## 8 Discussion
### Sensor Comparison  
Based on Figure 11, it can be estimated that the PCO camera has a limiting magnitude of approximately 14 at 100 ms, while the IDS camera has a limiting magnitude of around 10 at the same exposure time. As a result, numerous stars and, by extension, RSOs should be detectable using both sensors. Despite the fact that the PCO camera is ten times more expensive than the IDS camera, both systems are capable of capturing enough stars and RSOs to support the attitude determination and RSO detection algorithms. This estimation was validated during the mission as both systems were able to capture stars and RSOs. Figure 12 demonstrates that subpayload 1 detects stars at a frequency ten times higher than subpayload 2. The plot employs the Johnson V magnitude system, also known as visual magnitude, operating at an effective wavelength midpoint of 551 nm, a part of the UBV photometric system used for classifying stars. The histogram reveals that most stars detected by subpayload 1 (represented in blue) have magnitudes ranging approximately from 5.5 to 7.5, with the peak frequency observed between magnitudes 6 and 7. In contrast, subpayload 2 (depicted in red) shows fewer detections and a more limited magnitude range. Subpayload 1 demonstrates a higher sensitivity in star detection with a quantum efficiency of 82% and a spectral responsivity range of 350-800 nm. In contrast, subpayload 2 exhibits a peak quantum efficiency of 64% within a wavelength range of 400-800 nm. The magnitudes of sources extracted from images were found to be significantly lower than those evaluated from Equation (9). This discrepancy can be attributed to the parameters used in source extraction and the correlation of celestial objects within the catalogue. However, the most significant factor reducing the detection of dimmer magnitudes is the noise present in the sensors. Furthermore, the images used for extraction were captured at the end of the mission, a time when the sun was 9 degrees below the horizon, further contributing to this reduction. Figure 14 clearly illustrates subpayload 1's sensor's ability to capture stars as bright, well-defined points against the dark sky, demonstrating high contrast. In comparison, subpayload 2's sensor produces a contrast map with a more homogeneous appearance, with stars appearing less distinct against the background. This comparison is essential as it uncovers the relative constraints of subpayload 2's sensor, potentially caused by increased noise levels, decreased dynamic range, or differences in ISO and overall imagingsystem quality. The contrast map for subpayload 1, with its distinct and high contrast dots, is not only a confirmation that the sensor can detect stars but also a testament to its enhanced capabilities for photometric and astrometric analysis. High contrast is crucial for precise photometry, which depends on accurate measurements of object intensities, and for detecting faint resident space objects (RSOs), which require clear distinction from background noise. The contrast map of subpayload 1 provides evidence of functional sensors and indicates an imaging system that has the potential to significantly improve the quality and reliability of astronomical data. In Figure 15, the pixel intensity distributions from the images captured by the subpayloads yields distinct insights. Subpayload 1 exhibits a higher dynamic range, as evidenced by the broader spread of pixel intensities, ranging from the minimum to the maximum values within the images. This wide range is indicative of subpayload 1's capacity to capture a full spectrum of brightness, from deep blacks to bright highlights. In addition, subpayload 1 demonstrates a greater variation in the brightness of features within the images. This is characterized by distinct peaks at multiple intensity levels in the histogram. Such variation is indicative of the payload's ability to resolve differences in brightness with high fidelity.