Effects of aerosols and surface shadowing on bidirectional reflectance measurements of deserts

Desert surfaces are probably one of the most stable of the Earth's natural targets for remote sensing. The bidirectional reflectance properties of the Saudi Arabian desert was investigated during the Summer Monsoon Experiment (Summer Monex). A comparison of high-altitude with near-surface measurements of the White Sands desert showed significant differences. These discrepancies have been attributed to forward scattering of the dust-laden atmosphere prevalent during Summer Monex. This paper is concerned in general with modeling the effects of atmospheric aerosols and surface shadowing on the remote sensing of bidirectional reflectance factors of desert targets, and in particular with comparing the results of these models with flight results. Although it is possible to approximate the latter, it is felt that a surface reflectance model with a smaller specular component would have permitted using a more realistic set of atmospheric conditions in the simulations

Non-Orthogonal Multiple Access for Hybrid VLC-RF Networks with Imperfect Channel State Information

The present contribution proposes a general framework for the energy efficiency analysis of a hybrid visible light communication (VLC) and Radio Frequency (RF) wireless system, in which both VLC and RF subsystems utilize nonorthogonal multiple access (NOMA) technology. The proposed framework is based on realistic communication scenarios as it takes into account the mobility of users, and assumes imperfect channel-state information (CSI). In this context, tractable closed-form expressions are derived for the corresponding average sum rate of NOMA-VLC and its orthogonal frequency division multiple access (OFDMA)-VLC counterparts. It is shown extensively that incurred CSI errors have a considerable impact on the average energy efficiency of both NOMA-VLC and OFDMAVLC systems and hence, they should not be neglected in practical designs and deployments. Interestingly, we further demonstrate that the average energy efficiency of the hybrid NOMA-VLCRF system outperforms NOMA-VLC system under imperfect CSI. Respective computer simulations corroborate the derived analytic results and interesting theoretical and practical insights are provided, which will be useful in the effective design and deployment of conventional VLC and hybrid VLC-RF systems

The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste

[EN] This study describes a model-based method for real-time optimization of the key filtration parameters in a submerged anaerobic membrane bioreactor (AnMBR) treating urban wastewater (UWW) and UWW mixed with domestic food waste (FW). The method consists of an initial screening to find out adequate filtration conditions and a real-time optimizer applied to a periodically calibrated filtration model for minimizing the operating costs. The initial screening consists of two statistical analyses: (1) Morris screening method to identify the key filtration parameters; (2) Monte Carlo method to establish suitable initial control inputs values. The operating filtration cost after implementing the control methodology was (sic)0.047 per m(3) (59.6% corresponding to energy costs) when treating UWW and 0.067 per m(3) when adding FW due to higher fouling rates. However, FW increased the biogas productivities, reducing the total costs to (sic)0.035 per m(3). Average downtimes for reversible fouling removal of 0.4% and 1.6% were obtained, respectively. The results confirm the capability of the proposed control system for optimizing the AnMBR performance when treating both substrates. (C) 2018 Elsevier Ltd. All rights reserved.This research work was possible thanks to financial support from Generalitat Valenciana (project PROMETEO/2012/029) which is gratefully acknowledged. Besides, support from FCC Aqualia participation in INNPRONTA 2011 IISIS IPT-20111023 project (partially funded by The Centre for Industrial Technological Development (CDTI) and from the Spanish Ministry of Economy and Competitiveness) is gratefully acknowledged.Robles Martínez, Á.; Capson-Tojo, G.; Ruano García, MV.; Seco Torrecillas, A.; Ferrer, J. (2018). Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste. Waste Management. 80:299-309. https://doi.org/10.1016/j.wasman.2018.09.031S2993098

Evaluation of sensor, environment and operational factors impacting the use of multiple sensor constellations for long term resource monitoring

