esa-sceva/satcom-chunk-collection huggingface dataset:
# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 4 Experimental Result
### Dataset  
#### 4.1.1 The PhysioNet/CinC 2016 Challenge Dataset  
The PhysioNet/CinC 2016 Challenge [30] Dataset is composed of training-a, training-b, training-c, training-d, training-e, and training-f as shown in Table 1. The data collected from healthy subjects, including children and adults, as well as patients with heart disease, included 3240 data, including 2575 normal and 665 abnormal data. The length of the signal consisted of data from a minimum of 5.31 s to a maximum of 122 s, as shown in Figure 12, and was resampled at 2000 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**PhysioNet/CinC 2016 Challenge Dataset**} \\ \hline
**Data** & **Normal** & **Abnormal** & **Total** \\ \hline training-a & 117 & 292 & 409 \\ \hline training-b & 386 & 104 & 490 \\ \hline training-c & 7 & 24 & 31 \\ \hline training-d & 27 & 28 & 55 \\ \hline training-e & 1958 & 183 & 2141 \\ \hline training-f & 80 & 34 & 114 \\ \hline Total & 2575 & 665 & 3240 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the PhysioNet/CinC 2016 Challenge Dataset.  
Table 2 shows the number of signals divided into 5 s for each class of the PhysioNet/CinC 2016 Challenge Dataset, and the number of divided signals can be constant or increase depending on the length of the original signal. Since the PhysioNet/CinC 2016 Challenge Dataset was divided into 5 s, which is the minimum length, and signals shorter than 5 s were not used, the number of split signals remained constant for signals shorter than 10 s, and the number of split signals increased for signals longer than 10 s.
# Fractional Order Viscoelastic Model for Stress Relaxation of Polyvinyl Chloride Geomembranes
## 5 Discussion  
### Analyses of Model Parameters  
The FOVM established in Section 3 contains two parameters: the fractional derivative order \(\beta\) and viscoelastic parameter \(K\). As reported in Section 4.2, the constant \(\beta\) indicates no correlation with the tensile rate and initial strain, and hence it has no impact on the variation of relaxation stress with time, whereas the viscoelastic parameter \(K\) varies with the tensile rate and initial strain.  
Figure 7 presents the linear relationship between \(K\) and tensile rate \(v\) at three initial strains. The linear function \(K=av+b\) was used for fitting, and the fitting parameters are described in Table 3. As shown, all the correlation coefficients exceeded 0.96, verifying the accuracy of fitting results. Moreover, the viscoelastic parameter \(K\) decreased linearly with the tensile rate, that is, the lower the tensile rate, the more obvious the viscoelasticity.  
Figure 8 presents the relationship between \(K\) and initial strain \(\varepsilon_{0}\) at three tensile rates. The function \(K=\varepsilon_{0}+d\) was chosen for fitting, and the results are listed in Table 4. The correlation coefficients of the transverse specimens, in the range of 0.943 to 0.989, were all greater than 0.94, indicating an outstanding linear relation with relatively high accuracy, whereas no linear relation between \(K\) and \(\varepsilon_{0}\) of the longitudinal specimens was noted. For the transverse specimens, the viscoelastic parameter \(K\) tends to decrease linearly with the increasing initial strain, or, a smaller initial strain corresponds to a larger \(K\), suggesting more remarkable viscoelasticity. The reason is that the smaller initial strain results in less work done by axial tension, and a shorter time for viscoelastic materials to adjust internal energy. Whereas, no linear relation between \(K\) and \(\varepsilon_{0}\) was observed in the longitudinal specimens, which may be likely due to their residual stresses.  
In general, the viscoelastic parameter \(K\) in the FOVM is negatively correlated with both the tensile rate and initial strain, while \(\beta\) is a constant, irrelevant to the initial strain, direction of the specimen, and tensile rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fitting** & \multicolumn{3}{c}{\(\varepsilon_{0}\) **(T)**} & \multicolumn{3}{c}{\(\varepsilon_{0}\) **(L)**} \\ \cline{2-7}
**Parameters** & **40.55\%** & **58.78\%** & **81.09\%** & **40.55\%** & **58.78\%** & **81.09\%** \\ \hline \(a\) & \(-0.079\) & \(-0.140\) & \(-0.129\) & \(-0.094\) & \(-0.069\) & \(-0.060\) \\ \(b\) & 7.33 & 7.24 & 6.70 & 7.79 & 6.87 & 6.90 \\ \(\text{R}^{2}\) & 0.980 & 0.988 & 0.990 & 0.961 & 0.993 & 0.987 \\ \hline \hline \end{tabular} Note: T represents the transverse specimen, and L represents the longitudinal specimen.  
