Tunable diode laser absorption spectroscopy (TDLAS) tomography is a well-established combustion diagnostic technique for imaging two-dimensional cross-sectional distributions of critical flow-field parameters. As two key metrics in TDLAS tomography, reconstruction accuracy and efficiency are generally traded off to satisfy either the requirement of highfidelity image retrieval or rapid tomographic data inversion. In this paper, a novel quality-hierarchical temperature imaging network for TDLAS tomography is developed based on stacked Long Short Term Memory (LSTM). From limited line-of-sight TDLAS measurements, this network outputs two reconstructed temperature images, i.e. a coarse-quality image and a fine-quality image, with different numbers of network layers and consequently different computational costs. The coarse-quality image provides more timely temperature reconstruction, which can satisfy realtime dynamic monitoring of turbulence-chemistry interactions with a temporal resolution of tens of kilo frames per second. On the other hand, the fine-quality image, that can be stored and utilized for offline analysis and diagnosis, further details the temperature reconstruction with more accurate features. Both numerical stimulation and lab-scale experiment validated the accuracy-efficiency trade-off achieved by the proposed qualityhierarchical temperature imaging network.

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The Peer-to-Peer network has been widely used to share large files anonymously. Because of its open nature, nodes can join and leave Peer-to-Peer networks freely. But there are few limited methods to ensure their trust. Malicious nodes can expose great security threats to Peer-to-Peer networks such as spreading inauthentic data and viruses. But handling all the malicious nodes in the same way causes wastes of the network. In this paper, we first introduce the existing ways of handling malicious nodes in the Peerto-Peer network and then present a better approach dealing with malicious nodes which has a better performance than before.

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