# EMF Assessment Utilizing Low-Cost Mobile Applications
## 2 Materials and Methods
### Measured Data Processing  
#### 2.3.1 Data Preprocessing  
The focus of this study was solely on 4G LTE networks. This is due to the app's limitations in providing information about neighbor cells in 5G networks and because 4G coverage is nearly 100% along the selected route for all three operators, whereas 5G coverage is much lower. Furthermore, the 5G network in Greece follows Non-Standalone Architecture (NSA), meaning the 4G network is utilized even when a smartphone is connected to 5G [14; 15].  
The parameter used by the app to determine the received electromagnetic power on the smartphones was the RSRP. RSRP is an indicator used in handover and cell reselection decisions and is calculated as the average power of the resource elements carrying reference signals [16]. The utilized RSRP values concerned only the 4G LTE network and were available for the main cell and each of the 17 neighbor cells for every smartphone, i.e., mobile operator in this study. The total received (reference) power for each smartphone was calculated by summing the RSRP value for the main cell and all 17 neighbor cells after converting from decibel milliwatts (dBm) to milliwatts (mW). The total received reference power for all smartphones was the sum of these calculations for all three smartphones. Similarly, the received power from the exposimeter was calculated as the sum of the power flux densities of all 4G bands, which were 791-821 MHz, 1805-1880 MHz, and 2620-2690 MHz in September 2023, with the 2110-2170 MHz band also added for 4G in October 2023 based on app data and spectrum allocation information [17; 18].  
Figure 2: Measurement equipment and placement: (**a**) The exposimeter and the three smartphones using the mobile app; (**b**) Two bags and the electric scooter; Bag 1 contained the exposimeter, and Bag 2 contained the three smartphones in three separate compartments.  
To minimize fluctuations in the received power data from both the exposimeter and the app resulting from multipath propagation, shadowing, and similar effects, a sliding window smoothing technique was applied. Specifically, a moving average sliding window of size 2 was applied to the exposimeter data, and a window of size 8 was used for the app data to cover the same distance, given the difference in sampling rates of the two types of equipment.  
An important step for comparing the two received power values was to synchronize the four devices: the three smartphones and the exposimeter. Due to different sampling rates (four or six seconds for the exposimeter and one second for the smartphone application), achieving a common starting point using the timestamps resulted in one exposimeter data point corresponding to four (or six) measurement points from the application. As the exposimeter provides nearly instantaneous readings compared to the smartphone app, only one out of every four smartphone app data points was retained. The choice of which measurement point to retain was made by aligning the normalized received power from the exposimeter and the application. An example of this alignment for a portion of randomly selected route repetition of the first route is presented in Figure 3.  
The final stage of the data processing procedure involved filtering out excessively high and low values from both the smartphones and the exposimeter data. Specifically, the  
Figure 3: Example of alignment for a portion of a randomly selected route, with the x-axis representing the timestamp and the y-axis the normalized value of received power: (**a**) Before alignment; (**b**) After alignment.  
mid-90% of the data was retained after eliminating values that fell between the 5 th and 95 th percentiles. This filtering was applied to every route to remove outlier samples.