esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Studies of High-Power Optical Parametric Oscillators Pumped by a Pulsed Fiber Laser
## 2 Experimental Setup  
The schematic diagram of OPO was almost similar to the setups described previously [17; 18; 19; 20]. The pump beam was a linear-polarized YDFL delivering a maximum power of 207 \(\pm\) 10 W. To enhance optical-optical conversion efficiency, the pump polarization was rotated to match the \(e\to e+e\) interaction in the nonlinear crystal. According to the OPO cavity configuration, the theoretical waist radius of signal mode at the center of the crystal was estimated by the nonlinear optics software SNLO (Version 79) [22] (AS-Photonics, LLC., Albuquerque, NM, USA). As shown in Figure 1, the pump beam, passing through the fiber collimator, was focused by a plano-convex lens into the MgO:PPLN crystal to match the mode of the signal beam. The MgO:PPLN crystal, whose two end faces were optically polished and coated with an anti-reflection-coat (T \(>\) 99%) at 1.064 \(\upmu\)m, 1.4 to 1.6 \(\upmu\)m, and 3.4 to 4.2 \(\upmu\)m, had dimensions of 2 \(\times\) 1 mm\({}^{2}\) with different lengths. A single domain period of 29.45 \(\upmu\)m was designed to match the phase of the pump, signal, and idler beam at 1.064 \(\upmu\)m, 1.47 \(\upmu\)m and 3.8 \(\upmu\)m, respectively. Theoretically, the crystal temperature for phase-matching is \(\sim\)75 \({}^{\circ}\)C. Two sapphire flakes were used to sandwich the crystal for even heating and were housed in an oven with a thermal stability of \(\pm\)0.1 \({}^{\circ}\)C. The signal-resonant linear cavity, whose length was constant at 42 mm comprised one plate mirror, M1, and one concave mirror, M2. The two mirrors, M1 and 2, were highly transmitting (T \(>\) 99.5%) for the idler beam (3.0 to 4.2 \(\upmu\)m) and pump beam (1.064 \(\upmu\)m). In addition, the mirror, M1, was highly reflecting (R \(>\) 99.8%) for the signal beam (1.3 to 2.1 \(\upmu\)m), while M2, as the output coupler for the idler beam, was reflecting (\(\sim\)80%) for the range of 1.3 to 2.1 \(\upmu\)m. M3 was highly reflecting at 1.064 \(\upmu\)m and 1.3 to 2.1 \(\upmu\)m for 45\({}^{\circ}\) angle of incidence and was used to separate the output idler beam from the residual pump beam and leaking signal beam.
## 3 Numerical Results
### Secrecy Outage Probability  
To simulate the secrecy outage probability (SOP), the SINR corrected by the LIS was generated, and an additional eavesdropper channel with a Nakagami distribution with LoS access to the LIS signal was considered. This additional channel has a line of sight. The summation was truncated at index 100 to calculate the SOP, and the approximation  
Figure 6: Error probability for \(m=1\) (Rayleigh), 16-QAM.  
Figure 7: Error probability for \(m=2\), 4-QAM.  
closely matched the simulated SOP values via Monte Carlo. It was assumed that the Von Mises concentration parameter was \(\kappa=2\), and the direct link had unity variance.  
It can be observed in Figure 8, where an eavesdropper channel with \(m=2\) and \(\Omega=1\) is considered, that the higher the number of reflectors, the lower the SOP will be. This demonstrates that the LIS contributes to the confidentiality of information.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Environments Considered  
The three environments considered in [3] are the city center, city peripheral area, and open area (park), but for comparison with the TOA and TDOA localisation schemes, the city center and the park are chosen to represent a dense multipath environment (NLOS), and a LOS scenario, respectively. These are shown in Figure 6 below. Ray tracing is run against each of these areas to generate both the training data, and the data used for performance evaluation.
