esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Quality Evaluation Model with Taguchi Cost Loss Index
## 3 Process Quality Evaluation by Statistical Testing  
In this section, we use a statistical testing method to evaluate process quality by means of the confidence interval of the Taguchi cost loss index. As described in Section 2, interval \((LC_{PM},UC_{PM})\) is the \(100(1-\alpha)\%\) confidence interval of the Taguchi cost loss index, rewritten as follows:  
1. When \(\delta_{U}<0\), then the lower confidence limit \(LC_{PM}\) is as follows: \[LC_{PM}=1/3\sqrt{\left(\hat{\delta}_{0}-t_{\alpha/4;N-m}\times\sqrt{\frac{ \hat{\gamma}_{0}^{2}}{N}}\right)^{2}+\frac{(N-m)\hat{\gamma}_{0}^{2}}{\chi_{ \alpha/4;N-m}^{2}}},\] (30) and the upper confidence limit \(UC_{PM}\) is as follows: \[UC_{PM}=1/3\sqrt{\left(\hat{\delta}_{0}+t_{\alpha/4;N-m}\times\sqrt{\frac{ \hat{\gamma}_{0}^{2}}{N}}\right)^{2}+\frac{(N-m)\hat{\gamma}_{0}^{2}}{\hat{ \chi}_{1-\alpha/4;N-m}^{2}}}.\] (31)When \(\delta_{L}\)\(>\)0, then the lower confidence limit \(LC_{PM}\) is as follows:  
\[LC_{PM}=1/3\sqrt{\left(\delta_{0}+t_{\alpha/4;N-m}\times\sqrt{\frac{\hat{\gamma} _{0}^{2}}{N}}\right)^{2}+\frac{(N-m)\hat{\gamma}_{0}^{2}}{\lambda_{\alpha/4;N-m }^{2}}}, \tag{32}\]  
and the upper confidence limit \(UC_{PM}\) is as follows:  
\[UC_{PM}=1/3\sqrt{\left(\delta_{0}-t_{\alpha/4;N-m}\times\sqrt{\frac{\hat{ \gamma}_{0}^{2}}{N}}\right)^{2}+\frac{(N-m)\hat{\gamma}_{0}^{2}}{\lambda_{1- \alpha/4;N-m}^{2}}}. \tag{33}\]  
When \(\delta_{L}\)\(\leq\)0\(\leq\)\(\delta_{U}\), then the lower confidence limit \(LC_{PM}\) is as follows:  
\[LC_{PM}=\sqrt{\frac{\lambda_{\alpha/4;N-m}^{2}}{(N-m)}}\left(\frac{1}{3\hat{ \gamma}_{0}}\right), \tag{34}\]  
and the upper confidence limit \(UC_{PM}\) is as follows:  
\[UC_{PM}=\sqrt{\frac{\lambda_{1-\alpha/4;N-m}^{2}}{(N-m)}}\left(\frac{1}{3\hat{ \gamma}_{0}}\right). \tag{35}\]  
As noted by [PERSON] et al. [35], the process capability level is \"Capable\" for \(C_{PM}\)\(\geq\) 1.00. To determine whether the process capability level is capable or not, we adopted the following null hypothesis \(H_{0}\):\(C_{PM}\) = 1.00 versus the alternative hypothesis \(H_{1}\):\(C_{PM}\)\(\
eq\) 1.00. Let \(\alpha\) be the significance level of the test, then the three statistical testing rules can be described as follows:  
1. If \(LC_{PM}\)\(>\) 1.00, then reject \(H_{0}\) and conclude that \(C_{PM}\)\(>\) 1.00 (consider cutting costs).
2. If \(UC_{PM}\)\(<\) 1.00, then reject \(H_{0}\) and conclude that \(C_{PM}\)\(<\) 1.00. Then, the process needs to improve.
3. If \(LC_{PM}\)\(\leq\) 1.00 \(\leq\)\(UC_{PM}\), then do not reject \(H_{0}\) and conclude that \(\hat{C_{PK}}\)\(=\)\(c\) (maintain the status quo).  
Next, these three statistical testing rules can be adopted to evaluate the process quality and decide whether the process needs to improve, maintain its status quo, or cut its costs.
