esa-sceva/satcom-chunk-collection huggingface dataset:
# Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
## 4 Case Study: Identifying Competition Patterns between Taxi and Ride-Hailing Services
### Data Description  
The taxi and ride-hailing service datasets in Xiamen, Fujian Province, China were used in the case study. There were six districts in the study area (Figure 6). To detect bivariate spatial flow patterns, taxi and ride-hailing service datasets during the morning peak period (8:00-10:00) were collected on 4 June 4 2019 (Tuesday). Figure 7 displays the spatial distribution of these two datasets in the whole study area: the taxi service dataset contains 29,222 spatial flows and the ride-hailing service dataset contains 53,135 spatial flows. As one can see, the spatial distribution of both types of flows is roughly the same; both types are mainly located in the Huli, Siming, Jimei, and Haicang districts. Moreover, a significant number of flows due to ride-hailing are located in the Tongan and Xiangan districts, while only a small fraction of taxi flows is located in these two districts. We selected ten network-constrained scales (range 200 m to 2000 m with step size 200 m) in the case study. The number of Monte Carlo simulations was set to 999, and the significance level \(\alpha\) was set to 0.05.  
Figure 6: Study area.
## 3 An Improved Objective Function Based on SVDM
### Selection of Key Parameters in Improved Objective Function
#### 3.3.1 Selection of the Damping Ratio  
The frequency response is used to replace the mode shape and natural frequency terms in the traditional objective function of Equation (1). The frequency response depends on the assumed damping ratio of the modeled structure, so that the selection of the damping ratio has a certain impact on the optimization process. When the damping ratio of the model is zero, the extreme point of \(h_{aq}(\mathbf{\mu},\omega)\) is exactly in the position of \(\overline{\omega}_{i}\). However, an obvious steep peak will occur in the curve of \(h_{aq}(\mathbf{\mu},\omega)\) at the position of \(\overline{\omega}_{i}\), which is not conducive to optimization. On the other hand, when the damping ratio is large, the curve at the extreme point will be relatively gentle, but the peak value may not be in the position of \(\overline{\omega}_{i}\), and it might be affected by other modes. After calculating and comparing the damping ratio of each mode, it has been decided to use the typical value of 0.01.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 3 Key Technologies of IoSB
### Wireless Communication  
Reliable transmission of information is the guarantee of data application, especially for real-time application just like shared bicycle. The quality of communication is affected by communication standard, network topology, base station capacity, etc. The following part will introduce some relevant communication technologies.  
Long Term Evolution (LTE) and 3G are standards for wireless communication on all user mobile phones, and they are also standards for a high speed on data terminals [77]. Taking into account of the development of wireless communication infrastructures all over the world, especially in developing countries, these technologies are available everywhere, suitable for broadband connectivity and long range applications, such as IoSB. However, for now, 3 G/4 G communication modules have higher cost and power consumption, which is overkill for IoSB in terms of transmission performance.  
Low Power Wide Area (LPWA) technology is designed for the transmission distance more than 3 km in the complex urban environments and 15 km in open area with strong penetrability. Compared with 3 G/4 G communication, LPWA is more suitable to be applied in scenarios such as tracking, smart parking, smart agriculture, and shared bicycle. NB-IoT and eMTC are wide-area IoT technologies that can be integrated with existing cellular networks and features of low cost, high reliability, and high security [78]. NB-IoT, which has been used in the latest generation of OFO bicycle, has four advantages: (1) Super coverage; (2) Low power; (3) Low cost; (4) Massive connection [34]. With the maturity of network standards and related hardware, NB-IoT has been used in monitoring of water level [79], smart hospitals [80], car parking system [81], etc.
## 3 Results
### Impacts Results  
The analysis of the impacts variable (Table 6), based on a five-cluster k-means model, shows that U14 basketball players distribute their mechanical load across a range of intensities during match play, an analysis from the 717 quarters of the 24 games. In Zone 1 (<133.45 contacts/min), players recorded 6.69% of total impacts, with a cluster center of 120.05 contacts/min. Zone 2 (133.45-158.75 contacts/min) accounted for 23.01% of impacts, centered at 147.39 contacts/min.  
