthening and likely to creep. This view is reinforced by geodetic inversions, which show low to zero coupling close to the trench. However, these locations are remote from the land; hence the models derived from terrestrial stations are not suïŹƒciently well constrained. Moreover, the updip region can also be seismogenic, as demonstrated by tsunami earthquakes and shallow, slow slip events. To better understand the controls on the updip limit, we analyze high-resolution seismic data to image an erosive margin with documented intense seismicity. Developing a high-resolution model of the seismic velocity and reïŹ‚ectivity of a region where we have exceptionally good knowledge of the temporal and spatial distribution of slip will provide new insights into these controls that can be transferred to studies of seismic slip and crustal structure elsewhere. These results will also have a signiïŹcant impact on statistical forecasts of future earthquake activity, which can aïŹ€ect seismic hazard evaluation and mitigation plans. The thesis is motivated by two themes, which are interrelated and addressed in a holistic seismic approach. One theme is mainly focused on the ïŹ‚uid-pressure variations revealed by the reïŹ‚ectivity of the seismic proïŹles along the updip limit to identify the shallow, velocity strengthening part of the plate boundary. Another theme is the study of the structure of the updip limit and the hanging upper plate caused by the subducting oceanic ridge

    BDS GNSS for Earth Observation

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    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

    Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning

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    This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a widely-used method of data transmission in wireless communications, when abrupt changes occur in the channel. In highly mobile applications, new dynamics appear that might make channel tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with time. Simple examples might be the di erent channel dynamics a train receiver faces when it is close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being reborn, and so tap birth-death detection is of the essence. In order to improve the quality of communications, we delved into mathematical methods to detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/ Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt change detection by informing and inspiring the creation of low-complexity implementations for real-world channel tracking. In particular, two such novel trackers were created: the Simpli- ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF) schemes. The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies the three following heuristics for tap birth-death detection: a) detect death if the tap gain jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly converged into approximately zero (memory detection); c) detect birth if the tap gain is far from zero. The precise parameters for these three simple rules can be approximated with simple theoretical derivations and then ne-tuned through extensive simulations. The status detector for each tap using only these three computationally inexpensive threshold comparisons achieves an error reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations. This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems. The underlying RST framework for the SMAP was then extended to combined death/birth and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate SNR detection. The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy. These proposed Neural-Kalman schemes can work as novel trackers for multipath channels, since they are robust to wide variations in the probabilities of tap birth and death. Such robustness suggests a single, low-complexity NNKF could be reusable over di erent tap indices and communication environments. Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to model, detect and track such changes, providing a geometric justi cation for this and additional non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring campaigns is included. For this generalized framework of abrupt channel changes that includes partial tap lateral hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for integration in NNKF schemes. Finally, the newly found understanding of abrupt changes and the interactions between Kalman lters and neural networks is leveraged to analyze the neural consequences of abrupt changes and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts (HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process. The forecasting problem in the presence of expert disagreements is illustrated with a hopping LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their existence in the areas of neuroscience and nance. Apart from providing speci c examples, practical guidelines and historical (out)performance for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural- Kalman architecture for ever granular stereotyping providing a practical solution for continual learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera
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