esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Discussion
### Data Description and Preprocessing  
The opening, closing, highest, and lowest trade prices for natural gas from 4 April 1990-26 October 2023, and for gold from 2 January 1990-26 October 2023, are among the data analyzed in this study. Investing Financial Information provides the data instance for each index. Renowned as one of the premier financial websites globally, Investing Financial Information offers real-time updates and comprehensive market data on a diverse array of financial instruments, including stocks, funds, foreign currencies, futures, bonds, and digital currencies. One can download all of the experiment's data at [[https://www.investing.com/commodities/real-time-futures](https://www.investing.com/commodities/real-time-futures)]([https://www.investing.com/commodities/real-time-futures](https://www.investing.com/commodities/real-time-futures)), accessed on 11 June 2024. To facilitate effective model training and evaluation, the closing price data were partitioned into training and test sets. Specifically, the training set comprises the initial 80% of the data, employed to train the LSTM model parameters. Then, the remaining 20% of the data are reserved as a test set, serving as a robust validation mechanism for assessing the performance and accuracy of the proposed predictive model. It is worth noting that, in order to reduce the volatility of the dataset and enhance the predictive performance and stability of the proposed model, we normalize the raw data (both training and test sets) using Equation (16). This normalization process ensures that the raw data, both in the training and test sets, are standardized to a common scale, thereby facilitating more robust and reliable model training and evaluation, where \(x_{min}\) and \(x_{max}\) are the minimum and maximum values of the original sequence, respectively.  
\[x_{i(norm)}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{16}\]  
To facilitate this predictive process, we adopt the moving window method, a widely employed technique for feature extraction from observed time series data. The closing prices of the previous t trading days are used in each prediction to forecast the closing price of the following trading day t + 1. This method systematically constructs features and labels by sliding a window over the time series data, as depicted in Figure 4.
# Design of an Energy Harvester Based on a Rubber Bearing Floating Slab Track
## 5 Parameter Optimization Design
### Piezolectric Layer Radius  
In the study of the effect of piezoelectric layer radius on the energy-harvesting structure, parametric scanning was defined for the numerical model. The radius R scan range was from 6 to 23 mm, and R = range (6, 1, 23) was defined in the parameter value list. The peak stress, peak vertical displacement, peak voltage, and total electricity of the spherical energy harvester under different piezoelectric layer radii were also obtained.  
The peak stress under different piezoelectric layer radii is shown in Figure 18, and the peak stress increased with the increase in the piezoelectric layer radius. The radius R of the piezoelectric layer increased from 6 mm to 23 mm, and the peak stress of the energy harvester increased from 1.02 MPa to 1.57 MPa, with an increase of 53.9%. Figure 19 shows the variation of the peak vertical displacement, which decreased and then increased with the radius of the piezoelectric layer. The minimum displacement value existed when the radius was between 18 and 21 mm, which was 2.41 \(\times\) 10\({}^{-4}\) mm. With the change of the  
Figure 16: Peak voltage under different piezoelectric layer thicknesses.  
Figure 17: Total electricity under different piezoelectric layer thicknesses.  
piezoelectric layer radius, the peak vertical displacement decreased from 2.87 \(\times\) 10\({}^{-4}\) mm to 2.43 \(\times\) 10\({}^{-4}\) mm in general, which was a decrease of 15.5%.  
As can be seen from Figure 20, the peak voltage increased with the increase in the piezoelectric layer radius and then decreased, and the maximum value was 6.7 V when the piezoelectric layer radius R = 16 mm. With the increase in radius, the peak voltage of the energy harvester increased from 5.6 V to 6.5 V, which was 16% higher. The change of total electricity is shown in Figure 21. It can be seen that the total electricity increased with the increase in the piezoelectric layer radius, and it increased from 1.13 \(\times\) 10\({}^{-8}\) J to 2.3 \(\times\) 10\({}^{-7}\) J with the increase in the piezoelectric layer radius R from 6 mm to 23 mm, with a total increase of 20.4 times.  
