docs: cleanup and refine the wording

Author: ekarad1um

docs/algorithms/gedad/how-it-works.md

@@ -1,25 +1,25 @@
 # Overview of the GEDAD Algorithm
 
-Here we provide a detailed explanation of the Gyroscope Euclidean Distance Anomaly Detection (GEDAD) algorithm, which initially developed for gyroscopes, and now it has been extended to 3-axis accelerometers while retaining its original name. The GEDAD algorithm consists of two core phases: **learning** and **inference**.
+Here we provide a detailed explanation of the **G**yroscope **E**uclidean **D**istance **A**nomaly **D**etection (GEDAD) algorithm, which initially developed for gyroscopes, and now it has been extended to 3-axis accelerometers while retaining its original name. The GEDAD algorithm consists of two core phases: **learning** and **inference**.
 
-## Data Acquisition and Pre-processing
+### Data Acquisition and Pre-processing
 
-The process begins with data acquisition, the data from a 3-axis accelerometer is collected via I2C and stored in a circular buffer. Before using the data, it undergoes a linear transformation where it is multiplied by a coefficient `alpha`, and added with another coefficient `beta`.
+The process begins with data acquisition. While vibration data is collected from a 3-axis accelerometer via I2C and stored in a circular buffer, later the data undergoes a linear transformation where it is multiplied by a coefficient `alpha`, and added with another coefficient `beta`.
 
 ![Sampling from 3-Axis Accelerometer](sampling_example.png)
 
 ### Learning Phase
 
-The objective of the learning phase is to establish a baseline template of "normal vibration" for the device.
+The objective of the learning phase is to establish a **baseline template of normal vibration** for the measuring device.
 
 ![Learning Process Example](learning_example.png)
 
-1.  **Template Generation**: First, a `sliding Window` of 3-axis acceleration data, sized to cover a complete normal operational cycle, is collected to serve as the **template data**.
-2.  **Distance Calculation**: The algorithm then randomly samples `N` short data segments or named `chunks` from identical positions within each channel of the template. Each chunk is then slid across the entire template of its corresponding channel with a defined `sliding Step`, calculating the Euclidean (L2) distance at each position.
-3.  **Threshold Calculation**: Next, outliers are filtered from these distances (e.g., using the 3σ rule; specifically, values less than a given EPS). For each channel, the remaining distances are sorted to identify the `M` smallest values. An average "threshold" is then computed for each channel from these `M` distances, defining the boundary between normal and abnormal states.
-4.  **Parameter Calibration**: Finally, an additional parameter, `C`, is determined by finding the maximum number of consecutive instances where the Euclidean distance is below the threshold during a subsequent comparison. This parameter is stored to enhance detection accuracy in the next phase.
+1.  **Template Generation**: First, a `sliding window` of 3-axis acceleration data, sized to cover a complete normal operational cycle, is collected to serve as the **template data**.
+2.  **Distance Calculation**: The algorithm then randomly samples `N` short data segments or named `chunks` from identical positions within each channel of the template. Each chunk is then slid across the entire template of its corresponding channel with a defined `sliding step`, calculating the Euclidean (L2) distance at each position.
+3.  **Threshold Calculation**: Next, outliers are filtered from these distances (e.g., using the 3σ rule; specifically, values less than a given EPS). For each channel, the remaining distances are sorted to identify the `M` smallest values. An average **threshold** is then computed for each channel from these `M` distances, defining the boundary between normal and abnormal states.
+4.  **Parameter Calibration**: Finally, an additional parameter, `C`, is determined by finding the median counts of consecutive instances where the Euclidean distance is below the threshold during a subsequent comparison. This parameter is stored to enhance detection accuracy in the next phase.
 
-#### Inference Phase
+### Inference Phase
 
 During inference, the algorithm compares real-time 3-axis acceleration data against the established **template data** to identify any vibrations that do not match the normal *fingerprint*.
 
@@ -32,7 +32,7 @@ The process is similar to training, but it uses real-time data segments for comp
 
 The actual computation is more complex, involving the fusion of anomaly scores across channels and the use of the parameter `C`.
 
-#### **Summary and Future Work**
+## Summary and Future Work
 
 In summary, the algorithm's advantages include: