The role of sulfur cycling in arsenic behavior under reducing conditions is not well-understood in previous investigations. This study provides observations of sulfur and oxygen isotope fractionation in sulfate and evaluation of sulfur cycling-related biogeochemical processes controlling dissolved arsenic groundwater concentrations using multiple isotope approaches. As a typical basin hosting high arsenic groundwater, the western Hetao basin was selected as the study area. Results showed that, along the groundwater flow paths, groundwater δS, δO, and δC increased with increases in arsenic, dissolved iron, hydrogen sulfide and ammonium concentrations, while δC decreased with decreasing Eh and sulfate/chloride. Bacterial sulfate reduction (BSR) was responsible for many of these observed changes. The δS indicated that dissolved sulfate was mainly sourced from oxidative weathering of sulfides in upgradient alluvial fans. The high oxygen-sulfur isotope fractionation ratio (0.60) may result from both slow sulfate reduction rates and bacterial disproportionation of sulfur intermediates (BDSI). Data indicate that both the sulfide produced by BSR and the overall BDSI reduce arsenic-bearing iron(III) oxyhydroxides, leading to the release of arsenic into groundwater. These results suggest that sulfur-related biogeochemical processes are important in mobilizing arsenic in aquifer systems.

Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams. Current mmWave beam training and channel estimation techniques do not normally make use of the prior beam training or channel estimation observations. Intuitively, though, the channel matrices are functions of the various elements of the environment. Learning these functions can dramatically reduce the training overhead needed to obtain the channel knowledge. In this paper, a novel solution that exploits machine learning tools, namely conditional generative adversarial networks (GAN), is developed to learn these functions between the environment and the channel covariance matrices. More specifically, the proposed machine learning model treats the covariance matrices as 2D images and learns the mapping function relating the uplink received pilots, which act as RF signatures of the environment, and these images. Simulation results show that the developed strategy efficiently predicts the covariance matrices of the largedimensional mmWave channels with negligible training overhead. ,, ,,

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This paper proposes a nondestructive evaluation method based on deep learning using combined ground‐penetrating radar (GPR) and electromagnetic induction (EMI) data for autonomic and accurate estimation of the cover thickness and diameter of reinforcement bars. A real‐time object detection algorithm—You Only Look Once–version 3 (YOLO v3)—is adopted to automatically identify the reinforcement bar reflected signals from radargrams, with which the range of the cover thickness is roughly predicted. Subsequently, EMI data, accompanied with the cover thickness range, are imported to a one‐dimensional convolutional neural network (1D CNN), pretrained by calibrated EMI and GPR data, to simultaneously estimate the cover thickness and reinforcement bar diameter. Testing with the on‐site GPR data shows that YOLO v3 is superior to Single Shot Multibox Detector method in GPR hyperbolic signal identification. Testing of 1D CNN with the EMI and GPR data collected in an in‐house sand pit experiment shows that the estimation accuracy of the cover thickness and reinforcement bar diameter is, respectively, 96.8% and 90.3% with a permissible error of 1 mm. Further, an experiment with concrete specimens demonstrates that among the 22 estimated values (including the reinforcement bar diameter and cover thickness), there are 17 values accurately estimated, while the inaccurately estimated values have an error up to 2 mm. The experimental results show that the proposed method can autonomically evaluate the reinforcement bar diameter and cover thickness with a high accuracy.

In this paper, an approximate closed-form probability density function expression for the sum of lognormal-Rician turbulence channels with Rayleigh pointing errors is developed. The results of Kolmogorov-Smirnov goodness-of-fit statistical tests show that the proposed approximation is highly accurate across a wide range of channel conditions. Also, the analysis of approximation error is presented in detail, and it indicates that a more efficient approximation can be achieved for larger coherence parameter r and smaller variance σ 2 z . To reveal the importance of proposed approximation, the closed-form expressions for the ergodic capacity, outage probability, and bit-error rate are derived in terms of Meijer's G-function. The performance of multiple-input multiple-output (MIMO) free-space optical (FSO) systems with equal gain combining (EGC) diversity technique are analyzed in detail under different scenarios, including the number of transmit and receive apertures, turbulence channels, and presence of pointing errors. It is observed that MIMO technology can offer a significant improvement in FSO performance when compared with the single-input single-output (SISO) systems. The ergodic capacity and BER performance at high signal-to-noise ratio are also obtained to provide further insights. Numerical results demonstrate the accuracy of the proposed approach.