esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Machine Learning-Based IDS on the Cloud without FS Method
#### 4.1.1 LSTM-Based IDS on Cloud without FS Method  
We studied different LSTM models with varying numbers of layers and neurons to find the optimal LSTM model for deployment on the cloud side of an IoT network for an IDS. The details of the LSTM model are shown in Table 3. To determine the optimal learning rate for training, Model1 and Model2 are trained using learning rates of 0.1, 0.01, and 0.001 with 50, 80, and 100 epochs. The results indicate that the best learning rate for training the LSTM model is 0.001. Therefore, Model3 and Model4 are trained using a learning rate of 0.001 with 50 epochs. The training and testing results of Model1-Model4 are shown in Table 3. The results show that Model 1, trained with a learning rate of 0.001 and 50 epochs, gives the detection accuracy of 94.2%.
## 3 Results and Discussion
### Characterization of the DP Composite
#### 3.1.2 Thermo-Physical Properties of the DP Composite  
Figure 4 shows the DSC curves of paraffin and the DP composite. The melting and solidification temperatures of paraffin were 21.75 \({}^{\circ}\)C and 19.85 \({}^{\circ}\)C, and the corresponding thermal latent heat values were 110.7 J/g and 121.6 J/g, respectively. The melting and solidification temperatures of the DP composite were 22.94 \({}^{\circ}\)C and 21.75 \({}^{\circ}\)C, and the corresponding latent heat values were 13.75 J/g and 18.75 J/g, respectively. According to Equation (1), we can calculate that the impregnation ratio of paraffin in the DP composite was 14%. These results are consistent with those of [PERSON] et al. [24]. In their study, the paraffin was encapsulated by diatomite and high-density polyethylene to prepare SSPCM, and the melting and solidification enthalpy were approximately 14.75 J/g and 16.95 J/g, respectively.  
It is worth noting that the melting temperature of DP is higher than that of pure paraffin. This might be related to the special pore structure and natural convection of paraffin. For pure paraffin, when the ambient temperature is higher than the melting temperature of pure paraffin, the paraffin surrounding and in contact with the environment will melt at first, and then the heat will flow to the unmelted paraffin by the driving force of natural convection, which could accelerate the melting of the pure paraffin. However, when the paraffin distributes evenly in the pores of diatomite, the flow of the paraffin is limited, especially in the unconnected pores, so the natural convection phenomenon is greatly weakened when paraffin is stored in the unconnected pores of diatomite [38].  
Figure 5 shows the TGA curves of paraffin and DP. The paraffin began to decompose at around 180 \({}^{\circ}\)C and then volatilized completely at 315 \({}^{\circ}\)C. In contrast, the decomposition of DP occurred at temperatures ranging from 160 \({}^{\circ}\)C to 230 \({}^{\circ}\)C. The DP composite was more sensitive to temperature change during the heating process than the pure paraffin due to the higher thermal conductivity of DP. Although the DP has a lower decomposition temperature, it still meets the requirements of thermal stability for practical applications. According to the results shown in Figure 5b, the content of paraffin within DP was approximately 15 wt. %, which is close to the result of DSC (14 wt. %).  
Figure 4: DSC curves of paraffin and diatomite/paraffin.
## 6 Machine Learning and 5G Security
### Machine Learning Applications in 5G Security
#### 6.2.2 Security Predictions Using Machine Learning  
Predictive security is a proactive approach in cybersecurity where ML can play an integral role in different areas, particularly in 5G networks. Security predictions using ML involve forecasting future security incidents by leveraging historical and real-time network data. This ability to foresee potential threats offers a strategic advantage, allowing the network to take proactive measures to minimize or prevent harm. A variety of machine learning techniques are applicable to this task, including classification, regression, and time-series forecasting. For instance, logistic regression, random forest, and gradient boosting can be utilized for predicting security incidents based on historical data patterns [126; 109].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**MI. Techniques in 5G Security** & **Applications** & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Weaknesses**} & \multicolumn{2}{c}{**Evaluation Metrics**} \\ \hline \multirow{3}{*}{Neural networks} & \multirow{3}{*}{-} & \multirow{3}{*}{Deep packet inspection -} & - & High capability for feature representation - & \multirow{3}{*}{-} & Resource-intensive Risk of overfitting in small datasets & - & Area Under the Receiver Operating Characteristic (AUROC) \\  & & & & complex patterns & & - & F1 score \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._Furthermore, Long Short-Term Memory (LSTM) networks, a type of RNN specialized in processing sequences of data, have been effectively used in time-series forecasting for predictive security. They can model the temporal dependencies in network traffic data, thereby predicting future anomalies or intrusions. One prominent application of ML for security predictions in 5G is the anticipation of DDoS attacks. Through analyzing patterns in network traffic, ML algorithms can predict an impending DDoS attack and implement preventive measures, thus averting network downtime and potential loss of service. However, while ML holds considerable promise for predictive security, it is crucial to note that prediction models must continually evolve in response to the dynamic nature of 5G networks and the ever-changing landscape of cyber threats [8]. The role of machine learning in 5G security is shown in Table 11.
