esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Attacker Model
#### 3.1.4 Iterative Solution  
If the initial budget \(D\) cannot meet feasibility requirements, the opponent will continue to reduce \(D\) and repeatedly solve the above feasibility problem until the minimum value \(D_{min}\) is found, which can still cause congestion on the target link.
## 2 Related Work
### Machine Learning
The weights of examples that are misclassified are increased by the base learning algorithm, and the weight of examples that are correctly classified are decreased [25]. The naive Bayes (NB) classifier is a machine-learning algorithm that greatly simplifies learning by assuming that features are independent given class, that is, \(P(X|C)=\prod\sum_{i=1}^{n}P(X_{i}|C)\), where \(X=(X_{1},\cdots,X_{n})\) is the vector of features, and C is the class. Naive Bayes classifiers assign the most likely class to a given subject depending on its feature vector. Although it includes unrealistic assumptions, NB is considered remarkably successful in practice compared to other more sophisticated algorithms. Its applications include medical diagnosis [26] and food quality classification [27]. As shown in Table 1, we will explain the advantages and disadvantages of algorithms used in machine learning training. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Advantages** & **Disadvantages** \\ \hline SVM/SVR & High performance, high accuracy, good handling of high dimensional data [17,28,29] & Lack of transparency in high dimensional data, extensive memory requirements [17,28,29] \\ \hline linear discriminant analysis & Low computational cost Easy to implement Discriminate different groups & Requires normal distribution Linear decision \\  & Visual representation makes clear understanding [27,30] & boundaries Limited to two classes [27,30] \\ \hline K-Nearest Neighbors & Intuitive and simple, easy to implement for multiclass problems [31,32] & Computationally expensive in large datasets, performance depends on dimensionality [31,32] \\ \hline Artificial Neural Networks & Good at handling large datasets, detect all possible interactions between prediction variables, implicit detection of complex non-linear relationships & High hardware dependencies (GPU), Unevplained behavior of the network, between dependent and independent variables [33,34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of machine-learning algorithms used.
## 4 Validation and Applications
### High-Performance Centrifugal Compressor (HPCC)
Although overestimated at the design speed, the total pressure ratio characteristic computed by the throughflow solver matched well with the experimental data and the 3D-CFD results in the whole operating range. At the design mass flow rate, the throughflow results showed relative errors of 8.3% and 4.9% compared to the experimental data and 3D-CFD results, respectively. This should be attributed to the underestimated slip effects as the isentropic efficiency predicted by the throughflow solver was in good agreement with the experiment at the design mass flow rate. At the off-design speed, the throughflow results showed even better accuracy compared with the experimental data and the 3D-CFD results. Regarding the isentropic efficiency characteristic, the throughflow and 3D-CFD results showed good agreement at all the different speed lines. At the design speed, both calculations matched well with the experimental data, whereas they underestimated the efficiency at lower speeds. It can also be seen from Figure 9 that the choke mass flow rate was accurately predicted at the design speed by the throughflow solver with a value of 5.07 kg/s, which was almost the same as the experimental data and 3D-CFD results. For the near-stall conditions, it was observed that the throughflow calculation could not reach the stall boundary shown in the experimental characteristics, especially for the 60%-80% speed. This problem may have been caused by the setup of the loss correlations or the boundary conditions and will be further studied in later work. Table 4 further reports the detailed contributions of each loss to the total loss in the throughflow simulation of the HPCC at different operating conditions. Figure 8: Convergence histories of HPCC simulation. Figure 9: Performance characteristics of the HPCC. (**a**) Total pressure ratio; (**b**) isentropic efficiency. The meridional flow fields obtained by the throughflow and 3D-CFD simulations are compared at the design mass flow rate in Figure 10 in terms of the relative Mach number. In the comparison, the 3D solution was circumferentially averaged. It can obviously be seen that both calculations predicted similar subsonic flow fields with a maximum relative Mach number of about 0.8 at the leading edge of the blade tip. The blockage effects resulting from the blade thickness of the splitter were successfully captured by the throughflow solver as it was predicted by the 3D solution, which locally exhibited a positive gradient of the relative Mach number at the leading edge of the splitter. In addition, the low Mach number region at the impeller exit near the shroud was predicted by both different calculation methods despite the slight discrepancy in its strength. This kind of low-energy flow means that the blade leakage effects were successfully simulated by the present throughflow solver.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)
### Computational Complexity  
The algorithm adds \(\mathcal{O}(|S||A|)\) operations per timestep compared to the standard Q-learning's \(\mathcal{O}(1)\). However, practical implementations can reduce this through  
* Locality-sensitive hashing for approximate similarity search;
* Sparse similarity matrices;
* Parallel batch updates.  
