esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technological Platform for the Wireless Sensor Node
### Lightweight PaRTiKle Operating System Design
#### 3.2.1 PaRTiKle Architecture  
Figure 2 sketches the PaRTiKle architecture. Contrary to other small embedded RTOS, which are implemented as a library that is linked to the application, PaRTiKle has been designed as a real kernel with a clean and well-defined separation between the kernel and application execution spaces. All kernel services are provided via a single entry_point, which improves the robustness and simplifies porting PaRTiKle to other architectures and environments.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 3 Methodology  
In the previous section, it was demonstrated that in the case of flexural waves, high-order sinusoidal variation along the pipe's circumference will be created. In ideal scenarios, where a perfect axisymmetric wave hits a perfect axisymmetric feature, the received amplitude in each transducer must be the same. However, due to the imperfections of the transducers and the testing conditions, there will be variance along each of the received amplitudes from the transducers; nonetheless, they all will have the same phase. Similar to the pipe ends, defects would generate a significant amount of torsional mode. However, they also do create flexurals with respect to the defect size and location. For signals coming from the backward direction, it is observed that the phases would practically be the inverse of each other for each transducer.  
Based on these observations, by detecting the linearity between the phases of the sensors, the axisymmetric waves can be detected. Since not all of the phases would become the same in the case of defect signals, a measure of similarity between the phases must be defined, which can then be the threshold for detection.
## 2 Computational Details  
We implemented the whole theoretical calculation, namely the structural relaxation and electronic calculation, in the dispersion-corrected DMol\({}^{3}\) package based on density functional theory (DFT) [23]. To obtain the accurate electronic behavior of the analyzed systems, generalized gradient approximation, within the Perdew-Burke-Ernzerhof (GGA-PBE) function and in line with the HSE06 function, was adopted to treat the electron exchange-correlation interaction for comparison [24]. It is known that PBE may underestimate the bandgap, while HSE06 may slightly overestimate it. The DFT-D method proposed by [PERSON] was employed to better understand the Van der Waals force and long-range interactions [25; 26]. Double numerical plus polarization (DNP) was selected as the atomic orbital basis set, with a global orbital cut-off radius of 5.0 A in order to ensure the accuracy of total energy [27; 28]. In terms of Monkhorst-Pack \(k\)-point setting, we determined \(7\times 7\times 1\) for geometric optimizations and \(10\times 10\times 1\) electronic structure calculations [29].  
A \(4\times 4\times 1\) supercell of the InN monolayer was established, which contains 16 In atoms and 16 N atoms, with a vacuum region of 15 A to prevent the interaction between adjacent units. The lattice constant of the InN monolayer calculated in this work was 3.62 A, consistent with the previous work (3.63 A [30]).  
To evaluate the adsorption strength of the Pd-InN monolayer towards gas molecules, the adsorption energy (\(E_{\text{ad}}\)) is determined, expressed as follows: \(E_{\text{ad}}=E_{\text{Pd-InN}}+E_{\text{gas}}-E_{\text{Pd-InN/gas}}\), where \(E_{\text{Pd-InN}}\) and \(E_{\text{Pd-InN/gas}}\) represent the total energy of the Pd-InN monolayer before and after gas adsorption and the \(E_{\text{gas}}\) is the energy for isolated CO or NO molecules. It is undouble that a negative \(E_{\text{ad}}\) represents the exothermicity of the gas adsorption process. Additionally, the Hirshfeld method is used to consider the charge transfer (\(Q_{\text{T}}\)) between the Pd-InN monolayer and gas molecules, defined as the charge of the adsorbed gaseous species. Based on this definition, a positive \(Q_{\text{T}}\) represents the electron-losing property of related gas molecule.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### XFEM/GFEM Enrichment Functions for LEFM  
In the original partition of unity finite element method (PU-FEM) [48], enrichment functions were used as a means of improving the overall accuracy of the approximation, thus enrichment was applied globally, i.e., for all nodes of the FE mesh. For problems involving localized phenomena, such as fracture, enrichment functions are only needed locally, thus, in the XFEM [44; 97] only a subset of the nodes is enriched to increase the efficiency of the method. This type of enrichment was subsequently also adopted in the GFEM rendering the two methods almost identical. In fact, in more recent publications [92] almost no distinction is made between the two methods.  
