esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Security-related stress/neutralization, value conflicts, and deterrence are the three main categories of factors driving noncompliance. According to a literature review, workers often consider IPs as difficult to follow, which later becomes a type of stress known as security-related stress. Employees neglect the organization's IPs in order to avoid tension. According to several studies, employees regarded IPs as external and stressful to obey [11; 46]. Employees unwittingly formed noncomplaining behavior and used those tactics to neutralize their wrongdoing [12]. These tactics are based on the theory of neutralization. Five neutralization methods were initially included in the theory. The first is a denial of liability, in which the perpetrator says he or she is not liable for the breach [50; 110]. The second is the denial of damage, in which they argue that what they did was the best way to mitigate harm to the organization. The third is that the perpetrator claims close ties to the organization. They argue that what they did was wrong, but their goal was to protect and help the organization. The fourth neutralization is condemnation of those who oppose them. It means that perpetrators justify their misconduct by condemning anyone who points out the violators' wrongdoing. The last one is the victim's denial of wrongdoing. They agree that the victims should be prosecuted using this technique. Typically, this is used to justify attacks on minority groups such as homosexuals [111]. Similarly, along with neutralization, employees can establish value conflicts, such as believing that work is more important than adhering to organizational IPs, which leads to noncompliance with organizational IPs [42; 78; 112]. **RQ3**: What are the best possible transformation steps of behavior as analyzed in studies from noncompliance to compliance? Based on the above discussion, we have formulated transformation steps from noncompliance to compliance and developed a process model (Figure 5). These steps' layout is based on the previously described factors and their theoretical underpinnings in Section 5.1.
## 2 Operating Principle
### Gaussian Weighted KNN Algorithm  
KNN is a classification algorithm that does not require a complicated training process compared to other machine learning algorithms. For a testing sample, the classification is achieved by using the Euclidean distances between the testing sample and several training samples in the feature space. Nevertheless, one of the disadvantages of the KNN algorithm is that when the data in the training set are skewed, i.e., one of the classes is much more frequent, the prediction may be biased [28]. To address such a problem, the Gaussian weighted KNN algorithm is applied. The Euclidean distance between the testing sample and training sample in feature space is provided into a Gaussian function for optimizing the sample weight at different distances. The Euclidean distance is closer, and the weight of the distance is higher. In the Gaussian weighted KNN algorithm, \(k\) samples, which are closest to the testing sample, are found in the training set. Then, among these \(k\) training samples, if the sum of weights of one class is largest, the testing sample is identified as this class. Assuming the feature space \(\chi\) is an six-dimensional real vector space \(R^{6}\), \(x_{i},x_{j}\in\chi\), \(x_{i}=\left(x_{i}{}^{(1)},x_{i}{}^{(2)},\ldots x_{i}{}^{(6)}\right)^{T}\), \(x_{j}=\left(x_{j}{}^{(1)},x_{j}{}^{(2)},\ldots x_{j}{}^{(6)}\right)^{T}\), the Euclidean distance \(L_{p}\) between the training sample \(x_{j}\) and testing sample \(x_{i}\) is defined as  
\[L_{p}(x_{i},x_{j})=\left(\sum_{l=1}^{6}\left|x_{i}{}^{(l)}-x_{j}{}^{(l)} \right|^{2}\right)^{\frac{1}{2}}, \tag{10}\]  
Meanwhile, the Gaussian function can be expressed as  
\[f\big{(}L_{p}\big{)}=ae^{\frac{-(L_{p}-b)^{2}}{2\xi^{2}}}, \tag{11}\]  
where \(a\), \(b\), \(c\in R\) are the coefficients. By weighting the Euclidean distance, the Gaussian weighted KNN algorithm can overcome the problem caused by skewed training sets, as shown in Figure 6.  
The green square shown in Figure 6 represents a testing sample, and the Gaussian weighted KNN algorithm calculates the Euclidean distance between the testing sample and training samples (blue triangles denote the training samples of category A, while red circles indicate the training samples of category B) to find \(k\) training samples, which are  
Figure 6: Schematic diagram of identification by the Gaussian weighted KNN algorithm.  
closest to the testing sample. For instance, assuming \(k=3\), then three training samples that are closest to the testing sample are found. These three training samples consist of two training samples of category A and one training sample of category B. The Euclidean distances between the testing sample and these three training samples are then weighted by using the Gaussian function in Equation (11), and the Gaussian weighted values are denoted as \(w_{1}\), \(w_{2}\), and \(w_{3}\), respectively. Since the sum of the weighted values (\(w_{1}+w_{2}\)) of category A is greater than that (\(w_{3}\)) of category B, the testing sample is thus identified as category A.
