esa-sceva/satcom-chunk-collection huggingface dataset:
# Kalman Filter-Based Differential Privacy Federated Learning Method
## 4 Experiment and Analysis  
### Experiment Setting  
The public datasets MNIST [19], FMNIST [20], and CIFAR10 [21] were selected to train and test. The dataset was partitioned to simulate a federated learning environment, (1) independently and identically distributed (IID) for each TN [1]: the training data were shuffled and then partitioned into 100 clients each receiving an equal amount of examples; (2) non-independently and identically distributed (Non-IID) for each TN [22]: we distributed the data among 100 TNs, such that each TN contained samples of only two kinds; the number of samples among the TN followed a power law, as detailed in Table 2.  
Using CNN as the training model for MNIST and FMNIST, the network architecture consisted of two fully connected layers. The convolutional layers used \(5\times 5\) convolutions with stride 1, followed by a ReLU and \(2\times 2\) max pools, with 32 channels in the first layer and 64 channels in the second layer. Then the first convolution output was \(32\times 14\times 14\) after pools, and the second convolution output was \(64\times 7\times 7\) after pools. Last, we had two fully connected layers and used resnet50 [23] to train the model for CIFAR10.  
The experimental environment was an Intel(R) Xeon(R) W-2295 [EMAIL_ADDRESS] GHz, 64 GB RAM, NVIDIA GeForce RTX 3080 GPU, and the experimental platform was Pytorch. According to the related works [10; 11; 12], the parameters of this paper were set as shown in Table 3. Parameter definitions.
## 3 Results
### BSN Inhibited Src Activation  
Due to STAT3 being activated by JAK tyrosine kinases [29], we next showed that BSN could affect JAK1 and JAK2 activation in HepG2 and C3A cells. BSN suppressed JAK1 and JAK2 phosphorylation (Figure 5A,B). Under the same conditions, the levels of non-phosphorylated JAK1 and JAK2 remained the same. The Src kinase family may also activate STAT3 ([PERSON] et al., 2012). As a result, we tested whether BSN affected Src kinase activation in C3A cells. BSN suppressed c-Src kinase constitutive phosphorylation (Figure 5A,B).  
Figure 4: BSN induced apoptosis by downregulating STAT3 signaling pathways in hepatic carcinoma cells. (**A**) We treated HepG2 and C3A cells with 0–200 \(\mu\)mol BSN for 4 h. Prepared whole-cell extracts and used anti-_p_-STAT3 antibody to perform Western blotting. The results were from three independent experiments. (**B**,**C**) Silencing STAT3 aborted BSN-mediated apoptosis. A specific SAT3 siRNA or control siRNA were transfected into cells. The cells were transfected and incubated with or without BSN (200 \(\mu\)mol) for 48 h. Western blotting was used to measure STAT3 transcriptional targets such as Bcl-2 and cleaved caspase-3. Experiments were conducted in triplicate. [PERSON]’s post hoc test was used in this study to analyze the data. \(p\) < 0.05 was considered significant. Graph Pad prism was used. Data are presented as the mean \(\pm\) SD. Furthermore, *_p_ < 0.05 vs. control.
# Pulsed Electric Fields (PEF) to Mitigate Emerging Myotoxins in Juices and Smoothies
## 3 Results and Discussion
### Identification of Degradation Products  
Several degradation products of ENs and BEA were tentatively identified after PEF treatment in juice and smoothie matrices (Table 1). For this purpose, the samples were injected in LC-ESI-qTOF-MS equipment, operating in the full scan modality. For BEA, the degradation product with m/z 517.3705, corresponded to BEA with the loss of one unit of phenylalanine (Phe) and hydroxyvaleric acid (HyLv). In a previous study, [PERSON] et al. [30] also observed a degradation product from BEA with the loss of these two structural components (Phe + HyLv).  
