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Based on the observation monthly climatic data collected from 2766 weather observation stations on global during the period from 1981 to 2010, and the climatic scenarios data of SSP1_2.6、SSP1_4.5 and SSP1_8.5 scenarios released by CMIP6, the mean annual biotemperature, average total annual precipitation and potential evapotranspiration ratio on spatial resolution of 0.1º× 0.1º were respectively obtained by operating a high accuracy and speed method of surfacing modeling (HASM) (Yue, 2010, Yue et al., 2016) during all the four periods from 2020 to 2050 per decade. The method for surface modelling of land cover scenarios (SMLCS) has been developed to simulate the scenarios of land cover in Eurasia （Fan et al., 2019, 2020, 2021）. Finally, the scenario dataset of land cover under scenario SSP1_2.6、SSP1_4.5 and SSP1_8.5 were simulated by the SMLCS method from 2020 to 2050. 采用1981-2010年全球2766个气象观测站的观测月气候数据，以及CMIP6发布的SSP1_2.6、SSP1_4.5和SSP1_8.5情景的气候情景数据。通过运行高精度面建模方法（HASM）(Yue, 2010, Yue et al., 2016)，分别获得2020-2050年间每10年的空间分辨率为0.1º×0.1º的平均生物温度数据、多年平均年降水和潜在蒸散比率数据。采用自主研发的土地覆被情景曲面建模（SMLCS）方法（Fan et al., 2019, 2020, 2021），实现了SSP1_2.6、SSP1_4.5和SSP1_8.5情景的2020-2050年间每10年的全球土地覆被变化情景模拟。

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Spatio-temporal variations in extreme drought in China during 1961–2015 Spatio-temporal variations in extreme drought in China during 1961–2015

This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84).

Wind turbine roll decay curves Wind turbine roll decay curves

As the most complex component in the transmission system, the operating state of the wind turbine gearbox has a tremendous impact on the monitoring of the health status and operation control of the wind turbine equipment. Abnormalities in wind turbines that lead to downtime not only result in a loss of electrical energy, but also a significant increase in maintenance costs. Therefore, with the wind turbine gearbox as the main object of study, the following studies were carried out: For microscopic local conditions in gearbox gear systems, a method for obtaining modal data using finite element simulation analysis of single tooth faults is proposed. Using a combination of deep auto-encoder structures and BP structures for secondary training strategies, a linear and non-linear performance evaluation method is proposed, which takes into account the relationship between performance and efficiency. Hyper-parameter configuration in deep transfer structures is often arbitrary, so a hierarchical transfer network structure hyper-parameter searching method is proposed to address the gearbox planetary system fault classification problem. The algorithm is validated using the classical LeNet-5 reconfiguration transfer application on a modal dataset of the planetary system. Finally, a stability validation and results analysis of the algorithm performance is carried out. A compressed sensing-based sparse signal decomposition method is proposed, and the structure of the transfer network is redesigned to achieve deep migration learning from rolling bearing faults to gear faults. A new network architecture was designed using a plug-and-play attention module. Pre-training models were designed and produced for fault data to improve the accuracy and recognition speed of fault diagnosis model classification. Finally, the effects of the same number of samples in the source and target domains and different distributions of sample features on the performance of the transfer learning method and the effects of hyper-parameters on the final performance of the network structure are verified.

The energy transfer of Nd3+/Yb3+ in silicate glass with temperature was studied. The fluorescence spectra and fluorescence lifetime were measured at a series of different temperatures, and the energy transfer efficiency at different temperatures was calculated 研究了Nd3+/Yb3+在硅酸盐玻璃中能量传递随温度的变化，测量了一系列不同温度下荧光光谱和荧光寿命，并计算了不同温度下的能量传递效率

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change. Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.

A series of silver-modified phosphotungstic acid catalysts AgxH3-xPW12O40 (x=1, 2, 3) were synthesized by incorporating silver nitrate into phosphotungstic acid. The structure, stability and acidity of the catalysts synthesized were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermal gravimetric analysis-derivative thermogravimetric analysis (TGA-DTG) and 31P-TMPO magic-angle spinning nuclear magnetic resonance (31P-TMPO MAS-NMR) spectroscopy. The effects of various reaction parameters, such as methanol/oil molar ratio, amount of catalyst, reaction time, and reaction temperature on the catalytic transesterification of soybean oil and methanol with Ag2HPW12O40 were investigated. The results demonstrated that Ag2HPW12O40 had the best catalytic activity, superior biodiesel yield and excellent durability. The high catalytic activity of the catalyst was attributed to Brønsted-Lewis acid synergy. With 6 wt.% Ag2HPW12O40 catalyst, the yield of biodiesel reached 96.4% with a methanol/soybean oil molar ratio of 32/1, a reaction temperature of 150℃ and a reaction time of 20 h.