## 5 Experiments and Results
### Fault Detection and Classification
#### 5.2.2 Optimization of Model Parameters and Hyperparameter Tuning  
In the pursuit of enhancing the efficiency and accuracy of our models, we adopted a strategic two-phase methodology for the optimization of the model hyperparameters. The initial phase utilized a random search technique to broadly investigate a vast array of hyperparameter values, setting the stage for a more focused fine-tuning phase. Furthermore, we engaged in Bayesian optimization, a more targeted approach, to refine our selection to the most advantageous parameters. This combination leveraged the wide-reaching capability of random search with the targeted precision of Bayesian optimization. Central to our optimization efforts was the application of Bayesian optimization to identify the ideal decision threshold for our ensemble model. This process incorporated a probabilistic model that was progressively refined with each evaluation of hyperparameter performance, guiding us efficiently through the parameter space to identify an optimal balance for the precision-recall trade-off. Through this analytical process, we determined a decision threshold of 0.7, within an investigatory range of 0.5 to 0.8, thereby optimizing our model's predictive performance by achieving a superior balance between sensitivity and specificity.  
For the adjustment of other hyperparameters, we set the learning rate within a spectrum from \(1\times 10^{-5}\) to \(1\times 10^{-4}\), to calibrate the convergence speed against the stability of the learning process. Batch sizes ranged from 16 to 128, tailored to maximize computational efficiency while ensuring precise gradient estimation. We opted for the Adaptive Moment Estimation (Adam) as our optimizer for its adeptness in dynamically adjusting the learning rates. To mitigate the risk of overfitting, we established a weight decay range from \(1\times 10^{-4}\) to \(1\times 10^{-2}\), promoting a balance between model adaptability and regularization. Furthermore, we implemented an early stopping mechanism to halt training upon detecting no improvement in validation loss over a specified number of epochs, thereby preventing overtraining. This comprehensive and methodical approach to hyperparameter tuning underpins the superior performance of our ensemble model in identifying anomalies.
## 4 Discussion  
### Noise Levels with Membership Function  
The scheme in this work assumes that the filtering process is a linear combination of the products of three SMs with membership functions. The lower Z areas are assigned to heavy noise, middle areas are assigned to medium noise, and higher Z areas are assigned to the edges or less noisy areas. Each pixel in the noisy image is assumed to partially belong to three partitions and has membership partition rates in the same positions. SM5 is used for heavy noise and creates a product with the left area rates. SM3 and SM0.5 are used for medium, less noisy areas, or edges and create a product with the rates from these areas. These areas are shown in Figure 3.  
To prove that the above assumption is correct, if the SMs corresponding to the originally assigned noisy areas are changed (i.e., arbitrarily exchanging SM5, SM3, SM0.5 to any area), a product is obtained with the rates from these areas. A noisy Barbara image (\(\mu=100,\ \sigma=10,\ 20\%\) randomly added) was filtered with a 3 \(\times\) 3 and a 5 \(\times\) 5 average filter and a Gaussian filter (\(\sigma=0.5\)) was applied to this noisy image to make three SMs, respectively. The results from both PSNR and SSIM for all exchanged cases were obviously worse than those obtained before. This effect is consistent with the previous reports on Moran statistics [34].  
However, if the SM5 assigned to heavily noisy areas and SM3, SM0.5 are used to obtain a product with the rates from the left areas or middle areas or exchanged using the same SM in two areas, the PSNR and SSIM results for all cases are slightly lower than or equal to those obtained before. This indicates that proper SMs selection is important.