For LEFM problems, two types of enrichment functions, i.e., specializations of function \(\Phi\left(\mathbf{x}\right)\), are most commonly used to represent the discontinuities and singularities introduced in the solution by the crack. In the following, these enrichment functions are presented along with possible alternatives from the literature. Furthermore, some common problems, associated with their use, are identified and possible remedies discussed.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 1 Introduction
In contrast to conventional retraining with AEs, the introduction of derived variables (DVars) effectively bridges the recognized gap in the existing literature and provides valuable insights to improve the resilience of ML-based network traffic classifiers. DVars, an automated approach designed to enhance the robustness of ML-based network traffic classification systems, introduces controlled randomness into the data, thus enhancing its diversity. The core of this research focuses on the improvement of three state-of-the-art shallow ML-based network traffic classifiers: decision tree (DT), random forest (RF), and k-neighbors (KNN) in response to adversarial attacks, with a specific focus on countering the Jacobian-based Saliency  
Figure 1: Adversarial attack in computer vision [21]. Figure 2: Adversarial attack on network traffic classification systems [22]. Map Attack (JSMA). While the primary intent of the JSMA was originally designed to be attacking image classification systems, its perturbation crafting procedures could be used to preserve the underlying logic of a network traffic flow [22]. The JSMA only perturbs the most appropriate features in the decision boundary without affecting the remaining features, which can preserve the correlations among most features of a network flow; hence, this underscores the strategic motivation behind its adoption. Our findings show the profound impact of adversarial attacks on these models and underscore the remarkable efficacy of DVars in restoring their performance in the presence of black-box attacks. The main contributions of the work presented in this paper are as follows:  
* A method that improves the robustness of ML-based network traffic classification models against adversarial attack is proposed by integrating DVars into training. The DVars follows the logic of adding randomness to the input data. In particular, our proposed approach preserves the underlying logic and maliciousness of the network flow. * Evaluation of the proposed method on the CSE-CIC-IDS-2018 dataset, with a specific focus on improving the accuracy of network traffic classification models when subjected to AEs. According to experimentation and analysis, our approach shows considerable improvements in the performance of the models. * Investigation of the impact of AEs on ML-based network traffic classification models. Using experiments and analysis, we explore the effect of AEs on the performance and robustness of the investigated models. The remainder of this paper is organized as follows: Section 2 provides a detailed review of related studies on ML-based network traffic classification, common AE generation methods, and defenses. Section 3 presents the impact of AEs on ML-based network traffic classification models, and Section 4 presents our novel defensive method based on DVars. Section 5 presents the dataset and evaluation metrics used in assessing the performance of DVars. Section 6 provides the results and analysis, and Section 7 concludes the work and discusses future directions.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 3 The Proposed Strategy  
We consider a clustered smart energy network in this paper. Instead of testing each meter, we perform a centralized testing on cluster-heads only. Then, the fault state of cluster-members can be verified by corresponding fault-free cluster-heads in a decentralized manner. To be more specific, the \(SFBT\) works in three phases. In the first phase, \(SFBT\) finds the Minimal Dominating Set (\(MDS\)) of the network such that each dominator serves as a cluster-head while the corresponding dominatees become cluster-members. During the phase two, \(SFBT\) employs both \(STUKF\) and \(MB\) to detect the faulty cluster-heads in a centralized manner. In the last phase, an \(MTS\) is developed to determine the fault state of each cluster-member for decentralized fault diagnosis.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Symbols** & **Descriptions** \\ \hline \(C_{i}\) & \(i\)-th cluster \\ \(CH_{i}\) & \(i\)-th cluster-head of \(C_{i}\) \\ \(s_{i}\) & \(i\)-th meter \\ \(Neg_{i}\) & Set of neighboring meters of \(s_{i}\) \\ \(x(k)\) & State of a meter at time \(k\) \\ \(x_{i}\) & data of \(s_{i}\) \\ \(x_{i}\) & Estimated value of \(x_{i}\) \\ \(\mu_{i}\) & Sample mean of \(s_{i}\) \\ \(e_{i}\) & Sample variance of \(s_{i}\) \\ \(Med\) & Median of the data \\ \(MAD\) & Median Absolute Deviation \\ \(SD\) & Standard Deviation \\ \(\eta\) & Threshold for identifying fault state \\ \(p_{r}\) & Probability of a meter being faulty \\ \(p\) & Probability of a faulty meter detected as fault-free \\ \(STUKF\) & Strong Tracking Unscented Kalman Filter \\ \(MB\) & Modified Bayes’ classification algorithm \\ \(MTS\) & Modified Three Sigma test \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notions.
