The integration of low earth orbit （LEO） satellite with 5G in the mobile communication system presents extensive application prospects. To ensure effective establishment of communication links between users and base stations， as well as reliable data transmission， time-frequency synchronization technology plays a crucial role. However， traditional timing and frequency synchronization algorithms face limitations when dealing with millimeter wave frequency bands， large bandwidths， high Doppler frequency shifts and change rates， and low signal-to-noise ratios in low-orbit satellite channels. These limitations result in decreased estimation accuracy. For the uplink service data channel of a 5G-based low orbit satellite communication system， a joint timing and frequency synchronization algorithm based on weighted embedded synchronization sequences is proposed to improve the accuracy of timing and frequency offset estimation. Simulation results demonstrate that the proposed timing and frequency synchronization algorithm outperforms traditional algorithms with comparable complexity in terms of synchronization performance.

Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.

The palm-leaf manuscript is a treasure of human civilization， which is the scripture written or engraved on the processed leaves of palm trees. Originating in ancient India， it was gradually introduced into China in the historical period， and reached a period of great prosperity in the Tang Dynasty. Chinese ancestors had a clear understanding of the materials used to make the palm-leaf manuscript. According to our study， the “Doro” （多罗） tree in ancient books should be talipot（Corypha umbraculifera）， while the “Beiduo” （贝多） tree should be palmyra（Borassus flabellifer）. The word “Beiduo”， which appeared many times in Chinese classical poetry， has become a synonym of Buddhist classics. Material evidence of ancient palm-leaf manuscript has been unearthed in many places in Xinjiang， among which Powell’s manuscript as well as Spitzer’s manuscript unearthed in Kuqa were of great significance， with the latter being considered to be the earliest physical evidence of palm-leaf manuscript all over the world. In addition， ancient palm-leaf manuscripts were also unearthed in the Mogao Caves in Dunhuang， which was also of great importance. To date， palm-leaf manuscripts are mainly preserved in Xizang， as well as Xishuangbanna in Yunnan Province， where they have been well organized and protected.​ The characters on the palm-leaf manuscripts were carved and written， respectively， which should be adapted to their preservation conditions.

Regime shifts and their underlying mechanisms in ecosystems are a critical issue in ecological research， with profound implications for predicting ecological risks under global change. This review systematically synthesizes the theoretical advances in alternative stable state （ASS） of ecosystem multi-stability， focusing on three key dimensions： micro-macro process coupling， mechanisms of threshold response， and the role of regulatory nodes in ecosystem resilience. By integrating methodologies such as ASS theory， potential landscape modeling， and bifurcation analysis， we highlight how climate change and anthropogenic activities are driving critical ecosystems （e.g.， coral reefs， Amazon rainforest， Arctic permafrost） toward tipping points， while hysteresis effects and irreversible potentials exacerbate recovery challenges. Emerging approaches combining network theory and energy （carbon） flux analysis offer novel insights for cross-scale early warning， yet bridging micro-scale mechanisms with macro-scale patterns remains a critical challenge. This review provides a theoretical framework for ecological threshold management and underscores the urgent need for interdisciplinary approaches to address planetary-scale regime shift risks.

The convergence of broadband LEO satellite communication systems with 5G is the development trend of satellite communication. To meet the demand for high-speed data transmission, multibeam technology based on large-scale digital phased arrays is indispensable. The peak-to-average ratio problem of multibeam signals induces the non-linear distortion of onboard power amplifiers, while the non-linear distortion of power amplifiers leads to serious interference between multibeams. A digital pre-distortion structure for multibeam is proposed for low-orbit satellite communication systems using large-scale phased arrays. A pre-distortion model is developed to solve the problems of non-linearity, intermodulation distortion and multibeam interference. Through simulation experiments, it is found that the proposed structure is more advantageous than the traditional pre-distortion structure in terms of performance and complexity balance, providing a feasible solution for the implementation of multibeam and overcoming non-linearity on board.

The ramp event of wind speed is a large increase or decrease in wind speed within a short period, causing a significant change in wind farm power, affecting the safe operation of the grid and even triggering accidents such as frequency reduction and voltage collapse. This paper selects the simultaneous data of wind turbines and meteorological towers in wind farms, identifies the ramp events by the swinging door algorithm (SDA), analyzes the duration, magnitude and change rate of the ramp events, and discusses the influence of mountainous terrain on them. In this paper, the recognition algorithm of the ramp event of wind speed and power is designed based on the SDA, and the algorithm parameters are set as follows: the time threshold 4 h, wind speed threshold 6 m·s^-1, and power threshold 1 000 kW. For the recognition of ramp events in other wind turbines, this paper suggests using 2/3 value of the difference between rated wind speed and cut-in wind speed as the wind speed threshold parameter, and 2/3 value of rated power as the power threshold parameter. The terrain influence on the ramp event is significant, and the ramp event is more related to the altitude and average wind speed at the turbine, and the time proportion of the ramp event under different terrain ranges from 6.5% to 9.8%, with the average value of 7.8%.

