esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges in Intrusion Detection Systems
### Challenges of Data Imbalance  
The Pervasiveness of Data Imbalance: In cybersecurity, data imbalance is a widespread issue that is particularly evident in IDS [37; 38]. Normal traffic data typically dominate the dataset, while anomalous attack traffic is relatively rare [39]. The imbalance of sample distribution can cause the model to lean towards majority-class samples during training while ignoring minority-class samples [40].  
The challenges posed by data imbalance include the following:  
Model Bias: Given the predominance of normal traffic, models are prone to becoming biased towards these data during training, failing to detect minority class attacks effectively [41].  
Introduction of Noise and Information Loss: Traditional oversampling methods, such as SMOTE, can introduce noise, leading to overfitting [42]. On the contrary, using undersampling methods can lead to the loss of key information and weaken the overall detection capability of the model [43].  
Computational Complexity: Advanced techniques like Generative Adversarial Networks (GANs) excel at generating minority class samples. However, their training involves multiple sources of complexity, such as iterative optimization, high computational costs, and hardware requirements, which limit their feasibility in real-world applications [44].  
The problem of imbalanced samples in the dataset can increase false-positive and false-negative rates, thereby compromising the overall performance of IDS [45]. Overcoming data imbalance issues is particularly important when dealing with stealth attacks such as Advanced Persistent Threats (APTs) and zero-day exploit [46; 47]. Table 4 shows the impact of data imbalance on model performance.
## 4 Results and Analysis
### Electromechanical Coupling Analysis  
As shown in Figure 2, the crystal orientation of flexible PVDF piezoelectric film made it suitable for the detection of circumferential stress and strain of the pipe wall. Circumferential strain and circumferential stress signals at Point1 were compared with the voltage signal of the piezoelectric thin film labeled PVDF1, as shown in Figure 8a,b. The correlation coefficients were 0.9437 and 0.9570, respectively. Comparison of signals measured at Point2 and Point3 are listed in Table 2. Thus, it can be concluded that the voltage variation of PVDF piezoelectric film sensor was highly positively correlated with the variation of circumferential stress and strain of the pipe wall.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Circumferential Strain and Fluid** & **Circumferential Stress and Fluid** \\  & **Pressure** & **Pressure** \\ \hline Point1 and Plane1 & 0.9256 & 0.9225 \\ Point2 and Plane2 & 0.9318 & 0.9311 \\ Point3 and Plane3 & 0.9223 & 0.9261 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient.  
Figure 7: Comparison curves of **(a)** fluid pressure and circumferential strain at Point1; **(b)** fluid pressure and circumferential stress at Point1.  
In conclusion, the PVDF piezoelectric film sensor can be applied to indirectly measure the pressure fluctuation of internal fluid by measuring the variation of circumferential stress and strain of the pipe wall.
## 3 Simulation Results and Discussion
### Linearly Polarized MIMO RDRA (Design-1)
Consequently, the said design is LP, and to excite the DRA strip, a separate-edge port was applied. Design-1 provided a narrow impedance-matching bandwidth of 7.19% while in parallel, high mutual-coupling, as illustrated in Figure 5. The band achieved via Design-1 covers a range from ~3.61 GHz to 3.95 GHz, having return losses for S\({}_{11}\)/S\({}_{22}\) of \(<-10\) dB and for S\({}_{21}\)/S\({}_{12}\) of \(<-15\) dB. Design-1 had only one minima (resonance) in the S11 curve at 3.68 GHz due to the main radiator. Figure 4: Simulated S\({}_{11}\) and gain of Design-1. Figure 3: S\({}_{11}\) of the RDRA feed at various distances from the edge. The simulated results of Design-1 were undesirable for a MIMO system; for the best performance, the design of the linearly polarized DRA must be modified to generate circular polarization and widen the S11 bandwidth along with that reducing shared coupling. Modifications with in the design are discussed with in the subsequent subsection.
