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With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing.

The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

Identification of the community structure of phytoplankton is an important link in studying the structure and function of aquatic ecosystems. However, research on the community structure of phytoplankton in the Golmud area is very scarce at present. To explore the composition of phytoplankton in the Golmud area, eukaryotic phytoplankton and environmental parameters were sampled in September 2020. The composition and diversity of the eukaryotic phytoplankton community were determined by microscopic observation and Illumina high-throughput sequencing technology. The results showed that a total of 40 species of eukaryotic phytoplankton from 4 phyla were identified by microscopic observation, and 154 species of eukaryotic phytoplankton from 7 phyla were obtained by high-throughput sequencing, mainly Bacillariophyta and Chlorophyta. The dominant species were Dunaliella sp., Teleaulax sp., Parvodinium mixtum, and Lindavia radiosa. The eukaryotic phytoplankton density in summer was 0.09–12.08 × 105 cells/L, the total biomass was 0.002–0.55 mg/L, and the concentration of chlorophyll-a was 0.00–0.91 μg/L. Multiple α diversity indices showed that the diversity of water in different salinities was in the order of freshwater > brackish > saline. The β diversity results showed that the eukaryotic phytoplankton community composition was more similar in water with the same salinity. The redundancy analysis results of water with different salinities showed that environmental factors susc as salinity, total phosphorus concentration, and dissolved oxygen affected the eukaryotic phytoplankton community structure, among which salinity had the greatest influence.

This study determined the transcriptomes of eight Oedogoniales species, including six species from Oedogonium and two species from Oedocladium to conduct phylotranscriptomic and evolutionary analyses. 155,952 gene families and 192 single-copy orthogroups were detected. Phylotranscriptomic analyses based on single-copy orthogroups were conducted using supermatrix and coalescent-based approaches. The phylotranscriptomic analysis results revealed that Oedogonium is polyphyletic, and Oedocladium clustered with Oedogonium. Together with the transcriptomes of the OCC clade in the public database, the phylogenetic relationship of the three orders (Oedogoniales, Chaetophorales, Chaetopeltidales) is discussed. The non-synonymous (dN) to synonymous substitution (dS) ratios of single-copy orthogroups of the terrestrial Oedogoniales species using a branch model of phylogenetic analysis by maximum likelihood were estimated, which showed that 92 single-copy orthogroups were putative rapidly evolving genes. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses results revealed that some of the rapidly evolving genes were associated with photosynthesis, implying that terrestrial Oedogoniales species experienced rapid evolution to adapt to terrestrial habitats. The phylogenetic results combined with evolutionary analyses suggest that the terrestrialization process of Oedogoniales may have occured more than once.