## 1 Introduction
All of the above-mentioned efforts have the goal of accurately predicting the final stress state after forming, which is crucial for springback determination, and research in the field has been focused on this aspect [18]. However, it is rather uncommon to find research focused on complex components employed in industrial applications. There are many process parameters which have a great effect on the final stress reached by the material during the stamping forming process, hence determining dimensional accuracy of parts [19]. The die clearance, blank-holder force, die radius, and coefficient of friction are key parameters of the sheet metal-forming process that drive the thinning and thickening. Many studies have been carried out on tool geometry as well as the variability of process parameters to show how this affects springback [20; 21; 22]. Despite the many advances in simulation and development [23], the day-to-day sheet metal-forming process keeps facing difficulties. When designed parts are pushed to allowed extreme tolerances, even the minimum microstructural variation in the material could lead to a significative divergence in the final stress state after forming. This is the reason why changes in the press setup should be made to correct these deviations, even between different batches of metal blanks. This setup could lead to a large effect on the quality of the final sheet metal-forming process, including undesirable springback of the final part. The objective of this study is to investigate the effect of blank-holder force (BHF) on a real industrial component--a gas cooktop part made from unstable austenitic steel 1.4301, which is characterized by its complex geometry. Unlike many studies that focus on simpler components, this research provides valuable insights into the practical applications of sheet metal forming, using springback as the determining factor for manufacturing quality. The work not only presents experimental results but also compares them with predictions from the widely recognized Barlat Yld2000 numerical model, which is used to predict the yield behavior of anisotropic materials. The paper offers a comprehensive overview of the entire process, from the experimental determination of model parameters to the correlation with simulated results, detailing the experimental setup for measuring springback displacement and describing the full simulation chain from the forming process to springback measurements. Ultimately, both experimental and numerical results are analyzed to evaluate the performance of the process and validate the relationship between BHF and springback.
## 2 Dataset Description and Processing
### Data Processing  
To provide a clean and meaningful feature space to feed into the proposed CSIDTL learning process, a set of well-defined steps are followed, namely, feature extraction, a list of denoising algorithms, a set of outlier removals, filtering, smoothing, scaling, and balancing. Figure 5 addresses this data processing methodology by introducing these steps in the form of six different layers. The order of the presented layers is very important for the process to run smoothly. It should be mentioned that this section and the next ones contain many interesting tools and metrics that are used and discussed. Therefore, describing the mathematical background of these tools and their demonstration is not appropriate for this paper in terms of length and number. Instead, a set of very interesting references already well known in the literature will be cited and referred to when describing these tools. Furthermore, this work concentrates solely on the mathematical background of the innovative new formulas that are the focus of this paper and makes them easy to present for replication in programming terms.  
Figure 4: Dataset generation goals and scenarios.  
Figure 5: Illustration of the data processing methodology.  
Finally, it should be noted that the tools described in the following section might be well known in the literature. However, the main contribution of this paper in terms of data processing lies in the use of these tools in the form of different layers (which will be explained in the next sub-sections) in specific order. In addition, the combination of these different tools is specifically designed to tackle problems presented by data. The order of these layers is defined based on the authors' area of expertise in maintaining feature scales according to the algorithmic requirements of deep learning. The following sub-sections will also give examples of layer order and the repetition of certain layers to solve similar problems.
