esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 4 Methodology
### ML Model  
In this subsection, we describe the architectural choices for the machine learning intrusion detection module. This model consists of a convolutional part followed by the time series processing part. A complete overview of the architectural choices is displayed in Figure 2.  
First, our model uses two convolutional layers that work on the feature axis. These layers have a dual purpose: they compress the feature space and merge information. This is crucial because the original dataset contains 44 features, some of which may be less useful or relevant. To address this, we employ a convolutional layer to identify and enhance important information during training. The output then passes through a max pooling layer to further emphasize significant feature aspects. It is worth noting that the time steps remain unchanged, and the convolution only affects the feature axis. As a result, we obtain a time series object with fewer features while preserving the same number of time steps.  
Let \(X\) be the input time series with \(N\) time steps and \(D\) features. The output \(Y_{conv}\) after the convolutional layers and max pooling can be represented as follows:  
\[Y_{conv}=MaxPool(Conv_{2}(Conv_{1}(X))) \tag{4}\]  
Then, the modified time series is passed into the time module, which comprises two LSTM layers with attention incorporated in between. The initial LSTM layer processes the time series object and concatenates information. However, due to the persistence of dense and information-rich input, we opt to not merely utilize the concatenated output of the LSTM; instead, we collect the outputs of each time step (LSTM cell). The output of the first LSTM is a time series with the same number of time steps as the input of the LSTM, where each timestep has accumulated information from previous states. Consequently, we feed the time series produced by the first LSTM into an attention layer. This attention layer operates as a self-attention module, assessing the correlation between all LSTM cell outputs, in other words, between all the time steps within the time series. This approach aids the model in concentrating on important information from previous time steps, which proves valuable  
Figure 2: Architecture of our multivariate time series prediction model.  
in predicting potential attacks. The resultant attention map, denoted as \(A\), in conjunction with the output from the first LSTM layer (all cell outputs \(H_{1}\)), are fused and presented as input to the second LSTM layer, as follows:  
\[H_{att}=Attention(H_{1}) \tag{5}\]  
\[H_{LSTM_{2}}=LSTM_{2}([H1,A]) \tag{6}\]  
With knowledge of the attention map at each time step, this second layer can effectively interconnect information, further enhancing its ability to spotlight significant past events within the time series object.  
Lastly, the output of the second LSTM (only the last accumulated state) is fed to a fully connected layer, which produces a binary label, indicating the presence of an attack:  
\[Y_{final}=FC(H_{LSTM_{2}}) \tag{7}\]
## 5 Numerical Results
### Effect of Varying \(m\)  
The chosen weight for the partition of unity of the Taylor series is a bell-shaped Gaussian function. To control the width of the Gaussian function, a constant, called \(m\), is introduced in the weight equation. In the previous results, the constant \(m\) was kept fixed (\(m=1\)). In this section, the same benchmark will be performed again; however, \(m\) will be varied instead of \(N_{e}\). In order to ensure that the present new basis is as close as possible to the Gaussian basis, a minimum value of \(N_{e}=5\) is selected, as shown in Section 4. The results of varying \(m\) are depicted in Figure 7. Evidently, the accuracy is a function of \(m\). In addition, the first (\(O=1\)) and second (\(O=2\)) truncation orders show inconsistent results at low \(\varepsilon\) values. In contrast, the zeroth truncation order (\(O=0\)) provides consistent and reliable results with reasonable accuracy.  
Figure 6: The log10 of the norm of the error of approximating the given function \(f(x)\) and its first two derivatives using Gaussian RBF-TPU with different truncation orders for a range of \(\varepsilon\) and \(N_{e}\) values. The rows are for function, first derivative, and second derivative approximation, while the columns are for zeroth, first, and second orders of truncation. \(\varepsilon_{k}\) ranges between [0, 1] and \(m=1\).  
It should be emphasized that the results of Figure 7 are repeated for different model functions, number of grid points, grid distributions, and \(N_{e}\) values. However, it is not plotted because the results are not found to vary significantly. In other words, the previously mentioned parameters have a negligible effect on the accuracy. Subsequently, careful study of these results allows choosing proper values for \(m\) and \(\varepsilon\) regardless of the given data or the problem. These values can be inferred from Figure 7. In brief, large values of \(m\) and small values of \(\varepsilon\) should be chosen, and vice versa. In order to remove the dependency of the accuracy on \(m\) and \(\varepsilon\), the range of \(\varepsilon_{k}\) should be investigated.
