write-up

docs/quality-management/quality-control.markdown

@@ -41,28 +41,28 @@ The purpose of this document is to provide Quality Control (QC) guidelines for
 
 #### Objective
 
-To perform initial calibration and validation of the raw sensor data in laboratory conditions. 
+To perform initial calibration and validation of the sensor depth calculations at different heights in laboratory conditions. 
 
 #### Procedure
 
-1. Using measuring tape, fix mount locations at heights h1, h2, h3, h4 from the surface. 
-2. Miount the sensor at these known heights in order starting from h1 and collect data every 30 seconds for at least 30 minutes, totaling at least 60 measurements.
-3. Place an object of known height on the surface, beneath the sensor for the next 30 minutes and record the measurements.
-4. Remove the object and move the sensor onto the next height and repeat the process until the sensor has been tested and recorded at all the heights h1, h2, h3 and h4.
+1. Using measuring tape, fix mount locations at heights h1, h2, and h3 from the surface. If three different mounts aren't available, vary the single mounting location's height accordingly. While fixing the mount, using a spirit leveler, make sure the mount is vertical to the mounting surface and horizontal to the ground plane. 
+2. Mount the sensor at these known heights in order starting from h1. Now collect data for at least 60 minutes, with a duty cycle of 60 seconds.
+3. After the second step is complete, place an object of known height on the surface, beneath the sensor for the next 60 minutes and record the measurements.
+4. Stop the sensor before removing the object and move the it onto the next height and repeat the process until the sensor has been tested and recorded at all the heights h1, h2, h3 so on.
 
 #### Calibration
 
 Calculate offset value for every heights by taking median of these measurements. Invert the distance values and add this calculated offset values to obtain depth values.
 
-#### Data Validation
+#### Depth Data Validation
 
 Since measurement of depth is relative to another vertical elevation from a known reference point, the height of a known object is used to simulate validation of depth level captured by the sensor.
 
-At every given test height h1, h2, h3, and h4, the average of the observed height of the object calculated from the depth values shall be Real Object's height +- noise floor. 
+At every given test height h1, h2, and h3, the average of the observed height of the object calculated from the depth values shall be Real Object's height +- noise floor. 
 
 #### Records
 
-Calibration offsets and depth averages are recorded for every sensor along with the sensor unique identifiers shall be recorded on this google sheets for reference.
+Calibration offsets and depth averages are recorded for every sensor along with the sensor unique identifiers shall be recorded on [this](https://docs.google.com/spreadsheets/d/1FdsjeI8EHIygVHqMOj_p9l_yOaDbCCXwaQY8SxioX9Q/edit?usp=sharing) google sheets for reference.
 
 ### 2.2 Noise floor Testing
 
@@ -72,11 +72,11 @@ The objective of this test is to perform noise floor testing of the ultrasonic s
 
 #### Procedure
 
-Data collected from 2.1 is passed through this algorithm to test the noise floor of the sensor.
+Data collected from 2.1 is passed through a [python test script](https://github.com/floodsense/testing/blob/main/scripts/qc_test.py) to test the noise floor of the sensor.
 
 #### Records
 
-Noise floor for every sensor along with the sensor unique identifiers shall be recorded on this google sheets for reference.
+Noise floor for every sensor along with the sensor unique identifiers shall be recorded on [this](https://docs.google.com/spreadsheets/d/1FdsjeI8EHIygVHqMOj_p9l_yOaDbCCXwaQY8SxioX9Q/edit?usp=sharing) google sheets for reference.
 
 ## References
 

docs/real-time-data-pipeline.markdown

@@ -23,7 +23,7 @@ permalink: /real-time-data-pipeline/
 - [3.0 Flood Depth Data Flow Pipeline](#30-flood-depth-data-flow-pipeline)
  * [3.1 Data Flow Pipeline Overview](#31-data-flow-pipeline-overview)
  * [3.2 Data Processing Methodology](#32-data-processing-methodology)
- + [Message Syntax Check - TTN Console](#message-syntax-check---ttn-console)
+ + [Message Syntax Check](#message-syntax-check)
  + [Distance to Depth Conversion](#distance-to-depth-conversion)
  + [Erroneous Depth Data Filter](#erroneous-depth-data-filter)
  + [Data Storage - InfluxDB](#data-storage---influxdb)
@@ -34,7 +34,7 @@ permalink: /real-time-data-pipeline/
 
 ## 1.1 Background
 
-This project uses advanced IoT flood sensors to measure Real-time Flood Depth Data (FDD) on a city-wide scale using [Industrial grade Ultrasonic](https://www.maxbotix.com/ultrasonic_sensors/mb7389.htm) ranging technology. The Ultrasonic rangefinder readings are distance measurements and they are passed through a real-time data pipeline to calculate depth measurements.
+This project uses advanced IoT flood sensors to measure Real-time Flood Depth Data (FDD) on a city-wide scale using [industrial grade ultrasonic](https://www.maxbotix.com/ultrasonic_sensors/mb7389.htm) ranging technology. The ultrasonic rangefinder readings are distance measurements and they are passed through a real-time data pipeline to calculate depth measurements.
 
 ## 1.2 Purpose
 
@@ -104,7 +104,7 @@ This section explains the data processing methodology implemented in the real-ti
 
 There are three main components where all these stages takes place: TTN Console, Node-Red and InfluxDB. The following sections explain these stages in detail.
 
-### Message Syntax Check - TTN Console
+### Message Syntax Check 
 
 Payload decoder has been implemented on the TTN console to verify and decode the raw payload of incoming messages from end-nodes.