esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Quantitative Analysis of Fault Tree
#### 3.4.1 Obtaining the Triangular Fuzzy Number of the Basic Events of the Fault Tree  
(1) Obtain the triangular fuzzy number of the basic event of the fault tree  
In order to obtain the probability of occurrence of 19 bottom events such as the coal mine gas monitoring system X1-X19, 10 on-site monitoring experts were selected to fill in the expert questionnaire. An expert questionnaire is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Basic** & **Event Name** & **Suggested** & **Order of** & **Suggested** & **Linguistic** & **Linguistic** & **Linguistic** \\
**Event** & **Event Name** & **Value** & **Magnitude** & **Magnitude** & **Variable Value** & **Variable Value** & **Remark** \\ \hline X1 & Industrial computer host failure & \(10^{-5}\) & & & & & \\ X2 & Display failure & \(10^{-5}\) & & & & & \\ X3 & Monitor substation failure & \(10^{-5}\) & & & & & \\ X4 & Gas sensor failure & \(10^{-3}\) & & & & & \\ X5 & Temperature sensor fault & \(10^{-5}\) & & & & & \\ X6 & Wind speed sensor fault & \(10^{-5}\) & & & & & \\ X7 & Power sensor failure & \(10^{-5}\) & & & & \\ X8 & Switch sensor failure & \(10^{-5}\) & & & & \\ X9 & Breaker failure & \(10^{-5}\) & & & & \\ X10 & PLC controller failure & \(10^{-6}\) & & & & \\ X11 & Mechanically break the line & \(10^{-5}\) & & & & \\ X12 & Self-aging short circuit & \(10^{-5}\) & & & & \\ X13 & Poor line contact & \(10^{-4}\) & & & & \\ X14 & Abnormal power failure & \(10^{-3}\) & & & & \\ X15 & High communication error rate & \(10^{-8}\) & & & & \\ X16 & Short circuit to ground & \(10^{-4}\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expert questionnaire on the probability of basic events.  
Through the feedback of the 10 expert questionnaires from the field, the MATLAB software was used to program the arithmetic mean of the X1-X19 probability interval. In addition, the average value of the probability interval and the left and right distribution of the occurrence of each bottom event were calculated, as shown in Table 3.
# Parallel Complex Quadrature Spatial Modulation
## 4 Performance Analysis  
Building upon the work in [26], let  
\[\mathbf{x} =\frac{1}{\sqrt{P}}\sum_{i=1}^{P}\big{(}\mathbf{h}_{i_{l}}s_{a}+ \mathbf{h}_{k_{i}}s_{b}\big{)}, \tag{10}\] \[\hat{\mathbf{x}} =\frac{1}{\sqrt{P}}\sum_{i=1}^{P}\big{(}\mathbf{h}_{i_{l}}\hat{s} _{a}+\mathbf{h}_{k_{i}}\hat{s}_{b}\big{)}\]  
Figure 2: Optimized rotation angle for PCQSM with \(N_{t}=8\), \(N_{r}=4\).  
be two noiseless received codewords. The pairwise error probability (PEP) is accordingly given by  
\[Pr[\mathbf{x}-\hat{\mathbf{x}}|\mathbf{H}]=Q\left(\sqrt{\frac{\|\mathbf{x}-\hat{ \mathbf{x}}\|^{2}}{2\sigma_{n}^{2}}}\right)=Q\Big{(}\sqrt{\xi}\Big{)}, \tag{11}\]  
where  
\[\zeta=\frac{1}{2\sigma_{n}^{2}}(\mathbf{x}-\hat{\mathbf{x}})^{H}(\mathbf{x}- \hat{\mathbf{x}}), \tag{12}\]  
and \(\zeta\) is determined in the the same way as in [22], assuming \(P\) antenna subgroups. The average PEP assuming \(N_{r}\) receive antennas is thereby determined as follows,  
\[\bar{P}_{e}(\mathbf{x}-\hat{\mathbf{x}})=\gamma^{N_{r}}\sum_{u=1}^{N_{r}-1} \binom{N_{r}-1+u}{u}[1-\gamma]^{u}, \tag{13}\]  
where \(\gamma=\frac{1}{2}\left(1-\sqrt{\frac{\zeta/2}{1+\zeta/2}}\right)\). Averaging (13), the BER of the proposed scheme is upper-bounded by the average bit-error probability (ABEP), and this is given by  
\[P_{b}=\frac{1}{R^{2R}}\sum_{i=1}^{2R}\sum_{u=1}^{2R}\bar{P}_{e}(\mathbf{x}- \hat{\mathbf{x}})e_{i,u}, \tag{14}\]  
where \(R\) denotes the total spectral efficiency of the system and \(e_{i,u}\) is the number of bits that are erroneously detected in the error event \(\bar{P}_{e}(\mathbf{x}-\hat{\mathbf{x}})\).
