Études des systèmes de communications sans-fil dans un environnement rural difficile

Les systèmes de communication sans fil, ayant de nombreux avantages pour les zones rurales, peuvent aider la population à bien s'y établir au lieu de déménager vers les centres urbains, accentuant ainsi les problèmes d’embouteillage, de pollution et d’habitation. Pour une planification et un déploiement efficace de ces systèmes, l'atténuation du signal radio et la réussite des liens d’accès doivent être envisagées. Ce travail s’intéresse à la provision d’accès Internet sans fil dans le contexte rural canadien caractérisé par sa végétation dense et ses variations climatiques extrêmes vu que les solutions existantes sont plus concentrées sur les zones urbaines. Pour cela, nous étudions plusieurs cas d’environnements difficiles affectant les performances des systèmes de communication. Ensuite, nous comparons les systèmes de communication sans fil les plus connus. Le réseau sans fil fixe utilisant le Wi-Fi ayant l’option de longue portée est choisi pour fournir les communications aux zones rurales. De plus, nous évaluons l'atténuation du signal radio, car les modèles existants sont conçus, en majorité, pour les technologies mobiles en zones urbaines. Puis, nous concevons un nouveau modèle empirique pour les pertes de propagation. Des approches utilisant l’apprentissage automatique sont ensuite proposées, afin de prédire le succès des liens sans fil, d’optimiser le choix des points d'accès et d’établir les limites de validité des paramètres des liens sans fil fiables. Les solutions proposées font preuve de précision (jusqu’à 94 % et 8 dB RMSE) et de simplicité, tout en considérant une multitude de paramètres difficiles à prendre en compte tous ensemble avec les solutions classiques existantes. Les approches proposées requièrent des données fiables qui sont généralement difficiles à acquérir. Dans notre cas, les données de DIGICOM, un fournisseur Internet sans fil en zone rurale canadien, sont utilisées. Wireless communication systems have many advantages for rural areas, as they can help people settle comfortably and conveniently in these regions instead of relocating to urban centers causing various overcrowding, habitation, and pollution problems. For effective planning and deployment of these technologies, the attenuation of the radio signal and the success of radio links must be precisely predicted. This work examines the provision of wireless internet access in the Canadian rural context, characterized by its dense vegetation and its extreme climatic variations, since existing solutions are more focused on urban areas. Hence, we study several cases of difficult environments affecting the performances of communication systems. Then, we compare the best-known wireless communication systems. The fixed wireless network using Wi-Fi, having the long-range option, is chosen to provide wireless access to rural areas. Moreover, we evaluate the attenuation of the radio signal, since the existing path loss models are generally designed for mobile technologies in urban areas. Then, we design a new path loss empirical model. Several approaches are then proposed by using machine learning to predict the success of wireless links, optimize the choice of access points and establish the validity limits for the pertinent parameters of reliable wireless connections. The proposed solutions are characterized by their accuracy (up to 94% and 8 dB RMSE) and simplicity while considering a wide range of parameters that are difficult to consider all together with conventional solutions. These approaches require reliable data, which is generally difficult to acquire. In our case, the dataset from DIGICOM, a rural Canadian wireless internet service provider, is used

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980

Statistical modeling of aircraft engine fuel burn

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 169-177).Fuel burn is a key driver of aircraft performance, and contributes to airline costs and aviation emissions. While the trajectory (ground track) of a flight can be observed using surveillance systems, its fuel consumption is generally not disseminated by the operating airline. Emissions inventories and benefits assessment tools therefore need models that can predict the fuel flow rate profile and fuel burn of a flight, given its trajectory data. Most existing fuel burn estimation tools rely on an architecture that is centered around the Base of Aircraft Data (BADA), an aircraft performance model developed by EUROCONTROL. Operational data (including trajectory data) are generally processed in order to generate the inputs needed by BADA, which then provides an estimate of the fuel flow rate and fuel burn. Although a versatile tool that covers a large number of aircraft types, BADA makes several assumptions that are not representative of real-world operations. Consequently, the reliance on BADA results in errors in the fuel burn estimates. Additionally, existing fuel burn modeling tools provide deterministic predictions, thereby not capturing the operational variability seen in practice. This thesis proposes an alternative model architecture that enables the development of data-driven, statistical models of fuel burn. The parameters of interest are the instantaneous fuel flow rate (that is, the mass of fuel consumed per unit time) and the fuel burn (cumulative mass of fuel consumed over a particular phase or the entire trajectory). The new model architecture uses supervised learning algorithms to directly map aircraft trajectory variables to the fuel flow rate, and subsequently, fuel burn. The models are trained and validated using operational data from flight recorders, and therefore reflect real-world operations. A physical understanding of aircraft and engine performance is leveraged for feature selection. An important characteristic of statistical methods is that they provide both estimates of mean values, as well as predictive distributions reflecting the variability and uncertainty. Locally expert models are developed for each aircraft type and for each of the flight phases. The Bayesian technique of Gaussian Process Regression (GPR) is found to be well-suited for modeling fuel burn. The resulting models are found to be significantly better than state-of-the-art aircraft performance models in predicting the fuel flow rate and fuel burn of a trajectory, giving up to a 63% improvement in total airborne fuel burn prediction over the BADA model. Finally, the Takeoff Weight (TOW) of an aircraft is recognized as an important variable for determining the fuel burn. The thesis therefore develops and evaluates a methodology to estimate the TOW of a flight, using trajectory data from its takeoff ground roll. The proposed statistical models are found to result in up to a 76% smaller error than the Aircraft Noise and Performance (ANP) database, which is used currently for TOW estimation.by Yashovardhan Sushil Chati.Ph. D

Happiness and environmental quality

Subjective wellbeing — happiness — is of increasing interest to economists, including environmental economists. There are several reasons for thinking that environmental quality (EQ), defined as high levels of environmental goods and low levels of environmental ‘bads’, will be positively related to happiness. Quantitative evidence on this remains limited, however. Some papers use cross-sectional data aggregated at country level, but it is open to doubt whether these aggregated measures reflect individuals’ real EQ exposures. Other papers use individual-level data, but in general have spatial data at very coarse resolution, and consider a limited range of EQ variables, exclusively around individuals’ homes. This thesis reports two related strands of work. The first designs, implements and analyses data from two new cross-sectional surveys. It builds on earlier work by using spatial data at very high resolution, and advanced Geographical Information Systems (GIS) techniques; by simultaneously considering multiple EQ characteristics, around both homes and workplaces; and by investigating the sensitivity of results to the choice of happiness indicator. The second strand develops and implements a new methodology focused on individuals’ momentary experiences of the environment. It extends a protocol known by psychologists as the Experience Sampling Method (ESM) to incorporate satellite (GPS) location data. Using an app for participants’ own smartphones, called Mappiness, it collects a panel data set comprising millions of geo-located responses from thousands of volunteers. EQ indicators are again joined to this data set using GIS. Results of the first strand of work are mixed, but support some links between happiness and the accessibility of natural environments, providing quantitative (including monetary) estimates of their strength. The second strand demonstrates that individuals are significantly and substantially happier outdoors in natural environments than continuous urban ones. It introduces a valuable new line of evidence on this question, which has great potential for future development