\end{table}
Table 3: Fitting parameters by fitting a line between \(K\) and tensile rate \(v\).  
Figure 7: Relationship between and tensile rate v at three initial strains.
## 4 Outage Performance
With the increase of \(R_{2}\), the outage probability of two users first remains stable and then increases rapidly in the higher \(R_{2}\) range. For a given \(R_{2}\), a higher \(R_{1}\) leads to a higher outage probability. This result further confirms that it is not advisable to set too high a data rate at the user side, especially for the far user. Because the QoS of the near user is guaranteed first, and then the far user is opportunistically assisted, it is difficult to achieve a high data rate at the far user.
## Materials and Methods
### Data Collection  
Fiber optic Raman spectroscopy (Figure 1) equipped with 785 nm laser (TA pro, Toptica, Germany), a bifurcated fiber-optic probe (N.A. = 0.22, InPhotonics, USA) with an integrated filtering and steering micro-optics, a spectrometer with an adjustable reflection type grating (iHR320, line density of 1200 L/mm, Horiba Scientific, UK), and a thermoelectrically cooled charge-coupled device (CCD) detector (Synapse, 1024 \(\times\) 256 with each pixel size of 26 \(\upmu\)m, Horiba Scientific, UK) was utilized for measuring the oil samples. The laser light was focused onto 25 \(\upmu\)L of sample dropped in Al multi-well plate and multiple Raman measurements (\(n\) = 3) were taken on each sample. Each Raman spectrum was acquired in the fingerprint region from 800 to 1800 cm\({}^{-1}\). The spectral resolution of the system is 1.7 cm\({}^{-1}\). The Raman measurements on all mixtures were performed in a time span of two months with different power levels (80-150 mW) and integration times (10-30 s). Each spectrum is an average of 2 scans.
## 3 Results
### Evaluation of Cell Proliferation  
After cytotoxicity evaluation, the product was tested on dermal fibroblasts to evaluate its proliferative capacity to promote cell renewal by the BrdU assay. NHDF cells were treated with the two highest non-cytotoxic concentrations (0.625 and 1.25 mg/mL) for 24 h. In Figure 2, treatment with the product showed a greater proliferative rate compared to untreated cells (CTRL). After 24 h treatment with 0.625 mg/mL, the percentage of BrdU positive cells was equal to 55.12% versus 38.46% of control cells, indicating a significant increase of 43% (* \(p\) values \(\leq\) 0.05). A similar result was confirmed after treatment with the highest concentration used of 1.25 mg/mL (+32% compared to untreated cells, * \(p\) values \(\leq\) 0.05).
## 3 Calculation of Mechanical Variables Using a Strong Form-Based Meshfree Method
### Verification of Synthetic Structural Analysis through Comparison with Experimental Results  
In this section, the calculation results of the kinematic and kinetic variables such as strain, stress, force, and displacement are compared with the values measured through actual experiments to verify the performance of the synthetic structural analysis. Figure 21 compares the force measured by the load tester and that calculated by the synthetic structural analysis. The trend of strain change in the process of gradual loading demonstrates that the strain values calculated by the synthetic structural analysis and those measured through experiment agreed well. When the load applied on the rubber beam exceeded a certain threshold, a slight slip phenomenon occurred at the support, and plastic behavior was observed. Subsequently, a change in the strain curve was observed, which did not occur during the numerical analysis in which an elastic material was assumed. As the synthetic structural analysis uses the displacement values obtained from the TP or the expanded TP, it can reasonably simulate the phenomenon of strain curve change.  