Figure 19: Peak vertical displacement under different piezoelectric layer radii.  
Figure 20: Voltage under different piezoelectric layer radii.  
Figure 18: Peak stress under different piezoelectric layer radii.  
When the radius of piezoelectric layer varied from 6 mm to 23 mm, the peak stress and vertical displacement were within the safety range, so the electrical properties were given priority to determine the radius size. When the radius of the piezoelectric layer R was 16 mm, the peak voltage of the energy harvester can be taken to the maximum value of 6.7 V, and the minimum output voltage requirement (0.4-1.5 V) can be met. At this time, the total electricity was \(1.17\times 10^{-7}\) J, so the optimal radius of piezoelectric layer was 16 mm.
## 5 Conclusions  
The outcome of this study is the evaluation of different machine learning algorithms on the 68,722 SCADA real-time datasets using the following combined machine learning performance metrics: high accuracy, earliest training time, fastest prediction speed, negligible MCE, and less computation power requirement. Based on these combined machine learning performance metrics using the 68,722 datasets, it was concluded that the coarse tree algorithm showed the best performance, and is regarded as the most suitable for the detection of MitM attacks in a process control network of an oil and gas installation. This study can be improved upon by evaluating more machine learning algorithms as well as the use of more real-time SCADA datasets which improve the detection of other forms of cyber-attacks. More real-time SCADA data samples are required in order to develop very accurate and reliable anomaly detection models. Because the tree algorithms performed best, it is recommended to perform more hyperparameter tuning of the ensemble tree, which will aid the trade-off between accuracy and training time. The use of different datasets is a helpful approach to validation. However, such datasets should have a comparable relationship to avoid misjudgment of performance. In this work, the coarse tree had a limitation of confusing the MiTM attack with a backdoor attack, and more work will be required to handle multiclass detection and classification. In our approach, we solved this problem by employing an ensemble tree but it came with a training time burden.  
Conceptualization, U.O.O., C.I.N. and F.K.O.; methodology, all authors contributed equally; software, U.O.O., C.I.N. and M.M.A.; validation, U.O.O., C.I.N., F.K.O., C.C.M., J.-K.C.O. and I.O.A.; formal analysis, U.O.O. and C.I.N.; investigation, all authors contributed equally; resources, U.O.O. and C.I.N.; data curation, U.O.O., C.I.N. and M.M.A.; writing--original draft preparation, all authors contributed; writing--review and editing, U.O.O. and C.I.N.; visualization, U.O.O., C.I.N. and M.M.A.; supervision, F.K.O., J.-K.C.O., I.O.A. and C.I.N.; project administration, U.O.O. and C.I.N.; funding acquisition, U.O.O. and C.I.N. All authors have read and agreed to the published version of the manuscript.  
This research work received no external funding  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors wish to thank [PERSON] of the Networked Systems Laboratory, IT-Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea for assisting in the simulation during the preliminary stage of the project.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 14: Graph demonstrating the proportion of all the identified attacks that may not be attacked (false discovery rates (FDR)) across all evaluated datasets (**a**) SCADA Pressure, (**b**) WUSTL-2018, (**c**) ORNL PowerGrid (**d**) TON_IoT.
## 5 Conclusions  
In this study, the plastic deformation of a flat plate under slamming impact was investigated numerically. To simulate fluid-structure interactions, the CEL method, which combines the advantages of Eulerian and Lagrangian formulations, was applied, and the permanent deformation in the plate was measured and compared with previous experimental results. The following conclusions were obtained:  
* Similar to the experiment, the simulation result showed that the relative deformation of the plate increased as the drop height increased and the plate thickness decreased. In the experiment, various patterns of deformation were observed, while the simulation results all showed deformation of the same shape.
* A comparison between the CEL-based simulation and experimental results indicated good agreement between them in terms of the maximum deflection range. Two cases (h: 1 m, t: 5 mm and h: 1 m, t: 8 mm) show relatively large differences, where the amount of deflection was small. The result of this study presents that the CEL technique is suitable for analyzing slamming impacts and the resulting deformation on a plate.