# Wide Scanning Angle Millimetre Wave 1 \(\times\) 4 Planar Antenna Array on InP at 300 GHz
## 2 Design of the Unit-Cell  
Based on the capacitive feed coupling technique presented in [19], a unit radiating element (also referred as unit-cell) is designed such that the input impedance is a conjugate match of the output impedance of a UTC-PD. The developed UTC-PD presents a complex output impedance of 15 + j8 \(\Omega\). In order to provide a conjugate matching, the input port impedance of the antenna structure is defined as 15 - j8 \(\Omega\).  
The UTC-PD has a ground-signal-ground (GSG) output. Therefore, the signal pad is connected to the top feed patch employing a via etched in the epitaxially grown BCB Layer 1 (see Figure 1a) on top of a large gold ground plane deposited using Gold Layer 1. The BCB is a low-k dielectric compatible with InP processes which has a relative dielectric constant (\(\varepsilon\)) of 2.3 @ 300 GHz with a loss tangent (8) of 0.01. It provides the ability to realise antenna elements over an InP substrate. The ground plane shields the mm-wave signal from back-radiating through a high dielectric constant InP substrate underneath. The epitaxial layer stack-up is shown in Figure 1a. A single 4 \(\upmu\)m thick via is etched through the BCB Layer 1 with the same dimensions as the signal pad (Figure 1b).  
On top of BCB Layer 1, a gold feed patch is deposited. A thin layer of silicon oxyntiride (SiOxN) is also deposited between the Gold Layer 1 and Gold Layer 2 to implement the biasing circuit. The SiOxN layer increases the adhesion between Gold and BCB and provides a substrate layer between the biasing structure and the ground. The biasing circuit is implemented based on a microstrip line with two radial stubs acting as a shorting capacitor for the mm-wave signal. The microstrip connects to a bonding pad of 60 \(\upmu\)m \(\times\) 60 \(\upmu\)m. The square radiation patch is deposited on top of a 10 \(\upmu\)m thick BCB Layer 2 with dimensions of 306.4 \(\upmu\)m \(\times\) 306.4 \(\upmu\)m. The planar dimensions of BCB layers are 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m; the size of the unit cell is increased in the x-direction by 100 \(\upmu\)m to provide bonding pads for DC biasing and ground. The structure of the unit cell is presented in Figure 2.  
The unit cell was simulated using Ansys HFSS v17. The simulated gain along \(\theta\) (angle relative to the z axis) and return loss assuming a port impedance of \(15-\beta\)\(\Omega\) are presented in Figure 3. The unit cell can provide a gain of 2.5 dBi with a matched impedance bandwidth of 10 GHz.  
Figure 1: (**a**) Proposed epitaxial layer stack-up of InP, (**b**) pad dimensions of UTC-PD.  
Figure 2: 3D structure of the unit cell.