For ENA, one degradation product was identified at m/z 475.3261, which corresponds to the loss of isoleucine (Ile) and HyLv. This degradation product was also previously identified by [PERSON] et al. [31]. For ENA1, the degradation product m/z 475.3244, corresponding to the loss of valine (Val) and HyLv, was identified. For ENB, two degradation products were observed. The degradation product 1 (m/z 437.1936), characterized as the sodium adduct of ENB, with the loss of Val and HyLv. The degradation product 2 with m/z 527.2000 corresponds to the loss of Val and was previously  
Figure 3: Percentage of reduction (%) of ENs and BEA in H\({}_{2}\)O vs. acidified H\({}_{2}\)O at pH = 4 after pulsed electric fields (PEF) treatment.  
reported by [PERSON] et al. [31]. Finally, for ENB1, the degradation product m/z 443.1674 was identified as the loss of Val and HyLv (Figure 4).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Mycotoxin** & **Degradation Product** & **[M+H]\({}^{+}\) (m/z)** & **Lost Fragment** & **Molecular Structure** \\ \hline ENA degradation product & [ENA-HyLv-Vle+H\({}_{2}\)O]\({}^{+}\) & 475.3261 & HyLv+Val & \\ \hline ENA1 degradation product & [ENA1-HyLv-Val+H\({}_{2}\)O]\({}^{+}\) & 475.3244 & HyLv+Val & \\ \hline ENB degradation product 1 & [ENB-HyLv-Val+Na]\({}^{+}\) & 437.1936 & HyLv+Val & \\ \hline ENB degradation product 2 & [ENB-Val+H\({}_{2}\)O]\({}^{+}\) & 527.2000 & Val & \\ \hline ENB1 degradation product & [ENB1-HyLv-Val]\({}^{+}\) & 443.1674 & HyLv+Val & \\ \hline \hline \end{tabular}
\end{table}
Table 1: LC-ESI-qTOF-MS data (MS1) of the degradation products obtained in juice and smoothie samples after PEF treatment.  
The degradation products that were observed confirmed the reduction effect of PEF on emerging mycotoxins. The degradation products originated from the loss of structural aminoacidic fragments of original molecules such as HyLv, Val, Ile, or Phe.
## 6 Analysis of Parameter Sensitivity
### Influence of Anisotropy Ratio  
Figure 10 shows the influence of changes in anisotropy ratio on the limit supporting force of shield excavation face. It can be seen that \(\sigma_{\rm s}\) decreases linearly with the increase in \(k\). When \(k=1\), the strength of soil is isotropic. When \(k<1\), the anisotropy of soil strength is the strength of the soil when the direction of large principal stress is vertical. At this time, the strength of the soil at this time is greater than that when the direction of the large principal stress is horizontal, and the ultimate supporting force of excavation face is greater than that of isotropic soil. This is because the frictional force of surrounding soil around the sliding crack surface on the sliding prisms and wedges decreases relatively, which makes the ultimate support force of the excavation surface increase. When \(k>1\), it is  
Figure 9: Relationship between supporting force of excavation face and overburden pressure on sliding block.  
the opposite. In engineering practice, the situation of \(k<1\) is often encountered, so the calculated limit supporting force will be unsafe if the anisotropy of soil strength is not considered.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 3 Performance Evaluation of AG-PSMF
### Tracking Accuracy for Different Values of \(N_{\text{max}}\) and \(R\)  
This part evaluates the tracking performance of AG-PSMF under the following input:  
\[u=\sin(10t)+\alpha\varepsilon(t),0\;\text{s}\leq t\leq 4.2\;\text{s}. \tag{7}\]  
The performance is analyzed through the average magnitude of \(|\hat{u}-x_{1}|\) (AME), which is defined as follows:  
\[\text{AME}\triangleq\frac{1}{42000}\sum_{k=0}^{42000}|\hat{u}-x_{1}|, \tag{8}\]  
where \(\hat{u}\) is the signal component of the input, i.e., \(\hat{u}=\sin(10t)\).  
Figure 7 shows AME under different values of \(N_{\text{max}}\) and \(\alpha\), and Figure 8 provides AME of Figure 7 under \(\alpha=0.1\). One can observe that AME converges to its minimum value as \(N_{\text{max}}\) reaches around \(N_{\text{max}}=50\) under a certain value of \(\alpha\).  
The influence of \(R\) is shown in Figure 9, which gives AME for different values of \(R\) and \(\alpha\). In addition, Figure 10 provides AME of Figure 9 under \(\alpha=0.1\). It is shown that, under a certain value of \(\alpha\), AME dramatically increases and converges to a certain value as \(R\) increases in the range \(R>0.0001\). On the other hand, in the case of \(R<0.0001\), AG-PSMF produces smaller AME than that of the case \(R>0.0001\), but the value is slightly larger compared with the case of \(R=0.0001\).  