* Various shape deformations, i.e., deformed shapes with one or two peaks, were observed in the experiment, whereas the simulation results primarily indicated deformations with only one peak. The difference in the maximum deformation might be due to the difference in the deformation shape.
* Two additional studies can be performed in the future. One is to accurately measure the initial deformation of the plate before an experiment and apply it to a simulation study. This would allow various modes of deformation to be observed in the simulation. The other is to consider air pockets, which are primarily caused by the slamming of flat plates, in the simulation. In this regard, air, which is a compressible fluid, must be included in the analysis. By performing the two studies above, the deformation of the flat plate due to slamming can be predicted more accurately.  
Figure 13: Comparison of maximum deflection ranges.  
**Author Contributions:** Conceptualization, J.-H.K.; methodology, J.-H.K.; validation, Y.I.P.; formal analysis, S.-H.P.; investigation, S.-H.P.; writing--original draft preparation, Y.I.P.; writing--review and editing, Y.I.P.; visualization, S.-H.P.; supervision, J.-H.K.; project administration, J.-H.K.; funding acquisition, J.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
### Problem Formulation  
The aim is to find a solution to the following problem. There are \(b\) points where the coordinates of their positions are known. There is a requirement to find the coordinates of the positions of the \(c\) centers. Each point will be assigned to exactly one center which is closest to it. Variable \(c\) is an integer greater than or equal to 1 and \(b\) is an integer greater than or equal to \(c\).  
The objective is to find the positions of the centers so that the sum of the distances between all the centers and their assigned points will be minimal.  
A practical example is a company with a number of existing branches which needs to set up a number of centers that will provide services to these branches. The locations of the branches are known; the centers will be built as new. The location of these centers and the branches they will serve are not specified.  
The mathematical formulation of this problem is as follows. Let \(B=\left\{B_{1},\ B_{2},\ldots,B_{b}\right\}\) be a set of points (\(b\geq 1\)); the coordinates of these points are known. Let \(C=\left\{C_{1},\ C_{2},\ldots,C_{c}\right\}\) be a set of centers (\(b\geq c\)); the coordinates of these points are not known as they are the subject of the optimization.  
The Euclidean distance between a center and any point in two-dimensional space is calculated according to Formula (1). In general, any space, number of dimensions and distance function can be used.  
\[d\left(C_{i},B_{j}\right)=\sqrt{\left(B_{X_{j}}-C_{x_{j}}\right)^{2}+\left(B_ {y_{j}}-C_{y_{j}}\right)^{2}}\ \text{for}\ i=1...c\ \text{and}\ j=1...bw \tag{1}\]  
where \(d\left(C_{i},B_{j}\right)\) is the distance between center \(C_{i}\) and point \(B_{j}\); \(C_{x_{j}}\), \(C_{y_{j}}\) are coordinates of the center \(C_{i}\), and \(B_{x_{j}}\), \(B_{y_{j}}\) are coordinates of point \(B_{j}\).  
The objective function of the MFLP problem is shown in Formula (2). It corresponds to the sum of distances between all points and their assigned centers which are closest to them.  
\[D=\sum_{i=1}^{b}\min(d(C_{1},B_{i}),d(C_{2},B_{i}),\ldots,d(C_{c},B_{i})) \tag{2}\]  
The optimization goal is in Formula (3); the aim is to find the locations of \(c\) centers so that the objective function \(D\) is minimal. In the case of two-dimensional space, there are \(2c\) continuous optimization variables.  
