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ABSTRACTThe GaoFen-14 (GF-14) satellite is China’s most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic mapping at 1:10,000-scale without ground control points (GCPs). The technical parameters of payloads will change once the satellite enters orbit. As a result, strict on-orbit geometric calibration is necessary. This study performs the on-orbit geometric calibration of the GF-14 stereo camera using ground calibration data. The exterior orientation errors are corrected by a comprehensive bias matrix, internal orientation errors are described by a fifth order polynomial. The results of Yinchuan calibration field show that the planar accuracy is better than 286 m (RMS) before calibration, and improved to 2.11 m (front camera) and 1.51 m (back camera) by external orientation, and further improved to 1.46 m (front camera) and 0.99 m (back camera) after internal calibration. The front intersection accuracy reaches 0.67 m in plane (RMS) and 1.10 m in elevation (RMS), respectively. Checking by multiple global check points (CKPs), the planar and elevation RMS reaches 2.38 m (5.09 m in CE90) and 2.08 m (3.43 m in LE90), respectively.

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning (ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following: (1) The stacking ensemble learning, using RF-XGB-CB-LR model, significantly enhances flood susceptibility simulation. (2) In addition to rainfall, key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland. (3) In the northeast of the study area, heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management. [ABSTRACT FROM AUTHOR]

Co-B catalyst, W-modified Co-B (Co-W-B) catalyst, and Mo-modified Co-W-B (Co-W-Mo-B) catalyst powders were synthesized via chemical reduction of metal salt solutions at room temperature. The optimal hydrogen production rate was obtained when the ratio of the reducing agent (NaBH4) to the metal solution was 4:1. Among the ternary catalysts, the Co0.1-W0.9-B catalyst showed the optimal hydrogen production rate when the content of W doping was 0.9. Furthermore, the hydrogen production rate of the quaternary catalyst was enhanced when the content of Mo doping was 0.075, denoted as Co0.1-W0.9-Mo0.075-B. The catalyst powders were characterized by SEM, XRD, XPS, and BET. The hydrogen production rates of the Co-B catalyst, Co0.1-W0.9-B catalyst, and Co0.1-W0.9-Mo0.075-B catalyst were investigated for the decomposition of alkaline sodium borohydride (NaBH4) in a water–ethanol mixture solution. The Co0.1-W0.9-Mo0.075-B catalyst exhibited the highest hydrogen production rate compared to Co-B and Co0.1-W0.9-B catalyst powders. The optimal hydrogen production rate of the Co0.1-W0.9-Mo0.075-B catalyst is 4600 ml·min−1·g−1, which is nearly 1.9 times higher than that of the Co-B catalyst. Moreover, the Co0.1-W0.9-Mo0.075-B catalyst exhibited good stability, retaining 73% of its catalytic activity after five cycles, and had a low apparent activation energy of 41.51 kJ·mol−1. [ABSTRACT FROM AUTHOR]

Traditionally, the assessment of heavy metal concentrations using remote sensing technology is sample-intensive, with expensive model development. Using a mining area case study of Daxigou, China, we propose a cross-time-domain transfer learning model to monitor heavy metal pollution using samples collected from different time domains. Specifically, spectral indices derived from Landsat 8 multispectral images, terrain, and other auxiliary data correlative to soil heavy metals were prepared. A cross time-domain sample transfer learning model proposed in the paper based on the TrAdaBoost algorithm was used for the Cu content mapping in the topsoil by selective use of soil samples acquired in 2017 and 2019. We found that the proposed model accurately estimated the concentration of Cu in the topsoil of the mining area in 2019 and performed better than the traditional TrAdaBoost algorithms. The goodness of fit (R2) of the test set increased from 0.55 to 0.66; the relative prediction deviation (RPD) increased from 1.37 to 1.76; and finally, the root-mean-square deviation (RMSE), decreased from 8.33 to 7.24 mg·kg−1. The proposed model is potentially applicable to more accurate and inexpensive monitoring of heavy metals, facilitating remediation-related efforts. [ABSTRACT FROM AUTHOR]

Fault tree of automotive brake system and tire were set up according to their typical failure modes. We use the likelihood function and belief function of D-S theory respectively as the fault tree to analyze the upper and lower bounds of bottom event fault probability interval,we also apply the interval analysis theory to construct the AND operator,OR operator,and we conduct numerical computation on bottom event fault probability interval to solve the quantitative calculation problem of fault tree,thus to obtain probability interval of the top event fault,and reliability of automobile tire and brake system are analyzed and predicted. The calculation results show that this method is effective and feasible, which provide a reference design for improvement and the adjustment of automotive brake system and tire.