1,656 research outputs found

    IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments

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    Distributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities. Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc. This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts

    Acoustic Propagation Variation with Temperature Profile in Water Filled Steel Pipes at Pressure

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    Conventional pressure leak testing of buried pipelines compares measurements of pressure with pipe wall temperature. An alternative proposed method uses acoustic velocity measurements to replace pipe wall temperature measurements. Early experiments using this method identified anomalous results of rising acoustic velocities thought to be caused by air solution. This research investigated the anomalous acoustic velocity measurements by evaluation of acoustic velocity variation with pressure, temperature and air solution. Quiescent air solution rate experiments were carried out in water filled pipes. Computer modelling of the air bubble shape variation with pipe diameter was found to agree with bubble and drop experiments over the pipe diameter range from 100 mm to 1000 mm. Bubbles were found to maintain constant width over a large volume range confirmed by experiments and modelling

    Engineered Emulsions Stabilised by Thermoresponsive Branched Copolymers for Pharmaceutical Applications

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    This research work explored thermoresponsive emulsions and investigated their potential in delivering drugs through in situ gelling pharmaceutical formulations. Employing thermoresponsive branched copolymer surfactants (BCSs), this study established their efficacy in creating stable emulsions with reversible gelation triggered by changes in temperature. While previous research had shown BCSs' capacity to transition emulsions to gels via pH alteration, this study innovatively proposed the concept of thermoresponsive emulsions that respond at physiological temperatures. The focus was on generating materials capable of shifting from a liquid to a gel state upon warming, promising enhanced healthcare technologies like in situ gel-forming materials for diverse drug delivery routes. The thermoresponsive BCSs used to stabilise the emulsions that showed sol-gel transition upon heating were synthesised with a lower critical solution temperature (LCST) monomer, a hydrophilic macromonomer, a crosslinker and a hydrophobic chain transfer agent. All these components were proven to contribute to the gelation behaviour. The research investigated the interplay between temperature and BCS structure at both macro and nanoscales, dissecting how these engineered emulsions react to temperature shifts. Moreover, the emulsions held the potential for solubilisation of various drug chemistries and explored their drug delivery activities via in situ gelation. This thesis evaluated the rheology of the engineered emulsions based on polymer architecture, branching, molecular weight, and hydrophobic end groups, influencing gel formation on heating. Furthermore, poly(ethylene glycol) methyl ether methacrylate’s role in controlling emulsion responsiveness was highlighted, with longer poly(ethylene glycol) chains inducing thermogelation and shorter chains causing emulsion breakdown upon mild heating. The ratio of LCST monomer to hydrophilic macromonomer tightly governed gelation temperature. Expanding these findings, the research explored various pharmaceutically relevant oils in the emulsion system, along with additives to enhance stability. The addition of methylcellulose significantly improved stability, and small-angle neutron scattering (SANS) helped to understand the gelation mechanism and the nanoscale processes within BCS-stabilised emulsions. Furthermore, these emulsion systems were investigated as pharmaceutical formulations, analysing drug release mechanisms and compatibility with nasal spray devices. These advanced emulsions showed promise in controlled drug release and nasal spray device compatibility. In summary, this thesis showed a new frontier in drug delivery through temperatureresponsive emulsions, offering smart dosage forms with transformative potential. The work not only advances understanding in thermoresponsive engineered emulsions but also lays the groundwork for personalised medicine and targeted drug delivery, promising improved patient outcomes and reduced dosing frequency

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Contributions to improve the technologies supporting unmanned aircraft operations

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    Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

