esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Heart Sounds Using Convolutional Neural Network
## 3 Results
### Performance Comparison between the Fully Connected Layer and the Global Average Pooling Layer  
In the proposed method, the fully connected layers of the conventional CNN were replaced by a GAP layer to avoid overfitting and to improve the model's performance. To demonstrate the advantages of using a GAP layer, we performed another experiment on the model with one fully connected layer followed by the dropout strategy with a dropout rate of 0.25. The comparison of the average performances, in terms of five-fold cross-validation, between the fully connected layer and the global average pooling layer is depicted in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Mean** \\ \hline Sensitivity & 0.919 & 0.838 & 0.741 & 0.943 & 0.909 & 0.87 \\ Specificity & 0.735 & 0.92 & 0.944 & 0.937 & 0.795 & 0.866 \\ MCC & 0.593 & 0.797 & 0.703 & 0.906 & 0.606 & 0.721 \\ Macc & 0.827 & 0.879 & 0.8425 & 0.94 & 0.852 & 0.868 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification performance of the proposed method based on five-fold cross-validation.  
Figure 4 shows that the proposed model with a GAP layer is more powerful for normal and abnormal heart sound classification than the model with a fully connected layer. The mean accuracy and sensitivity of the former are approximately 2 percentage points and 6 percentage points higher than the latter, respectively. The MCC observed from the proposed model is about 4 percentage points higher than the model with fully connected layer. However, the specificity of the fully connected layer is better than that of the GAP layer. From a clinical standpoint, sensitivity is more important than specificity, so the classification results from the model with a GAP layer are promising.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 4 Single-Difference Ambiguity Analysis
However, when using the other two different satellites as a reference respectively, the L1 SD ambiguities were estimated to be 7. Similarly, for the L2 SD ambiguities, when using five different satellites as a reference respectively, the estimated values were 63; when using  
Figure 1: Time series of the float SD carrier-phase ambiguities (fractional) of reference satellite obtained for KOKV-KOVB baseline using L1 and C1 code (**top**) and using L2 and P2 code (**bottom**). Figure 2: Time series of the SD float carrier-phase ambiguities (fractional) of reference satellite obtained for ONSA-SPT0 baseline. the other one satellite as a reference, the L1 SD ambiguity was estimated to be 62; and using the rest of the satellites as a reference, the L1 SD ambiguity was estimated to be 61. It can be concluded that there are differences in SD ambiguity resolution using different reference satellites, which affects the accuracy of the time difference solution obtained by Equations (10) and (11). Clustering the results of these SD carrier-phase ambiguities, it can be determined that the L1 SD ambiguity is 6 and the L2 SD ambiguity is 63 for G18. For the ONSA-SPT0 baseline, Figure 4 illustrates the GPS IF combination SD carrier-phase ambiguities of G12, G13, G24, G25, G27, and G29. Taking G13 as an example, it can be observed that the IF SD carrier-phase ambiguities for each visible satellite are different, and it is not the integer number of cycles because IF combined observations cause the ambiguity to lose integer properties. After clustering the results of these SD carrier-phase ambiguities, the final IF SD ambiguity of G13 was determined to be 5.6. Figure 4: GPS SD IF ambiguities of G12, G13, G24, G25, G27, and G29 for ONSA-SPT0 baseline. Figure 3: GPS L1 and L2 SD ambiguities of G07, G08, G10, G13, G18, G24, G27, and G28 for KOKV-KOVB baseline.
## 2 Sensing Method
### Piezolectric Sensing  
The piezoelectric effect stems from the linear electromechanical coupling between the mechanical and electrical states within crystalline materials lacking inversion symmetry. This effect is a reversible phenomenon: materials demonstrating the piezoelectric effect also manifest the reverse piezoelectric effect, wherein an applied electric field gives rise to the internal generation of a mechanical strain. The piezoelectric effect finds application in pressure sensing [37; 38], whereas the inverse piezoelectric effect is frequently employed for the generation of ultrasonic waves. The advantages of this sensor lie in its ability to convert mechanical energy into electrical energy, thereby enabling self-power supply and eliminating the necessity for external power sources. Additionally, it features a simple structure and relatively high sensitivity. However, this type of device exhibits a greater sensitivity to dynamic signals and a lower sensitivity to static signals, which may impose certain limitations in long-term monitoring scenarios [39].
