## 2 Materials and Methods
### Statistical and Graphical Tools  
The measured values, both BBM and PEM, were analyzed numerically using Statgraphics Centurion version 19.2.01 (64-bits) software [16] and IBM SPSS v.24 [17]. Using these programs, the means and variances were obtained and the fitting of the probability functions was performed.  
The geographic analysis of the data was carried out using R version 4.3.2. This programming language allows for complex statistical analyses and their visualization, highlighting its capacity to process geographic data with libraries such as sp, sf, raster, gstat and automap [18; 19; 20; 21; 22]. The sp and sf libraries were used to read and process vector spatial data, while the raster library was employed for handling raster data. Ordinary kriging was carried out using the gstat package. To support this, the automap package was used to automate the fitting of the semivariogram, thereby streamlining the process. It also allows for the use of genetic algorithms from specific libraries, such as GA: Genetic Algorithm [23]. Genetic algorithms are a kind of classical optimization technique and are still used as part of more modern schemes such as the multimethod ensemble approach [24; 25]. In this case, it is only a matter of finding the optimal value of two variables, so classical techniques such as genetic algorithms are suitable for the optimization process. These capabilities have led to its selection as a programming language. The interpolation technique used in this study was ordinary kriging, using the specific libraries gstat and automap [19; 22]. Interpolation by kriging has been used for mapping with BBM and PEM measurements and to calculate the whole-body specific absorption rate (SAR) [15; 26; 27].  
The gstat library was used for the development of the comparative mapping [22]. The adjustment of the semi-variogram was performed manually, using information from similar statistical analyses [3; 28]. In the case of the BBM, the fit was performed with a spherical function, while the uncalibrated exposimeter (PEM) dataset showed a better fit with an exponential function. As explained in detail below, the measurements made with the PEM were corrected using a single constant for the whole range of values (PEM-1) or two different constants depending on whether there was LOS to any of the emitters at the measurement point (PEM-2). The adjustment of the semivariogram was, in both cases, conducted using a spherical function. For the integration of the spatial interpolation process with the genetic algorithm, the automatic adjustment of the automap library was used; this serves to adjust the sample to the function with the smallest error present. For this case, the following distributions were used: Spherical, Exponential, Gaussian, Spatio-Temporal Error and Matern's Covariance [19].  
The kappa variable, in the estimation of the semivariogram, allows the smoothness of the random field to be adjusted. In other words, it favors a better fit. In this case, a variable kappa value was used to test different levels of smoothness and select the one that best fitted the data. For the interpolation process, a rectangular interpolation grid of \(5\times 10^{6}\) cells was generated. In the interpolation adjustment, the function resulting from the automatic adjustment of the semi-variogram is used. Other parameters used in the interpolations are: nmax and maxdist. The parameter nmax is used to limit the number of neighboring points that influence the estimation of a value at a specific location. The maxdist parameter, on  
Figure 1: Distribution of measuring locations and BTS.  
the other hand, defines the maximum distance up to which neighboring points should be considered for interpolation. In this case, the value 10 was used for the nmax parameter and 20 for the maxdist parameter.