The figures also show that stronger noise intensity results in larger AME for a certain value of \(N_{\max}\) and \(R\).
## 2 Research Aim and Hypotheses  
The primary objective of this research study was to investigate the recognition of decision-making processes when presenting individuals with multiple stimulus alternatives in an 8-AFC decision-making task through eye-tracking patterns. The study aimed to explore how individuals perceive and process information when making decisions and how this process may be influenced by the presentation of multiple stimuli. By examining these factors, the study sought to shed light on the underlying cognitive mechanisms involved in decision-making and to provide insights into how these mechanisms may be leveraged to improve decision-making processes in various domains. The information that this investigation will give us using the behavior of gaze as an implicit measure can contribute to the comparison of physiological measures and self-reports. In this context, the following hypotheses were postulated:  
* First, it was suggested that there would be a correlation between liking and eye-tracking metrics (H1). The eye-tracking metrics examined in this research comprise three variables, namely (1) gaze time, which is the duration of time that a participant's eyes remain fixated on a particular AOI, (2) visits, which is the number of times that a participant's gaze visits a particular AOI, and (3) time to first gaze, which is the duration of time that elapses between the presentation of a visual stimulus and the participant's first fixation on a particular AOI. Consequently, H1 was divided into three sub-hypotheses: there would be a correlation between liking and gaze time (H1.1), visits (H1.2), and time to first gaze (H1.3).
* On the other hand, one of the objectives of this research is to examine whether there are differences in eye-tracking metrics and liking scores among the products selected. Thus, it is postulated that the selected stimuli would exhibit distinct patterns for eye-tracking metrics and liking compared to the various distractors (H2).
* Additionally, we suggest that the Gaze Cascade Model would occur in the virtual environment (H3). As described earlier, the Gaze Cascade Model has been considered an indicator of users' decision-making process. However, this has not been extensively studied in virtual environments, and given the increasing popularity of VR in the product evaluation process, it is interesting to see if the decision-making process occurs in the same way during the virtual experience.
* Finally, it is proposed that we can predict the user's decision by analyzing the gaze time evolution in the last three seconds prior to the product selection (H4).
# Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating
###### Abstract  
Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating to reduce the springback of 45 steel sheets and investigated the effects of bending parameters on springback behavior. Initially, a thermodynamic coupling model and a static model were created utilizing the VOC hardening model and the von Mises yield criterion in the ANSYS workbench 2022 R2 software. The springback behavior and stress distribution of the sheets were then investigated under different temperatures (room temperature and 800 \({}^{\circ}\)C) and bending angles (15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\)). Simultaneously, the experiments were performed to investigate springback behavior and guarantee the accuracy of the model. The results indicate that the springback reached a minimum value at the 45\({}^{\circ}\) bending angle at room temperature while increasing with the increasing bending angle under 800 \({}^{\circ}\)C local heating. The springback under local heating can be decreased by 75.2% and the error of the bending angle can improve by 2-7\({}^{\circ}\) compared with samples processed in room temperature.  
