# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Satellite Information Data Mining
However, the variance, peak, and mean values show clear differences and can be used as statistical features to distinguish between scenes. In particular, the variance shows the most noticeable difference. In outdoor environments, the variance of the accuracy factor is generally low with values of 0.0060, 0.0015, and 0.0057, respectively. In contrast, in semi-outdoor environments, where objects like buildings cause obstructions, the variance is significantly higher with values of 0.2775, 0.1318, and 0.1644. The number of visible satellites can also serve as an important marker for scene classification. Changes in the number of visible satellites occur continuously, containing rich temporal information. Even at the same location, the number of visible satellites may vary under different weather conditions, as shown in Figure 4a. The figure depicts tests conducted under three weather conditions as the environment transitions from outdoor to indoor, then to semi-indoor, and back to outdoor. Figure 3: DOP change graph: (**a**) outdoor DOP change graph, and (**b**) indoor DOP change graph. On rainy days, the overall number of visible satellites is lower, likely due to the thicker cloud layers, which affect satellite signal reflection and refraction. Most of the time in outdoor environments, the number of visible satellites ranges between 20 and 25, but it fluctuates significantly when switching from outdoor to indoor. In deep indoor environments, the number of visible satellites drops to 2 or fewer. On cloudy days, the number of visible satellites mostly ranges from 25 to 30, which is likely due to cloud movement. This condition shows the greatest overall fluctuation. On sunny days, the number of visible satellites is the highest, typically ranging between 30 and 35. The transitions between indoor and outdoor environments under the three different weather conditions reveal that the number of visible satellites does not immediately decrease upon moving indoors from outdoors. This is because the terminal maintains the tracking of satellite signals until the signal lock is lost, which occurs with a lag that is variable. Similarly, when moving outdoors from indoors, the number of visible satellites does not increase immediately. This delay is due to the time required for the terminal to capture, track, and process satellite signals, which involves solving the ephemeris. This process is influenced by both the terminal's performance and environmental factors. To minimize the influence of weather, environment, and equipment variability on the parameters, the number of visible satellites is processed using a time-series differencing method. The differencing formula is shown in the following equation, where D_num_sateN \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Form** & **Variance** & **Peak** & **Min** & **Mean** \\ \hline Outdoor & 0.0060 & 1.3700 & 0.9500 & 1.1140 \\ Semi-outdoor & 0.2775 & 5.1000 & 0.9300 & 1.7588 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PDOP outdoor vs. semi-outdoor statistics comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Form** & **Variance** & **Peak** & **Min** & **Mean** \\ \hline Outdoor & 0.0015 & 0.7600 & 0.5300 & 0.5912 \\ Semi-outdoor & 0.1318 & 3.2700 & 0.5000 & 1.0238 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HDOP outdoor vs. semi-outdoor statistics comparison. Figure 4: Visible satellite map: (**a**) variation in the number of visible satellites. (**b**) Variation in the rate of change of visible satellites in different windows. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Form** & **Variance** & **Peak** & **Min** & **Mean** \\ \hline Outdoor & 0.0057 & 1.1600 & 0.7700 & 0.9434 \\ Semi-outdoor & 0.1644 & 3.9100 & 0.7700 & 1.4238 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VDOP outdoor vs. semi-outdoor statistics comparison. represents the number of difference features.