## 4 Numerical Example and Comparative Analysis  
### Applicable Example  
In this section, we shall present a numerical example to select strategic suppliers under supply chain risk with SVNNs in order to illustrate the method proposed in this paper. There is a panel with five possible strategic suppliers \(O_{i}(i=1,2,3,4,5)\) to select. The experts select four attributes to evaluate the five possible strategic suppliers: 1 C1 is the technology level; 2 C2 is the service level; 3 C3 is the risk managing ability; and 4 C4 is the enterprise environment risk. The five possible strategic suppliers \(O_{i}(i=1,2,3,4,5)\) are to be evaluated using the SVNNs by the decision-maker under the four above attributes (whose weighting vector \(\omega=(0.10,0.40,0.35,0.15)^{T}\)), as listed in the following matrix:  
\[\widetilde{R}=\left[\begin{array}{ccccc}(0.6,0.9,0.2)&(0.7,0.4,0.4)&(0.4,0. 7,0.2)&(0.6,0.8,0.3)\\ (0.8,0.5,0.2)&(0.8,0.6,0.3)&(0.8,0.5,0.5)&(0.9,0.6,0.2)\\ (0.7,0.8,0.3)&(0.6,0.8,0.4)&(0.6,0.4,0.2)&(0.7,0.4,0.3)\\ (0.9,0.2,0.4)&(0.7,0.4,0.5)&(0.5,0.5,0.3)&(0.6,0.7,0.3)\\ (0.7,0.6,0.5)&(0.5,0.9,0.2)&(0.8,0.7,0.3)&(0.6,0.9,0.4)\end{array}\right].\]  
Then, we utilize the proposed operators to select the best strategic suppliers under supply chain risk.  
**Step 1.** According to \(w\) and SVNNs \(O_{ij}(i=1,2,3,4,5,j=1,2,3,4)\), we can aggregate all SVNNs \(O_{ij}\) by using the DGSVNNWBM (DGSVNNWGBM) operator to derive the SVNNs \(O_{i}(i=1,2,3,4,5)\) of the alternative \(O_{i}\). The aggregating results are in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **DGSVNNWBM** & **DGSVNNWGBM** \\ \hline O1 & (0.5720, 0.6135, 0.2929) & (0.5692, 0.6209, 0.2951) \\ O2 & (0.8157, 0.5549, 0.3423) & (0.8150, 0.5552, 0.3454) \\ O3 & (0.6253, 0.5980, 0.3033) & (0.6249, 0.6066, 0.3051) \\ O4 & (0.6377, 0.4583, 0.3888) & (0.6345, 0.4610, 0.3903) \\ O5 & (0.6431, 0.7999, 0.2927) & (0.6395, 0.8064, 0.2951) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The aggregating results of strategic suppliers by the DGSVNNWBM and DGSVNNWGBM (\(R=(1,1,1,1)\).)  
**Step 3.** According to the Table 2 and the scores, the order of the strategic suppliers is listed in Table 3, and the best strategic suppliers is O\({}_{2}\).
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 5 Parameter Analysis  
The method used in this paper can be used to solve the scattering problem of seismic waves by an arbitrary-shaped canyon. In this section, we will take semicircular and trapezoidal anyons as examples to research the effects of canyon shape, porosity \(n_{s}\), incidence angle \(\gamma_{1}\), and dimensionless frequency \(\eta\) on the ground surface motion. The parameters analyzed in the two cases are as follows: incidence angles \(\gamma_{1}=5^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\), where \(90^{\circ}\) is the vertical incidence and \(5^{\circ}\) is the approximate horizontal incidence; the porosity of saturated soil \(n_{s}=0.1\), \(0.3\), \(0.34\), corresponding to 'hard soil','medium hard soil', and'soft soil', respectively [35]; the dimensionless frequencies \(\eta=1\), \(2\), \(4\), and \(6\), representing low frequency, mid frequency, mid-high frequency, and high frequency, respectively. For each canyon shape and dimensionless frequency, the number of truncated terms \(N\) in the equations is tested when calculating the surface displacement response. The value of \(N\) is continuously increased until the result converges or numerical stability is reached, and the test method can be seen [21]. In general, the parameters are discussed from three angles: incident wave property (incidence angle and dimensionless frequency), canyon site property (canyon shape), and half-space site property (porosity of saturated soil). As shown in Figure 3, the cases of semicircular canyon Figure 3a and trapezoidal canyon Figure 3b are calculated, respectively. For the two cases, plot the ground surface displacements of the \(x\)- and \(y\)-components in the range of \(x/a\in[-4,\,4]\). For convenience, the whole drawing region is divided into three categories: the front side (in which the waves arrive first \(x/a\in[-4,\,-1]\)), the backside (\(x/a\in[1,\,4]\)), and the surface of the canyon (\(x/a\in[-1,\,1]\)). Please see Section 4 for calculation parameters.  