The highest proportion of impacts occurred in Zone 3 (158.76-181.45 contacts/min), representing 36.54% of the total, with a cluster center of 170.74 contacts/min. Zone 4 (181.46-206.59 contacts/min) comprised 24.69% of impacts, centered at 192.45 contacts/min, while Zone 5 (>206.59 contacts/min) represented 9.07%, with a cluster center of 221.46 contacts/min (Table 6). These results suggest that the greatest number of impact-related actions occur within moderate-to-high-number contact zones, reflecting the physical demands of competitive youth basketball.  
The analysis of impact frequency among U14 male basketball players revealed that the majority of in-game physical contacts occurred within moderate-to-high-intensity zones (Figure 6). Specifically, Zone 3 (158.76-181.45 contacts/min) accounted for the largest proportion of impacts at 36.54%, followed by Zone 4 (181.46-206.59 contacts/min) with 24.69%, and Zone 2 (133.45-158.75 contacts/min) with 23.01%. Combined, these three  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Impacts (cont/min)** & **Zone 1** & **Zone 2** & **Zone 3** & **Zone 4** & **Zone 5** \\ \hline Cluster centers & 120.05 & 147.39 & 170.74 & 192.45 & 221.46 \\ Ranges & \textless{}133.45 & 133.45 to 158.75 & 158.76 to 181.45 & 181.46 to 206.59 & \textgreater{}206.59 \\ n & 48 & 165 & 262 & 177 & 65 \\ \% & 6.69 & 23.01 & 36.54 & 24.69 & 9.07 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the five-k means cluster of the variable impacts.  
Figure 5: Graphical interpretation of results from five-k means cluster of the variable player load.  
zones represented 84.24% of all recorded impacts, indicating that most physical contact during gameplay occurs within a sustained moderate-to-high intensity range.  
Zone 5, corresponding to very high-intensity impacts (>206.59 contacts/min), comprised 9.07% of total impacts, with a cluster center of 221.46 contacts/min. This suggests that U14 players are capable of engaging in frequent and intense physical contact during competition. In contrast, only 6.69% of impacts occurred in Zone 1 (<133.45 contacts/min), reflecting a relatively low occurrence of minimal-contact phases.
## 4 Test Results and Analysis
### Sensibility Analysis of Factors  
#### 4.2.1 Density  
We averaged the severe test of each factor, as shown in Table 5. The range of corresponding influencing factors can be obtained through the difference of the average values of five levels of different factors. As shown in Table 4, the range of Portland cement/gypsum was the largest, followed by filling materials/bonding materials, and the range of mica powder/standard sand and silicone oil content was close. To some extent, it can be explained that the factor of Portland cement/gypsum was sensitive to the parameter of sample density and was the main control factor. On the other hand, the proportion of filling material can be appropriately increased or the proportion of silicone oil can be reduced to increase the density of the sample.
## 2 Materials and Methods
### Infiltration Experiments for Assessing Soil Physical Quality of Agricultural Soil
We employed the BEST averaged parameters to derive several key parameters from the soil water retention curve (Equation (1)): the permanent wilting point soil water content (\(\theta_{PWP}\) m\({}^{3}\) m\({}^{-3}\)) at \(h=-150\) m, the field capacity soil water content (\(\theta_{FC}\), m\({}^{3}\) m\({}^{-3}\)) at \(h=-1\) m, and the saturated volumetric water content of the soil matrix (\(\theta_{m}\), m\({}^{3}\) m\({}^{-3}\)) at \(h=-0.1\) m. These parameters, in conjunction with \(\theta_{s}\), were utilized to estimate the following capacitive SPQ indicators: air capacity (\(AC\), m\({}^{3}\) m\({}^{-3}\)), plant-available water capacity (\(PAWC\), m\({}^{3}\) m\({}^{-3}\)), relative field capacity (\(RFC\), \(-\)), and soil macroporosity (\(p_{MAC}\), m\({}^{3}\) m\({}^{-3}\)) (Table 2). The indicators considered in this study, along with the associated optimal ranges or critical limits, were selected based on the study by [PERSON] et al. [42]. \begin{table}