According to the T/CEEIA 415 standard, taking the drive motor slot insulating material as the research object, three kinds of interstitial aramid insulating papers with 0.25 mm thickness are selected for the oil compatibility test, and based on the changes of the physical, chemical, electrical, and mechanical properties of the insulating paper surfaces in different cycles, the degree of damage and the degradation factors of the materials are elaborated in detail. The results indicate that in the oil compatibility test, the porous structure and surface capillarity enhance the densification of the paper-based structure in the short term, which is beneficial to the electrical performance. However, with the increase of the test time, the diffusion of moisture in the automatic transmission fluid in the oil-paper insulating system is intensified, which triggers the formation of insulating defects and results in the attenuation of the mechanical strength. When screening insulation materials, changes in mechanical properties are more intuitive and accurate. In addition, due to the destruction of the hydrogen bond stabilizing configuration, the amide bond and the C＝O bond strength are changed, and the dielectric properties of the lower density paper deteriorate significantly.

Based on the perspective of human-earth relationships, firstly, this article analyzes the theoretical connotation of “Lucid waters and lush mountains are invaluable assets” from the perspectives of philosophical meaning, geographical logic, and economic thinking. Secondly, from the three aspects of serving the rural revitalization, building a beautiful China, and building a community of human destiny, the era value of “Lucid waters and lush mountains are invaluable assets”. Subsequently, from the perspective of coordinated development of human-earth relations, from the four aspects of highlighting the participation of people, the value of excavation land, maintaining human-land balance, and the standardized human and ground behavior, it proposed the development of the transformation from “lucid waters and lush mountains” to “gold and silver mountains”. Finally, according to the key issues that have not been resolved in the practical path, it is pointed out that future research should be strengthened on the selection of transformation path and model, the valuation of ecological products, and the evaluation of transformation efficiency of the “Two Mountains”.

The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79～1.96 t/hm², (0.14～0.37)×10⁸ t and (0.14～0.23)×10⁸ hm², respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm², and 0.37×10⁸ t, respectively, while the cultivated grain area peaked in 1993 at 0.23×10⁸ hm². 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×10⁴ t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×10⁴ t, with a decline of 15.5%.

With the rapid development of broadband low-orbit satellite systems, communication frequency bands such as Ku and Ka tend to be saturated gradually, and non-geostationary orbit (NGSO) satellites will inevitably cause interference to geostationary orbit (GSO) satellites operating at the same frequency. At present, a spatial isolation strategy is often adopted to avoid interference. NGSO satellites always produce the strongest interference to the collinear area. Increasing the isolation angle can reduce the interference, but it will greatly lose the coverage of the LEO satellite. This paper proposes an interference avoidance strategy based on sidelobe nulling of the transmit beam. The antenna array is divided into row and column elements by establishing the LEO satellite coordinate system. In the dimension of column elements, the robust LCMV algorithm is used to realize wide nulling. In the dimension of row elements, it is expanded in combination with beam direction, and finally forms a “null band” in the direction of the collinear area. Through simulation analysis, the proposed strategy can effectively reduce the interference avoidance isolation area of LEO satellites while avoiding collinear interference. The algorithm has low complexity and is easy to implement on satellites.

Astana Cemetery is located in Turpan Basin, Xinjiang. Due to the local extremely arid climate, large numbers of plant remains and documents were well preserved in Astana Cemetery, providing valuable materials for exploring the agricultural production and plant utilization of Turpan people during the Jin to Tang dynasties. Archaeobotanical method was applied on the Chinese jujube (Ziziphus jujuba Mill.) remains unearthed from the Astana Cemetery. Combined with relevant documents, the cultivation and utilization of Chinese jujube during the Jin to Tang dynasties were discussed. It showed that Chinese jujube was a common and important characteristic cash crop in ancient Turpan. The indigenous people used Chinese jujube in various ways, including eating in fresh and further processing it into dried jujube, jujube cake or dry powder of jujube. In addition, the medicinal value of Chinese jujube and the excellent characteristics of jujube wood were also recognized and utilized by ancient Turpan people.

