Parametric analysis of an L-band deployable offset reflector for CubeSats

Thanks to the advances in the miniaturization and improved power consumption efficiency of electronics, computers, cell phone technologies, etc., today’s spacecrafts and payloads are reducing their size and increasing their performance. However, not all systems can be reduced, as their dimensions are determined by the laws of physics. This study is focused on the design of an L-band reflector antenna for a CubeSat-based Earth observation mission devoted to measure the surface soil moisture. Two configurations of deployable parabolic reflector antennas and meshes are presented from the mechanical point of view. The electromagnetic analyses including the antenna feeder are also presented. It is found that the regular circular mesh performs slightly better than the irregular one, although requires a more careful manufacturing process.This work was supported in part by the Project “Sensing With Pioneering Opportunistic Techniques-SPOT,” Spanish Agencia Estatal de Investigación under Grant RTI2018-099008-B-C21 and EU ERDF funds, and in part by UPC-CommSensLab María de Maeztu Unit under Grant MDM-2016-0600.Peer ReviewedPostprint (author's final draft

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Characterization of user mobility trajectories by implementing clustering techniques

Current and legacy technologies for wireless communications are facing an explosive demand of capacity and resources, triggered by an exponential growing of traffic, mainly due to the proliferation of smartphones and the introduction of demanding multimedia and video applications. There is the anticipation that future generation of wireless communications systems, 5G, will attend the growing demand on capacity and network resources, along with the necessity for blending novel technology concepts including Internet of Things, machine communications, the introduction of heterogeneous network architectures, massive arrays of antennas and dynamic spectrum allocation, among others. Moreover, self-organizing networks (SON) functions incorporated in present mobile communication standards provide limited levels of proactivity. Therefore, it is foreseen that future network are required of highly automation and real-time reaction to network problems, topology changes and dynamic parameterization. The flexibility to be introduced in 5G networks by incorporating virtualized hardware architecture and cloud computing, allow the inclusion of big data analytics capabilities for finding insights and taking advantage of the vast amounts of data generated in the network system. The full embodiment of big data analytics among the Radio Access Network optimization and planning processes, allow gathering an end to end knowledge and reaching the individual user level granularity. The purpose of this work is to provide a case of study for smartly processing collected data from mobility traces by using a hierarchical clustering function, an unsupervised method of data analytics, for characterizing the different user mobility trajectories to extract an individual user mobility profile. The methodology proposed references a knowledge discovery framework which uses Artificial Intelligence processes for finding insights in collected network data and the use of this knowledge for driving SON functions, other optimization and planning processes, and novel operator business cases

Soil water management: evaluation of infiltration in furrow irrigarion systems, assessing water and salt content spatially and temporally in the Parc Agrari del Baix Llobregat area.

Sustainability of irrigated agriculture is a growing concern in the Baix Llobregat area. Although irrigated land accounts for a substantial proportion of food supply to the local market, it has been, and still is increasingly degraded by poor agricultural management. This dissertation focuses on ways to evaluate furrow irrigation and to assess soil water content and soil salinity (temporally and spatially) under usual farmers's management practices. This dissertation meets these goals through an extensive study of relevant literature and the implementation of practical research. The latter was carried out with a case study on representative fields of the area. Empirical and stochastic models were applied to evaluate furrow irrigation as well as to monitor water flow and solute transport in the root zone. This research produced a number of key findings: first, evaluating furrow irrigation confirmed that 40-43 % of the applied water would have been saved in the study fields if irrigation was stopped as soon as soil water deficit was fully recharge taking the amount of water needed for salt leaching into account, and that the application efficiency (AE) would increase from 48% to 84% and from 41% to 68% (Field 1 and Field 2, respectively). Second, the predictions of soil water content using ARIMA models were logical, and the next irrigation time and its effect on soil water content at the depth of interest were correctly estimated. Third, considering the linear relationship eb-sb, by transforming the Hilhorst (2000) model, which is based on the deterministic linear relationship eb-sb, into a time- varying Dynamic Linear Model (DLM) enabled us to validate this relationship under field conditions. An offset esb=0 value was derived that would ensure the accurate prediction of sp from measurements of sb. It was shown that the offset esb=0 varied for each depth in the same soil profile. A reason for this might be changes in soil temperature along the soil profile. The sp was then calculated for each depth in the root zone. Fourth, by using a (multiple input--single output) transfer function model, the results showed that soil water content and soil temperature had a significant impact on soil salinity, and soil salinity, predicted as a function of soil water and soil temperature, was correctly estimated. Finally, applying the analysis of variance (ANOVA), the results showed that the irrigation frequency, according to the farmer's usual management practice, had statistically significant effects on soil salinity behaviour, depending on soil depth and position (furrow, ridge). Moreover, it was shown that at the end of the crop's cycle the farmers left the field with less soil salinity, for each depth, than at the beginning of the crop's agricultural cycle