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 4 Proposed Haar Wavelet Decision Feedback Channel Estimation Method
After the IFFT operation, the Haar wavelet decomposition is performed to obtain the detail coefficients \(\mathbf{d}_{i}\). Based on the Equation (9), the \(\hat{\sigma}\) is obtained as the threshold to reduce the AWGN in time-domain which is presented as  
\[\hat{\mathbf{h}}_{\text{Haar}}(n)=\left\{ \begin{array}{cc}\hat{\mathbf{h}}_{\text{DF }}(n),&\hat{\mathbf{h}}_{\text{DF}}(n)\geq\hat{\sigma},\\ 0,&\hat{\mathbf{h}}_{\text{DF}}(n)<\hat{\sigma}.\end{array} \right. \tag{10}\]  
After setting the insignificant channel coefficients that below the threshold to be zero, the noise-removed channel coefficients are transformed into frequency-domain coefficients \(\hat{\mathbf{H}}_{\text{Haar}}\) in the FFT block. Finally, through these procedures, the proposed Haar wavelet method improves the data transmission rate and spectrum efficiency while suppressing the AWGN effectively.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 4 Computer Simulation
### Numerical Analysis
The loss of system rate is defined as a ratio against the maximum system rate. In the figure, the colors show the loss level, and the boundaries are drawn at every 1% of loss. Essentially, the storage limit and the time window should be smaller in terms of the installation cost and prefetching performance. Figure 8: The system rate vs. the backhaul capacity. Figure 9: The average download time vs. the backhaul capacity. Particularly concerning the latter, a small time window can prevent a communication performance deterioration from mis-prefetching if the context information contains an error. However, as shown in the figure, setting both values too small may result in non-negligible degradation of the system rate. A large amount of data cannot be stored in an overly limited storage capacity, and prefetching cannot be conducted in time for demand traffic in too short a time window. Therefore, appropriate values should be chosen to fully obtain the benefits of prefetching. For example, we recommend selecting 4 GB and 35 s as the storage and time window, which provides only 1% loss from the ideal case. In addition, mmWave edge cloud is beneficial from the viewpoint of power consumption. The energy efficiency of the network \(\eta_{\rm P}\) (bit/J) is defined as:  
\[\eta_{\rm P}=\frac{L}{T_{\rm M}P_{\rm M}+T_{\rm S}P_{\rm S}}, \tag{6}\]  
Figure 11: Normalized loss of the system rate (1 Gbps backhaul capacity, 60 GHz access, and WPF with 200 UE). Figure 10: The system rate vs. the number of users (1 Gbps backhaul capacity and 500 s time window). where \(L\) [bit] is the total traffic size occurring in the network, \(T_{\text{M}}\) and \(T_{\text{S}}\) [s] are the total time duration that the macro cell and small-cell BSs consume to transfer data. \(P_{\text{M}}\) and \(P_{\text{S}}\) [W] denote the power consumption, i.e., the transmit power in macro/small-cell BSs as shown in Table 4. Figure 12 shows the comparison of the energy efficiency \(\eta_{P}\) in four cases. Employing mmWave small cells can significantly improve the energy efficiency compared to the case with only macro cells. This is because the mmWave can deliver more traffic data in a shorter communication duration. Furthermore, introducing MEC assisted by prefetching is more effective in saving power consumption. In the 60 GHz band, energy efficiency of the RR-based prefetching is 3.2-times higher than the case without prefetching, and improvement of the proposed WPF is enhanced 5.0 times. In 28 GHz band, the improvement is small but still effective; the RR-based and proposed prefetching schemes attain 1.5- and 1.7-times higher energy efficiency compared with the conventional one.
