157 | | - ''Problem'': found impracticality in using binary classification (with/without RFI) on transmitting signal spectrograms. As they are pre-RFI and contain no features that reveal the impact of signal interference, Tx graphs are not suitable as input for training CNN, since the model would become a simple color detector (hue of spectrograms based on intensity of power, e.g., orange = RFI, blue = no RFI), which reduces the entire purpose of using ML for RFI detection |
| 157 | - **Problem**: found impracticality in using binary classification (with/without RFI) on transmitting signal spectrograms. As they are pre-RFI and contain no features that reveal the impact of signal interference, Tx graphs are not suitable as input for training CNN, since the model would become a simple color detector (hue of spectrograms based on intensity of power, e.g., orange = RFI, blue = no RFI), which reduces the entire purpose of using ML for RFI detection |