## 4 Vibration Signal Analysis
### EMD Frequency Analysis Result  
The EMD method is used to analyze the vibration of 16 test points, but due to the large amount of data, this paper only shows the analysis result of test point 1. As shown in Figure 6, IMF1-IMF15 and the signal residual components are obtained by decomposition.  
Figure 5: Time domain statistical characteristics of different test points: (**a**) mean of 16 test points, (**b**) variance of 16 test points, and (**c**) RMS of 16 test points.  
Column a is the time-domain waveform of the IMF obtained by EMD method, column b is the FFT spectrum of column a, and column c is the local enlarged view of column b.  
Figure 6: EMD analysis results of test point 1: (**a**) IMFs of test point 1, (**b**) FFT spectrum of IMFs, and (**c**) enlarged view of the FFT spectrum.  
It can be seen from Figure 6 that IMF1 of the EMD analysis results has frequency components of 181.8 Hz, 184.0 Hz, 176.4 Hz, 187.3 Hz, 168.1 Hz, 197.7 Hz, 203.9 Hz and 141.6 Hz (The frequency components are sorted by descending order of amplitude.). In IMF2, the frequency components are 101.2 Hz, 78.9 Hz, 65.3 Hz, 94.9 Hz and 59.3 Hz. In IMF3, the frequency components are 45.2 Hz, 37.0 Hz, 48.1 Hz, 59.3 Hz, 54.5 Hz and 65.8 Hz. Both IMF2 and IMF3 have a frequency component of 59.3 Hz. From the above results, it can be found that IMFs decomposed by the EMD method have modal aliasing problem, which affects the accuracy of the signal's decomposition. The other 15 test points also have the same modal aliasing as test point 1.
## 2 Ti6 Al4V  
### General Information  
Titanium is the fourth-most abundant structural metal in the earth's crust [9]. However, titanium rarely exists in a concentrated form. As a result, titanium has a relatively short production history. The element titanium was discovered in England by the Reverend [PERSON] in 1790, and was first purified in the United State by [PERSON] at the General Electric Company in 1906 [10; 11]. In the 1950s, numbers of companies managed to meet the challenge to produce titanium. The value of aluminum and vanadium as alloy additions was established in the \"Workhorse alloy\" Ti6 Al4V, which was patented by Crucible Steel in 1954 [12]. Since then, Ti6 Al4V has been widely used for lightweight parts in variety of industrial products [13].  
The chemical composition of Ti6 Al4V is shown in Table 1.  
Ti6 Al4V is designed for a good balance of strength, ductility, fatigue and fracture properties. Ti6 Al4V has been used as an important structural material in advanced aircraft since the 1960s [14]. Nippon Steel and the Sumitomo Metal Corporation acquired qualifications in 1985 from Rolls-Royce and started commercial production of titanium alloys for aircraft engines. Furthermore, the company concluded a long-term agreement with Airbus in 2002 and has been consistently supplying titanium for Airbus's airframes [5].  
