Since the last IAEA Fusion Energy Conference in 2018, significant progress of the experimental program of HL-2A has been achieved on developing advanced plasma physics, edge localized mode (ELM) control physics and technology. Optimization of plasma confinement has been performed. In particular, high-N H-mode plasmas exhibiting an internal transport barrier have been obtained (normalized plasma pressure N reached up to 3). Injection of impurity improved the plasma confinement. ELM control using resonance magnetic perturbation (RMP) or impurity injection has been achieved in a wide parameter regime, including Types I and III. In addition, the impurity seeding with supersonic molecular beam injection (SMBI) or laser blow-off (LBO) techniques has been successfully applied to actively control the plasma confinement and instabilities, as well as the plasma disruption with the aid of disruption prediction. Disruption prediction algorithms based on deep learning are developed. A prediction accuracy of 96.8% can be reached by assembling convolutional neural network (CNN). Furthermore, transport resulted from a wide variety of phenomena such as energetic particles and magnetic islands have been investigated. In parallel with the HL-2A experiments, the HL-2M mega-ampere class tokamak was commissioned in 2020 with its first plasma. Key features and capabilities of HL-2M are briefly presented.

Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This paper provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has a high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has a poor calculation efficiency. Finally, the semi-analytical Monte Carlo model was used to quantitatively analyze the three main factors ( LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.

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For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable gate array (FPGA) can meet the requirements of design. In the coordinate transformation scheme, 250,656 LUTs, 499,268 registers, and 388 DSP48s are used. Furthermore, 27,218 LUTs, 45,823 registers, 456 RAM/FIFO, and 267 DSP48s are used in the bilinear interpolation module; (3) the values of root mean square errors (RMSEs) are less than one pixel, and the other statistics, such as maximum error, minimum error, and mean error are less than one pixel; (4) the gray values of the georeferenced image when implemented using FPGA have the same accuracy as those implemented using MATLAB and Visual studio (C++), and have a very close accuracy implemented using ENVI software; and (5) the on-chip power consumption is 0.659 W. Therefore, it can be concluded that the proposed georeferencing method implemented using FPGA with second-order polynomial model and bilinear interpolation algorithm can achieve real-time geographic referencing for remotely sensed imagery.