esa-sceva/satcom-chunk-collection huggingface dataset:
# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
###### Abstract  
In this study, we propose a systematic process design method using a convolutional neural network (CNN) for the uniform strain distribution of a Ti-6242 impeller during forging. A convolutional neural network (CNN) is a machine learning algorithm optimized for processing grid-like data, such as images, by identifying patterns within the data. To achieve the design goal with a simple process, we propose a methodological process in which the initial billet passes through three steps: upsetting, preform forging, and target impeller forging. We used the CNN model in the upsetting and preforming steps to enable our proposed design method to be applied to various impeller shapes. We trained a CNN model with two different types of datasets: one to derive the preform shape suitable for the target impeller forging and another to determine the shape of the initial billet that was upset for impeller preform forging. The proposed forging process resulted in a reduction in the mean strain, strain standard deviation, and maximum strain by up to 38.6%, 52.5%, and 59.7%, respectively, compared with the impeller forging processes proposed in previous studies. Consequently, the strain of the forged product was been homogenized, thereby reducing the possibility of defects. This process design method can be used in fields such as aerospace that require high-quality forging.  
T1-6242; impeller forging; convolutional neural network (CNN); uniform strain distribution; preform design; hot deformation 2023
## 3 Results And Discussion
### Delay  
The graph shown in Figure 3 indicates a delay under different velocities of the mobile node. Figure 3 shows the decrease in delay, as the model acquires less than 1 ms delay with an increase in the velocity. Hence, the graph indicates a significant improvement in delay using the proposed hybrid approach compared to the existing approach and other single optimization methodologies.  
This improvement is because in the proposed approach, the use of hybrid optimization helps in improving the static base algorithms, such as RSS. The use of RSS helps in the selection of a static threshold, and this static threshold is optimized via an optimization algorithm. In most of the cases, the optimization approach does not converge and provides a random answer, which further reduces the performance of delay. In the proposed approach, the cuckoo search and genetic algorithm are also used. This methodology iteratively improves the performance of the cuckoo search.
## 3 Displacement Estimation Method
### Vertical Displacement Estimation  
Stress characteristics were examined using a vertical bending strain that had been separated from the coupled strain, as discussed in this section. Considering the distribution characteristics of the vertical bending deformation of a tunnel, it is appropriate to model the tunnel as a semi-infinite elastic foundation beam. A conjugate beam method (CBM) was applied to accurately calculate the vertical displacement of the tunnel using the separated strain [43; 44].  
The conjugate beam theory takes the bending moment distribution M(x) on the actual beam as the load distribution on the conjugate beam, then the corresponding bending moment distribution on the conjugate beam is equivalent to the vertical deformation distribution y(x) of the actual beam. For a Euler beam, the relationship between its bending strain, curvature, and bending moment can be expressed as follows:  
\[\mathrm{k}(x)=\frac{\epsilon_{\mathrm{my1}}(\mathrm{x})-\epsilon_{\mathrm{my2} }(\mathrm{x})}{\mathrm{H}}=\frac{\mathrm{M}(\mathrm{x})}{\mathrm{EI}} \tag{12}\]  
Thus,  
\[\mathrm{M}(\mathrm{x})=\mathrm{EI}\frac{\epsilon_{\mathrm{my1}}(\mathrm{x})- \epsilon_{\mathrm{my2}}(\mathrm{x})}{\mathrm{H}} \tag{13}\]  
If the load distribution \(\mathrm{q}_{\mathrm{i,y}}^{\prime}\) of the conjugate beam equals \(\frac{\epsilon_{\mathrm{my1}}-\epsilon_{\mathrm{my2}}}{\mathrm{H}}\), then the bending moment distribution of the conjugate beam is exactly the same as the vertical deformation distribution \(\mathrm{v}_{\mathrm{y}}(\mathrm{x})\) of the actual beam. Assuming that the length of each sensing unit is equal, it can be denoted as \(L\). Based on the balance principle, the support reaction force at the left fulcrum of the conjugate beam can be determined as follows:  
\[\overline{\mathrm{R}}=\frac{L}{n}\mathrm{\sum}_{i=1}^{n}q_{i,y}^{\prime}(n-i+ 0.5). \tag{14}\]  
Therefore, the bending moment of the middle point of each element of the conjugate beam, that is, the vertical displacement of the same point in the actual beam, can be expressed as:  
\[\
u_{i,y}=L^{2}\mathrm{\sum}_{j=1}^{i}q_{j,y}^{\prime}(i-j)+\frac{q_{i,y}^{ \prime}\mathrm{L}^{2}}{8}-\overline{\mathrm{R}}L(\mathrm{i}-0.5)(i=1,2,\cdots,n) \tag{15}\]
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Parameter Estimation
#### 2.2.2 Pearson III (PE3)  
For estimation with MOM, the distribution parameters have the following expressions [17; 18; 19; 20]:  
\[\alpha=\left(\frac{2}{C_{s}}\right)^{2} \tag{35}\]  
\[\beta=\frac{\sigma}{2}\cdot C_{s} \tag{36}\]  
\[\gamma=\mu-\alpha\cdot\beta \tag{37}\]  
where \(C_{s}\) represents the skewness coefficient.  
