esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A non-thermal plasma-water system using a microsecond pulsed high-voltage power supply was investigated with air, nitrogen, oxygen, and argon gas feedings individually. Optical emission spectroscopy (OES) was utilized to characterize the primary active species inside the plasmas generated by different gas feedings. The OES method was also employed to estimate the neutral gas and electron temperatures. The pH and the oxidation-reduction potential (ORP) of plasma-activated water (PAW) were measured in the liquid phase. An ion chromatography system (ICS) was employed to present the PAW activity, such as nitrite and nitrate species. Moreover, hydrogen peroxide as a secondary active species inside the activated water, generated by the gases mentioned above, was measured by potassium permanganate titration. It was found that the gas species have a noticeable effect on the pH level as well as the ORP of PAW. In the cases of argon and oxygen plasmas, the pH level of PAW does not change significantly. In contrast, the pH values of PAW generated by air and nitrogen plasmas decline sharply during the treatment time. Moreover, the gas species have a significant impact on the concentrations of nitrite, nitrate, and hydrogen peroxide generated in PAW. The activated water generated by oxygen plasma provides the highest level of hydrogen peroxide. Although the consumed power of argon plasmas was half of the other plasma sources, it provides relatively high hydrogen peroxide contents compared to the nitrogen and air plasmas.  
microsecond pulsed discharge; non-thermal atmospheric pressure plasmas; plasma-activated water; plasma-water interaction +
Footnote †: journal: Applied Sciences
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 4 Experimental Work
### Experimental Results  
#### 4.3.1 Non-Destructive Test  
First, a non-destructive test was carried out. In this test, a midspan loading was applied (see Figure 8 Case X).  
As is shown in Figure 9, strain rosette gages were used to measure the strains in three directions. The shear strain \(\gamma_{w}\) and the principle strain direction angle \(\varphi\) can be calculated by Equations (26) and (27) respectively.  
\[\gamma_{w}=\varepsilon_{0^{\circ}}+\varepsilon_{90^{\circ}}-2\varepsilon_{45 ^{\circ}} \tag{26}\]  
\[\varphi=\frac{1}{2}\text{arctan}\bigg{(}\frac{\gamma_{w}}{\varepsilon_{90^{ \circ}}-\varepsilon_{0^{\circ}}}\bigg{)} \tag{27}\]  
where \(\varepsilon_{0^{\circ}}\), \(\varepsilon_{90^{\circ}}\), \(\varepsilon_{45^{\circ}}\) are the strains in horizontal direction, vertical direction, and \(45^{\circ}\) direction respectively, as shown in Figure 11.  
Figure 12 shows the measured shear strain distributions of the CSWs at sections A and B for specimen S3-t2d3. It can be found that the shear strain distributions are almost uniformly distributed along the direction of the web height. Because the effect of the concrete flanges, the shear strain near flanges shows some deviations. The principal strain direction angles of CSWs are given in Table 12. It can be seen that the direction angles are close to \(45^{\circ}\), which indicates that CSWs for HCGs are almost in a pure shear stress state and barely carry axial forces. The results are similar to CSWs of straight girders [22].  
Figure 11: Schematic view of the strain rosette gage.  
Figure 10: Photograph of the test setup: (**a**) Test setup; (**b**) Strain gauge configurations on CSWs; (**c**) Load transducers on supports; (**d**) Displacement transducers.  
According to structural mechanics, the total shear force of section A and B is 25 kN when applying 50 kN vertical load at midspan. The average shear strain in the same section is used to calculate \(Q_{w}\), the shear force carried by the CSWs, which can be calculated by Equation (28) due to the uniformly distributed shear strain along the direction of the web height.  
\[Q_{w}=G(\gamma_{o}+\gamma_{i})dt \tag{28}\]  
where \(\gamma_{o}\) and \(\gamma_{i}\) are the average shear strain of the outer web and the inner web respectively. Table 13 shows the ratios of \(Q_{w}\) to the total shear force \(Q_{total}\) of specimen S3-t2d3, and the average ratio is 76%.
# Evaluation of Garlic Landraces from Foggia Province (Puglia Region; Italy)
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
The results of the PCA using the chemical parameters showed that the first two PCs explained approximately 70% of the total variability, attributing 42% to PC1 and 26% to PC2 (Figure 2).  