Figure 22a shows a force-displacement plot for the entire duration of the loading test. From approximately 60 s after the loading starts to the time the deflection exceeds 10 mm or the load exceeds 1000 N, the behavior of the material seems to enter the nonlinear or plastic stage. After this point, nonlinearity becomes evident, and the experimental value increasingly diverges from the value measured by synthetic structural analysis. However, the results of the synthetic structural analysis correspond with the experimental results in the linear elastic region, as shown in Figure 22b. Figure 23a-c displays the results of the displacements in the \(x\)- and y-directions, and the von Mises stress calculated by the synthetic structural analysis applying the expanded TP when the displacement reached approximately 10 mm after 60 s and the load exceeded 1000 N. In fact, Figure 23c was plotted by doubling the domain of influence when calculating the PDM approximation. Thus, it shows the same result as Figure 16b, while the scale of the stress value was the same as that in Figure 14e or  
Figure 21: Comparison of the change in strain over time measured in the experiment and the strain value calculated by the synthetic structural analysis.  
Figure 20: Convergence rates for the PDM simulation with a fixed number of TPs.  
Figure 14f. Therefore, we determined that the analysis result was slightly more consistent with the experimental result when the expanded TP was used than when the TP was used.
## 3 Machine Learning (ML)
### Advanced ML Methods
#### 3.1.2 Auto Features Engineering Approach with Deep Neural Network (DNN)
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Ref.**} & \multicolumn{1}{c}{**ML Model**} & \multicolumn{1}{c}{**Explanation**} \\ \hline  & [50] & Deep CNN Network & Intraoperative glioma tumor detection \\  & [51] & Deep GAN Network & Overcoming limitation of fluorescent probe \\  & & & in SWIR by converting NIR-a to NIR-b results \\  & [52] & Deep Iter-Net & Segmentation on vasculature, in vivo \\  & [53] & Deep FCC Network & 3D rendering on fluorescent \\  & & & results with the FMT method \\  & [54] & Deep Scale-recurrent & Image 3D reconstructing \\  & & Network & for increase SNR from deeper tissue layers \\  & [55] & Deep U-Net & Enhancing the precision of \\  & & & surgical resection of gliomas \\ \hline  & [56; 57; 58] & Deep Network & Automated coronary plaque classification \\  & & & for risk assessment \\  & [59; 60] & Deep Network & Cardiac tissue characterization \\  & & & for detection of Kawasaki disease’s biomarkers \\  & [61] & Decision Tree & Coronary plaque classification \\  & & with smaller dataset for training the ML model \\  & [62; 63] & SVM & Automated stent coverage analysis \\  & & & and detection \\  & [64] & Decision Tree & Automatic stent detection \\  & & from IV-OCT pullback results \\  & [65] & Bayesian Network & 3D stent detection from IV-OCT results \\  & [66] & Deep Network & Stent detection under deep tissue coverage \\  & [67] & Deep U-Net & Automated dispersion compensation \\  & [68] & Deep GAN & Synthesize PS-OCT images from conventional \\  & & Network & OCT images to tackle the limitation of \\  & & & conventional OCT system in providing \\  & & & birefringence-related contrast \\  & [69] & Deep U-Net & Quantifying wound morphology as an \\  & [70] & Deep FCC & \\  & & Network & automatic method to monitor wound healing \\  & [71] & SVM & Enhancing imaging rate \\  & & & of functional phase-related OCT extensions \\  & [71] & SVM & Quantification of lipid content \\  & & with OCT and its modalities on phantoms \\ \hline  & [72] & KNN & Enhancing intraoperative tumor delineation in \\  & & & mouse, in vivo \\  & [73] & Deep Network & Quantification of water and lipid in phantoms \\  & [74] & KNN and SVM & Measuring skin parameters on humans \\  & [75] & SVM & Quantitative label-free brown adipose tissue \\  & [76] & Partial least squares & Quantitative description of edema description \\  & [77] & Bayesian classifier & Distinguishing malignant kidney tissue \\  & & from normal tissues \\  & [78] & Deep U-Net & Lymph nodes segmentation and size measurement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examplesof applications of ML models in different SWIR modalities. blocks, has remarkably deep variants and has demonstrated superior performance across complex tasks such as disease prediction and image registration/fusion, showcasing its potential to unravel complex patterns within data.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 2 Topology Based Scalability  
This section provides reviews of different topology-based scalability approaches. These approaches are classified into two architectures--centralized controller design and distributed controller design. The distributed controller design is further classified into three categories--flat topology, hierarchical topology and hybrid topology. The following subsections provide details on these designs and their respective strengths and weaknesses regarding control plane scalability.