## 3 Results
### Buckling Analysis
#### 3.4.1 Eln+/+ Mice  
The analysis of the Eln+/+ mouse artery buckling is conducted for the geometry and using the Fung constants of the fifth specimen from [19], as this turned out to be the most accurate description presented in [19] for the Eln+/+ artery. This behavior is reviewed by additional calculations performed for the aforementioned geometry of the artery but using the Holzapfel constitutive relation with the \"B\" set of constants, described in Section 3.3.1, which proved to be very accurate. The response of the artery axis, defined as the relation between the internal pressure \(p_{int}\) and the displacement \(u_{A}\), is shown for the Eln+/+ FEM analyses in Figure 6a. The buckling modes that were observed during the analysis and some reference experimental comparisons are shown in Figure 7.  
The buckling pressures are collected in Table 2, namely, the experimental values \(p_{exp}\) from [19], the FEM values derived from the current analysis of post-buckling response \(p_{BuckFEM}\), and the FEM values \(p_{uA,FEM}\) for the pressure at \(u_{A}=D_{out}/2\). The \(p_{exp}\), according to [19], is the pressure value at which the maximum lateral displacement of the carotid artery axis is equal to the radius of the unloaded artery. Thus, a direct comparison of \(p_{exp}\) and \(p_{uA,FEM}\) is possible. Consequently, a relative error is calculated between the FEM estimations and experimental values for every stretch level and constitutive relation under consideration. The \(p_{Buck,FEM}\) is determined in a typical way, like in [37; 38], by finding the intersection point of a curve tangent to the post-buckling response and the second one tangent to the initial response (before buckling occurs). This value is also introduced here,  
Figure 6: The relation between the internal pressure (\(p_{int}\)) and the horizontal displacement of the carotid artery (\(u_{A}\)) for the Fung and the Holzapfel material laws and for different stretches: (**a**) Eln+/+ mouse model; (**b**) Fbln5\(-/-\) mouse model.  
because it is the theoretical moment when the phenomenon is initiated and may be a reference in further studies.  
Further discussion of the results of Eln+/+ mouse model arteries' buckling will be provided in Section 4.3.1.
## 4 Experimental Results
### Integrated Detection Algorithm  
#### 4.2.1 Effectiveness of IDA Detection Algorithm against Paralysis Attacks  
In practice, the ability of the IDA algorithm to detect paralysis attacks depends on the effectiveness of the Data Washing algorithm in improving the model accuracy. Table 6 shows the accuracy improvements obtained by the Data Washing algorithm for the original dataset and the datasets attacked by the TensorFlowClog and Category Diverse algorithms with two different clip rates (0.05 and 0.1). In the absence of any attack, the Data Washing algorithm results in almost no change in the accuracy. However, for both attack methods, the Data Washing algorithm results in a notable improvement in the accuracy. Hence, both attacks can be theoretically detected using the IDA algorithm, provided that an appropriate threshold value is set (see Figure 3). It should be noted that the IDA algorithm does not seek to identify every attacked data point in the dataset, but simply to determine whether or not the dataset has been attacked.
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## Principles
### Signal Processing Procedure  
Wavelet transform (WT) is a new transformation analysis method. WT inherits and develops the thought of short-time Fourier transformed localization, while overcoming shortcomings such as the window size not changing with frequency. It can automatically adapt to the requirements of time-frequency signal analysis and can focus on any detail of the signal to solve the difficult problem of the Fourier transform. It is the ideal tool for signal time-frequency analysis and processing.  
The continuous wavelet coefficient [13] can be expressed as  
\[W_{f}(m,n)=|m|^{-1/2}\int f(x)\psi^{*}\bigg{(}\frac{x-n}{m}\bigg{)}dx \tag{20}\]  
where \(m\) is the scale parameter, \(n\) is the shift parameter, \(f(x)\) is the signal to be analyzed, \(\psi(x)\) is the mother wavelet, and \(\psi^{*}(x-n)/m\) is its conjugate function. The amplitude of \(W_{f}(m,n)\) is positively correlated with the similarity of the mother wavelet and the signal [14]. In this paper, Gaussian wavelet is chosen as the mother wavelet.  