## 3 The Influence of Structure Parameters  
It is well known that slight changes of nanostructures' parameters will largely shift the structural color. How much the parameters of nanostructures influence the structural color is the topic to be discussed in this section. To systematically analyze the influences of the nanostructure parameters on structural colors, such as the diameter of nanodisk, the height of nanodisk, the tilt angle, the thickness of dielectric layer, and the thickness of substrate, we chose a set of parameters in which the nanostructure reflected the red wave band to perform numerical simulations using the finite-difference time-domain  
Figure 2: The reflection spectra and the field distribution. (**a**) The reflection spectra with \(D=190\) nm and \(p=290\) nm. (**b**) The reflection spectra with \(D=115\) nm and \(p=290\) nm. (**c**) The reflection spectra with \(D=100\) nm and \(p=190\) nm. (**d**) The electric dipole resonances with \(D=190\) nm, \(p=290\) nm and \(\lambda\) = 600 nm. (**e**) The magnetic dipole resonances with \(D=190\) nm, \(p=290\) nm and \(\lambda=680\) nm. (**f**) The CIE 1931 chromaticity diagram obtained from the reflection spectra with different \(D\) and \(p\). \(D\) varies from 30 nm to 200 nm. \(p\) takes the value of 140 nm, 190 nm and 290 nm, respectively.  
method. According to the simulation results in Section 2, these parameters include 190 nm for \(D\), 290 nm for the period \(p\), and 130 nm, 70 nm, 140 nm for \(h\), \(H_{\text{d}}\), \(H_{\text{s}}\), respectively.
# Key Strata Identification of Overburden Based on Magnetotelluric Detection: A Case Study
## 2 MT Detection Method
### Influencing Factors  
Resistivity is a parameter used to describe the physical properties of rocks under ideal conditions. Under natural conditions, in addition to rock components, there are many factors that affect the rock's resistivity, including structure, porosity, water content, etc. [26]. Due to a stratum being a combination of many materials and structures, it is difficult to accurately calculate its resistivity. Consequently, resistivity is replaced by AR in the research and engineering. Thus, AR is not a real resistivity value, but a parameter reflecting the comprehensive characteristics of overlying strata, and a comprehensive product that reflects the conductivity and topography of strata. Formula (5) is the calculation of AR.  
\[\rho_{s}=K\frac{\Delta U_{MN}}{I}, \tag{5}\]  
where \(K\) is electrode coefficient; \(I\) is measured current, A; \(\Delta U_{MN}\) is potential difference, V.  
Because there are a large number of fractures, structures, water, and other fluids or other conductive media in the strata, the resistivity value of a stratum is less than its AR. The AR of overlying strata with high porosity and permeability obviously depends on the fluid and conductivity. In general, dense strata have fewer pores, fluids, and higher AR, and vice versa. Meanwhile, the main influencing factors of AR are shown in Figure 4.
## 4 Aggregated Channel Attenuation Model
### Atmospheric Turbulence  
The FSO channel in this paper was modeled using the M distribution. This statistical model, which has gained popularity in recent years, characterizes surface irradiance fluctuation of unbounded light waves. It is a comprehensive model that encompasses all channel conditions ranging from weak turbulence to strong turbulence and unifies most of the statistical models of irradiance fluctuation proposed worldwide, including the LN distribution and Gamma-Gamma distribution. Additionally, the M distribution demonstrated excellent agreement with experimental data [18]. The PDF of the M distribution can be expressed as:  
\[f_{h_{a}}(h_{a})=A\sum_{k=1}^{\beta}a_{k}(h_{a})^{\frac{\alpha+k}{2}-1}K_{a-k} \left(2\sqrt{\frac{\alpha\beta h_{a}}{\xi\beta+\Omega^{\prime}}}\right) \tag{9}\]  
where  