\[Minimize(D) \tag{3}\]
###### Abstract  
Transparent conductive oxides are essential materials for many optoelectronic applications. For new devices for aerospace and space applications, it is crucial to know how they respond to the space environment. The most important issue in commonly used low-Earth orbits is proton radiation. This study examines the effects of high-energy proton irradiation (226.5 MeV) on thin films of aluminium-doped zinc oxide (AZO) and indium tin oxide (ITO). We use X-ray diffraction and electron microscopy observations to see the changes in the structure and microstructure of the films. The optical properties and homogeneity of the materials are determined by spectrophotometry and spectroscopic ellipsometry (SE). Analysis of the chemical states of the elements with X-ray photoelectron spectroscopy (XPS) gives insight into what proton irradiation changes at the surface of the oxides. All measurements show that ITO is less influenced than AZO. The proton energy and fluence used in this study simulate about a hundred years in low Earth orbit. This research demonstrates that both transparent conductive oxide thin films can function under simulated space conditions, with ITO showing superior resilience. The ITO film was more homogenous in terms of the total thickness measured with SE, had fewer defects and adsorbates present on the surface, as XPS analysis proved, and did not show a difference after irradiation regarding its optical properties, transmission, refractive index, or extinction coefficient.  
Keywords:transparent conductive oxides; proton irradiation; thin films; space application
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 4 Discussion
### Tidal Distributions  
It is known that the impacts of the tides on the saltwater intrusions in estuaries are also important factors. Tides are astronomical phenomena which are cyclically affected by the phases of the moon, and tend to be the driving forces of saltwater intrusions [33]. In the YRE, the tides have size and period variations within half-monthly cycles. The salinity levels of the YRE also display one-level and one-half cycle variations [34]. In the eigenvalues which reflect the intensity levels of the tides, the tidal ranges have been found to be more objective in reflecting the characteristics of tide intensities [35]. It has been observed that when the tidal range is higher, the larger tide levels move farther forward, resulting in higher levels of saltwater intrusions until finally the maximum level of salinity is reached [36].  
Table 10 and Figure 13 show that under the average river discharge of the dry seasons (16,542 m\({}^{3}\)/s) at Datong station, the mean salinity levels of the selected stations of the SB displayed different tidal ranges as follows: \(<\)1.5 m; 1.5 to 2 m; 2 to 2.5 m; 2.5 to 3 m; 3 to 3.5 m; and \(>\)3.5 m. It was observed that at the same rate of flow, as the tidal range increased, the salinity levels of each station continuously increased. From upstream to downstream along the SB, the stations generally showed rising trends. The salinity of the SB was found to be distributed as \"high-low-high\". These results were consistent with those of the previous related studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Discharge** & \multicolumn{2}{c}{**Dongfengxisha**} & \multicolumn{2}{c}{**Baogang**} & \multicolumn{2}{c}{**Chenhang**} & \multicolumn{2}{c}{**Qingcaosha**} \\ \cline{2-9}
**(m\({}^{3}\)/s)** & **Total** & **Successive** & **Total** & **Successive** & **Total** & **Successive** & **Total** & **Successive** \\ \hline
27,856 & 93 & 4 & 0 & 0 & 0 & 0 & 0 & 0 \\
18,112 & 614 & 196 & 360 & 133 & 453 & 154 & 364 & 157 \\
16,331 & 655 & 204 & 496 & 175 & 556 & 203 & 500 & 275 \\
14,832 & 686 & 318 & 572 & 266 & 608 & 283 & 552 & 316 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The increasing total and the longest successive time of salinity exceeding 0.45% at four reservoirs in the four scenarios in a typical month (unit: h).
## 3 Deep-Learning-Based Channel Estimation Methods  
Deep learning is transforming the paradigms and methods of physical layer communications, with application areas including channel estimation, resource allocation, and signal detection [45; 46]. Conventional channel estimation methods work well in systems with a clear and linear representation of the input-output relationship. However, when the wireless environment becomes more complicated, the input-output relationship exhibits nonlinear characteristics, potentially reducing the effectiveness of conventional techniques. Deep learning aims to enable machines to learn to deal with complex and highly nonlinear relationships between input datasets and desired outputs without human intervention [47]. As stated in Section 1, the existing research on deep-learning-based channel estimation can be broadly classified into two main categories: data-driven and model-driven approaches. Therefore, this section will describe deep-learning-based channel estimation according to these two approaches and introduce their representative studies.
