Drinking water safety improvement and future challenge of lakes and reservoirs

To meet the Sustainable Development Goal(SDG)target 6.1,China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality.Since 2000,China has invested heavily in the water infrastructure of numerous reservoirs,with a total storage capacity increase of 4.704×1011m3(an increase of 90.8%).These reservoirs have significantly enhanced the available freshwater resources for drinking water.Concurrently,efforts to improve water quality in lakes and reservoirs,facilitated by nationwide water quality monitoring,have been successful.As a result,an increasing lakes and reservoirs are designated as centralized drinking water sources(CDWSs)in China.Among the 3441 CDWSs across all provinces,40.8%are sourced from lakes and reservoirs,32.6%from rivers,and 26.6%from groundwater in 2023.Notably,from 2016 to 2023,the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces.This progress has enabled561.4 million urban residents to access improved drinking water sources in 2022,compared to 303.4 million in 2004.Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources.Nevertheless,the nationwide occurrence of algal blooms has surged by 113.7%from the 2000s to the 2010s,which is a considerable challenge to drinking water safety.Fortunately,algal blooms have been markedly alleviated in past four years.However,it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms,and associated toxic microcystin and odor compounds.

Forward link outage performance of aeronautical broadband satellite communications

High-throughput satellites(HTSs) play an important role in future millimeter-wave(mm Wave) aeronautical communication to meet high speed and broad bandwidth requirements. This paper investigates the outage performance of an aeronautical broadband satellite communication system’s forward link, where the feeder link from the gateway to the HTS uses free-space optical(FSO) transmission and the user link from the HTS to aircraft operates at the mm Wave band. In the user link, spot beam technology is exploited at the HTS and a massive antenna array is deployed at the aircraft. We first present a location-based beamforming(BF) scheme to maximize the expected output signal-to-noise ratio(SNR) of the forward link with the amplify-and-forward(AF) protocol,which turns out to be a phased array. Then, by supposing that the FSO feeder link follows Gamma-Gamma fading whereas the mm Wave user link experiences shadowed Rician fading, we take the influence of the phase error into account, and derive the closed-form expression of the outage probability(OP) for the considered system. To gain further insight, a simple asymptotic OP expression at a high SNR is provided to show the diversity order and coding gain. Finally, numerical simulations are conducted to confirm the validity of the theoretical analysis and reveal the effects of phase errors on the system outage performance.

DIUN:Deeper Inception U-Network for Recovering Partial Pixelated Images

In our daily life,it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes.To prevent these phenomena and recover the true information from pixelated images,it is meaningful to research an effective reconstruction method for recovering pixelated images.This paper aims at recovering the artificial partial pixelated images via deep learning(DL).To abstract more abundant features and enhance the repair ability of DL model,we propose a new DL structure,called deeper inception U-Net,to act as the generator of a generative adversarial network.We combine the feature loss with structural similarity index measure loss as the context loss to minimize the distance between feature maps of clear images and the generated images,which helps to improve the quality of repair images.After obtaining inception features,we use fusion layer to adaptively learn featuresin each inception block.To evaluate the performance of our model,we introduce a new home dataset that contains 10174 clear home images with corresponding pixelated images.A series of experiments show that our model has ability to rebuild pixelated images.