# Adaptive Model-Free Coupling Controller Design for Multi-Axis Motion Systems
## 5 Conclusions  
We have introduced an adaptive model-free coupling controller while using RFNN for overcoming the affection of system uncertainties, force ripple, and frictional force in a three-axis CNC machine tool feed drive system, and this method did not have restrictions on the trajectories. By the concept of cross-coupling control (CCC), the considered system is transferred into a nonlinear time-varying system due to the time-dependent coordinate transformation; tangential, normal, and bi-normal components of desired contour. Herein, we propose a model-free adaptive coupling controller design approach for multi-axis linear motor system with uncertainty and nonlinear phenomena. The adaptive model-free controller design was established based on an error system by time-varying coordination transformation in order to minimize the contour errors. Specifically, an adaptive controller was developed to control for highly nonlinear system. Through the simulation results, we know that the proposed controller is effective in reducing contour error. The stability of closed-loop system is guaranteed by the Lyapunov method and the adaptation of RFNN is also obtained. Simulation results are introduced to illustrate the effectiveness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FNN** & **RFNN** \\ \hline Max contour error (mm) & 16.1006 & 15.3143 \\ \hline Total contour error (mm) & 113410.19 & 61780.23 \\ \hline RMSE (mm) & 9.4631 & 4.6759 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison performance of FNN and RFNN.  
Figure 8: Comparison results of adaptive controller using RFNN and fuzzy neural network (FNN), (a) system trajectory in X-Y-Z plane, (b) performance of contour error; (c) trajectories of each axis, (d) Control effort.  
**Author Contributions:** [PERSON] and [PERSON] initiated and developed the ideas related to this research work. Both of them developed the presented novel methods, derived relevant formulations, and carried out the performance analyses of simulation and experimental results. [PERSON] wrote theC.[PERSON]'s guidance and Professor [PERSON] finalized the paper. All authors have read and agreed to t paper draft under he published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank the Editor and reviewers for their constructive comments and suggestions. This work was supported in part by the Ministry of Science and Technology, Taiwan, under contracts MOST-108-2634-F-005-001, 10\({}^{\prime}\)-2634-F-005-001, 106-2218-E-005-003, and 105-2221-E-005-049-MY3.  
**Conflicts of Interest:** The authors declare no conflict of interests.
###### Abstract  
Fiber Bragg gratings are sensitive elements in fiber optic sensor networks, and this paper discusses the practicalities of using neural network algorithms to determine their central wavelengths. The problem is to determine the central wavelength of a single sensor, the parameters of which are obtained using a low-resolution spectrum analyzer. The configuration of the neural network and the algorithm for producing the training and control datasets are specified. The training results for the selected neural network configuration demonstrated that the proposed method could determine the position of the central wavelength with a resolution two and a half orders of magnitude higher than the resolution of the input data sampling. The obtained results demonstrate that the approach makes it possible to determine the FBG central wavelength shift with an error not exceeding -0.5 pm at a spectrum analyzer resolution of 167 pm.  
spectrum shift determination; neural network; stochastic optimization; Adam optimization method; artificial intelligence; fiber optics sensor; fiber Bragg gratings; central wavelength determination methods
## 4 Diversity Performance
### Mean Effective Gain (MEG)  
The mean effective gain of the antenna measures the antenna performance of the multi-path environment. The ratio of the received power to the total incident power is the measure of the mean effective gain. The mean effective gain ratio is less than 3 for the effective MIMO antenna performance which shows compliance with this figure of merit. The mean effective gain is calculated from the following formula:  
\[\text{MEG}=\frac{1-\frac{(|S_{\bar{w}}|^{2}-|S_{\bar{w}}|^{2}|)}{2}}{1-\frac{(| S_{\bar{w}}|^{2}-|S_{\bar{w}}|^{2}|)}{2}} \tag{7}\]  
Figure 11 shows the MEG of the MIMO antenna. The figure shows that the MEG for the MIMO antenna is less than 3 for the entire band.  
Figure 11: Total active reflection coefficient (TARC) and mean effective gain (MEG) of the proposed MIMO system.  
Figure 10: Envelope correlation coefficient (ECC) and diversity gain of antenna vs. frequency.