Locality analysis and its hardware implications for graph pattern mining

En aquest treball hem abordat l'acceleració d'aplicacions GPM des de la perspectiva oferta per l'arquitectura NDP. Hem desenvolupat una nova eina de simulació, basada en la integració de dos coneguts simuladors: ZSim (per als cores i les caches) i Ramulator (per a la memòria). Hem hagut de dissenyar específicament aquesta integració perquè la implementació disponible per a la utilització conjunta de tots dos simuladors no aprofita les tècniques que fa servir ZSim per reduir la pèrdua de precisió. Després hem implementat al simulador un accelerador GPM que utilitza l'arquitectura NDP (NDMiner), que representa l'estat de l'art. L'eina de simulació permet realitzar un detallat ``profiling'' de NDMiner, molt útil per identificar els seus punts febles. D'aquesta manera, el simulador facilita el disseny d'estratègies per millorar el rendiment de l'accelerador. Mitjançant una sèrie dexperiments en simulació, hem elaborat una sèrie de propostes concretes per solucionar els problemes detectats i millorar NDMiner.En este trabajo, hemos abordado la aceleración de aplicaciones GPM desde la perspectiva ofrecida por la arquitectura NDP. Hemos desarrollado una nueva herramienta de simulación, basada en la integración de dos conocidos simuladores: ZSim (para los cores y las caches) y Ramulator (para la memoria). Hemos tenido que diseñar específicamente esta integración porque la implementación disponible para la utilización conjunta de ambos simuladores no aprovecha las técnicas que usa ZSim para reducir la pérdida de precisión. Luego hemos implementado en el simulador un acelerador GPM que utiliza la arquitectura NDP (NDMiner), entendemos que representa el estado-del-arte al respecto. La herramienta de simulación permite realizar un detallado ``profiling'' de NDMiner, muy útil para identificar sus puntos débiles. De esta forma, el simulador facilita el diseño de estrategias para mejorar el rendimiento del acelerador. Mediante una serie de experimentos en simulación, hemos elaborado una serie de propuestas concretas para solucionar los problemas detectados y mejorar NDMiner.In this work, we have addressed the acceleration of GPM applications from the perspective offered by the NDP architecture. We have developed a new simulation tool, based on the integration of two well-known simulators: ZSim (for the cores and the caches) and Ramulator (for the memory). The need to carry out this integration arises from the fact that the implementation available for the joint use of both simulators does not take advantage of the techniques that ZSim uses to reduce the loss of precision. We have implemented in simulation a state-of-the-art GPM accelerator based on the NDP architecture (NDMiner). The new simulation tool allows a detailed NDMiner profiling to identify its weak points. Therefore, it helps to design strategies that alleviate those bottlenecks and improve their performance. Consequently, after realizing experiments with the new simulator, we have elaborated a series of concrete proposals to solve some of the problems detected and to improve NDMiner.Outgoin

Clustering and visualization of Lithium-Ion battery data for second life applications