In aerospace, Ti6 Al4V is mainly used for airframe and engine parts. An expansion of the demand on the market is expected due to the requirement of low fuel consumption by aircraft. In airframes, it is used for structural materials, such as bolts and seat rails; in engines, due to the relatively low allowable temperature of about 300 \({}^{\circ}\)C of the material, it is used for fan blades, fan cases and engine suspension, where the working temperatures are lower than 300 \({}^{\circ}\)C [5]. Additive manufactured parts, such as cabin brackets and bleed pipes, are already being applied in test aircraft of new models, such as Airbus A320 neo and A350 XWB parts. The current knowledge can already allow the parts produced by additive manufacturing to replace those which were produced by conventional process [15].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Chem. Element wt.\%** & **Al** & **V** & **Fe** & **O** & **N** & **C** & **H** & **Ti** & **Others (Each)** & **Others (Total)** \\ \hline Min. & 5.50 & 3.50 & 0 & 0 & 0 & 0 & 0 & balance & 0 & 0 \\ Max. & 6.75 & 4.50 & 0.40 & 0.20 & 0.05 & 0.08 & 0.015 & balance & 0.10 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of Ti6 Al4V.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
###### Abstract  
Soy is a growing protein source; however, the isoflavunes it contains are of concern, as they exhibit estrogenic activities whose toxicological limits might be exceeded. Reducing their concentrations to safe levels while preserving nutritional quality in soy foodstuffs is therefore a matter of public health. The main objective of this paper is to develop at pilot scale a process for isoflavunes' extraction from soybeans, and to show its feasibility and efficiency. The study was conducted by first optimizing the previously obtained laboratory treatment key factors. These data were then transposed to the pilot level. Finally, the process was adjusted to technical constraints which appeared at pilot scale: the mandatory use of drenching and the exploration of granulometry analysis. The involved steps were validated by monitoring the genistein and daidzein content variations through statistical analysis of the data of an ELISA and a Folin-Cicolact assay. Additionally, isoflavunes' recovery from treatment waters for their valorisation and the water cleaning by means of filtration, centrifugation and resin adsorption were carried out. The results showed that the most successful pilot treatment developed involved soybean dehulling, drenching, washing and drying and almost halved isoflavues while preserving the main nutritional characteristics. A combination of techniques led to almost complete recovery of isoflavones from process waters.  
Keywords:genistein; daidzein; consumer exposure; manufacturing process; soybean; isoflavremoval +
Footnote †: journal: _foods_
## 4 Discussion  
In Figure 4, we studied the dependences of mode properties on the ratio of \(b/a\). Here, we compared the GCENW with two GCCNW waveguides with radii equal to \(a\) and \(b\). Figure 7a-c shows the normalized energy distributions of the GP modes for graphene-coated nanowire with \(R=a=1~{}\upmu\)m, GCENW with \(a=1~{}\upmu\)m and \(b=3~{}\upmu\)m, and graphene-coated nanowire with \(R=b=3~{}\upmu\)m, respectively. In Figure 7d we compared the propagation length of these three GP modes. The red line shows the propagation length of the GP mode in GCENW, with \(a=1~{}\upmu\)m and \(b/a\) ranging from 1 to 3. The gold and blue lines show the propagation length for the plasmon modes of Figure 7a, and 7c, which are \(L_{\text{P}}=86.6~{}\upmu\)m and \(L_{\text{P}}=95.7~{}\upmu\)m, respectively. Therefore, the GP mode in GCENW had a longer propagation length for \(b/a>\)1.8 when comparing with GCCNW plasmon modes (with \(R=1~{}\upmu\)m and \(R=3~{}\upmu\)m).  
Meanwhile, we already knew that the effective mode area of the GP mode was smaller than that of the GCCNW plasmon mode with \(R=a=1~{}\upmu\)m, seen in Figure 4b. As for the GCCNW plasmon mode with \(R=b=3~{}\upmu\)m, the effective mode area was about \(2.66\times 10^{-2}~{}A_{0}\), which is enlarged by one order of magnitude when compared with the case of \(R=a=1~{}\upmu\)m.  
