esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Empirical Study
### Empirical Study on the Reliability of RPEM Economic Evaluation
#### 3.2.4 Calculation of Economic Reliability of Mines  
(1) Establish target state function  
Taking the ore extraction grade of 21.04% as an example and referring to Equation (13), a target state function with the ore extraction grade as a variable is established, as shown in Equation (18):  
\[Z=\frac{F}{p}=Z(21.04\%) \tag{18}\]  
(2) Solution to Economic Reliability  
According to the basic principles and methods of RPEM in Section 1, with its coefficient of variation of 0.1, two symmetrical value points for an ore grade of 21.04% can be obtained according to Equations (1) and (2),  
\[x_{i1}=\mu_{xi}+\sigma_{xi}=21.04\%+2.1\%=23.14\% \tag{19}\]  
\[x_{i2}=\mu_{xi}-\sigma_{xi}=21.04\%-2.1\%=18.94\% \tag{20}\]  
By using the cash flow calculation method for mines, the cash flows for ore grades of 23.14% and 18.94% are calculated, resulting in \(Z_{1}\) = 1.2814 and \(Z_{2}\) = 1.1464. Its mean \(\mu_{z}\) and variance \(\sigma_{z}\)2 of the state function for ore grade variables within the interval of 21.04% and 22.36% can be calculated, as shown in Equations (21) and (22):  
\[\mu_{z}=\frac{1}{2}(Z_{1}+Z_{2})=\frac{1}{2}\times(1.2814+1.1464)=1.2139 \tag{21}\]  
\[\sigma_{z}^{2}=\sum_{j=1}^{2^{n}}P_{j}Z_{j}^{2}-\mu_{z}^{2}=0.0091 \tag{22}\]  
According to the calculation results of Equations (21) and (22), Equation (7) is used to solve the reliability index of the state function \(\beta\), as shown in Equation (23).  
\[\beta=\frac{\mu_{z}-1}{\sigma_{z}}=\frac{0.2139}{0.0954}=2.2421 \tag{23}\]  
At this point, when the ore extraction grade is 21.04%, the economic reliability \(R\) of the mine is:  
\[R=\phi(2.2421)=98.75\% \tag{24}\]  
Within the variation range of ore grade [21.04%, 22.36%], with a coefficient of variation of 0.1, taking 11 sets of data, the cash flow, reliability indicators, and economic reliability of each group of data are calculated separately according to the above steps. The results are shown in Table 2.  
Repeating the above process, the cash flow, reliability indicators, and economic reliability of its variables when they change within its value range can be obtained.
## 3 Comparative Analyses Results
### CMORPH Products
#### 3.2.3 C/N-Based Product  
Figure 19a,b presents the precipitation amounts estimated by DATABOURG during both the April and June case studies. The DATABOURG product uses an innovative method to estimate and process precipitation at high spatial and temporal resolutions as described in Section 2.1.3. No similar observations-estimations comparison at this spatial scale has been conducted by using DATABOURG estimations but it has been previously found that the dataset is closer to rain-gauge observations than the estimations of the German DWD radars of Neuheilenbach [22].  
For the April case (Figure 19a,c), DATABOURG estimated moderate- to high-intensity precipitation amounts over the south of France, most notably over the WMC, SAL, and CEV areas. The locations of the precipitation patterns agree well with the observations made by RADOME (Figure 2a). Moreover, the distance between the maximum estimated by DATABOURG and the maximum observed by RADOME was less than 20 km for the WMC, DUR, and CEV areas (Table 2). However, the DATABOURG maximums were lower than the RADOME maximums by more than 20 mm (Table 2). An exception concerns the DATABOURG maximum over the JUR area being close to the observation but this was located 108.96 km away from its RADOME counterpart. In Figure 19c, we can identify an area of underestimation across the eastern part of the WMC area and the areas located in the east of the country. Over the northeastern part of the CEV area, a local overestimation area was detected, corresponding to the highest-intensity precipitation amounts estimated by DATABOURG. The medians of the DATABOURG estimations inside each area of interest (Table 3) were lower than the medians of the RADOME observations by less than 7 mm, confirming the underestimation observed previously. The mean absolute biases (Table 3) confirm the presence of a slight underestimation over the study areas (ranging from \(-3.11\) mm over CEV to \(-6.83\) mm over the WMC area).  