Figure 2: Principal component analysis bi-plot (PC1 vs. PC2) showing the spatial distribution of the main chemical traits as affected by genotypes. Refer to Table 1 for the acronyms of garlic genotypes. Following a previous analysis of correlation, 3-vinyl-1.2-dithiocyclohex-5-ene is representative of a group of seven volatile S-compounds (di-methyl-sulphide, propylene-sulphide, methyl-2-propenyl-disulphide, trans-propenyl-methyl-disulphide, di-methyl-trisulphide, methyl-allyl-trisulphide, and Allicin-S-oxo-di-allyl-di-sulphide); di-allyl-disulphide (DADS) is representative of a group of two volatile S-compounds (di-allyl-sulphide-DAS and 2-vinyl-1.3-dithiane); Isorhametin is representative of 3-hydroxy-methyl-phenol.  
The group of 'Anzano', 'Monteleone', 'Cortigli', and 'Peschici' landraces separated from the 'PanniD', 'Panni' landraces (low side of PC2) and also from the CG (left side of PC1).  
The former group clustered for the concentration of phenolic compounds (Isorhamnetin, Ferulic acid Caffeic-acid-O-hexoside-2, and Rutoside1) (highly and positively correlated to PC1) and for the high concentration of anions and cations (highly and positively correlated to PC2). 'Peschici' and 'Monteleone' set in an extreme position of this quadrant, mainly characterized by the highest mineral (K) concentration and phenols, as also underlined by Anova (Tables 2 and 5). 'Anzano' and 'Cortigli' clustered quite in a cloud-graphical space, suggesting a substantial homogeneity in chemical composition.  
Conversely, the CG tended to cluster separately on the left side of PC1 for the general negative correlation with phenolic compounds and for the positive correlation with the volatile S-compounds (di-allyl-disulphide-DADS and 3-vinyl-l,2-dithiocyclohex-5-ene), with a phenolic compound as Quercetol (Quercetin) (exclusively present in GC, Table 5), citric acid and sucrose.  
The PC2 axis separated the two Panni Municipality landraces from all the other landraces mainly for the general negative correlation with all the minerals and phenols, except for Hyperoside (Hyperin), exclusively present in 'PanniD' (Table 5).
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Delamination Measurements Based on Electrical Conductivity
#### 4.2.3 Electrical Potential Change Method  
In addition to ERCM, the electrical potential change method (EPCM) was also employed to sense CFRP delamination. EPCM measures the change in electric potential difference based on the changing electric current at the two electrodes installed at two opposite ends of a specimen [152].  
In ERCM and EPCM, two electrodes are used to send current and different numbers of electrodes are used to measure voltage. The difference between them lies in the potential measurement direction. In ERCM, the applied current line coincides with the electric potential line. In EPCM, the two lines do not coincide with each other [52; 153; 154]. The one-dimensional (1-D) ERCM is enough for sense the damage distribution. A two-dimensional (2-D) method is required for determining the damage location. In the 2-D ERCM, a large array of contacts is required to cover the surface. According to the ERCM mechanism, each surface requires three contacts, which cannot be realized in the two-dimensional resistance method. The 1-D EPCM avoids the disadvantages since it can be applied with only the contacts along specimen edges. The current is applied with one pair of contacts and other contacts are used to measure the voltage [155].  
Early in the 1990s, [PERSON] et al. at the Tokyo Institute of Technology explored EPCM and largely contributed to the development of non-destructive testing of CFRP [156]. [PERSON] combined the response surface method with EPCM to determine a delamination in the laminate [157; 158]. However, extensive experimental data are required in these models for calibration. The calibration is performed for a given experimental condition. [PERSON] et al. also employed a normalization method to improve the delamination detection performance. The improved method showed better detection performance for the delamination near specimen edges, but the low performance for the delamination in the middle of the specimen [159]. A new two-stage estimation method was used to solve the problem and showed better performance than FEM analysis [160].
###### Abstract  
Model-based tracking of the movement of the tibofemoral joint via a biplane X-ray imaging system has been commonly used to reproduce its accurate, three-dimensional kinematics. To accommodate the approaches to existing clinical asynchronous biplane fluoroscopy systems and achieve comparable accuracy, this study proposed an automated model-based interleaved biplane fluoroscopy image tracking scheme (MIBFT) by incorporating information of adjacent image frames. The MIBFT was evaluated with a cadaveric study conducted on a knee specimen. The MIBFT reproduced skeletal poses and tibofemoral kinematics that were in good agreement with the standard reference kinematics provided by an optical motion capture system, in which the root-mean-squared (Rms) errors of the skeletal pose parameters ranged from 0.11 to 0.35 mm in translation and 0.18 to 0.49\({}^{\circ}\) in rotation. The influences of rotation speed on the pose errors were below 0.23 mm and 0.26\({}^{\circ}\). The MIBFT-determined bias, precision, and Rms error were comparable to those of the reported model-based tracking techniques using custom-made synchronous biplane fluoroscopy. The results suggested that the further use of the clinical imaging system is feasible for the noninvasive and precise examination of dynamic joint functions and kinematics in clinical practice and biomechanical research.  
