Tsunamis from source to coast

Tsunami disasters pose a significant threat to coastal communities. In the last decades, tsunamis caused enormous destruction and exceeding 250000 fatalities. International efforts led to sig-nificant advances in tsunami science and research, but recent events demonstrated some limi-tations. Thus, it is essential to increase our knowledge of the source to coast tsunami phenom-enon. A better understanding of potential tectonic structures and other generation mechanisms is needed, especially in complex geologic domains or where sources are unknown. Furthermore, we need to improve Tsunami Warning Systems (TWSs) to provide timely alerts for communi-ties in the near field. Therefore, potential tsunamigenic sources in the diffuse plate boundary setting and the near field of the southwest Iberian margin (SWIM) are investigated. For the March 31, 1761, trans-atlantic tsunami, numerical modelling has been used to propose a structure that agrees with tsunami travel times, tsunami observations, macroseismic data, and kinematic plate modelling. Since there exists a description of a tsunami for the November 11, 1858, Sétubal earthquake, its source has been investigated using macroseismic analysis. The analysis suggests a local structure in a compressive regime with weak to moderate tsunamigenic potential. Future tsu-nami hazard assessments need to include the sources of the investigated events. To quickly estimate the tsunami impact, the Tsunami Runup Predictor (TRP), an empirical source-to-coast method to instantly provide first-order estimates of the tsunami runup based on waveform parameters has been developed. The TRP is helpful for emergency managers and evacuation planning for near-field events. Moreover, the author of this thesis contributed to the tsunami impact assessment of September 28, 2018, Palu tsunami, where tsunamis generated by multiple sources caused runup heights up to 9.2 m. However, for local sources, tsunami warning remains challenging; thus, communities need to be prepared how to respond appropriately to earthquakes and tsunamis with or without warning

Evaluating Sustainability of Mass Rapid Transit Stations in Dubai

A transport business that has reached financial sustainability is one that is providing a service at a price that not only covers its costs but also creates a profit for upcoming contingencies. A focus on rail infrastructure financial sustainability is of paramount importance to guarantee the availability of punctual rail transport to remote potential users. To evaluate the sustainability of mass rapid transit on the relation among hypothetical key aspects of sustainability—perception of property; willingness to pay for maintenance, repair, and operations; confidence in the Roads and Transport Authority; and citizen participation in the rail project—and railway service punctuality, the most important result variable, was studied according to the specialized literature on rail transport sustainability. Leading information was collected by means of personal questionnaires of more than 1000 railway users according to the Krejcie Morgan formula for the calculation of the sample size knowing the population size. Qualitative plus quantitative information was gathered from different ways (technical test of the rail system, discussions with users, focus-group discussions, and interviews with key informers).The outputs by means of the statistical analysis allowed understanding two key perceptions. Firstly, beyond a half decade after construction, during a system intervention, a smaller perception of public property for the railway system was related to better service punctuality. This idea contrasts with the vast majority of the publications, which highlight a regular, direct relationship between perception of property and sustainability of railway systems. Secondly, in spite of three-quarters of users accepting that they would contribute monetarily for maintenance, repair, and operations service, such payments were not imminent because of the lack of confidence in the Roads and Transport Authority. In this situation, more than one-third of the metro stations were identified as non-punctual, beyond a half decade after construction