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 2 Materials and Methods
### Characterization of Tool Electrodes and Silicon Workpieces  
An _Olympus SZX-12_ optical microscope, [PERSON], Tokyo, Japan, with _Image Pro Plus 7_, (version 7), Media Cybernetics, Stoneham, MA, USA, has been used for characterization. To characterize the tool electrodes, exposure was set to \(-2/3\), light intensity set to 8-10, and magnification set to 3.5\(\times\), 6.25\(\times\), 10\(\times\), 12.5\(\times\), or 16\(\times\) depending on the size and shape of the electrode under the optical microscope. For each magnification, the image of the ruler with the marking was taken using the same magnification, which was used as a measurement base for _Image Pro Plus 7_, Media Cybernetics, Stoneham, MA, USA, to determine the dimensions of the legs and other areas of tool electrodes.  
The material removal rate (MRR) is the amount of material removed from the workpiece for a unit period. There are two ways to calculate the material removal rate. For the 1 st case, the dimensions of the machined geometry can be measured through the microscope  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Optimization** & **Peak Current** & **Pulse on** & **Pulse off** & **Discharge** & **Machining** & **Depth of** \\
**Level** & **(A)** & **Time (μs)** & **Time (μs)** & **Time (s)** & **Time (min)** & **Machining (in)** \\ \hline Level 1 & 4 & 100 & 50 & 0.8 & 49.5 & 0.128 \\ Level 2 & 6 & 150 & 75 & 0.8 & 49.5 & 0.0672 \\ Level 3 & 8 & 200 & 100 & 0.8 & 49.5 & 0.0227 \\ \hline \end{tabular}
\end{table}
Table 4: Various process parameters for die-sinking EDM for silicon workpieces and graphite electrodes.  
\begin{table}
\begin{tabular}{c c} \hline
**Criteria** & **Description** \\ \hline C code & 408 \\ Peak current & 4 A \\ Pulse on time & 100 μs \\ Pulse off time & 50 μs \\ Discharge voltage & 235 V (DC) \\ Discharge time & 0.8 s \\ Discharge height & 0.039\({}^{\prime\prime}\) \\ Slag removing speed & 60\% \\ Discharge server speed & 70\% \\ Gap voltage & 50 \\ Swing radius & 0 \\ Electrode polarity & Positive \\ \hline \end{tabular}
\end{table}
Table 5: Optimized process parameters for die-sinking EDM for silicon workpieces and graphite electrodes.  
Figure 2: Experimental setup in die-sinking EDM (**a**) before machining and (**b**) during machining.  
and image processing software. After that, the volume is calculated by multiplying the length, breadth, and depth of the hole. The machining time is recorded in the EDM machine and the MRR can be determined. The second way of estimating the MRR is to calculate the weight difference before and after machining. Using the density of the workpiece material and machining time, the material removal rate can be calculated. The commonly used unit for the MRR is mm\({}^{3}\)/min. In this work, the MRR was computed both ways and then cross-checked for accuracy.
## 2 Problem Description
### Cooperation Scheme of Wind Power and DR Resource  
Leveraging the complementary characteristics of wind and DR resources, a coordinated trading strategy for WPPs and DR aggregators providing energy and frequency regulation is proposed and the scheme is presented in Figure 1. To achieve higher revenue, WPPs and DR aggregators are formed as a coalition, to participate in the energy and regulation markets. For the energy part, the coalition submits bids to the day-ahead energy market and clear imbalance in the real-time market. For the regulation part, the coalition can strategically respond by using wind energy or DR resources when the regulation signalsare updated to maximize total expected profits. Specifically, in the cooperation scheme, the up-regulation is provided by DR resources through measures like reducing electricity consumption among consumers under its management, while the down-regulation is offered from wind power by adjusting the power output of wind turbines within the allowed range.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 3 Influence of Retail Store Products and Propagation Phenomena on a Tag Sensitivity
