esa-sceva/satcom-chunk-collection huggingface dataset:
# Directional Support Vector Machines
## 4 Symmetric Directional SVM  
For directional data, the model \(f(x_{1}|w_{0},w_{1})\) has to be adapted, as it should be periodic, continuous, and naturally take positive and negative values in the circular domain (so it can aim to label positive and negative examples correctly). Arguably, the most natural extension of the linear model in \(\mathbb{R}\) is the piecewise linear model in \([0,2\pi)\); see Figure 1b,c. Note that, now, the partition of the input space requires two thresholds.  
Motivated by this observation, we explore models of the form:  
\[f(x|w_{0},w_{1},\theta)=w_{0}+w_{1}g(x+\theta), \tag{9}\]  
where we start by investigating the following specific realizations for \(g(x)\):  
Figure 1: Toy examples of Support Vector Machine (SVM) in \(\mathbb{R}\) and in the unit circle. (**a**) Standard linear model. (**b**) Piecewise linear model in the unit circle. (**c**) Unfolding the piecewise linear model in the unit circle.  
1. \(g(x)=g_{t}(x)\), where \(g_{t}(x)\) is the triangle wave with unitary amplitude, period \(2\pi\), and maxima at \(x=2k\pi\), \(k\in\mathbb{Z}\). This function is piecewise linear, and so, it is close to the linear version in the standard domain.
2. \(g(x)=g_{s}(x)\), where \(g_{s}(x)=\cos(x)\). This option can be seen as a rough approximation to the intuitive choice \(g_{t}(x)\), but as we will see, analytically more tractable.
## 4 Test Verification
### Simulation Result
Therefore, the ENOB after calibration is not compared between work [31] and others. In Equation (19), quantization noise and Gaussian white noise are not considered. Therefore, the calibration of time skew is affected by quantization noise and Gaussian white noise. Figure 11 shows the performances of calibration time skew in a 2-channel 6.4 GS/s TI-ADCS under different white noise levels. * \({}^{1}\) O is offset. \({}^{2}\) G is gain. \({}^{3}\) T is time skew. \({}^{4}\) B is bandwidth mismatch. \(M\) is the channel number. \(L\) is filter length. \(L_{\text{s}}\) is the number of samples. \(N=5\) is the length of the calibration coefficient. \end{table}
Table 4: Compared with other techniques. As shown in Figure 11, there is a positive correlation basically between the SNDR of TI-ADCS and the SNR of sub-ADC when the SNR of sub-ADC is in the range of 0\(\sim\)100 dB. Therefore, the proposed technique can effectively calibrate the gain, time skew, and bandwidth mismatch at the SNR of 0\(\sim\)100 dB. In Figure 12, there is a positive correlation between the SNDR of TI-ADCS and the ENOB of sub-ADC when the ENOB of sub-ADC is in the range of 1 bit-16 bit. It can be seen from Figures 11 and 12 that the proposed technique is effective when the SNR of sub-ADC is in the range of 0\(\sim\)100 dB or the ENOB of sub-ADC is in the range of 1\(\sim\)16 bits. The minimum value of \(\sum(e_{i}[n])^{2}\) is obtained by the least square method. Hence, the cumulative error \(\sum(e_{i}[n])^{2}\) is affected by quantization error and white Gaussian noise. In Figures 13 and 14, the cumulative errors of the proposed technique are respectively shown under different quantization errors and Gaussian white noise of sub-ADC. It can be seen from Figures 13 and 14 that the cumulative error is stable and approaches 0 when the SNR is greater than 30 dB and the ENOB is greater than 5 bits. It can be concluded from Figures 11-14 that the proposed technique will be affected by the SNR and ENOB. However, the proposed technique has a good effect in the 30-100 dB of SNR or the 5\(\sim\)16 bit of ENOB. Figure 11: The performance of the proposed technique with different SNR of the input signal. Figure 12: The performance of the proposed technique with different ENOB of the sub-ADC.