In large state spaces, computational cost can be managed by (1) pre-computing similarity neighborhoods, (2) using dimensionality reduction on state representations, or (3) implementing selective update strategies that prioritize high-similarity pairs.
## 3 Background
### Authentication  
Authentication is the process of verifying the identity of one entity by another, where an entity can be a user (human), a system, or any other entity [19]. Most IT deployments often use user-server scenarios, where the user needs to authenticate themselves to the server to tailor experiences and to allow for additional security schemes such as authorization.  
Authentication has the sole purpose of identifying a subject, unlike authorization, where the latter focuses on granting permissions to resources based on the authenticating subject's rights, defined through, e.g., roles or access-control lists. Often, authentication and authorization are both referred to under the unifying term 'Auth', but it is essential to distinguish the difference.  
Traditionally, digital authentication was based solely on username-password combinations, and in more advanced IT systems, digital certificates have also been utilized for digital authentication, often in corporate or governmental deployments. Today, the IT landscape is a bit different, with the use of multiple authentication options and even the combination of several authentication methods used to increase the security of the authentication process and therefore protect the underlying assets. Different authentication methods are often referred to as factors. Combining multiple authentication factors is known as multi-factor authentication. There are three principal factors of authentication as follows [20]:  
* [noitemsep,topsep=0 pt]
* Something you know (e.g., password).
* Something you have (e.g., a phone or security key).
* Something you are (e.g., face, fingerprint, iris, etc.).  
In today's world, the consensus is that single-factor authentication is not secure enough in most cases, and most IT systems, therefore, allow, encourage, or require at least two factors for authentication. This is also to comply with laws, especially in high-risk sectors such as the financial sector, where regulations require the usage of multi-factor authentication [21].
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Application of the Methodology
#### 2.4.7 Machine Learning Classifiers  
There are many possible machine learning methods to predict stroke using medical data. RF is a bootstrapping algorithm that uses many trees for decision-making to achieve better predictions. In this method, every tree is educated in parts of the data, and the outcome is obtained from all the trees' results. DT is a classical technique in which a learning algorithm partitions the data into subgroups depending on the values of specific attributes until a decision has been made or a termination has been invoked. SVM algorithms attempt to find the optimal hyperplane for classifying observations into different classes. They are helpful when working with large datasets, and the model can detect linear and non-linear interactions between the variables. However, they require high computation power, and the results are dependent upon the kind of kernel function, especially for large datasets.  
In the second research, the authors employed ten models to forecast stroke. TabNet is an algorithm for tabular data with so-called neural networks with attention. LR and AGD--the logistic regression model of this study--employs AGD and a maximum of 100 iterations. Neural networks consist of five hidden layers and are used to discover complicated data relations. RF constructs its prediction out of 100 decision trees. Gradient Boosting uses 100 estimators to enhance the learner through a step-by-step training process. CatBoost is designed for categorical features, and the number of boost gradients is 100. LightGBM is a histogram and parallelism-efficient model with 100 estimators. XGBoost interacts with weak learning with rectification to avoid over-training, and it runs 100 times. Balanced Bagging focuses on sampling and five Random Forest classifiers to deal with class imbalance. Specifically, XGBoost is based on 100 estimators and provides probabilistic prediction and estimation of uncertainties with Bayesian methods.  
In the third research, several classification models were used to predict stroke. Among them are NB, which is based on [PERSON] theorem and the assumption of feature independence; SVM, RF, ensemble learning methods combining multiple decision trees to improve prediction accuracy; AdaBoost, a boosting technique that improves performance by combining weak classifiers; and XGBoost, an advanced gradient boosting model known for its efficiency and ability to handle complex data.
