esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 VR-PEKS Scheme and Security Definition
### Security Model
#### 4.3.1 KC-IND-CKA Security  
The KC-IND-CKA security is defined by the following interactive game between adversary \(\mathcal{A}\) and challenger \(\mathcal{C}\).  
(1) The challenger \(\mathcal{C}\) inputs a security parameter \(\lambda\) and calls the _KeyGen_ algorithm to generate DR's key pair \((pk,sk)\) and key \(k\). Then, he sends \(pk\) to adversary \(\mathcal{A}\).  
(2) The challenger \(\mathcal{C}\) generates a keyword ciphertext \(CT_{w}\) for keyword \(w\) chosen by the adversary \(\mathcal{A}\) and sends it to \(\mathcal{A}\).  
(3) The challenger \(\mathcal{C}\) generates the keyword trapdoor \(T_{w}\) for keyword \(w\) chosen by the adversary \(\mathcal{A}\) and sends it to \(\mathcal{A}\).  
(4) Adversary \(\mathcal{A}\) sends two keywords \(w_{0}\), \(w_{1}\) to challenger \(\mathcal{C}\) that he wishes to challenge, with the restriction that adversary \(\mathcal{A}\) has not requested trapdoor \(T_{w_{0}}\) or \(T_{w_{1}}\) before. After receiving the keywords, the challenger \(\mathcal{C}\) chooses a random bit \(b\in\{0,1\}\) and sends the ciphertext \(CT_{w_{0}}\) to adversary \(\mathcal{A}\).  
(5) The adversary \(\mathcal{A}\) can continue to adaptively choose any keyword \(w^{\prime}\) to request its ciphertext \(CT_{w^{\prime}}\) and trapdoor \(T_{w^{\prime}}\) as long as \(w^{\prime}\
eq w_{0},w_{1}\).  
(6) The adversary \(\mathcal{A}\) outputs a guess \(b^{\prime}\in\{0,1\}\) of the keyword ciphertext. When \(b^{\prime}=b\), the adversary \(\mathcal{A}\) wins the game. The possibility that adversary \(\mathcal{A}\) can win the game is defined as  
\[Adv_{\mathcal{A}}^{KC-IND-CKA}(\lambda)=\left|\Pr\left[b^{\prime}=b\right]- \frac{1}{2}\right|\]
## 3 Spectrum Management Issues
### Cognitive Radio
#### 3.2.4 Channel Estimation and Optimization  
The limiting factor that has been left as a challenge is designed as a multilayer complex algorithm for the more agile cluster. In [122], a channel status evaluation parameter, i.e., Signal-to-Noise Ratio (SNR), is observed. The research is conducted by implementing the Second- and Fourth-Order Moment technique based on Kalman filter theory. The simulation proves that in the range of SNR (0-15 dB), the estimation and prediction methods exhibit low error rates with a high prediction error of up to 0.14 dB. Therefore, this method is viable and can be utilized to estimate the channel quality of the CR system. The authors in [123] have presented an energy-efficient resource allocation for the CR-enabled 5G network and proposed an alternative optimization framework to optimize the variables of subcarrier assignment and power allocation. It has adopted a heuristic subcarrier assignment and a convex approximation technique. The results are estimated to achieve efficient energy efficiency in comparison with the conventional resource allocation scheme. However, the proposed approach is based on some impractical assumptions, such as a single user in one cell, a single antenna for transmitter and receiver, and well-synchronized PUs and SUs.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 2 Synthesis and Characterization of DWNTs
### The Catalytic Chemical Vapor Deposition Method
#### 2.2.1 The Supported Catalyst Method
(**g**)-(**i**) Vertically-aligned DWNTs grown from a thickness-controlled Fe film. Reproduced with permission from [116], Copyright Nature Publishing Group, 2006. (**g**) SEM image, (**h**) TEM image and (i) wall number and diameter distributions of CNTs for various Fe film thicknesses. (**j**)-(**l**) Layer number controlled by sulfur addition. Reproduced with permission from [120], Copyright Elsevier, 2007. TEM image of a SWNT nucleating from Fe nanoparticles with the area activated by FeS-Fe eutectic formation (**j**), and the proposed growth model for (**k**) SWNTs and (**l**) DWNTs by sulfur addition. with a high selectivity of 84% without any post-purification (Figure 6b-e). While the other molecular reaction enhancer, benzaldehyde, also yielded MWNTs with a prolonged catalyst