\begin{tabular}{l l r r} \hline
**Soil Physical Quality Indicator** & **Description** & **Ranges or Critical Limits** & **Evaluation Class** \\ \hline Soil organic carbon content, \(OC\) [g & & \(30\leq OC\leq 50\) & optimal \\ kg\({}^{-1}\)] & Strong indirect effects & & \(30\leq OC\leq 50\) & intermediate \\  & on soil physical quality & \(23\leq OC<30\) and \(50< OC\leq 60\) & poor \\  & & \(OC<23\) and \(OC>60\) & poor \\ \hline Structure stability index, \(SSI\) [\%] = & & \(SSI>9\) & stable structure \\
1.724 OC\%/(silt\%+clay\%) \(\times\) 100 & & \(7<SSI\leq 9\) & low risk of structural degradation \\  & & \(5<SSI\leq 7\) & high risk of degradation \\  & & \(SSI\leq 5\) & structurally degraded soil \\ \hline Air capacity, \(AC\) [m\({}^{3}\) m\({}^{-3}\)] = \(\theta_{s}\) - & & \(\geq\)0.14 & good \\ \(\theta_{FC}\) & Root zone aeration & \(0.10\leq AC<0.14\) & intermediate \\  & & \(<\)0.10 & poor \\ where \(\theta_{FC}\) [m\({}^{3}\) m\({}^{-3}\)] is the field capacity (gravity drained) soil water content, corresponding to \(h=-1\) m. & & \\ \hline Plant-available water capacity, & & \(\geq\)0.20 & ideal \\ \(PAWC\) [m\({}^{3}\) m\({}^{-3}\)] = \(\theta_{FC}-\theta_{PWP}\) & Soil’s ability to store & & \\  & and provide water & \(0.15\leq PAWC<0.20\) & good \\  & available to plant roots & \(0.10\leq PAWC<0.15\) & limited \\  & & \(<\)0.10 & poor \\ where \(\theta_{PWP}\) [m\({}^{3}\) m\({}^{-3}\)] is the permanent & wilting point soil water content, corresponding to \(h=-150\) m. & & \\ \hline Relative field capacity, \(RFC\) [\(-\)] = & Soil’s ability to store & \(0.6\leq RFC\leq 0.7\) & optimal \\ \(\theta_{FC}/\theta_{s}\) & water and air relative & & \\  & to the soil’s total pore & \(<\)0.6 & limited (water limited soil) \\  & volume & & \\ \hline \end{tabular}
\end{table} 
Table 2: Estimated values of the selected soil physical quality (SPQ) indicators along with their corresponding optimal ranges or critical thresholds for the sampled soils at the Ottawa site.
# Research Status and Prospects of High-Voltage Pulse Plasma Rock-Fracturing Technology
## 5 Conclusions  
Pulsed discharge plasma rock fracturing is a new and efficient rock-fracturing technology, which is expected to solve the problems of high wear and tear of mechanical equipment, high cost, and serious pollution during the traditional rock-fracturing process, with a wide range of prospects for application in many fields, such as tunnel boring, infrastructure construction, rocky foundation construction, building demolition, etc. Combined with the research results of this technology in recent years, this paper first introduces the key to this technology, which is to enhance the intensity of shockwaves generated in the process of electric explosion; secondly, it systematically summarizes the main factors affecting the rock fracturing; and finally, it discusses the popularization and application of this technology in the field of tunnel boring.  
1. Pulsed discharge plasma rock fracturing is a process of multi-physical field coupling, which brings together many disciplines such as acoustic, optical, thermal, electric, force, etc. Moreover, this technology has a low cost and high safety, it does not pollute the environment, and it has high efficiency, which can greatly improve the effectiveness of rock fracturing.
2. At present, the main research means for pulsed discharge plasma rock fracturing are laboratory experiments and simulations. Experimental methods can visually reflect the interaction process between the plasma and the rock. It can provide effective validation for the simulation. The simulation method can save time and reduce the cost of consumption, and the combination of the two can better reveal the mechanism of rock fracturing and determine the relationship between rock-fracturing efficiency and rock-fracturing energy consumption.