## 4 Electromagnetic Performance Analysis  
According to the basic parameters shown in Table 1, the finite element analysis model of the AFPMBLDCM was established, and its electromagnetic performance was analyzed, including static magnetic field analysis, transient magnetic field analysis and parameter optimization. In order to improve the efficiency of the analysis and solution and save calculation time, the half-equivalent AFPMBLDCM model was established on the premise of ensuring calculation accuracy and the motor's cyclic symmetry, as shown in Figure 5. In addition, manual meshing, motion boundary setting and master-slave boundary setting were carried out.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 4 Deployment of AccFlow  
In this section, we address the deployment of AccFlow in real networks and its interaction with other security protocols. Although AccFlow offers advantages like flexible control and real-time response to attacks, it also faces scalability challenges due to the centralized network architecture. The centralized controller must create an entry in the routing table for each distinct flow, which can strain CPU and storage resources. To overcome this scalability issue, we propose the use of flow aggregation and a virtual centralized controller. These solutions aim to optimize AccFlow's performance and ensure its practicality in large-scale networks.  
Flow aggregation involves grouping similar flows together, reducing the number of distinct flows and, consequently, the load on the controller. The virtual centralized controller concept employs multiple coordinated processors to serve as the centralized controller, allowing for distributed handling of flow management. Together, these measures enhance AccFlow's scalability, making it suitable for deployment in both local area networks (LANs) and wide area networks (WANs).  
By addressing scalability concerns and optimizing AccFlow's performance, we aim to make it a viable and effective solution for enhancing network security and mitigating DoS and DDoS attacks.
## 4 Findings Results
### Descriptive Findings
#### 4.1.3 The Distribution of Publications by Field, Type, and Journal  
The number of publications by area, type, and journal provides important insights into the evolution of this domain over time. According to Figure 5, the most published articles are in the fields of computer science, engineering, decision sciences, business management, and accounting. This suggests that the application of AI in demand forecasting is interdisciplinary, impacting multiple areas. The high volume of publications in computer science and engineering indicates that AI and machine learning models require a strong technical foundation, which is essential for advancements in algorithms, computational techniques, and system designs for effective demand forecasting.  
Figure 4: The fluctuation in publication counts over the years.  
On the other hand, publications in decision sciences, business, and management focus on the practical applications of these technologies to enhance decision-making processes, optimize operations, and improve overall supply chain efficiency. This interdisciplinary approach ensures that artificial intelligence solutions are not only technically robust but also aligned with business goals and management practices.  
Moreover, demand prediction challenges have been addressed using various methods, such as machine learning, deep learning, blockchain, and time series forecasting, in these studies. Machine learning and deep learning are particularly noteworthy because they can process large amounts of data and recognize complex patterns, enabling more accurate and dynamic predictions. Although less widely adopted, blockchain technology holds significant potential to improve demand forecasting accuracy by enhancing transparency and traceability in supply chains. Time series forecasting remains a classic methodology that is often integrated into sophisticated AI models to enhance prediction quality.  
Overall, the diversity of publications in these areas reflects the complex nature of AI research in anticipating supply chain needs, highlighting the collaborative effort to synchronize technological innovations with practical applications. This trend indicates an ongoing and extensive effort to leverage AI for solving complex prediction problems, ultimately making supply chains more resilient and efficient.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 3 Methodology
### Deep Learning Architecture  
Followed by data pre-processing, a DNN model as shown in Figure 3 is constructed to predict the MTF. The structure of the model is determined in a heuristic fashion. There are three hidden layers, each of which has 10 nodes respectively. The final node constitutes a single node for outputting a regression result. For the training, Adam optimizer, ReLU activation function and Xavier initializer are used. ReLU activation function is applied after every multiplication of weight plus bias but is omitted at the final layer. The objective function is the Mean Squared Error (MSE), which calculates the mean of the squared difference between the target value and each batch samples. The optimal hyper-parameters including learning rate, iteration and batch size are discovered using a small portion of training data (15%) based on grid-search. Early stopping is implemented to break the training procedure. Table 4 shows the top 5 results when different hyper-parameters are applied for the training of the model.  
