Review of Image Quality Measures for Solar Imaging

Observations of the solar photosphere from the ground encounter significant problems caused by Earth’s turbulent atmosphere. Before image reconstruction techniques can be applied, the frames obtained in the most favorable atmospheric conditions (the so-called lucky frames) have to be carefully selected. However, estimating the quality of images containing complex photospheric structures is not a trivial task, and the standard routines applied in nighttime lucky imaging observations are not applicable. In this paper we evaluate 36 methods dedicated to the assessment of image quality, which were presented in the literature over the past 40 years. We compare their effectiveness on simulated solar observations of both active regions and granulation patches, using reference data obtained by the Solar Optical Telescope on the Hinode satellite. To create images that are affected by a known degree of atmospheric degradation, we employed the random wave vector method, which faithfully models all the seeing characteristics. The results provide useful information about the method performances, depending on the average seeing conditions expressed by the ratio of the telescope’s aperture to the Fried parameter, \(D/r_{0}\). The comparison identifies three methods for consideration by observers: Helmli and Scherer’s mean, the median filter gradient similarity, and the discrete cosine transform energy ratio. While the first method requires less computational effort and can be used effectively in virtually any atmospheric conditions, the second method shows its superiority at good seeing (\(D/r_{0}<4\)). The third method should mainly be considered for the post-processing of strongly blurred images.     

A spatially explicit reinforcement learning model for geographic knowledge graph summarization

Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.     

Site formation processes in loess areas: A case study from moravany-žakovska,western slovakia

The site of Moravany-Žakovska in western Slovakia is situated in a loessic terrain and is dated to the end of the Late Glacial Maximum. This article shows the importance of analyzing the 0.01-0.1 mm fraction to interpret the sedimentological environment and a number of measured anthropogenic indices, such as burnt quartz grains, charcoal, radiolarite chips, microflakes, and microfragments of bone. Analysis of the profile of the site through time reveals the relationship between occupational levels and phases of slope wash that occurred during wetter climatic episodes; these are separated by phases of aeolian sedimentation. At the same time, analysis of anthropogenic indices in this size fraction permitted the identification of occupational levels, including those not recorded by macrofinds. Analysis of anthropogenic indices in horizontal exposures enabled the identification of latent settlement structures such as hearths, areas of radiolarite processing, zones where mineral dyes were prepared, and other structures not recorded by macrofinds.     

Balanced cross-section restoration in a complicated folded hinterland structure: Shilbilisaj profile,Talas ridge,Caledonian Tien Shan

Conventional cross-section balancing techniques based on layer length measuring can be applied only for foreland structures. To analyse complicated hinterland structure with numerous small-scale folds, this balancing technique requires the reliable and detailed tracing of the morphology of any layer throughout the cross-section, which is unattainable. We present a special kinematic method of balancing cross sections based “on the geometry of the folded domain” which enables the structural restoration of hinterland regions. We apply the method to restore the detailed structural section along the Shilbilisaj River, having a length of 26 km. We divided this section into 40–60 so-called “domains” each including 2–7 folds. Our method uses the fold's morphology to determine the strain ellipsoid, which describes the deformation of each domain and is used to restore its pre-folded state. By combining the pre-folded states of the domains, we reconstruct the entire profile, and calculate shortening values as K = L₀/L₁ (initial to final length). The overall shortening value for the profile is 4.49, incrementally varying along the section from 3.79 to 5.53. The comparable results of two independently performed reconstructions emphasize the reliability of the applied balancing method.