3. The factors affecting the efficiency of pulsed discharge plasma rock-fracturing technology are mainly discharge wire parameters, circuit parameters, electric medium properties, rock properties, the blasting process, etc. At present, the first two types of research are more promising, focusing mainly by improving the intensity of the shockwave generated in the discharge process to obtain a higher efficiency of rock fracturing.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## Appendix A Details of the Derivation of the Fisher Information Matrix
### Bounds Derivation  
Hereafter we provide details on the derivation of the FIM (9). Given the signal model (3), the derivative of the parameters vector excluding the noise variance \(\sigma_{n}^{2}\), \(\tilde{\  
\[\Delta_{\mathbf{\theta}}(nT_{s};\tilde{\mathbf{\epsilon}}) \triangleq\mathbf{e}(nT_{s};\tilde{\mathbf{\epsilon}})\mathbf{e}(nT_{s}; \tilde{\mathbf{\epsilon}})^{H}-\mathbf{I}_{2}\] \[=\left[ \begin{array}{cc}0&e^{-j\Delta\varphi}e^{2\pi f_{s} \Delta h\
u T_{s}}\\ e^{j\Delta\varphi}e^{-2\pi f_{s}\Delta h\
u T_{s}}&0\end{array} \right], \tag{101}\]  
with  
\[\Delta\psi=\psi_{1}-\psi_{0}=\Delta\phi+\omega_{c}(b_{1}\tau_{1}-b_{0}\tau_{0} ),\;\Delta b=b_{1}-b_{0},\;\Delta\phi=\phi_{1}-\phi_{0}. \tag{102}\]  
Similarly to [16], taking the limit of (100) when \(N_{1}^{\prime}\) and \(N_{2}^{\prime}\) are very large leads to an integral form  
\[\lim_{(N_{1},N_{2})\to(-\infty_{+},+\infty)} \left(\frac{\partial\Delta\mathbf{\alpha}}{\partial\tilde{\mathbf{ \epsilon}}^{T}}\right)^{H}\left(\frac{\partial\mathbf{\Lambda}\mathbf{\alpha}}{ \partial\tilde{\mathbf{\epsilon}}^{T}}\right)\] \[=F_{s}\left(\mathbf{Q}(\tilde{\mathbf{\epsilon}})\left[ \begin{array} []{cc}\mathbf{W}_{0}&\left(\mathbf{W}^{\Delta}\right)^{H}\\ \mathbf{W}^{\Delta}&\mathbf{W}_{1}\end{array} \right]\mathbf{Q}(\tilde{\mathbf{ \epsilon}})^{H}\right)^{*}, \tag{103}\]  
where \(\mathbf{W}_{0}\) and \(\mathbf{W}_{1}\) are derived and studied in the single source case in [17; 18] and  
\[\mathbf{W}^{\Delta}\triangleq\left[ \begin{array}{ccc}W_{1,1}^{\Delta}&W_{1, 2}^{\Delta}&W_{1,3}^{\Delta}\\ W_{2,1}^{\Delta}&W_{2,2}^{\Delta}&W_{2,3}^{\Delta}\\ W_{3,1}^{\Delta}&W_{3,2}^{\Delta}&W_{3,3}^{\Delta}\end{array}
## 4 Denoising Methods
### BP Denoising
The power of the sparse signal remains basically unchanged after being compressed and measured, resulting in a sharp drop in the signal-to-noise ratio and affecting the performance of compressed sensing reconstruction. It can be seen from Figure 2d that the noise reduction effect of the BPDN algorithm is not ideal.
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## Materials and Methods
### Methods  
Perovskite films were sputtered using a Korovus HEX system (Korovus Technology Ltd., Newington, UK) employing an argon plasma at rate of 0.05 nm/s up to 200 nm thick deposits, monitored using a quartz cell microbalance. To guarantee a homogeneous deposition rate and thickness, the substrates were fixed on a rotating sample holder set to 20 rpm during the deposition process. All substrates were located on the sample holder, obtaining the deposition of the films on the same run while equalizing the deposition parameters for all the samples. After the deposition step, each sample was divided into four pieces, one of which was used as a zero reference (\"as prepared\" sample), while the others were exposed to an aggressive environment.  
The stability tests were carried out in a home-built climatic chamber in which temperature was maintained at 50 \(\pm\) 1 \({}^{\circ}\)C with the relative humidity (RH) set to 43% using a saturated K\({}_{2}\)CO\({}_{3}\) aqueous solution [25]. The nature of the crystalline phases constituting the samples was determined by X-ray diffraction (XRD) analysis performed by means of a model D8 Bruker diffractometer working in Bragg-Brentano mode. The optical properties were monitored using an ultraviolet-visible light (UV-Vis) Jasco V-670 double beam spectrometer equipped with an integration sphere, while the surface chemical composition was determined via X-ray photoelectron spectroscopy (XPS) constituting a VSW Scientific Instrument Limited model TA10 (Manchester, UK) equipped with a nonmonochromatic X-ray source and a VSW Scientific Instrument Limited model HA100 (Manchester, UK) hemispherical analyzer with a 16-channel detector. The spectra were collected in constant pass energy mode (22 eV) and the peaks were analyzed using CasaXPS software after Shirley-type background subtraction.
