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Existing space application networks in space stations are mainly fiber-optic cable-based networks due to their low size, weight, and power (SWaP) values. While fiber networks in space stations offer data transmission at high speeds with minimal signal loss, their major disadvantage is the lack of flexibility and mobility when new and unplanned space scientific equipment is added to the network. To enhance the flexibility of space networks while increasing their throughput, this paper introduces the hybrid space network (HSN), a new space network architecture that incorporates an optical wireless link, to meet the ever-increasing demands for larger bandwidth and higher mobile access capabilities in space scientific experiments. To best utilize the HSN's system performance, we propose a multi-priority-based network scheduling scheme, which can dynamically adapt to the requirements of mass tasks and select the best transmission procedure. Through simulations, we find that by adding optical wireless communication (OWC) links to the state-of-the-art deterministic FC-AE-1553 space network, the HSN's bandwidth can be increased by 20 times with an average latency reduction of 87.3%. We believe that the proposed HSN's architecture may ultimately shape the future of space stations' wireless connectivity, and in the meantime, innovate many advanced space applications with larger data rates and mobility requirements. [ABSTRACT FROM AUTHOR]

In order to implement sustainable management of water resources in the Desert Oasis, it is necessary to quantify the changes in the groundwater recharge process. Using the Cl−, stable hydrogen and oxygen isotope datasets extracted from 113 groundwater samples in the years of 2000 and 2020, and combining these with the MixSIAR model, changes in the groundwater recharge process and recharge source contributions in the Minqin Oasis (MO) in northwestern China area before and after ecological treatment were quantitatively analysed. Overall, confined groundwater was the greatest contributor to the recharge of shallow groundwater in the MO area in 2000. However, after 10 years of ecological rehabilitation, surface water has become the most important recharge source of shallow groundwater, with its contribution increasing by 10.6% between 2000 and 2020 ~ 2021. River water diversion projects have reversed falling groundwater levels in the MO area. Of these projects, that in the MO has seen groundwater levels rise at a rate of 0.09 m·year−1 from 2008 to 2019. This coincides with the increase in the contribution of surface water to groundwater recharge in 2020. In addition, insignificant changes were observed in the contributions made by lateral groundwater flow to groundwater recharge in the MO area in the years of 2000 and 2020 ~ 2021, indicating that groundwater in the upper and middle reaches of the MO area have contributed very little to the restoration of groundwater levels in the area's lower reaches. All these factors would appear to indicate that regional ecological management has had a lasting impact, and that the exploitation of groundwater in this desert oasis area in an inland river basin should be based upon rational planning and integrated, comprehensive management strategies. [ABSTRACT FROM AUTHOR]

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively. [ABSTRACT FROM AUTHOR]