esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Dingo Optimization Algorithm Applied to SHE-PWM Modulation Strategy
## 2 Materials and Methods  
### Selective Harmonic Elimination Method  
In cascaded H-bridge inverters, the staircase voltage output is a consequence of the addition of each DC source (\(V_{dc}\)) from each H-bridge cell (\(m\)), where the number of cells are equal to the numbers of sources (\(s\)). The relationship between the number of levels (\(n\)) and number of sources is given by \(2s+1\). Figure 1 shows an ideal single phase sine voltage waveform (\(f(t)_{v}\)) and the multilevel inverter staircase voltage output for un period (\(T\)). Where, the peak phase voltage is equal to \(sV_{dc}\) and the angles, defined for a quarter-wave symmetry are subjected to:  
\[0\leq\alpha_{1}\leq\alpha_{2}\leq\ldots\leq\alpha_{(s-1)}\leq\alpha_{s}\leq 9 0^{\circ}. \tag{1}\]By applying Fourier analysis [10], the MLI staircase output showed in Figure 1 is defined as follows:  
\[f(t)_{V}=\begin{cases}\frac{4V_{dc}}{n\tau}\cos(\text{n}\alpha_{1})+\ldots+\cos( \text{n}\alpha_{n})&\text{for odd n}\\ 0&\text{for even n}.\end{cases} \tag{2}\]  
SHE aims for the elimination of low-order harmonics as it keeps the fundamental component equal to a given amplitude. In a three-phase system, as in this study, the triplen harmonics are canceled out. Thus, Equation (2) is solved to eliminate the fifth, seventh, eleventh, and thirteenth harmonics while keeping the amplitude of the fundamental component equal to \(M\). Then, Equation (2) can be rewritten as:  
\[\begin{split}\cos(\alpha_{1})+\cos(\alpha_{2})+\ \cdots+\cos( \alpha_{5})=M\\ \cos(5\alpha_{1})+\cos(5\alpha_{2})+\ \cdots+\cos(5\alpha_{5})=0\\ \cos(7\alpha_{1})+\cos(7\alpha_{2})+\ \cdots+\cos(7\alpha_{5})=0\\ \cos(11\alpha_{1})+\cos(11\alpha_{2})+\ \cdots+\cos(11\alpha_{5})=0\\ \cos(13\alpha_{1})+\cos(13\alpha_{2})+\ \cdots+\cos(13\alpha_{5})=0\end{split} \tag{3}\]  
where \(M=(V_{1})/(4\ sV_{dc}\tau)\) is defined as the modulation index for \(0\leq M\leq 1\) and \(V_{1}\) is equal to the desired fundamental component [26]. Then, subjected to Equation (1), Equation (3) can be expressed as an optimization problem [10; 17] as follows:  
\[\begin{split}\text{min}f(\alpha_{1},\alpha_{2},\ldots,\alpha_{5} )=&\left[\sum_{i=1}^{5}\cos(\alpha_{i})-M\right]^{2}+\\ &\left[\sum_{i=1}^{5}\cos(5\alpha_{i})\right]^{2}+\ldots+\left[ \sum_{i=1}^{5}\cos(13\alpha_{i})\right]^{2}.\end{split} \tag{4}\]  
Once the objective function is defined, and previous to the definition of the modified dingo optimization algorithm (mDOA), the standard DOA is described in the next section.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 3 Methods
### Data Preprocessing  
For the categorical feature \"Protocol\", comprising \"TCP\", \"UDP\", and \"ICMP\", one-hot encoding is utilized to represent them as \([1,0,0]\), \([0,1,0]\), and \([0,0,1]\), respectively. Among these protocols, TCP (Transmission Control Protocol) is a connection-oriented and reliable transport layer protocol that establishes connections through a three-way handshake mechanism and employs acknowledgment responses, timeout retransmission, and other mechanisms to ensure data integrity and ordered delivery. It is widely utilized in scenarios such as HTTP/HTTPS and FTP. In contrast, UDP (User Datagram Protocol) is a connectionless and unreliable transport layer protocol that does not guarantee data reliability but offers low overhead and high efficiency, making it suitable for latency-sensitive applications like real-time video streaming and online gaming. ICMP (Internet Control Message Protocol), a network layer protocol, is designed for transmitting control messages and error reports. It supports network diagnostics and error feedback, serving as a critical tool for network management and fault troubleshooting.  