Huanjing-1A （HJ-1A） CCD1 has four reflected solar bands in visible and near-infrared bands. The on-orbit absolute radiometric calibration frequency of HJ-1A/CCD1 is limited to the annual site calibration. Cross calibration， as a supplement means of site calibration， can achieve high-frequency on-orbit absolute radiometric calibration. In this paper， we select Terra/MODIS as the reference sensor to perform long time series cross calibration on HJ-1A/CCD1 based on Dunhuang calibration site. By limiting conditions such as the imaging angle difference （less than 20°）， the imaging time difference （less than 2 hours）， cloud cover， and imaging quality between HJ-1A/CCD1 and Terra/MODIS， a total of 147 effective cross calibration image pairs were screened out from September 2008 to December 2021. Then the 6SV v2.1 radiative transfer model was used to calculate the spectral band adjustment factor， and ultimately achieving long time series cross calibration of HJ-1A/CCD1. The results show that： 1） The cross-calibration coefficients calculated in this article are highly consistent with the officially published calibration coefficients， with an average relative difference of less than 2.25%. The calibration uncertainty is within 5.34%. 2） The long time series cross calibration results showed that after one year on-orbit operation， the gain status of HJ-1A/CCD1 was adjusted on October 20， 2009， resulting in a sudden change in the cross-calibration coefficients on the 409th day after launch. After the adjustment， the overall radiometric performance was relatively stable. 3） The radiometric performance of HJ-1A/CCD1 showed a slow and fluctuating downward trend from October 2009 to December 2021， with an annual attenuation rate of less than 3.10%. The method proposed in this paper can effectively improve the radiometric calibration frequency and accuracy， and can be used for radiometric performance monitoring over the whole life cycle of HJ-1A/CCD1.

Image completion is widely used in unwanted object removal and media editing, which aims to find a semantically consistent way to recover corrupted images. This paper is based on generative adversarial network (GAN) inversion, which leverages a pre-trained GAN model as an effective prior to filling in the missing regions with photo-realistic textures. However, existing GAN inversion methods ignore that image completion is a generative task with hard constraints, making final images have noticeable color and semantic discontinuity issues. This paper designs a novel bi-directional perceptual generator and pre-modulation network to seamlessly fill in the images. The bi-directional perceptual generator uses extended latent space to help the model perceive the non-missing regions of the input images in terms of data representations. The pre-modulated networks utilize a multiscale structure further providing more discriminative semantics for the style vectors. In this paper, experiments are conducted on Places2 and CelebA-HQ datasets to verify that the proposed method builds a bridge between GAN inversion and image completion and outperforms current mainstream algorithms, especially in FID metrics up to 49.2% enhancement at most.

Low earth orbit (LEO) communication satellites can break through terrain constraints and work with 6G to build an integrated space-ground information network. In terms of the beam scheduling problem of satellites for fixed terminals on the ground, a beam scheduling strategy that can achieve dual optimization of interference and delay is proposed, considering that the uneven distribution of global user demands exists. The model with the optimization goal of minimizing the queuing delay and co-channel interference is constructed, combining with constraints such as transmit power as well as carrier-to-noise ratio. By means of step-by-step optimization, a beam-hopping scheme including demand clustering, time slot allocation and beam position matching is designed. When it comes to the interference optimization problem in the beam position matching process, a genetic algorithm-based chromosome crossover mechanism of “beam position self-crossover within a cluster” is proposed. The simulation results show that the improved genetic algorithm can reduce the co-channel interference by 32% to 58% compared with the other algorithms. Besides, the proposed strategy can schedule the beam within the resource allocation period while achieving dual optimization of delay and interference.

This article takes 567 national level (168) and provincial level (511) traditional villages in Shandong Province as subjects of investigation. Firstly, a spatial econometric model is used to analyze the spatial distribution characteristics of traditional villages in Shandong Province. Secondly, the Delphi method and SPSS factor analysis method are used to screen and analyze the evaluation indicators. A total of 28 indicators including village cultural resources, village ecological and natural resources, institutional management, tourism infrastructure, and village economic vitality are obtained. Finally, the AHP entropy weight method is used to combine weights to score the selected indicators. A scoring table for tourism development potential of traditional villages in Shandong Province is constructed, and a multi-objective linear weighted function model is used for scoring. The results showed that: 1) The overall spatial distribution of traditional villages in Shandong Province is uneven, mostly distributed in mountainous areas or underdeveloped areas of cities with relatively uneven development and underdeveloped infrastructure; 2) Among various indicators, the cultural resources (0.331 2) and institutional management (0.144 0) of the village play an important role, followed by tourism infrastructure (0.143 7), village ecological and natural resources (0.143 4), and village economic vitality (0.097 7); 3) The evaluation results show that there are 135 key traditional villages (S≥7.9) in Shandong Province, accounting for only 23.81% of the total number of traditional villages in Shandong Province; there are 295 traditional villages (6.2<S<7.9), accounting for 52.03% of the total number of traditional villages in Shandong Province; there are 137 traditional villages with a focus on protection (S≤6.2), accounting for 24.16% of the total number of traditional villages in Shandong Province; 4) Based on the characteristics of various traditional villages, corresponding development strategies are proposed in this paper.

