Space-borne multi-angle polarization remote sensing is considered to be one of the most important tools to obtain global aerosol parameters in assessment of climate change. Accurate calibration is a prerequisite for quantitative polarization remote sensing. However, most research focuses on the polarization calibration methods and the monitoring of the calibration coefficient stability. Few studies investigate the polarization calibration verification of the subsequent new satellite sensors. The Directional Polarimetric Camera (DPC) onboard the Chinese Terrestrial Ecosystem Carbon Inventory Satellite (abbreviated as TECIS, with the Chinese name ”Gou Mang”) is a brand-new polarization sensor. To evaluate the polarization calibration of this sensor, we propose a verification scheme for polarization calibration, which can verify both the degree of polarization (DoP) and polarized reflectance. The accuracy of DoP is verified by using sunglint on the ocean. When detecting the sunglint, constraints such as observational geometry, cloud identification, and wind speed are introduced, and the polarized reflectance is atmospherically corrected according to the marine aerosol model and aerosol optical depth from MODIS. The accuracy of polarized reflectance at the Top of Atmosphere (TOA) is verified based on AERONET inversion products and the Bidirectional Polarization Distribution Functions (BPDF). Experiments show that the accuracies of DoP of the three polarization channels (490, 670, 865 nm) of DPC are 5.57%, 2.07%, and 1.97% respectively, and the average accuracy of the multi-angle polarized reflectance at the TOA of the 865nm channel is 0.209%.

One of the most common threats to online service system's reliability is disk failure. Many disk failure prediction techniques have been developed to predict failures before they actually occur, allowing proactive steps to be taken to minimize service disruption and increase service reliability. Existing approaches for disk failure prediction do not differentiate among various types of disk failure. In industrial practice, however, different product teams treat distinct types of disk failures as different prediction tasks in large-scale online service systems like Microsoft 365. For example, hardware operation team is concerned with physical disk errors, while database service team focuses on I/O delay. In this paper, we propose MTHC (Multi-Task Hierarchical Classification) to enhance the performance of disk failure prediction for each task via multi-task learning. In addition, MTHC introduces a novel hierarchy-aware mechanism to deal with the data imbalance problem, which is a severe issue in the area of disk failure prediction. We show that MTHC can be easily utilized to enhance most state-of-the-art disk failure prediction models. Our experiments on both industrial and public datasets demonstrate that such disk failure prediction models enhanced by MTHC performs much better than those models working without MTHC. Furthermore, our experiments also present that the hierarchical-aware mechanism underlying MTHC can alleviate the data imbalance problem and thus improve the practical performance of various disk failure prediction models. More encouragingly, the proposed MTHC has been successfully applied to Microsoft 365 online service systems, and averagely reduces the number of virtual machine interruptions by 10% per month.

This paper has put forward an innovated CA model to sub-pixel mapping. It has the advantage of easily constructing the rules of CA and the potential for controlling the spatial scale in sub-pixel mapping freely. The neural networks have played an important role in the whole work.