model-based tracking; fluoroscopy; image registration; kinematics; knee 2020, 10, 8426; doi:10.3390/app10238426
## 2 Methodology of the Proposed SWB-BPF
In this case, Equation (9) remains unchanged, while Equation (10) becomes  
\[-{Z_{s}}^{-1}\cot\theta_{s}=-2{Z_{o}}^{-1}\tan\frac{\theta}{2}\]  
The stubs depicted in Figure 3a,b have been substituted with the stubs illustrated in Figure 4a,b. Initially, a conventional stub, as depicted in Figure 4a, was positioned at the centre of the UTL using matrix Equation (11) [45]. The given configuration generates a wideband response with poor selectivity, with one TP observed at 12 GHz, as depicted in Figure 5a. \[[A]=\left( \begin{matrix}1&0\\ j\frac{1}{Z_{s}}\tan\theta&1\end{matrix} \right) \tag{11}\]  
To increase the design flexibility, the stub in Figure 4a is replaced with a stub shown in Figure 4b. Furthermore, two more identical stubs are placed on the opposite side of the transmission line. This adjustment leads to a modified configuration, as depicted in Figure 1. The corresponding equation of Figure 4b is given below. \[[A]=\left( \begin{matrix}1&0\\ -j\frac{1}{Z_{s}}\cot\theta&1\end{matrix} \right) \tag{12}\]  
The way the stubs are arranged significantly enhances the filter's performance, as demonstrated in Figure 5b. The filter exhibits a wide and flat passband, excellent IL, and sharp rejection levels at the lower and upper stopband, with good out of band suppression up to 25 GHz, respectively. Figure 4: (**a**) Conventional stub. (**b**) Proposed shunt stub. Figure 5: S-parameter responses: (**a**) response of the wideband filter loaded using a single conventional stub; (**b**) response of the proposed broadband filter. In Figure 1, when the source and load impedances (\(Z_{o}\)) are well matched, then the relation of the reflection coefficient, \(\mid S_{11}\mid\), and power transmission coefficient, \(\mid S_{21}\mid\), can be obtained, and are given below:  
\[S_{11}=\frac{A+(B/Z_{o})-CZ_{o}-D}{A+(B/Z_{o})+CZ_{o}+D} \tag{13}\]  
\[S_{21}=\frac{2}{A+(B/Z_{c})+CZ_{c}+D} \tag{14}\]
## 3 Error Overboundings against IMU Faults
### Error Overboundings  
For the error-state EKF, the innovation at epoch k can be expressed as follows:  
\[\begin{split}\vec{\gamma}_{k}&=\vec{Z}_{k}-H_{k} \hat{X}_{k}^{\ -\cdot}\\ &=H_{k}\vec{X}_{k}+\vec{v}_{k}-H_{k}\hat{X}_{k}^{\ \cdot\cdot}\\ &=H_{k}\left(\[PERSON]_{k}\vec{X}_{k-1}+\vec{\omega}_{k}\right)+\vec{ v}_{k}-H_{k}\left(\[PERSON]_{k}\hat{X}_{k-1}+\vec{f}_{\ddot{X}_{k}}\right).\\ &=H_{k}\[PERSON]_{k}\underbrace{\left(\vec{X}_{k-1}-\hat{X}_{k-1} \right)}_{-\delta X_{k-1}}+H_{k}\vec{\omega}_{k}+\vec{v}_{k}-H_{k}\vec{f}_{ \ddot{X}_{k}}\\ &=-H_{k}\[PERSON]_{k}\delta X_{k-1}+H_{k}\vec{\omega}_{k}+\vec{v}_{k} -H_{k}\vec{f}_{\ddot{X}_{k}}\end{split} \tag{28}\]  
As Equations (27) and (28) show, the final EKF state error and EKF innovation using the error-state EKF were the same as that obtained by the full-state EKF in [18]. Based on the method proposed in [18] for the full-state EKF, the error overboundings against the IMU faults for the error-state EKF can be expressed as Equation (29), which was originally derived in [18] for the full-state EKF.  
\[PL_{error-overboundings}=-V_{k,p}\overrightarrow{\gamma}_{k}\pm K_{md,IMU} \bigg{\{}\sigma_{V_{k,p}\overrightarrow{v}_{k,p}}+\sigma_{K_{k,p}\overrightarrow {v}_{k,p}}\bigg{\}}. \tag{29}\]  
As we expected, the final error overboundings equation (Equation (29)) can be expressed in an identical manner to those derived based on the full-state EKF in the previous study [18].
