In order to ensure the accuracy and reliability of the observational data from the remote sensor during its on-orbit operation, a sampling correction approach with the interpolation sliding window (ISWSCA) is proposed on the basis of the quadratic fitting and interpolation correction. The ISWSCA mitigates the effects of the inhomogeneous distribution of the reflective properties on the lunar surface, significantly enhancing the sampling correction accuracy of the lunar observation data by incorporating a subpixel correction term. Based on the lunar observation data from the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager (MERSI)-II collected between 2018 and 2023, the feasibility of the ISWSCA is verified, and the ISWSCA improves the sampling correction accuracy by 2.13% and 5.25% during low and high lunar phases in comparison to the scaling method of the lunar full-disk irradiance (LFDISM), together with the correction accuracy improved by 1.37% and 6% in low and high spatial resolutions. The ISWSCA gives the long time series of the normalized calibration coefficient, together with the calibration uncertainty of the effective data being analyzed, and the results show that the on-orbit stability of the FY-3D satellite is excellent in the visible (VIS) and near-infrared (NIF) bands, with the attenuation rate below 1.29% and the calibration uncertainty within 2.11%. This study has an important significance for the on-orbit radiation calibration of the spatial remote sensor.

Built on the WeChat social platform, WeChat Mini-Programs are widely used by more than 400 million users every day. Consequently, the reliability of Mini-Programs is particularly crucial. However, WeChat Mini-Programs suffer from various bugs related to execution environment, lifecycle management, asynchronous mechanism, etc. These bugs have seriously affected users' experience and caused serious impacts.

Node.js has become a widely-used event-driven architecture for server-side and desktop applications. Node.js provides an effective asynchronous event-driven programming model, and supports asynchronous tasks and multi-priority event queues. Unexpected races among events and asynchronous tasks can cause severe consequences. Existing race detection approaches in Node.js applications mainly adopt random fuzzing technique, and can miss races due to large schedule space.In this paper, we propose a dynamic race detection approach NRace for Node.js applications. In NRace, we build precise happens-before relations among events and asynchronous tasks in Node.js applications, which also take multi-priority event queues into consideration. We further develop a predictive race detection technique based on these relations. We evaluate NRace on 10 realworld Node.js applications. The experimental result shows that NRace can precisely detect 6 races, and 5 of them have been confirmed by developers.

Hyperspectral unmixing is a technique for selecting endmembers (pure spectral constituents) and their abundances (proportions). Recently, sparse unmixing is a semi-supervised method in which mixed pixels are represented in the form of combinations of a number of pure spectral signatures from a large spectral library. Compared with other methods, the sparse unmixing method exhibits significant advantages. However, most of these sparse unmixing methods were implemented in spatial domain, where the information is too scattered, redundant and susceptible to noise. In this paper, we propose a new unmixing method called spectral-spatial weighted sparse unmixing in the transform domain (SSTSU) to impose the abundance sparsity and enhance the anti-noise performance. The experimental results show that the proposed algorithm has better anti-noise performance and unmixing results compared with other advanced sparse unmixing methods.