The DATABOURG estimations for the June case (Figure 19b) reveal an area of high-intensity precipitation amounts (more than 110 mm) extending from Central to northeast NMC area. A larger moderate-intensity precipitation amount (more than 60 mm) surrounded the latter in a southwest-northeast axis, matching the RADOME observation, although these areas were more extensive than their RADOME counterparts. The difference appeared in the extent of the high-intensity precipitation pattern estimated by DATABOURG, as the distance separating the DATABOURG maximum and the RADOME maximum was 157.18 km (Table 2) further to the northeast, despite having a very close maximum to the observation. In Figure 19d, we can see the large overestimation pattern linked to the extent of high-intensity rainfall estimations from the DATABOURG compared  
Figure 19: As in Figure 4, but for the DATABOURG product.  
to RADOME observations. Over the NMC, the median of the accumulated precipitation estimated by DATABOURG was more than 14 mm over the one computed by RADOME (Table 3). This overestimation pattern was +8.94 mm, on average, over the NMC.
# Audio Time Stretching Using Fuzzy Classification of Spectral Bins
## 5 Conclusions  
In this paper, a novel TSM method was presented. The method is based on fuzzy classification of spectral bins into sinusoids, noise, and transients. The information from the bin classification is used to preserve the characteristics of these distinct signal components during TSM. The listening test results presented in this paper suggest that the proposed method performs generally better than a state-of-the-art algorithm and is competitive with commercial software.  
The proposed method still suffers to some extent from the fixed time and frequency resolution of the STFT. Finding ways to apply the concept of fuzzy classification of spectral bins to a multiresolution time-frequency transformation could further increase the quality of the proposed method. Finally, although this paper only considered TSM, the method for fuzzy classification of spectral bins could be applied to various audio signal analysis tasks, such as multi-pitch estimation and beat tracking.  
**Acknowledgments: This study has been funded by the Aalto University School of Electrical Engineering. Special thanks go to the experience director of the Finnish Science Center Heureka [PERSON], who proposed this study. The authors would also like to thank Mr. [PERSON] for providing expert help in the beginning of this project, and [PERSON] for proofreading.**  
Figure 8: Mean opinion scores for eight audio samples using four TSM methods for (**a**) medium (\(\alpha=1.5\)), and (**b**) large (\(\alpha=2.0\)) TSM factors. The rightmost bars show the average score for all eight samples. PV: phase vocoder; HP: harmonic–percussive separation [28]; EL: elastique [36]; PROP: proposed method.  
**Author Contributions:** [PERSON] and [PERSON] planned this study and wrote the paper together. [PERSON] developed and programmed the new algorithm. [PERSON] conducted the listening test and analyzed the results. [PERSON] supervised this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 3 TSV Reliability
### Noise Coupling
Both models included equivalent circuits for TSV, RDL (redistribution layer), metal interconnects, and substrates. Modeling TSV structures and simulating them in the time domain is one of the most critical issues in this field. Thus, future research should focus on developing more accurate and efficient modeling and simulation techniques to address the challenges posed by TSV coupling in 3D IC design [71; 72].
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Deployment of the Neutral Host Platform
#### 4.4.2 Platform Deployment Validation  
Given the importance of ensuring the proper deployment and functionality of the conceived platform, multiple validation tests were executed. In general, tests were designed taking into account the components involved, the defined interactions, as well as the expected results. Therefore, two groups of tests were conducted:  
* _Individual tests:_ All elements of the platform were individually tested after accomplishing the deployment of each component to corroborate their functionality. These tests validated the attainment of the expected behavior of every developed module and feature.
* _Integration tests:_ To verify the proper interaction between components of the platform, specific integration tests were performed. Particularly, the performance of these tests validated the entire workflow involved in the lifecycle automation of a neutral host framework, in terms of infrastructure (registration, configuration, and removal), slices (creation, activation, and removal), and services (onboarding, instantiation, and removal).
## 6 Results and Discussion
### Analysis for Frequency and Conductivity (Permittivity Is Fixed)  
In the second example, the relative permittivity of the background medium is chosen as \(\epsilon_{r\,\mathrm{mm}}=40\). Figure 5 represents the image plot of the amplitude variation of the functional \(F(f,\sigma_{\mathrm{mm}})\) as a function of the frequency and conductivity of the matching medium, where the frequency is altering in a range from 0.8 to 2 GHz and the conductivity from 0.01 to 0.03 S/m.  
As can be seen from Figure 5, two pairs with values \((f,\sigma_{\mathrm{mm}})=(1.375\,\mathrm{GHz},0.028\,\mathrm{S/m})\) from the yellow-colored region and \((f,\sigma_{\mathrm{mm}})=(1\,\mathrm{GHz},0.012\,\mathrm{S/m})\) from the darker blue-colored region are chosen. These pairs are the corresponding respective highest and lowest amplitude values of the functional \(F(f,\sigma_{\mathrm{mm}})\).  