Once the hyper-parameters are optimized, the best regression result is attained and the optimal number of samples is determined, the next step is to implement classification using class-labeled data. The deep learning model is similar to the one used for the regression but the objective function as well as the activation function used at the final layer. Since it is a multi-class classification problem, categorical cross-entropy loss is minimized for optimization and softmax function is applied at the final layer of the DNN. Here, an area under the ROC curve (AUC) is calculated to see if there is any improvement by comparing the classification results between the augmented and the raw data.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Learning** & \multirow{2}{*}{**Iteration**} & **Batch** & \multirow{2}{*}{**Layer**} & **Number** & **Validation** & \multirow{2}{*}{**RMSE**} & **MAE** & \(R^{2}\) \\
**Rate** & & & **Size** & & **of Samples** & **Set** & & \\ \hline \(3\times 10^{-4}\) & 3,125,550 & 32 & [10; 10; 5] & 384 & Val 5 & 5.584 & 5.582 & 0.678 \\ \hline \(4\times 10^{-6}\) & 119,400 & 16 & [10; 10; 5] & 128 & Val 2 & 5.705 & 5.000 & 0.738 \\ \hline \(3\times 10^{-4}\) & 3,349,350 & 64 & [10; 10; 5] & 768 & Val 5 & 5.861 & 5.170 & 0.645 \\ \hline \(2\times 10^{-5}\) & 4,336,650 & 256 & [10; 10; 5] & 1280 & Val 2 & 5.880 & 5.630 & 0.720 \\ \hline \(2\times 10^{-5}\) & 4,282,800 & 256 & [10; 10; 5] & 768 & Val 2 & 6.120 & 5.420 & 0.698 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Top 5 results with varying hyper-parameters.  
Figure 3: DNN architecture.
## 4 Results
### Global Explainability Results
#### 4.1.2 Effect of Regularization Parameter  
Figures 1 and 2 illustrate the effect of the explainability regularization parameter \(\lambda_{1}\) on the precision and fidelity of our frameworks. As \(\lambda_{1}\) increases, we observe a consistent improvement in fidelity scores across all datasets, with only minimal impact on accuracy. This confirms that our frameworks effectively balance the trade-off between accuracy and explainability, allowing users to control this balance through the regularization parameter.  
For example, for the ML-100k dataset with the pre hoc framework, as \(\lambda_{1}\) increases from 0.01 to 1.0, fidelity improves from 0.8207 to 0.9410 (a 14.6% increase), and accuracy remains relatively stable (0.7840 to 0.7740). Similarly, for the HELOC dataset, fidelity improves from 0.7482 to 0.8454 (a 9.3% increase) as \(\lambda_{1}\) increases, with accuracy actually improving slightly from 0.7591 to 0.7719. These results demonstrate that our frameworks can achieve high fidelity without sacrificing accuracy.  
The co hoc framework exhibits an even more favorable trade-off curve, particularly in the HELOC dataset, where both accuracy and fidelity improve simultaneously as \(\lambda_{1}\) increases. This suggests that the joint optimization approach not only aligns the behavior of the two models but also enhances their complementary strengths. The plateau observed in fidelity scores at higher \(\lambda_{1}\) values (above 0.5) indicates an optimal operating point beyond which additional regularization yields diminishing returns. This behavior provides practical guidance for hyperparameter selection in real-world applications, where setting \(\lambda_{1}\) between 0.25 and 0.5 offers the best balance between model performance and interpretability.
## 3 Results
### Vertebral Artery Dominance (VAD)  
In the present study, the differences in the diameter of VAs were observed. Among study subjects, the right dominant VA was found in 28.5% (165 out of 579) and left dominant VA was in 44.7% (259 out of 579) of subjects. The codominant VA was found in 26.8% (155 out of 579) of cases. The right dominant VA in males and females was 30.1% and 26.1% while the left dominant VA was 42% and 48.5% in males and females, respectively. On the other hand, the codominant VA was observed with a frequency of 27.8% and 25.3%, in males and females. There were no significant differences in VA diameter and sex (\(p>0.05\)). VA arose from arch of aorta in 3.5% of courses (41 out of 1158), and all these were left-sided. In one case (0.08%), the right VA arose from the right common carotid artery (Figure 