The parameter estimation with the L-moments method is performed numerically (definite integrals) based on the equations using the quantile of the function. An approximate form of parameter estimation can be adopted. Thus, for the estimation with the L-moments, the shape parameter \(\alpha\) can be evaluated numerically with the following approximate forms, depending on L-skewness (\(\tau_{3}\)) [20]:  
\[\begin{array}{l}\alpha=\exp\left(\begin{array}{l}-3.164791927-5.108735285 \cdot\ln(|\tau_{3}|)-4.116014079\cdot\ln(|\tau_{3}|)^{2}-\\ 2.985250105\cdot\ln(|\tau_{3}|)^{3}-1.327399577\cdot\ln(|\tau_{3}|)^{4}-0.3739 44875\cdot\ln(|\tau_{3}|)^{5}-\\ 0.065421611\cdot\ln(|\tau_{3}|)^{6}-0.006508037\cdot\ln(|\tau_{3}|)^{7}-0.00028 1969\cdot\ln(|\tau_{3}|)^{8}\end{array}\right)\end{array} \tag{38}\]  
\[\begin{array}{l}\text{if }\frac{1}{3}<|\tau_{3}|\leq\frac{2}{3}:\\ \\ \alpha=\exp\left(\begin{array}{l}-3.9918551-10.781466\cdot\ln(|\tau_{3}|)-2 1.557807\cdot\ln(|\tau_{3}|)^{2}-\\ 33.8752604\cdot\ln(|\tau_{3}|)^{3}-35.0641585\cdot\ln(|\tau_{3}|)^{4}-22.921163 \cdot\ln(|\tau_{3}|)^{5}-\\ 8.5491823\cdot\ln(|\tau_{3}|)^{6}-1.3855653\cdot\ln(|\tau_{3}|)^{7}\end{array}\right)\end{array} \tag{39}\]  
\[\begin{array}{l}\text{if }\frac{2}{3}<|\tau_{3}|<1:\\ \\ \alpha=\frac{67.08589624\cdot\tau_{3}^{4}-29.150288079\cdot|\tau_{3}|^{5}+5.3 64968945\cdot\tau_{3}^{6}}{1+0.0005134\cdot|\tau_{3}|+0.00063644\cdot\tau_{3} ^{2}}\end{array} \tag{40}\]The scale parameter \(\beta\) and the position parameter \(\gamma\) are determined with the following expressions:  
\[\beta=L_{2}\cdot\sqrt{\tau}\cdot\frac{\Gamma(\alpha)}{\Gamma\Big{(}\alpha+\frac{ 1}{2}\Big{)}} \tag{41}\]  
\[\gamma=L_{1}-\alpha\cdot\beta \tag{42}\]
## 1 Introduction
Such data partition in federated learning settings is known as vertically partitioned data. To the best authors' knowledge, this research is the first one that addresses the challenge of designing intrusion detection systems based on the federated learning principles and applicable for the vertically partitioned data. Another challenge that is considered in the paper is the lack of datasets that could be used to model vertically partitioned data. The authors propose a possible solution to construct an appropriate dataset on the basis of the dataset that describes the functioning of the secure water treatment facility. Thus, the novelty of the paper lies in the research of the applicability of federated learning for intrusion detection in the case of vertical data partition, i.e., when collaborating entities have information about one object but are presented by a different set of attributes. The main contribution of the paper is as follows:  
* the paper analyses existing intrusion detection systems based on the federated learning, and shows that the proposed solutions mainly focus on the specific type of data distribution across collaborating clients, namely horizontal partition when all clients share a similar set of attributes about different samples;
* the paper proposes an approach for the construction of intrusion detection system in case of vertically partitioned data and an architecture of the intrusion detection system based on federated learning;* the paper evaluates the efficiency of the proposed system on the experiments in terms of accuracy, training and inference time parameters and outlines that, although existing federated learning frameworks allow constructing efficient intrusion detection systems in case of vertically partitioned data in terms of accuracy, these solutions are not practically applicable due to extremely large values of temporal parameters. The experiments demonstrated that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using centralized machine learning. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. Thus, to make the application of federated learning in the case of vertically partitioned data practically useful, it is required to either reconsider the privacy preserving techniques used to secure input data or an elaborate approach that is able to transform vertically partitioned data into horizontally partitioned data. The paper is structured as follows: Section 2 describes the distinctive features of federated learning. Section 3 considers existing solutions for intrusion detection based on federated learning. Section 4 provides an approach and an architecture of the intrusion detection system that allows for training intrusion detection models. Section 5 describes the results of the experiments. The paper ends with conclusions and future work prospects.
