Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

X‐ray is a critical factor in generating electrons in the lower ionosphere, however, the cumulative effects of consecutive X‐ray flares on the lower ionosphere remain understudied due to the scarcity of historical events. From May 8 to 15, 2024, intense solar eruptions occurred, featuring multiple consecutive X‐ray flares. By leveraging data from space‐based satellites and ground‐based ionosondes during these consecutive X‐ray flare events, we investigate the correlations between extreme ultraviolet (EUV) radiation, soft X‐rays, hard X‐rays, solar energetic proton fluxes and ionospheric parameters (minimum reflection frequency and E‐layer critical frequency). And then we analyze the physical mechanisms governing the lower ionosphere's response to consecutive X‐ray flares. The key findings include: Consecutive X‐ray flares consistently induced significant responses in the dayside lower ionosphere; When two consecutive X‐ray flares occur very close together, then the previous X‐ray ionization will reduce the effect of the subsequent X‐ray flare on the ionospheric electron density increase; High‐energy protons can directly penetrating Earth's magnetic field to trigger intense ionospheric responses; X‐ray flare effects on the lower ionosphere typically lasted about 1 hr, however, when accompanied by solar proton events, subsequent high‐energy protons could compound with later X‐ray flares to produce cumulative effects.

The small number of pixels in the current linear mode avalanche photodiode (LM-APD) array limits its three-dimensional (3D) imaging resolution. We use an optical phased array-based beam array subdivided pixel method to improve the 3D imaging resolution, using an optical phased array to generate a beam array with the same number of pixels as the LM-APD array and matching positions and controlling each sub-beam in the beam array to scan in the field of view of the corresponding pixel. The sub-beam divergence angle in the beam array is smaller than the instantaneous field of view angle of a single pixel in the LM-APD array. The sub-beam scanning in a single pixel’s field of view realizes the multiple acquisition of the target 3D information by the LM-APD array, thus improving the resolution of the LM-APD array. The distribution of the beam array in the far field is simulated, and the main performance parameters of 3D imaging are analyzed. Finally, a liquid crystal phase modulator is used as an optical phased array device to conduct experiments on a target 20 m away, and the results prove that our method can improve the resolution from 4 × 4 to 8 × 8, which can be improved at least four times.

The linear mode avalanche photodiode (LM-APD) array has the capability of real-time 3D imaging for moving targets, which is a promising 3D imaging means in space. The main system parameters of the LM-APD array 3D imaging system, the characteristics of the space target itself, and the relative positional relationship between them will affect the 3D imaging results at the same time, and there is a need for an appropriate simulation method to describe the space target point cloud acquired by the LM-APD array 3D imaging system under different conditions. We propose a simulation method for the 3D imaging of space targets with LM-APD arrays, which takes the characteristics of the space targets and the relative position into consideration, and build a link from the laser to the receiving system to simulate the echo waveform of each pixel in the LM-APD array. The experiment results under different conditions show that the proposed simulation method can accurately describe the imaging results of the LM-APD array 3D imaging system for space targets with different shapes, materials, and motion states, which providing theoretical and data support for the design of LM-APD array 3D imaging systems.

Total electron content (TEC) is an important characteristic parameter of the ionosphere, which has a great impact on applications such as navigation error correction. The paper addresses the need for short- and medium-term forecasting of regional ionospheric TEC. Firstly, we analyze the frequency domain characteristics of ionospheric TEC, and analyze the data in the frequency domain based on the characteristics of trend, periodicity and abruptness of ionospheric TEC changes influenced by solar activity, combined with the Prophet algorithm. Then, according to its frequency domain characteristics, Prophet is used to achieve hour-by-hour prediction. The computer modeling results show that the RMSE of the 7-day forecast is better than 1.262 TECU during the geomagnetically quiet period, and the method is suitable for the short- and medium-term forecasting of ionospheric TEC with good forecast accuracy and time efficiency.