Multi-hop relaying using energy harvesting

In this letter, the performance of multi-hop relaying using energy harvesting is evaluated. Both amplify-and-forward and decode-and-forward relaying protocols are considered. The evaluation is conducted for time-switching energy harvesting as well as power-splitting energy harvesting. The largest number of hops given an initial amount of energy from the source node is calculated. Numerical results show that, in order to extend the network coverage using multi-hop relaying, time-switching is a better option than power splitting and in some cases, decode-and-forward also supports more hops than amplify-and-forward

Stability of Y/MCM-48 composite molecular sieve with mesoporous and microporous structures

AbstractY/MCM-48 composite molecular sieve was hydrothermally synthesized at different crystallization temperatures and crystallization times using ethyl orthosilicate as Si source and cetyltrimethyl ammonium bromide as template with the aid of fluoride ions and was characterized by X-ray diffraction, N2 physical adsorption technique, scanning electron microscopy and transmission electron microscopy. The thermal, hydrothermal, acidic, and basic stabilities of the Y/MCM-48 composite were investigated. The results show that Y/MCM-48 composite molecular sieve with meso- and microporous structures was synthesized successfully at 120°C for 36h. The Y/MCM-48 composite has the surface area of 864m2/g and the average pore size is ca. 2.48nm. The bi-porous structure in composite molecular sieve still maintains its stability even after thermal treatment at 800°C for 4h or hydrothermal treatment at 100°C for 48h. After treatment in 1mol/L hydrochloric acid solution or 1mol/L sodium hydroxide solution for 48h, the Y/MCM-48 composite exhibits good acidic stability. The acidic stability is superior to the basic stability at the same treatment time

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00\% when 10 training samples each class

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation

Multi-Hop Relaying Using Energy Harvesting

In this letter, the performance of multi-hop relaying using energy harvesting is evaluated. Both amplify-and-forward and decode-and-forward relaying protocols are considered. The evaluation is conducted for time-switching energy harvesting as well as power-splitting energy harvesting. The largest number of hops given an initial amount of energy from the source node is calculated. Numerical results show that, in order to extend the network coverage using multi-hop relaying, time-switching is a better option than power splitting and in some cases, decode-and-forward also supports more hops than amplify-and-forward

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future