## 3 Event-Triggered Extended-State Observer
Using a transformation (\(E_{i}(t)=TE_{i}(t)\), where \(T=\text{diag}\{R_{i}^{T}(\psi_{i}(t)),I_{3},I_{3}\}\)), it follows that  
\[\dot{E}_{t}(t)=A_{0}E_{t}(t)+r_{i}S_{T}E_{t}(t)-B_{i}e_{i}\dot{\
u}_{i}(t)-\dot {R}_{i}D_{i}q(t), \tag{41}\]where \(\hat{H}_{i}=\text{diag}\{R^{T}(\psi_{i}(t)),R^{T}(\psi_{i}(t)),R^{T}(\psi_{i}(t))\}\), \(S_{T}=\text{diag}\{S^{T},0_{3},0_{3}\}\), and  
\[A_{0}=\left[ \begin{array}{ccc}-3I_{3}&I_{3}&0_{3}\\ -3I_{3}&0_{3}&I_{3}\\ -I_{3}&0_{3}&0_{3}\end{array} \right], \tag{42}\]  
\[S=\left[ \begin{array}{ccc}0&-1&0\\ 1&0&0\\ 0&0&0\end{array} \right]. \tag{43}\]  
\(\square\)  
**Theorem 2**. : _The observer error dynamics are bounded if there exists a positive definite matrix (\(P\)) satisfying the inequalities used in stability analysis, and the inequalities are defined as_  
\[A_{0}^{T}P+PA_{0}+\varrho I_{9}-\bar{r}_{i}(S_{T}^{T}P+PS_{T}) \leq 0, \tag{44}\] \[A_{0}^{T}P+PA_{0}+\varrho I_{9}+\bar{r}_{i}(S_{T}^{T}P+PS_{T}) \leq 0, \tag{45}\]  
_where \(\varrho\) is a positive constant, and \(\bar{r}_{i}\) is the upper bound of \(r_{i}\)._  
Proof. : Consider the Lyapunov function:  
\[V_{o}=\frac{1}{2}E_{t}(t)^{T}PE_{t}(t). \tag{46}\]  
Differentiating \(V_{o}\) with respect to time  
\[\dot{V}_{o} =\frac{1}{2}E_{t}(t)^{T}(A_{0}^{T}P+r_{i}S_{T}^{T}P+PA_{0}+r_{i}PS_ {T})E_{t}(t)\] \[\quad-E_{t}(t)^{T}PB_{i}\varepsilon_{i}\dot{\sigma}_{i}(t)-E_{t}( t)^{T}P\dot{H}_{i}D_{i}q(t)\] \[\leq -\frac{\varrho}{2}\|E_{t}(t)\|^{2}+\epsilon_{i}\|E_{t}(t)\|\|PB_ {i}\|\|\dot{\sigma}_{i}(t)\|\] \[\quad+\|E_{t}(t)\|\|PD_{i}\|\|q(t)\|. \tag{47}\]  
It is worth noting that \(2\epsilon_{i}\|PB_{i}\|\|\dot{\sigma}_{i}(t)\|+2\|PD_{i}\|\|q(t)\|\leq\varrho c _{o_{1}}\|E_{t}(t)\|\), \(0<c_{o_{1}}<1\), leading to  
\[\dot{V}_{o}\leq -\frac{\varrho}{2}(1-c_{o_{1}})\|E_{t}(t)\|^{2}\leq-c_{o_{2}}V_{o}, \tag{48}\]  
where \(c_{o_{2}}=\varrho(1-c_{o_{1}})/\lambda_{\max}(P)\). Then, the state \(E_{t}(t)\) is bounded. Given that \(\|T^{-1}\|=1\) and \(E_{i}(t)=T^{-1}E_{t}(t)\), the estimation error (\(E_{i}(t)\)) is bounded. \(\square\)
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 3 Switchable Receiver Front-End Architecture: HARRF LNA and SPDT Integration  
The design of the switchable wideband receiver frontend, incorporating the Low Noise Amplifier (LNA) and Single Pole Double Throw (SPDT) Switch, involves generating multiple design scenarios to optimise the performance of the communication infrastructure for both 5G and satellite applications. The cascade topology is selected for the LNA due to its superior gain performance and impedance matching compared to common-source and common-gate configurations. This ensures compliance with low-noise performance requirements across multiple wireless standards spanning a wide frequency spectrum.  
