esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Evaluation Method
### Fuzzy Comprehensive Evaluation
#### 3.5.1 First-Level FCE  
Step 1: Calculate the first-level FCE single-factor evaluation matrix \(\mathbf{R_{i}}\).  
\[\mathbf{R_{i}}=\left[r_{ijk}\right]_{n\times p} \tag{49}\]  
where \(r_{ijk}\) is the fuzzy membership degree of \(u_{ij}\) to the \(k\)th element in the comment set; \(i=1,2,\dots,N;j=1,2,\dots,n;k=1,2,\dots,p\).  
Step 2: Calculate the first-level FCE weight vector \(\mathbf{A_{i}}\).  
The weight vector \(\mathbf{A_{i}}\) of evaluation indicator (sub-criterion layer) is calculated using the quantitative TOPSIS method (based on Section 3.4.1).  
\[\mathbf{A_{i}}=[a_{i1},a_{i2},\dots,a_{in}] \tag{50}\]  
where \(a_{ij}\) is the weight coefficient of each evaluation index \(u_{ij}\).  
Step 3: Calculate the first-level FCE set \(\mathbf{B_{i}}\).  
The first-level FCE set \(\mathbf{B_{i}}\) (Equation (51)) is calculated using weight vector \(\mathbf{A_{i}}\) (Step 2) of evaluation index \(U_{i}\) and single-factor evaluation matrix \(\mathbf{R_{i}}\) (Step 1).  
\[\begin{split}\mathbf{B_{i}}&=\mathbf{A_{i}}\bullet \mathbf{R_{i}}\\ &=[a_{i1},a_{i2},\dots,a_{in}]\bullet\left[r_{ijk}\right]_{n \times p}\\ &=\left[b_{i1},b_{i2},\dots,b_{ip}\right]\end{split} \tag{51}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(n\) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline RI & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 \\ \hline \end{tabular}
\end{table}
Table 2: Average random consistency index (RI).
## 2 Materials and Methods
### Data Processing  
Emission fluxes were calculated based on CO\({}_{2}\) concentration changes (ppm min\({}^{-1}\)). Firstly, linear regression analysis was performed on 30 data [(21; 22)]. Secondly, the CO\({}_{2}\) emission flux (\(\mu\)mol m\({}^{-2}\) d\({}^{-1}\)) was calculated with Equations (1)-(3).  
\[\rm Flux(CO_{2})=K\times S \tag{1}\]  
\[\rm K=\frac{86400\times P}{10^{6}\times R\times T_{c}}\times\frac{V}{A} \tag{2}\]  
\[\rm S=\frac{\rm\Delta C}{\rm\Delta t} \tag{3}\]  
where K is the accumulation factor of the chamber (mol min ppm\({}^{-1}\) m\({}^{-2}\) d\({}^{-1}\)); S is the rate of change in CO\({}_{2}\) concentration (ppm min\({}^{-1}\)); P is barometric pressure (mbar); R is the ideal gas constant, 0.0831451 (bar L K\({}^{-1}\) mol\({}^{-1}\)); T\({}_{c}\) is the temperature inside the chamber (\(K\)); V is the net volume of the chamber (m\({}^{3}\)); and A is the net area of the chamber entrance (m\({}^{2}\)).  
Thirdly, the Michaelis-Menten rectangular hyperbola model was used to calculate NEE [(23)]. The equation used was (4).  
\[\rm NEE=\frac{(PPFD\times-P_{max})}{K_{m}+PPFD}-R_{eco} \tag{4}\]  
where NEE is the net CO\({}_{2}\) flux of the rice ecosystem (\(\mu\)molCO2 m\({}^{-2}\) s\({}^{-1}\)), PPFD is the photosynthetic photon flux density (\(\mu\)molphotons m\({}^{-2}\) s\({}^{-1}\)), P\({}_{max}\) is the maximum photosynthetic rate, K\({}_{m}\) is an adjustment constant, and Reco is the respiration rate of the rice ecosystems (\(\mu\)molCO2 m\({}^{-2}\) s\({}^{-1}\)). For this, PPFD, P\({}_{max}\), and K\({}_{m}\) data from [PERSON] et al. [(21)] were used. The daily NEE for each phenological stage is the average of the fluxes from three analyzers. To verify the normality of the data, the Anderson-Darling test was used, which turned out to be non-parametric. Spearman correlation (\(R\)) was performed between the environmental variables, NEE, and Reco. Additionally, the Mann-Whitney U test was applied to assess significant differences between the results, at a significance level of 5%.
## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper
Consequently, feature extraction is one of the major challenges in designing BCI systems; it is determined based on the features and on the appropriate transformation. Although the answer to what are the most emotion-relevant EEG features is still under investigation, power features from different frequency bands are still the most popular in the context of emotion recognition. Studies [26; 197; 218] have shown that power spectral density (PSD) extracted from EEG signals performs well on distinguishing affective states. Several machine learning algorithms have been used as emotion classifiers, such as support vector machine (SVM), K-nearest neighbors (K-NN), linear discriminant analysis (LDA), random forest, Naive Bayes (NB) and Artificial Neural Network (ANN). In general, therefore, the choice of which classification algorithm can be used when designing a BCI depends largely on both the type of brain signal being recoded and the type of application that is being controlled. However, SVM based on frequency domain features as power spectral density (PSD) is shown as the most commonly used method. Recently, Deep learning methods have been applied to the EEG-based emotion recognition filed. In [49], they proposed a deep belief network (DBN) classifier to recognize three categories of emotions (positive, neutral, and negative). Their experimental results show that the DBN models obtain higher accuracy than SVM and K-NN methods. Also, recurrent neural networks have been used in [74; 208; 229]. In [206], [PERSON] et al. proposed auto-encoder-based deep learning method to investigate emotion recognition on the SEED dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Method** & **Number of Articles** & **References** \\ \hline \multirow{6}{*}{**Condation**} & Frequency domain: power spectral density, & & [26; 49; 63; 74; 104; 105; 107; 116; 123; 149; 161; 169; 176, \\  & band power \(>>\) using Fourier Transform & & 193; 197; 200; 203; 204; 207; 208; 216; 218; 225] \\ \cline{2-4}  & Time domain: Activity, mobility and & & \\  & complexity \(>>\) using Hiorth Parameters, & & 11 & [107; 117; 200; 203; 204; 206; 213; 216; 220; 222; 224] \\ \cline{2-4}  & Fractal dimension \(>>\) using Higuchi Method & & \\ \cline{2-4}  & Wavelet domain: Entropy, Energy \(>>\) using & & \\  & Wavelet Transform & & & \\ \cline{2-4}  & Statistical features: Median, Standard & & \\  & deviation, Kurtosis symmetry, etc. & & & \\ \hline \multirow{6}{*}{**Condation**} & Support Vector Machine (SVM) & 24 & [49; 104; 106; 107; 116; 117; 157; 176; 186; 190; 193; 196, \\  & & & 197; 202; 204; 213; 216; 218; 220; 225; 226; 227; 228] \\ \cline{2-4}  & K-Nearest Neighbor (k-NN) & 10 & [49; 104; 107; 190; 204; 207; 213; 216; 218; 228] \\ \cline{2-4}  & Linear Discriminant Analysis (LDA) & 4 & [26; 123; 176; 227] \\ \cline{2-4}  & Artificial Neural Network (ANN) & 7 & [105; 176; 190; 204; 216; 223; 227] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational methods to extract and classify emotional features from EEG.
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 4 Communication with an ROS  
In this work, an ROS was used to communicate with all different systems of the robot. Software packages called _nodes_ run independently from each other. In order to exchange information, the _nodes_ send messages, enclosed in the form of _topics_. Messages are received from publishers and sent to subscribers directly from the master server of the ROS. Therefore, nodes do not directly communicate with each other.  
Figure 3 shows a reduced scheme of the nodes and their communication messages on the robotic platform. Two nodes are used to communicate with the hardware: (a) the _serial_node_ receives signals from piezoelectric-force sensors on each finger and encapsulates the signal in a message, which is sent to the ROS; and (b) the _dynamixel_manager_ reads from the ROS the commands for each motor and returns messages about the state of these. A node called _Force_Position_controller_ is in charge of reading \"sensor signals\" topics and  
Figure 2: (**a**) An arm with three degrees of freedom, placed on a mobile robot (iRobot Create). On the back of the robot is a data acquisition board, as well as a 12 V battery to power the system. (**b**) Force sensors placed on each fingertip of the gripper.  
writing commands to \"motor-speed\" topics. It is at this node where all control computation is performed.