lifetime (Figure 6c-f), it was proposed that multilayer formation originates from the aromatic structure-based \(\pi\)-\(\pi\) interaction between dissociated reaction-enhancer molecules. In the case where a planer substrate was used as a supporting material (such as SiO\({}_{2}\)/Si, Al\({}_{2}\)O\({}_{3}\)), the resulting film of metal nanoparticles is usually deposited on the film by a physical method, such as sputtering, thermal evaporation, electron beam evaporation, _etc._ The layer number of CNTs is usually controlled by the thickness of the catalyst metal nanoparticle film. [PERSON] _et al_. [PERSON] _et al_. (2016) prepared a thin film of Fe, varying its thickness by the sputtering technique, and the synthesis reaction was carried out over the substrate. By using the water-assisted CVD process, 2.2 mm-long vertically-aligned DWNTs were grown with a selectivity as high as 85% (Figure 6g-h) [127]. The number of walls of the CNTs thus obtained was precisely controlled by the thickness of the Fe thin film, and this approach was used to get the optimum thickness for the selective growth of SWNTs and for DWNTs. When the thickness of the Fe film is below 1.2 nm, SWNTs were predominantly grown, whereas DWNTs were predominantly grown when the thickness was thicker than 1.6 nm, with the transition region occurring between 1.2 nm and 1.6 nm (Figure 6i). This threshold sensitively varied when the purity of the sputtering target was changed, but the same trend was observed. The selective growth of DWNTs was also achieved by optimizing the Fe concentration of the carbon source [PERSON] _et al_. (2016).
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Measurement Techniques  
Delay and data rate measurement tests are carried out over the different use cases. Delay is measured by attaching timestamps to the train monitoring messages, which makes it possible to quantify the E2E latency, including the delay introduced by transport and application levels. It is remarkable that this method supports measures over MPTCP because the MQTT protocol maintains the same TCP connection during the whole dialogue. Similarly, the iperf3 traffic generator is used to measure the instant data rate of the communication, allowing us to set up TCP flows and taking advantage of the MPTCP subsystem.
## 3 Results  
The classification models, KNN, SVM, RF, and MLP, were trained using Scikit-learn, an open-source machine learning Python library. All models were trained with 10 Skfold, and a GridSearch was implemented to fine-tune the hyperparameters. The accuracy, precision, recall, specificity, F1-score, AUC-ROC, AUC-PR, Cohen's Kappa, and MCC performance metrics calculated from the confusion matrices of each model are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Performance Metrics**} \\ \cline{2-10}  & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **AUC-ROC** & **AUC-PR** & **Cohen’s Kappa** & **MCC** \\ \hline SVM & 98.17 & 98.2 & 97.97 & 97.3 & 98.07 & 99.85 & 98.89 & 97.96 & 97.96 \\ RFC & 96.67 & 96.62 & 96.35 & 97.14 & 96.46 & 99.94 & 99.42 & 96.29 & 96.3 \\ MLP & 98.62 & 98.59 & 98.41 & 98.67 & 98.48 & 99.99 & 99.9 & 98.47 & 98.47 \\ KNN & 91.28 & 91.42 & 90.4 & 94 & 90.38 & 99.06 & 95.55 & 90.29 & 90.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics of all classification models using the test dataset.  
The performance metrics in the figures above and below show the evaluation of the four ML models trained and tested on a test dataset and provide information about how well each model performed in terms of accuracy, precision, recall, specificity, F1-score, AUC-ROC, AUC-PR, Cohen's kappa, and Matthews correlation coefficient (MCC).  
According to Figure 5, a confusion matrix of the classification of the SVM model, out of 872 predictions, the overall accuracy was 98.17%, in which 15 misclassifications were found, out of which four instances were misclassified as Starkrimson.  
Examining the results of the RFC in Figure 6, having an accuracy of 96.67%, a total of 20 misclassifications occurred. Similar to SVM, the highest confusion was between the Red Braeburn and Starkrimson varieties.  