## 3 Results
### Automotive Multi-Layer System on Metal Substrate
#### 3.1.3 Thickness Measurement  
Inline quality control of multi-layer car body paint with THz TDS is commonly done with reflectometry, since it is faster, more robust, and less expensive compared to ellipsometry. In addition, THz TDS reflectometry does not require any moving parts such as motorized rotary stages. Figure 6 shows the THz TDS reflectometry measurement at 8\({}^{\circ}\) angle of incidence using a focused measurement spot with a diameter of about 1 mm, which is much smaller, compared to the ellipsometric spot size. A comparable setup to the one described in [40] was used for the THz TDS reflectometry measurements. The modelling of this measurement data is based on the dispersion curves, which were previously measured with the THz ellipsometer. Here, only the thicknesses were employed as free parameters.  
The agreement between the measurement and the model of the time-domain signal is very good for both the absolute value and the phase of the measured transfer function. The resulting thicknesses are summarized in Table 1. The values are in good agreement with confocal CC thickness measurements and microsection results. The differences between ellipsometry and reflectometry results are probably due to the different spot sizes and measurement positions. The single layer root-mean-square roughness measured with a confocal microscope is about 0.4 \(\upmu\)m for the E-coat, 0.5 \(\upmu\)m for the BC and 0.1 \(\upmu\)m for the CC. These roughness could also contribute to the thickness variations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **THz Ellipsometry (\(\upmu\)m)** & **THz Reflectometry (\(\upmu\)m)** & **Confocal Microscope (\(\upmu\)m)** \\ \hline Clear coat (CC) & 39.5 \(\pm\) 0.2 & 39.6 \(\pm\) 0.1 & 40.0 \(\pm\) 0.9 \\ \hline Base coat (BC) & 44.9 \(\pm\) 0.4 & 42.8 \(\pm\) 0.1 & - \\ \hline E-coat & 17.1 \(\pm\) 0.5 & 18.1 \(\pm\) 0.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thickness results of a three-layer paint on a metal substrate.  
Figure 5: Stack calibration of the BC layer as part of the full stack on a metal substrate: (**a**) Ellipsometric measurement (black) and best fit of the theoretical model (red); (**b**) Dispersion based on the ellipsometric measurement shown in (**a**).
# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 2 Related Work  
### Fiducial Methods  
ECG-based authentication applications are typically based on two approaches, namely, fiducial and non-fiducial methods. The fiducial approach utilizes a feature extraction process, wherein the points of interest are within a heartbeat wave. Usually, the heartbeat wave consists of a P-QRS-T complex, which represents the activity of a beating human heart, acquired using electrodes placed on different parts of the body. These complexes are then used to extract latency and amplitude features [4; 6]. Such approaches generally rely on robust heartbeat segmentation and fiducial peak point detection [7; 8; 9; 10]. Thus, manual feature engineering efforts are necessary to capture a heartbeat from an original ECG signal. Moreover, precise estimation is important to annotate the peak locations of the P-QRS-T complex of a signal. After this fiducial information is detected, the amplitude and time-interval between corresponding points are measured. The entire process of identifying peak data points of the QRS complex and calculating the time-interval features are considered as the pre-processing component or the signal processing phase in ECG-based biometric authentication applications, as shown in Figure 1.
## 8 Presentation of Results  
### Presentation of Failure Prediction Model Results, Comparisons and Discussions  
Two approaches for predicting the target variable CP01 in failure were used: the first was based on machine learning algorithms and the second on time series processes.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 4 Evaluation
### Evaluation Results
As a result, the delivery delay continues to increase as the number of destinations increases. Figure 11 shows the delivery delay with a varying number of edge nodes. Similar to the transmission count, the delivery delay decreases as the number of edge nodes increases. The reason is that with more edge servers, there is more chance to exploit the edge servers for forwarding and retransmissions. As the communication coverage of edge servers is much larger than that of sensor nodes, the delay on multiple hops is saved. Furthermore, the reduction also becomes smaller as the number of edge servers becomes larger. The reason is that when there are too many edge servers, most of the transmissions are performed by the edge servers and there is less optimization space for the delivery delay of transmissions in sensor networks. Figure 9: The transmission count with a varying number of edge servers.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids
### Material and Method  
In the previous part of the paper, most popular models of electrical discharges formation and propagation in liquids are detailed. Presented models cannot be, in general, used interchangeably,  
Figure 3: Ion dissociation under the influence of the field: (**a**) no ionization at low intensity fields; (**b**) ionization in the area of high intensity.  
and utility of specific description depends on electrical parameters of the discharge and material parameters of the region, where breakdown process occurs.  
