## 5 Discussion  
The interface speed is a parameter that should be highlighted for designing a camera payload, especially with regard to communication between the CPU and the camera sensor. It will affect the time taken for data transfer and, eventually, increase the power consumption during the mission execution. The USB3.0 and Ethernet interfaces are currently considered the high-speed communication options offered by the COTS camera sensor. A similar study was also applied to a Korean 6U CubeSat using an Ethernet interface design [47]. They had integrated a medium resolution of 3 m with an Intel CPU SBC. Thus, it shows an essential balance in terms of using high-end components for both CPU and camera sensors with high-speed communication. In current technology, the camera sensor has evolved from a single band to hyperspectral on a miniaturized scale. Multiple bands of images are significantly helpful for remote sensing applications. The fundamental challenges are the considerable data size for both the DL training algorithm and the captured images by the camera payload in addition to the processing power onboard a small satellite. Furthermore, the downlink capabilities of small satellites could be described as other challenges in addition to the amount of power generated from the solar panels per orbit. For instance, [PERSON] et al. [44] indicated how critical the power limitation is for onboard processing on HYPSO-1, which is another 6U CubeSat equipped with a hyperspectral imaging payload. In addition, HYPSO-1 utilizes Self-Organizing Maps (SOMs) on sea scenes with an overall accuracy above 90%. The SOM algorithm consumed a maximum computational time of 380 s. This accuracy and computational time could be improved by applying the CNN model, as the results shown in this paper for wildfire image classification reached 98% accuracy with a runtime of 137 s. Therefore, the implementation of ML or DL onboard a CubeSat is significantly affected by the computational costs. A similar 6U CubeSat, PhiSat-1, demonstrated the Deep Neural Network (DNN) for cloud detection onboard using hyperspectral images. [PERSON] et al. [33] discussed that the test set achieved 92% accuracy with 1% _FP_ using dataset images from Sentinel-2. The CNN-based algorithm showed a considerably impressive result, which consumed 0.16 mWh of energy. The DL runtime could be reduced with the support of the Myriad 2 VPU hardware accelerator integrated onboard PhiSat-1. However, in this paper, the CNN model of MiniVGGNet implemented onboard KITSUNE demonstrated 98% accuracy, 0% of _FP_, and consumed 0.10 Wh of energy without VPU installed. The significant difference in power consumption could be argued, but the power was calculated by turning on RPi CM3+, executing the image classification algorithm, and finally turning it off. In the KITSUNE design, the RPi CM3+ is not continuously on to avoid the anomaly of SEL, which was observed in TID radiation on the ground test. On top of that, machine learning was generally executed on the ground. [PERSON] et al. [48] discussed how and why image processing was conducted after downloading the image data at the GS. There is always a significant issue in comparing the downlink capability between a traditional satellite and a CubeSat. However, our study to classify images onboard is advantageous for the nanosatellite class and complemented the low latency of downlink communication. Challenges with regard to computational costs, power, and time were tackled and improved in the mission plan strategy. The ultimate goal of the DL approach onboard a CubeSat is to have the re-training model capability. Images captured by the payload can be utilized and classified directly to update the dataset labels. In this study, the KITSUNE design was tested and showed promising results for conducting a re-training model onboard, but several issues will be raised.