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams

Parking guiding system with occupation prediction

Parking availability is an increasingly scarce and expensive resource within large cities, and this problem is considered to be one of the most critical transportation management system inside a big city. To approach this problem a proof of concept is presented as a way to guide a driver to the possible free parking lot through a prediction process using past data, correlated with traffic, weather conditions and time period features (year, month, day, holidays, and so on). A feature selection was performed by the study of data patterns, in order to understand the parking lot affluence and how certain features influence them, as well as to comprehend the sudden changes in the total occupation of the parking lot and which features really matter and have an impact on the total occupation. Those conclusions helped to create a robust and efficient predictive model in order to predict the parking lot availability rate more accurately. Three algorithms were used to build the predictive models as a way to test the most efficient and accurate one, namely Gradient Boosting Machine, Decision Random Forest and Neural Networks. Various types of models were tested with the aim of improving the results obtained, as well as understanding the impact of each of the processing of the data used. To complement this, a decision algorithm was created to guide the driver to the most optimal parking lot that presents better conditions, taking into account the location and driver characteristics, like the park more likely to have an available parking space, closer to the user’s current position or a more attractive price for the driver. Finally, these developments are integrated into a mobile application in order to work like an interface that the driver can interact.A disponibilidade de estacionamento é um recurso cada vez mais escasso e caro nas grandes cidades, e este problema é considerado um dos mais críticos nos sistemas de gestão de transportes dentro de uma grande cidade. Para abordar este problema, uma prova de conceito é apresentada como uma forma de guiar um condutor para o parque de estacionamento com lugares disponíveis através de um processo de previsão usando dados passados, correlacionados com o tráfego, condições climáticas e características do período de tempo (ano, mês, dia, feriados, e assim por diante). Uma seleção de características foi realizada pelo estudo de padrões de dados, a fim de entender a afluência do estacionamento e como certas características os influenciam, bem como para compreender as mudanças repentinas na ocupação total do estacionamento e quais características realmente importam e têm um impacto sobre a ocupação total. Essas conclusões ajudaram a criar um modelo preditivo robusto e eficiente a fim de prever a taxa de disponibilidade do estacionamento com mais precisão. Três algoritmos foram usados para construir os modelos preditivos como forma de testar o mais eficiente e preciso, a saber: Gradient Boosting Machine, Decision Random Forest e Neural Networks. Foram também testados vários tipos de modelos com o objetivo de melhorar os resultados obtidos, bem como compreender o impacto de cada um dos processamentos de dados utilizados. Para complementar, foi criado um algoritmo de decisão para orientar o condutor para o parque de estacionamento mais indicado e que apresente melhores condições, tendo em conta a localização e as características do condutor, como o mais provável de ter um lugar de estacionamento disponível, mais próximo da posição atual do utilizador ou um preço mais atrativo para o condutor. Finalmente, estes desenvolvimentos são integrados numa aplicação móvel de forma a que o utilizador consiga aceder através de uma interface

Robust Design of Machine Learning based GNSS NLOS Detector with Multi-Frequency Features

The robust detection of non-line-of-sight (NLOS) signals is of vital importance for land-based and close-to-land safe navigation applications. Their reception and use without adapted mitigation may induce unacceptable inaccuracy and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms have recently shown as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging approach that must however, be tackled carefully to avoid biased estimation results and guarantee generalized algorithms for different scenarios, receivers, antennas and their specific installations and configurations. This work has provided new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focused on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based only on single frequency features. The detection by means of logistic regression not only provides a binary LOS/NLOS decision, but also an associated probability which can be used in the future as a mean to weight specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state of the art, reaching more than 90% detection accuracy in the validation scenarios evaluated

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

Advancements in Real-Time Simulation of Power and Energy Systems

Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics

Hands-on Science. Advancing Science. Improving Education

The book herein aims to contribute to the advancement of Science to the improvement of Science Education and to an effective implementation of a sound widespread scientific literacy at all levels of society. Its chapters reunite a variety of diverse and valuable works presented in this line of thought at the 15th International Conference on Hands-on Science “Advancing Science. Improving Education

EBook proceedings of the ESERA 2011 conference : science learning and citizenship

This ebook contains fourteen parts according to the strands of the ESERA 2011 conference. Each part is co-edited by one or two persons, most of them were strand chairs. All papers in this ebook correspond to accepted communications during the ESERA conference that were reviewed by two referees. Moreover the co-editors carried out a global reviewing of the papers.ESERA - European Science Education Research Associatio