To improve model training efficiency and enhance the stability of the training process, continuous variables undergo data normalization. Using Equation (1), each feature value is transformed linearly to lie within the range \([0,1]\), where \(x\) denotes the continuous feature value, Max represents the feature's maximum value, and Min signifies the minimum value. The categorical feature \"Protocol\", consisting of \"TCP\", \"UDP\", and \"ICMP\", is represented using one-hot encoding, resulting in \([1,0,0]\), \([0,1,0]\), and \([0,0,1]\), respectively. To enhance the efficiency and stability of the training process, continuous variables are normalized. Each feature value is linearly scaled to the range \([0,1]\) using Equation (1), where \(x\)is the continuous feature value, Max is the feature's maximum value, and Min is its minimum value.  
\[x_{\text{normalized}}=\frac{x-\text{Min}}{\text{Max}-\text{Min}} \tag{1}\]  
For redundant features, invalid features (e.g., those with all zero values), null values, and arithmetic field characters in the dataset, invalid data features are removed. In addition, incomplete and anomalous data are excluded from the dataset.
## 2 Concept
### Single- and Multi-Target Scenarios
#### 2.2.4 Scenario IV  
This scenario also provides two targets within the same range resolution cell. However, the cross-range distance between target #5 and target #6 is closer as target #5 is located within the overlap area of the sub-channels #1 and #2, while target #6 is located in the main beam area of sub-channel #2. Comparably to scenario III, the time gating operation does distinguish between both targets so that a coding filter superposition is observed once more:  
\[s_{\mathrm{r,IV}}(t)=s_{\mathrm{t}}(t)*h_{\mathrm{sys}}(t)*h_{\mathrm{fs}}(t- \tau_{\mathrm{IV}})*(h_{1}(t)+2\cdot h_{2}(t)) \tag{10}\]  
In this scenario, the spectral analysis of \(s_{\mathrm{r,IV}}(t)\) will deliver features from both coding filter transfer functions \(H_{1}\) and \(H_{2}\). However, as target #5 provides both signatures and target #6 solely the signature of \(H_{2}\), the latter characteristic is more distinct. This may lead to the interpretation that the observed range cell provides two targets, which are located in sub-channel #2's main beam and within the overlap area to sub-channel #1.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Image Preprocessing  
Image preprocessing refers to some image processing to the original image before doing the main algorithm operation, in order to make the features of interest parts more prominent or adapt to the subsequent algorithm. Common image preprocessing includes smoothing, image threshold segmentation and so on.  
Image smoothing is a method to reduce the gray difference between some pixels and adjacent pixels in the image. The purpose of smoothing is to reduce image noise and image blur. In order to preserve the edge sharpness and detail of the image and suppress the noise at the same time, the median filter was used to smooth the original image and got the filtered image.  
Image threshold segmentation is to divide the picture scene into two categories: target object and background object. Image segmentation is the basis of image recognition. The Gray threshold method is a common image segmentation method, it includes the direct threshold method and optimal threshold method. Optimal threshold method is a method to determine the optimal segmentation threshold by using statistical knowledge. Its representative method is the maximum inter class variance method (Otsu) [22]. For the laser image under interference in the environment, the maximum inter-class variance method, which determines segmentation threshold automatically, was used to binary the filtered image to eliminate the background interference caused by adverse environment. The gray histogram is divided into two groups at a certain threshold, and the threshold is determined when the variance between the two groups is maximum. Further, the method can effectively distinguish the cross laser-pattern from the background [23]. However, when the stray light is too bright, it brings noise to the acquired image, causing low discrimination to background and cross laser-pattern; this affects the accuracy of the segmentation threshold. In order to avoid this problem, the image acquisition system uses a band-pass filter film matching with the laser frequency and dust shield to form a darkroom, preventing the affects of stray light.