## 4 Experimental Results and Analysis
### Visual Analysis of the Results  
To verify the reliability of our proposed co-word relation, we randomly selected 200 enterprise data points from two datasets for visualization, as shown in Figure 3. In the figure, Figure 3a is the visualization result for the Xiuzhou District dataset, and Figure 3b is the visualization result of the Zhuji City dataset. We used nodes to represent enterprise entities, edges to represent co-word relations between enterprises, nodes of different colors to represent different enterprise categories, and then used a force-oriented layout to distribute them in the graph. The co-word relation graph we designed shows associations between most of the same type of enterprises, such as the blue and green nodes in Figure 3a and the purple nodes in Figure 3b.  
T-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used nonlinear dimensionality reduction technique that operates by minimizing the Kullback-Leibler di  
Figure 3: Visualization of the co-word relationship of enterprise names. (**a**) Co-word relationship diagram of 200 enterprise names in Xiuzhou City. (**b**) Co-word relationship of 200 enterprise names in Zhuji City.  
vergence between the distributions of data points in high-dimensional and low-dimensional spaces. t-SNE is effective at preserving the local structure of the original high-dimensional data in the lower-dimensional space, making it especially advantageous for data visualization. To visually assess the model's performance in feature learning, t-SNE was applied to the minor classification features of the Xiuzhou and Zhuji datasets after training, with the results presented in Figure 4. As illustrated in the figure, data points belonging to the same category exhibit obvious clustering patterns in the low-dimensional space. For instance, in Figure 4a, the printing industry (red) and the textile industry (orange) are clearly clustered, while in Figure 4b, the real estate industry (brown) and the textile industry (blue) form separate clusters. These results indicate that our model can effectively capture semantic features and co-word relationships, thereby enabling accurate industry classification.
## Appendix A
The CDF of the non-central Chi-square distribution can be expressed as  
\[P(T,n-m,\lambda_{i})=\int_{0}^{T}f(x,n-m,\lambda_{i})dx \tag{13}\]  
Therefore,  
\[\frac{\partial[P(T,n-m,\lambda_{i})]}{\partial\lambda_{i}}=\frac{\partial \Big{[}\int_{0}^{T}f(x,n-m,\lambda_{i})dx\Big{]}}{\partial\lambda_{i}}=\int_{ 0}^{T}\frac{\partial[f(x,n-m,\lambda_{i})]}{\partial\lambda_{i}}dx \tag{14}\]  
We assume  
\[g(x,n-m,\lambda_{i})=\frac{1}{2}e^{-(x+\lambda_{i})/2}\bigg{(}\frac{x}{\lambda _{i}}\bigg{)}^{(n-m)/4-1/2} \tag{15}\]  
then  
\[ \begin{array}{l}\frac{\partial[f(x,n-m,\lambda_{i})]}{\partial\lambda_{i}}= \frac{\partial[g(x,n-m,\lambda_{i})]I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x} \big{)}]}{\partial\lambda_{i}}\\ =\frac{\partial[g(x,n-m,\lambda_{i})]}{\partial\lambda_{i}}I_{(n-m)/2-1}\big{(} \sqrt{\lambda_{i}x}\big{)}+\frac{\partial[I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{ i}x}\big{)}]}{\partial\lambda_{i}}g(x,n-m,\lambda_{i})\end{array}  \tag{16}\]  
where  