## 3 The Proposed Architecture
### Application Layer  
The application layer covers the application protocols used in the IIoT context such as websockets, Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), Hypertext Transfer Protocol (HTTP) and Secured Message Queuing Telemetry Transport (SMQTT). Mirai malware, IRCTelenet, Injection, DoS, DDoS and buffer overflow are the main threats regarding the application layer. Advanced encryption standard (AES), RSA, Attribute-Based Encryption (ABE) and input validation are the key security solutions to tackle the application layer threats.
## 2 Materials and Methods
### Environment Model  
To accurately assess sensor placement, we rely on environment models proposed in our previous research that closely represent the real environment based on descriptions of free and occupied spaces. These models, which typically describe the layout of the ground plane, are created through three-dimensional polygonal modeling. Although these models are primarily three-dimensional, they can also be simplified into two-dimensional representations if necessary by focusing on their ground planes. Three distinct test environments were defined by altering their shapes and the arrangement of obstacles within them. The first environment, seen in Figure 1a, features a U-shape without obstacles, representing a scenario where multiple sensors are necessary due to the curved layout obstructing visibility. In the second environment, Figure 1b, a simple quadratic shape with multiple obstacles, limits the coverage of individual sensors. The third environment, Figure 1c, simulates scenarios where obstacles isolate parts of the environment, testing the optimization algorithm's response to discontinuities and the placement of sensors in both isolated and non-isolated areas.  
To effectively calculate the spatial coverage, the environment is divided into a final number of voxels based on a rasterization parameter \(r_{v}\) that determines the granularity of  
Figure 1: 3D isometric views of used environment models.  
coverage. The number of voxels depends on the layout, the dimensions of the free and occupied spaces, and the selected rasterization parameter, and is given in Table 1. In general, it is assumed that a higher number of voxels provides a more accurate description of the world.  
By employing both two-dimensional and three-dimensional spatial representations and adjusting the rasterization parameter, we can evaluate the performance of optimization algorithms on test cases of varying complexity.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 1 Introduction
The challenge is further compounded by the limited availability of alternative navigation aids, as many aviation systems rely predominantly on satellite-based technologies like GPS. Considering these developments, the need for comprehensive research into RFI, its mitigation, and its implications for the aviation industry is more pressing than ever. Understanding the mechanisms of interference, exploring innovative mitigation techniques, and developing robust regulatory frameworks are essential steps in safeguarding the future of aviation. This research aims to contribute to these efforts by investigating the sources, impacts, and mitigation strategies for RFI, with the ultimate goal of enhancing safety and efficiency in the aviation sector. By addressing these challenges, the aviation industry can not only maintain operational integrity but also adapt to the rapidly evolving technological landscape. This paper will explore these aspects in detail, shedding light on the critical role of RFI management in ensuring the continued safety and reliability of global air travel. The reliance on advanced electronic and wireless systems in aviation has made RFI a critical challenge. RFI disrupts essential communication, navigation, and surveillance systems, posing risks to safety and efficiency. With emerging technologies like 5G, IoT, and UAVs increasing the likelihood of interference in VHF and UHF bands, addressing RFI has become vital. This research focuses on identifying RFI sources, analyzing its impacts, and proposing mitigation strategies using real-time monitoring and simulations to ensure -aviation system safety and reliability. The primary research objectives of this study are as follows:  
* Review multiple research papers covering RFI interference to figure out the methodologies and technologies used in identifying the RFI. * Identify the sources of RFI in aviation, including external factors like terrestrial transmitters, on-board electronic equipment, portable electronic devices, and weather. This paper is organized as follows: Section 2 provides the theoretical background, Section 3 provides a literature review, Section 4 provides a discussion of the results, and Section 5 concludes the paper.
