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<p>This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat's Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.</p>

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This study focuses a novel method to remove the human pathogens cryptosporidium parvum from water by silica particles coated with functionalized self-assembled monolayers. The results of this investigation clearly show that the pathogen can efficiently and completely be removed at pH ranges of drinking water by stirring the coated particles in the contaminated water for up to 60 min and finally filtrating the powder. The removal is believed to be caused by electrostatic attraction and immobilization of pathogen on the surface of the particles. At higher pH vales, even chemisorption may occur.

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