\[\left\{\begin{matrix}A=\frac{2a^{\frac{\beta}{2}}}{\varsigma^{1+\frac{\alpha} {2}}\Gamma(\alpha)}\Big{(}\frac{\xi\beta}{\xi\beta+\Omega^{\prime}}\Big{)}^{ \beta+\frac{\alpha}{2}}\\ a_{k}=\left(\begin{matrix}\beta-1\\ k-1\end{matrix}\right)\frac{(\xi\beta+\Omega^{\prime})^{1-\frac{\alpha}{2}}}{ (k-1)!}\Big{(}\frac{\Omega^{\prime}}{\xi}\Big{)}^{k-1}\Big{(}\frac{\alpha}{ \beta}\Big{)}^{\frac{k}{2}}\end{matrix}\right. \tag{10}\]  
\(\alpha\) represents the effective number of large-scale units in the scattering environment, \(\beta\) which is related to the diffraction effect caused by small-scale eddies, and \(K_{v}(\cdot)\) representing the modified Bessel function of order v. The classical scattering component's average power is given by \(\xi=E\Big{[}\left|U_{S}^{G}\right|^{2}\Big{]}=2b_{0}(1-\eta)\), where \(U_{S}^{G}\) denotes the component of quasi-forward scattering of eddy currents along the propagation axis, \(0\leq\eta\leq 1\) denotes the ratio between the scattered power coupled with the LOS and the power of all scattered components. The average power of the total scattering component is represented by \(2b_{0}=E\Big{[}\left|U_{S}^{G}\right|^{2}+\left|U_{S}^{G}\right|^{2}\Big{]}\), where \(U_{S}^{G}\) denotes the energy scattered to the receiver by the eddy current on the propagation axis. The parameter \(\Omega^{\prime}=\Omega+2\eta b_{0}+2\sqrt{2b_{0}\Omega\eta}\cos(\phi_{A}-\phi _{B})\) represents the contribution of the coherent average power, where \(\Omega\) represents the average power of the LOS, \(\phi_{A}\) and \(\phi_{B}\) are the deterministic line-of-sight component and the scattering component coupled to it, respectively. \(\Gamma(\cdot)\) is the gamma function.
###### Abstract  
This paper presents TCReC, an innovative model designed for reconstructing network traffic characteristics in the presence of packet loss. With the rapid expansion of wireless networks driven by edge computing, IoT, and 5G technologies, challenges such as transmission instability, channel competition, and environmental interference have led to significant packet loss rates, adversely impacting deep learning-based network traffic analysis tasks. To address this issue, TCReC leverages masked autoencoder techniques to reconstruct missing traffic features, ensuring reliable input for downstream tasks in edge computing scenarios. Experimental results demonstrate that TCReC maintains detection model accuracy within 10% of the original data, even under packet loss rates as high as 70%. For instance, on the ISCX-VPN-2016 dataset, TCReC achieves a Reconstruction Ability Index (RAI) of 94.02%, while on the CIC-IDS-2017 dataset, it achieves an RAI of 94.99% when combined with LSTM, significantly outperforming other methods such as Transformer, KNN, and RNN. Additionally, TCReC exhibits robustness across various packet loss scenarios, consistently delivering high-quality feature reconstruction for both attack traffic and common Internet application data. TCReC provides a robust solution for network traffic analysis in high-loss edge computing scenarios, offering practical value for real-world deployment.  
Keywords:edge computing; network traffic analysis; packet loss; network traffic characteristic reconstruction +
Footnote †: journal:  
Article
## 1 Introduction
### Denial of Service (DoS) Attacks  
A DoS attack is any event that diminishes or eliminates a network's capacity to perform its expected function. The aim is to deprive the nodes' communication of network resources, in terms of dropping data packets and reducing the network bandwidth by preventing authorized users from accessing resources [6].  
Figure 1 shows the taxonomy of DoS attacks. In MANETs, DoS attacks are divided into two main attacks, called full packet drop attacks (black hole attacks) and partial drop packet attacks (gray hole attacks). Black hole attacks can be further expanded into three attacks, including single hole attacks, multiple attacks, and collaborative attacks. As their names imply, a single node or more than one node can partake in malicious activities. On the other hand, a gray hole attack is a partial packet drop attack. It can also be divided into two attacks, i.e., sequence-based gray hole attacks and smart gray hole attacks [7].