## 3 Results
### Regression Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Algorithm** & **MAE (All)** & **MAE (Short)** & **MAE (Long)** \\ \hline \multirow{8}{*}{5} & SVR (Linear) & \(5.7\pm 1.1\) & \(6.6\pm 2.7\) & \(4.9\pm 1.2\) \\  & SVR (RBF) & \(5.4\pm 0.9\) & \(5.6\pm 2.1\) & \(5.3\pm 1.2\) \\  & SVR (Poly) & \(5.8\pm 0.6\) & \(5.2\pm 1.1\) & \(6.5\pm 1.3\) \\  & kNNR & \(6.6\pm 1.2\) & \(6.7\pm 2.1\) & \(6.4\pm 0.9\) \\  & PolR & \(9.0\pm 5.0\) & \(9.2\pm 4.4\) & \(8.6\pm 6.0\) \\  & LinR & \(5.8\pm 1.3\) & \(7.2\pm 3.0\) & \(\mathbf{4.7\pm 1.1}\) \\  & RF & \(5.5\pm 1.3\) & \(6.3\pm 2.7\) & \(4.8\pm 0.9\) \\  & XGB & \(5.7\pm 1.2\) & \(6.7\pm 2.6\) & \(4.8\pm 1.0\) \\  & LSTM & \(\mathbf{4.0\pm 1.1}\) & \(\mathbf{2.6\pm 0.9}\) & \(4.8\pm 1.7\) \\  & Naive approach & \(7.0\pm 1.1\) & \(8.3\pm 2.8\) & \(6.0\pm 2.6\) \\ \hline \multirow{8}{*}{6} & SVR (Linear) & \(4.3\pm 0.8\) & \(4.8\pm 1.6\) & \(\mathbf{3.8\pm 1.0}\) \\  & SVR (RBF) & \(4.3\pm 0.7\) & \(4.2\pm 1.3\) & \(4.5\pm 1.1\) \\  & SVR (Poly) & \(6.1\pm 0.9\) & \(5.4\pm 0.9\) & \(6.7\pm 1.3\) \\  & kNNR & \(5.4\pm 0.6\) & \(5.4\pm 1.1\) & \(5.2\pm 0.7\) \\  & PolR & \(4.9\pm 1.0\) & \(5.5\pm 2.1\) & \(4.3\pm 1.1\) \\  & LinR & \(4.3\pm 0.8\) & \(4.9\pm 1.6\) & \(\mathbf{3.8\pm 1.0}\) \\  & RF & \(4.3\pm 0.6\) & \(4.6\pm 1.2\) & \(4.1\pm 0.9\) \\  & XGB & \(4.7\pm 0.6\) & \(4.9\pm 1.4\) & \(4.1\pm 0.9\) \\  & LSTM & \(\mathbf{3.6\pm 0.8}\) & \(\mathbf{2.4\pm 1.0}\) & \(4.6\pm 1.4\) \\  & Naive approach & \(5.5\pm 0.9\) & \(5.1\pm 1.6\) & \(5.9\pm 1.8\) \\ \hline \multirow{8}{*}{7} & SVR (Linear) & \(4.0\pm 0.8\) & \(4.6\pm 1.4\) & \(3.4\pm 0.6\) \\  & SVR (RBF) & \(\mathbf{3.6\pm 0.6}\) & \(3.9\pm 1.1\) & \(\mathbf{3.2\pm 0.5}\) \\  & SVR (Poly) & \(4.1\pm 0.8\) & \(3.7\pm 0.8\) & \(4.4\pm 1.5\) \\  & kNNR & \(4.7\pm 0.6\) & \(4.8\pm 0.9\) & \(4.5\pm 0.5\) \\  & PolR & \(4.5\pm 1.1\) & \(5.0\pm 1.7\) & \(4.0\pm 1.2\) \\  & LinR & \(3.9\pm 0.8\) & \(4.6\pm 1.5\) & \(3.3\pm 0.6\) \\  & RF & \(3.8\pm 0.7\) & \(4.3\pm 1.3\) & \(3.3\pm 0.5\) \\  & XGB & \(4.1\pm 0.7\) & \(4.8\pm 1.3\) & \(3.5\pm 0.6\) \\  & LSTM & \(\mathbf{3.6\pm 0.7}\) & \(\mathbf{2.8\pm 1.6}\) & \(4.6\pm 1.6\) \\  & Naive approach & \(4.1\pm 1.0\) & \(5.0\pm 1.8\) & \(\mathbf{3.2\pm 1.1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RNT prediction results (mean \(\pm\) SD MAE) for the three surgical tasks and for all, short, and long training sessions. The best MAE is shown in bold. worse than LinR, whereas for Tasks 6 and 7, it is significantly worse than the best ML models. However, as described previously, LSTM shows the best overall performance in all three surgical Tasks, irrespective of the number of training trials.
