esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Penetration Experiments
### Experimental Results  
Figure 6 shows a typical moment that the projectile contacts the target in the penetration process. Detailed experimental data, including the DOP and crater diameter (d\({}_{\rm c}\)), are listed in Table 1.  
The frontal damages of targets are shown in Figure 7. When the projectile hits the target, concrete around the contact location is ejected out and forms a circular fragment cloud. The projectile with a speed of 410 m/s bounced off the target, while the one at a striking velocity of 664 m/s was stuck in the target. The projectile impact formed an obvious funnel-shaped crater on the impact surface first and then a tunnel deep into the targets. An evident spalling phenomenon could be observed at the edge of the crater. Moreover, the concrete around the tunnel is crushed into powders by ultra-high compressive stress.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Target** & **Plain Size (m)** & **Thickness (m)** & \(f_{\epsilon}\) **(MPa)** & \(V_{f}\) & \(m\) **(kg)** & \(V_{0}\) **(m/s)** & **DOP (cm)** & \(\mathbf{d_{\epsilon}}\) **(cm)** \\ \hline U-1 & 2.0 \(\times\) 2.0 & 0.9 & 148.3 & 2\% & 11.9 & 410 & 42.1 & 51.3 \\ U-2 & 2.0 \(\times\) 2.0 & 1.5 & 148.3 & 2\% & 11.9 & 664 & 78.6 & 78.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Penetration tests data.  
Figure 5: Experimental setup for penetration tests.  
Figure 6: A certain moment in the penetration process.  
As listed in Table 1, the diameter of the crater increases with the growth of the striking velocity of the projectile. In the present penetration tests, The DOP included both the depth of the crater and the depth of the tunnel. The DOP of the projectiles at striking velocities of 410 m/s and 664 m/s is 42.1 cm and 78.6 cm, respectively, which also shows an increasing tendency.
Moreover, NMF simultaneously quantifies both spatial structures (muscle weight vectors) and temporal activation coefficients of muscle synergies [25; 26; 27], providing insights into muscle group contributions and their dynamic adaptations over time, overcoming limitations of traditional analytical methods. Despite increasing interest in tennis biomechanics, most existing studies have primarily focused on single-muscle activation or joint kinematics, overlooking the integrated, time-dependent coordination among multiple muscle groups. Moreover, few studies have explored how muscle synergy structures adapt across different skill levels using physiologically interpretable methods such as NMF. This study addresses this gap by applying synergy-based analysis to reveal neuromuscular control differences that underline skill-dependent variations in stroke execution. Therefore, this study employs the NMF algorithm to systematically analyze the neuromuscular control strategy variations in lower limb muscle synergy structures between elite and amateur tennis players while executing the forehand open stance stroke. By quantifyingthe number of synergy modules, activation timing synchronization, and spatial distribution of muscle weights [28], this study seeks to examine variations in muscle coordination and activation characteristics among players of varying skill levels. Focusing on the intrinsic mechanisms of motor skill control, this study further elucidates the role of muscle synergy patterns in movement control and energy transfer. By comparing and analyzing the muscle synergies of players at different skill levels, the findings will not only optimize existing motor skill control theories but also serve as a valuable reference for coaches and sports scientists, promoting neuromuscular-based training approaches to enhance overall athletic performance. Although skill level in tennis lies on a continuum, this study employed a binary classification (elite vs. amateur) to emphasize distinct contrasts in neuromuscular control. This simplification is acknowledged as a methodological limitation, and future studies may consider more nuanced stratification to reflect the spectrum of player abilities.
