In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

Urban fringes are of great significance to urban development as connecting hubs between urban and rural areas. However, there are many problems in urban fringes, including disorderly spatial layout, waste of social resources, and low quality of human settlements. Rapid and accurate identification of urban fringes has important practical significance for optimizing urban spatial layout, controlling urban unlimited expansion, and protecting land resources. Given the lack of suitable and high-quality fringe extraction models for small- and medium-sized urban areas, this study was based on Gaofen-2 (GF-2) imagery, Suomi National Polar-orbiting Partnership Visible Infrared Imager Radiometer Suite (NPP-VIIRS) imagery, point of interest (POI) data, and WorldPop data, taking the landscape disorder degree, POI kernel density, and night light intensity as urban feature factors and constructing a fringe extraction model of small- and medium-sized urban areas (FEM-SMU). Taking Hantai District in China as the study area, the results of the model were compared to the landscape disorder degree threshold method and POI kernel density breakpoint analysis method, while the generality of the model was further tested in Shangzhou and Hanbin Districts. The results show that the FEM-SMU model has evident improvements over the conventional methods in terms of accuracy, detail, and integrity, and has higher versatility, which can better meet the research needs of small- and medium-sized urban fringes.

In wireless networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. With the rapid growth and increasing diversity of traffic in wireless networks, differentiating and serving various types of traffic to enhance network performance has become a pressing issue that needs to be addressed. Deep Reinforcement Learning (DRL) optimizes decision-making through the interaction between an agent and its environment, enabling it to handle complex state spaces and high-dimensional data, making it particularly suitable for dynamically adjusting the contention window and adapting to environmental changes in real time. Access Category (AC) is used to differentiate various types of traffic to support different quality of service (QoS) requirements. Therefore, this paper proposes the integration of DRL techniques into the EDCA mechanism, utilizing AC to differentiate network traffic, while the DRL technology adjusts the contention window to adequately ensure QoS for different types of traffic and enhance overall network performance. This paper proposes a CW back off scheme based on DRL for differentiating ACs. The scheme uses DRL technology to observe the channel collision rate and sense the current network conditions, thereby adaptively adjusting the CW size for ACs. In addition, it dynamically adjusts the back off strategy according to the perceived current network conditions to optimize the data transmission process. In the range of 20–120 stations, the scheme was tested in both single AC and multiple AC traffic scenarios, demonstrating excellent performance. The collision rate consistently remained below 18%, while the normalized throughput was maintained above 76%. Additionally, there is a significant improvement compared to existing deep reinforcement learning-based optimization schemes. Experimental results show that this scheme effectively discriminates between different ACs, resulting in lower latency and higher throughput for high-priority traffic. Furthermore, adaptively adjusting the CW size and improving the back off strategy maintains a low collision rate and stable throughput even under heavy load conditions, significantly improving the overall network performance.

The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

Permafrost, which underlies roughly one-fifth of global land area, is highly vulnerable to ongoing climate warming and its degradation has important implications for hydrological processes, carbon cycling, ecosystems, and infrastructure stability. It remains challenging to accurately project its future distribution due to various factors. The Surface Frost Index (SFI) threshold is a critical parameter that captures first-order thermal forcing for projecting future permafrost distribution. Here, the future global annual frozen ground distribution datasets are produced using an optimized frost number threshold ( F _at ) calibrated via kappa coefficient-based accuracy assessment. By comparing simulated outputs with benchmark maps, we identify the optimal F _at . Applying this threshold to downscaled CMIP6 data (0.25° resolution), we generate annual frozen ground distributions (2020–2099) under four SSP scenarios (SSP126, SSP245, SSP370, SSP585). The datasets reveal accelerated permafrost degradation, with mid-century (2040–2060) losses of 19 ± 3% to 28 ± 3% and late-century (2080–2099) losses escalating to 21 ± 3% to 61 ± 5%, peaking under SSP585. These high-resolution projections provide critical insights for assessing climate impacts and guiding cryospheric adaptation strategies.