L'ús de bateries de segona vida és una de les estratègies més prometedores per a resoldre el problema del reciclatge de piles en el futur. L'objectiu d'aquesta recerca és aportar solucions pràctiques de garbellat, reagrupació i reutilització donant un nou ús a les piles de liti retirades. Donar una segona oportunitat a aquestes bateries retirades no sols té beneficis ecològics i mediambientals, sinó que també és rendible des del punt de vista econòmic. Per a aquest projecte, s'ha aprofitat el conjunt de dades que va registrar la NASA per a observar el comportament de les bateries elèctriques d'ió-liti al llarg de la seva vida per a realitzar un estudi que permet predir la vida útil d'una bateria. Mitjançant diferents tècniques d'aprenentatge automàtic, principalment SVM, s'ha aconseguit entrenar un model que pugui predir aquesta característica. D'aquesta manera es pretén aportar en un camp on la sostenibilitat i l'economia circular són cada vegada més importants. En el futur, es podrien implementar estratègies basades en l'ús de bateries de segona vida en diferents sectors, des de l'emmagatzematge d'energia fins a la mobilitat elèctrica. Realitzant aquest projecte es pretén continuar pel camí de la recerca a partir de tècniques de ML i IA per a contribuir a una economia més sostenible i responsable.The use of second life batteries is one of the most promising strategies to solve the problem of battery recycling in the future. The objective of this research is to provide practical solutions of screening, regrouping and reuse by giving a new use to retired lithium batteries. Giving a second chance to these retired batteries not only has ecological and environmental benefits, but is also cost-effective from an economic point of view. For this project, the data set recorded by NASA to observe the behavior of lithium-ion electric batteries over their lifetime has been used to conduct a study to predict the lifetime of a battery. Using different machine learning techniques, mainly SVM, it has been possible to train a model that can predict this characteristic. In this way, it aims to contribute to a field where sustainability and circular economy are becoming increasingly important. In the future, strategies based on the use of second life batteries could be implemented in different sectors, from energy storage to electric mobility. This project aims to continue on the path of research based on ML and AI techniques to contribute to a more sustainable and responsible economy

Method for forecasting ionospheric electron content fluctuations based on the optical flow algorithm

We present the optical flow algorithm for forecasting the rate of total electron content index (OFROTI). It consists of a method for predicting maps of rapid fluctuations of ionospheric electron content in terms of global navigation satellite system (GNSS) dual-frequency phase measurements of the rate of change of total electron content index (ROTI). The forecast is made in space and time, at horizons up to more than 6 h. These forecast maps will consist of the ROTI spatial distribution in the northern hemisphere above 45° latitude. The prediction method models the ROTI spatial distribution as a pseudoconservative flux, i.e., exploiting the inertia of the flux of ROTI to determine the future position. This idea is implemented as a modification of the optical flow image processing technique. The algorithm has been modified to deal with the nonconservation of the ROTI quantity in time. We show that the method can predict both, the local value of ROTI and also the regions with ROTI above a given level, better than the prediction using the current map as forecast, i.e., predicting by a current map from horizons of 15 min up to 6 h. The method was tested on 11 representative active and calm days during 2015 and 2018 from the multi-GNSS (GPS, GLONASS, Galileo, and Beidou) multifrequency measurements of more than 250 multi-GNSS receivers above 45°N latitude, including the high rate (1 Hz) measurements of Greenland geodetic network (GNET) network among the International GNSS Service network.This work is funded by ESA ITT “Forecasting Space Weather Impacts on Navigation Systems in the Arctic (Green-land Area)” Expro+, Activity No. 1000026374. The GNET GNSS observations from Greenland was kindly provided by The Danish Agency for Data Supply and Efficiency, in the Danish Ministry of Energy, Utilities and Climate, Copenhagen, DenmarkPeer ReviewedPostprint (author's final draft

Design, implementation and testing of a wire scanner prototype

A wire scanner is an electro-mechanical device which measures the transverse density profile of a particle beam. It consists on a rotating fork with a thin carbon wire attached to its endpoints. The wire intersects the beam in an intermittent manner. Minimizing the wire vibrations is important to improve the accuracy of the measurement. This work will focus on two points: the evelopment of an optimal motion pattern for the fork to reduce the wire vibrations, and the design and assembling of an xperimental setup to test de behaviour of the system under those motion patterns. The setup will be a simplification of the real system. The main actuator (an electrical motor) will be replaced by a piezo-actuator with a lever arm which will transform the linear displacement of the actuator into rotations. The actuator will be driven with the designed motion patterns, and the resulting vibrations will be measured by means of two electronic readout systems based on a Wheatstone bridge