## 3 Results and Discussion
### Construction of Deformation and Warpage Using Gauge Data  
After the proof that an FEM simulation can successfully describe the thermally-induced deformation of the PCB assembly, the discrete curvature data at different points from the FEM model representing the data obtained from the strain gauges were employed for constructing the deformation and warpage of the PCB assembly. Since the PCB's curvature may not be constant across the entire surface, a simple beam model with multiple curvatures for an out-of-plane deformation calculation is proposed and shown in Figure 12. This model is based on a beam theory with an assumption of a small deflection. The \(k_{i}\) is a constant curvature of a segment \(i\) between the length \(L_{i-1}\) and \(L_{i}\). The associated equations of deflection \(W_{i}\) within the segment \(i\) are listed as follows:  
\[\begin{array}{l}x=0\Rightarrow W_{0}=0\\ 0\leq x\leq L_{1}\Rightarrow W_{1}(x)=\frac{1}{2}k_{1}x^{2}\\ L_{1}\leq x\leq L_{2}\Rightarrow W_{2}(x)=W_{1}(L_{1})+k_{1}L_{1}(x-L_{1})+ \frac{1}{2}k_{2}(x-L_{1})^{2}\\ L_{2}\leq x\leq L_{3}\Rightarrow W_{3}(x)=W_{2}(L_{2})+[k_{1}L_{1}+k_{2}(L_{2 }-L_{1})](x-L_{2})+\frac{1}{2}k_{3}(x-L_{2})^{2}\\ \bullet\\ \bullet\\ \end{array} \tag{4}\]  
\[L_{i-1}\leq x\leq L_{i}\Rightarrow W_{i}(x)=W_{i-1}(L_{i-1})+\sum\limits_{m=1} ^{i-1}[k_{m}(L_{m}-L_{m-1})](x-L_{i-1})+\frac{1}{2}k_{i}(x-L_{i-1})\]  
The curvature data can be obtained from the strain gauges by back-to-back attaching on the PCB. To validate this proposed approach, curvature data at one, three, and six points (shown in Figure 13a obtained from the FEM simulation to represent the data from the strain gauge measurement were selected for calculating the out-of-plane deformation of the PCB assembly using this simple model. The results are shown in Figure 13b,c for out-of-plane deformations of the PCB assembly along line A-D and warpage, respectively, in comparison with the FEM result (baseline). It is shown that the more data used, the closer the deformed curve is to the baseline simulation. In addition, the warpage can reach the baseline within a 7% to 2% difference by using only one-point to six-point data. Therefore, the curvature data from the strain gauge measurement can be successfully applied to determine the out-of-plane deformation and warpage of the PCB assembly under a solder reflow process.  
Figure 12: The simple beam model for calculating out-of-plane deformation.