As shown in Figure 7, the MLPs confusion matrix shows the highest accuracy of 98.62% and the least misclassifications of 11 instances. Unlike all three models, the highest confusion was among the Red Braeburn and Fuji varieties.  
Figure 5: SVM confusion matrix.  
Figure 6: RFC confusion matrix.  
Figure 8 also represents the confusion matrix according to KNN. The model achieved the lowest accuracy rate of 91.2% compared to the remaining three. A total of 78 misclassifications were observed, of which 18 instances belonging to Red Braeburn were confused with Starkrimson.  
As detailed in each table above, confusion matrices representing the classification results of all models for 10 different apple varieties were tested using a separate test dataset.  
The matrices represented actual classes in their rows, and the predicted classes are provided in the columns with the number of instances that were classified into each class given in each cell. In all the matrices examining cells with the highest off-diagonal values, we can observe that in all four models, Red Braeburn and Starkrimson are the most often confused with each other, likely due to the natural resemblance of the two varieties.
## 2 Experimental Setup and Production of the Datasets
### EEG Data Preprocessing and Accommodation  
EEG data preprocessing is conducted as follows: The recorded EEG data are baseline corrected and filtered. Baseline correction is carried out using a first order Savitzky-Golay filter to produce a signal approximation that is then subtracted from the original signal. In that case, the baseline correction is  
Figure 1: (**a**) 64 electroencephalogram (EEG) channel layout used in the experiments. Reference electrodes attached to the ears; (**b**) Virtual disposition of the six sound directions with respect to the user. A stimulus is being produced from Direction 3; (**c**) Task constitution.  
conducted for the period from \(-100\) ms before the stimulus onset until the end of the trial (i.e., end of the silent period after the stimulus offset). This then becomes an example in the training or testing datasets.  
For the filtering process, we use Butterworth coefficients to make a bandpass filter with low and high cutoff frequencies of 0.1 Hz and 8 Hz, respectively. Once the correction and filtering are completed, the data are then down-sampled to 25 Hz to reduce the size of the generated examples.  
To generate the training and test sets that will be input into the CNN, trials are divided into two groups, randomly: those with and without the target stimuli. Each trial constitutes an example in the training or test set, so there are around 180 examples for each session. As there are 12 sessions for each task and a sixth of the trials correspond to when the stimuli were heard, a total of approximately 360 target and around 1800 non-target examples can be obtained for a single subject in one task. The target and non-target examples are distributed as closely as possible into a 50/50 relation among the training and test sets. Regardless of the trial length, the examples have a matrix shape of \(28\times 64\), which corresponds to 1100 ms of recordings along the 64 EEG channels after the stimuli were presented. This is done to assure each example contains the same amount of information.
## 6 Conclusions  
The automated collection of data streamed through sensor deployments has led to a massive increase in the quantity of environmental and other data available. The real-time requirement of streamed data and its sheer quantity means that automated data QC is necessary to ensure that the data collected is fit for purpose.  
We have designed and implemented a novel prototype expert system for automated data quality assessment, which incorporates not only data statistics, but also the expert knowledge of domain specialists. The framework has been successfully applied for evaluating and presenting the quality of data collected through a marine sensor network in South-Eastern Tasmania, Australia. Overall, in the case of the temperature sensors which are less susceptible to biofouling, the approach detailed in this paper produced error bars within 50% of the manually determined value approximately 80% of the time, and produced error bars within a factor of three approximately 99% of the time. The approach outlined here is able to assess data quality of large datasets in a timely manner and it is being applied to real-time data streaming from the TasMAN network.