The amplitudes of the light field are given by the expressions,  
\[A(m,n)=\sqrt{\Big{\{}Im\Big{[}W_{f}(m,n)\Big{]}\Big{\}}^{2}+\Big{\{}Re\Big{[}W _{f}(m,n)\Big{]}\Big{\}}^{2}} \tag{21}\]  
where \(Im\Big{[}W_{f}(m,n)\Big{]}\) is the imaginary part of \(W_{f}(m,n)\), \(Re\Big{[}W_{f}(m,n)\Big{]}\) is the real part of \(W_{f}(m,n)\).  
Hence, the phase is retrieved from the following equation:  
\[\Phi(m,n)=tan^{-1}\Bigg{\{}\frac{Im\Big{[}W_{f}(m,n)\Big{]}}{Re\Big{[}W_{f}(m, n)\Big{]}}\Bigg{\}} \tag{22}\]  
Then, through phase unwrapping, the deformation information of the measured object is obtained [15]. According to Equation (19), the object displacements can be obtained. The whole signal processing flowchart is shown in Figure 4.  
Figure 4: Signal processing flowchart.
# Modeling and Analyzing a Multi-Objective Financial Planning Model Using Goal Programming
## 2 Materials and Methods
### Objective Function
#### 2.8. GP Model  
In view of the above, the GP model (16) is created and formulated as follows, based on the established goal constraints.  
\[\begin{split}&\text{Min}\ d_{1}^{-}+d_{2}^{+}+d_{3}^{-}+d_{4}^{-}+d_{5 }^{-}+d_{6}^{-}+d_{7}^{-},\\ &\text{subjectto},\\ &\alpha_{11}x_{1}+\alpha_{12}x_{2}+\alpha_{13}x_{3}+\alpha_{14}x_{ 4}+\alpha_{15}x_{5}+\alpha_{16}x_{6}+\alpha_{17}x_{7}+\alpha_{18}x_{8}+\alpha_ {19}x_{9}+\alpha_{110}x_{10}+\alpha_{111}x_{11}+d_{1}^{-}-d_{1}^{+}=\tau_{1}, \\ &\alpha_{21}x_{1}+\alpha_{22}x_{2}+\alpha_{23}x_{3}+\alpha_{24}x_{ 4}+\alpha_{25}x_{5}+\alpha_{26}x_{6}+\alpha_{27}x_{7}+\alpha_{28}x_{8}+\alpha_ {29}x_{9}+\alpha_{210}x_{10}+\alpha_{211}x_{11}+d_{2}^{-}-d_{2}^{+}=\tau_{2}, \\ &\alpha_{31}x_{1}+\alpha_{32}x_{2}+\alpha_{33}x_{3}+\alpha_{34}x_{ 4}+\alpha_{35}x_{5}+\alpha_{36}x_{6}+\alpha_{37}x_{7}+\alpha_{38}x_{8}+\alpha_ {39}x_{9}+\alpha_{310}x_{10}+\alpha_{311}x_{11}+d_{3}^{-}-d_{3}^{+}=\tau_{3}, \\ &\alpha_{41}x_{1}+\alpha_{42}x_{2}+\alpha_{43}x_{3}+\alpha_{44}x_{ 4}+\alpha_{45}x_{5}+\alpha_{46}x_{6}+\alpha_{47}x_{7}+\alpha_{48}x_{8}+\alpha_ {49}x_{9}+\alpha_{410}x_{10}+\alpha_{411}x_{11}+d_{4}^{-}-d_{4}^{+}=\tau_{4}, \\ &\alpha_{51}x_{1}+\alpha_{52}x_{2}+\alpha_{53}x_{3}+\alpha_{54}x_{ 4}+\alpha_{55}x_{5}+\alpha_{56}x_{6}+\alpha_{57}x_{7}+\alpha_{58}x_{8}+\alpha_ {59}x_{9}+\alpha_{510}x_{10}+\alpha_{511}x_{11}+d_{5}^{-}-d_{5}^{+}=\tau_{5} \\ &\alpha_{61}x_{1}+\alpha_{62}x_{2}+\alpha_{63}x_{3}+\alpha_{64}x_{ 4}+\alpha_{65}x_{5}+\alpha_{66}x_{6}+\alpha_{67}x_{7}+\alpha_{68}x_{8}+\alpha_ {69}x_{9}+\alpha_{610}x_{10}+\alpha_{611}x_{11}+d_{6}^{-}-d_{6}^{+}=\tau_{6}, \\ &\alpha_{71}x_{1}+\alpha_{72}x_{2}+\alpha_{73}x_{3}+\alpha_{74}x_{ 4}+\alpha_{75}x_{5}+\alpha_{76}x_{6}+\alpha_{77}x_{7}+\alpha_{78}x_{8}+\alpha_ {79}x_{9}+\alpha_{710}x_{10}+\alpha_{711}x_{11}+d_{7}^{-}-d_{7}^{+}=\tau_{7}, \end{split} \tag{16}\]  