## 3 Principle and Implementation of Markov Chain Random Carrier Frequency Modulation
### Markov Chain Principle and Three-State Random Number Generation Process
In Figure 5, it can be seen that if the current carrier frequency of the system belongs to state 1, the next generated carrier frequency of the system can only belong to state 2 or 3, and the probability of belonging to state 3 is \(P_{1}\), while the probability of belonging to state 2 is \(1-P_{1}\). If the current carrier frequency of the system belongs  
Figure 5: State parameter transition diagram of three-state Markov chain carrier frequency. to state 2, then the probability of the next generated carrier frequency still belonging to state 2 is \(P_{2}\), while the probabilities of being in state 1 or 3 are both \((1-P_{2})/2\). If the current carrier frequency of the system belongs to state 3, the next generated carrier frequency of the system can only belong to state 1 or 2, where the probability of being in state 1 is \(P_{1}\), and the probability of being in state 2 is \(1-P_{1}\). If the current carrier frequency of the system belongs to state 3, the next generated carrier frequency of the system can only belong to state 1 or 2, where the probability of belonging to state 1 is \(P_{1}\), and the probability of belonging to state 2 is \(1-P_{1}\). Therefore, the transition probability matrix of the three-state Markov chain can be expressed as  
\[P= \begin{bmatrix}p_{11}&p_{12}&p_{13}\\ p_{21}&p_{22}&p_{23}\\ p_{31}&p_{32}&p_{33}\end{bmatrix} = \begin{bmatrix}0&p_{1}&1-p_{1}\\ (1-p_{1})/2&p_{2}&(1-p_{1})/2\\ 1-p_{1}&p_{1}&0\end{bmatrix}  \tag{14}\]  
where \(p_{1}\) and \(p_{2}\) take the values of \((0,1)\). The three-state Markov chain random carrier frequency generation result is shown in Figure 6. Compared with the conventional random carrier frequency generation results, the performance of random numbers is further improved, and the randomized carrier frequency distribution is more uniform, which verifies the effectiveness of the three-state Markov chain random number algorithm.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### Simulation Configuration
* DRBF\({}_{\mathtt{RQ}}\), ADBF\({}_{\mathtt{RQ}}\): They are the proposed mechanisms implementing the duplication suppression scheme and the re-queuing scheme as proposed in Sections 3.2 and 3.3, respectively. The duplication ratio in (2) was used in DRBF\({}_{\mathtt{RQ}}\) (\(\alpha=1.0\)) whereas the approximated one in (3) was used in ADBF\({}_{\mathtt{RQ}}\) (\(\delta=0.1\), \(\mu=1000\)) (We determined the values of \(\delta\) and \(\mu\) heuristically after performing many simulations under various configurations). Meanwhile, DRBF and ADBF denote mechanisms that the re-queuing scheme without the re-queuing scheme. We observed and compared the performance in various aspects with the indices summarized in Table 1. We repeated the simulations 10 times with a different distribution of nodes and obtained the average values of these performance indices.
## 3 Control Strategy and Optimization Function  
### Optimization Function Justification  
To utilize a recent heuristic optimization method for creating a controller, the initial step involves establishing an objective function that considers the necessary constraints and specifications. The integral absolute error (IAE), integral-squared error (ISE), integral-time-squared error (ITSE), and integral time absolute error (ITAE) are performance indices that are typically considered in the control design. IAE- or ISE-based tuning cannot decrease the settling time as effectively as the ITAE criterion. Furthermore, a large controller output is provided by an ITSE-based controller for a quick change in set point. The literature has shown that the ITAE is more effective than other performance criteria [47]. The ITAE is used as the optimization objective function for the controller gains in this study, and it is given by  
\[J=ITAE=\int\limits_{0}^{Tsim}t|\Delta f|dt \tag{14}\]  
where \(T_{sim}\) is the time range of the simulation and \(\Delta f\) is the frequency deviation of the system.
## 3 Results and Discussion
### Mathematical-Statistical Analysis for Measured Values  
After the experiments and partial analyses of the measured results were finished, the experimental data were subjected to statistical analysis (Table 8).  