## 4 Selection and Simulation Analysis for Wavelet Packet  
### Selection of the Best Wavelet Packet Base  
According to the wavelet packet decomposition in Equations (19)-(24), the decomposition effects of the reference signal for different wavelet packet bases are also different. In order to effectively decompose the complicated signal into the narrowband line spectrum signals with different frequency bands, a complex excitation is used as the input signal, which contains 37 Hz, 60 Hz, and 110 Hz signals with white noise. In the process, the distribution should be balanced and there is no significant endpoint effect, and the energy needs to be concentrated as much as possible. Based on the different natures of the wavelet base, the Haar, db, sym, and bior wavelet bases are used, respectively. The number of decomposition layers is five layers; the frequency band is 32 layers. Then, the decomposition effect is illustrated in Figures 4-7, respectively.  
Figure 3: The structure diagram of WPTDDM-FxLMS algorithm for the MIMO system.  
Figure 4: (a) Haar wavelet tree termination node; (b) energy spectrum of Haar wavelet decomposition.  
Figure 5: (a) db5 wavelet tree termination node; (b) energy spectrum of db5 wavelet decomposition.  
Figure 6: (a) sym20 wavelet tree termination node; (b) energy spectrum of sym20 wavelet decomposition.  
Figure 7: (a) bior3.3 wavelet tree termination node; (b) energy spectrum of bior3.3 wavelet decomposition.  
It can be observed in Figure 4 that the energy spectrum of the frequency band is relatively divergent by the Haar wavelet, and the maximum energy is only 14.5. The energy spectrum in frequency bands 2, 3, 4, 6, and 7 is much higher, and the energy is also divided into four or even five frequency bands. Evidently, this decomposition signal does not effectively realize the separation of the tri-band line spectrum signal. In Figure 5, the energy spectrum is concentrated in frequency bands 3, 4, 6, and 7; the maximum was up to 24, and the energy spectrum of the other frequency bands is lower. Thus, the db5 wavelet is much better than the Haar wavelet.  
In Figure 6, the energy is concentrated in three effective frequency bands (3, 4, and 6), and the energy spectra of other frequency bands are very low. The line spectrum performance is very prominent and more convenient to control. In Figure 7, the energy spectrum is highly concentrated in three frequency bands (2, 3, and 5), but the energy of the low-frequency line spectrum is too small; thus, it is obviously not adverse for AVC implementation. Depending on the analysis above, the sym20 can be the optimal wavelet packet for decomposing the tri-band signal mentioned above.
## 6 Performance and Discussion
### Discussion
[2] have shown in their experiments that the voxel-based direct method (similar to ROI_CL) and the atlas-based method (similar to ROI_AAL) for AD vs. CN independent test in ADNI outperformed cortical thickness and hippocampus volume-based classification. Also, methods using the whole brain reached specificity over 90% than those based on the hippocampus. Besides in prodromal AD detection (CN vs. MCIc), the sensitivity was substantially lower as in our case. In comparison to the cortical thickness and hippocampus analysis, VBM-based methods are simple and direct, SPM software can be used to generate tissue probability maps without extra parcellation (with parcellation error during registration) as required by the Freesurfer software ([[https://surfer.nmr.mgh.harvard.edu/](https://surfer.nmr.mgh.harvard.edu/)]([https://surfer.nmr.mgh.harvard.edu/](https://surfer.nmr.mgh.harvard.edu/)) (accessed on 21 February 2021)), which accounts for heavy time consumption and hardware operation. Similarly, contrary to deep neural networks like CNN, which are beneficial for standalone classification purposes and claiming the state-of-the-art performance in image classification. However, considering very few CNN architectures being designed for 3D MRI scans [45] and the significances of ROIs analysis as discussed in Sections 3.2 and 6.1 we were interested in VBM. In VBM, we follow an established preprocessing pipeline to extract and visualize the group cluster difference for generating ROIs of significant regions, hence is more specific in voxel-based group statistics analysis, whereas CNN is a gradient-loss supervised algorithm for image-attributes based feature extraction process primarily focused on classification rather than ROI detection.
