Spanish National GPS Reference Station Network (ERGPS)

The Instituto Geográfico Nacional de España, thought its geodesy department, since 1997 has carried out the establisment of a GPS Reference Station Network (ERGPS) delivered all around Spain which allows millimetric co-ordinate results, as well as velocity fields in a Global Reference System (ITRFxx). It serves as support for other geodetic networks. Some of these stations are being integrated into the EUREF (EUropean REference Frame) Permanent Station Network. The ERGPS forms the zero order of the Spanish new geodesy

Some techniques for automated, resource-aware distributed and mobile computing in a multi-paradigm programming system

Distributed parallel execution systems speed up applications by splitting tasks into processes whose execution is assigned to different receiving nodes in a high-bandwidth network. On the distributing side, a fundamental problem is grouping and scheduling such tasks such that each one involves sufñcient computational cost when compared to the task creation and communication costs and other such practical overheads. On the receiving side, an important issue is to have some assurance of the correctness and characteristics of the code received and also of the kind of load the particular task is going to pose, which can be specified by means of certificates. In this paper we present in a tutorial way a number of general solutions to these problems, and illustrate them through their implementation in the Ciao multi-paradigm language and program development environment. This system includes facilities for parallel and distributed execution, an assertion language for specifying complex programs properties (including safety and resource-related properties), and compile-time and run-time tools for performing automated parallelization and resource control, as well as certification of programs with resource consumption assurances and efñcient checking of such certificates.

Evaluación y análisis de la trazabilidad metrológica en redes para la verificación y calibración de instrumental GNSS (Global Navigation Satellite System)

Los estudios realizados hasta el momento para la determinación de la calidad de medida del instrumental geodésico han estado dirigidos, fundamentalmente, a las medidas angulares y de distancias. Sin embargo, en los últimos años se ha impuesto la tendencia generalizada de utilizar equipos GNSS (Global Navigation Satellite System) en el campo de las aplicaciones geomáticas sin que se haya establecido una metodología que permita obtener la corrección de calibración y su incertidumbre para estos equipos. La finalidad de esta Tesis es establecer los requisitos que debe satisfacer una red para ser considerada Red Patrón con trazabilidad metrológica, así como la metodología para la verificación y calibración de instrumental GNSS en redes patrón. Para ello, se ha diseñado y elaborado un procedimiento técnico de calibración de equipos GNSS en el que se han definido las contribuciones a la incertidumbre de medida. El procedimiento, que se ha aplicado en diferentes redes para distintos equipos, ha permitido obtener la incertidumbre expandida de dichos equipos siguiendo las recomendaciones de la Guide to the Expression of Uncertainty in Measurement del Joint Committee for Guides in Metrology. Asimismo, se han determinado mediante técnicas de observación por satélite las coordenadas tridimensionales de las bases que conforman las redes consideradas en la investigación, y se han desarrollado simulaciones en función de diversos valores de las desviaciones típicas experimentales de los puntos fijos que se han utilizado en el ajuste mínimo cuadrático de los vectores o líneas base. Los resultados obtenidos han puesto de manifiesto la importancia que tiene el conocimiento de las desviaciones típicas experimentales en el cálculo de incertidumbres de las coordenadas tridimensionales de las bases. Basándose en estudios y observaciones de gran calidad técnica, llevados a cabo en estas redes con anterioridad, se ha realizado un exhaustivo análisis que ha permitido determinar las condiciones que debe satisfacer una red patrón. Además, se han diseñado procedimientos técnicos de calibración que permiten calcular la incertidumbre expandida de medida de los instrumentos geodésicos que proporcionan ángulos y distancias obtenidas por métodos electromagnéticos, ya que dichos instrumentos son los que van a permitir la diseminación de la trazabilidad metrológica a las redes patrón para la verificación y calibración de los equipos GNSS. De este modo, ha sido posible la determinación de las correcciones de calibración local de equipos GNSS de alta exactitud en las redes patrón. En esta Tesis se ha obtenido la incertidumbre de la corrección de calibración mediante dos metodologías diferentes; en la primera se ha aplicado la propagación de incertidumbres, mientras que en la segunda se ha aplicado el método de Monte Carlo de simulación de variables aleatorias. El análisis de los resultados obtenidos confirma la validez de ambas metodologías para la determinación de la incertidumbre de calibración de instrumental GNSS. ABSTRACT The studies carried out so far for the determination of the quality of measurement of geodetic instruments have been aimed, primarily, to measure angles and distances. However, in recent years it has been accepted to use GNSS (Global Navigation Satellite System) equipment in the field of Geomatic applications, for data capture, without establishing a methodology that allows obtaining the calibration correction and its uncertainty. The purpose of this Thesis is to establish the requirements that a network must meet to be considered a StandardNetwork with metrological traceability, as well as the methodology for the verification and calibration of GNSS instrumental in those standard networks. To do this, a technical calibration procedure has been designed, developed and defined for GNSS equipment determining the contributions to the uncertainty of measurement. The procedure, which has been applied in different networks for different equipment, has alloweddetermining the expanded uncertainty of such equipment following the recommendations of the Guide to the Expression of Uncertainty in Measurement of the Joint Committee for Guides in Metrology. In addition, the three-dimensional coordinates of the bases which constitute the networks considered in the investigationhave been determined by satellite-based techniques. There have been several developed simulations based on different values of experimental standard deviations of the fixed points that have been used in the least squares vectors or base lines calculations. The results have shown the importance that the knowledge of experimental standard deviations has in the calculation of uncertainties of the three-dimensional coordinates of the bases. Based on high technical quality studies and observations carried out in these networks previously, it has been possible to make an exhaustive analysis that has allowed determining the requirements that a standard network must meet. In addition, technical calibration procedures have been developed to allow the uncertainty estimation of measurement carried outby geodetic instruments that provide angles and distances obtained by electromagnetic methods. These instruments provide the metrological traceability to standard networks used for verification and calibration of GNSS equipment. As a result, it has been possible the estimation of local calibration corrections for high accuracy GNSS equipment in standardnetworks. In this Thesis, the uncertainty of calibration correction has been calculated using two different methodologies: the first one by applying the law of propagation of uncertainty, while the second has applied the propagation of distributions using the Monte Carlo method. The analysis of the obtained results confirms the validity of both methodologies for estimating the calibration uncertainty of GNSS equipment.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Community Structurein Soil Porous System.