Furthermore, we obtained that the FWHM of \(S_{\text{z}}\) at the tip was 250 nm for \(b=2~{}\upmu\)m and \(a=1~{}\upmu\)m (140 nm for \(b=1~{}\upmu\)m and \(a=0.5~{}\upmu\)m, seen in Figure 2a). For GCCNW plasmon mode with \(R=1~{}\upmu\)m and \(R=3~{}\upmu\)m, the FWHMs of \(S_{\text{z}}\) were approximately 400 nm and 1000 nm, respectively. As the field outside the circular wire was radially polarized, the circular wire shows relatively weak mode confinement. Additionally, the whole graphene layer led to large loss. As for GCENW, the nanofocusing effect of the elliptical nanowire tip resulted in strong modal field confinement, just as the conical metal nanowires. At the same time, only a small fraction of graphene contributed to loss, thus the propagation loss was reduced when compared with circular wire. These results indicated that the plasmon modes in GCCNW were less confined when compared to that of the GCENW, and provided a reasonable explanation for the smaller effective mode area of the GP mode in GCENW.
## 3 Results and Discussion
### Influence of \(NO_{x}\) Acidity and RH on SOA (Experiments: Tol-7 to Tol-9)
#### 3.4.3 Influence of \(\mathrm{NO_{x}}\) Acidity and RH on SOA Light Scattering  
The light scattering characteristics of toluene SOA were also significantly influenced by H\({}_{2}\)SO\({}_{4}\) seeding under various \(\mathrm{NO_{x}}\) and RH conditions. The increase in \(\mathrm{B_{scat}}\) with [\(\mathrm{NO_{x}}\)]/[\(\mathrm{\Delta{HC}}\)] mirrored that of the SOA mass in all three H\({}_{2}\)SO\({}_{4}\) seeded experiments (e.g., Tol-7, Tol-8 and Tol-9) (see Figure S11 in the Supplementary Materials). Upon comparing the seeded experiments to their non-seeded counterparts (Tol-1, Tol-2 and Tol-4, respectively), it can be seen that the response of \(\mathrm{B_{scat}}\) to increasing [\(\mathrm{NO_{x}}\)]/[\(\mathrm{\Delta{HC}}\)] differs and that acid seeding increased the absolute value of \(\mathrm{B_{scat}}\) by factors of 1.25-2.5 in the NP regime and 1.5 to 3 in the NR regime (see Figure 15a,b). These increases in \(\mathrm{B_{scat}}\) can be attributed to the presence of sulfuric acid seeds, which are likely to have increased the surface area and scattering potential of the formed SOA. The relative enhancement of \(\mathrm{B_{scat}}\) in the H\({}_{2}\)SO\({}_{4}\)-seeded SOA was highest (2.5- to 3-fold) at the lowest RH (20%) indicating that the contribution of H\({}_{2}\)SO\({}_{4}\) seeds to the aerosol mass fraction was highest at 20% RH. The response of the MSC to increases in [\(\mathrm{NO_{x}}\)]/[\(\mathrm{\Delta{HC}}\)] was similar in both the H\({}_{2}\)SO\({}_{4}\) seeded and non-seeded experiments (see Figure S12a,b in the Supplementary Materials). However, like the \(\mathrm{B_{scat}}\) values, the absolute MSC values were significantly higher in the H\({}_{2}\)SO\({}_{4}\) seeded experiments than in their non-seeded counterparts, under both NP and NR conditions (see Figure S12 in the Supplementary Materials). A rationale for the higher MSC in the seeded case is that H\({}_{2}\)SO\({}_{4}\) also acts as a strongly scattering substance and increases the surface area of the resulting SOA. The highest MSC values were observed at the intermediate RH (65%) in both the NP and NR SOA regimes (see Figure S12a,b). However, the effects of acidity and RH on the chemical composition of the SOA and the resulting changes in its light scattering properties remain unclear. It seems most likely that the higher values of \(\mathrm{B_{scat}}\) and MSC observed for seeded SOA under both NP and NR conditions can be mainly attributed to the enhancement of surface growth of SOA particles caused by H\({}_{2}\)SO\({}_{4}\) seeds.  
