考题猜想 03 阅读理解常考必刷20篇-2023-2024学年高二英语下学期期中考点大串讲（人教版2019）

考题猜想 03 阅读理解常考必刷20篇（原卷版） 选择性必修第二册、第三册 Passage 1 Certain areas near the moon’s poles stay everlastingly in shadow, never receiving direct sunlight. Recent studies suggest these so-called permanently shadowed regions (PSRs) contain rich ice resource that could show details about the early solar system; they could also help future visitors make fuel and other resources. But these areas are hard to photograph from satellites moving around the moon and thus are a challenge to study. The few photos PSRs reflect are often flooded by camera noise and quantum effects (量子效应). Now researchers have produced a deep-learning algorithm (算法) to cut through the interruption and to see these dark zones. “Our images enable scientists to identify the features of craters and boulders (陨石坑和巨石),” says Valentin Bickel, a planetary scientist at the Max Planck Institute of Solar System Research in Germany and lead author of a Nature Communications study testing the new algorithm. The researchers used more than 70,000 images of completely dark lunar areas — with no light signal — together with details about the camera’s temperature and position in orbit to train their algorithm to recognize and remove camera noise. Next they dealt with the rest noise through information learned from millions of sunlit lunar photos, together with copied versions of the same images in shadow. Ignacio Lopez-Francos, a study co-author and engineer at the NASA Ames Research Center, says using such man-made shadow was necessary because sunlit PSR images do not exist. A similar technique is also used in low-light digital camera photography. The researchers used their algorithm to analyze the size and number of craters and boulders in several PSRs that might be explored by NASA’s Artemis moon program. They also found the likely origins of some boulders and established a potential route for an astronaut through a PSR on the moon, avoiding obstacles and slopes steeper than 10 degrees. “It’s an interesting application