Soil is well recognized as a highly complex system. The interaction and coupled physical, chemical, and biological processes and phenomena occurring in the soil environment at different spatial and temporal scales are the main reasons for such complexity. There is a need for appropriate methodologies to characterize soil porous systems with an interdisciplinary character. Four different real soil samples, presenting different textures, have been modeled as heterogeneous complex networks, applying a model known as the heterogeneous preferential attachment. An analytical study of the degree distributions in the soil model shows a multiscaling behavior in the connectivity degrees, leaving an empirically testable signature of heterogeneity in the topology of soil pore networks. We also show that the power-law scaling in the degree distribution is a robust trait of the soil model. Last, the detection of spatial pore communities, as densely connected groups with only sparser connections between them, has been studied for the first time in these soil networks. Our results show that the presence of these communities depends on the parameter values used to construct the network. These findings could contribute to understanding the mechanisms of the diffusion phenomena in soils, such as gas and water diffusion, development and dynamics of microorganisms, among others.

Evolution of the Riparian forest corridor in a large Mediterranean river system

The well-documented re-colonisation of the French large river basins of Loire and Rhone by European otter and beaver allowed the analysis of explanatory factors and threats to species movement in the river corridor. To what extent anthropogenic disturbance of the riparian zone influences the corridor functioning is a central question in the understanding of ecological networks and the definition of restoration goals for river networks. The generalist or specialist nature of target species might be determining for the responses to habitat quality and barriers in the riparian corridor. Detailed datasets of land use, human stressors and hydro-morphological characteristics of river segments for the entire river basins allowed identifying the habitat requirements of the two species for the riparian zone. The identified critical factors were entered in a network analysis based on the ecological niche factor approach. Significant responses to riparian corridor quality for forest cover, alterations of channel straightening and urbanisation and infrastructure in the riparian zone are observed for both species, so they may well serve as indicators for corridor functioning. The hypothesis for generalists being less sensitive to human disturbance was withdrawn, since the otter as generalist species responded strongest to hydro-morphological alterations and human presence in general. The beaver responded the strongest to the physical environment as expected for this specialist species. The difference in responses for generalist and specialist species is clearly present and the two species have a strong complementary indicator value. The interpretation of the network analysis outcomes stresses the need for an estimation of ecological requirements of more species in the evaluation of riparian corridor functioning and in conservation planning.

Computational Fluid Dynamics Expert System using Artificial Neural Networks

The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations. As a results of this, Computational Fluid Dynamic (CFD) solvers are widely used in the aeronautical field. These solvers require the correct selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on the proper choice of these parameters. In this paper we create an expert system capable of making an accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver. Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time required for the convergence of a CFD solver.

