日侧极光卵的可见光多波段观测特征——中国北极黄河站首次极光观测初步分析

中国在斯瓦尔巴特(Svalbard)群岛新奥尔松(Ny-Alesund)地区建立了一个永久性科学考察站--北极黄河站。北极黄河站(78．92°N,11．93 °E)的修正磁纬为76．24°。2003年11月黄河站建立了一套多波段(427．8nm,557．7nm,630．0nm)单色极光全天空CCD成像观测系统,并于2003年12月11日-2004年2月25日极夜期开展了首次极光越冬观测,获得了1200多小时的连续观测数据。利用这1200多小时观测数据制作了以时间为横轴的南北向极光活动图,对日侧极光卵的极光活动特征进行了研究。初步分析发现,沿日侧极光卵不论磁静日还是磁扰日,在三个波段上都同时观测到了四个重复出现的极光活动区,即0900MLT(磁地方时)附近的 A区、1330MLT的B区和1630MLT的C区以及1200MLT附近的G区。A区内的极光活动由低纬向高纬运动,强度逐渐变弱,形态由较宽的弧状极光向冕状极光演变;B区内的极光活动由高纬向低纬方向运动,形态呈现为冕状极光向较窄的极光弧的演变,极光强度逐渐增强;C区极光活动向高纬方向展宽,强度逐渐变弱,极光由单弧向多重弧演化;而在A与B之间存在一个纬度范围相对较窄的极光活动区G区,该区的极光呈现暗弱的冕状极光。日侧极光活动随时间从午前、正午到午后呈现出一个系统的光谱变化:绿色-红色-绿色。A区的位置出现在卫星探测的午前极光峰之中;而正午附近极光强度暗弱的G区,可部分地解释卫星观测的所谓“正午极光空白区”;卫星观测到的1500MLT附近的“极光热点”区在地面观测中可以区分为以红色极光为主的B区和以绿色极光为主的C区。     

一组高精度的回旋同步加速辐射的近似式

本文对回旋同步加速辐射的发射系数 ην/BN和吸收系数KνB/N作了精确的数值计算 ,采用幂指数δ为电子能谱指数δ的多项式的幂函数拟合 ,获得了一组新的 ην/BN和KνB/N的近似式 ,在大多数爆发出现的δ和谐波系数S(=ν/νB)范围内 ,其精度好于±7%和± 1 7% ,比幂指数为δ的线性拟合的相应精度改善很多     

太阳强磁区的精细结构形态

作者希望通过对国内外太阳物理学家多年来在太阳磁场精细结构方面的研究成果的回顾，探讨立足现有和将有的仪器设备，可能和应该从事的高分辨太阳磁场的研究工作。为突出重点，侧重评述１０００Ｇ以上的强磁场精细结构特征及与之相关的亮度特征的精细结构。     

综合物探在我省煤矿生产中的应用

河北局从1955年于开滦建立了我国第一个煤田地震勘探队和电法队以后,进行了大量的方法研究及技术积累,其综合勘探技术水平有了很大的提高。特别是在解决煤矿复杂地质构造及水文地质问题方面,取得了较好的地质效果。到目前为止,已经做了百余个针对破碎带、断层、陷落柱导水、突水通道检测、小窑采空区积水和煤系地层含水层分布等方面的地质勘探工作,积累了丰富的经验。实践证明,综合勘探技术在河北省找煤、矿井水文地质勘探以及环境地质、灾害地质等方面发挥着越来越大的作用。     

煤炭三维地震勘探时深转换误差分析及对策

煤炭三维地震勘探对煤层作时深转换时常用的方法是：首先利用钻孔处的目的层铅垂深度除以钻孔处目的层的反射时间值的一半，算出各个钻孔处煤层的大平均速度，然后利用内插法绘制测区速度分布平面图。之后，把速度平面上各点的速度乘以时间平面图上反射时间的一半，得出深度平面图。该时深转换易产生误差，有些情况其误差甚至超过了规范要求。针对测区的实际情况，列举了在二层速度情况下用大平均速度作时深转换时误差分析的一些例子，并提出了如何消除误差的对策。     

重大突发事件中提升志愿服务应急能力的现实思考

重大突发事件的应对处置能力是国家治理体系和治理能力的重要组成部分,也是国家治理能力现代化的重要体现。志愿服务作为新时代社会治理的重要元素,在重大突发事件应对中发挥着独特的作用。然而从近年来重大突发事件应对的现状来看,无论是在质还是量上,志愿服务都难以满足应急工作的需要。通过对近年来重大突发事件的梳理和对灾区的实证调研,提出了重大突发事件中增强志愿服务应急法治能力建设,加强“平战结合”的专业建设能力,提升“互联网+”多方合作的联动协调能力,制定志愿服务应急的精准激励保障能力等方面的思考和建议,对于改善重大突发事件中志愿服务应急能力困境具有一定的价值。     

轨道交通沿线不同区段土地利用的差异性测度

为了研究轨道交通沿线的土地利用差异性变化,该文提出了一种面向城市总规用地、遥感影像数据探讨土地利用差异性的分析方法,为引导城市与轨道交通协同发展提供支撑,以合肥1号轨道交通为例,运用了ArcGIS空间和SPSS统计分析方法.研究表明,在时间维度方面:①用地的动态度、变化率和变化量大小排序均呈现出二环以外＞一二环之间＞一环以内.②在用地面积变化上,相交于二环以外,一环以外、一二环之间区域的用地面积变化不突出;空间维度方面:①轨道交通各站点周边用地均呈现多元复合型,而不同站点周边用地的主导功能有所差异.②各站点400～600m范围用地的信息熵和均衡度大小趋于较高值的平稳状态,200 m范围内信息熵和均衡度波动大.③各站点周边商业、居住和道路用地半径维数较高,道路、商业用地边界维数较高.     

Real-time fusion of multiple videos and 3D real scenes based on optimal viewpoint selection

3D real scenes are a digital virtual space that represents and portrays the real world in a photorealistic, three-dimensional, and sequential manner. Existing methods for constructing and updating 3D models, such as oblique photography and laser scanning, are difficult to meet the demand of perceiving the real world intuitively, dynamically, and in real time. In recent years, the method of integrating rapidly rising video data and 3D models has become increasingly popular. Compared with existing methods, it enhances the real-time perception of 3D scenes by taking advantage of the real-time character of videos and the intuitive character of 3D models. In this article, we propose a real-time fusion method of multiple videos and 3D real scenes based on optimal viewpoint selection. To begin, 3D reconstruction and video camera calibration were used to prepare the basic data for the fusion of videos and 3D model. Second, a visible-surface detection-based video space restoration method was provided, and the overlapping region between multiple videos was determined. Third, to split the overlapping region into the corresponding camera spaces, a segmentation method based on optimal viewpoint selection was given. Finally, the 2D videos were dynamically fitted to the 3D model using the dynamic texture mapping method, while accomplishing the fusing and rendering of the 3D real scene. After experimental verification, the overall effect of the multiple videos and 3D real scene fusion system implemented using the method proposed in this article is better, while the algorithm is less time-consuming and efficient in rendering.     

Integrating domain knowledge and graph convolutional neural networks to support river network selection

Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine-tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.