## 2 Affine Projection Sign Algorithm Based on Step-size Adjustment
### Apsa  
In the conventional APSA, the desired output signal is given by  
\[d(n)=\mathbf{u}^{T}(n)\mathbf{w}^{o}+v(n), \tag{1}\]  
where \(n\) is the time index; superscript \(T\) represents transposition of matrix or vector; \(\mathbf{w}^{o}=[w_{0}^{o},w_{1}^{o},...,w_{M-1}^{o}]^{T}\) denotes an unknown system weight vector, which needs to be estimated; \(\mathbf{u}(n)=[u(n),u(n-1),...,u(n-M+1)]^{T}\) is the input signal vector with length \(M\); and \(v(n)\) is the background noise with the addition of an interference signal.  
Let \(\mathbf{U}(n)\), \(\mathbf{d}(n)\), \(\mathbf{e}(n)\), and \(\mathbf{e}_{p}(n)\) be the input matrix, desired signal vector, error vector, and _a posteriori_ error vector, respectively, as follows:  
\[\mathbf{U}(n)=[\mathbf{u}(n),\mathbf{u}(n-1),...,\mathbf{u}(n-K+1)], \tag{2}\]  
\[\mathbf{d}(n)=[d(n),d(n-1),...,d(n-K+1)]^{T}, \tag{3}\]  
\[\mathbf{e}(n)=\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n-1), \tag{4}\]\[\mathbf{e}_{p}(n)=\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n), \tag{5}\]  
where \(K\) is the affine projection order. The APSA algorithm can be derived by minimizing the \(\ell_{1}\)-norm of _a posteriori_ error vector \(\mathbf{e}_{p}(n)\) with a constraint on the filter coefficients, as  
\[\min_{\mathbf{w}(n)}\|\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n)\|_{1}, \tag{6}\]  
\[\text{subject to }\|\mathbf{w}(n)-\mathbf{w}(n-1)\|_{2}^{2}\leq\mu^{2}, \tag{7}\]  
where \(\mu^{2}\) is a parameter which is used to ensure the stability of the weight coefficients vector \(\mathbf{w}(n)\) during updating. Using the method of Lagrange multipliers [19], the cost function can be solved by combining (6) and (7). Then, the updated equation for the weight vector is  
\[\mathbf{w}(n)=\mathbf{w}(n-1)+\mu\frac{\mathbf{U}_{s}(n-1)}{\sqrt{\xi_{1}+\| \mathbf{U}_{s}(n-1)\|_{2}^{2}}}, \tag{8}\]  
where \(\mu>0\) is the step size; \(0<\xi_{1}\ll 1\) is to avoid division by zero; \(\|\cdot\|_{2}\) is the \(\ell_{2}\)-norm of the vector; and \(\mathbf{U}_{s}(n-1)=\mathbf{U}(n-1)\text{sgn}(\mathbf{e}(n-1))\) with \(\text{sgn}(\mathbf{e}(n-1))\) being the sign operation on each element of the error vector.
## 6 Conclusions  
Considering the spline friction of friction components, the detailed numerical model and experimental method were established to study the FT attenuation in a wet multi-disc clutch. Under various working conditions, the contact pressure and FT obtained by bench test show a good agreement with the numerical model, which also verifies the existence of spline friction. The conclusions can be summarized as follows: the spline friction is an important factor affecting the attenuation of the axial contact pressure and FT of the friction components. With the increase of applied pressure, the attenuation of the contact pressure along the axial direction increases gradually, whereas the growth rate of the average single-pair FT decreases. In addition, the average single-pair FT decreases gradually with the increasing friction pairs under the same applied pressure. To be more specific, it is not reliable to obtain a substantial enhancement of FT by continuing to add friction components when the friction pair reaches a certain number. Therefore, in order to reduce the effect of spline friction, it suggests to choose the reasonable structural parameters for friction component, to improve the manufacturing processes of spline and to increase the lubrication flow at the spline.  
[PERSON] is responsible for the theoretical modeling and research funding. [PERSON] did the academic writing and the numerical analysis. [PERSON] not only formulated the specific experimental scheme, but also provided the academic and experimental guidance. [PERSON] and [PERSON] did part of the experiments and organized the experimental data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant NO. 51775045 and NO. 51975047), and the basic product innovation research project of the Ministry of Industry and Information Technology of China (JCCPCX201705).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Applied Pressure (MPa)**} & **0.2** & **0.4** & **0.6** & **0.8** & **1.6** \\ \hline Calculated & \(\mu_{\text{fs}}=0.1\) & 30.747 & 60.136 & 88.694 & 117.44 & 222.4 \\ torque (N\(\cdot\)m) & \(\mu_{\text{fs}}=0.15\) & 29.977 & 58.664 & 86.56 & 114.64 & 217.37 \\ \hline Relative error & \(\mu_{\text{fs}}=0.1\) & 4.6 & 6.89 & 6.49 & 10.3 & 7.26 \\ (\%) & \(\mu_{\text{fs}}=0.15\) & 1.98 & 4.28 & 3.93 & 7.69 & 4.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total calculated torque and relative error.
