## 4 Results  
Input data containing information about measurement dates \(D\) (21.9.2020 [DMY]), measurement times \(C\) (7:52 [HOUR: MIN]), information about received \(RSSI\) signal strength (\(-32.375\) [dBm]), current temperature \(TE1\) (17.3 [\({}^{\circ}\)C]), pressure \(TL\) (990.52 [hPa]), current temperature \(TE2\) (15.4 [\({}^{\circ}\)C]), air humidity \(VL\) (49.099998 [\(\%\)]), current temperature \(TE3\) (11.187 [\({}^{\circ}\)C]), wind speed \(RV\) (0.403695 [m/s]), wind speed \(SV\) (0.396887 [m/s]), particle concentration \(KC\) (13 [mg/m\({}^{3}\)]), and visibility \(VL\) (4000 [m]) should be stored in a CSV file, where the individual values in the line are separated by a decimal point. The decimal point, therefore, represents the column separator. The individual rows are arranged one below the other, forming an input data set. The measurements were performed in a certain period with a minute interval between individual measurements. This set of input data was obtained from the weather station of the Technical University in Kosice located on the PK13 building and contains almost 120,000 records from the dates from 21.9.2020 to 13.12.2020. An example of measured data is shown in Figure 8.  
21.9.2020,7:52,-32.375,17.3,990.52,15.4,49.099998,11.187,0.403695,0.396887,13,4000  
21.9.2020,7:53,-25,17.35,990.505,15.55,49.1999985,11.85,0.415297,0.397262,13,4000  
21.9.2020,7:54,-25,17.55,990.525,15.4,48.95001,11.281,0.415297,0.397262,11.5,4000  
21.9.2020,7:55,-25,17.55,990.56,15.55,48.8999995,11.35,0.415297,0.397262,12,4000  
21.9.2020,7:56,-25,17.4,990.545,15.7,48.25,11.375,0.4133635,0.3971995,13,4000  
21.9.2020,7:57,-25,17.4,990.54,15.55,48.049999,11.375,0.415297,0.397262,13,4000  
21.9.2020,7:58,-25,17.5,990.605,15.55,48.3500005,11.375,0.4133635,0.3971995,13,4000  
We can also imagine the input data in the form of the matrix shown below:  
\[M= \begin{bmatrix}D_{1}&C_{1}&RSSI_{1}&TE1_{1}&TL_{1}&TE2_{1}&VL_{1}&TE3_{1}&RV1 _{1}&RV2_{1}&KC_{1}&VI_{1}\\ D_{2}&C_{2}&RSSI_{2}&TE1_{2}&TL_{2}&TE2_{2}&VL_{2}&TE3_{2}&RV1_{2}&RV2_{2}&KC_{2 }&VI_{2}\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ D_{n}&C_{n}&RSSI_{n}&TE1_{n}&TL_{n}&TE2_{n}&VL_{n}&TE3_{n}&RV1_{n}&RV2_{n}&KC_{n }&VI_{n}\end{bmatrix}  \tag{6}\]  
From the initial look at the set of input data, it was clear that not all parameters affect the RSSI value to the same extent. Therefore, we decided to use correlation to determine the magnitude of the impact of individual parameters on the RSSI value. To calculate the correlation, a program in the Python programming language was created. Three correlation methods were used for the calculation--the method using [PERSON]'s correlation coefficient (\(\rho_{\text{p}}\)), then the method with [PERSON]'s correlation coefficient (\(\rho_{\text{s}}\)), and finally the method with [PERSON]'s correlation coefficient (\(\rho_{\text{k}}\)). We always compared the column of parameter values of the received RSSI signal with the column of one of the parameters of climatic conditions. All functions return two values--the first being the correlation coefficients, which were stored, and then the values used to test the hypothesis, which are not used in our proposal, so they are discarded. The outputs are rounded to five decimal places and calculated for all correlation functions. The results of [PERSON]'s method, which compares the linear dependence between two sets of data, in our case specifically between the RSSI parameter and individual parameters of climatic conditions, appeared to be the best. The correlation coefficient is between \(-1\) and \(+1\) where \(0\) represents no correlation and \(-1\) and \(+1\) represent the same linear relationship. A positive number means that with a gradual increase in the RSSI value, a specific parameter of climatic conditions increases. On the contrary, a negative number means that as the RSSI value increases, the specific parameter of climatic conditions decreases.