Figure 2: Comparison with the published results of a semicircular canyon in a single-phase elastic half-space [21].
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
###### Abstract  
Low latency video transmission is gaining importance in time-critical applications using real-time cloud-based systems. Cloud-based Virtual Reality (VR), remote control, and AI response systems are emerging use cases that demand low latency and good reliability. Although there are many video transmission schemes that claim low latency, they vary over different network conditions. Therefore, it is necessary to develop methods that can accurately measure end-to-end latency online, continuously, without any content modification. This research brings these applications one step closer to addressing these next generation use cases. This paper analyzes the cause of end-to-end latency within a video transmission system, and then proposes three methods to measure the latency: timecode, remote online, and lossless remote video online. The corresponding equipment was designed and implemented. The actual measurement of the three methods using related equipment proved that our proposed method can accurately and effectively measure the end-to-end latency of the video transmission system.  
real-time video; timecode; remote online; lossless remote video online +
Footnote †: journal: Applied  
doi: Article
## 6 Recent Advancements in Analytical Methods
### Instrumental Analysis
#### 6.4.3 HPLC-Ms  
Although fluorescence detection is suitable for a wide range of food samples, researchers often suggested to use MS-based techniques to confirm BPA in complex food matrices and to reduce errors in quantification [18, 110]. Therefore, systems such as LC-MS or GC-MS are commonly used for BPA quantification. In MS methods, internal standards such as isotope labelled \({}^{13}\)C-BPA [13] and deuterated BPAd\({}_{16}\)[88, 111] has been frequently used to overcome the loss of analytes during sample preparation. In this regard, LC-MS and LC-MS/MS demonstrate excellent sensitivity and precision for the analysis of BPA in food, environmental and biological samples. In most cases, negative ion electrospray ionization (ESI) has been used. Compared to LC-MS, LC-MS/MS is more specific and selective for BPA as it allows single or multiple reaction monitoring (SRM or MRM) thus giving more confidence in peak identification as compared to LC-MS [8, 96]. The study conducted by [PERSON] et al. showed that the LOD of BPA by LC-MS and LC-MS/MS was 0.1 ng/mL for both methods, with LC-MS/MS being more precise (%RSD = 1.2) than LC-MS (%RSD = 3.2) [8]. LOQ was reported as 1 ug/kg for the determination of BPA in meat samples by combining accelerated solvent extraction with LC-MS/MS [84]. Moreover, the UHPLC-MS/MS method for the detection of conjugated metabolites of BPA and BPS in foods of animal origin described by [PERSON] et al. showed LODs in muscle samples of 0.02, 0.09, 0.04, 0.12, and 0.50 ug/kg for the metabolites BPA-monoglucuronide (BPA-1G), BPA-diglucuronide (BPA-2G), BPA mono-sulfate (BPA-1S), BPA-disulfate (BPA-2S) andBPS-monoglucuronide (BPS-1G), respectively [7]. Finally, the average recovery of BPA from meat samples was reported to range from 91% to 108% when using LC-MS/MS [76; 84].