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
## 3 Theory on Broadband Pumped OPA
### Gain Mechanism of Signal Pulse  
With a given initial seed, the gain bandwidth of the signal pulse is mainly determined by the phase-matching condition, which in the case of DC-OPA, depends on the initial bandwidth and chirp of the input pump and seed pulses. As previously investigated in various works [110; 111; 112; 113; 114], the chirp-compensation scheme is favorable in such conditions. Here, we investigate the chirp-compensation scheme in the NIR to Mid-IR spectral region and the result is depicted in Figure 4. The pump and seed wavelengths cover 740-860 nm and 1000-1800 nm respectively,  
Figure 3: **(a)** Spectra of injected seed pulse (yellow solid line), and spectra of amplified signal pulse phase (blue solid line) together with its phase (green solid line) before phase compensation, \(\omega_{s}\) is the signal frequency divided by its center frequency. Inset: Calculated spatial profile for the obtained signal pulse. **(b)** Temporal profile (red solid line) and phase (green solid line) of the amplified signal pulse after phase compensation.  
and a BBO crystal cut at \(\theta=20^{\circ}\) is employed. The phase-matching efficiency is given by \(sinc^{2}(\Delta kL/2)\), where \(\Delta k=\overset{\rightarrow}{k_{p}}-\overset{\rightarrow}{k_{s}}-\overset{ \rightarrow}{k_{i}}\) is the phase-mismatch and \(L=2\) mm is the crystal thickness.  
As observed in the figure, signal wavelength components spanning 1100-1700 nm can be phase-matched to pump wavelength components in the 740-860 nm range, and the relationship of the phase-matched pump-signal wavelengths is almost linear in this range. When pump and seed pulses of enough bandwidths are injected into the BBO crystal with the appropriate chirps, broadband phase-matching can be expected with a linear chirp-compensation fitting.  
The instantaneous frequency of the interacting pulses in a chirp manipulated OPA can be expressed as follows:  
\[\omega_{p}(t)=\omega_{p0}+C_{p}t \tag{1}\]  
\[\omega_{s}(t)=\omega_{s0}+C_{s}t \tag{2}\]  
\[\omega_{i}(t)=\omega_{p}(t)-\omega_{s}(t)=\omega_{i0}+C_{i}t \tag{3}\]  
in which \(\omega_{m}(t)\) (\(m=p,s\), and \(i\)) is the time-dependent frequency of the pump, signal, and idler pulses, respectively, \(\omega_{m0}\) [rad] is the central angular frequency and \(C_{m}\) [rad/fs] is the temporal chirp of each pulse, \(C_{i}=C_{p}-C_{s}\). The linear chirp-compensation fitting is denoted as the blue line in Figure 1, the line is slightly curved because of the reciprocal relationship between the wavelength \(\lambda\) and the angular frequency \(\omega\), the \"linear\" fitting actually refers to the linear chirp of the frequency, i.e., \(C_{m}\) being a constant value. In order to obtain phase-matching for as many signal wavelengths as possible, a pump-to-seed chirp ratio \(A=C_{s}/C_{p}=1.8\) is selected. The feasibility of the chirp-compensated OPA scheme will be experimentally verified in the following section.
## 4 Proposed Algorithm for Multi-Agent Configuration Change
### Trajectory Reference Generation
#### 4.1.2 DMPC-Based Reference Generation
```
1 Initialization\(C_{input}\), \(C_{output}\), \(N\), \(T\), \(A\), \(B\), \(\mathbf{p}_{0}\), \(\mathbf{u}_{\text{max}}\), \(\mathbf{v}_{\text{max}}\), \(\mathbf{u}_{\text{min}}\), \(\mathbf{v}_{\text{min}}\)
2 Defining the initial state of formation \(\{X_{k}^{i}|\mathbf{p}_{k}^{i},p_{k}^{i},p_{k}^{i}(k)\}\), \(i\in V\), the affine image \(I(r)\), the changing matrix \([A,b]\) and trajectory sequences \(\{p^{*}(t)\}=[I_{n}\otimes A(t)]r+I_{n}b(t)\)
3 whileIter\(K_{iter}\leq 4500\) and not ReachTargetPosition do
4 BroadcastLastPrediction() \(\rightarrow\sum_{k-1}\)
5 for\(k=0,1,\ldots,+\infty\)do
6 for all agent \(i\in N=1,2,\ldots\)do
7\(\{\mathbf{u}_{i}|k\}\leftarrow\text{Reset Input}\left(\{X_{k}^{i}|\mathbf{p}_{k}^{i}, \mathbf{v}_{k}^{i},p_{t}^{*}(k)\}\right)\)
8 Input\(\{X_{k}^{i}|\mathbf{p}_{k}^{i},\mathbf{v}_{k}^{i},p_{t}^{*}(k)\}\)
9 Calculate\(\mathbf{f}(X^{*})\) and store\(\left\{\mathbf{u}_{d}^{*},X_{k}^{i}\right\}\) (Broadcast)
10 Update\(X_{k}^{i}\)