Figure 15: (**a**,**b**): Effects of water vapour and acidity on the \(\mathrm{B_{scat}}\) of toluene SOA. (**a**) \(\mathrm{B_{scat}}\) of NP SOA for the \(\mathrm{H_{2}SO_{4}}\) seeded and non-seeded experiments at the three RHs; (**b**) \(\mathrm{B_{scat}}\) of NR SOA for the \(\mathrm{H_{2}SO_{4}}\) seeded and non-seeded experiments at the three RHs. The solid and dashed lines are eye guides.
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 2 Theory of BDS-3 TGD/DCB Correction Models
### Correction Model with Precise Satellite Clocks  
At present, the BDS-3 precise satellite orbit and clock products of Wuhan University (WUM), as well as the BDS-3 DCB products of CAS is all derived from the B1I/B3I ionospheric-free LC. Therefore, when single-frequency users used the precise ephemeris, \(B_{j}\) can be described below:  
\[\left\{\begin{array}{c}B_{B1I}=-\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}} \cdot DCB_{B1I-B3I}=-\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot TGD_{1} \\ B_{B3I}=-\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot DCB_{B1I-B3I}=-\frac{ f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot TGD_{1}\\ B_{B1C}=-\left(-DCB_{B1C-B3I}+\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot DC _{B1I-B3I}\right)=-\left(-TGD_{B1 Cp}+\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2} }\cdot TGD_{1}\right)\\ B_{B2a}=-\left(-DCB_{B2a-B3I}+\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot DC _{B1I-B3I}\right)=-\left(-TGD_{B2 ap}+\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2} }\cdot TGD_{1}\right)\end{array}\right. \tag{6}\]  
Figure 3: Comparison of BDS-3 broadcast TGD with DCB.  
For dual-frequency users, when users used the precise ephemeris, \(B_{i/j}\) can be then expressed below:  
\[B_{B1I/B3I}=0 \tag{7}\] \[B_{B1C/B2a} =\frac{f_{B1C}^{2}}{f_{B1C}^{2}-f_{B2a}}\cdot DCB_{B1C-B3I}-\frac{f _{B2a}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot DCB_{B2a-B3I}-\frac{f_{B1I}^{2}}{f_{ B1I}^{2}-f_{B3I}^{2}}\cdot DCB_{B1I-B3I}\] \[=\frac{f_{B1C}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}TGB_{B1C}p-\frac{f_{B2a }^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot TGD_{B2 ap}-\frac{f_{B1I}^{2}}{f_{B1I}^{2} -f_{B3I}^{2}}\cdot TGD_{1}\]
## 1 Introduction
### Experimental Procedures
This parameter is NA which is considered to be promising in structural health monitoring as a sensitive universal parameter [64]. Two strain gauges are placed on horizontal surfaces along top and bottom of the concrete beam to measure its contraction or expansion against bending to determine the neutral axis. The ANN modeling was done for each reinforcement design in each water-cement ratio. Each beam specimen has 68 data sets comprising of 5 input parameters (NA, A1, A2, A3, PPA) and one output (Load). The total number of data sets used for each ANN model derived was 3 specimen \(\times\) 68 data sets which is equal to 204 data sets. Using the experimental results in the test, linear normalization of data was done for all the 18 RC beam specimens. It converted all datasets to a minimum value of 0.0 and maximum value of 1.0 to eliminate the scaling of original data with small and large values. Shown in Figure 6 is a sample ANN model with 5 IN-3 HN-1 ON, where IN is the number of input parameters, HN is the number of hidden nodes, and ON is the number of output parameter. The ANN network architecture in this paper was defined as IN-HN-ON. The transfer function for each layer was varied, being L1 the first layer transfer function and L2 the second layer transfer function. In particular, LOGSIG and TANSIG were varied in L1 to obtain a model. In the neural network, the 204 data sets per design were divided into 60% for training, 20% for validating and another 20% for testing. The training set was used in updating the weights and biases. The validation set was used to make sure that the network was generalizing and was used to stop the training of the network before overfitting occur. The testing set was used to measure on how good the model can generalize. The network training function used in this paper was Levenberg-Marquardt optimization where the target performance goal measured used mean square error. This function is one of the fastest backpropagation algorithms available. This algorithm was proven to give good results in the quality control for the resistance spot welding [65], prediction of the strength of mineral admixture concrete [66] and predicting residual strength of non-linear ultrasonic evaluated damaged concrete [8]. Figure 5: Ultrasonic and four-point bending test setup. The ANN network when trained, validated, and tested to attain a performance goal produced stochastic results. Each run of the model produced unique weights and biases that were saved for further analysis. The number of hidden nodes were varied from 3, 4, 5, 6 and 11 to come up with sixty distinct ANN models. The transfer function was varied for the first layer using hyperbolic tangent sigmoid function (TANSIG) and log-sigmoid transfer function (LOGSIG), while the second layer transfer function was linear transfer function (PURELIN). Single inner layer was used throughout the modeling to arrive at the simplest model in predicting the damage in terms of load. A previous study gave good estimate of the number of hidden nodes needed [67].