## 4 Results and Discussion
### Development of the Optimized Prediction Model for Corn Yield  
Crop yield can differ by land surface variables according to season. To find the best months for crop yield prediction, we conducted an analysis using the three aforementioned periods: (1) GS, (2) JA, and (3) OC. For corn yield prediction, we built six AI models (MARS, SVM, RF, ERT, ANN, and DNN) for the three periods (GS, JA, and OC). In particular, we carried out the parameter optimization for the DNN models according to the procedure presented in Figure 3. The results of the sensitivity analysis according to DNN parameters are illustrated in Figure 4. The best structure of the hidden layers was determined to be 300-300 nodes, which was selected from several layer structures, including 200-200, 300-300, 500-500, 200-200-200, 300-300-300, and 500-500-500 nodes. From the loss function, such as sum of square errors (SSE), mean of square errors (MSE), and cross entropy (CE), SSE was selected as the best one. The adaptive gradient (AdaGrad) algorithm was better than the root mean square \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & **GS(\({}^{\text{(1)}}\)** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **MJ(\({}^{\text{(2)}}\)** & **JJ(\({}^{\text{(3)}}\)** & **JA(\({}^{\text{(4)}}\)** & **AS(\({}^{\text{(5)}}\)** & **MJJ(\({}^{\text{(6)}}\)** & **JA(\({}^{\text{(7)}}\)** & **JAS(\({}^{\text{(8)}}\)** \\ \hline EVI\({}^{\text{(9)}}\) & 0.824 & \(-\)0.045 & 0.320 & 0.857 & 0.833 & 0.428 & 0.205 & 0.673 & 0.917 & 0.694 & 0.556 & 0.860 & 0.856 \\ LAI\({}^{\text{(10)}}\) & 0.494 & \(-\)0.375 & \(-\)0.122 & 0.537 & 0.620 & 0.233 & \(-\)0.245 & 0.348 & 0.635 & 0.521 & 0.222 & 0.560 & 0.603 \\ GPP\({}^{\text{(11)}}\) & 0.654 & \(-\)0.214 & 0.032 & 0.699 & 0.675 & 0.514 & \(-\)0.092 & 0.594 & 0.719 & 0.660 & 0.452 & 0.694 & 0.718 \\ PPT\({}^{\text{(12)}}\) & 0.450 & 0.159 & 0.313 & 0.349 & 0.259 & 0.136 & 0.318 & 0.390 & 0.414 & 0.271 & 0.391 & 0.462 & 0.377 \\ TMAX\({}^{\text{(13)}}\) & \(-\)0.054 & 0.144 & 0.124 & \(-\)0.414 & \(-\)0.250 & 0.169 & 0.143 & \(-\)0.185 & \(-\)0.369 & \(-\)0.025 & \(-\)0.065 & \(-\)0.219 & \(-\)0.208 \\ SM\({}^{\text{(14)}}\) & 0.472 & 0.395 & 0.489 & 0.540 & 0.479 & 0.365 & 0.561 & 0.596 & 0.583 & 0.485 & 0.509 & 0.535 & 0.503 \\ \hline \multicolumn{10}{c}{(1) Growing Season; (2) May-June; (3) June-July; (4) July-August; (5) August-September; (6) May-June-July; (7) June-July-August; (8) July-August-September; (9) Enhanced Vegetation Index; (10) Leaf Area Index; (11) Gross Primary Production; (12) Precipitation; (13) Maximum air temperature; (14) Soil Moisture.} \\ \end{tabular}
\end{table} 