## 2 Experimental  
Starting powders were prepared by mixing Si powder (1-2 \(\upmu\)m, Beijing Xing Rong Yuan Technology Co., Ltd. Beijing, China), silicon nitride (Si\({}_{3}\)N\({}_{4}\)) powder (prepared in our laboratory with 87% \(\upalpha\) phase, 1-2 \(\upmu\)m grain size, and 99% purity [23]), ammonium fluoride (NH\({}_{4}\)F, analytical pure, Sinopharm Chemical Reagent Beijing Co., Ltd., Beijing, China), W powder (200 nm, Beijing Xing Rong Yuan Technology Co., Ltd. Beijing, China), and polytetrafluoroethylene (PTFE, Shanghai 3F New Materials Technology Co., Ltd., Shanghai, China) powder (components of the mixtures and corresponding roles in the synthesis process are listed in Table 1). Mixtures were ball milled for 8 h in alcohol using Si\({}_{3}\)N\({}_{4}\) balls as the medium with the ball/mixture weight ratio of 4:1, then dried and sieved. The sieved mixture was placed in a graphite vessel, then evacuated and backfilled with general industrial nitrogen to \(\sim\)4-6 MPa. A W heating coil was used to ignite the nitridation reaction system [1; 2].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **SC** & **W-SC** & **Role** \\ \hline Si (wt.\%) & 85 & 85 & Raw materials \\ \hline Si\({}_{3}\)N\({}_{4}\) (wt.\%) & 8 & 8 & Diluent [23] \\ \hline NH\({}_{4}\)F (wt.\%) & 2 & 2 & Active diluent [23] \\ \hline W (wt.\%) & 0 & 5 & Catalyst \\ \hline PTFE (vol.\%) & 50 & 50 &
\begin{tabular}{c} Create C element\(\backslash\)spatial for whisker growth\(\backslash\)supply combustion energy [1] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components of reactants. PTFE: polytetrafluoroethylene.  
The composition and crystal structure of as-synthesized whiskers were investigated by X-ray diffraction (XRD, TTRIII, Rigaku Corporation, Tokyo, Japan). The morphologies were observed by field emission scanning electron microscope (FESEM, SUPRA(tm) 55, Zeiss, Germany). The intrinsic structure of whiskers was characterized by transmission electron microscopy (TEM, FEI TECNAI G2 F20, FEI, USA).
# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
###### Abstract  
Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.  
artificial intelligence; artificial immune system; cyber security; danger theory; deep learning; dendritic cell; internet of things; IoT; network security +
Footnote †: journal: Applied Sciences
## 3 Optimization of the PMPP
### Numerical Example  
We use the following numerical example in order to illustrate our proposed sequential approach for solving the joint production and maintenance optimization problem.  
We consider a single machine \(M\) that has to satisfy, over a finite horizon \(H\). \(\Delta t\), a stochastic demand that follows a Gaussian law with a mean \(\hat{d}(k)\) and a variance \(V_{d}\left(k\right)\). The number \(H\) of periods \(\Delta t\) is equal to 24, with \(\Delta t=1\). To satisfy the demand, we set a service level.  
We consider that the deterioration of the production system follows a Weibull distribution with parameters \(\gamma\) and \(\beta\). From Equation (13), we determined the average number of failures assuming that after each preventive maintenance action, the equipment is in the state \"as good as new\".  
The following data are used for the computations:  
* \(C_{pr}=3\) monetary units (mu)/unit of product/period.
* \(U_{max}=15\) products.
* \(\alpha=0.95\).
* \(C_{s}=5\) mu/unit of product/period.
* \(S_{0}=20\).
* \(V_{d}(k)=1.21\)
* \(C_{pm}=212\) mu.  
\(C_{cm}=1000\) mu.
* \(\Delta t=1\).
* \(\lambda_{0}=0.2\).
* \(\gamma=2\).
* \(\beta=100\).
* \(H=24\). \(\Delta t\)  
Table 2 presents the average demand in each period.