## 4 Applications and Limitations
Additionally, the reliability of CFD-VR visualized data is completely dependent on the accuracy of CFD simulations, and there are several standard methods to prove the validity of the numerical simulations to neurosurgeons. Furthermore, while the real-time CFD simulation capability during the changing of geometry and flow conditions made by the surgeon has not been developed extensively, the final decision about the selection of devices and methods is made by the operating surgeon, not the computer. While the rendering of CFD data in a VR environment is not very time-consuming, the computation time of CFD simulations for some complicated cases is significant, but it can be reduced considerably (from several days to even some minutes) by high-performance computing.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 3 Results and Discussion
### Model Development and Evaluation for Full-Spectrum and Feature Bands  
Based on the previously discussed selected feature bands, we compared the modeling performance across three spectral ranges: the full spectrum (1100-2500 nm), 1100-1470 nm, and 1800-2050 nm. The 1100-1470 nm range includes the O-H stretching first overtone absorption peak of water molecules at 1450 nm, while the 1800-2050 nm range contains the O-H combination band characteristic peak at 1940 nm. Three Partial Least Squares Regression (PLSR) models corresponding to these bands were developed to predict ethylene glycol water content. The models' performance was evaluated using the training set correlation coefficient (Rc), root mean square error of calibration (RMSEC), validation set correlation coefficient (Rp), and root mean square error of prediction (RMSEP).  
Figure 3: NIR spectra of ethylene glycol samples after first-derivative smoothing preprocessing.  
As shown in Table 5, the PLSR model utilizing the 1800-2050 nm feature band outperformed the full-spectrum model, exhibiting higher Rc and Rp values alongside significantly lower RMSEC and RMSE. This improvement is primarily attributed to the inclusion of the O-H combination band (1940 nm), which demonstrates the strongest spectral response to water content. In contrast, the 1100-1470 nm band performed less effectively, showing higher RMSEC and RMSE values than the full-spectrum model. This discrepancy likely results from the weaker signal intensity of the O-H first overtone peak (1450 nm) in the 1100-1470 nm range, which is more susceptible to interference from the ethylene glycol matrix and other factors, thereby diminishing prediction accuracy.  
These findings confirm that the 1800-2050 nm band, characterized by the prominent O-H combination absorption peak at 1940 nm, is the optimal spectral interval for quantitative water analysis in ethylene glycol. While the 1100-1470 nm band is unsuitable for standalone use due to weak signals and potential interference, the full-spectrum model still offers practical value in certain scenarios, providing balanced performance.
## 5 Experimental Results
### Security Analysis Against Evasion Attacks  
Given that our system will operate in an adversarial context, it is crucial to evaluate how easily an attacker can deceive the proposed framework. This topic intersects with the study of adversarial examples. An adversarial example is a sample generated by inserting a small amount of noise into an original sample (with minimal distortion) such that it is recognized incorrectly by the targeted model [28]. As reported by [29], deep neural networks are vulnerable to adversarial examples. To assess the robustness of our method against evasion attacks, we compare it to a baseline model that utilizes manually crafted features (i.e., base features (BS-F)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Train**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-7}  & **Time (ms)** & **Speed** & **Memory (MB)** & **Time (ms)** & **Speed** & **Memory (MB)** \\ \hline Baseline & 138.8 & 7835 & 308.55 & 7.3 & 495,262 & 308.55 \\ \hline \multirow{2}{*}{AFF\_CGE} & Phase I & 159.8/Epoch & 10,209 & 578.5 & 46.7 & 154,540 & 578.54 \\ \cline{2-7}  & Phase II & 131.2 & 12,413 & 578.54 & 6.9 & 1,047,637 & 578.54 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of experimental results for time and space complexity of models.  
In our analysis, we conduct gradient descent-based evasion attacks (GD attacks) against the target classifiers under a perfect knowledge scenario. In this scenario, we assume the attacker possesses complete information about the targeted classifier, including the feature space, classifier type, and trained model parameters. We employed the fast gradient sign method (FGSM) attack [30], utilizing the Foolbox library to implement it [31]. The FGSM works by applying a perturbation to the original input \(X\) in order to generate an input \(X^{\prime}\) that the model misclassifies. A critical parameter in FGSM is epsilon, which regulates the extent of perturbation between \(X\) and \(X^{\prime}\). Thus, epsilon serves as a pivotal attack parameter in our experiments.  
Our findings, as illustrated in Figure 8, depict how the False Negative (\(FN\)) rate varies with epsilon across different attack scenarios. The experimental data reveal that as epsilon increases, the \(FN\) rate rises and eventually stabilizes. Moreover, our results indicate that attackers encounter greater difficulty in evading our approach compared to traditional methods. This suggests that our framework enhances classifier security against evasion attacks when compared to conventional techniques.