L-bending; springback; finite element analysis; induction local heating; thermodynamic coupling model +
Footnote †: journal: Applied  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating. _Appl. Sci._**2024**, _14_, 6253. https:////doi.org/10.3390/app14146253
The results showed that the model could achieve accurate performance prediction for more than 2000 h. [PERSON] et al. [31] proposed a new grey neural network model (GNNM) method, which combined the particle swarm optimization (PSO) and the moving window method to predict the degradation of PEM fuel cell under different operating conditions. The influence of load current, inlet temperature, input hydrogen pressure and inlet relative humidity are considered. Three PEM fuel cell aging experiments were used to demonstrate that the proposed method can accurately predict the degradation for PEM fuel cell on different applications. Based on the literature review, machine learning was mainly used for the long-term degradation process prediction of PEM fuel cells. The cell performance degradation process in the initial period (generally hundreds of hours) was measured, and the data was used to predict the degradation process in the future time through machine learning. However, to the best of authors' knowledge, the study of machine learning modeling to predict the full-process degradation process of the PEM fuel cell at different operating conditions still lacks. In this study, we propose a data-based degradation prediction method to simulate the degradation behaviors of the PEM fuel cell in the DEA and anode recirculation modes. We firstly used artificial neural network (ANN) to predict the degradation behaviors of the PEM fuel cell, but the results were not satisfactory. Considering that the initial value and change evolution of the PEM fuel cell performance both change under different operating conditions, we try to prediction them separately to improve the accuracy by the multivariate polynomial regression (MPR) method and ANN, respectively. Some previous works also reported the reasonable combination of algorithms can achieve a higher accuracy [32]. Therefore, we propose a combined the MPR method and ANN approach, M-ANN. The main idea is using the MPR method to predict the initial value under different conditions, and using ANN to predict the variation value over time. The workflow of the proposed method, M-ANN, is shown in Figure 1 for predicting the PEM fuel cell degradation process in the DEA mode. We use a verified physical model to simulate the degradation characteristics of the PEM fuel cell in the DEA and anode recirculation modes, and the simulation data is used instead of experimental data. The potential of using experimental data for the proposed modeling method in the future is also discussed. Figure 1: Workflow of the ANN-based and M-ANN based models for predicting the PEM fuel cell degradation process in the DEA mode.
## 4 Results and Discussion
### Test of the Zinc Pseudo-Membrane
#### 4.2.1 RED Test  
RED with Maximum Power Point Tracking  
The stack was tested with 4 runs. Before each run, the OVC was measured (Table 9). Voltage, current and power density are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Run** & **OCV (V)** \\ \hline
1 & 0.434 \\
2 & 0.575 \\
3 & 0.407 \\ mean & 0.472 \\ \hline \end{tabular}
\end{table}
Table 8: OCV of the stack before each constant current run.  
Figure 6: Results of the MPPT test of the RED stack equipped with normal AEMs (AMX) and pseudo CEMs (Zn).  
\begin{table}
\begin{tabular}{c c} \hline
**Run** & **OCV (V)** \\ \hline
1 & 0.166 \\
2 & 0.185 \\
3 & 0.250 \\
4 & 0.253 \\ mean & 0.214 \\ \hline \end{tabular}
\end{table}
Table 9: OCV of the stack before each MPPT run.  
RED Tested with Constant Current  
Voltage, current and power density of the constant current (20 mA) experiment are shown in Figure 7.  
Discussion of the RED Tests  
OVC values of the Zn-AMX system using a ZnCl\({}_{2}\) solution were less than half of those of the CMX-Ag/AgCl system with an NaCl solution. That is probably due to the electrochemistry at the zinc surface, but interaction of the ZnCl\({}_{3}\)\({}^{-}\) anion at the AMX can also play a role.  
Between each run, the feed solutions were switched, and a large asymmetry is seen in the voltage and current plot. However, the system is symmetrical and we cannot explain this anomalous behavior. The power density of this Zn-AMX stack is a factor of 10 lower than that achieved with the CMX-Ag/AgCl stack.
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 3 Optical Amplifiers in Telecommunications Networks
### Amplifiers for PONs
In general, we suggest that use of the Brillouin amplifier is not feasible in this area of optoelectronics due to specific properties [93; 94; 95; 96; 97; 98; 99; 100; 101]. EDFAs can be employed for analog radio frequency (RF) overlay video services or WDM-PONs, where the C-band or L-band is used [102; 103; 104; 105; 106; 107]. Other types of fiber amplifiers can be used for PONs: A thulium-based amplifier downstream and a praseodymium-based amplifier upstream [108; 109; 110; 111; 112; 113; 114]. Raman amplifiers can be used for PONs; however, if we take into account the cost and hazardous optical power, it is not the best solution for downstream transmission [115; 116; 117; 118; 119]. SOAs can be used as one of the most suitable candidates for future next-generation long-reach PONs. The low cost, sufficient gain and small size positions SOAs for future development [120; 121; 122; 123].