## 3 Wireless Sound-Based Channel
### Wavelet Transform  
Unlike the Fourier transform, which decomposes a signal solely into its frequency components to analyze the spectral content, the wavelet transform breaks down a signal into various scales, reflecting the level of resolution, and time locations, which represent the temporal location of the wavelet function along the time-axis. This approach provides a comprehensive analysis that encompasses both the frequency and time domains [34]. Wavelet transforms achieve high temporal resolution at the cost of frequency resolution at higher frequencies. Conversely, at lower frequencies, they offer high-frequency resolution but with reduced temporal accuracy, a behavior similar to that of the human ear [20].  
This analysis is enabled by the wavelet transform's inherent scaling and translation properties. Specifically, the scaling function facilitates a detailed examination in terms of frequency, while the translation function permits temporal analysis. Following this logic, the wavelet series can be expressed as shown in Equation (1):  
\[f(x)=\sum_{j}\sum_{k}c_{jk}\psi_{jk}(t) \tag{1}\]  
where \(c_{jk}\) are the wavelet coefficients at each of the scales and temporal positions and \(\psi_{jk}\) are the wavelet function scaled and translated, as given by Equation (2):  
\[\psi_{jk}(t)=2^{j/2}\psi(2^{j}t-k) \tag{2}\]  
To calculate the wavelet coefficients \(c_{jk}\), it is essential to select a wavelet function along with the corresponding low-pass and high-pass filters. For our analysis, we have chosen the Haar wavelet due to its simplicity and capacity to detect transients or sudden changes in an audio signal. The associated filters for the Haar wavelet, which belongs to the orthogonal wavelets family, are as follows in Equations (3) and (4):  
\[H_{Low}=\frac{1}{\sqrt{2}}[1,1] \tag{3}\]  
\[H_{High}=\frac{1}{\sqrt{2}}[1,-1] \tag{4}\]  
Here, the low-pass filter computes the sum of adjacent elements, while the high-pass filter calculates their difference. The resultant of the high-pass filter produces the detail coefficients (cD), and the post-filtered signal contains residual noise. Passing the signal through a reconstruction high-pass filter produces the signal details (D). On the other hand, the result of the low-pass filter is the approximation coefficients (cA). In these coefficients, the smoothed signal is stored and when passing it through a low-pass reconstruction filter, the approximation of the signal (A) is obtained [21].  
And the Equation (1) can be rewritten as Equation (5):  
\[f(x)=\sum_{j}\sum_{k}\big{(}cA_{jk}\phi_{jk}(t)+cD_{jk}\psi_{jk}(t)\big{)} \tag{5}\]  
where \(\phi_{jk}(t)\) and \(\psi_{jk}(t)\) are the scaling and translation function that corresponds to the \(cA\) and the \(cD\), respectively.