This model can skip the use of EMFSs and circuit system simulation, and directly predicts SI metrics including EH/EW, IL/RL, impulse response, and COM values according to physical parameters. Meanwhile, the optimization of links can be achieved by using this model to predict the corresponding COM values and equalizer parameters for the links with different equalizer configurations. Furthermore, Bayesian optimization based on the Gaussian process (GP) is used to optimize the hyperparameters under the same conditions for different combinations of models. Compared with the prediction of high-speed link performance using traditional ML [6; 7; 8; 9; 10], DNN-Transformer can directly use the physical parameters of the link for analysis, and its prediction accuracy is significantly better. For the prediction of simulation data such as impulse responses, DNN-Transformer can capture more features and its prediction ability is more accurate than that achieved when using an RNN [14] or LSTM [20] model alone to create a surrogate model. The results show that the DNN-Transformer model can achieve more effective prediction. The performance and feasibility of the proposed method is illustrated by the prediction data and graphical results given in this paper. The main contributions of this paper can be summarized as follows:  
1. Based on the key physical parameters of channels, neural network models are used to directly analyze link performance, and this obviates the time-consuming processes of EMF solving and circuit system simulation. 2. Through neural network models, the accurate prediction of multiple SI indicators and equalizer parameters is achieved. Additionally, we show that SI analysis and link optimization can also be rapidly achieved. 3. A DNN-Transformer cascaded model is proposed, Bayesian optimization is used to tune the hyperparameters of this model, and its superior performance is demonstrated by comparing it with other models. The rest of this paper is organized as follows: Section 2 discusses the basic principles of this work, including the principles of the SI and COM methods. Section 3 describes how the simulation dataset was created. In Section 4, the design idea of the DNN-Transformer model and test metrics are presented. Section 5 gives the numerical results to demonstrate the prediction performance of our model. Finally, Section 6 concludes the paper.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 6 Conclusions  
The main objective of this work has been to evaluate the impact of CF on the SQIs in PPG measurements. The combination of the selected SQIs provides a comprehensive evaluation of the quality of the PPG signals from multiple perspectives, ensuring that various aspects of the signal are captured and analyzed. The comparative results show  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Window Size (s)** & **Min \(P_{\text{SQI}}\) (\%)** & **Max \(P_{\text{SQI}}\) (\%)** & **Mean \(P_{\text{SQ}}\)** & **SD \(P_{\text{SQ}}\) (\%)** \\ \hline
1 & 0.02 & 6.04 & 1.26 & 0.84 \\
5 & 0.23 & 13.15 & 2.18 & 1.44 \\
10 & 0.33 & 13.06 & 2.90 & 1.82 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive statistics of \(P_{\text{SQ}}\) for different window sizes.  
Figure 6: Impact of window sizes on the analysis of \(P_{\text{SQ}}\) (in blue). For reference, the original PPG signal from the left index finger of Subject 1 is displayed in gray.  
that the normalized CF range of 0.4 to 0.6 is identified as optimal for capturing a detailed and complex signal, suitable for most PPG-based physiological analyses. However, when peripheral perfusion is critical and maximizing the sensitivity of \(P_{SQL}\) is required, the CF range of 0.2 to 0.4 should be considered. This reflects the need for a compromise based on the specific priorities of the physiological analysis. Furthermore, it is recommended that CF be expressed in terms of normalization, which enables better comparison between different studies, datasets, and subjects. Using normalized CF values provides more consistent results applicable to a broader population. It is suggested that CF calibration is crucial to improve the precision and reproducibility of PPG measurements. Our analysis suggests that the results can be generalized to other wavelengths.  
Conceptualization, J.L.C.; Formal analysis, A.S.M. and B.d.S.; Funding acquisition, J.S. and J.C.G.-N.; Investigation, J.L.C. and A.S.M.; Project administration, J.C.G.-N. and J.S.; Resources, J.S.; Supervision, J.C.G.-N. and J.S.; Validation, J.L.C.; Writing--original draft, J.L.C., A.S.M. and B.d.S.; Writing--review and editing, J.L.C., A.S.M. and B.d.S. All authors have read and agreed to the published version of the manuscript.  
We acknowledge VUB for the SRP (Strategic Research Program) LSDS and the IOF-GEAR TECH4 HEALTH project funding and ETRO.RDI BAS funding. We also thank the Belgian Development Cooperation through VLIR-UOS (Flemish Interuniversity Council-University Cooperation for Development) in the context of the Institutional University Cooperation program (IUC 2019 Phase 2 UO) with the Universidad de Oriente (Cuba). This work has been supported through the OZR4103 VUB project funding and partially supported by the FWO-Flanders FWOSB106 Ph.D. grant and by Innoviris-Brussels through the ILSF projects BRGEOZ403 and BRGEOZ422.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 3 Design and Optimization  
### Model of Compact VCDM  
The exploded 3D drawing for the model of a 69-element compact VCDM is shown in Figure 2. The structure of compact VCDM mainly includes ten components listed in Table 1. The pupil diameter in the front shell is 17 mm. The diameter of the thin mirror is 24 mm and is mounted on the O-ring. 69 micro VCAs are mounted on the substrate. The inter-actuator distance is 2.5 mm and the diameter of the substrate is 26 mm. A cylindrical micro VCA contains a permanent magnet and a voice coil. One end of the strut is attached to a micro VCA, while the other is attached to the back of the thin mirror. The strut transmits the force of an actuator to the thin mirror and deforms the local surface of the thin mirror. The shaft of a strut is mounted with a spring in the spring-fixing plate. This structure provides the micro VCA with axial stiffness, which overcomes the problem that an actuator's mover is directly suspended on the mirror in traditional VCDMs [21].