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gender** & **Male (676)** & **Female (482)** & **Chi-Square Test** \\ \hline \multirow{4}{*}{**V2**} & UE-V2 (\%) & 10.35\% (70/676) & 10.58\% (51/482) & \(\begin{array}{l}X^{2}=0.001\\ p=0.979\end{array}\) \\ \cline{2-5}  & MM (\%) & 1.18\% (8/676) & 1.45\% (7/482) & \(\begin{array}{l}X^{2}=0.018\\ p=0.892\end{array}\) \\ \hline \multirow{4}{*}{**V3**} & PFIA (\%) & 0.30\% (2/676) & 0\% (0/482) & \(\begin{array}{l}X^{2}=0.228\\ p=0.541\end{array}\) \\ \cline{2-5}  & PP (\%) & 0.30\% (2/676) & 1.04\% (5/482) & \(\begin{array}{l}X^{2}=1.489\\ p=0.135\end{array}\) \\ \hline \multicolumn{2}{c}{**Side**} & **Right side** & **Left side** & \\ \hline \multirow{4}{*}{**V2**} & UE-V2 (\%) & 5.44\% (63/1158) & 5.01\% (58/1158) & \(\begin{array}{l}X^{2}=0.148\\ p=0.701\end{array}\) \\ \cline{2-5}  & MM (\%) & 0.35\% (4/1158) & 0.95\% (11/1158) & \(\begin{array}{l}X^{2}=2.431\\ p=0.119\end{array}\) \\ \hline \multirow{4}{*}{**V3**} & PFIA (\%) & 0.17\% (2/1158) & 0 & \(\begin{array}{l}X^{2}=0.501\\ p=0.500\end{array}\) \\ \cline{2-5}  & PP (\%) & 0.17\% (2/1158) & 0.43\% (5/1158) & \(\begin{array}{l}X^{2}=0.575\\ p=0.452\end{array}\) \\ \hline \end{tabular}  
\begin{tabular}{l c c c} \hline \hline \multicolumn{2}{c}{UE-V2: unusual entrance of VA; MM: midline migration; PFIA: persistent first intersepmental artery; PP: para-condylar processes; V2: intraformainal segment of vertebral artery; V3: extracranial segment of vertebral artery.} \\ \end{tabular}
\end{table}
Table 1: Association between V2 and V3 segments variations with sex or side.  
Figure 6: (**A**) A coronal CTA maximum intensity projection image shows the right vertebral artery(solid arrow) originating from the right common carotid artery (dashed arrow). Right-sided UE-V2 variant is also present in this patient. (**B**) The left vertebral artery is seen arising from the aortic arch in another patient (arrow).
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 2 Continuous Wavelet Transform (CWT) for Fault Diagnosis
### Morlet Wavelet for CWT  
For wavelet analysis is important to choose a wavelet function whose shape is similar to the vibration signal portion caused by the mechanical fault. The Morlet wavelet is an excellent choice for continuous wavelet analysis and is widely used in fault diagnosis [29] of rotating mechanical systems, particularly those where faults generate periodic impulses in the machine's vibration signals. In CWT, complex-valued wavelets, as opposed to real-valued wavelets, are preferred, because the complex-valued wavelets contain phase information. A sinusoidal wave multiplied in the time domain by a Gaussian function is known as a Morlet wavelet. A function is said to be analytic if its Fourier transform is zero for negative frequencies. The analytic function is complex and is entirely characterized by its real part [26]. The analytic version of the Morlet wavelet function is defined as a product of two exponentials, according to Formula (7)  
According to Formula (1), CWT generates complex coefficients (denoted in the sequel as \(T(a,b)\)) over the \(a\)-\(b\) plane for the real signal \(x(t)\) associated with the complex Morlet mother wavelet. The polar format of the coefficients is given in Formula (8).  
\[T(a,b)=|T(a,b)|e^{\;i\theta(a,b)}, \tag{8}\]  
where the modulus (magnitude) and phase for each coefficient based on the associated Cartesian coordinates are expressed as in Formula (9).  
\[|T(a,b)|=\sqrt{\text{Re}\left(T(a,b)\right)^{2}+\text{Im}\left(T(a,b)\right)^{ 2}},\;\phi(a,b)=\tan^{-1}[\frac{\text{Im}\left(T(a,b)\right)}{\text{Re}\left(T (a,b)\right)}] \tag{9}\]  
The 2D plot of \(\left|T(a,b)\right|^{2}\) is the scalogram, a wavelet energy density function that specifies at scale \(a\) and time location \(b\) which is the contribution to the signal energy. By integrating the scalogram across \(a\) and \(b\) parameters, the total energy in the signal is obtained using Formula (10) [21].  
\[E=1/C_{g}\int_{-\infty}^{\infty}\int_{0}^{\infty}\left|T(a,b)\right|^{2}\frac{ da}{a^{2}}db, \tag{10}\]  
where \(C_{g}\) is the admissibility constant, and its value depends on the chosen wavelet (admissibility condition, \(C_{g}<\infty\), is among the conditions that a wavelet should meet) [23].