## 5 Results  
This section provides the results of the proposed approach on the eWINE data set and the Radar data set. Considering that there are a number of hyper-parameters in this study and different scenarios in the eWINE data set, the analysis cannot be exhaustive for all the scenarios but it will mainly focus on the first scenario (Office 1) (indicated as scenario 1 (Office 1)) in this paper, even if a comparison of the different scenarios will be also provided. For the Radar data set, only one scenario is present. As described in Section 3.2, the term LF is used to identify the label flipping attack, the term FSP is used to indicate the attack where the feature space is perturbed by the factor \(S_{P}\), and the term TFP is used to indicate the attack where the perturbation is applied to the label template.  
Table 1 summarizes the hyper-parameters and the range of values considered in this study for both data sets. As described before, \(T_{P}\) is the hyper-parameter defining the percentage of the poisoned samples on the entire training data set. This parameter has a range from 0.1 (i.e., 10% of the data) to 0.5 (i.e., half of the data). This range is similar to the one adopted in similar poisoning attacks in the wireless communication domain [19, 20].  
The results were obtained using a four-fold partition of the entire data set, where three-fourths of the data set was used to create the training data set on which poisoning attacks were implemented and one-fourth of the data set was used for testing. The final results were obtained by averaging the results obtained for each partition.  
This section is structured in two different sub-sections: Section 5.1 for the eWINE data set and Section 5.2 for the Radar data set.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hyper-Parameter** & **Description** & **Ranges** & **Notes** \\ \hline \multirow{2}{*}{\(T_{P}\)} & Percentage of & (0.1, 0.2, 0.3, & \\  & poisoned samples & 0.4, 0.5) & \\ \hline \multirow{2}{*}{\(S_{P}\)} & Severity of the FSP and & (0.25, 0.5, 1, & This parameter \\  & TFP attacks & 4, 16) & does not apply \\ \hline \multirow{2}{*}{\(P_{SAN}\)} & Percentage of data set & (0.6, 0.7, 0.8, & \\  & for sanitization & 0.9, 0.95) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyper-parameters considered in this study and related ranges.
## 2 Spaceflight Environment and Ocular Risks
### Infections and Immune Suppression  
Microbial keratitis represents a significant threat during spaceflight, exacerbated by a combination of immune suppression, microgravity conditions, and microbial contamination. Stress-induced immune dysfunction, driven by activation of the hypothalamic-pituitary-adrenal (HPA) axis and reduced cytotoxic T-cell activity, renders the ocular surface vulnerable to opportunistic pathogens [11; 35]. Microgravity further destabilizes the tear film, weakening natural defenses and amplifying susceptibility to infections [22; 36].  
Microbial biofilms, inherently more resilient than planktonic bacteria, present a severe challenge in the confined spacecraft environment. Studies demonstrate that _Pseudomonas aeruginosa_ and _Staphylococcus aureus_ form denser biofilms under microgravity, exhibiting increased virulence and resistance to antimicrobials [37]. These adaptations, coupled with limited air circulation and sterilization challenges, significantly heighten the risk of ocular infections. Enhanced biofilm formation on spacecraft surfaces and water systems underscores the need for advanced countermeasures during long-duration missions [37].  
Microgravity and associated spaceflight conditions can also alter the human microbiome, including that of the ocular surface. While direct studies on the tear film microbiome in microgravity are limited, terrestrial research highlights the importance of a balanced ocular microbiome in maintaining eye health [38]. Disruptions to this balance, potentially induced by spaceflight, could increase susceptibility to ocular infections [39]. Therefore, maintaining the stability of the ocular surface microbiome is crucial for astronaut health during long-duration missions.  
Preventive measures, such as antimicrobial coatings on spacecraft surfaces and preemptive ocular defenses like antimicrobial contact lenses, are critical for managing these risks. Silver nanoparticle-infused materials and enhanced sterilization technologies have shown promise in reducing microbial adhesion and biofilm growth [37].  