Table 4: Correlation coefficients between the selected variables and corn yields according to combinations of months, 2006–2015.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Temporo-Spatial Parameters**} \\ \hline  & \multicolumn{2}{c}{**Fast**} & \multicolumn{2}{c}{**Self-Selected**} & \multicolumn{2}{c}{**Stubby Prostheses**} \\ \hline  & **R** & **L** & **R** & **L** & **R** & **L** \\ \hline \multicolumn{2}{c}{_Spatial parameters [m]_} \\ \hline
**Stride length** & 1.31 \(\pm\) 0.1 & 1.29 \(\pm\) 0.1 & 1.21 \(\pm\) 0.1 & 1.2 \(\pm\) 0.1 & 0.71 \(\pm\) 0.1 & 0.71 \(\pm\) 0.1 \\
**Step width** & 0.29 & & 0.26 & & 0.24 \\
**Step** & 0.66 \(\pm\) 0.1 & 0.57 \(\pm\) 0.1 & 0.62 \(\pm\) 0.1 & 0.54 \(\pm\) 0.1 & 0.32 \(\pm\) 0.1 & 0.34 \(\pm\) 0.1 \\ \hline \multicolumn{2}{c}{Speeds [m/s]} \\ \hline
**Velocity** & 0.83 \(\pm\) 0.02 & & 0.69 \(\pm\) 0.06 & & 0.75 \(\pm\) 0.06 \\
**Cad** & 1.51 & & 1.3 & & 2.14 \\ \hline \multicolumn{2}{c}{Scalar parameters [\%GC]} \\ \hline
**Stance** & 0.63 \(\pm\) 0.01 & 0.69 \(\pm\) 0.03 & 0.65 \(\pm\) 0.03 & 0.7 \(\pm\) 0.03 & 0.65 \(\pm\) 0.02 & 0.66 \(\pm\) 0.03 \\
**Double Stance** & 0.18 \(\pm\) 0.03 & 0.17 \(\pm\) 0.02 & 0.19 \(\pm\) 0.03 & 0.18 \(\pm\) 0.03 & 0.14 \(\pm\) 0.03 & 0.16 \(\pm\) 0.01 \\
**Swing** & 0.36 \(\pm\) 0.01 & 0.31 \(\pm\) 03 & 0.34 \(\pm\) 0.03 & 0.30 \(\pm\) 0.03 & 0.35 \(\pm\) 0.02 & 0.34 \(\pm\) 0.03 \\ \hline \multicolumn{2}{c}{Temporal parameters [s]} \\ \hline
**Stride** & 1.55 \(\pm\) 0.1 & 1.33 \(\pm\) 0.1 & 1.55 \(\pm\) 0.2 & 1.55 \(\pm\) 0.1 & 0.94 \(\pm\) 0.1 & 0.94 \(\pm\) 0.1 \\
**Stance** & 0.84 \(\pm\) 0.1 & 0.92 \(\pm\) 0.1 & 1.02 \(\pm\) 0.1 & 1.01 \(\pm\) 0.1 & 0.61 \(\pm\) 0.1 & 0.61 \(\pm\) 0.1 \\
**Double Stance** & 0.23 \(\pm\) 0.1 & 0.22 \(\pm\) 0.1 & 0.29 \(\pm\) 0.1 & 0.27 \(\pm\) 0.1 & 0.13 \(\pm\) 0.1 & 0.14 \(\pm\) 0.1 \\
**Swing** & 0.48 \(\pm\) 0.1 & 0.41 \(\pm\) 0.1 & 0.52 \(\pm\) 0.1 & 0.46 \(\pm\) 0.1 & 0.32 \(\pm\) 0.1 & 0.34 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and \(\pm\) standard deviation of temporo-spatial parameters for prosthetic gait (fast gait, self-selected gait speed and gait with stubby prostheses—stubbies). The vGRF (Figure 2) waveforms captured during the gait with standard prostheses were similar in shape to the letter \"M\". During the gait with both the self-selected and fast speed, vF1 and vF3 parameters (overweight at the initial weight acceptance phase and overweight during the terminal stance phase) reached higher values than vF2 parameter (underweight during the middle stance phase). During the gait with stubbies prosthesis, despite the time-shift in achievement of the parameter, there was no difference in vGRF values obtained by the vF1 parameter. The vF2 parameter reached the lowest value of approx. 30% for the stance phase (self-selected gait speed--approx. 50%, fast gait speed--70%). The vF3 parameter obtained maximum values much earlier than in other cases (50%, 70% and 80% for the stance phase in the gait cycle, respectively) and took longer stance phases.