The potential of light detection and ranging (LiDAR) technology in the application of individual tree segmentation and parameter estimation in desert Haloxylon ammodendron forests has not been explored. This study uses UAV LiDAR data to extract canopy height models (CHM) at spatial resolutions of 0.1, 0.25, 0.5, and 1 m on different interpolation methods, and applies the CHM seed point segmentation algorithm to segment individual trees in three types of Haloxylon ammodendron plots with different growth conditions. This study evaluates the impact of spatial resolution and growth conditions on segmentation accuracy, and verifies the extraction accuracy of tree height and crown width with field measurement data. The results show that the inverse distance weighting interpolation has a higher segmentation accuracy in this study. Spatial resolution is a key factor affecting the results of individual tree segmentation, with the best segmentation results obtained at a resolution of 0.25 m.Class III plots had the highest segmentation accuracy, which was 27% higher than that of Class II plots and 44% higher than that of Class I sample plots. The overlapping crowns of Haloxylon ammodendron in plot I make it difficult to distinguish the crown boundaries, while the independent crowns in plot III make it easier to achieve accurate segmentation. The R² of the tree height fitting model for all three types of plots is around 0.80, with RMSE less than 0.31 m. The R² of the canopy extraction fit for the Class I and II plots is around 0.70, with a slightly higher RMSE error, and the branches in a half dead state of Haloxylon ammodendron in plot III affect the extraction accuracy of crown width. This study demonstrates that LiDAR data has great potential for individual tree segmentation in desert Haloxylon ammodendron forests, which can provide data support for desert forests carbon sink estimation in Xinjiang.

Stochastic variance reduction methods have been successful in solving large scale machine learning problems, and researchers cooperate them with adaptive stepsize schemes to further alleviate the burden of parameter-tuning. In this article, we propose that there exists a trade-off between progress and effectiveness of adaptive stepsize arising in the SVRG-BB algorithm. To enhance the practical performance of SVRG-BB, we introduce the Katyusha momentum to handle the aforementioned trade-off. The linear convergence rate of the resulting SVRG-BB-Katyusha algorithm is proven under strong convexity condition. Moreover, we propose SVRG-BB-Katyusha-SPARSE algorithm which uses Katyusha momentum sparsely in the inner iterations. Numerical experiments are given to illustrate that the proposed algorithms have promising advantages over SVRG-BB, in the sense that the optimality gaps of the proposed algorithms are smaller than the optimality gap of SVRG-BB by orders of magnitude.

Coal-fired power plants are important contributors to CO₂ emissions in China. Due to the low timeliness of statistical data and inaccurate emission factors, the existing emission inventories gradually fail to reflect the CO₂ emissions of power plants. This study provides a method to estimate CO₂ emissions from power plants based on Orbiting Carbon Observatory 2/3 (OCO-2/3) satellite data and Gaussian plume model, retrieving the images of super-large coal-fired power plants (≥5 000 MW) in China from the OCO-2 (September 6,2014-October 1, 2021) and OCO-3 (August 6, 2019-October 1, 2021) dataset, and identifying a total of seven plumes near Tuoketuo, Waigaoqiao, and Jiaxing power plants. Using a combination of three atmospheric background value determination methods, the CO₂ emissions estimated by the Gaussian plume model range from 43 to 77 kt/d, with correlation coefficients ranging from 0.50 to 0.87. The uncertainties of individual plumes varied from 8% to 32% (1σ), with wind speed being the largest uncertainty (6%-31%), followed by background values (5%-18%), enhanced values (1%-21%), and plume rise (1%-8%). The estimates are verified to be in high agreement with Carbon Monitoring for Action, Carbon Brief, and the Global Power Emissions Database (Tuoketuo: (76.48±15.75), Waigaoqiao: (55.98±6.90), Jiaxing: (64.55±15.89) kt/d). This study helps monitor and estimate important point source carbon emissions, which is not only a prerequisite for the power industry to carry out carbon reduction efforts but also helps develop specific regional carbon reduction policies, thereby reducing anthropogenic carbon emissions.