Topological measure locating the effective crossover between segregation and integration in a modular network

We introduce an easily computable topological measure which locates the effective crossover between segregation and integration in a modular network. Segregation corresponds to the degree of network modularity, while integration is expressed in terms of the algebraic connectivity of an associated hypergraph. The rigorous treatment of the simplified case of cliques of equal size that are gradually rewired until they become completely merged, allows us to show that this topological crossover can be made to coincide with a dynamical crossover from cluster to global synchronization of a system of coupled phase oscillators. The dynamical crossover is signaled by a peak in the product of the measures of intracluster and global synchronization, which we propose as a dynamical measure of complexity. This quantity is much easier to compute than the entropy (of the average frequencies of the oscillators), and displays a behavior which closely mimics that of the dynamical complexity index based on the latter. The proposed topological measure simultaneously provides information on the dynamical behavior, sheds light on the interplay between modularity and total integration, and shows how this affects the capability of the network to perform both local and distributed dynamical tasks.

Performance assessment of a kinetically-powered network for herd localization

Developing a herd localization system capable to operate unattended in communication-challenged areas arises from the necessity of improving current systems in terms of cost, autonomy or any other facilities that a certain target group (or overall users) may demand. A network architecture of herd localization is proposed with its corresponding hardware and a methodology to assess performance in different operating conditions. The system is designed taking into account an eventual environmental impact hence most nodes are simple, cheap and kinetically powered from animal movements-neither batteries nor sophisticated processor chips are needed. Other network elements integrating GPS and batteries operate with selectable duty cycles, thus reducing maintenance duties. Equipment has been tested on Scandinavian reindeer in Lapland and its element modeling is integrated into a simulator to analyze such localization network applicability for different use cases. Performance indicators (detection frequency, localization accuracy and delay) are fitted to assess the overall performance; system relative costs are enclosed also for a range of deployments.

StreamCloud: An elastic and scalable data streaming system

Many applications in several domains such as telecommunications, network security, large scale sensor networks, require online processing of continuous data lows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static con?gurations that lead to either under or over-provisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation and a thorough evaluation of the scalability and elasticity of the fully implemented system.

Neural network controller for active demand side management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

Sustainable Air Traffic Management System Development Methodology

As it is defined in ATM 2000+ Strategy (Eurocontrol 2001), the mission of the Air Traffic Management (ATM) System is: “For all the phases of a flight, the ATM system should facilitate a safe, efficient, and expedite traffic flow, through the provision of adaptable ATM services that can be dimensioned in relation to the requirements of all the users and areas of the European air space. The ATM services should comply with the demand, be compatible, operate under uniform principles, respect the environment and satisfy the national security requirements.” The objective of this paper is to present a methodology designed to evaluate the status of the ATM system in terms of the relationship between the offered capacity and traffic demand, identifying weakness areas and proposing solutions. The first part of the methodology relates to the characterization and evaluation of the current system, while a second part proposes an approach to analyze the possible development limit. As part of the work, general criteria are established to define the framework in which the analysis and diagnostic methodology presented is placed. They are: the use of Air Traffic Control (ATC) sectors as analysis unit, the presence of network effects, the tactical focus, the relative character of the analysis, objectivity and a high level assessment that allows assumptions on the human and Communications, Navigation and Surveillance (CNS) elements, considered as the typical high density air traffic resources. The steps followed by the methodology start with the definition of indicators and metrics, like the nominal criticality or the nominal efficiency of a sector; scenario characterization where the necessary data is collected; network effects analysis to study the relations among the constitutive elements of the ATC system; diagnostic by means of the “System Status Diagram”; analytical study of the ATC system development limit; and finally, formulation of conclusions and proposal for improvement. This methodology was employed by Aena (Spanish Airports Manager and Air Navigation Service Provider) and INECO (Spanish Transport Engineering Company) in the analysis of the Spanish ATM System in the frame of the Spanish airspace capacity sustainability program, although it could be applied elsewhere.

Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Runtime variability is a key technique for the success of Dynamic Software Product Lines (DSPLs), as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work we address the problem of dynamic changes in feature models in sensor networks product families, where nodes of the network demand dynamic reconfiguration at post-deployment time.

Risk assessment of the spanish national railway system

The principal risks in the railway industry are mainly associated with collisions, derailments and level crossing accidents. An understanding of the nature of previous accidents on the railway network is required to identify potential causes and develop safety systems and deploy safety procedures. Risk assessment is a process for determining the risk magnitude to assist with decision-making. We propose a three-step methodology to predict the mean number of fatalities in railway accidents. The first is to predict the mean number of accidents by analyzing generalized linear models and selecting the one that best fits to the available historical data on the basis of goodness-offit statistics. The second is to compute the mean number of fatalities per accident and the third is to estimate the mean number of fatalities. The methodology is illustrated on the Spanish railway system. Statistical models accounting for annual and grouped data for the 1992-2009 time period have been analyzed. After identifying the models for broad and narrow gauges, we predicted mean number of accidents and the number of fatalities for the 2010-18 time period.