## 5 Results and Discussion
### Distortion Rate  
The distortion rate indicates the amount of distortion in the UVLC system. The technique and how effectively it eliminates the linear and nonlinear distortion in the UVLC system is essential for efficient transmission. Figure 6 displays the distortion rate for both the existing and recommended methods. Table 2 shows the comparison of the distortion rate (%). The suggested system's distortion rate is minimal, at 45%. It means that the recommended strategy eliminates UVLC system distortions. It may be computed utilizing the following formula and is commonly expressed in decibels (dB):  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Bit Error Rate (\%)** \\ \hline APNN [33] & 91 \\ TFCNN [34] & 79 \\ DSANet [35] & 62 \\ CE-LSTM [36] & 85 \\ AFL-DLE [Proposed] & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of bit error rate (%).  
Figure 5: The bit error rate of the proposed and existing techniques, namely APNN [33], TFCNN [34], DSANet [35] and CE-LSTM [36].  
\[SNR=10*log10(P_{\rm s}/P_{d}) \tag{14}\]  
\(P_{\rm s}\) denotes the power of the signal.  
\(P_{d}\) denotes the power of the noise or distortion.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 4 Predicted Results and Analysis
### State of Health Estimation with All Charging Range  
For Cell 1 and Cell 2, the prediction results and errors of SOH estimation are presented in Figures 5-8. We can observe that, the proposed method based on the SVM algorithm performs precise estimation. As can be seen in Figures 6 and 8, most of error can be limited within 1.6%. Moreover, for Cell 1, the maximum error, MSE and MARE are 0.98%, 6.5 \(\times\) 10\({}^{-6}\) and 0.26%, and for Cell 2, the maximum error, MSE and MARE are 1.87%, 9.6 \(\times\) 10\({}^{-6}\) and 0.21%, respectively. Hence, we can conclude that the proposed SVM model can achieve the reliable battery SOH estimation.  
Figure 5: Results of SOH estimation by support vector machine (SVM) with Cell 1 testing samples.
## 1 Introduction  
### Motivation  
The development of modern air defense systems, particularly radar systems, takes place in conditions of significant complexity in the air. The increase in the speed and maneuverability of air objects, the active use of electronic warfare, jamming systems, and signal simulators make it challenging to ensure the reliable and continuous tracking of targets. In such conditions, the issues of noise robustness and the adaptability of tracking systems become critical, especially at the initial stage of radar interaction with the target, when observations are still fragmentary, and the probability of the loss of tracking is the highest.  
Most modern radars do have basic adaptation and filtering mechanisms; however, they are not always able to work in conditions of unstable measurement reliability, signal loss, and variable interference levels. Typical filters, in particular, the Kalman filter, demonstrate high accuracy in stationary conditions; however, they are not adapted to work with intermittent or noisy data. Even promising intelligent modules mostly focus on classification or recognition but do not implement recursive filtering or prediction of the reliability of measurements in real time. This limits the ability of radars to work effectively in conditions as close as possible to combat conditions.  
The relevance of this research topic lies in the need to create a comprehensive intelligent tracking method that can predict the reliability of future measurements, adapt filtering parameters to changes in the interference environment, combine evaluation results from different sources, and provide stable tracking even while losing part of the data.  
Thus, the relevance of the problem is the need to create hybrid intelligent tracking methods that integrate stochastic modeling, recursive filtering, and artificial intelligence into a single adaptive architecture. Such approaches will ensure the stable and accurate operation of radars in real time, in conditions of uncertainty, high speeds, complex object dynamics, and the influence of intentional interference.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 2 Network Architecture and System Model
### Network Architecture  
We consider a one-cell environment with a single Evolved Node B (eNB). The network consists of a D2D-based V2V along with a conventional cellular user and a V2I link, as shown in Figure 1. The three users interfere with each other on the uplink. While earlier works have mostly considered the conventional cellular users (CUE) and V2I links as the same, we have considered them as two different types of users for the reason that V2I links are mostly mobile and hence encounter large-scale fading and the channel state information (CSI) is delayed at the base station [10], whereas, the CUEs are mostly either in a static position or moving very slowly; hence, they encounter small-scale fading.  
Figure 1: Network architecture. CUE: cellular user equipment; V2I: Vehicle to Infrastructure; V2V: Vehicle to Vehicle.  