### Retail Store Results  
#### 3.3.1 Simulation Model of a Measurement Scenario  
As the retail store model with different receiver setups was analyzed in previous sections, first the receiver trajectory scenario was simulated as performed in measurements for comparison and easier visualization of the measurement results. Figure 12 depicts the trajectory of a Higgs 4 tag (attached to cardboard and water product as in measurements) around the metallic shelves with products of mixed properties and arrangement (as in the real store during the measurements). Besides the power distribution along the trajectory, the results are also given in a form of 2D plot of \(P_{tag}\) vs receiver number, Figure 13, to highlight the variation of a signal level along the trajectory which covers LOS and non-LOS area (behind the shelves). Again, the receiver was an omnidirectional antenna in free space with gain \(G_{tag}=2\) dBi (representing tag antenna on a cardboard) and omnidirectional antenna of lower gain, \(G_{tag}=-12\) dBi (representing tag antenna on a water product). From these results, we can see that due to multipath fading, \(P_{tag}\) drops significantly in a non-LOS area and even in LOS at certain receiver positions (receiver positions 3, 6 and 35, see Figures 12 and 13) when the tag is attached to a water product. The variation of \(P_{tag}\) along the trajectory can even reach 20 dB from one receiver position to the next. On the contrary, when the tag is in free space, this variation is very small. The comparison of \(P_{tag}\) along the trajectory in free
## 4 Case Study
### Construction of the Bow-Tie Model for Describing the Failure of Storage Tank
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Work Experience (Year)** & **Education Level** & **Professional Relevance** & **Professional Title** & **Weight (\(\lambda_{i}\))** \\ \hline
1 & 21 & Master’s & Basically related & Senior & 0.382 \\
2 & 4 & PhD & Basically related & Deputy senior & 0.324 \\
3 & 11 & Master’s & Basically irrelevant & Deputy senior & 0.294 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experts’ information and their weights. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Basic Events** & **Expert No.1** & **Expert No.2** & **Expert No.3** & **Expert No.1** & **Expert No.2** & **Expert No.3** \\ \hline X1 & ([0, 0.2], [0.5, 0.8]) & ([0.1, 0.3], [0.4, 0.7]) & ([0, 0.2], [0.5, 0.8]) & ([0.4651, 0.7661]) \\ X2 & ([0.1, 0.3], [0.4, 0.7]) & ([0.1, 0.3], [0.4, 0.7]) & ([0.1, 0.3], [0.4, 0.7]) & ([0.1, 0.3], [0.4, 0.7]) & ([0.4, 0.7]) \\ X3 & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) \\ X4 & ([0.4, 0.6], [0.2, 0.4]) & ([0.5, 0.7], [0.1, 0.3]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4344, 0.6356], [0.1598, 0.3644]) \\ X5 & ([0.8, 1], [0, 0]) & ([0.8, 1], [0, 0]) & ([0.7, 0.9], [0, 0.1]) & ([0.7477, 1], [0, 0]) \\ X6 & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.3, 0.5], [0.2, 0.5]) & ([0.3400, 0.5409], [0.2, 0.4591]) \\ X7 & ([0.4, 0.6], [0.2, 0.4]) & ([0.3, 0.5], [0.2, 0.5]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.3693, 0.5700], [0.2, 0.4230]) \\ X8 & ([0.2, 0.4], [0.3, 0.6]) & ([0.2, 0.4], [0.3, 0.6]) & ([0.3, 0.5], [0.2, 0.5]) & ([0.2308, 0.4313], [0.2663, 0.5687]) \\ X9 & ([0.5, 0.7], [0.1, 0.3]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.4404, 0.6416], [0.1535, 0.3584]) \\ X10 & ([0.6, 0.8], [0, 0.2]) & ([0.4, 0.6], [0.2, 0.4]) & ([0.7, 0.9], [0, 0.1]) & ([0.5808, 0.7958], [0.2042]) \\ X11 & ([0.6, 0.8], [0, 0.2]) & ([0.7, 0.9], [0, 0.1]) & ([0.6, 0.8], [0, 0.2]) & ([0.6356, 0.8402], [0.1598]) \\ X12 & ([0.7, 0.9], [0, 0.1]) & ([0.8, 1], [0, 0]) & ([0.6, 0.8], [0, 0.2]) & ([0.7137, 1], [0, 0]) \\ X13 & ([0.5, 0.7], [0.1, 0.3]) & ([0.7, 0.9], [0, 0.1]) & ([0.7, 0.9], [0, 0.1]) & ([0.6354, 0.8479], [0.1521]) \\ X14 & ([0.5, 0.7], [0.1, 0.3]) & ([0.6, 0.8], [0, 0.2]) & ([0.5, 0.7], [0.1, 0.3]) & ([0.4695, 0.6707], [0.1, 0.3]) \\ X15 & ([0.6, 0.8], [0, 0.2]) & ([0.5, 0.7], [0.1, 0.3]) & ([0.5, 0.7], [0.1, 0. In order to verify the accuracy of the proposed model in calculating the probability of an event, the probability data obtained by IVIFNs were compared with the data from the literature [52], and the results are shown in Figure 6. It can be seen from the figure that the variability between the two sourcing data is small, which indicates that the proposed method for obtaining the probabilities of the basic events is feasible.