## 4 Classification Effect on the Dataset
### Classification Effect on Binary Classification Dataset
Although the individual classification algorithms do not exhibit high accuracy, the performance improves significantly after applying the feature concatenation method based on ANOVA. The results demonstrate that our feature extraction framework, when coupled with the XGBoost model, yields excellent performance across various evaluation metrics. The classification performance of various algorithms was compared, and the results show that our method outperforms others on multiple evaluation metrics. The balanced accuracy achieved was 0.978, indicating that the model has strong performance across both classes. The specificity reached 97.5%, demonstrating that the model is highly effective in identifying non-positive samples. The F1 score of 0.982 further emphasizes that the model maintains a good balance between precision and recall, while the precision value of 0.983 indicates that the model is highly accurate in predicting positive cases. The recall rate of 0.980 reflects the model's ability to correctly identify positive samples. Furthermore, the Matthews correlation coefficient (MCC) and Cohen's kappa coefficient, both at 0.954, suggest that the model's predictions are significantly better than random chance. The Hamming loss of 0.020 indicates that only 2% of the samples were misclassified, while the Jaccard index of 0.964 confirms a high level of agreement between predicted and actual results. These findings highlight the effectiveness and robustness of our feature extraction framework when used in conjunction with the XGBoost model. To further evaluate the impact of different classifiers on classification results, we performed experiments using MATLAB's Classification Learner tool (after sorting). The results are summarized in Table 3, which presents the performance of various classifiers in binary classification tasks. To make it easier to see which are the top 10 models of the Matlab2023b classification toolbox in the binary classification task, we visualized them in Figure 12. We compared the method in this paper with the popular EEG classification model CNN method based on deep learning, and from Figure 13, we found that the test set accuracy of the CNN method on the binary classification task was 0.93, which was not as high as the accuracy of the method proposed in this paper. This can illustrate the effectiveness of our method. The confusion matrix under the CNN method is shown in Figure 14. Therefore, our framework performs well across all indicators of the binary classification tasks. Figure 12: Accuracy % of the top ten models for binary classification tasks. Figure 13: Accuracy of the CNN method for binary classification task.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background  
There are three key factors that need to be considered in our discussion of flow correlation privacy:  
1. **Spatial flow privacy.** There should not be any addressing information in packets that can be correlated across different topological sub-network points of attachment (SNPA). If a node changes its SNPA such that it is now in a different part of the IP network, an attacker should not be able to correlate flows generated by that node while connected to a different SNPA. For privacy, the desire is for there to be completely new address values when an SNPA change results in a network topology change for a node.
2. **Temporal flow privacy.** There should not be any addressing information that can be correlated across time between different flows. An attacker should not be able to correlate multiple flows from the same node even on the same topological SNPA. For privacy, the desire is for there to be completely new address values when a new flow is created.
3. **Scope of trust.** If an entity other than the node using an address value is involved in the generation or allocation of that address value, that entity could be compromised or act with malicious intent. This, in turn, would lead to exposure of linkage information between a node and its address values. For privacy, the desire is for the scope of trust to be confined to the node itself.  
A summary of the various addressing schemes discussed in this section with respect to these key factors is given in Table 1.
## 3 Methods
### Description of Experiments
#### 3.4.4 Influence of Feature Randomness  
A recently published work [62] uses vocal acoustic features similar to the ones included in this study to detect essential tremor (ET), a neurological disorder which causes involuntary, rhythmic shaking. The authors reported an area under the ROC curve (AUC) of 0.97. Subsequently, in a letter to the editor [21], it was pointed out that the number of coefficients analyzed (6139) is much larger than the sample size of the corpus being analyzed (58). The authors of the letter repeated the classification experiment but employed random feature vectors in which each coefficient of the vectors was replaced with a random number between zero and one, obtaining an AUC = 0.90. This suggests that having many more coefficients (on the order of 100 times) than participants can provide high (and artificial) cross-validation results, even when there is no relevant information in the feature vectors. Specifically, increasing the number of features above the number of observations (recordings, in our case) increases the probability that one of the coefficients could be highly correlated with the class labels just by chance.  
In order to evaluate the influence of the feature vector length in our baseline experiments, we repeated the experiments and replaced every coefficient vector calculated using DARTH-VAT with those of random numbers ranging between zero to one. We selected the DARTH-VAT dimension since that corresponds to the longest feature vector length in our experiments. The labels and folds were kept as in the baseline experiments, and we hypothesized that classification accuracy would increase in a fashion inversely proportional to the size of each corpus. Then, a grid search was performed over the same range of hyperparameters used in the baseline.
## 4 Simulation and Performance Evaluation
### Performance Analysis under Different Parameters
#### 4.2.2 The Impact of Event Area's Radius \(r_{e}\)  
We study the impact of event area's radius \(r_{e}\) on the network performance. The event area's radius increases from 20 m to 60 m. The parameters we set are shown as follows: (1) the number of all nodes is 500; (2) \(Th\) is 70%; (3) The event area changes by period, and the period is 800 s; (4) one source node produces one data packet in one second; (5) the velocity of sink is 5 m/s. The performance metrics here are network's lifetime, residual energy's variance and number of data packets collected.  