###### Abstract  
Creep analysis is crucial when dealing with thick rotating cylinders exposed to a steady load or stress at a higher temperature. These cylinders present a fundamental constituent in a variety of dynamic engineering applications, such as pressure vessels, hydraulic cylinders, gun barrels, boilers, fuel tanks, aerospace technologies, nuclear reactors, and military equipment. Thus, severe mechanical and thermal loads cause significant creep and reduce service life. Hence, the prediction of creep in such axisymmetric components, including pressure vessels, subjected to steady load at elevated temperatures is extremely important and quite a complex task. Thus, in this study, the creep behavior in a rotating thick-walled cylinder made of Al-SiC\({}_{\text{p}}\) composite subjected to constant load as well as internal and external pressures is investigated, both analytically and numerically, using FEM. A wide range of rotational speeds effect on the process is also included. The creep behavior is assumed to follow the [PERSON] constitutive model, and for stress failure analysis, von Mises yield criteria are adopted. The effect of internal and external pressures, as well as the rotational speed on the stresses, strains, and strain rates in the cylinder, is studied and presented. Both finite element analysis (FEA) and [PERSON]'s theory were used to determine the radial, tangential, and longitudinal displacements and corresponding stresses, as well as the equivalent Von Mises stresses and strain rate distributions in the cylinder revolving about its own axis. It is observed that with the increase of the internal pressure in the cylinder, the strain rate increases. Meanwhile, when subjecting the cylinder to both external and internal pressures, the strain rates tend to decrease. For instance, it was also found that stress and strain rates were higher for the 1000 rad/sec rotational speed of thick cylinder in comparison with lower rotational speeds of 300 and 500 rad/sec. Also, it is noticed that the variation in these values at the inner radius was more than those found at the outer radius. All results of the stresses, strains, and strain rate distributions obtained are found to be in full agreement with the published data. Furthermore, all plotted results of the stresses, strains, and strain rate distributions obtained through the analytical approach were found to be in exceptional compliance with those solutions obtained using finite element analysis (FEA).  
Creep; aluminum composite; silicon carbide (Si-C); functionally graded material (FGM); rotating thick cylinders; strain rates; finite element analysis (FEA)
## 4 Seismic Response CFST Structure with BRBs under Earthquake Sequences  
### Sequence-Type Ground Motion  
An earthquake sequence is formed by multiple earthquake ground motions composed of foreshock, mainshock, and aftershock or a combination thereof (e.g., mainshock and aftershock). The sequence-type ground motions constructed in this study refer to an earthquake sequence that comprises a large earthquake (mainshock), with a foreshock before the mainshock and an aftershock after the mainshock. Due to the lack of actual recorded sequence-type ground motions, most researchers rely on synthetic methods to construct earthquake sequences. To date, the main synthetic methods are divided into two types, which include the repetition method and the randomized method. The principle of the repetition method is to equate the characteristics of the aftershocks (foreshocks) with those of the mainshock and consider the aftershocks (foreshocks) as one or more repetitions of the mainshock to form an earthquake sequence. In contrast, the principle of the randomized method is to randomly select an earthquake from the non-mainshock set to be aftershocks or foreshocks.  
In this study, the repeated method is employed to construct foreshock-mainshock-aftershock sequences. Based on the Gutenberg Richter law [51, 52], a real ground motion is selected as the mainshock, and the PGA of the mainshock is scaled for the aftershocks (foreshocks) with a specific scaling coefficient. As suggested by [PERSON] [51], the PGA scaling coefficient (\(\delta\)) can be derived without the involvement of specific information of ground motion records. However, the recommended \(\delta\) of 0.8526 possibly overestimates the intensities of the foreshocks and aftershocks, which poses certain limitations. Considering the uncertainty of the ratios between the PGAs of the foreshocks (aftershocks) and the mainshock, \(\delta\) is taken as 0.4, 0.6, and 0.8 in this study, respectively, to investigate the influence of this parameter on the structural seismic responses.  
Between two consecutive seismic events, a time interval is generally applied to better simulate the actual scenario of an earthquake sequence. If the time gap is very short or neglected, it is possible that the structure is not at rest when the subsequent earthquake excitation initiates, which aggravates the errors between the simulation and the actual situation. On the other hand, an excessively long time interval between two consecutive seismic events reduces the calculation efficiency. To balance error reduction in simulation and efficiency improvement in the calculation, a time interval of 50 s is applied in this study to give the structure enough time to stop the vibration of the structure due to damping.