Herpes simplex virus (HSV) reactivation poses a significant concern during spaceflight. Stress-related immune suppression, isolation, and disrupted circadian rhythms can lead to reactivation of latent HSV-1, resulting in stromal keratitis characterized by inflammation, edema, and scarring, severely compromising visual acuity [11]. Prophylactic antiviral therapy, such as acyclovir or valacyclovir, is recommended primarily for astronauts with a history of recurrent HSV infections or those at high risk for reactivation [40] This targeted approach aims to minimize the risk of antiviral resistance associated with widespread prophylactic use [41].
## 3 Distributed Cognition-Based Data Analysis and Results
### Reliability and Validity Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **Item** & \begin{tabular}{c} **Factor.** \\ **Loading** \\ \end{tabular} & \
\begin{tabular}{c} **Robustness** \\ **Level** \\ \end{tabular} &
\begin{tabular}{c} **Cronbach’s** \\ **Alpha** \\ \end{tabular} & **CR** & **AVE** \\ \hline \multirow{4}{*}{LF} & LF1 & 0.781 & Moderate & & & \\  & LF2 & 0.927 & Strong & & & \\  & LF3 & 0.899 & Strong & & & \\  & LF4 & 0.885 & Strong & & & \\ \hline \multirow{4}{*}{PF} & PF1 & 0.641 & Moderate & & & \\  & PF2 & 0.906 & Strong & & & \\  & PF3 & 0.824 & Strong & & & \\  & PF4 & 0.879 & Strong & & & \\ \hline \multirow{4}{*}{EF} & EF1 & 0.47 & Weak & & & \\  & EF2 & 0.734 & Moderate & & & \\  & EF3 & 0.653 & Moderate & & & \\  & EF4 & 0.682 & Moderate & & & \\ \hline \multirow{4}{*}{PU} & PU1 & 0.885 & Strong & & & \\  & PU2 & 0.968 & Strong & & & \\  & PU3 & 0.979 & Strong & & & \\  & PU4 & 0.972 & Strong & & & \\ \hline \multirow{4}{*}{PEU} & PEU1 & 0.861 & Strong & & & \\  & PEU2 & 0.938 & Strong & & & \\  & PEU3 & 0.945 & Strong & & & \\  & PEU4 & 0.925 & Strong & & & \\  & PEU5 & 0.943 & Strong & & & \\ \hline \multirow{4}{*}{PE} & PE1 & 0.528 & Weak & & & \\  & PE2 & 0.838 & Strong & 0.771 & 0.748 & 0.564 \\  & PE3 & 0.826 & Strong & & & \\  & PE4 & 0.77 & Moderate & & & \\ \hline \multirow{4}{*}{SA} & SA1 & 0.9 & Strong & & & \\  & SA2 & 0.953 & Strong & & & \\  & SA3 & 0.961 & Strong & & & \\  & SA4 & 0.954 & Strong & & & \\ \hline \multirow{4}{*}{WTCTU} & WTCTU1 & 0.993 & Strong & & & \\  & WTCTU2 & 0.998 & Strong & & & \\ \cline{1-1}  & WTCTU3 & 0.999 & Strong & & & \\ \cline{1-1}  & WTCTU4 & 0.998 & Strong & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of validity and reliability analysis. higher than the corresponding row-column correlation coefficients, indicating that each latent variable has good discriminative validity. It should be noted that the extremely high correlation (r = 0.969) between PEU and PU may reflect conceptual overlap. It is suggested that subsequent studies refine the theoretical boundaries between the two. Overall, the measurement model has good discriminative validity in the main constructs.
## 2 Materials and Methods
### Model Evaluation  
The predictive performance of the models was evaluated using k-fold cross validation, repeated ten times, which consists of dividing the data into subsets, using one of the subsets as a test and the others to estimate the model, increasing reliability and unbiasedness during estimation [58]. The following were used to evaluate the cross-validation results: the root mean square error (RMSE), the mean absolute error (MAE), Lin's concordance correlation coefficient (CCC), and the coefficient of determination (R2).  
The modeling process was performed with R software version 4.1.3 [36], with the aid of the packages neuralnet [59], kernlab [60], Cubist [61], gbm [62], and rpart [63] for the ANNs, SVM, Cub, BRTs, and CART models, respectively. The packages caret [64] and DescTools [65] were used for evaluation.