## 4 Factors Affecting the Successful Introduction of New-Age Nanotechnologies in Pavement Engineering
### Hydrophobic Characteristics of an Alkoxy-Silane Modification and/or Treatment
#### 4.7.1 General  
As previously mentioned, Alkoxy-silane modifications are designed to attach firmly to the minerals present in the stone/aggregate/soil to form (for example), strong Si-O-Si bonds. This example logically assumes that silicon atoms are present in large quantities in the stone/aggregate/soil that is being treated. Although this assumption applies to a large percentage of the crust of the earth, this is not always correct and the principle inherent in this assumption, cannot be uniformly applied, especially when using highly weathered materials. The applicability of the alkoxy-silane modification not only depends on the primary minerals comprised of the stone/aggregate/soil, but also to the condition of the stone/aggregate/soil (weathering due to chemical decomposition) quantified by the presence of secondary minerals [9]. It follows that the applicability and expected behaviour of any nano-modification of a stabilising agent, will depend on a detailed analysis of the mineralogy of the material. Enough detail must also be obtained to enable the identification of the chemical decomposition that is already present in the material under specific climatic conditions [9].  
As discussed previously, alkoxy-silane treatments alone will not be effective to fulfil the normal role of a stabilising agent when treating stone/aggregate/soil. A larger molecule is required to act as a binder bridging the gaps between the stone/aggregate/soil particles. This immediately points to another important aspect related to the applicability of the nanotechnology product to be used, i.e., compatibility with the stabilising agent (such as bitumen emulsion or equivalent) to which the nano-particle (in this case alkoxy-silane or HCT-modified alkoxy-silane as an aggregate adhesive) needs to attach. It follows that the applicability or compatibility of an alkoxy-silane is affected by both the (Figure 1):  
* Mineralogy with which it is to be firmly bonded;
* Characteristics of the stabilising agent, the bitumen emulsion and/or compatible polymers(s) to which it is added as a modification.
# Spatially Adaptive Regularization in Image Segmentation
## 1 Introduction
Too large values of \(\lambda\) may produce oversegmentation, while too small values of \(\lambda\) may produce undersegmentation [9]. Furthermore, a constant value of \(\lambda\) may not be suitable for the whole image, i.e., different regions of the image may need different values. In recent years, spatially adaptive techniques have been proposed, focusing on local information, such as gradient, curvature, texture, and noise estimation cues--see, e.g., [10]. Space-variant regularization has been also widely investigated in the context of image restoration, using TV and its generalizations--see, e.g., [11; 12; 13; 14; 15]. In this work, we propose some techniques for setting the parameter \(\lambda\) in an adaptive way based on spatial information, in order to prevent excessive regularization of smooth regions while preserving spatial features in nonsmooth areas. Our techniques are based on the so-called cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique. The resulting locally adaptive segmentation model can be solved either by smoothing the discrete TV term--see, e.g., [16; 17]--and applying optimization solvers for differentiable problems, such as spectral gradient methods [18; 19; 20; 21], or by using directly optimization solvers for nondifferentiable problems, such as Bregman, proximal and ADMM methods [22; 23; 24; 25; 26; 27; 28]. In this work, we use an alternating minimization procedure exploiting the split Bregman (SB) method proposed in [24]. The results of numerical experiments on images with different characteristics show the effectiveness of our approach and the advantages coming from using local regularization. The rest of this paper is organized, as follows. In Section 2 we propose our spatially adaptive techniques. In Section 3, we describe the solution of the segmentation model using those techniques by the aforementioned SB-based alternating minimization method. In Section 4 we discuss the results obtained with our approaches on several test images, also performing a comparison with the CEN model and a segmentation model developed for images with texture. The results show the effectiveness of our approach and the advantages coming from the use of local regularization. Some conclusions are provided in Section 5.