Main topic of the paper is developing universal models and calculating procedure for the complex analysis of high voltage electric discharges. Models consider the pre-breakdown and breakdown stage of the discharge. Additionally, calculations of thermal phenomena in the time between the discharges are realized.  
Before development of numerical models, some tests were performed using physical model of discharge system. Based on measurements of electrical parameters of discharges, it was possible to select appropriate model of ionization. These tasks are detailed in Section 3 of the paper.  
In Section 4, general concept of HVED modeling is presented. It is assumed, that the mentioned issue can be solved using coupled electromagnetic and thermal fields. Both field and circuit models are in use.  
Electrostatic calculations, described in Section 5, are used to analyze the streamers propagation in models of liquids by using FEM (ANSYS) system. Electric field distribution and gas bubble models are used to determine streamers geometry in the pre-breakdown stage. Utility of additional analytical equations enables to determine speed of streamers propagations.  
In the Section 6, procedure for analysis of breakdown (arc) stage of discharge is detailed. FEM (ANSYS) numerical model with plasma channel is used. Plasma conductivity and cross section of the channel are determined by analytical equations. Momentary values of voltage drop and current in plasma channel are computed using coupled circuit model (composed of resistor and capacitor) of power supply. Such solution enables to determine transient characteristics of discharge, including power supply capacitor discharge.  
Section 7 presents the analysis of temperature distribution in the time between the discharges. The computational fluid dynamics procedures are used to guarantee the precise analysis of convection heat transfer in liquid environment. Based on the calculations results obtained in this stage, it is possible to determine the minimal break time between discharges.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
## 6 Conclusions  
This study numerically investigated the performance of a tubular solid oxide fuel cell with a novel current collector design. The newly developed design has a trapezoidal shape for the flow channels. Different numbers of flow channels, such as four, eight, and twelve, were investigated. Additionally, a comparison between the developed and traditional cell designs was reported. The following points are concluded from this study:  
1. The numerical model could predict the cell performance with the newly developed current collector designs. 2. The developed trapezoidal design achieves high performance compared with the traditional design. 3. Increasing the number of channels enhances cell performance by increasing the contact surface, leading to efficient current collection. 4. Increasing the number of channels from 8 to 12 did not achieve a significant high-performance difference, and any further improvement should be directed to the SOFC microstructure. 5. The design with twelve channels gave a high concentration of hydrogen at the outlet compared to the designs with four and eight channels. However, it achieved a greater performance than the designs with four and eight channels. Figure 12: Current density distribution contour through the cell. **Author Contributions:** Conceptualization, K.I.A. and M.H.A. ; methodology, K.I.A. ; software, K.I.A. ; validation, K.I.A. and M.H.A. ; formal analysis, K.I.A. and M.H.A. ; investigation, K.I.A. and M.H.A. ; resources, K.I.A. and M.H.A. ; data curation, K.I.A. and M.H.A. ; writing--original draft preparation, K.I.A. and M.H.A. ; writing--review and editing, K.I.A. and M.H.A. ; visualization, K.I.A. and M.H.A. ; supervision, K.I.A. ; project administration, K.I.A. ; funding acquisition, K.I.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research and the APC was funded by Deanship of Scientific Research (DSR) at King Abdulaziz University, Saudi Arabia, Jeddah, under Grant no. **G: 1500-135-1440**. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** This work was supported by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Saudi Arabia, Jeddah, under Grant no. **G: 1500-135-1440**. The authors wish to thank the DSR for their technical and financial support. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclatures**