## 4 Results
### Load Spectra  
Figure 8 shows the load spectra of the spiral level gear during the four tractor operations. The load spectrum is expressed as 3D Map of a range, mean, and number of cycles. Figure 8a shows the load spectrum during moldboard plow tillage, showing the highest number of cycles in the mean of 500-700 Nm and the range of 0-200 Nm. Figure 8b shows the load spectrum during subsoiler tillage, showing the highest number of cycles in the mean of 750-1250 Nm and the range of 0-100 Nm. Figure 8c,d show the load spectra during rotary tillage and baler operation, showing the highest number of cycles in the mean of 0-100 Nm and the range of 0-50 Nm. The 3D Load spectra were converted into stress and used to predict the fatigue life, as detailed below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Field Operations**} & \multicolumn{4}{c}{**Spiral Bevel Gear Torque (Nm)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Working Period (A)**} & \multicolumn{2}{c}{**Steering Period (B)**} \\ \cline{2-5}  & **Max.** & **Avg. \(\pm\) Std.** & **Max.** & **Avg. \(\pm\) Std.** \\ \hline Moldboard plow tillage & 750.7 & 614.7 \(\pm\) 45.1 & 784.8 & 214.0 \(\pm\) 202.1 \\ Subsoiler tillage & 1324 & 876.5 \(\pm\) 293.9 & 1205 & 400.3 \(\pm\) 238 \\ Rotary tillage & 617.8 & 47.2 \(\pm\) 46.3 & 481.4 & 305.1 \(\pm\) 74.1 \\ Baler operation & 584.0 & 55.9 \(\pm\) 49.5 & 358.9 & 212.8 \(\pm\) 87.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical description of the spiral bevel gear torque by field operations during working and steering periods.  
Figure 7: Dynamic load profile of spiral bevel gear by field operations: (**a**) moldboard plow tillage, (**b**) subsoiler tillage, (**c**) rotary tillage, and (**d**) baler operation.
## 2 Motion Model and Failure Effect
\begin{array}{l}r=\|\mathbf{r}\|\left[H_{x}\ H_{y}\ H_{z}\right]=\mathbf{H}=\mathbf{ r}\times\mathbf{V}\ i_{f}=\arccos(H_{z}/\|\mathbf{H}\|)\\ \Omega_{f}=-\arctan\bigl{(}H_{x}/H_{y}\bigr{)}\ \Bigl{[}e_{fx}\ e_{fy}\ e_{fz} \bigr{]}=\mathbf{e}_{f}=\frac{r_{x}\psi}{\mu}-\frac{r}{r}\ e_{f}=\|\mathbf{e}_{f}\|\\ \tan\omega_{f}=\frac{e_{fz}}{(e_{fy}\sin\Omega_{f}+e_{fz}\cos\Omega_{f})\sin i _{f}}\\ a_{f}=\frac{\|\mathbf{H}\|^{2}}{\mu\bigl{(}1-\frac{e_{f}^{2}}{f}\bigr{)}}\ \cos\theta_{f}=\frac{\mathbf{r}e_{f}}{r_{f}}\ \phi_{f}=\omega_{f}+\theta_{f}\\ \phi_{f}=\arctan\biggl{(}\frac{r_{z}}{(r_{y}\sin\Omega_{f}+r_{x}\cos\Omega_{f} )\sin i_{f}}\biggr{)}\end{array} \right. \tag{2}\]  
where \(\mu\) denotes the geocentric gravitational constant; \(r_{x}\), \(r_{y}\), and \(r_{z}\) denote the three components of \(\mathbf{r}\). Constraints on the altitudes of perigee and apogee of the target orbit are required to ensure the safety and mission viability of the payload in the target orbit:  
\[\left\{ \begin{array}{l}h_{p}\geq h_{p\text{min}}\\ h_{a}\geq h_{x}+\delta_{0}\end{array} \right. \tag{3}\]  
where \(h_{p}\) is the perigee altitude of the target orbit; \(h_{p\text{min}}\) is the minimum safe altitude, which is set to 110 km in this paper; \(h_{x}\) is the altitude of the failure point; \(\delta_{0}\) is the height adjustment margin, which is set to 5 km in this paper. The launch vehicle flies at a programmed angle in the first stage at a low altitude, which is influenced by the atmosphere, so there are limited flight adjustment measures when the launch vehicle power system failure occurs. In contrast, the launch vehicle flies at a higher altitude in the second flight stage, where the influence of aerodynamic forces can usually be ignored, and there is more room for adjustment when the power system failure occurs. Therefore, the main flight stages studied are the secondary and higher vacuum flight phases.
# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
## 2 Methodology  
The aim of this paper is to design a road segmentation model that can be used later for real time road extraction from aerial images. The first challenge was determining the design elements that can empower our model in the segmentation task: we got our inspiration from U-Net [31] and ResBlocks [26; 27], and after testing a number of models, we established our current model. The second step is to preprocess the dataset by dividing it into two sets: a training and a test set. The images were cropped to generate consistent sized images in the dataset. The output of this model was a binary mask for road and background segmentation, which had the same size as the input images. All these steps will be explained in detail in the next sections.
## 3 Heat Effect on Steel Cylinder
### Influence of Laser Spot Radius on Heat Effect  
Figure 2 reveals the thermal responses of the 15 mm thickness steel cylinder subjected to different power density lasers. Continuous laser irradiations on targets were at the power of 1.5 kW, 2.0 kW, 2.4 kW, and 2.8 kW, respectively, with the laser spot diameter of 10 mm. The simulation results within 300 s of the front surface spot center temperature and the corresponding inner surface center temperature are shown in Figure 3.  
For comparison, simulation results of the lasers with a spot diameter of 20 mm at the same power levels are shown in Figure 4.  
For the simulation result of a 20 mm diameter laser spot, the temperature of the front surface does not exceed 3250 K within 300 s, and there is no vaporization ablation. Under the laser power of 1.5 to 2.8 kW, the front surface temperature exceeds 1720 K, and a certain depth of molten ablation forms on the surface of the steel cylinder. However, the temperature of the back surface does not exceed 1720 K, which means the steel cylinder cannot be penetrated within 300 s.  
\begin{table}
\begin{tabular}{c c} \hline \hline Parameter & Value \\ \hline Laser power [kW] & 1.5, 2.0, 2.4, 2.8 \\ Laser wavelength [\(\mu\)m] & 1.06 \(\pm\) 0.02 \\ Laser spot radius [mm] & 10, 20 \\ Target thickness [mm] & 15, 13, 9, 7 \\ Latent heat of vaporization [J/g] & 6430 \\ Melting latent heat [J/g] & 247 \\ Melting temperature [K] & 1670 \\ Initial temperature [K] & 273, 300 \\ Irradiation time [s] & 10 \\ Convective heat transfer coefficient [W/(m\({}^{2.\circ}\)C)] & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loading conditions for simulation.  
Figure 1: Schematic diagram of 2.4 kW incident laser power density distribution at 10 mm spot diameter.  
distribution in Figures 4 and 5, it can be seen that when the laser power is greater than 2.8 kW and the laser spot diameter is less than 10 mm, the steel cylinder (15 mm) can be penetrated within 272.2 s. The melting-through mechanism of this process is that the steel cylinder forms through holes under the action of vaporization back pressing.  
Figure 3: Center temperature of front and back surface of the steel cylinder under 1.5–2.8 kW laser power. (**a**) Front surface, (**b**) Back surface.  
Figure 2: Temperature distribution on 15 mm thickness cylinder subjected to 2.4 kW incident laser with spot diameter of 10 mm.
## 4 Overview of Pseudo-Dynamic Testing and Result Analysis
### Experimental Results and Analysis
#### 4.5.2 Maximum Seismic Response Load and Displacement
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Specimen** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Input** \\ **Seismic Ground** \\ **Motion** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Input Seismic** \\ **Ground** \\ **Motion** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Load** \\ **(cm/s\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Maximum** \\ **Load** \\ **(km)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Measure** \\ **Displacement** \\ **(Failure Mode)** \\ \end{tabular} } \\ \cline{3-3}  & & & 200 & 272.5 & 70.2 & Collapse (shear failure) \\ \cline{3-3}  & & 200 & 419.5 & 23.8 & Small (flexural cracks) \\ \cline{3-3}  & & 300 & 592.9 & 46.6 & Moderate (flexural shear cracks) \\ \cline{3-3}  & & 400 & 711.9 & 85.6 & Large (flexural shear failure) \\ \hline \hline \end{tabular}  
* The degree of seismic damage was determined in accordance with JBDPA [46] and [PERSON] et al. [47].  