# Blockchain Integrated IoT for Food Supply Chain: A Grey Based Delphi-DEMATEL Approach
## 3 Methodology
### Grey Delphi  
[PERSON] and [PERSON] [69] proposed the Delphi approach. It is a popular survey methodology for synthesizing the opinion of experts on a specific issue to reach a mutual consensus [70; 71]. The conventional Delphi method has some limitations such as multiple rounds to reach consciences, subjectivity, and expert biasness. These limitations could be overcome with the grey theory integration [72]. Therefore, Delphi and grey set theories are combined to create the grey Delphi approach. The following are the steps for the grey Delphi:  
Step 1: Identification of CSFs of B-IoT adoption in FSC  
This step involves identifying the potential CSFs of B-IoT adoption in FSC through the literature review. Based on these CSFs, a questionnaire is developed to collect the data from the experts.  
Step 2: Response collection from expertsThe developed questionnaire is being sent to experts, and they are required to provide their responses using the linguistic scale. The linguistic scales and grey numbers for each scale are listed in Table 1.  
Step 3: Overall evaluation using grey number  
In accordance with Table 1, the responses have been converted into corresponding grey numbers. This grey number is used for integrating the responses of the experts. Consider the case of an expert panel consisting of \(k\) members. The evaluation of the factor \(\otimes\)\(G_{i}\) is as follows:  
\[\otimes\)\(G_{i}\)\(=\)\(\dfrac{\left(\otimes\)\(G_{i}^{1}\)\(+\)\(\otimes\)\(G_{i}^{2}\)\(+\)\(\ldots\)\(+\)\(\otimes\)\(G_{i}^{h}\)\(+\)\(\ldots\)\(+\)\(\otimes\)\(G_{i}^{k}\)\(\right)}{k}\)  
where \(\otimes\)\(G_{i}\) is the overall evaluation of CSF's importance and \(\otimes\)\(G_{i}^{h}\) denotes the \(h\)th expert's evaluation of CSF '\(i\)' of B-IoT adoption.  
Step 4: Whitening of the grey number  
The grey number having the interval (\(\otimes\)\(G_{i}\)\(=\)\([\underline{G}\), \(\overline{G}]\)\(=\)\([\)\(G^{\prime}\)\(\in\)\(G\)\(]\(\underline{G}\)\(\leq\)\(G^{\prime}\)\(\leq\)\(\underline{G}\)\(]\)) and their equivalent whitensation value is \(\widehat{\otimes}\). The whitenisation of grey numbers is obtained through Equation (6) as mentioned in preliminary section.  
Step 5: Setting threshold limit and CSFs selection  
The final step of the grey Delphi method involves selecting and rejecting CSFs. In order to determine the significance of the factor, an overall score is calculated and compared to a threshold value (\(\lambda\)). If the value of \(\widehat{\otimes}\)\(\geq\)\(\lambda\), then the factor is selected; otherwise, it is rejected.