## 3 Results and Discussion
The average error ranged from 0.715 m with external antenna to 1.439 m without external antenna. Finally, the low-cost receiver QLV2, used in test M without RTK differential correction and external antenna, provided an average error value of 0.550 m. If this receiver is compared to the others, tests A and B, performed with the S7 receiver, provided better positioning accuracy, while worse accuracy was obtained in test H performed with the S5 receiver (without RTK and external antenna) and in test L (also without RTK and external antenna) performed with the TMMP receiver. Instead, there were no statistically significant differences between the average positioning error of the QLV2 receiver and that of the S7 (used without differential correction both with and without external antenna in tests C and D), as well as between the QLV2 receiver and both the S5 (used with RTK differential correction and without external antenna in test F) and the TMMP (used in test I). By comparing the receivers used in RTK mode (S7 and S5), i.e., tests A-E and B-F, no statistically significant differences were obtained. The purpose of the PCA is to obtain a small number of linear combinations of the ten variables which account for most of the variability in the data. In this case, ten components were extracted. Together, they account for 100.0% of the variability in the original data. Table 6 reports all the ten components extracted from PCA performed on the data. The first column lists the Eigen values of the correlation matrix, ordered from the highest to the lowest. As a correlation matrix was analysed, the variables were standardised to have unit variance, so the total variance was 10. As the Eigen values are the variances of the principal components, the first principal component had a variance of 4.0987, explaining 41% (4.0987/10) of the total variance; the second component had a variance of 2.9029, explaining the 29% of the total variance, and so on for the other components. Following the well-known and most-used Kaiser's criterion [26] and the scree plot (not shown in the paper) [27], which suggest retaining components with Eigen values higher than 1, three components were extracted which explain 81% (41+29+11) of the total variance of the data components. The factorability tests provide indications of whether or not it is likely to be worthwhile to attempt to extract factors from a set of variables. The Kaiser-Meyer-Olkin (KMO) statistic provides an indication of how much common variance is present. For factorisation to be worthwhile, KMO should normally be at least 0.6. Since KMO = 0.714463, factorisation is likely to provide interesting information about any underlying factor. The score biplot (Figure 6) gives us a feeling for the similarities and differences among the different tests. It shows that test L is opposite to B, E and M, while test I is opposite to C, F and H.  
The results of the evaluation carried out by [PERSON] et al. [7] with reference to the GNSS receiver Navcom SF-3050 showed that, generally, the low-cost receivers do not achieve the same positioning accuracy as the survey-grade ones. All low-cost single-frequency (L1) receivers evaluated by these authors, during static applications, could achieve centimetre-level accuracy in rural environments and perform better when using high quality versus a low-quality antenna. However, during dynamic applications, they could not determine an RTK position at any time. Among the low-cost L1 frequency receivers tested by the above authors, Reach showed a centimetre-level 95% accuracy. Among the tested L1/L2 frequency receivers, Piksi Multi showed a positioning accuracy of 0.048 m, while Eclipse P307 had an accuracy of 0.069 m [7]. The results obtained during the performed tests are perfectly aligned with those achieved by [PERSON] et al. [10]: dual-frequency (L1/L2) GNSS receivers were better than single-frequency (L1) receivers.