Lunar surface potential and electric field

The Moon has no significant atmosphere, thus its surface is exposed to solar ultraviolet radiation and the solar wind. Photoemission and collection of the solar wind electrons and ions may result in lunar surface charging. On the dayside, the surface potential is mainly determined by photoelectrons, modulated by the solar wind;while the nightside surface potential is a function of the plasma distribution in the lunar wake. Taking the plasma observations in the lunar environment as inputs, the global potential distribution is calculated according to the plasma sheath theory, assuming Maxwellian distributions for the surface emitted photoelectrons and the solar wind electrons. Results show that the lunar surface potential and sheath scale length change versus the solar zenith angle, which implies that the electric field has a horizontal component in addition to the vertical one. By differentiating the potential vertically and horizontally, we obtain the global electric field. It is found that the vertical electric field component is strongest at the subsolar point,which has a magnitude of 1 V m-1. The horizontal component is much weaker, and mainly appears near the terminator and on the nightside, with a magnitude of several mV m-1. The horizontal electric field component on the nightside is rotationally symmetric around the wake axis and is strongly determined by the plasma parameters in the lunar wake.

Element-Arrangement Context Network for Facade Parsing

Facade parsing aims to decompose a building facade image into semantic regions of the facade objects.Considering each architectural element on a facade as a parameterized rectangle,we formulate the facade parsing task as object detection,allowing overlapping and nesting,which will support structural 3D modeling and editing for further applications.In contrast to general object detection,the spatial arrangement regularity and appearance similarity between the facade elements of the same category provide valuable context for accurate element localization.In this paper,we propose to exploit the spatial arrangement regularity and appearance similarity of facade elements in a detection framework.Our element-arrangement context network(EACNet)consists of two unidirectional attention branches,one to capture the column-context and the other to capture row-context to aggregate element-specific features from multiple instances on the facade.We conduct extensive experiments on four public datasets(ECP,CMP,Graz50,and eTRIMS).The proposed EACNet achieves the highest mIoU(82.1%on ECP,77.35%on Graz50,and 82.3%on eTRIMS)compared with the state-of-the-art methods.Both the quantitative and qualitative evaluation results demonstrate the effectiveness of our dual unidirectional attention branches to parse facade elements.