From the analysis of variance (Table 8) for the total machining force \(F\), it follows that the most significant influence is shown by the engagement angle \(\alpha\), which directly affects the value of the maximum width of the removed layer, reaching up to 75.9% of the linear model. This fact is manifested above all in a more pronounced inclination in the direction of the \(s\) axis. From the scattering analysis, it also follows that, within a given range of cutting conditions, the response has a quadratic character  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & **\(p\)-Value** & **Contribution (\%)** \\ \hline Model & 5 & 5,050,835 & 1,010,167 & 59.30 & 0.003 & 99.00\% \\ Linear & 2 & 1,409,645 & 704,822 & 41.37 & 0.007 & 75.90\% \\ \(s\) (mm) & 1 & 1,178,227 & 1,178,227 & 69.16 & 0.004 & 4.45\% \\ \(a\) (\({}^{\circ}\)) & 1 & 339,360 & 339,360 & 19.92 & 0.021 & 71.45\% \\ Square & 2 & 1,177,729 & 588,864 & 34.57 & 0.008 & 23.08\% \\ \(s\) (mm) & 1 & 1,113,985 & 1,113,985 & 65.39 & 0.004 & 21.83\% \\ \(a\) (\({}^{\circ}\)) & 1 & 637,743 & 63,743 & 3.74 & 0.149 & 1.25\% \\ \(2\)-Way Interaction & 1 & 832 & 832 & 0.05 & 0.839 & 0.02\% \\ Error & 3 & 51,106 & 17,035 & & & 1.00\% \\ Total & 8 & & & & & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The output from the analysis of variance for the total machining force \(F\).  
Figure 9: Time recording of the evolution of the cutting force components for selected cases (**top—**detail of one pass; **bottom—**the progress as a whole).  
in the direction of the \(s\) axis; i.e., approximately at the mean values of the step \(s\), this force reaches its maximum.  
The response area is defined as the second-degree polynomial. The precision of the presented model reaches 99%, with a prediction coefficient of 87.02% and a 95% confidence interval setting, or \(\pm\)5% on both sides. In terms of the statistical significance of the individual members, this model can be considered correct and sufficiently accurate for further research, for simulation and verification of the machining process and for subsequent follow-up applications in professional practice.  
The response area equation (Figure 10) shows the influence of the trochoid parameters: step \(s\) (mm) and engagement angle \(\alpha\) (\({}^{\circ}\)), with the trochoid constant radius \(R_{p}\) = 5 mm; namely, the impact on the change in the total machining force \(F\) (2):  
\[F=-462+6131\cdot s+53.3\cdot\alpha-4664\cdot s^{2}-0.3\cdot\alpha^{2}-1.43 \cdot s\cdot\alpha \tag{2}\]
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 3 Materials and Methods
### Sewer Pipe Defect Detection Method Based on Improved YOLOv4
#### 3.3.1 Overall Architecture of the YOLOv4 Model  
The overall structure of YOLOv4 is shown in Figure 5. Compared with YOLOv3, YOLOv4 has three main innovations in the network structure, including the backbone feature extraction network CSPDarknet53, the spatial pyramid pooling (SPP) module and the path aggregation network (PANet). CSPDarknet53 adds the Cross Stage Partial connections (CSP) module [23] on the basis of Darknet53. The CSP module can reduce computation and memory costs while improving the accuracy of the model. As shown in Figure 5, CSPDarknet53 consists of five residual structures CSP_ResBlock_Body, whose network structure is shown in Figure 6. After the input images pass through the backbone network, three feature maps of different scales P1, P2 and P3 are obtained, which are responsible for the detection of large, medium and small targets, respectively. These three feature maps will be sent to the Neck network for feature fusion.  
Figure 4: Different types of data augmentation: (**a**) raw image; (**b**) horizontal flip; (**c**) vertical flip; (**d**) color tuning; and (**e**) random scaling.  
Figure 5: Architecture of YOLOv4. In the figure, the yellow part is the backbone network (CSPDarknet53), the blue part is the SPP module, the green part is the PANet module and the purple part is YOLO Head.  
YOLOv3 combines semantically rich high-level features with low-level spatial information through a top-down path. However, this method may cause the loss of spatial information due to the long path. For the high-level feature layer used to detect large objects, the spatial information may need to be propagated through hundreds of layers to be fused with the high-level semantic information. PANet adds a bottom-up path on the basis of FPN, which greatly shortens the distance between low-level features and high-level features, enhancing the fusion ability between different feature layers.