## 3 Results and discussion
### Effect of the Photocatalyst Load
Although the details are different, in both cases, \(k\) varies non-monotonically with the catalysts concentration, in line with reports by other authors [62; 63]. The curve relative to \(a\)-TiO\({}_{2}\) NFs exhibits a sharp maximum at 0.33 mg mL\({}^{-1}\); in the case of the ZnO NFs, the variation of \(k\) with the photocatalyst load is smoother and the optimal load value is higher (0.66 mg mL\({}^{-1}\)). However, in both cases, very small relative amounts of the photocatalyst, compared to those frequently utilized [26; 27; 57], allow the dye to be effectively degraded. The initial improvement in the discoloration efficiency reflects the increase in the number of the photocatalyst active sites and the consequent enhancement in the formation of the hydroxyl (OH\({}^{\bullet}\)) and superoxide (O\({}_{2}\)\({}^{\bullet}\)) radicals, responsible for the dye degradation [62; 63]. The worsening at higher loads may be due to two cooperative mechanisms [62; 63]. On one hand, a higher concentration of the active material favors the agglomeration of the catalyst NPs/NFs, which reduces the active surface area. On the other hand, it reflects on greater turbidity of the catalyst suspension, which increasingly hampers the light penetration. It is worth noticing that, after load optimization, for ZnO NFs, a significant improvement with respect to the previously reported results [21] is obtained (namely, a rate constant of 7.94 \(\times\) 10\({}^{-2}\) min\({}^{-1}\) for 0.66 mg mL\({}^{-1}\) load against 6.06 min\({}^{-1}\) for 1.00 mg mL\({}^{-1}\)). This finding (i.e., higher activity with lower load) may be useful to scale up the process on an industrial scale. Outstandingly, \(a\)-TiO\({}_{2}\) NFs exhibit even higher activity toward the MB degradation (\(k\) = 1.12 \(\times\) 10\({}^{-1}\) min\({}^{-1}\)) at a smaller load (0.33 mg mL\({}^{-1}\)) compared to ZnO NFs.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
###### Abstract  
Recent advances in mobile communication require that indoor/outdoor environment information be available for both individual applications and wireless signal transmission in order to improve interference control and serve upper-layer applications. In this paper, we present a scheme to identify the indoor/outdoor environment using GPS signals combined with machine learning classification techniques. Compared to traditional schemes, which are based on received signal strength indicator (RSSI), the proposed scheme promises a robust approach with high accuracy, smooth operation when moving between indoor and outdoor environments, as well as easy implementation and training. The proposed scheme combined information from a certain number of GPS satellites, using the GPS sensor on mobile devices. Then, data are collected, preprocessed, and classified as indoor or outdoor environment using a machine learning model that is optimized for the best performance. The GPS input data were collected in the Kookmin University area and included 850 training samples and 170 test samples. The overall accuracy reached 97%.  
I/O detection; GPS signal; machine learning; positioning applications. +
Footnote †: journal: Accepted: 6 January 2020; Published: 10 January 2020
## 3 Results
### Radial Strength of the Tubular Stents  
Figure 18a shows the radial load-diameter curves of Stents B and C. The expanding force, normalized by the initial stent axial length (\(l_{\text{a}}\)) and labeled as the length-normalized force, decreased with an increasing outside diameter. As seen in Figure 18, radial strength was herein defined by drawing a straight line parallel to the linear loading section of the curve with an offset of 0.1 mm [19]. The radial strength of Stent C was 4.48 N/mm. However, the linear loading section in the radial loading curve of Stent B was difficult to distinguish from the rest of the curve, and in this case the length-normalized force at the same diameter as Stent C was chosen for comparison, and it was 0.73 N/mm. Stent C was found to have a much higher radial scaffolding strength than Stent B.  
Commercially available coronary artery stents are required to have an inside diameter of 3.0 mm after deployment [21]. In this research, Stents B and C were expanded further to an inside diameter of 3.0 mm. Stent C fractured but Stent B survived, allowing evaluation of its radial strength. The radial strength was 0.692 N/mm at the offset of 0.1 mm, as shown in Figure 18b. Assuming that the radial loading uniformly acted on the outer surface of the tubular stent, the radial strength could be transformed into a corresponding pressure. The radial strength of Stent B, after it was expanded to an inside diameter of 3.0 mm (0.692 N/mm), corresponded to a pressure of 74 kPa. [PERSON] and [PERSON] [22] provided the radial strengths of eight bare metal stents characterized by the stents collapse pressure, and all of them were above 110 kPa. Therefore, the radial strength of Stent B used in this research would need to be further enhanced.  