In Figure 15, black line represents the impact of \(r_{e}\) on lifetime of network. The network's lifetime decreases by radius increasing. The network's lifetime decreases fast firstly. Then speed of decrease becomes slow after that. As radius becomes larger, more nodes become source nodes. That means sink can collect more data packets in the same time. However, the lifetime of network is decreasing. The blue line represents the whole number of data collected in lifetime. We can find that the number of data packets collected is also decreased, and it decreases fast from 40 to 50. We can study the balance condition of network from red line. We can find that the red line is generally on the rise. Hence the network is balanced well when the radius is 20.  
According to the three performance metrics, we can find that our algorithm can have a better performance with a small event area.
## 3 Results
### Hfq Is Required for Antibiotics Persistence in Y. ruckeri
Exponential (**f**) and biofilm resuspended (**g**) cells of the WT and \(\Delta\)_lifq_ strains were exposed to 1 mg mL\({}^{-1}\) ampicillin, 0.05 mg mL\({}^{-1}\) oxolinic acid, 0.05 mg mL\({}^{-1}\) ciprofloxacin, 0.01 mg mL\({}^{-1}\) forfenicol or 1 mg mL\({}^{-1}\) polymyxin B for 24 h, and the survival of persister cells was measured as CFUs per 1 mL at 24 h. Asterisks represent statistical differences with respect to the wild-type strain (\({}^{*}p<0.01;^{**}p<0.001;^{**}p=0.0001\)). Data represent the means \(\pm\) standard deviations (\(n=3\)). found that the single-gene knockout of _relA_ affected the persister level to the same extent as the double-gene knockout mutant \(\Delta\)_relA_\(\Delta\)_lfq_ but to a greater extent than the single-gene knockout of _lfq_, for all the antibiotics treatments (Figure 4a-e). These results indicates that the _relA_ gene is epistatic over _lfq_ regarding this phenotype. Figure 4: Persister cells production of the \(\Delta\)_relA_ and \(\Delta\)_relA_\(\Delta\)_lfq_ strains exposed to antibiotics. Stationary cells of the WT, \(\Delta\)_lfq_, _ArelA_ and _ArelA_\(\Delta\)_lfq_ strains were exposed to 1 mg mL\({}^{-1}\) ampicillin (**a**), 0.05 mg mL\({}^{-1}\) oxaliplatin acid (**b**), 0.05 mg mL\({}^{-1}\) ciprofloxacin (**c**), 0.01 mg mL\({}^{-1}\) Borrelinol (**d**) or 1 mg mL\({}^{-1}\) polymyxin B (**e**) for 24 h, and the survival of persister cells was measured as CFUs per 1 mL at 24 h. Asterisks represent statistical differences with respect to the wild-type strain (\({}^{\star}\)_p_ < 0.01; \({}^{\star\star}\)_p_ < 0.001; \({}^{\star\star}\)_p_ = 0.0001). Data represent the means \(\pm\) standard deviations (\(n=3\)). To further investigate the interaction between the _hftq_ and _reLA_ genes, the replication in bacterial cultures and the ATP levels of stationary phase cells, from the single and double \(\Delta\)_relA_ backgrounds strains, were determined as described above. We observed that the faster replication and hypermetabolic status previously detected in the \(\Delta\)_hftq_ strain was increased in the absence of _relA_, in both the single and double deletion backgrounds (\(\Delta\)_relA_ and \(\Delta\)_relA_\(\Delta\)_lfq_ strains), confirming the epistatic effect observed above (Figure 5a,b). Together, these data indicate that the regulatory effect of RelA on persistence, energy metabolism and bacterial proliferation exceeds that of Hfq.
## 2 Proposed Methodology
The last one, Scenario IV, is the case where both E-OLTCs and LVRs are equipped with automatic tap changing, so they both serve as real-time voltage regulators. This paper is not concerned with mechanical tap-changing transformers which require de-energization when operated. The results from each of these scenarios contain inputs and outputs which are then exported, prepared, and used as examples to train the DNN. The training examples are first shuffled, then separated into a training set (around 75%) and a test set (around 25%). The training set will adjust the weights of the DNN and make the model learn by maximally decreasing the loss function. After the model has been trained, the DNN is fed the test set which it has not seen before, so it will predict the outputs by itself. The performance of the ML model is measured using the loss function and by being compared to tap positions found by the deterministic method through PowerFactory. A simplified methodology flowchart is provided in Figure 3.  
Figure 2: The 10 kV feeder model first half, including the added LVRs.