# Chemical Migration from Wine Contact Materials
## 5 Wine Migration Studies on Chemical Contaminants
### Analytical Methods
#### 5.2.3 Elemental Analysis  
The migration of elements from cork stoppers to aqueous alcoholic simulants has been investigated with inductively coupled plasma atomic emission spectrometer (ICP-AES) for major and minor element determination (Ba, Mn, and Al), while a quadrupole-based inductively coupled plasma mass spectrometer (ICP-MS) was used for trace element determination (Cr, Fe, Ni, Cu, Zn, Pb, Cd, As, Se) [76]. The effect of ethanol on the quantification of the elements was investigated, and it was shown that its presence is the main matrix effect in the determination of some elements like Mn and Fe, making the use of ethanol matrix standards compulsory for the generation of calibration curves. On the contrary, for other elements like Pb, Cd, Ni, Fe, and Cu, it was shown that the use of the internal standard allows appropriate correction for the presence of ethanol in the solution matrix. The quantified concentrations, expressed as micrograms per kilogram of cork, ranged between 4 \(\mu\)g kg\({}^{-1}\) (Cd) and 28,000 \(\mu\)g kg\({}^{-1}\) (Al).
# Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
## 3 Prediction of Carsharing Demand Based on GBDT
### Importance of Parameters and Characteristics  
(1) Parameter Settings  
The following parameters need to be set when the GBDT is constructed: \(L\) is the loss function; \(M\)ax\(Depth\) is the maximum depth of the tree; \(N\) is the model iteration times; \(v\) is the step size of each iteration.  
When the parameters of the GBDT model are being adjusted, the iteration times and iteration steps should be determined first. Generally, the maximum depth of the tree should not exceed 20, so that the model is not too complicated or overfitted. Considering the accuracy of the model, the step size is set to 0.1. The root mean square error and mean absolute error are used as loss functions, and the parameters are optimized in the range of iteration times from 100 to 300 to select the iteration times with the highest accuracy of the model. Similarly, the step size and the depth of the tree are adjusted by grid search, and the parameter values that make the model work best are found.  
To understand the contribution of each characteristic to the model prediction, the relative importance of each characteristic needs to be calculated. With the characteristic \(j\) taken as an example, the relative importance calculation formula is shown in (17).  
\[F_{j}=\frac{1}{M}\sum_{m=1}^{M}\hat{F}_{j}(T_{m}) \tag{17}\]  
where \(M\) is the iteration times;  
\(\hat{F}_{j}(T_{m})\) is the importance of characteristic \(j\) in a single tree, as shown in Formula (18).  
\[\hat{F}_{j}(T_{m})=\sum_{t=1}^{N-1}i_{t}(v_{t}=j) \tag{18}\]  
where  
\(N\) is the number of tree leaf nodes;  
\(N-1\) is the number of non-leaf nodes;  
\(v_{t}\) is the feature associated with node \(t\);  
\(i_{t}\) is the average loss reduction value of node \(t\) after classification.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Time-Frequency Peak Filtering (TFPF)
#### 2.2.2 Influence of Fixed Window Length of TFPF  
In the TFPF algorithm, the choice of window length will influence the denoising result. That is, TFPF with fixed window length cannot effectively suppress random noise and preserve the amplitude of the effective signals at the same time. This paper explains the problem of TFPF through the synthetic data. By adding random noise to the synthetic data, noisy data is obtained. Then, window lengths of 7, 11, and 15 can be selected, respectively, to deal with the noisy data. The original data and denoising results of each window length are presented in Figure 1. In order to better show the difference of the denoising results, two areas (sampling point 200-220 and 770-920) are enlarged, respectively, in Figure 2. It can be observed that the short window length has better signal fitting degree at the signal peak than the long window length in Figure 2a. In Figure 2b, the denoising quality of long window length is more obvious than the short window length.