OpenCyber Academy
Engineering · Chapter 3 · English
Course code: OCA-118Chapter 03: OCA-118-C003

Chapter 3 — Physics for computing

Open, free learning for anyone who wants to protect people and technology. No account is required to read this text or its worked answers.

Lesson map

Lesson 03 published. Map, example, EX007.

flowchart TD
  N0["Learning outcomes"]
  N1["3.1 Measurement and data"]
  N2["3.2 Error and drift"]
  N3["3.3 Units and conversions"]
  N4["3.4 Metadata for measurements"]
  N0 --> N1 --> N2 --> N3 --> N4
  N4 --> EX["Worked example"]
  EX --> Q["Exercise EX007"]

Logical drawing

flowchart LR
  N0["Learning outcomes"]
  N1["3.1 Measurement and data"]
  N2["3.2 Error and drift"]
  N3["3.3 Units and conversions"]
  N4["3.4 Metadata for measurements"]
  N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX007"]

[Learning outcomes]
    |
    v
[3.1 Measurement and data]
    |
    v
[3.2 Error and drift]
    |
    v
[3.3 Units and conversions]
    |
    v
[3.4 Metadata for measurements]
    |
    v
[Worked example] --> [Exercise EX007]

Learning outcomes

You will (1) relate physical measurement to data quality; (2) explain sensor error and drift; (3) record measurement metadata.

3.1 Measurement and data

Tidewater's flow sensors convert physical quantities into numbers. Kofi learns that every measurement has uncertainty from the instrument, the installation and the environment. A flow sensor near a bend in the pipe reads differently from one on a straight run. Data governance must therefore record how each value was measured, because a number without its measurement context can be misused. He notes that ISO 8000's emphasis on requirements includes knowing whether data are precise enough for their intended use. Context travels with values.

3.2 Error and drift

Sensor accuracy changes over time. Deposits inside pipes and ageing electronics cause drift, a slow systematic change in readings. Kofi studies synthetic data for a sensor that reads two percent high after three years. If the drift is not detected, leakage estimates fall because supplied volume appears higher than reality. He proposes comparing sensors against calibrated reference meters annually and recording calibration dates as metadata, so analysts can exclude or correct readings outside their calibration period. Drift is invisible without an independent reference measurement.

3.3 Units and conversions

A common data error in water utilities is unit confusion: cubic metres versus litres, or hourly versus daily flows. Kofi finds a synthetic case where one district's readings were stored in litres while others used cubic metres, inflating that district's demand a thousandfold in a report. He recommends storing units explicitly with each value and validating that units match the expected ones at ingestion. The physics lesson becomes a governance rule: never store a measured value without its unit. Units are mandatory fields.

3.4 Metadata for measurements

Kofi designs a metadata record for each sensor: identifier, location, type, unit, accuracy class, installation date, last calibration date and owner. The record lets analysts judge whether readings meet their needs and lets engineers schedule maintenance. Prof. Varga points out that metadata are data too and need their own quality rules, such as calibration dates not lying in the future. Kofi adds the record to his governance notes as the first example of metadata management. Each field has a defined format and an owner responsible for keeping it current.

Worked example

Kofi explains measurement uncertainty, models a two-percent drift's effect on leakage, finds a litres-versus-cubic-metres error and designs sensor metadata with calibration dates.

Synthetic case. Sensor data are synthetic; ISO 8000-1:2022 is real.

Errors and limits

Calibration reduces but does not remove uncertainty; report it.

Exercises

Worked answers

EX007. Supplied volume looks higher, so estimated leakage falls.

EX008. It inflated that district's demand a thousandfold.

EX009. Unit, last calibration date and owner.