Figure 6 represents the amplitude changes of the transmitted electric field in the innermost layer of the arm structure along the line \(x_{2}=0\) dep  
Figure 4: Total scattered field computed outside and inside the arm model having an internal object in presence and absence of the matching medium at \(f=0.5\,\mathrm{GHz}\).  
Figure 5: Amplitude variation of the functional \(F(f,\sigma_{\mathrm{mm}})\) with \(\epsilon_{r\,\mathrm{mm}}=40\).  
matching medium parameters taken from Figure 5. The ratio of the areas under the curves plotted in Figure 6 yields in 4.67, from which one can conclude that a noticeable part of the field energy can enter the arm construct. The scattered electric field by the internal object located in the innermost layer of the arm structure is computed on the observation circles of radii 0.3 m and 0.025 m. The amplitude variations versus observation angle are depicted in Figure 7 using the pair \((f,\sigma_{\text{mm}})=(1\,\text{GHz},0.012\,\text{S/m})\) from Figure 5, with \(\epsilon_{\text{rmm}}=40\) in the presence and \(\epsilon_{\text{rmm}}=1\) in the absence of the matching medium, respectively.  
It is observed from Figure 7 that the scattered field changes both inside and outside the arm model vary for different background spaces.  
Figure 6: Comparison of the total transmitted field amplitude variations along the line \(x_{2}=0\) in the innermost layer of the model for the \((f,\sigma_{\text{mm}})\) pairs.  
Figure 7: Total scattered field computed outside and inside the arm model having an internal object in presence and absence of the matching medium at \(f=1\,\text{GHz}\).
## 5 Simulation and Results
### Performance of Our Proposed Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Beam** \\ **Hopping** \\ **Scheme** \\ \end{tabular} & \begin{tabular}{c} **Traffic** \\ **Distribution** \\ \end{tabular} & \begin{tabular}{c} **Throughput** \\ **(Gbps)** \\ \end{tabular} & \begin{tabular}{c} **Access** \\ **Success Rate** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Delay (ms)** \\ \end{tabular} & \begin{tabular}{c} **Delay** \\ **Variance** \\ \end{tabular} \\ \hline \multirow{2}{*}{Polling-BH} & \begin{tabular}{c} even \\ uneven \\ \end{tabular} & 1.734 & 93.2\% & 90.38 & 5910 \\  & \begin{tabular}{c} **1.172** \\ \end{tabular} & **63.4\%** & **178.25** & **11,336** \\ \hline \multirow{2}{*}{Random-BH} & \begin{tabular}{c} even \\ uneven \\ \end{tabular} & 1.222 & 65.7\% & 248.94 & 4754 \\  & \begin{tabular}{c} **0.852** \\ \end{tabular} & **46.1\%** & **214.55** & **7393** \\ \hline \multirow{2}{*}{GAWIC-BH} & \begin{tabular}{c} even \\ uneven \\ \end{tabular} & 1.804 & 97.0\% & 71.99 & 85 \\  & \begin{tabular}{c} **1.790** \\ \end{tabular} & **96.9\%** & **59.27** & **1471** \\ \hline \multirow{2}{*}{Proposed Algorithm} & \
\begin{tabular}{c} even \\ uneven \\ \end{tabular} & 1.808 & 97.2\% & 66.06 & 74 \\  &
\begin{tabular}{c} **1.797** \\ \end{tabular} & **97.3\%** & **52.95** & **1188** \\ \hline \hline \end{tabular}  
* Small traffic demand is defined as: Poisson arrival intensity is 400 packages under even distribution, 2100 in hotspots and 210 in non-hotspots under uneven distribution.  
\end{table} 
Table 4: Comparison of the results of each algorithm in the two scenarios when traffic is small 1. Figure 12: Comparison in uneven distribution scenario. (**a**) Throughput. (**b**) Access Success Rate. (**c**) Average Delay. (**d**) Delay Variance.
# Insights into the Issue of Deploying a Private LoRaWAN
## 3 Experimental Environment
### Test Device  
For our measurements we used a certified LoRaWAN Field Test Device from Adeunis RF (ARF8123 AA), with the following parameters [63]:  
* Static node (no movement during measurement).
* Transmit power of 14 dBm (25 mW).  
Figure 5: Topology of our LoRaWAN experimental single-gateway network.  