Figure 2 shows the suggested receiver frontend module design for HARRF 5G and Satellite receiver systems. The system uses two LNAs to collect X-band and K/Ka-band  
Figure 1: The HARRF design flow.  
signals. Selecting the frequency band uses an SPDT switch. Transmission lines connect the SPDT switch to the LNAs, decreasing chip footprint. This receiver frontend design switches between X-band and K/Ka-band modes using a voltage-driven SPDT switch. The SPDT switch receives and routes the incoming RF signal as the primary driving stage.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 5 The Experimental Results and Evaluation Metrics
### Analysis of the Results
It can be inferred that the Rayleigh fading channel exhibits strong sensitivity to wave height. Moreover, when the region is affected by larger ocean waves, that is, greater wave heights result in more signal fluctuation and instability due to the harsher transmission conditions, thereby increasing the MSE. On the other hand, lower wave heights create a more stable transmission environment, enhancing the signal quality and reducing the MSE. When compared to the influence of the \(\lambda\) parameters on the MSE, wave height mainly impacts the physical stability of the signals. In contrast, Rayleigh fading brings about statistical variations in the channel conditions. Evidently, both factors exert a notable influence on the system performance, underscoring the importance of accounting for environmental variability in designing robust maritime communication systems. The experimental results demonstrate that the model proposed in this paper can automatically capture and analyze the potential impact of environmental factors, such as ocean waves, on the characteristics of wireless communication channels. Moreover, it makes corresponding adjustments during the actual prediction process, enhancing the accuracy of the predictions and the robustness of the system. This method not only strengthens the system's ability to cope with complex environmental changes but also highlights its application potential in dynamic maritime communication scenarios.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 4 Results
### Can Vegetation Water Status Maps be Produced using VOD?  
The method developed in this study for VOD mapping at plot scale has the potential to become operational since it only uses free and open access S2 and S1 data. VOD maps at plot scale were computed for barley, fallow, oat, and wheat plots located in our study zone, but rapeseed plots were excluded in this mapping process. Each VOD map was obtained using four SAR images (18 days), and the attributed date to each VOD map was that of the last used SAR image, as explained in the method section.  
Figure 7 shows four VOD-VV maps with assigned dates 12 February 2018, 2 March 2018, 7 April 2018, 25 April 2018, and 13 May 2018 (Figure 6(a)-e). Figure 7 also shows NDVI maps for dates close to those assigned to VOD maps (Figure 6(f)-j). For most plots, VOD values are lower than 0.3 on 12 February 2018 and 2 March 2018 (Figure 6(a),b). On 7 April 2018 (Figure 6(c)), we can observe a spatial heterogeneity in VOD values. The study area appears to be divided into two zones of VOD values. The first zone at the East (delineated by a black polygon) is dominated by VOD values mostly lower than 0.3, while the other zone at the West (delineated by a red polygon) is dominated by higher VOD values (mostly between 0.3 and 0.5). This spatial heterogeneity is more pronounced on 25 April 2018 (Figure 6(d)) where many plots have high VOD values between 0.5 and 0.8 (mainly plots displayed in orange and red color codes), while small areas in the eastern part of the study area present low to medium VOD values (mainly plots situated in the black dashed ellipsoid). In the VOD map on 13 May 2018, most of the plots with high VOD values on 25 April 2018 present a decrease in VOD to attain low to medium VOD values whereas the majority of the plots with low to medium VOD values on 25 April 2018 present larger VOD values on 13 May 2018 (Figure 6(d),e). One possible explanation is that crops with high VOD values on 25 April 2018 were soon before those with low VOD values and have reached a more advanced stage in the growth cycle.  
Figure 6: VOD values computed from VH polarization (VOD-VH\({}_{\text{A},\text{av}}\) and VOD-VH\({}_{\text{D},\text{av}}\)) as a function of NDVI\({}_{\text{av}}\) values. (**a**) Barley, (**b**) Fallow, (**c**) Oat, (**d**) Wheat, (**e**) Rapeseed.  
Figure 7: _Cont_.  
