## 3 Results  
### Test 1: Dry-Land Salom  
The participant completed 25 runs for each trajectory (short/direct and long/less direct, Figure 2B), which were significantly different for distance (46.95 \(\pm\) 0.31 and 57.21 \(\pm\) 0.22 m, t\({}_{(57)}\) = 131.5, \(p\) < 0.0001) and time (t\({}_{(57)}\) = 3.998, \(p\) = 0.0002), taking 51.21 \(\pm\) 5.88 and 57.52 \(\pm\) 5.13 s, respectively. The starting threshold for the horizontal accuracy (Hrms) was met on every occasion and resulted in a mean \(\pm\) SD (range) of 0.22 \(\pm\) 0.04 (0.21-0.24) m and 0.23 \(\pm\) 0.06 (0.20-0.25) m, for the short and long trajectories, respectively. Overall, the PNT solution data, processed via ArcGIS Pro, consisted of 24,008 data points, with no data set errors determined through a visual inspection of trajectory plots. The number of satellites within the constellation, and thus usable by the device, was 20 \(\pm\) 1 (17-21), and resulted in HDOP 0.68 \(\pm\) 0.03 (0.63-0.78) and PDOP 1.36 \(\pm\) 0.07 (1.20-1.58). According to the classification [26] of dilution of precision, HDOP was ideal, VDOP excellent, and PDOP excellent. The overall Hrms value was 0.23 \(\pm\) 0.02 (0.18-0.28) m.  
When data were analysed and compared between the short and long trajectories (n = 10,310 and n = 13,698), the mean \(\pm\) SD (95% CI) for Hrms was 0.23 \(\pm\) 0.04 (0.21-0.25) and 0.24 \(\pm\) 0.05 (0.22-0.26) m, respectively. The Hrms values were normally distributed (K2 = 1.149, p = 0.563 and K2 = 1.456, \(p\) = 0.483) and showed no significant difference (t\({}_{(50)}\) = 0.395, \(p\) = 0.695). The number of satellites used within the constellation by the device had normal data distribution (K2 = 2.838, \(p\) = 0.242 and K2 = 3.97, \(p\) = 0.137) but differed significantly (18 \(\pm\) 2 (17-19) and 19 \(\pm\) 1 (18-19), t\({}_{(50)}\) = 2.504, \(p\) = 0.016). The subsequent effect of satellite geometry led to differences for the normally distributed (K2 = 4.572, \(p\) = 0.102 and K2 = 0.021, \(p\) = 0.990) HDOP data (0.74 \(\pm\) 0.09 (0.70-0.78) and 0.69 \(\pm\) 0.03 (0.68-0.70), t\({}_{(50)}\) = 2.673, \(p\) = 0.011) and not normally distributed (K2 = 16.31, \(p\) = 0.0003 and K2 = 4.944, \(p\) = 0.084) PDOP data (1.45 \(\pm\) 0.22 (1.36-1.54) and 1.31 \(\pm\) 0.10 (1.27-1.36), t\({}_{(50)}\) = 2.749, \(p\) = 0.009) for the short and long trajectories, respectively. As per the overall classification of precision of error, the HDOP was ideal, and PDOP excellent. The frequency distribution of horizontal accuracy and dilution of precision data is shown in Figure 3.  
Figure 2: (**A**) Dry-land simulated slalom course with marked trajectories (obtained from the Arrow 100 and proceed with ArcGIS Pro), and (**B**) the 25 runs completed and overlayed on the control trajectories. Colour coding is used to signify trajectory speed, where red is the fastest speed and green the slowest. A one-sample \(t\)-test with comparison to the known total distance (tape-measured distance) of the two dry-land slalom course trajectories (short 47.3 and short 57.8 m) showed significant difference (Figure 4A) for the short trajectory (46.95 \(\pm\) 0.30 m, 95% CI discrepancy \(-\)0.48-- 0.22 m, CV = 0.65 %, \(t_{(29)}\) = 5.741, \(p<0.0001\)) and the long trajectory (57.21 \(\pm\) 0.22 m, 95% CI discrepancy \(-\)0.69-- 0.50 m, CV = 0.38%, \(t_{(29)}\) = 12.91, \(p<0.0001\)). However, the overall percentage differences from the measured distance were small for both the short (\(-\)0.74 \(\pm\) 0.64%) and long (\(-\)1.09 \(\pm\) 0.42%) trajectories (Figure 4B). Figure 4: Dry-land slalom simulation test showing (**A**) the mean \(\pm\) SD for the short (SC) more direct and long (LC) less direct trajectories, measured with the PNT solution, and (**B**) the percentage differences of the short and long trajectories as measured by the PNT solution compared to the known measured trajectory.