## 3 Experimental Results
### P300  
The descriptive statistics for the mean amplitude and peak latency of the P300 component are presented in Table 5. A 2(fatigued, non-fatigued) \(\times\) 3(simple task interruption, complex task interruption, pause interruption) repeated-measures ANOVA was conducted, and the results are summarized as follows: The main effect of fatigue state on P300 mean amplitude was not significant (frontal: (\(F(1,446)=0.158,p=0.692\)); central: (\(F(2,445)=0.042,p=0.837\)); parietal: (\(F(1,446)=0.927,p=0.336\)). However, a significant interaction between fatigue state and interruption type was observed, particularly in the frontal (\(F(1,446)=6.002,p<0.01,\)\(\eta_{p}^{2}=0.013\)) and central regions (\(F(1,446)=2.373,p=0.038,\)\(\eta_{p}^{2}=0.005\)). Descriptive statistical analysis indicated that under fatigue conditions, the P300 amplitude in the frontal region gradually decreased following task and pause interruptions as the experiment progressed, while the P300 amplitude in the central region increased after task interruptions. For P300 peak latency, the fatigue state significantly affected the frontal region (\(F(1,446)=9.573,p=0.002,\)\(\eta_{p}^{2}=0.021\)) but had no significant effects on the central (\(F(1,446)=1.154,p=0.283\)) or parietal regions (\(F(1,446)=0.038,p=0.854\)). The interruption type had a significant effect on P300 peak latency in the central (\(F(1,446)=4.031,p=0.018,\)\(\eta_{p}^{2}=0.035\)) and parietal regions (\(F(1,446)=3.226,p=0.041,\)\(\eta_{p}^{2}=0.017\)), but not in the frontal region (\(F(1,446)=0.658,p=0.518\)). To further analyze the effects of trial type on P300 mean amplitude and peak latency, a 2(fatigued, non-fatigued) \(\times\) 3(simple task interruption, complex task interruption, pause interruption) \(\times\) 3(pre-interruption trials, interruption trials, recovery trials) three-way repeated-measures ANOVA was conducted for the frontal, central, and parietal regions.
# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
## 4 Results and Analysis
### Practical Implications  
Water management authorities can apply the proposed logistic model and vulnerability assessment to identify areas of concern within the WDN. As illustrated in the previous sections, the model can aid in producing distribution network maps that identify the vulnerability of various WDNs based on multiple static and dynamic factors. Insights from the maps are critical in guiding decision making regarding the essential areas of concern, while helping to prevent risks. The model could support decision making concerning the vulnerable areas to upgrade the water distribution network considering the maximum, minimum, and average drops in the varying factors (flow, pressure, chlorine). The solution should also efficiently support resource allocation and distribution by ensuring preventive maintenance and response efforts focused on critical areas. The results illustrate that water management authorities can use the proposed model for early leak detection, vulnerability assessment, and leak likelihood evaluation. The maps are also critical in establishing the location of water leaks.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 2 Research and Development of Unsealed Borehole Leakage Detection Technology Based on Flow Method
### Selection of the Main Components of the Air Leakage Detection Device
#### 2.2.4 Selection of Flow Gauge  
This device determines the position of air leakage based on changes in the flow of air-gas mixture extracted from the borehole, so the choice of flow gauge is critical. Gas meters, rotors, and other flow gauges are mostly used to test gas flow in a barotropic environment. Considering that the flow gauges used in gas extraction are mostly under negative pressure conditions, after substantive investigations and selections, the wet flow gauge that can be used in the negative pressure environment of underground gas extraction is selected for this device. The specific model of the flow gauge can be chosen according to the actual working conditions of the coal mine.
