The Global L-band Observatory for Water Cycle Studies (GLOWS) is designed as a follow-on to the Soil Moisture Active Passive (SMAP) observatory launched in 2015. While GLOWS is essentially copying many aspects of the SMAP mission, a key change has been made in the antenna technology. SMAP uses a reflector antenna and to reduce mission costs GLOWS uses a metamaterial lens antenna. This type of antenna is less efficient, so it must be proven that GLOWS can achieve the same uncertainty levels in soil moisture measurements as SMAP. In this work, a unified framework for modeling and analyzing GLOWS' ability to meet all mission and measurement requirements is developed. A model for the uncertainty effects of the lens antenna is developed and used to show that so long as the lens efficiency is above a threshold determined by the accuracy of the lens physical temperature knowledge, GLOWS will also be able to achieve all measurement requirements. It is shown that GLOWS is able to copy the design parameters of SMAP and achieve the same mission requirements.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, a three-dimensional bending solution of doubly-curved shells subjected to mechanical, thermal and hygrothermal load is studied. Through-the-thickness temperature of the shell is modeled by Fourier's heat conduction equation. Fick's moisture diffusion law equation is used to determine the hygro-thermal profile through-the-thickness. The partial differential equations are solved by using the Navier closed form summations which are valid only for shells with constant radii of curvature among the midsurface and with simply supported boundary conditions on its shell's edges. The shell governing equations are solved by discretizing the thickness profile via Legendre's grid distribution and by using the Differential Quadrature Method (DQM). The Layerwise capabilities of the method is guaranteed by imposing the inter-laminar continuity of out-of-the-plane stresses, displacements, temperature and hygrothermal load thickness profile. The zero-stress condition for the transverse shear stresses is imposed due to the fact that no mechanical loads are applied in those directions. Results for cylindrical, spherical panels and rectangular plates are presented. Comparisons are made with Layerwise and three-dimensional solutions available in literature. The results have strong accuracy and a benchmark problem is delivered. / Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica

Weather stations provide key information related to soil moisture and have been used by farmers to decide various field operations. We first evaluated the discrepancies in soil moisture between a weather stations and nearby field; due to soil texture, crop residue cover, crop type, growth stage and duration of temporal dependency to recent rainfall and evaporation rates using regression analysis. The regression analysis showed strong relationship between soil moisture at the weather station and the nearby field at the late vegetative and early reproductive stages. The correlation thereafter declines at later growth stages for corn and wheat. We can adduce that the regression coefficient of soil moisture with four-day cumulative rainfall slightly increased with an increase in the crop residue resulting in a low root mean square error (RMSE) value. We then investigated the effectiveness of machine learning techniques such as random forest regression (RFR), boosted regression trees (BRT), support vector regression, and artificial neural network to predict soil moisture in nearby fields based on RMSE of a 30% validation dataset and to determine the relative importance of predictor variables. The RFR and BRT performed best over other machine learning algorithms based on the lower RMSE values of 0.045 and 0.048 m3 m-3, respectively. The Classification and Regression Trees (CART), RFR and BRT models showed soil moisture at nearby weather stations had the highest relative influence for moisture prediction, followed by the four-day cumulative rainfall and Potential Evapotranspiration (PET), and subsequently followed by bulk density and Saturated Hydraulic Conductivity (Ksat). We then evaluated the integration of weather station data, RFR machine learning, and remotely sensed satellite imagery to predict soil moisture in nearby fields. Soil moisture predicted with an RFR algorithm using OPtical TRApezoidal Model (OPTRAM) moisture values, rainfall, standardized precipitation index (SPI) and percent clay showed high goodness of fit (r2=0.69) and low RMSE (0.053 m3 m-3). This research shows that the integration of weather station data, machine learning, and remote sensing tools can be used to effectively predict soil moisture in the Red River Valley of the North among a large diversity of cropping systems.

