## 2 Related Work  
The primary objectives of change detection in remote sensing include identifying the spatial locations and types of changes, quantifying those changes, and evaluating the accuracy of the results. Previous studies in _Space Scientific Reviews_ provide detailed insights into the equipment and mission plans for Jupiter's scientific exploration [3]. Juno's status reports, covering phases such as the preliminary design [4; 5], launch year [6], early cruise [7], Earth flyby [8], and the period leading up to Jupiter's arrival [9], offer a comprehensive overview of the mission's progress. These publications collectively enhance our understanding of Juno's trajectory and its scientific contributions. Simply stated, these computer simulations provide quick insights that enhance space exploration efficiency. In addition, they help supply more information. The one-minute accuracy-based periodic sampled position and velocity data downloaded from the Juno project of the National Aeronautics and Space Administration (NASA) were found to be 3D non-linear and non-stationary signals. We searched for the answer to identify the time moments of the trajectory modification based on these specific time series. Because of the non-stationarity property of the data, classical statistical methods are less usable to evaluate particular events. Trajectory analysis is one of the most important aspects of successful activities on space missions. Traditionally, trajectory analysis has relied heavily upon first-order derivatives of parameters such as velocity and acceleration. Nevertheless, a number of recent contributions suggest that second- or higher-order derivatives can investigate different dynamic properties of systems more deeply. As an example, in 2019, [PERSON] and his team focused on using second-order derivatives of GPS carrier phase data to find small disturbances in the ionosphere. They realized that by looking at these derivatives, they could more easily identify tiny changes in how signals travel [10]. In the analysis of spacecraft trajectories, the use of higher-order derivatives can be especially beneficial. [PERSON] and [PERSON] (2023) [11] suggested a method using real-order derivatives and generalized norms for feature extraction and signal processing. The authors stressed that the use of higher-order derivatives has the ability to identify complex features in dynamic signals, thus enhancing the accuracy of condition monitoring and fault detection of mechanical systems. Recent years have seen developments in spacecraft trajectory analysis bring about new methods for enhancing mission design and operations. One such development involves the use of diffusion models to create ballistic spacecraft trajectories. [PERSON] et al. (2024) [12] employed a new generative model that utilizes score-based diffusion models to create Earth-to-Mars transfers. Their method showed that it was possible for such models to learn and reproduce the inherent dynamics of interplanetary trajectories, making it an excellent mission design tool for the future. Traditional trajectory analysis techniques tend to prioritize the analysis of first-order derivatives like velocity and acceleration. In the current research, the usefulness of the inclusion of higher-order derivatives to describe the finer dynamics of spacecraft movement was also discussed. For instance, [PERSON] et al. (2024) [13] established a solution with the inclusion of the perturbative effect of the J\({}_{2}\) (the second zonal harmonic coefficient in the expansion of the Earth's gravitational potential) by employing perturbative techniques to obtain solutions that include the intricate effect of the complex gravitational forces to improve the fidelity of the trajectory predictions. Through the use of compositional diffusion modeling, [PERSON] et al. [14] presented an improved approach for generating spacecraft trajectories in their research from 2025.