\[ \begin{array}{l}\frac{\partial[g(x,n-m,\lambda_{i})]}{\partial\lambda_{i}}= -\frac{1}{8}e^{-(x+\lambda_{i})/2}\Big{(}\frac{x}{\lambda_{i}}\Big{)}^{(n-m)/4 -1/2}\Big{(}2+\frac{n-m-2}{\lambda_{i}}\Big{)}\\ \frac{\partial[I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x}\big{)}]}{\partial\lambda _{i}}=\frac{\partial[I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x}\big{)}]}{\partial \sqrt{\lambda_{i}x}}\times\frac{\partial\sqrt{\lambda_{i}x}}{\partial\lambda_{ i}}=\frac{1}{2}\sqrt{\frac{x}{\lambda_{i}}}\frac{\partial[I_{(n-m)/2-1}\big{(} \sqrt{\lambda_{i}x}\big{)}]}{\partial\sqrt{\lambda_{i}x}}\end{array}  \tag{17}\]  
For the modified Bessel function of the first kind,  
\[\frac{\partial\Big{[}I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x}\big{)}\Big{]}}{ \partial\sqrt{\lambda_{i}x}}=I_{(n-m)/2-2}\Big{(}\sqrt{\lambda_{i}x}\Big{)}- \frac{n-m-2}{2\sqrt{\lambda_{i}x}}I_{(n-m)/2-1}\Big{(}\sqrt{\lambda_{i}x} \Big{)} \tag{18}\]  
Combining (17) and (18),  
\[\frac{\partial\Big{[}I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x}\big{)}\Big{]}}{ \partial\lambda_{i}}=\frac{1}{2}\sqrt{\frac{x}{\lambda_{i}}}\bigg{[}I_{(n-m)/2- 2}\Big{(}\sqrt{\lambda_{i}x}\Big{)}-\frac{n-m-2}{2\sqrt{\lambda_{i}x}}I_{(n-m )/2-1}\Big{(}\sqrt{\lambda_{i}x}\Big{)}\bigg{]} \tag{19}\]  
Inserting (17) and (19) into (16),  
\[\frac{\partial[f(x,n-m,\lambda_{i})]}{\partial\lambda_{i}}=-\frac{1}{4}e^{-(x+ \lambda_{i})/2}\bigg{(}\frac{x}{\lambda_{i}}\bigg{)}^{(n-m)/4-1/2}h(x,n-m, \lambda_{i}) \tag{20}\]  
where \(h(x,n-m,\lambda_{i})\) is assumed by  
\[h(x,n-m,\lambda_{i})=\frac{n-m-2+\lambda_{i}}{\lambda_{i}}I_{(n-m)/2-1}\Big{(} \sqrt{\lambda_{i}x}\Big{)}-\sqrt{\frac{x}{\lambda_{i}}}I_{(n-m)/2-2}\Big{(} \sqrt{\lambda_{i}x}\Big{)} \tag{21}\]In summary, inserting (A20) into (A14), and combining (A11) and (A8) gives  
\[c_{2}=\frac{b_{i}^{2}\times S_{i,i}\times URA}{2\sigma^{4}}\int_{0}^{T}e^{-(x+ \lambda_{i})/2}\bigg{(}\frac{x}{\lambda_{i}}\bigg{)}^{(n-m)/4-1/2}h(x,n-m,\lambda _{i})dx\] (A22)
## 3 Simulated Micro-Doppler Signatures
### Theoretical Considerations
By generating thrust through the rotation of the four motors, each with two blades attached, the UAVs can support their weight and ascend. Different combinations of rotation speeds result in different UAV flight motions. In this study, the weight and size of the target are discriminating factors for which the micro-Doppler signature belongs to which target. For heavier UAVs, more thrust is required to lift the drone, resulting in a higher detected micro-Doppler bandwidth. Figure 2: Plots related to a single propeller drone. (**a**) power spectral density; (**b**) spectrogram.
## 6 Results and Discussions
### End-to-End Delay  
The delay of transmitted bytes from Access Point 1 to Access Point 4 using the shortest path routing method and the SDN-based energy-aware routing method is described in this section.  
In the shortest-path routing method, Access Point 1 transmits bytes to Access Point 4 through the shortest path via Access Point 2, as demonstrated in Figure 12. The delay is lower due to there being two links between Access Point 1 to Access Point 4, Access Point 1 to Access Point 2 and Access Point 2 to Access Point 4. In the Proposed SDN-based energy-aware routing method, Access Point 1 transmits the bytes to Access Point 3, then Access Point 3 transmits the packet to Access Point 5. Finally, the bytes arrive at Access Point 4. According to the transmission path and associated links, the delay is higher, as shown in Figure 12. The differences in delay increase as the links increase. However, in the industry environment, energy consumption is more critical than a slight difference in delay.