Le réchauffement climatique est l'un des principaux problèmes de ce siècle. Les politiques européennes soutiennent le développement des énergies renouvelables afin d'atteindre, d'ici 2020, l'objectif fixé par l'Union Européenne (UE). Ce dernier stipule que 20% de l'énergie produite devra provenir d'une source renouvelable. La combustion de la biomasse sous forme de bois est la plus importante des ressources renouvelables. Par conséquent, le développement de celle-ci est fondamental pour atteindre l'objectif de l'UE. Les plaquettes forestières sont des morceaux de bois déchiquetés. Elles présentent de nombreux avantages pour la production d'énergie thermique, en particulier, elles possèdent un bilan carbone neutre. Lors de la combustion, la présence d'eau dans le bois ne produit pas d'énergie mais provoque une perte de chaleur dans le processus de l'évaporation de l'eau. Par ailleurs, plus il y a d'eau moins il y a de bois pour une masse donnée de combustible. De ce fait, la connaissance de la teneur en eau dans le bois permet de déterminer son pouvoir calorifique et donc son prix. Dans cette thèse, plusieurs techniques fondées sur l'étude de la permittivité ont été proposées afin de prédire la teneur en eau des plaquettes forestières à grande échelle. Les systèmes externes fondés sur la réflectivité électromagnétique du matériau sont peu fiables en raison de leur dépendance à l'état de surface du combustible. Celui-ci est très variable en fonction des conditions d'utilisation et climatiques. Les dispositifs internes sont plus performants. Des systèmes internes capacitifs et à base de résonateurs ouverts ont été étudiés. Un prototype de dispositif à résonateur a été réalisé et testé en conditions réelles de production. / Global warming is one of the major problems of this century. Thus, European policies support the development of renewable energies in order to reach the target set by the European Union by 2020: 20% of the energy consumption must come from renewable resources. The combustion of wood biomass is the larger of the renewable energies and thus the increase of it is a key factor to get the expected target. Wood chips are shredded wood that present many advantages for the production of thermal energy. In particular, they are considered a carbon neutral fuel. During combustion, the water content does not produce energy but causes a heat loss in the process of the evaporation of the water. Moreover, for a given mass of biofuel, the larger water content, the smaller the wood available. Therefore knowing the moisture content (MC) allows to determine the calorific value of the biofuel and then its price. In this thesis several techniques based upon the study of the permittivity of large samples of wood chips have been proposed to predict the MC. External systems based on measurements of the reflected energy by the material are not reliable because of their dependence on the surface of the fuel, which can be very changeable under rough industrial and weather conditions. Internal devices are more efficient. They are based on either a capacitive technology or on open resonator technology. Due to promising results, a prototype of a resonator device was implemented and tested in real operating conditions.

Failure of viable seed to germinate results in poor stands and often in lower yields. Some of the more important factors that affect germination of seed are temperature, moisture, aeration, and alkalinity.

Sweet corn is rapidly becoming an important cash crop in many of the irrigated regions of the intermountain West. The lacome from this crop is relatively small in comparison to that from other major agricultural products; however, sweet corn production has a definite place in the economy of the region. One of the factors limiting increasing acreage of sweet corn in many areas is low yields. Yields have been increased naterially in recent years with the development of hybrid varieties, superior to the standard varieties previously used. Better cultural practices are improving yields, but much remains to be done in this phase of the work. Plant growth is affected by many complex factors, two of which are soil fertility and moisture. A considerable number of investigations have been carried out where either fertility or moisture effects have been studied independently. Four studies have been made where the two factors have been studied jointly in the same experiment. Recent trends in experimental research tend to emphasize the value of studying two or more factors at the same time, thereby making it possible to study interaction that otherwise would not have been observed. This study was an attempt to determine the effects of various soil fertility and soil moisture levels on the yield of sweet corn. These factors were studied simultaneously. By studying the relationships that exist between moisture and fertility, it is theoretically possible to arrive at the optimum level for both factors, thus obtaining maximum yields. Studies leading to the data reported in this paper were conducted on Nibley silty clay loam at the Utah State Agricultural College Forage Crops Experimental Farm near Nibley, Utah.

