# Telescopic Network of Zhulong for Orbit Determination and Prediction of Space Objects
## 4 Discussion  
(1) Increasing the number of telescope systems  
The Zhulong telescopic network is planned to exceed nine telescope systems to obtain more measurement data. If these devices are installed globally, it is possible to obtain continuous observation data for space objects. Multiple telescope systems can simultaneously observe the same space object, enabling the acquisition of more precise data and enabling more accurate applications, such as improving orbit accuracy and establishing space object models [37]. These data will help us to better understand the movement trajectory and possible influence of space objects, making an important contribution to the development of the field of space science. (2) Improving OP through machine learning  
With the rapid development of Artificial Intelligence (AI) and machine learning (ML) technology [38], some research results have been achieved based on historical orbital measurement data, using ML to enhance OP accuracy. However, there is relatively little research on improving the OP of ML based on sparse angle data due to the scarcity of angular data obtained by telescopes. Future research can explore the data conditions (such as accuracy and distribution) under which machine learning can effectively enhance the accuracy of traditional OD and OP. Through in-depth research, valuable insights may be discovered, such as the optimal data conditions for selecting the most suitable ML \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NORAD** & **OD Start** & **OD** & **files** & **OD Fitting** & **OD** & **1-Day OP** \\
**ID** & **Date in 2023** & **Duration** & **Number** & **Errors/\({}^{\infty}\)** & **Errors** & **Errors** \\ \hline \multirow{6}{*}{41240} & Jul. 3 & 2 & 2 & 2 & 1703 & 1842 \\  & Jul. 3 & 5 & 3 & 2 & 312 & 352 \\  & Jul. 3 & 6 & 4 & 2 & 731 & 742 \\  & Jul. 3 & 10 & 6 & 3 & 106 & 212 \\  & Jul. 3 & 11 & 7 & 3 & 178 & 314 \\  & Jul. 7 & 2 & 2 & 2 & 904 & 1086 \\  & Jul. 12 & 2 & 3 & 2 & 201 & 206 \\  & Sep. 25 & 3 & 2 & 2 & 666 & 689 \\  & Oct. 4 & 1 & 3 & 2 & 203 & 275 \\ \hline \multirow{6}{*}{46984} & Jun. 16 & 10 & 2 & 1 & 120 & 129 \\  & Jul. 3 & 1 & 3 & 2 & 57 & 218 \\  & Jul. 3 & 11 & 4 & 2 & 348 & 480 \\  & Jul. 25 & 1 & 2 & 1 & 811 & 1044 \\  & Jul. 25 & 2 & 4 & 2 & 583 & 616 \\  & Sep. 26 & 2 & 3 & 2 & 284 & 288 \\  & Sep. 26 & 11 & 4 & 2 & 92 & 42 \\ \hline \multirow{6}{*}{1328} & Jul. 23 & 3 & 2 & 2 & 318 & 395 \\  & Jul. 25 & 2 & 2 & 2 & 502 & 503 \\ \cline{1-1}  & Sep. 25 & 2 & 3 & 2 & 534 & 549 \\ \cline{1-1}  & Sep. 25 & 3 & 4 & 2 & 107 & 109 \\ \cline{1-1}  & Sep. 25 & 4 & 5 & 2 & 177 & 179 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of outlier coincidence errors in orbit determination prediction. method to enhance OP or the data distribution conditions under which ML can effectively improve OP accuracy. Such research will provide us with a deeper understanding of how to effectively utilize sparse data for ML to further improve the accuracy of OP. This will provide an important reference for future calculations of space object orbits and help us better understand the movement trajectory and possible influence of space objects. In comparing the use of machine learning for OD and OP, we have found that the current literature primarily focuses on enhancing the accuracy of OP. Three additional references have been added to the reference list [23; 39; 40]. Specifically, the first article utilizes Two-Line Element (TLE) data, while the second relies on simulated datasets. 3. Characterization detection of space objects Using telescopes, detailed information about space objects can be observed and collected, including their position, velocity, shape, size, and composition. This information is crucial for understanding the characteristics of space objects, their trajectory of movement, and their potential influence. The Zhulong telescopic network can also be used for the characterization detection of space objects. For instance, spectral analysis can help us understand the composition of an object.