## 2 Theoretical Background
### The Use of a Cascaded Complementary Filter for Estimation of Headwind Gusts  
A novel cascaded architecture of the complementary filter (Figure 6) employs a non-linear and linear version of the complementary filter within one framework. \(K_{p}\), \(K_{i}\), and \(\alpha\) are the filter constants. The proposed architecture does not require any mathematical modelling of the system and is computationally inexpensive [27]. The cascaded complementary filter transfer function is represented by Equation (14). The amplitude and phase shift of the CCF is presented in Figure 7.  
\[EST=\frac{(1-\alpha)\cdot s^{2}+\alpha\cdot Kp\cdot s+\alpha\cdot Ki}{s^{2}+ \alpha\cdot Kp\cdot s+\alpha\cdot Ki}\cdot LF+\frac{\alpha\cdot s^{2}}{s^{2}+ \alpha\cdot Kp\cdot s+\alpha\cdot Ki}\cdot HF \tag{20}\]  
Referring to the dependencies (1), (2), (12), (13) and (20), the complete law of headwind estimation (including temporary gusts) can be formulated as (21), and finally, as Equation (22).  
\[U_{g\text{-}est\_CCF}=\frac{(1-\alpha)\cdot s^{2}+\alpha\cdot Kp\cdot s+\alpha \cdot Ki}{s^{2}+\alpha\cdot Kp\cdot s+\alpha\cdot Ki}\cdot U_{g\text{-}lo}+ \frac{\alpha\cdot s^{2}}{s^{2}+\alpha\cdot Kp\cdot s+\alpha\cdot Ki}\cdot u_{ g\text{-}hi} \tag{21}\]  
Figure 5: Bode diagram (amplitude and phase) for non-linear CF.  
\[U_{g\text{-est\_CCF}}= \frac{(1-a)\cdot z^{2}+\text{-}k\cdot Kp\cdot s+\text{-}k\cdot Ki}{s ^{2}+\text{-}k\cdot P\cdot s+\text{-}k\cdot Ki}\text{:}(GS_{x}-TAS) \tag{22}\] \[+\frac{s\cdot z^{2}}{s^{2}+\text{-}k\cdot P\cdot s+\text{-}k\cdot Ki }\text{:}(k_{1}\text{:}\hat{q}-k_{2}\text{:}q+k_{3}\text{:}\delta_{H}+k_{4} \text{:}u)\]
## 2 Materials and Methods  
### Raw Materials  
Commercially available aluminum of ~99.5% purity purchased from KMI Company LLP, Astana, Kazakhstan, was used as the matrix material. This material was selected as the basis for this study because of its low initial mechanical characteristics to more clearly assess the effect of introducing microsilica on the formation of the structure and the improvement in the performance of AMCs. For convenient placement in the cavity of a cylindrical graphite melting crucible with dimensions \(\mathcal{O}84\)\(\times\)\(161\) mm (Yihui Casting Technology Co., Ltd., Guangzhou, China), the initial commercial aluminum ingots were pre-cut into blocks using wet (cooled) cutting technology. This method ensures a high accuracy of material separation and minimizes the risk of thermal damage or structural defects caused by local overheating. Unconsolidated powder of microsilica (commercial density \(\rho_{\mathrm{ms}}\)\(\approx\) 2.45 g/cm\({}^{3}\)) of gray-black color, a byproduct of ferrosilicon production at \"YDD Corporation\" LLP, Karaganda, Kazakhstan, was used as a reinforcing material. Scanning electron microscopy (SEM) was used to analyze the initial morphology and particle size of microsilica. Energy-dispersive spectroscopy (EDS) was used to determine the elemental composition and identify the possible impurities. These examinations were performed using a crossbeam 540 SE microscope (Carl Zeiss, Oberkochen, Germany). The results are shown in Figure 1.  
The SEM image in Figure 1 shows the characteristic spherical morphology of the microsilica particles, with an average particle size of approximately 130 nm and a basic size distribution of 50-200 nm. These data confirm the presence of particles with a good degree of size uniformity, contributing to their efficient use in composite materials. The elemental analysis results by the EDS method confirmed that the microsilica consisted mainly of silicon (Si) and oxygen (O), with mass fractions of 40.8 \(\pm\) 0.1% and 52.8 \(\pm\) 0.1%, respectively. Other elements such as potassium (K), iron (Fe), and sodium (Na) were present in trace amounts, confirming the high purity (-94% SiO\({}_{2}\)) of the material.  
Figure 1: Morphology, size distribution, and elemental composition of microsilica particles.