Finally, results show that between 12 February 2018 and 25 April 2018 VOD-VV values tend to increase together with NDVI. However, VOD-VV (Ascending, 18h00) starts to decrease on 13 May 2018 while NDVI values remain high (Figure 7e.j). This was expected since VOD peaked earlier than NDVI (Figure 3).
## 5 Conclusions  
This study evaluates landslide susceptibility based on EAP for different return periods by incorporating 14 factors related to geology, topography, and hydrology. Six machine learning models were trained to assess landslide risk. The following conclusions are drawn.  
The best-fit distribution models for most stations in the study area are the generalized extreme value and lognormal distributions. The spatial distribution maps of EAP for the different return periods show higher values in coastal and western Zhejiang mountainous regions and lower values in the central and northern Zhejiang areas. As the return period increases, EAP generally shows an upward trend.  
Among the six machine learning models used here, XGB outperformed the others with an AUC of 0.96 and an accuracy of 89%. Feature importance assessments using methods such as SHAP values and the mean decrease in Gini indicate that the most significant contributing factor to the model is EAP, followed by the TRI, the slope, the distance to roads, and the SPI.  
From the landslide susceptibility assessments under varying return periods (5, 10, 20, and 50 years), we identified three high-susceptibility zones: the southern coastal areas, the western mountainous region, and the hilly Shaoxing-Tiazhou area in central Zhejiang. In contrast, the central and northeastern plains predominantly fall within lower-risk zones.  
Overall, this study combined the recurrence period of EAP with landslide susceptibility maps, offering a fresh perspective that contributes to a more comprehensive assessment of landslide disaster risk. Future work could explore more advanced, interpretable, and integrated machine learning models to advance the field further.  
Conceptualization, C.C. and H.G.; methodology, C.C. and H.G.; validation, C.C., Y.L. ([PERSON]) and D.Z.; formal analysis, D.Z. and D.L.; investigation, D.Z.; data curation, D.Z., Y.L. ([PERSON]) and D.L.; writing--original draft preparation, C.C. and H.G.; writing--review and editing, C.C., H.G., Y.W., Y.L. ([PERSON]) and Y.L. ([PERSON]); visualization, C.C. and Y.L. ([PERSON]); project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key R&D Program of China (Grant No. 2023 YFE0103500) and the Public Welfare Technology Application Research Program of Jinhua (Grant No. 2023-4-186).  
Not applicable.  
Data Availability Statement: Data associated with this research are available and can be obtained by contacting the corresponding author.  
We would like to thank the editor and the anonymous reviewers for their helpful work on improving this paper.  
The authors declare no conflicts of interest.
## 3 Results
### CART Model  
The CART analysis showed that PGFFI, AGRFI, AGRFP, and VSIL were associated with runners with ITBS. The first variable selected was PGFFI, with a cut-off value of 0.019 BW*s. Those with PGFFI greater than 0.019 BW*s had a lower chance of being in the ITBS group (node 2: protective profile--proportion of participants with ITBS = 16.7%; proportion of participants without ITBS = 83.3%). In runners with PGFFI less than or equal to 0.019 BW*s, the AGRFI entered the model with 0.012 BW*s as the cut-off. Those with PGFFI less than or equal to 0.019 BW*s and AGRFI less than or equal to 0.012 BW*s \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Total** & **ITBS+** & **ITBS\(-\)** & **Mean Difference** & & \\