and  
\[x_{1},x_{2},x_{3},x_{4},x_{5},x_{6},x_{7},x_{8},x_{9},x_{10},x_{11},d_{1}^{-},d_{ 1}^{+},d_{2}^{-},d_{2}^{+},d_{2}^{-},d_{3}^{+},d_{4}^{-},d_{4}^{+},d_{5}^{-},d_{ 5}^{+},d_{6}^{-},d_{6}^{+},d_{7}^{+},d_{7}^{+}\geq 0.\]  
Additionally, the following case study was examined to illustrate the effectiveness of the proposed GP model.
## 2 Materials and Methods
### Ammonia Emissions Flux Calculations  
For calculation of the flux of NH\({}_{3}\) emissions from the manure being processed inside the manure composite agitation hall, the extraction fans were disabled, allowing gaseous NH\({}_{3}\) concentrations to rise until they reach peak concentration, probably as a result of equilibrium in the concentration gradient at the manure-air boundary. The difference between normal (ventilated) NH\({}_{3}\) concentrations and saturated NH\({}_{3}\) concentrations was divided by the time taken for this increase to occur, and the results were averaged across the four internal NH\({}_{3}\) sampling channels.  
\[E_{\text{NH3}}=(\text{C}_{\text{NH3(sat.)}}-\text{C}_{\text{NH3(vnt.)}})/\Delta t \tag{1}\]  
Equation (1) demonstrates how the NH\({}_{3}\) emission rate was calculated, where C\({}_{\text{NH3(sat.)}}\) is the point at which NH\({}_{3}\) concentrations in the air reach equilibrium with NH\({}_{3}\) concentrations at the manure surface (i.e., there is no longer a concentration gradient, leading to a stabilization of NH\({}_{3}\) concentration in the air), C\({}_{\text{NH3(vnt.)}}\) is the ambient concentration of NH\({}_{3}\) under ventilated conditions (i.e., when there is negative pressure and emitted NH\({}_{3}\) is being constantly removed and scrubbed by the ventilation system during ordinary operation), and \(\Delta t\) is the change in time in minutes between these two states following the disengagement of the ventilation system.
## 1 Introduction
Thus, we use this crystal's data for the frequency down-conversion into the radiation possessing wavelength equal to 4 \(\upmu\)m and 10 \(\upmu\)m, and the tellurium crystal data [39] for the frequency down-conversion to wavelength 24 \(\upmu\)m.  
