## 5 Juno Trajectory Geometry
### Statistical Properties of Position, Velocity and Acceleration Components  
They are designed to convey high-level information at a glance, providing broad knowledge about the uniformity, skew, volatility, and outliers of a set of data at a glance. They offer a practical method for visually representing the variety of replies a large group provides, in addition to their other qualities. It is easy to determine where most of the data are located and compare the characteristics of the various groupings easily. In the boxplot in Figure 4, each box visually represents the median million kilometer over different position components in the barycenter for the Juno spacecraft in the sample data. For example, the median for position y is approximately \(-\)300,000 km. The minimum value at boxplot y is about \(-\)600,000 km and the maximum is about 200,000 km. This graphic illustrates that the distance at point \(d\) exceeds that at the other places, which are z, y, and x in succession. Several versions of the boxplot are feasible: represent outliers with dashed lines, where an outlier is often defined as any point 1.5 IQR (inter-quartile range) distance measurements from the box. The scores range from the lower to upper quartile and are referred to as the interquartile range. There are a few alternative variants of the boxplot, including displaying outliers using dashed lines, where an outlier is generally understood to be any point that is further than 1.5 IQR distance measurements from the box. In the plots, the whiskers can only be drawn a maximum of 1.5 IQR distance measurements out from the box. If, however, the whisker is going to be drawn beyond the bounds of the data vector, then it is redrawn at the data's  
Figure 4: Boxplot of the position component (for the time interval between 7 August 2011 and 4 January 2023). Figure 3: Trajectory of Juno in the last 1/4 th (L4 TH) time interval. border (i.e., it is clamped to sidestep it exceeding). The box's upper bound is unequal to the box's lower bound, except for the boxplot (x). All the boxplots show the negative skewness of the dataset, except the boxplot (x), which is normal. A boxplot does not reveal the actual distribution of the data. For example, the boxplot z in Figure 5 shows that the data are skewed left due to a higher median and a longer lower tail than the upper tail. The traveled distance concerning the acceleration is described in Figure 6. Based on the figure, all the components have positive and negative acceleration values except component (a). To plan and adapt the spacecraft's trajectory, we must understand how its speed changes (i.e., whether it is accelerating or decelerating). This is critical for operations like orbital insertion, rendezvousing with other spacecraft, and landing on a celestial body. Acceleration determines the forces exerted on the spacecraft. High accelerations may cause stress on the spacecraft's construction. Simply said, velocity informs you how fast the spacecraft is moving, and acceleration tells you how quickly that speed is changing. This information is critical to precise control and mission success. As can be seen in Figure 7, the boxplot can be a useful tool to visualize the distribution of the acceleration derivative data. The first derivative of acceleration, referred to as jerk, offers crucial insights that go beyond the information provided by basic acceleration when studying the motion of a spacecraft. Jerk quantifies the rate at which the acceleration is changing. To execute a seamless maneuver, it is essential to minimize the jerk, hence reducing abrupt movements and vibrations experienced by both the spacecraft and its passengers. Figure 5: Boxplot of the velocity component (for the time interval between 7 August 2011 and 4 January 2023). Figure 6: Boxplot of the acceleration component (for the time interval between 7 August 2011 and 4 January 2023). Examining jerk aids in attaining seamless acceleration patterns that save gasoline.