## 5 Discussion  
It is shown that our results for a selected region can be applied to other orbit data obtained by the same satellite. If the obtained nine parameters are correct, the calibrated result should match with the WMM regardless of when the measurement is performed, as long as the WMM is steady. WMM coefficients are estimated using data measured during magnetically quiet periods. The combined error of the WMM 2020 is 129 nT in the total magnetic field [15] (p. 67). In Table 9 below, we list the Kp-index of when the measurement was performed. From this table, we can say that the magnetic field was quiet, and the WMM is an adequate \"solution\" for the genetic algorithm. Although the calibrated results obtained by parameters derived from an additional data set provided reduced error values, as shown in Tables 4, 7 and 8, we still could not reduce the oscillation to the degree we desired. To determine the measured source of oscillation, we ran the GA simulation by dropping some of the parameters from the subject of the search. We applied the GA for specific low-latitude regions of each data set. In each simulation, only six parameters, instead of nine, were searched. Table 10 shows the results. In the table, without gain means that we searched only the offset (X, Y, and Z) and non-orthogonal angles (\(\lambda\), \(\rho\), \(\phi\)) while fixing the gain values (a, b, c) to 1. The errors when gain and offsets were not considered were larger than the error when non-orthogonal angles were not considered. According to the results, the main reasons for the high oscillations are offsets and gain. \begin{table}
\begin{tabular}{c c c c c} \hline
**Data Set** & **Equation Used to** & **Error of Selected** & **Error of** & **Error of Data** \\
**Number** & **Calculate the Error** & **Region** & **Whole Orbit** & **Set 3** \\ \hline
2 & e\({}_{\text{m}}\) & 0.42 & 0.45 & 0.50 \\
2 & e & 0.01 & 0.03 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 7: Errors before and after calibration by using the parameters derived from Data Set 2 of Uguisu. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **Data Set** & **Date** & \multicolumn{2}{c}{**Measurement Time**} & \\  & & & & **(UTC)** & & **Kp-Index** \\ \hline \multirow{4}{*}{Raavana-1} & 1 & 17 June 2019 & Start & End & \\  & 2 & 14 February 2021 & 05:23 & 06:52 & 1.667/2.000 \\  & 3 & 11 April 2021 & 05:09 & 06:39 & 2.333/1.667 \\ \hline \multirow{4}{*}{Uguisu} & 1 & 17 June 2019 & 10:15 & 11:44 & 0.667 \\  & 2 & 14 February 2021 & 04:40 & 06:09 & 1.333/1.667 \\ \cline{1-1}  & 3 & 11 April 2021 & 04:41 & 06:13 & 2.333/1.667 \\ \hline \multirow{2}{*}{NepaliSat-1} & 1 & 17 June 2019 & 10:15 & 11:44 & 0.667 \\  & 2 & 14 February 2021 & 05:13 & 06:41 & 1.667/2.000 \\ \hline \end{tabular}
\end{table} 
Table 8: Errors before and after calibration by using the parameters derived from Data Set 1 of NepaliSat-1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Data Set** & **Equation Used to** & **Error of Selected** & **Error of** & **Error of Data** \\
**Number** & **Calculate the Error** & **Region** & **Whole Orbit** & **Set 3** \\ \hline
2 & e\({}_{\text{m}}\) & 0.42 & 0.45 & 0.50 \\
2 & e & 0.01 & 0.03 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 7: Errors before and after calibration by using the parameters derived from Data Set 2 of Uguisu. Next, the solar cell output pattern and the magnetometer output pattern for each axis were checked to confirm the reason for the high oscillations observed. The solar cell graph pattern and the magnetometer graph pattern have similar behavior, proving that each axis' oscillation of magnetometers stems from the satellites' rotation. One example is shown in Figure 15; the solar cell current is shown in mA, and the magnetic field is shown in mG. Each axis of the magnetometer data has an oscillation due to satellite rotation. However, we should not observe an oscillation in the total magnetic field.