Our paper is organized as follows. We begin with the set of nonlinear Schrodinger equations accounting for the group velocity dispersion (GVD) of the laser pulses. Then, based on the multi-scale method, we derive the modified set of equations and solve them without using the energy non-depletion approximation for HFW in the framework of the long pulse duration approximation. This solution demonstrates that the effective energy conversion of HFW to LFW is possible, even if the incident IFW's intensity is small. The latter part of our study contains computer simulation results, showing how GVD influences energy conversion efficiency. That section is divided into two parts: investigation of the amplification process (small incident LFW's intensity) and the generation process (incident LFW's intensity is equal to zero). In both cases, we demonstrate that a highly-effective frequency down-conversion process occurs under certain conditions.
# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 4 Experimental Results
### Violence and Fall-down  
The precision rate of violence detection was 0.960, and an example of the resulting image is shown in Figure 13. In the violence scenario, many personnel appeared, and outstanding results could be derived by showing clear movements. In the failed video, it was detected as violence before the violence behavior took place, and a false detection occurred. However, we expected that performance will be improved if additional dataset configuration and violence definition are added. The precision rate of fall-down was 0.880, which seems insufficient compared with other results. In this paper, training was conducted by dividing behavior recognition into normal behavior, fall-down behavior, and violence behavior. Algorithms were used to compensate for the behavior overlap problem between the acts of violence and fall-down, but they were not completely compensated. In addition, the detection failed because there were parts that were difficult to clearly distinguish the fall-down behavior according to the angle of the image. To clearly judge this and improve the performance, it is expected to show better performance if additional networks such  
Figure 12: Intrusion/Loitering Experimental Results: (**a**) Intrusion Experimental Results, (**b**) Loitering Experimental Results.  
as pose estimation are combined. The performance of the proposed abnormal behavior analysis method shows an outstanding accuracy of 0.941 on average, which is the result of a real-time experiment through VLC streaming. If network improvement and additional data sets are constructed, the performance will be further improved, and the proposed method alone is suitable for use in real environments.
## 3 A Contextual Bandit-Based Reinforcement Learning Approach
### Operational Workflow of the Contextual Bandit Framework  
This provides an overview of the core operational flow for our contextual bandit-based reinforcement learning approach. The principal steps are as follows:  
* Each classifier \(ci\) configures its upper and lower boundaries (\(L\_i\) and \(U\_i\)) and initializes a pool of possible actions as increments of \(\mu\).
* For each incoming data point, \(ci\) generates a prediction \(Pi\).
* The selection function, guided by \(Pi\), determines which decision boundary to apply from the available action set.
* Based on the selection function's parameters, the algorithm may either leverage known successful actions or explore alternative actions.
* The chosen boundary is then applied. If the boundary suggests that further refinement is needed, the data instance is passed on the subsequent classifier \(ci+1\); otherwise, the prediction stands.
* The executed action is assessed against a specified set of criteria, producing a computed reward. This feedback updates the action value estimates, informing future decision-making processes.
* This classification-and-evaluation cycle continues until \(i<n-1\), ensuring that each instance progresses through the chain of classifiers as necessary.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 2 Materials and Methods
### Evaluation of the Antimicrobial Effect of TiHA against Biofilms  
To evaluate the antimicrobial effect of TiHA against biofilms formed by single bacterial species (_S. sanguitis, A. naeslundii, S. aureus,_ or _E. coli_), the specimens were placed in petri dishes and soaked in 20 mL of fresh medium containing 1 mL of each bacterial culture, and then incubated at 37 \({}^{\circ}\)C for 12 h. After incubation, the specimens were gently and carefully washed with sterile distilled water, three times, to remove non-adherent cells. Each specimen was placed on a wet filter paper and then irradiated with UVA for 2 h. The bacteria that adhered to the back of the specimens, which received no irradiation, were removed with a cotton swab saturated with 70% ethanol. After irradiation, the specimens were soaked in 5 mL of PBS, and then exposed for 15 min to ultrasound using an ultrasonic cleaner B-220 (Branson Ultrasonic, Danbury, CT, USA) set at approximately 50 kHz to remove the attached bacteria. After serial dilution with PBS, the microorganism suspensions were plated to determine CFU values. The bacteria from non-irradiated specimens used as negative controls were placed in a shade for 2 h without irradiation, as described above.  
