Commander Mirza orders all sensor data normalized before the deep scan begins.
Before we run the deep scan, I want all sensor data normalized. We can't have one sensor's scale drowning out another — each data source must contribute equally.
Just like CSS resets create a consistent baseline for styling, normalization creates a consistent baseline for data. Without it, the analysis system will be biased toward sensors with larger numerical ranges.
Every frontend developer knows CSS resets. Without them, browsers apply inconsistent default styles. Normalization is the data equivalent — without it, features with large values (like pixel counts in the thousands) dominate features with small values (like percentages from 0-1).
Z-score (mean=0, std=1): Use when you need to preserve the spread of your data. Most common for neural network inputs.
Min-max (range 0-1): Use when you need bounded values, like pixel data or probabilities.
Think of it like choosing between normalize.css and a full reset — each has its place depending on what consistency you need.
Normalize the Archimedes sensor data using z-score normalization.