## 4 Results
### Evaluation Metrics  
This study adopted three metrics--Intersection over Union (IoU), Dice coefficient (Dice), and Accuracy (Acc)--to evaluate the segmentation performance of the model, with the calculation formulas shown in Equations (8)-(10).  
\[IoU=\frac{TP}{TP+PN+FP} \tag{8}\]  
\[Dice=\frac{2\times TP}{2\times TP+FN+FP} \tag{9}\]  
\[Acc=\frac{TP+TN}{TP+FN+TN+FP} \tag{10}\]  
where _TP_ (True Positive) represents the overlap between the actual cell area and the predicted cell area; _FP_ (False Positive) represents the region predicted as cells that is actually the background; _FN_ (False Negative) represents the region predicted as the background that is actually cells; and _TN_ (True Negative) represents the overlap between the actual background area and the predicted background area.  
Furthermore, this study also employed three metrics--Root Mean Square Error (RMSE), Mean Relative Error (MRE), and Coefficient of Determination (\(R^{2}\))--to measure the error between the label values and predicted values, with the calculation formulas shown in Equations (11)-(13).  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right)}^{2}}} \tag{11}\]  
\[MRE=\frac{1}{n}{\sum_{i=1}^{n}{\left|{\frac{{y_{i}-\hat{y}_{i}}}{{y_{i}}}} \right|}} \tag{12}\]  
\[R^{2}=1-\frac{{\sum_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right)}^{2}}}{{\sum_{ i=1}^{n}{\left({y_{i}-\overline{y}_{i}}\right)}^{2}}} \tag{13}\]  
where \(y_{i}\) represents the proportion of correctly predicted cell pixels to the total number of pixels in the image, \(\overline{y}\) represents the proportion of cell pixels in the label, and \(\hat{y}\) represents the mean of the predicted values, while \(n\) is the number of samples. RMSE reflects the deviation between predicted and true values, and is sensitive to outliers. MRE represents the magnitude of the relative error. \(R^{2}\) reflects the goodness of fit between the predicted and true data, with values closer to one indicating a better fit.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 2 Related Work
### Examples of AI-Based Malware Detection Technology
#### 2.5.2 Research on NVIDIA's DL-Based Malware Detection Technology  
As shown in Figure 7, NVIDIA conducted an experiment to detect malicious files by training a DL model with the byte information of files to detect malicious files in Windows executable files. As for the system configuration, the system for detecting malicious files consisted of preprocessing the byte information of executable files, training a DL model with the byte information, and classifying normal and malicious files [14; 15].  
To preprocess the input value for learning the DL model, the byte information (hexadecimal) of the executable file was converted into a form suitable for the input value of the learning algorithm. It learned by applying a gate structure that performed two convolutional operations on a convolutional neural network and adding a structure for classifying whether it was normal or malignant. Normal files and malicious codes were classified using the DL results.  
To derive the classification criteria, NVIDIA used the class activation map (CAM) method to find the basis for classifying executable files as malicious codes. In the learning process of the CAM method, the value that has the greatest influence on the normal/malignant classification is identified inversely to confirm the part of the input byte information that affects the classification. This process is illustrated in Figure 8 [15].  
The validity of the proposed model (Malconv) was verified by comparing its accuracy with that of the learning model using byte block and file meta information as feature information by NVIDIA.  
As seen in the experiment, NVIDIA guaranteed good detection performance even for small changes in malicious code by considering the generality, robustness, scalability, throughput, explainability, and dependency on feature information when designing a system. Additionally, for a general-purpose system, a dataset for training was used. By using only byte information, the process of extracting feature information was reduced, and a system was built that was suitable for processing a large amount of malicious code and could help administrators understand the detection results.  
Figure 7: DL-based malware classification model architecture.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 2 Materials and Methods
### Model
We also take advantage of the logarithmic scaling to define our threshold; once the cumulative sum reaches 30, any further singular values will be very small and are, therefore, discarded. Regularization strength in \(\lambda\) is thus capped at 30\(\lambda\), and all subspace dimensions that would receive higher regularization are discarded, automatically defining \(r\), the length of \(\mathbf{q}\) and the dimensions of \(\mathbf{F}\) and \(\mathbf{G}\) in the process.