Figure 6: Coverage map of the public LoRaWAN network in the Czech Republic (upper-right corner) and coverage estimation of the nearest public network gateways in the selected location.  
* Spreading factor 12.
* ABP activation (OTAA was not supported by public network operator that time), fixed data rate (adaptive data rate was not supported by public network that time).
* Sensitivity up to \(-140\) dBm, \(0\) dBi wire antenna (Thermolast K TC7 AA).
* Communication range up to \(15\) km.
* Device temperature limits \(-30\) to \(+70\)\({}^{\circ}\)C.  
To ensure fairness, the frequency plan was chosen based on the public operator's plan to provide identical conditions for both networks (see Table 2). To minimize the possibility of internetwork collisions, measurements were conducted in different time slots.
## Appendix A  
GitHub Link: [[https://github.com/lawansisa/ML_MFO_GWO_DPGWO](https://github.com/lawansisa/ML_MFO_GWO_DPGWO)]([https://github.com/lawansisa/ML_MFO_GWO_DPGWO](https://github.com/lawansisa/ML_MFO_GWO_DPGWO)) (accessed on 1 June 2023). **Figure A1. ** Statistical Inference of ML Models' Performance for Crack Depth Prediction. **(a)** Probability Plot for Crack Length-R2 Tr. **(b)** Probability Plot for Crack Length-R2 Tr. **(c)** Probability Plot for Crack Length-RMSETt. **(d)** Probability Plot for Crack Length-RMSETt. Figure A2: Factorial and Interaction Plots for Performance of ML Models for Crack Depth Prediction. (**a**) R\({}^{2}\) Factorial Plot for Training Dataset—D. (**b**) R\({}^{2}\) Factorial Plot for Testing Dataset—D. (**c**) RMSEFactorial Plot for Training Dataset—D. (**d**) RMSEFactorial Plot for Testing Dataset—D. (**e**) R\({}^{2}\) Interaction Plot for Training Dataset—D. (**f**) R\({}^{2}\) Interaction Plot for Testing Dataset—D. (**g**) RMSE Interaction Plot for Training Dataset—D. (**h**) RMSE Interaction Plot for Testing Dataset—D. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variable** & **Mean** & **StDev** & **Variance** & **Minimum** & **Q1** & **Median** & **Q3** & **Maximum** & **Range** & _p_-Value** \\ \hline R2 Tr & 0.8804 & 0.1061 & 0.0113 & 0.6983 & 0.7836 & 0.9058 & 0.9840 & 1.0000 & 0.3017 & 0.0950 \\ R2 Tr & 0.3076 & 0.0743 & 0.0055 & 0.1582 & 0.2564 & 0.3195 & 0.3561 & 0.4196 & 0.2614 & 0.3030 \\ RmseTr & 0.0940 & 0.0648 & 0.0042 & 0.0005 & 0.0203 & 0.1006 & 0.1476 & 0.1795 & 0.1790 & 0.0750 \\ RmseTr & 0.2699 & 0.0170 & 0.0003 & 0.2391 & 0.2579 & 0.2676 & 0.2825 & 0.3008 & 0.0617 & 0.9400 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical Analysis of ML Models’ Performance for Crack Width Prediction. Figure A3: Factorial and Interaction Plots for Performance of ML Models for Crack Width Prediction. (**a**) R\({}^{2}\) Factorial Plot for Training Dataset—W. (**b**) R\({}^{2}\) Factorial Plot for Testing Dataset—W. (**c**) RMSEFactorial Plot for Training Dataset—W. (**d**) RMSEFactorial Plot for Testing Dataset—W. (**e**) R\({}^{2}\) Interaction Plot for Training Dataset—W. (**f**) R\({}^{2}\) Interaction Plot for Testing Dataset—W. (**g**) RMSE Interaction Plot for Training Dataset—W. (**h**) RMSE Interaction Plot for Testing Dataset—W. Figure A4: Statistical Analysis of Output using different algorithms for crack depth and crack width. (**a**) Statistical analysis of output using MFO for Crack Depth. (**b**) Statistical analysis of output using GWO for Crack Depth. (**c**) Statistical analysis of output using DPGWO for Crack Depth. (**d**) Statistical analysis of output using MFO for Crack Width. (**e**) Statistical analysis of output using GWO for Crack Width. (**f**) Statistical analysis of output using DPGWO for Crack Width.
