# EMF Assessment Utilizing Low-Cost Mobile Applications
## 3 Results
### Conversion of RSRP to Electric Field Strength
#### 3.2.2 The Impact of Variability  
To evaluate whether the linear relationship between the RSRP and the power flux density was stronger in \"low variability\" areas, we compared the results based on variability. Each segment was identified as either a \"high-\" or a \"low-variability\" segment based on  
Figure 11: Mean squared error results between the calculated and measured power flux density values. Areas (a–d) are characterized by high CV values for both the exposimeter and app received power. the combined CV values of the power flux density measured by the exposimeter and the total RSRP value from the app. We introduced the combined CV value for each segment as a metric for this analysis, calculated using the combined standard deviation and mean values, as described in Equation (8). \[CV_{combined}=\frac{\sqrt{\sigma_{exposimeter}^{2}+\sigma_{application}^{2}}}{\mu_{ exposimeter}+\mu_{application}} \tag{8}\]  
where \(\sigma_{exposimeter}\) and \(\sigma_{application}\) is the standard deviation of the average received power obtained by the exposimeter and the app, respectively, for each segment, while \(\mu_{exposimeter}\) and \(\mu_{application}\) is the corresponding mean value. After the calculation of the combined CV for each segment, the segments were sorted, with the first half classified as low-variability segments. Each data point from all route repetitions was then allocated to its corresponding segment. Once allocated, the data points within each segment were smoothed, and any outlier values were removed. The entire dataset was subsequently split into low- or high-variability datasets based on the corresponding segment to which each data point was assigned. The low-variability dataset had a maximum CV value of 0.74 for the exposimeter and 1.2 for the app data. It is important to note that lower individual CV values may still occur in the high-variability dataset, as classification was based on combined CV values rather than individual ones. Assuming the measurements were taken in the far field, the power flux density was converted to electric field strength using Equation (9) [26]. \[E[V/m]=\sqrt{S[W/m^{2}]*120\pi} \tag{9}\]  
The measures used to compare the calculated (with the _Conversion Factor_) and measured electric field strength values were the Mean Absolute Error (\(MAE\)) and the Mean Absolute Percentage Error (\(MAPE\)), calculated based on Equation (10) and Equation (11), accordingly [27; 28], and the linear relationship between them was evaluated using [PERSON]'s correlation coefficient. \[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|E_{calculated,i}-E_{measured,i}\right| \tag{10}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\left(E_{calculated,i}-E_{measured,i}\right)}{E_{measured,i}}\right| \tag{11}\]  
where \(n\) is the number of data points in each variability dataset, \(E_{calculated,i}\) is the calculated electric field strength and \(E_{measured,i}\) the obtained electric field strength. To illustrate the results, we used a scatter plot where the x-axis represents the measured electric field strength values, and the y-axis represents the calculated values. Figure 12 shows the scatter plot for the variability datasets corresponding to the first route. The plot also includes the \(y=x\) line and the regression line. Additionally, the correlation coefficient, \(MAE\), and \(MAPE\) results between the calculated and measured values are displayed. It is evident that the calculations in the low-variability dataset were more accurate than in the high variability, having a greater correlation coefficient and lower \(MAE\) and \(MAPE\) values. Additionally, the angle between the regression line and the x-axis was closer to the ideal 45\({}^{\circ}\) in the low-variability dataset. The results could further improve in the low-variability areas by setting a lower acceptable CV value rather than separating the dataset in half.