## 3 Results

Table 3: Summary of the parameters of ellipses plotted with different values of the ellipse semiaxis dimension conformity coefficient \(\rho\) and the evaluated criteria for a single motion capture measurement sample, where \(\rho\)—the conformity coefficient of the ellipse’s semiaxis dimensions, \(a\)—semiaxis of the ellipse parallel to the directional line, \(b\)—semiaxis of the ellipse perpendicular to the directional line, \(a\)—angle of inclination of the directional line, \(x_{COG}\)—location of the ellipse’s center on the horizontal axis, \(y_{COG}\)—location of the ellipse’s center on the vertical axis, \(\Delta_{P}\)—uniformity ratio of the distribution of points with respect to the mean value of the radius vector, \(k\)—percentage of points inside the area plotted by the ellipse, and \(\Delta R\)—difference between the reference radius of curvature and the mean radius of curvature of the ellipse. Figure 9: Graph of areas of different concentrations of PM 2.5 solid particles during maintenance work in the green area with the use of tools powered by an internal combustion engine. is important in the implementation of the method. The criterion according to which sorting was performed was the concentration of PM 2.5 particulate matter (Table 4). Biochemical tests are another example for the applicability of the method of describing a set of points with elliptical areas. The adopted tests measured the effect of wheelchair resistance moment values on the muscle activity resulting from propelling the wheelchair. The tests involved a hybrid wheelchair that could assist the user with electric motors. These motors could have different settings for the assistance gain factor w. This complexity of variables meant that there were several curves on a single graph representing changes in muscle activity values for different gain factor settings. Using the described method, all these curves were included in a single area, which defines the possible physical activity for a given range of resistance moment values and any values of the assistance gain factor w (Figure 10).
# Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
## 3 Complex Dynamics Behavior
### Parameter Spaces  
The dynamical behavior of operator \(R_{\alpha}(x)\) depends on the values of parameter \(\alpha\). The parameter space is defined as a mesh in the complex plane where each point of this mesh corresponds to a different value of \(\alpha\). Its graphical representation shows the convergence analysis of a method of CMT (\(\alpha\)) family associated with this \(\alpha\) using one of the free critical points \(cr(\alpha)\) given in Proposition 4 as initial estimate. The resulting graphic is made in Matlab R2020a programming package with a resolution of \(1000\times 1000\) pixels. If a method converges to any of the roots starting from \(cr(\alpha)\) in a maximum of 80 iterations with a tolerance of \(10^{-3}\), the pixel is colored red; in other cases, the pixel is colored black.  
Each value of \(\alpha\) that belongs to the same connected component of the parameter space results in subsets of schemas with similar dynamical behavior. Therefore, it is interesting to find regions of the parameter space as stable as possible (red regions), because these values of \(\alpha\) will give us the best members of the family in terms of numerical stability.  
CMT(\(\alpha\)) family has a maximum of 9 free critical points. Of these, \(cr_{1}\), \(cr_{2}\), and \(cr_{3}\) have the same parameter space which corresponds to the stability surface of \(ex_{1}\) (see Figure 1), because they are pre-images of this point. The remaining free critical points, \(cr_{4}\) to \(cr_{9}\), are conjugated in pairs (see Proposition 4), which gives rise to 3 different parameter spaces. These parameter spaces, named \(P_{1}\) (for \(x=cr_{4},cr_{5}\)), \(P_{2}\) (for \(x=cr_{6},cr_{7}\)), and \(P_{3}\) (for \(x=cr_{8},cr_{9}\)), are shown in Figure 3.  
From Figure 3b,c, we observe that the parameter spaces \(P_{2}\) and \(P_{3}\) have similar characteristics; then, we can select any of them for analysis.  
On the one hand, if we choose values of \(\alpha\) inside the stability regions (red regions) of the parameter spaces, for example, \(\alpha=-1,0,1\), the methods associated with these parameters will show good dynamical behavior in terms of numerical stability. Furthermore, note that these particular values of \(\alpha\) simplify the iterative scheme of CMT(\(\alpha\)) family given in (29) by canceling a term in its third step. This is especially useful to improve the computational efficiency of the associated method because the processing times required to reach the solution are reduced (see Section 4).  
On the other hand, if we choose values of \(\alpha\) outside the stability regions (black regions) of the parameter spaces, for example \(\alpha=-300,\;200,\;400\), the methods associated with these parameters will show poor dynamical behavior in terms of numerical stability.  
The methods associated with the values of \(\alpha\) treated above are discussed in Section 3.5.
## 2 Materials and Methods
### Training and Testing  
We trained all six above-mentioned classifiers using three feature sets, meaning that each model has been trained three times using a separate set of selected best features. The aim of using a different number of features is to check the trend of overfitting/underfitting and to verify which classifier is most robust and resistant to overfitting/underfitting. For training and testing, we split the data in a 4:1 ratio, where we used 80% of the data for training all the models, and 20% of the data were reserved for testing purposes. In this binary classification, the attacked data are denoted by 1, and benign data by 0.