11 if\(C_{output}\
otin\varnothing\)then
12 LocalControl\(\rightarrow\) BuildCollisionCost (\(J(X^{*})\))
13 NegativeFeedback \(\rightarrow\Delta(\mathbf{u}_{d},\mathbf{v}_{d},\mathbf{p}_{d})\)
14 DMPCSolve \(\rightarrow\{p_{d}^{*}|k\},\min J(X^{*})\)
15 else
16 Predict\(\{X_{k},\sum_{k+1}|t=k,k+1,\ldots,k+N\}\)
17 DMPCSolve \(\rightarrow\{p_{d}^{*}|k\}\)
18 endif
19\(\left\{\mathbf{u}_{d,k}^{*}\right\}\leftarrow\text{UpdateReferenceOutput}\)
20 endfor
21 endfor
22 ReachTargetPosition \(\rightarrow\) CheckReach (\(X_{i},p_{i}^{*}\))
23\(k\to k+1\)
24 endwhile
```  
**Algorithm 1**Trajectory Reference Generation  
**Remark 1**. : _The \(J(X^{*})\) formulation requires only its neighboring nodes, making it adaptable to a variety of communication topologies. However, the algorithm demonstrates effectiveness when the necessary condition of strong interconnectivity within the formation fails in real-world scenarios._  
**Remark 2**. : _Note that an accurate multi-agent model is necessary in problem \(J(X^{*})\) to output future behavior, while in real-world scenarios, issues of asymptotic stability, robustness against model uncertainty, noise and coupling constraints could only be solved in this paper to some extent. Thus, the design of Equation (6) would mainly concentrate on the discrete dynamic points instead of the continuous ones._  
The objective of reference generation is to serve as the reference baseline for the following critic network and reward function in the MADRL algorithm.
## 3 Proposed TCN Model Architecture  
Different types of sequence data modeling and forecasting are performed in deep learning approaches with LSTM and gated recurrent units (GRUs). CNNs are superior in computer vision applications, such as image classification, object detection, autonomous cars, and biometric authentication problems. However, in sequence classification and forecasting problems, CNN has drawbacks due to a lack of memory. TCN was proposed in [25], which modified the CNN architecture to adopt sequence data and time-series prediction.  
For TCN, we consider one-dimensional (1-D) input sequence \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) of features or data. We can predict some output as \(Y=\{y_{1},y_{2},\ldots y_{m}\}\). The sequence modeling can be written as \(f:X\to Y\). The input-to-output mapping can be written as follows:  
\[y_{1},y_{2},\ldots,y_{m}=f(x_{1},x_{2},\ldots x_{n}). \tag{5}\]  
TCN workflow with the sequential data in each layer is shown in Figure 2. One of the characteristics of TCN is that it has an equal number of inputs and output sequences in each layer. This is ensured by adding zero padding to each layer shown in the white box of Figure 2. TCN uses casual convolution to stop the leakage of any information from the future to the past. This design can have a very long history size and can handle a network for which large size kernels are needed. To tackle this problem, dilation is introduced in TCN. For a 1-D sequence input \(\mathbf{x}\in\mathbb{R}^{n}\) with a filter \(f:\{0,1,\ldots.q-1\}\rightarrow\mathbb{R}\), the dilated convolution operation \(\mathcal{D}\) for sequence \(t\) is written as follows:  
\[\mathcal{D}(t)=(\mathbf{x}*_{d}f)(t)=\sum_{i=0}^{q-1}f(i)\cdot\mathbf{x}_{t-d \cdot i}, \tag{6}\]  
where \(d\) is the dilation factor, \(q\) is the filter size, and \(t-d\cdot i\) is the direction of the past. Thus, dilation is considered as a fixed step between every two filter taps. Figure 3a shows an example of dilation factors with \(d=2,3,4\). The receptive field for \(n\) layer convolutional network with kernel size \(q\) is as follows:  
\[r=1+n*(q-1). \tag{7}\]  
To complete the structure of TCN, the residual block is added for learning the identity mapping. If \(x\) is the input, then the output of the residual block is as follows:  
\[\beta(x)=\mathcal{F}(x)+x. \tag{8}\]  
In the case of TCN as shown in Figure 3b, the residual block consists of dilated causal convolution block, weight normalization block, rectified linear unit (ReLU), and dropout block. The same configuration is repeated two times, and after that, the input is added to the resulting value.