# Variational Quantum Circuits for Machine Learning.
## 2 Quantum Technology Approaches
### Superconducting Circuits  
One type of technology used to generate qubits is based on superconduction. These circuits are characterized by the use of qubits that can be selectively introduced or removed. Superconducting circuits can take several different architectures. For instance, superconduction-based qubits can be designed by the interconnection of superconducting electrodes with Josephson junctions. The system is based on the capacitive contribution of the Cooper pairs as well as on the inductive contribution of the jump between two electrodes [12]. Some examples of qubits based on these concepts are the charge qubit, the flux qubit, the phase qubit, and the fluxonium. The transmon (\"transmission line shunted plasma oscillation qubit\" [12]) is a type of superconducting charge qubit that was developed in 2007 to reduce sensitivity to noise. This technology is based on a multilevel system, used as qubits if all their dynamics are confined to two quantum levels. The parameters of a qubit cannot be perfectly obtained during its fabrication process, despite knowing its Hamiltonian capacity [13]. At present there is still a high discrepancy between two fabricated qubits. Superconducting qubits typically operate roughly in the range between 4 GHz and 8 GHz, an energy range that gives rise to the states of 0 and 1. Despite the complexity of superconduction circuits, control of the qubit entanglement is achieved by changing the direction of the circuit current corresponding to the 0 and 1 states [14].  
The transmon consists of two islands connected by a capacitor and a Josephson inductance. In other words, its operation is based on an LC oscillator. The use of a Josephson inductance provides inductive energy in the form of a cosine function. Consequently, the transition of the transmon from the first to the second excitement state is less than 300 MHz. In practice, this difference is sufficient to confine the dynamics to a subspace of two qubits using pulses of a duration of about 20 ns. For two-qubit quantum gates, two inductancesin parallel are needed. Figure 2 shows the structure of a transmon and the implementation of a quantum superconducting device.  
For this implementation, the quantum chip must be completely isolated and at a temperature of only a few tens of millikelvins above absolute zero. To achieve such low temperatures, cryogenic technologies are required. Therefore, a quantum computer requires the use of thermal insulators to prevent noise that would compromise the quality of the qubit. So far, due to the state-of-the-art technologies requiring a high investment, only a few companies such as Google, IBM, Rigetti, Intel, and Alibaba have ventured into developing quantum computers based on superconducting circuits.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 5 Evaluation Results
### Space-Based Time System Stability Evaluation
The mean value of the 24-h satellite clock offset fitting residual RMS of the anchor station is 0.094 ns. The estimated 24-h satellite clock offset fitting residual RMS mean is 0.21 ns. The residual RMS of the clock offset fitting directly observed by the anchor station is small, which is mainly caused by the very short link establishment time between the MEO satellite and the anchor station. To summarize, the satellite clock offset noise estimated by the centralized algorithm based on the inter-satellite link proposed in this paper is a more ideal method, since it has higher accuracy in satellite clock offset estimation and longer clock offset monitoring arc coverage. Figure 4: Comparison of fitting residuals of satellite clock offset obtained by different methods. (**a**) Fitting residual comparison on satellite C19 (MEO satellite). (**b**) Fitting residual comparison on satellite C59 (GEO satellite).
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 4 Scenarios of Application
### Presenting Other Relevant Data to the Decision Process  
To make conscienti and informed decisions, a GDSS must be capable of performing other tasks than the ones previously described. One of them should rely on providing all the necessary information for the decision-makers to reason, understand the context and create new intelligence. So, it is important, for instance, to present data about the other decision-makers' preferences and the possible reasons for their preferences. Other important tasks should consist of creating alerts to advise decision-makers to think twice about inconsistencies in the evaluation of their preferences regarding alternatives and criteria. Although much more can be implemented, here are some examples of features that can be implemented using the proposed framework in order to fulfill the previously referred tasks:  
* Statistical information and aggregation data on preferences, satisfaction predictions, importance predictions according to the decision-makers' behavior style, their levels of expertise, the preferences of the decision-makers considered most credible, among others;
* To measure the consistency level of the alternative evaluation that each decision-maker made;
* To alert the decision-makers of (too) inconsistent evaluations;* To suggest the alternative with greater consistency based on the criteria importance given by the decision-maker;
* To present the most consistent alternative to the other decision-makers based on the criteria importance given by them;
* To present statistical information about the criteria importance based on the respective prediction algorithm;
* To explore the motives behind subjective evaluations through the user's interaction with the System (in our example, it is possible to anticipate that the inconsistency in the evaluation of an alternative is due to the subjective greatness cuisineStyle criterion);
* To justify why a decision-maker prefers a particular alternative (in our example, the framework can demonstrate that [PERSON] prefers nosh because of the distance, transportExpenses and mealPrice criteria);
* To provide information to the decision-maker to enhance his/her final satisfaction;
* To explore the decision maturity throughout the process and by predicting individual and group satisfaction.