Search

Due to disparities in tolerance, resource availability, and acquisition of labeled training data between satellite-terrestrial integrated networks (STINs) and terrestrial networks, the application of traditional terrestrial network intrusion detection techniques to satellite networks poses significant challenges. This paper presents a satellite network intrusion detection system named Bi-LSTM sparse selfencoder (BLSAE-SNIDS) to address this issue. Through the development of an innovative unsupervised training Bi-LSTM stacked self-encoder, BLSAE-SNIDS facilitates feature extraction from satellite network traffic, diminishes dimensionality, considerably reduces training and testing durations, and enhances the attack prediction accuracy of the classifier. To assess the efficacy of the proposed model, we conduct comprehensive experiments utilizing STIN and UNSW-NB15 datasets. The results obtained from the STIN dataset demonstrate that BLSAE-SNIDS achieves 99.99% accuracy with reduced computational and transmission overheads alongside enhanced flexibility. Furthermore, results from the UNSW-NB15 dataset exhibit BLSAE-SNIDS’ proficiency in detecting various network intrusion attacks efficiently. These findings indicate that BLSAE-SNIDS suits general satellite security networks and offers a novel approach to designing security systems for polar satellite networks, thus exhibiting practical utility. [ABSTRACT FROM AUTHOR]

In the era of information and communication technology (ICT), the advancement of science and technology has led to a trend of diversification in map representation. However, the lack of professional knowledge means that there is still a challenge in determining the appropriate type of thematic map for land use expression. To address this issue, this paper proposes a knowledge recommendation method for land use thematic maps based on the theory of visualization dimensions. Firstly, we establish a knowledge ontology of land use thematic maps centered on spatial data, data characteristics, visualization dimensions, thematic map forms, and application scenarios. A land use thematic map knowledge graph is constructed through knowledge extraction and storage operations. Secondly, knowledge embedding is performed on the knowledge graph to enable the knowledge-based expression of map visualization elements. Finally, based on the knowledge elements of land use thematic expression, a similarity calculation model is established to calculate the similarity between input data and the spatial data characteristics, visualization dimensions, and application scenarios within the knowledge graph, deriving a comprehensive similarity result to achieve precise recommendation for land use thematic map forms. The results show that the method can provide a more accurate visualization reference for the selection of land use themes, meeting the diversified needs of land use thematic expression to a certain extent. [ABSTRACT FROM AUTHOR]

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (−log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high–low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network's underlying marker-assisted selection (MAS) and oil content. [ABSTRACT FROM AUTHOR]

Propylene is an essential chemical feedstock, which needs efficient and stable production technology to meet its increasing industrial demand. In this work, the Pt–Sn/MgAl2O4 catalysts fabricated via the incipient wetness impregnation method were used for propane dehydrogenation to propylene (PDH). The effect of Sn on reaction performance was investigated to determine the optimal loading amount. The characterization results from XRD, N2 physisorption, TEM, H2-TPR, NH3-TPD, and TGA elucidated the physicochemical characteristic evolution with varied Sn content in the catalysts. The Pt0.3Sn/MgAl2O4 achieved the best propane conversion of 40.9% and propylene selectivity of 82.5% at 600 °C. Sn addition promoted Pt dispersion, enhanced metal-support interactions, and inhibited the side reactions. The MgAl2O4 support could suppress the coke formation and maintain propylene selectivity. This work demonstrates the Pt–Sn/MgAl2O4 catalysts are effective and durable for PDH to meet propylene demand. [ABSTRACT FROM AUTHOR]

Hierarchical SAPO-34 zeolite catalysts were successfully synthesized via a one-step hydrothermal synthesis approach with the assistance of excess triethylamine (TEA). Compared with conventional SAPO-34 (SP-T3), SAPO-34 synthesized with a TEA/Al2O3 molar ratio of 9 (SP-T9) presents high catalytic performance in the ethanol dehydration reaction due to its pseudocubic morphology with abundant butterfly-like large secondary pores and slightly decreased surface acidity. Afterwards, a cost-effective and green route is developed for synthesizing hierarchical SAPO-34 catalysts by recycling waste mother liquids. In recycling processes, residual organic templates in the mother liquids are reused in the next synthesis of SAPO-34 without adding fresh templates. After three recycles of mother liquids, the synthesized SP-R3 catalyst still retains its high crystallinity, hierarchical structure, moderate acidity, and excellent catalytic performance in ethanol dehydration as compared to the initial SP-T9 catalyst. [ABSTRACT FROM AUTHOR]