Moderate resolution remote sensing data offers the potential to monitor the long and short term trends in the condition of the Earth’s resources at finer spatial scales and over longer time periods. While improved calibration (radiometric and geometric), free access (Landsat, Sentinel, CBERS), and higher level products in reflectance units have made it easier for the science community to derive the biophysical parameters from these remotely sensed data, a number of issues still affect the analysis of multi-temporal datasets. These are primarily due to sources that are inherent in the process of imaging from single or multiple sensors. Some of these undesired or uncompensated sources of variation include variation in the view angles, illumination angles, atmospheric effects, and sensor effects such as Relative Spectral Response (RSR) variation between different sensors. The complex interaction of these sources of variation would make their study extremely difficult if not impossible with real data, and therefore, a simulated analysis approach is used in this study. A synthetic forest canopy is produced using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and its measured BRDFs are modeled using the RossLi canopy BRDF model. The simulated BRDF matches the real data to within 2% of the reflectance in the red and the NIR spectral bands studied. The BRDF modeling process is extended to model and characterize the defoliation of a forest, which is used in factor sensitivity studies to estimate the effect of each factor for varying environment and sensor conditions. Finally, a factorial experiment is designed to understand the significance of the sources of variation, and regression based analysis are performed to understand the relative importance of the factors. The design of experiment and the sensitivity analysis conclude that the atmospheric attenuation and variations due to the illumination angles are the dominant sources impacting the at-sensor radiance

ESTIMATING LAND SURFACE ALBEDO FROM SATELLITE DATA

Land surface albedo, defined as the ratio of the surface reflected incoming and outgoing solar radiation, is one of the key geophysical variables controlling the surface radiation budget. Surface shortwave albedo is widely used to drive climate and hydrological models. During the last several decades, remotely sensed surface albedo products have been generated through satellite-acquired data. However, some problems exist in those products due to instrument measurement inaccuracies and the failure of current retrieving procedures, which have limited their applications. More significantly, it has been reported that some albedo products from different satellite sensors do not agree with each other and some even show the opposite long term trend regionally and globally. The emergence of some advanced sensors newly launched or planned in the near future will provide better capabilities for estimating land surface albedo with fine resolution spatially and/or temporally. Traditional methods for estimating the surface shortwave albedo from satellite data include three steps: first, the satellite observations are converted to surface directional reflectance using the atmospheric correction algorithms; second, the surface bidirectional reflectance distribution function (BRDF) models are inverted through the fitting of the surface reflectance composites; finally, the shortwave albedo is calculated from the BRDF through the angular and spectral integration. However, some problems exist in these algorithms, including: 1) "dark-object" based atmospheric correction methods which make it difficult to estimate albedo accurately over non-vegetated or sparsely vegetated area; 2) the long-time composite albedo products cannot satisfy the needs of weather forecasting or land surface modeling when rapid changes such as snow fall/melt, forest fire/clear-cut and crop harvesting occur; 3) the diurnal albedo signature cannot be estimated in the current algorithms due to the Lambertian approximation in some of the atmospheric correction algorithms; 4) prior knowledge has not been effectively incorporated in the current algorithms; and 5) current observation accumulation methods make it difficult to obtain sufficient observations when persistent clouds exist within the accumulation window. To address those issues and to improve the satellite surface albedo estimations, a method using an atmospheric radiative transfer procedure with surface bidirectional reflectance modeling will be applied to simultaneously retrieve land surface albedo and instantaneous aerosol optical depth (AOD). This study consists of three major components. The first focuses on the atmospheric radiative transfer procedure with surface reflectance modeling. Instead of executing atmospheric correction first and then fitting surface reflectance in the previous satellite albedo retrieving procedure, the atmospheric properties (e.g., AOD) and surface properties (e.g., BRDF) are estimated simultaneously to reduce the uncertainties produced in separating the entire radiative transfer process. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua are used to evaluate the performance of this albedo estimation algorithm. Good agreement is reached between the albedo estimates from the proposed algorithm and other validation datasets. The second part is to assess the effectiveness of the proposed algorithm, analyze the error sources, and further apply the algorithm on geostationary satellite - the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). Extensive validations on surface albedo estimations from MSG/SEVIRI observations are conducted based on the comparison with ground measurements and other satellite products. Diurnal changes and day-to-day changes in surface albedo are accurately captured by the proposed algorithm. The third part of this study is to develop a spatially and temporally complete, continuous, and consistent albedo maps through a data fusion method. Since the prior information (or climatology) of albedo/BRDF plays a vital role in controlling the retrieving accuracy in the optimization method, currently available multiple land surface albedo products will be integrated using the Multi-resolution Tree (MRT) models to mitigate problems such as data gaps, systematic bias or low information-noise ratio due to instrument failure, persistent clouds from the viewing direction and algorithm limitations. The major original contributions of this study are as follows: 1) this is the first algorithm for the simultaneous estimations of surface albedo/reflectance and instantaneous AOD by using the atmospheric radiative transfer with surface BRDF modeling for both polar-orbiting and geostationary satellite data; 2) a radiative transfer with surface BRDF models is used to derive surface albedo and directional reflectance from MODIS and SEVIRI observations respectively; 3) extensive validations are made on the comparison between the albedo and AOD retrievals, and the satellite products from other sensors; 4) the slightly modified algorithm has been adopted to be the operational algorithm of Advanced Baseline Imager (ABI) in the future Geostationary Operational Environmental Satellite-R Series (GOES-R) program for estimating land surface albedo; 5) a framework of using MRT is designed to integrate multiple satellite albedo products at different spatial scales to build the spatially and temporally complete, continuous, and consistent albedo maps as the prior knowledge in the retrieving procedure

