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Shale has been considered as good gas reservoir due to its abundant interior nanoscale pores. Thus, the study of the pore structure of shale is of great significance for the evaluation and development of shale oil and gas. To date, the most widely used approaches for studying the shale pore structure include image analysis, radiation and fluid invasion methods. The detailed pore structures can be studied intuitively by image analysis and radiation methods, but the results obtained are quite sensitive to sample preparation, equipment performance and experimental operation. In contrast, the fluid invasion method can be used to obtain information on pore size distribution and pore structure, but the relative simple parameters derived cannot be used to evaluate the pore structure of shale comprehensively and quantitatively. To characterize the nanoscale pore structure of shale reservoir more effectively and expand the current research techniques, we proposed a new method based on gas adsorption experimental data and the method of moments to describe the pore structure parameters of shale reservoir. Combined with the geological mixture empirical distribution and the method of moments estimation principle, the new method calculates the characteristic parameters of shale, including the mean pore size (), standard deviation (σ), skewness (Sk) and variation coefficient (c). These values are found by reconstructing the grouping intervals of observation values and optimizing algorithms for eigenvalues. This approach assures a more effective description of the characteristics of nanoscale pore structures. Finally, the new method has been applied to analyze the Yanchang shale in the Ordos Basin (China) and Longmaxi shale from the Sichuan Basin (China). The results obtained well reveal the pore characteristics of shale, indicating the feasibility of this new method in the study of the pore structure of shale reservoir. [ABSTRACT FROM AUTHOR]

Sentence clustering plays a pivotal role in theme-based summarization, which discovers topic themes defined as the clusters of highly related sentences to avoid redundancy and cover more diverse information. As the length of sentences is short and the content it contains is limited, the bag-of-words cosine similarity traditionally used for document clustering is no longer suitable. Special treatment for measuring sentence similarity is necessary. In this article, we study the sentence-level clustering problem. After exploiting concept- and context-enriched sentence vector representations, we develop two co-clustering frameworks to enhance sentence-level clustering for theme-based summarization-integrated clustering and interactive clustering-both allowing word and document to play an explicit role in sentence clustering as independent text objects rather than using word or concept as features of a sentence in a document set. In each framework, we experiment with two-level co-clustering (i.e., sentence-word co-clustering or sentence-document co-clustering) and three-level co-clustering (i.e., document-sentence-word co-clustering). Compared against concept- and context-oriented sentence-representation reformation, co-clustering shows a clear advantage in both intrinsic clustering quality evaluation and extrinsic summarization evaluation conducted on the Document Understanding Conferences (DUC) datasets. [ABSTRACT FROM AUTHOR]

The design and performance evaluation of the high-speed railway (HSR) communications are inseparable from the accurate and efficient high-speed channel simulator. The variation on path loss, shadowing and fast fading are the key characteristics of HSR channels. Although finite-state Markov chain (FSMC) channel models have been extensively investigated to describe fading channels, most of the previous channel simulators cannot reflect all the above key characteristics. In this paper, we propose a novel FSMC model based on channel simulator with the path loss, shadowing and fast fading in HSR. The accurate closed-form expression of state transition probabilities between channel states is derived to break the simplified assumption of first-order Markov chain. To validate the accuracy of the new FSMC channel simulator, the comparison analysis between the proposed channel simulator and the actual measurements are provided. [ABSTRACT FROM AUTHOR]