Figure 2 shows the distribution of the users in the cell. The CUEs are shown in red, scattered in the cell with a radius of 500 meters. The VUEs, on the other hand, are moving on a highway running through the center of the cell (shown in blue, black, and purple).
## 4 Case Analysis  
### Data Acquisition  
The instruments and equipment used in this case are the borehole photo-acoustic combined measurement system independently developed by Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. The instrument and equipment are mainly composed of five parts: probe 1, transmission cable 2, depth encoder 3, integrated control box 4 and computer 5. The physical image of the overall instrument and equipment is shown in Figure 7a. The core components of the probe in the hole are the optical measurement component and the acoustic scanning measurement component. The optical measurement component is mainly composed of an optical camera and an LED diaphragm component. The physical picture of the component is shown in Figure 7b. The acoustic scanning component is mainly composed of acoustic transducer, electronic compass, scanning motor and other components. The physical picture of the component is shown in Figure 7c. The communication mode is network signal transmission, and armored optical fiber cable is used as the signal transmission medium.  
The data source used in this case analysis is a rock landslide in Jinping County, Guizhou Province, China. The landform is broken and the terrain is high in the northwest and low in the southeast. The west and southwest are middle and low mountain areas, the middle and northeast are low mountain canyon areas, and the southeast is a low mountain hilly basin dam area. In the investigation and study of determining the weak layer, if only the traditional digital borehole camera system is used to collect the data of the borehole wall, the distribution position of the structural plane can only be roughly judged, and the type of the structural plane cannot be accurately identified. Therefore, in order to further identify the position distribution of the structural plane and make a correct judgment on whether it is a weak interlayer, the corresponding instruments and equipment of this technology are used to collect the data of optical image and acoustic scanning. The optical image and acoustic wave scanning data of some hole walls are shown in Figure 8. Figure 8a is a screenshot of some rock wall video images at the borehole depth of 28.5-29.5 m. Figure 8b is a pseudo color image of some borehole acoustic scanning data at the borehole depth of 28.5-29.5 m.  
Figure 7: Physical diagram of instrument and equipment. (**a**) Complete system, (**b**) optical imaging measurement module, (**c**) acoustic scanning measurement module.
# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 4 Results
### Clinical Data
#### 4.2.2 Quantitative Evaluation: DSC and HD  
We present a quantitative evaluation of GAN-based motion artifact correction methods using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) metrics. Our assessment encompasses image patches affected by motion artifacts, compared to reference patches, and evaluates the performance of the pix2 pix model and CycleGAN model in correcting these artifacts. To accurately measure the DSC and HD metrics, we segmented the vessel region using a seed-based region-growing technique that allows for semi-automatic annotation of the vessel. The reference patches were extracted from the phases of the 4D CT scans that exhibited the least motion artifacts, typically the cardiac phases with minimal motion disturbance. By comparing the corrected patches against these motion-minimized references, we were able to quantitatively assess the effectiveness of the pix2 pix and CycleGAN models in correcting motion artifacts.  
For a visual representation of these metrics, refer to Figure 7, which includes two subfigures--(a) displaying a violin plot of the DSC, and (b) a violin plot for the HD--illustrating the performance comparisons between the methods. CycleGAN demonstrates the highest median DSC (0.751), surpassing pix2 pix, indicating its superior effectiveness in mitigating motion artifacts. In terms of HD, CycleGAN achieves the lowest median value (5.385), signifying superior patch similarity to pix2 pix. Detailed numerical values and statistical significance values of these findings are summarized in Table 2, which provides a comprehensive comparison of the DSC and HD values for pix2 pix and CycleGAN.  
Figure 6: A qualitative example from the CAVAREV dataset test data showcasing severe motion artifacts. The set comprises the original mid-RCA test patch, the outcome from the pix2 pix model, the result of the proposed CycleGAN method, and the corresponding reference patch. Phase information (\(\phi\) from the 4D CT) is indicated above the patch. Radial artifacts are attributed to the limited number of projections (\(n=133\)).  