A COMPARATIVE STUDY OF MONO-STABLE AND BI-STABLE MAGNETIC SPRING BASED ENERGY HARVESTERS

Continuous advancements in electronics manufacturing have resulted in the widespread use of low-power sensors, necessitating the development of energy harvesters capable of generating electric power from abundant and free energy sources such as ambient vibrations. A rising interest in energy harvesting technology inspires the work discussed herein using magnetic interactions to target nonlinear energy harvesting, which is compatible with ambient vibration energy sources with a broad frequency spectrum and particularly rich in low frequencies. This research aimed to look into a magnetic-levitation-based vibration energy harvester that could be tuned from a mono-stable to a bi-stable configuration. An oscillating magnet is levitated between two stationary top and bottom magnets in a mono-stable arrangement. A bi-stable configuration is achieved by fixing a cluster of peripheral solid magnets around the harvester housing. Magnetic forces in magnetic-levitation-based harvesters have traditionally been represented by polynomial functions integrated into the equation of motion. Analytical models for the interaction of magnets were developed and integrated into the equation of motion in this study. The analytical model of magnetic force delivers more accurate results for the bi-stable configuration than those produced using polynomial functions, according to the findings from this study. The results demonstrated that adjusting the geometric ratios of the peripheral magnets in the bi-stable configuration can produce a variety of load-deflection properties. The bi-stable design exhibits inter-well, chaotic, and intra-well motion at varying accelerations during dynamic operation. The bi-stable architecture benefits from thinner peripheral magnets, especially at lower acceleration values. Lower energy barriers, improved frequency responses, and nearly zero stiffness at equilibrium position are all advantages of thinner peripheral magnets. The harvester moved towards mono-stability when thinner peripheral magnets were utilized, showing that mono-stability is the preferred mode for vibration energy harvesting under harmonic excitation. We also propose an experimental and theoretical platform for developing design platform and performing analysis on mono-stable magnetic springs used in vibration energy harvesting devices. The results reveal a high level of agreement between the model and the experiment. For linear and nonlinear stiffness coefficients, approximate analytical expressions are found. The findings indicate that the linear and nonlinear stiffness coefficients are linked. The stationary ring magnet\u27s outer diameter can be utilized to modify the energy harvesting system\u27s nonlinearity to provide linear, hardening nonlinear, or softening nonlinear responses. Designers can use this work to understand the behavior of magnetic spring-based harvesting systems and assess their performance concerning design factors. Other energy systems that use magnetic springs, such as energy sinks, could benefit from this research

Materials fatigue prediction using graph neural networks on microstructure representations

The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph. Here, grains constitute graph nodes connected by edges whenever grains share a common boundary. Fatigue loading causes some grains to develop slip markings, which can evolve into microcracks and lead to failure. This data set enables applying graph neural network variants on the task of binary grain-wise damage classification. The objective is to identify suitable data representations and models with an appropriate inductive bias to learn the underlying damage formation causes. Here, graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F$_1$-score of 0.34, outperforming phenomenological crystal plasticity (+ 68%) and conventional machine learning (+ 17%) models by large margins. Further, we present an interpretability analysis that highlights the grains along with features that are considered important by the graph model for the prediction of fatigue damage initiation, thus demonstrating the potential of such techniques to reveal underlying mechanisms and microstructural driving forces in critical grain ensembles