A mathematical expression was developed and tested which describes the relation between evapotranspiration and soil moisture. A general premise of this mathematical model is that the evapotranspiration-soil moisture relationship is determined by interaction of climatic, soil and plant factors. The basic model is dETa/dYs = -ke[1-(ETa/ETmx)] in which ETa is the actual evapotranspiration, Ys is the total soil water potential, k is a proportionality coefficient , E is the soil moisture extraction capacity of the atmosphere , and ETmx is the evapotranspiration that would occur from a particular crop-soil unit when soil moisture was not limiting. From this model the following expression was derived: ETa = (1/1+(Ys/Ys’)^[2.56/log (Ymi/Ymx)])g E0 where Ymx', Ys', Ymi are the soil potentials at which ETa is equal to 95%, 50% and 5% of ETmx' respectively; E 0 is the evaporation from a free water surface and expressed the evaporative demands of the atmosphere. The term g is a proportionality coefficient equal to ETmx/E0. A second formula was developed that expresses the same relationship in terms of soil water content, and was derived from the former by assuming a hyperbolic relationship between soil water potential and water content. These formulas, as well as various other models which are described in the literature, were tested using experimental data covering a wide range of climatic, soil and plant variables. It was concluded that: (a) Most models advocated in the literature are only adequate to describe the relation between evapotranspiration and soil moisture under particular climatic , soil and plant conditions. (b) The formulas derived from the proposed model provide a good fit for the evapotranspiration-soil moisture relationship under widely different circumstances. If proper values are chosen for the coefficients, these formulas yield relations that are similar to several of the models taken from the literature. Consequently, the proposed mathematical expression appears to be a general model of the manner in which plants use soil water under different vegetative and environmental conditions. (c) It seems possible to predict in a comparative way the pattern of soil water utilization in a soil-plant-atmosphere system . This may be done from a knowledge of the relations between the coefficients of the formulas and climatic, soil and plant factors influencing evapotranspiration.

Low fat Mozzarella cheese was made using exopolysaccharide-producing starter cultures consisting of single strains of Streptococcus thermophilus MR-1C and Lactobacillus delbrueckii ssp. bulgaricus MR-1R with or without the addition of mesophilic exopolysaccharide-producing adjunct mixed culture consisting of Lactococcus lactis ssp. lactis and Lactococcus lactis ssp. cremoris. A control cheese was made using a non-exopolysaccharide-producing starter culture consisting of S. thermophilus TA061 and Lactobacillus helveticusLH100. Cheeses were analyzed for moisture, melt, fat, and protein. Cheeses made with the addition of the mesophilic exopolysaccharide-producing adjunct culture showed significant differences in moisture, but not in melting properties when compared to cheeses made without adjunct culture. Cheeses made with both the exopolysaccharide-producing starter and exopolysaccharide-producing adjunct cultures showed a 4% increase in moisture, but the use of the exopolysaccharide-producing starter cultures alone produced a 3% increase in moisture over the control cheese. Melt also increased in these cheeses as moisture increased. The same cultures were used to determine the effects on moisture when the cheesemaking procedure was scaled up from 10-kg vats to using 454-kg horizontal blade double-O vats, and hand stretching was replaced by an Alfa Laval cooker stretcher machine. Cheese made using the exopolysaccharide-producing cultures showed a 2% increase in moisture over cheese made using non-exopolysaccharide-producing cultures. All of the cheeses made in the double-0 vats showed a decrease in moisture compared to cheeses made in the 10-kg stainless steel vats . Cheeses with elevated moisture levels showed increased melt.

The presence of moisture in the soil reduces its shear strength. After the rain or snowmelt, the high percentage of moisture in the subgrade of highway, causes the instability resulting into failure of the highway due to high water table. Therefore, it is essential to install a drainage system to remove the excess moisture from the subgrade of highways to avoid its failure. In the present study, six different soil samples have been studied to observe its failure by triaxial shear strength and corresponding moisture content was noted. The tension applied to remove moisture was converted to equivalent drain depth. The relationships were studied between shear strength versus drain depth and moisture content versus drain depth. From these relationships the design drain depth for different types of soils studied was recommended. The present study also suggest that the design drain depth for the highways can be approximated directly using water retention curve.

This thesis introduces the implementation of different supervised learning techniques for producing accurate estimates of soil moisture content using empirical information, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). The dfferent models employed extend over a wide range of machine-learning techniques starting from basic linear regression models through models based on Bayesian framework. Also, ensembling methods such as bagging and boosting are implemented on all models for considerable improvements in accuracy. The main research objective is to understand, compare, and analyze the mathematical backgrounds underlying and results obtained from dfferent models and the respective improvisation techniques employed.