## 4 Results of Numerical Simulation
### Description of Cracking Behavior  
#### 4.3.1 Effect of Loading Rate  
The effect of the shear rate on the specimen's stress distribution and fracture process is mainly explored in this section. Moreover, the numerical model of the shearing rate in 0.01 mm/s, 0.02 mm/s, and 0.03 mm/s: cases AI, AII, and AIII (Figure 11a(1)-a(3)), were gathered and presented in Figure 11. It is noteworthy that the crack initiation started from the bottom-right tip of the left edge-notched crack during the crack initiation stage in Figure 11a(1),a(2), but the crack initiation angles for cases AI (Figure 11a(1)) and AII (Figure 11a(2)) were different. However, the crack initiation started from the top-left corner of the left circular holes for case AIII (Figure 11a(3)). Specifically, when the shearing rate was under 0.01 mm/s and 0.02 mm/s, the crack initiation angle was 45\({}^{\circ}\) (Figure 11a(1),a(2), while for 0.03 mm/s, the crack initiation angle was 135\({}^{\circ}\) (Figure 11a(3)).  
As summarized in Figure 11, from the observations recorded by the crack propagation (Figure 11b(1)-b(3)), the number of cracks and the path of the crack expansion pattern between edge-notched cracks and holes, or between two holes in the rock bridges were basically the same. However, the order of the cracks that appeared was obviously different. In case AII (Figure 11b(2)), crack 2 started from the left hole and propagated towards the left edge-notched crack. Furthermore, crack 2 appeared at the rock bridge in the middle of the two holes, initiated from the left hole and expanded to the right hole (Figure 11b(1),b(3)).  
Figure 9: The normal displacement field and the persistency of different shearing rates and normal stress was 1.0 MPa. (**a(I)–a(3)**). The stage of crack initiation; (**b(I)–b(3)**). The stage of crack propagation; (**c(I)–c(3)**). The stage of crack coalescence.  
Additionally, crack 3 started from the inner wall of the circular hole, which expanded to the end where the stress was applied (Figure 11b(1)).  
In the crack coalescence stage (Figure 11c(1)-c(3)), the rock bridge between the holes and the edge-notch cracks on both sides were gradually penetrated by the cracks. It should be emphasized that there was no intersection between the generated cracks, and the cracks that penetrated the rock bridge from different paths. Additionally, the location of the spalling generated from the cracks' coalescence was sensitive to the shearing rate. In the case of AI (Figure 11c(1)), three spalling areas could be clearly observed, located near the circular hole and right edge-notched crack. Specifically, in Figure 11c(2),c(3), five spallings appeared, respectively. However, their locations were different. For example, All's (Figure 11c(2)) spalling areas were mainly produced in the circular hole and the right edge-notched crack; however, in AIII (Figure 11c(3)), the circular hole and the left and right edge-notched crack, all had spalling areas.