\end{table} 
Table 9: Maximum response load-displacement and the degree of seismic damage. The test frame retrofitted with the NBSD system exhibited seismic response strength about 1.53 times higher than the reference test frame (PD-FR) when the same input seismic ground motion was applied at 200 cm/s\({}^{2}\). The difference was even larger when the input seismic ground motion was higher: about 2.17 times at 300 cm/s\({}^{2}\) and about 2.61 times at 400 cm/s\({}^{2}\), as shown in the figures and table above. These results were also highly consistent with the ultimate failure patterns shown in Figures 14-19. The measured seismic response displacement was about 0.33 times higher at 200 cm/s\({}^{2}\), 0.66 times higher at 300 cm/s\({}^{2}\), and 1.21 times higher at 400 cm/s\({}^{2}\) in the test frame retrofitted with the NBSD system than in the reference frame, respectively. When the same input seismic ground motion was applied (200 cm/s\({}^{2}\)), the seismic response displacement was about 67% smaller, indicating that the NBSD-based seismic retrofitting method proposed in the present study proved to be highly effective in improving the seismic energy absorption capacity of the system. Figure 21: Comparison of seismic response shear stress–displacement curves. Figure 20: Comparison of seismic response shear stress–displacement curves.
## 3 Legislation Process and Contents of the E-Government Acts in Korea and the United States
### Main Content of the E-Government Act in Korea
#### 3.2.1 Democratic Aspect of Administration  
As the use of information and communication has spread rapidly in our society, the dysfunction of informatization such as hacking, infringement of privacy by leakage of personal information, and computer crime has expanded. In addition, Korea has had the experience of adopting the e-resident card policy in the face of two acutely opposing arguments on the efficiency of administration and the possible invasion of privacy. Therefore, in order to cope with this problem, the e-government principle centering on the citizens' convenience was formulated more specifically in Article 4. Furthermore, in order to prepare for the provision of citizen-centered services as well as the dysfunctions, the principle of protection of personal information and privacy was also specified.  
_Article 4_ (_Principles of Electronic Government_)  
_(1) Each administrative agency, etc. shall consider, among other things, the following matters in materializing, operating, and developing electronic government, and take measures necessary therefor:_  
1. _Digitizing public services and improving citizens' convenience;_
2. _Imnovating administrative affairs and improving their productivity and efficiency;_
3. _Ensuring the security and reliability of information systems;_
4. _Protecting personal information and privacy;_
5. _Expanding disclosure and sharing of administrative information;_
6. _Preventing duplicative investment and improving interoperability._  
Until the early 2000s, most administrative services in Korea were only available through civil servants and by visiting the relevant government agencies. However, in many countries--including the United States, the United Kingdom (UK), Australia, Singapore, and Hong Kong--services regarding taxes, birth notification, marriage reports, and death reports have been made available via the Internet. In accordance with this trend, and as a concrete means of implementation, Korea also stipulated the processing of non-visiting service delivery and the establishment and operation of the electronic petition service counter through virtual institutions in Article 9.  
_Article 9_ (_Processing of Civil Petitions without Appearance_)  
_(1) In order for civil petitioners to have their civil petitions, etc. processed without necessarily appearing in person at the relevant agency, the head of each administrative agency, etc. shall take measures, such as the improvement of relevant Acts and subordinate statutes and the establishment of facilities and systems as necessary._
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 2 Related Work
### Precise Outdoor Positioning
The fact of using a real vehicle or a similar platform that can emulate its behaviour to test the proposed positioning solution provides a better understanding of its performance within a real case scenario. In conclusion, UWB based positioning systems are widespread in indoor positioning applications. In comparison, little research has been made in terms of outdoor positioning, indicating that more work is needed in order to evaluate their performance in outdoor applications. Hence, this paper proposes an outdoor positioning system based on a sensor fusion between an UWB based ranging solution and an INS positioning system. This sensor fusion makes use of the UWB ranging estimates to calibrate the position computed from the vehicle's inertial and odometry data to avoid long term drifts and reduce its uncertainty. Furthermore, the proposed system has been validated in a real outdoor environment emulating a relevant setup for the PRoPART project. On the other hand, our proposal is based on an external antenna connected through a cable to achieve a flexible deployment of our solution. This cable solution does not comprise the accuracy of our system which obtains good results in real tests. To our best of knowledge, none of the research works found in the literature proposes such a flexible solution.