## 3 Results and Discussion
### Comparison of Activated Carbon from Coffee Grounds with Other Adsorbents  
The comparison of the adsorption capacity of CV and Cr (VI) on ACSCGs with that of other activated carbons reported in the literature is presented in Table 8. It can be seen from Table 8 that activated carbon prepared by activation of spent coffee grounds has a high adsorption capacity compared with other activated carbons. This may be mainly due to the initial carbon content, the activation process, as well as the basic morphology of the raw material. In addition, it can be observed that ACSCGs has a great advantage for adsorption at a pH around neutral. Therefore, ACSCGs can be considered as an effective adsorbent for the removal of Cr (VI) and CV from aqueous solutions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multicolumn{4}{c}{**CV**} & \multicolumn{4}{c}{**Cr (VI)**} \\ \cline{2-9}  & \(\Delta\)H\({}^{\circ}\) & \(\Delta\)S\({}^{\circ}\) & \(\Delta\)G\({}^{\circ}\) & **E\({}_{\text{a}}\)** & \(\Delta\)H\({}^{\circ}\) & \(\Delta\)S\({}^{\circ}\) & \(\Delta\)G\({}^{\circ}\) & **E\({}_{\text{a}}\)** \\  & **(kJ/mol)** & **(J/mol K)** & **(kJ/mol)** & **(kJ/mol)** & **(kJ/mol)** & **(J/mol K)** & **(kJ/mol)** & **(kJ/mol)** \\ \hline
25 & & & \(-7.80\) & & & & & \(-4.65\) & \\
30 & \(-26.28\) & \(-62\) & \(-7.49\) & & & & \(-1.18\) & \\
40 & \(-26.28\) & \(-6.87\) & & & & & \(-1.00\) & \\
50 & & & & \(-6.25\) & & & & \(-0.82\) & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Thermodynamic parameters of Cr (VI) and CV adsorption on ACSCGs.  
Figure 15: Evolution of ln (K\({}_{\text{d}}\)) as a function of \(1/\)T.
## 4 Results
### Characteristics of Pore Development  
#### 4.2.1 Intergranular Pores and Intergranular Dissolved Pores  
The intergranular pores and intergranular dissolved pores of dolomite are mainly formed in the same period of dolomitization in carbonate sediments, which are closely related to the genesis of dolomite. The reservoir rocks of this type of reservoir are mainly coarse powder crystal and grain dolomite with a mud powder crystal structure. The grain size of dolomite is generally 40-150 \(\upmu\)m, and the grain structure is relatively uniform. The reservoir space type of the intergranular pore-type reservoir of dolomite is mainly dolomite intergranular pores (locally developed intergranular dissolved pores at the same time), followed by microfractures. The intergranular pores and intercrystalline non-selective dissolutions of dolomite are mainly formed in the same period of dolomitization of carbonate sediments, which is closely related to the genesis of dolomite (Figure 3A,B). The pore wall is straight and smooth, and the pore size is generally 9-50 \(\upmu\)m for the intergranular dissolved pores (Figure 3C,D). The pore size is generally 30-200 \(\upmu\)m, unevenly distributed, and the size is very different. Its development degree depends on the rock structure and the strength of its dissolution. The intergranular pores are transformed by the later karstification, forming intergranular pores of composite genesis. In addition to some of these pores being filled and destroyed by calcite and dolomite, most of them are effective pores. The pore size is generally 2-15 \(\upmu\)m, and the surface porosity is generally 0.5-3.5%. After the late flood dissolution, the edge of intergranular pores has estuarine dissolution marks and the shape is irregular, which will expand to form intercrystalline non-selective dissolution. This kind of pore has good connectivity and the highest development frequency, so it is the best type of reservoir space in the region.  
Figure 3: Microphotographs of intergranular pores and intergranular dissolved pores in the Majiagou Formation in the Ordos Basin. **(A)** J2, 3607.15 m, intergranular pores, not filled; **(B)** T37, 3429.13 m, the intergranular pores are observed, and the larger pore size is about 20 \(\upmu\)m, a small amount of illite is distributed in the pores; **(C)** J2, 3492.79 m, intercrystalline non-selective dissolutions; and **(D)** J2, 3586.57 m, intergranular pores and intergranular dissolved pores (BSE).