The effect of TiHA on bacteria from human saliva was also evaluated. Saliva samples were provided by eight volunteers (four males and four females, 31 \(\pm\) 20 years old [mean \(\pm\) SD]). The volunteers were asked to refrain from oral activities (such as eating, drinking, chewing, sucking, brushing, and mouth rinsing) for at least 3 h before the collection of the saliva. The collected saliva was stored on ice and used for experiments within 30 min. The specimens (\(n=3\)) were incubated anaerobically at 37 \({}^{\circ}\)C for 12 h in petri dishes containing the mixture of saliva and fresh BHI at a ratio of 1:3, or only saliva. Other procedures, including UVA irradiation, the removal of bacteria from the specimens, and determining the CFU values, were performed as described above.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries  
The IoT integrates smart grids as modern electrical networks rely on digital communication technologies and devices to collect and analyze data in real time. This integration, depicted in Figure 1, enables energy suppliers to facilitate the grid integration of renewable sources, allowing for real-time control and monitoring of energy consumption and usage [9]. It enhances their energy management through informed decision-making, improving the grid's resiliency and reliability while reducing consumer costs [10]. In this section, the application of the smart grid in IoT is presented, along with the advantages of smart grid technology and the most commonly used communication types.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 2 Theoretical Background of Wavelet Transforms
### Discrete Wavelet Transform (DWT)  
The DWT is a discrete set of the wavelet scales and translations. It is specially adapted for the sampled value [6]. However, this transform decomposes the signal into a mutually orthogonal set of wavelets. This specificity represents the main difference between DWT and CWT. In fact, the DWT employs a dyadic grid, where the mother wavelet is scaled by power two (\(a=2i\)) and translated by an integer (\(b=k2^{j}\) ), where \(k\) is a location index running from 1 to \(2^{-j}N\) (\(N\) is the number of observations) and \(j\) runs from 0 to \(J\) (\(J\) is the total number of scales).  
The DWT is expressed by the following equation:  
\[\psi_{j,k}(t)=2^{-j/2}\psi(2^{-j}t-k). \tag{9}\]  
The DWT coefficients are obtained from the following expression:  
\[W_{j,k}=W(2^{j},k2^{j})=2^{-j/2}\int_{-\infty}^{\infty}f(t)\overline{\psi(2^{ -j}t-k)}dt. \tag{10}\]  
The means of the Inverse Discrete Wavelet Transform (IDWT) is calculated in order to reconstruct the original signal (or its parts) from the wavelet coefficients \(W_{j,k}\), such that:  
\[f(t)=\sum_{j=-\infty}^{\infty}\sum_{k=-\infty}^{\infty}W_{j,k}\psi_{j,k}(t). \tag{11}\]  
The DWT supports different mother wavelets, such as Harr, Daubechies, Biorthogonal, Symlet, Meyer and Coiflets. The Haar wavelet is the first and simplest mother wavelet, whereas the Daubechies wavelet is a family of orthogonal wavelets. In the literature, there are 20 functions for this family, namely from Daubechies \(N=1\) to Daubechies \(N=20\), where \(N\) refers to the number of wavelets and scaling parameter used for decomposing the signal. If \(N=1\), Daubechies is similar to Haar wavelet. The Symlet and Coiflet are defined as a modified version of Daubechies wavelets. However, Symlet is a Daubechies wavelet with increased symmetry. The difference between Daubechies and Coiflet is the vanishing moment of the scaling function. In addition, the Coiflet wavelet uses six scaling and wavelet function coefficients, which makes the analysis smoother. Finally, Biorthogonal wavelet deals with semi-orthogonal, biorthogonal or non-orthogonal wavelet bases. This family supports symmetric wavelets and uses two different wavelet functions and two scaling functions that may generate a different multi-resolution analysis.