## 4 Results and Discussion  
### Validation of the CFD Model  
As mentioned in Section 3.1, the Roots pump studied has been tested extensively under various operating conditions. The experimental tests were built to calibrate and validate the results simulated by the CFD model established. The experimental test with working fluids of the helium and air were used instead of hydrogen for the sake of safety. The schematic diagram of the experimental test rig is shown in Figure 4. It should be noted that the supplied air was suctioned by the Roots pump from the environment when the working fluid is air. As seen in Figure 4, the fresh helium was supplied by a helium tank at the start-up stage of the experimental test. Then, the helium supply could be cut off by a tee valve, and the helium recirculation was performed to recycle the supplied helium. The low- and high-pressure regulators were used to adjust the inlet and outlet pressure of the Roots pump, respectively. The flow rate of helium recirculation was measured by a Coriolis mass flow meter with a maximum range of 1000 L/min and an uncertainty of 0.8%. The pressure and temperature of Roots pump inlet and outlet were measured by the pressure and temperature sensors, respectively.  
The suction temperature of the air and helium is 30 \({}^{\circ}\)C and the suction pressure of the helium is 200 kPa, while that of the air is the ambient pressure. As shown in Figure 5, the flow rate simulated for helium with a speed of 6000 rpm and pressure difference of 15 kPa is calibrated by changing the clearances within the Roots pump to have the error as small as possible between the data measured and that simulated. Then, all the volume flow rates under other operating conditions are simulated and validated based on these clearances adjusted as above. Except for the clearance at the discharge end, which is 0.15 mm, the values of other clearances are all of 0.1 mm for the clearance between the rotors, the clearance between the rotor tip and the casing as well as the clearance between the rotor end face and the casing at the suction end. The values of 0.1 mm and 0.15 mm are the normally applied clearances in most commercially available roots pumps.  
Figure 4: The schematic diagram of the experimental test rig.  
Figure 5 indicates the flow rate comparison of Roots pump between the experimental values and the simulation results under various rotation speed and pressures difference with working as air and helium. As can be seen, the flow rate increases with the rising of the rotation speed and reduction of pressure difference, and this variation trend of the experiments and simulation is consistent. Moreover, the simulated flow rate under various conditions agreed very well with the measured data by the maximum deviations, which was less than \(\pm\)10% within the main operating speed range between 5000 rpm and 9000 rpm, except for the very low speed condition, used rarely. Thus, it is believable that the CFD modeling can be used to predict and evaluate the performance of the Roots pump under various operating conditions and with gases having different properties.
# Development of a Vibroacoustic Stochastic Finite Element Prediction Tool for a CLT Floor
## 3 Finite-Element Model
### Model Validation Criterion  
The established model needs to be validated by two different metrics. One is the normalized relative frequency difference (NRFD), which characterizes the discrepancies between the simulated and the measured eigen-frequencies, defined by the equation below.  
\[NRFD_{i}=\frac{\left|f_{ref_{i}}-f_{i}\right|}{f_{ref_{i}}}, \tag{1}\]  
where \(f_{ref_{i}}\) is the measured resonance frequency and \(f_{i}\) is the simulated resonance frequency.  
The other one is the modal assurance criterion (MAC), which quantifies the similarity of simulated and measured mode shapes, defined by the formula below.  
\[MAC=\ \frac{|{\left(\[PERSON]_{i}^{sim}\right)}^{T}(\[PERSON]_{j}^{exp})|^{2}}{\left( \[PERSON]_{i}^{sim}\right)^{T}\left(\[PERSON]_{i}^{sim}\right)\left(\[PERSON]_{j}^{exp} \right)^{T}\left(\[PERSON]_{j}^{exp}\right)} \tag{2}\]  
where \(\[PERSON]_{i}^{sim}\) is the \(i\)-th simulated mode shape and \(\[PERSON]_{j}^{exp}\) is the \(j\)-th measured mode shape. The range of the MAC number is from 0 to 1. When the MAC number equals 1, the simulated eigen-mode perfectly correlates to the measured one. However, MAC equals 0, which implies irrelevant simulated and measured eigen-modes. Different from the conventional calibration procedure, stochastic process calibration begins with comparing eigen-frequencies (NRFDs) between the simulated FRFs and measured FRFs. Therefore, several FRFs of different positions are needed to make sure the calibrated model validating at different positions, which can increase the credibility of the model. However, the FRFs calibration can only ensure consistency of simulated and measured eigen-frequency but not the mode order. The simulated eigen-frequency may be the same as the measured eigen-frequency, but they could have different mode shapes. Therefore, both indictors are needed to ensure the simulated eigen-frequencies correlated with the measured ones to keep the simulated mode order corresponding to the measured one.