    Design and optimisation of solar sail orbits in proximity of asteroids

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    A solar sail is a large reflective membrane which is capable of producing thrust for a spacecraft by the reflection of sunlight. Such a propellant-less propulsion system can offer solutions to high-energy missions which would be impossible for conventional propulsion systems. As a result, this technology has been proposed by many authors as the ideal candidate for a multiple asteroid rendezvous mission. At the time of writing, there are more than 30,000 known near-Earth asteroids (NEAs) alone. Adding to this those contained in the main belt and elsewhere in the solar system, the abundance of these small rocky worlds becomes apparent. Focusing only on the NEAs, there are many reasons for interest in missions to these bodies. In the first instance, they represent the earliest building blocks of the rocky worlds of the solar system, and are often still in pristine condition, similar to how they would have been since these earliest moments. As such, there is massive scientific interest in visiting and extracting samples of their constituent materials. There is another community which is also interested in the extraction of these materials: the future asteroid miners. This mining could provide propellant for deep space missions, materials for in-space infrastructure and potentially also in the return of minerals which are rare on Earth, and so of great value. However, although these bodies provide many opportunities, they are not without threat. Although the frequency of impacts of large bodies capable of causing considerable damage to Earth-based infrastructure is relatively low, there are still recent examples of just such events. With the potential for large scale loss of life due to an asteroid impacting populated areas, the science of planetary defence requires greater knowledge of the make-up of these bodies. Yet another reason for mission designers to examine further options in achieving efficient missions to these bodies. It would be beneficial, in terms of cost, for a single spacecraft to be able to carry out a mission to multiple asteroids. Such a high-energy mission is ideally suited to the solar sail. Although the literature has provided many works on orbital transfers to multiple bodies, the operation of the sail when in proximity of the asteroid has not received quite as much attention. It is in this phase of the mission, where the science objectives would be carried out, that this thesis focuses. There are numerous challenges which the sail faces in the near-asteroid environment. These include the irregular gravity field, the strength of the acceleration provided by the sail in a relatively weak gravitational field, the often fast rotational velocities of the asteroid and higher demands on slew rates for the sail due to the shorter period of low-altitude orbits. The work will consider three main proximity phases. The first operation is in the control of an orbit using the solar sail in an irregular gravity field. In this operation, the sail must counter the perturbative effects of a non-spherical body. This manifests in the rotation of the orbit node line, referred to as nodal regression. A new tool, referred to as the Control Transition Matrix (CTM), which aids in forcing a periodic orbit solution over multiple orbits is then presented. The second operation deals with the control of a sail at the point of and subsequent to the deployment of a lander and during the deployment of a series of small ChipSat probes. The landing conditions for deployments from various locations around the asteroid are analysed before the deployment is presented from a low-asteroid orbit. The control of the sail along a nominal orbit while the lander is still on-board is presented before the sail control subsequent to the lander deployment is considered. Given the high velocity impacts for a ballistic lander deployed at large distances from the surface, an alternative mission scenario of the deployment of small ChipSat probes is presented. These probes are envisaged to carry out their science goals during the descent and so the landing conditions are less important. The final operation is in the gravitational capture of the sail around the asteroid. This work provides a preliminary analysis of the capability of the sail in achieving this by using a simple on/off control law. Following this, a more detailed two-phase approach is presented. In the first “initial capture” phase, the sail uses the value of Jacobi constant in the 3 body system as a guide to reduce the orbit radius to within a defined region. After this, the “orbit shaping” phase aims to circularise the orbit at this radius. Subsequently, preliminary investigations into an optimal approach are presented. In controlling the effects due to the non-spherical asteroid shape, an optimally controlled solution, where a minimum effort control law is sought, is presented. Following this, a novel method of updating a control law was successfully applied to force a periodic orbit. In the work carried out on lander deployment, it was found that the sail was capable of maintaining a periodic orbit after the point of lander separation by application of time-delay feedback control. For the deployment of a series of small probes, it was found that maintaining a fixed attitude for the sail during the deployment was not considerably different in station-keeping performance compared with LQR control, and performed this with no effort required of the sail. Finally, in the work on capture, the two-phase approach provided successful capture trajectories down to the desired orbit radius. The work showed that, for reducing size of asteroid, there was a reduction in the time to capture. This is due to the fact that the same size of sail is used in the weakening gravity field of each asteroid. This makes the sail relatively more powerful and so able to affect quicker capture. It was also seen that long period capture trajectories are compounded by the need for the sail to spend periods of time waiting for the position of the Sun relative to the orbit to be in such a way as to permit the capture operations to proceed. There was also the successful demonstration of an optimally controlled capture which minimised the orbit semi-parameter over one orbit revolution. The work contained in this thesis provides preliminary analysis for the consideration of future solar sail mission designers in the proximity operations of a sail near an asteroid. The findings presented here have shown that the sail can be of considerable utility in these proximity operations. They also present challenges to the mission designer given the continuous thrust that they may provide. Where a powerful sail may benefit the interplanetary phase of a mission in reaching many more asteroids further from the Earth, this can also present a challenge in the relatively weak asteroid gravitational field. However, these challenges are not insurmountable and so the sail remains a promising option for these high-energy missions

    Set-based state estimation and fault diagnosis using constrained zonotopes and applications

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    This doctoral thesis develops new methods for set-based state estimation and active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii) discrete-time nonlinear systems whose trajectories satisfy nonlinear equality constraints (called invariants), (iii) linear descriptor systems, and (iv) joint state and parameter estimation of nonlinear descriptor systems. Set-based estimation aims to compute tight enclosures of the possible system states in each time step subject to unknown-but-bounded uncertainties. To address this issue, the present doctoral thesis proposes new methods for efficiently propagating constrained zonotopes (CZs) through nonlinear mappings. Besides, this thesis improves the standard prediction-update framework for systems with invariants using new algorithms for refining CZs based on nonlinear constraints. In addition, this thesis introduces a new approach for set-based AFD of a class of nonlinear discrete-time systems. An affine parametrization of the reachable sets is obtained for the design of an optimal input for set-based AFD. In addition, this thesis presents new methods based on CZs for set-valued state estimation and AFD of linear descriptor systems. Linear static constraints on the state variables can be directly incorporated into CZs. Moreover, this thesis proposes a new representation for unbounded sets based on zonotopes, which allows to develop methods for state estimation and AFD also of unstable linear descriptor systems, without the knowledge of an enclosure of all the trajectories of the system. This thesis also develops a new method for set-based joint state and parameter estimation of nonlinear descriptor systems using CZs in a unified framework. Lastly, this manuscript applies the proposed set-based state estimation and AFD methods using CZs to unmanned aerial vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most of the research work has already been published in DOIs 10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353, 10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484, 10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638, 10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042

    Astrophysics with the Laser Interferometer Space Antenna

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    The Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy, and, as such, it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and space-born instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed; ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or interme-diate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help making progress in the different areas. New research avenues that LISA itself, or its joint exploitation with upcoming studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

    Electron Thermal Runaway in Atmospheric Electrified Gases: a microscopic approach

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    Thesis elaborated from 2018 to 2023 at the Instituto de Astrofísica de Andalucía under the supervision of Alejandro Luque (Granada, Spain) and Nikolai Lehtinen (Bergen, Norway). This thesis presents a new database of atmospheric electron-molecule collision cross sections which was published separately under the DOI : With this new database and a new super-electron management algorithm which significantly enhances high-energy electron statistics at previously unresolved ratios, the thesis explores general facets of the electron thermal runaway process relevant to atmospheric discharges under various conditions of the temperature and gas composition as can be encountered in the wake and formation of discharge channels
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