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 6 Discussion  
In this study, we proposed a code-expanded RA scheme that considers preamble codeword utilization and PUSCH resource limitations. Most conventional CeRA schemes focus more on the preamble transmission success rate than on the overall RA success rate. To improve the RA success rate, we formalized the analysis model of the preamble transmission success rate, preamble codeword utilization, and PUSCH timeout rate. In addition, we proposed the first RA slot selection procedure and PUSCH waiting message to improve PUSCH resource utilization. In the performance evaluation section, we validated that the proposed CeRA-eSP exceeded the performance of the existing CeRA scheme with respect to the RA success rate. In future work, we will further improve the preamble selection process using machine learning techniques that have been considered in recent studies [19; 20; 21].  
**Author Contributions:** Conceptualization, J.Y., J.P. and S.K.; methodology, J.Y., J.P. and S.C.; software, J.Y. and S.P.; validation, J.P., C.Y. and S.C.; formal analysis, J.Y., J.O. and S.A.; investigation, J.Y., S.K. and J.O.; resources, J.Y.; data curation, S.A.; writing--original draft preparation, J.Y. and S.C.; writing--review and editing, S.C. and C.Y.; visualization, J.Y. and J.P.; supervision, J.Y. and S.A.; project administration, S.P.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute for Information and Communication Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-00368, Development of the 6G service targeted AI/ML-based autonomous-regulating medium access control (6G STAR-MAC)), Institute of Information and Communication Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-00704, Development of 3D-NET Core Technology for High-Mobility Vehicular Service), and the Institute for Information Communication Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2018-0-00969, Full duplex non-orthogonal multiple access (NOMA) optimization technologies using deep learning for 5G-based autonomous vehicular networks).  
Figure 11: (**a**) PUSCH wastage for different PUSCH resources and (**b**) PUSCH wastage for different numbers of devices.
## 3 Experiment Results  
In Figure 2, the results of X-ray detection show the evolution of fatigue cracks. In Figure 2a, there is only the prefabricated defect without any fatigue cracks, i.e., the normal state. Then, the hidden crack of 1.2 mm was generated after 1280 kc, which has been marked with a circle in Figure 2b and cannot be seen with the naked eye. At last, the macroscopic crack visible to eyes appeared after 1560 kc.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**C** & **Si** & **Mn** & **P** & **S** \\ \hline \(\leq\)0.20 & \(\leq\)0.35 & \(\leq\)1.40 & \(\leq\)0.045 & \(\leq\)0.045 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mechanical property of experimental material.  
Figure 1: Specimen size and testing lines (in mm).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Yield Strength** & **Ultimate Strength** & **Percentage Elongation after** \\ \(\sigma_{y}\)**MPa** & \(\sigma_{\text{b}}\)**/MPa** & **Fracture \#\%** \\ \hline \(\geq\)235 & 370–500 & \(\geq\)26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition of experimental material (wt.%).  
Figure 2: Series photos of X-ray detection for crack evolution.  
By referring to X-ray testing results, the tangential signals \(H_{p}(x)\) and normal component signals \(H_{p}(y)\) of different defect levels and their corresponding fatigue cycle numbers are extracted, as shown in Figure 3. The fluctuation of MMM tangent component \(H_{p}(x)\) of hidden crack state is bigger than that of the normal state, although a small jump of \(H_{p}(x)\) occurs on the welded joints in the normal state due to the welding residual stress in Figure 3a. The jump of \(H_{p}(x)\) grows higher when the hidden crack is generated. The value becomes highest when the macroscopic crack is generated. In Figure 3b, the peak-to-peak value of the normal signal component \(H_{p}(y)\) in weld position increases in turn from normal state to hidden crack and then to macroscopic crack, in which the defect levels can be identified by MMM curves. When macroscopic cracks are produced, the polarity varies along the normal component \(H_{p}(y)\) on the SCZs.  
Sometimes, due to the interruption of welding residual stress, it is difficult to distinguish between the hidden crack state (1260 kc) and the normal state (0 kc) only by MMM curves shown in Figure 4. The fluctuations of \(H_{p}(x)\) curve in the hidden crack state are very similar to that in the normal state, as shown in Figure 4a. At the same time their curves of \(H_{p}(y)\) overlap with each other in Figure 4b. However, in Figure 5, the result of X-ray detection showed that there was a hidden crack of 1.8 mm on left of the prefabricated defect which has been marked with a circle when the fatigue cycles reach 1260 kc.  
Figure 4: Unidentifiable defect levels by MMM signals for specimen #92.  
Figure 3: Identifiable defect levels by MMM signals for specimen #55.