We applied the Wilcoxon signed-rank test to assess performance differences between pix2 pix and CycleGAN. This non-parametric test confirms significant improvements in both DSC and HD for both methods over the motion-affected patch baseline (_p_-values \(<\) 0.001). Notably, CycleGAN achieves significantly higher DSC and lower HD than pix2 pix (_p_-value \(<\) 0.001), demonstrating its superior efficacy in mitigating motion artifacts.  
In summary, CycleGAN outperforms pix2 pix in motion artifact correction, with higher median DSC and lower median HD values. The reduced variability, indicated by narrower Interquartile Ranges (IQRs), underscores CycleGAN's robustness in mitigating motion artifacts across various image patches. These findings emphasize substantial and statistically significant performance improvements by CycleGAN over pix2 pix in terms of DSC and HD metrics.
## 3 Results and Discussion
### BPNN-QPSO Network Model for Optimizing Laser Cladding Process Parameters
#### 3.2.1 Analysis of Training Performance of BPNN-QPSO Model  
According to the data from the 22 training groups listed in Table 3, the iteration of the _fitness_ value (\(\sigma\)) calculated by Equation (8) is shown in Figure 8a. It can be seen that the weights and thresholds of BPNN optimized by PSO algorithm and QPSO algorithm reached the optimal solution after 15 iterations and 22 iterations, respectively. The obtained optimal weights and thresholds were used to optimize the weights and thresholds of BPNN. The mean square error (MSE) calculated by Equation (6) is shown in Figure 8b-d. It can be seen that the un-optimized BPNN training underwent 19 epochs, the BPNN optimized by PSO training underwent 69 epochs, and the BPNN optimized by QPSO training underwent 93 epochs, all of which converged to the set training goal of 0.0005. The experimental results and training results of coating micro-hardness are listed in Table 3. As shown in Figure 9, it can be seen that the maximum errors of BPNN, BPNN-PSO, and BPNN-QPSO training are 1.7%, 2.49%, and 1.86%, respectively. The correlation coefficient calculated by Equation (13) is shown in Figure 10, indicating that the training effects are highly correlated. The results show that the established BPNN-QPSO network model had a strong training ability, the maximum error of training was small, and the number of training epochs increased.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Dimensions of Particle Swarms (_ND_) & 20 \\ Number of iterations (QPSO) & 30 \\ Noise & 0.01 \\ contraction coefficient & 1 \\ Number of epochs (BPNN) & 5000 \\ Training goal & 0.0005 \\ Learning rate & 0.001 \\ Transfer function & tansig \\ Training function & trainbr \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters setup for BPNN-QPSO.  
Figure 8: Training performance of the network model: (**a**) _fitness_ iteration of standard error; (**b**) BPNN training process; (**c**) BPNN-PSO training process; (**d**) BPNN-QPSO training process.  
Figure 9: Comparison diagram of training values and experimental values of micro-hardness: (**a,b**) BPNN; (**c,d**) BPNN-PSO; (**e,f**) BPNN-QPSO.
# Pressure Transient Model of Water-Hydraulic Pipelines with Cavitation
###### Abstract  
Transient pressure investigation of water-hydraulic pipelines is a challenge in the fluid transmission field, since the flow continuity equation and momentum equation are partial differential, and the vaporous cavitation has high dynamics; the frictional force caused by fluid viscosity is especially uncertain. In this study, due to the different transient pressure dynamics in upstream and downstream pipelines, the finite difference method (FDM) is adopted to handle pressure transients with and without cavitation, as well as steady friction and frequency-dependent unsteady friction. Different from the traditional method of characteristics (MOC), the FDM is advantageous in terms of the simple and convenient computation. Furthermore, the mechanism of cavitation growth and collapse are captured both upstream and downstream of the water-hydraulic pipeline, i.e., the cavitation start time, the end time, the duration, the maximum volume, and the corresponding time points. By referring to the experimental results of two previous works, the comparative simulation results of two computation methods are verified in experimental water-hydraulic pipelines, which indicates that the finite difference method shows better data consistency than the MOC.  
Keywords:water-hydraulic pipelines; pressure transients; cavitation; finite difference method; method of characteristics +
Footnote †: journal: Applied Sciences