$**$Question $1$: Dissecting the Functionality of a Digital Kitchen Scale$**$

Delve into the workings of a "Precision Digital Kitchen Scale," a device integral to culinary precision, enabling accurate measurement of ingredients by weight. This scale converts the weight of ingredients into digital data that can be easily read, assisting in meticulous recipe adherence and dietary management.

a$.**$Research Phase$**$: Investigate the internal mechanisms and technologies that underpin the digital kitchen scale. Highlight the process from the initial weight detection to the digital display of the weight. Explore the types of sensors used, focusing on their accuracy and how they manage to capture minute changes in weight.

b$.**$System Integration Phase$**$: Break down and elaborate on the operational stages within the measurement system of the digital kitchen scale, encompassing:

$-$ The $**$sensing element$**$ that detects the weight of the items placed on the scale.

$-**$Signal conversion and processing$**,$ where the physical pressure exerted on the sensing element is converted into an electrical signal, then translated into a numerical weight value.

$-$ The $**$calibration system$**,$ ensuring that measurements are accurate and consistent over time, detailing how users can calibrate the scale for precision.

$-**$Digital display and user interface$**,$ which presents the weight measurement to the user and allows for the selection of units $($e$.$g$.,$ grams, ounces$).$

$**$Question $2$: Analysis of an Autonomous Soil Sampling Robot$**$

Explore the integration and operation of a theoretical "Soil Quality Assessment Robot $($SQAR$)"$ designed for precision agriculture. This robot autonomously navigates farmland, collects soil samples, and analyzes them on$-$site to determine soil moisture, pH$,$ and nutrient levels. This information is crucial for precision farming practices, enabling targeted irrigation and fertilization.

a$.**$Research Phase$**$: Delve into the types of sensors and mechanisms involved in robotic soil sampling and analysis. Describe the working principles sensors suitable for measuring soil moisture, pH$,$ and nutrient levels, emphasizing their applicability in autonomous robotics.

b$.**$System Integration Phase$**$: Identify and elaborate on the stages of the SQAR's measurement system. This includes:

$-$ The $**$sensing elements$**$ used for soil property measurements.

$-**$Signal conditioning$**$ required to convert the raw sensor signals into a form suitable for analysis.

$-$ The $**$data processing unit$**,$ which interprets the sensor data to assess soil quality.

$-**$Navigation and mobility control$**$ that enables the robot to move autonomously across varied terrain.

$-**$Sample collection mechanism$**,$ describing how the robot collects and prepares soil samples for analysis.

$**$Question $3$: Developing a Test Plan for Sensor Fusion Algorithm Evaluation$**$

Sensor fusion is crucial in autonomous vehicle systems, integrating data from multiple sensors to improve navigation accuracy and reliability. Construct an experimental test plan to assess the performance of a novel sensor fusion algorithm designed for autonomous vehicle guidance.

a$.**$Test Plan Design$**$: Draft a detailed test plan that identifies:

$-**$Objectives$**$: What you aim to prove or discover about the new sensor fusion algorithm.

$-**$Hypothesis$**$: Your prediction of how the sensor fusion algorithm will perform under test conditions.

$-**$Variables$**$:

$-**$Independent Variables$**$: The conditions you will change during the experiment $($e$.$g$.,$ sensor configurations, environmental conditions$).$

$-**$Dependent Variables$**$: The outcomes you will measure $($e$.$g$.,$ navigation accuracy, decision$-$making time$).$

$-**$Extraneous Variables$**$: External factors that could affect the results $($e$.$g$.,$ weather conditions, GPS signal quality$)$ and how you plan to control them.

$-**$Experimental Setup$**$: The layout of your testing environment, including the types of sensors used $($e$.$g$.,$ LiDAR, cameras, GPS$),$ and the data fusion techniques being evaluated.

b$.**$Data Analysis and Validation$**$:

$-**$Analytical Methods$**$: Outline simple yet effective methods to analyze the collected data. This could include comparing the average navigation accuracy under different sensor configurations or environmental conditions.

$-**$Accuracy and Reliability Assessment$**$: Propose straightforward techniques for evaluating the algorithm's accuracy and reliability. For example, compare the sensor fusion algorithm's performance against a baseline to see if it meets or exceeds expected outcomes.

$-**$Real$-$world Applicability$**$: Briefly discuss how the results of your experiment could translate to real$-$world scenarios. Consider any limitations observed during testing that might affect the algorithm's deployment in actual autonomous vehicles.

$**$Question $4$: Calibration of a Hypothetical Pressure Sensor$**$

You are provided with synthetic data for calibrating a new type of pressure sensor$,$ "Nano