Transient execution vulnerabilities have emerged as a critical threat to modern processors. Hardware fuzzing testing techniques have recently shown promising results in discovering transient execution bugs in large-scale out-of-order processor designs. However, their poor microarchitectural controllability and observability prevent them from effectively and efficiently detecting transient execution vulnerabilities.

High quality training samples are essential for crop mapping. However, since traditional sample acquisition methods are based on expert interpretation or field research, they are time-consuming and expensive. Using the unique time window of a crop, it is possible to distinguish a specific crop from other features. Therefore, using phenological information combined with machine learning methods for sample migration is a very feasible solution for crop mapping. In this study, we developed a yearly automated generated sample migration algorithm based on crop phenological features. Using image time series derived from data acquired by Landsat sensor systems 5, 7, 8 accessible through the Google Earth Engine cloud data platform, we developed a procedure for temporally displacing ground-truth soybean samples based on phenological features of the crop. With these data, we then generated annual maps of soybean in Heilongjiang Province, China. Overall accuracy of the temporally displaced soybean samples was higher than 95%, while the overall accuracy of the soybean maps obtained was more than 83%. This study provides a feasible approach for developing ground-truth samples from long term image time series, suitable for mapping the dynamics of crops across space and time.