High speed and low latency have become the development trend of mobile communication network. Orthogonal frequency division multiplexing (OFDM) technology has become the key technology of mobile communication network due to its efficient transmission method. Space time block coding (STBC) introduces time and space correlation in the signals sent by different antennas, so that diversity gains and coding gains are obtained for the multiple input multiple output-OFDM (MIMO-OFDM) receiver without sacrificing bandwidth. STBC is a signal coding technology that combines higher transmission rates and it is a combination of space and time transmission signals. STBC uses the spatial diversity of multiple transmitters and multiple receivers in space to improve the capacity and information rate of the OFDM system. It proceeds parallel multiplex transmission in the transmission channel, which effectively improves spectrum utilization. For obtaining diversity gains and spectrum efficiency, this paper introduces a variable pilot assisted channel estimation method with different modulation modes. In this paper, the new type pilot sequences with different modulation orders are proposed for improving spectrum efficiency. The proposed variable pilot assisted channel estimation method in MIMO-OFDM systems is adopted with different low and high order modulations. Simulation results show that the proposed variable pilot assisted channel estimation method has 1–2 dB signal-to-noise ratio (SNR) gains for bit error rate (BER) performance of MIMO-OFDM systems.

Timely and accurate identification of fault types at the early stage of minor faults is significant for cutting off fault evolution. In order to have a clear understanding of the pipeline robot’s own situation in the pipeline, this paper proposes a fault diagnosis system for pipeline robots based on sound signal recognition. This can effectively reduce the probability of serious faults such as shutdown and loss of control in the pipeline without affecting the safe operation of the pipeline robot, which is a key issue to improve the reliability of the pipeline robot. The system consists of a combination of three parts: hardware, software, and algorithm. On the one hand, Raspberry Pi is the core module, while on the other hand, it is also responsible for the data transmission between the various modules, including storing the original sound signals collected by the sensors and transmitting the diagnosis results to the upper computer software interface. The proposed system is validated on the dataset collected by the data experimentation platform. The experimental results show that the proposed fault prediction method obtains advanced results on this dataset, verifying the effectiveness and stability of the proposed fault diagnosis system for pipeline robots based on sound signal recognition.

Ground-based astronomical observations face inherent challenges from weather changes, target visibility window constraints, and observational requirements. Enhancing the efficiency and effectiveness of telescope operations has long been a key objective for many observatories because of the high cost of observational resources. In this study, we formalize observation scheduling as a time-dependent combinatorial optimization problem. To achieve this, we implement a pointer network with temporal attention that is capable of planning observations while accounting for time-varying factors such as moonlight interference, target altitude, and air mass, which impact the exposure time and image quality. To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. Furthermore, an exposure time calculator and an equipment kinematic model are incorporated to dynamically estimate the time costs during the decision-making process. The simulation results demonstrated that the trained model significantly outperformed both manual scheduling and a greedy algorithm in terms of theoretical reward scores and the total number of scheduled targets. Observation experiments conducted using a dual-telescope system at Muztaga observatory further validated the superiority of our approach, demonstrating a 45.8% enhancement in the total signal-to-noise ratio across all observed targets and a 24.1% increase in the number of completed tasks under the same observing conditions.

Ground-based optical observation of GEO space debris plays an essential role in tracking, identifying, cataloging, and classifying space debris. The factors that affect the brightness of space debris include size, surface material, illumination geometry, attitude, shape, position, and so on. In order to better understand the influence of the above factors on the brightness of space debris, the synthetic light curves are analyzed. The Ashikhmin–Shirley model is chosen to simulate the light curves of space debris. The effects of orbit, attitude, model parameters, and the location of the observation station on the synthetic light curves are analyzed by the control variable method. Based on the Markov Chain Monte Carlo method (MCMC), the optimal model parameters that characterize the surface material of space debris can be obtained by comparing the synthetic light curves with the observed light curves under certain shape, orbit, and attitude characteristics. The results are roughly in good agreement with those measured in the laboratory.

To improve the observation efficiency of space debris surveys, a basic sky survey observation strategy was developed, with the aim of observing more space debris based on the Wide Field Optical Telescope Array run by Shandong University. The characteristics of the telescope and dynamic changes in the movement and position of space debris are considered in this strategy. An objective function was designed based on these factors. Using the pixelated sphere method to finely divide the celestial area, applying the summation filtering method, and using a greedy algorithm, the benefit of the objective function can be maximized, thus generating the optimal sky survey observation strategy. Through simulation and observation experiments, we demonstrate that the greedy algorithm observation strategy significantly improves the number of space debris instances and the number of arc segments with respect to the conventional observation strategy. This not only improves the automation level of space debris observation tasks, but also significantly enhances the execution efficiency of telescopes for debris observation. It is very helpful for cataloging space debris and generating collision warnings.

To improve the observation efficiency of the space debris survey, a basic sky survey observation strategy has been developed, aiming to observe more space debris based on the Wide Field Optical Telescope Array running by Shandong University. The characteristics of the telescope and dynamic changes in the movement position of space debris are considered in this strategy. An objective function is designed based on these factors. By using the pixelated sphere method to finely divide the celestial area, applying the summation filtering method, and using the greedy algorithm, the benefit of the objective function can be maximized, thus generating the optimal sky survey observation strategy. We demonstrated, through simulation and observation experiments , that the observation strategy using the greedy algorithm significantly improved the number of space debris and the number of arc segments with respect to the conventional observation strategy. This not only improves the automation level of the space debris observation plan but also significantly enhances the execution efficiency of the telescopes for debris observation. It is very helpful for cataloging space debris and collision warnings.