**(N = 60)** & **(N = 30)** & **(N = 30)** & **(95\% CI)** & & \\ \hline Ground Reaction Force & & & & & & \\ Impulses, & & & & & & \\ BW*s & & & & & & \\ Vertical & 0.36 (0.02) & 0.36 (0.01) & 0.36 (0.02) & 0 (\(-\)0.009 to 0.013) & 0.836 & - \\ Anterior & 0.015 (0.002) & 0.014 (0.002) & 0.015 (0.001) & \(-\)0.001 (\(-\)0.001 to 0.001) & 0.303 & 0.50 \\ Posterior & 0.017 (0.002) & 0.016 (0.002) & 0.017 (0.002) & \(-\)0.001 (\(-\)0.002 to \(-\)0.001) & 0.049 * & 0.50 \\ Medial & 0.009 (0.004) & 0.009 (0.004) & 0.009 (0.003) & 0 (\(-\)0.002 to 0.002) & 0.858 & - \\ Lateral & 0.002 (0.001) & 0.002 (0.002) & 0.001 (0.001) & 0.001 (\(-\)0.001 to 0.001) & 0.086 & 0.63 \\ Instantaneous Loading Rates & & & & & & \\ Peaks, BW/s & & & & & & \\ Vertical & 67.78 (21.72) & 70.35 (19.35) & 65.20 (23.91) & 5.15 (\(-\)6.08 to 16.39) & 0.363 & 0.20 \\ Posterior & 9.81 (3.27) & 9.51 (2.95) & 10.11 (3.59) & \(-\)0.60 (\(-\)2.29 to 1.10) & 0.359 & 0.18 \\ Medial & 7.86 (3.64) & 7.59 (3.37) & 8.12 (3.93) & \(-\)0.53 (\(-\)2.42 to 1.36) & 0.756 & 0.14 \\ Lateral & 8.24 (4.20) & 8.34 (3.76) & 8.14 (4.66) & 0.20 (\(-\)1.98 to 2.39) & 0.564 & 0.05 \\ Ground Reaction Force Peaks, & & & & & & \\ BW & & & & & & \\ Vertical & 2.26 (0.20) & 2.25 (0.21) & 2.27 (0.20) & \(-\)0.02 (\(-\)0.13 to 0.08) & 0.658 & 0.10 \\ Anterior & 0.19 (0.03) & 0.19 (0.04) & 0.20 (0.03) & \(-\)0.01 (\(-\)0.02 to 0.01) & 0.627 & 0.28 \\ Posterior & 0.25 (0.05) & 0.24 (0.05) & 0.26 (0.05) & \(-\)0.02 (\(-\)0.04 to 0.01) & 0.168 & 0.40 \\ Medial & 0.09 (0.03) & 0.09 (0.02) & 0.10 (0.03) & \(-\)0.01 (\(-\)0.02 to 0.01) & 0.779 & 0.39 \\ Lateral & 0.05 (0.03) & 0.06 (0.03) & 0.05 (0.04) & 0.01 (\(-\)0.01 to 0.02) & 0.352 & 0.28 \\ Vertical Stiffness at Initial & & & & & & \\ Loading, kN/m & 69.49 (22.13) & 71.95 (21.86) & 67.02 (22.49) & 4.93 (\(-\)6.53 to 16.39) & 0.237 & 0.22 \\ \hline \hline \end{tabular} Values are expressed as mean (SD). ITBS, liitobial band syndrome, CI, confidence interval. * Statistically significant (\(p<0.05\)). Effect size of \(d=0.2\) was defined as small; \(d=0.5\) as medium; and d = 0.8 as large.  
\end{table} 
Table 1: General data in running cohort with and without ITBS (n = 60).
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 4 System Model  
The scenario taken into consideration has the scope to recreate a realistic deployment of IoT nodes, denser in service areas and absent in other locations. For example, IoT devices can be positioned at smart traffic junctions, in city parks, at waste collection points, in the parking lots, or into buildings, to name just a few. Thus, practically, we can state we have clusters of IoT nodes, characterized by close vicinity when they implement the same application requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline RUs Number & 2 & 4 & 5 & 6 & 8 & 10 \\ \hline Max. Packet Size for \(I_{MCS}=6\) [bits] & 176 & 392 & 504 & 600 & 808 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NB-IoT UL TBS in bits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CE0** & **CE1** & **CE2** \\ \hline \(P_{cc,\text{min}}\) [dBm] & \(-101\) & \(-111\) & \(-121\) \\ NPRACH Periodicity [ms] & 320 & 320 & 320 \\ NPRACH Subcarriers & 24 & 12 & 12 \\ NPRACH Format & 0 & 0 & 1 \\ NPRACH Repetitions & 2 & 8 & 32 \\ NPUSCH Repetitions & 2 & 8 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coverage Parameters.  
The spots scattered with NB-IoT nodes are considered not to be served adequately by the terrestrial infrastructure, and, for this reason, an unmanned aerial BS (UAB) equipped with NB-IoT radio access is sent to supply the service instead.