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 4 Results and Analysis
### Parameter Analysis
#### 4.3.1 Influence of \(k_{h}\) and \(\varphi\)  
Figure 8 demonstrates the trend of seismic bearing capacity \(p\) with the initial seismic acceleration coefficient \(k_{h}\) for different internal friction angle \(\varphi\). Through Figure 8, it can be found that \(p\) is negatively correlated with \(k_{h}\), and \(p\) decreases as \(k_{h}\) increases. For Figure 8d, \(p\) decreases by about 80% when \(k_{h}\) increases from 0 to 0.5 for \(\varphi=25^{\circ}\), while \(p\) decreases by about 87% when \(k_{h}\) increases from 0 to 0.5 for \(\varphi=35^{\circ}\).  
Figure 8: _Cont._  
Figure 10 illustrates the variation in bearing capacity \(p\) with \(c/\gamma B_{0}\) for different \(\varphi\). For Figure 9(a), \(p\) increases by about 1300% when \(c/\gamma B_{0}\) increases from 0 to 2 for \(\varphi=25^{\circ}\), whereas \(p\) increases by about 570% for \(\varphi=35^{\circ}\). For Figure 9(b), \(p\) increases by about 760% as \(c/\gamma B_{0}\) increases from 0 to 2 when \(\varphi=25^{\circ}\), and by about 400% for \(\varphi=35^{\circ}\). The slope of the curve increases significantly with increasing \(\varphi\). In conclusion, both \(q\) and \(c\) have a positive effect on the bearing capacity \(p\).
## 4 Conclusions
On the one hand, satellite remote sensing was characterized by a lower break-even point in hectares, especially considering low-resolution satellites. On the other hand, medium- and high-resolution satellite image prices were affected by a minimum required area for purchase, which made these sources of data less suitable for small fields compared to wide field and regional scale applications. In terms of spatial resolution, satellite images were characterized by a wide range of values, making their application suitable for different variable rate applications. Satellite image quality, assessed as CV of NDVI simulation, showed the consistency of satellite data with the reference sensors (UAV and plane). However, low resolution satellite data were characterized by a coarse spatial resolution, making it challenging to identify specific spatial features and often requiring eliminating border pixels [57]. Conversely, plane and UAV images allowed the identification of specific spatial features, thanks to their spatial resolution and optical quality. The main disadvantages of these latter platforms consist of the price per hectare, making them profitable for variable rate nitrogen application on grains only with a wide surface (plane) or in case of high spatial variability (UAV), increasing the potential benefits provided by variable rate nitrogen application. A parallel economic profitability of variable rate nitrogen application may be performed considering active proximal sensors (e.g., GreenSeeker, Yara N-Sensor, OptRx) as providers of VIs, since these sensors are widely used in some areas (e.g., USA, Australia). In this case the economic analysis should take into account the rates of deterioration of sensors and navigation satellite systems required for their use. Conceptualization, M.S. ; formal analysis, M.S. ; data curation, M.S. and F.M. ; writing, M.S. ; review and editing, A.K., S.G., A.C. and F.M. ; supervision, L.S. and F.M. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the project financed with BIRD 2020 funds, Dept. TESAF, University of Padova--Italy and by the Land Environment Resources and Health (L.E.R.H.) doctoral course. Price and quotation are not available due to privacy policy. The authors wish to thank the satellite, plane, and UAV providers who participated in the survey. The authors declare no conflict of interest.