Figure 17: Fracture spots in the struts of stents with (**a**) Pattern B and (**b**) Pattern C in comparison with those in the planar specimens.
## 4 Improved Tight Combination Model  
To prevent positioning failure due to uncontrollable and cumulative errors from IMUs, we improved the UWB/IMU combination model with new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error self-correction. As Figure 2 shows, first, the distance information obtained by the second integral of the IMU accelerometer was converted to the global coordinate. According to the triangular three-side principle, the UWB ranging information was filtered by removing the positioning error caused by NLOS, and the optimized UWB ranging information was converted to the location information as the UWB output and EKF input.  
In Figure 2, this improved model can be divided into four parts: (1) doubling the IMU acceleration integration by time to obtain distance information, as shown in module A; (2) UWB time non-synchronous correction to obtain range information, as shown in module B; (3) distance threshold setting and UWB ranging information optimization, as shown in module C; (4) improvement of the UWB/IMU tight combination positioning model establishment, as shown in module D.  
To make the cumulative errors from IMUs controllable, the positioning results are input into the module E, i.e., the module of analysis of the dynamic double-loop cumulative positioning error. Then, the analysis result can be judged according to whether the controllability is satisfied. If the result is \"yes,\" then the localization results will be analyzed for the performances of variance and deviation. Otherwise, if the result is \"no\", the current  
Figure 2: Improved UWB/IMU tight combination positioning model.  
positioning results will be ignored, and the workflow returns back to module D for new position calculation until the positioning system controllability is satisfied.
## 2 Measurement Methodology and Used Devices
Figure 8 represents the measured average electric current that was flowing through the first stage into the electric motor. A comparison of Figures 6 and 8 clearly shows that the increase in the electric current becomes substantial mainly when the inlet gas pressure is elevated, which leads to an increase in the amount of compressed gas and thus also to an increase in the compression work. The rated current for the used electric motor is 41 A, while the current protection device switches off the motor at a current of 43 A. This indicates that it is necessary to maintain the relative gas pressure at the inlet to the compressor at a maximum value of 10 kPa. The nominal relative output pressure is, according to the compressor technical data, 3 kPa. The motor rotation speed during the measured values ranged between the value of 1007 and the nominal value of 1460 min\({}^{-1}\) (Figure 9). By increasing the revolutions up to the nominal value of 1460 min\({}^{-1}\), the electric current was increased. A correlation between the revolutions and the motor current cannot be identified, as the electric current depends on the mechanical power of the electric motor. A rapid increase of the input relative pressure (Figure 6) changes the parameters of technical output, as defined by Formulas (5) and (6). Concurrently, there is a change in the thermal power due to the changing temperatures and flow rates of cooling water, affecting thus output parameters in Formula (1). Figure 8: Average electric current flowing into the electric motor through one phase. Figure 2:0 Relative gas pressure after the first and second stages. The measurement of the acoustic pressure level was carried out at the same conditions as stated in the compressor documentation (according to GAGI-PNEUROP): 1 m distance from the noise source; 1.5 m height from the floor. The measurement was carried out using the Norsonic 118 measuring device (measurement accuracy of \(\pm\)2.1 dB\({}_{\rm A}\), Gracey & Associates, Upper Dean, UK). The measurements were carried out at the same times as the measurements of the pressure, flow rate, and other parameters. They lasted 100 s with a sampling frequency of 8 Hz. Subsequently, the average acoustic pressure level \(L\) was determined. The measurement results are listed in Figure 2:1 The values do not exceed the maximum allowable level of acoustic pressure--80 dB\({}_{\rm A}\). The highest values were reached when the electric motor was running at the nominal rotation speed.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 4 Numerical Results
### Simulation Results
#### 4.1.1 Simulation Setup
The channel states of \(S\)-\(D\) and \(r_{i}\)-\(D\) are uniformly quantized to 6 levels, and the corresponding mutual information is also 6 levels. Thus the total number of states is \(6^{3}=216\). There are 4 relay nodes in the simulated UWA network, so the size of Q-table is \(216\times 4\). The propagation delay is calculated by distance and speed of sound in the simulation. In the practical UWA network implementation, the delay can be obtained by the message exchanges of sensor nodes. The simulation parameters for SA-RL algorithm are set as Table 2.