esa-sceva/satcom-chunk-collection huggingface dataset:
# Explore Long-Range Context Features for Speaker Verification
## 2 Related Work  
### Speaker Verification System Overview  
As introduced in Section 1, two-stage speaker verification systems based on neural networks, which consist of a front-end and a back-end, are the predominant approach. As shown in Figure 1, the front-end, that is, the feature extractor, that extracts speaker embeddings from utterances, comprises three key components, a frame-level feature extractor, a pooling layer, and a segment-level feature extractor. The frame-level feature extractor uses CNN and TDNN to extract frame-level speaker information, the pooling layer converts frame-level features to segment-level features, and the utterance-level network further maps the utterance-level features to obtain speaker embeddings. In the training stage, the parameters of the feature extractor are optimized by using the softmax loss function. While in the test stage, speaker embeddings generated by the feature extractor are used to calculate the similarity between the enrollment utterance and the test utterance.  
X-vector [2] and ResNet [3] are two of the most representative speaker feature extractors. X-vector employs a multi-layer TDNN structure, which models temporal context information through the time-delay units. In contrast, ResNet uses stacked convolutional layers and incorporates a residual connection to mitigate the vanishing gradient problem caused by deep neural network architectures. Based on the x-vector, various TDNN architectures have been proposed, such as ETDNN [30], FTDNN [31] and D-TDNN [9], which improve performance by deepening the network and e  
Figure 1: Speaker verification system diagram.  
text modeling capability. The FTDNN architecture, in particular, employs singular value decomposition to reduce the number of parameters in the model. Furthermore, the ECAPA architecture utilizes inter-layer channel splitting and stacking to enhance its modeling capabilities. One of the most well-known variations of ResNet is Res2 Net [12], which splits the multi-group feature maps into multiple groups and stacks them together, resulting in substantial improvements in speaker verification tasks.
## 3 Results and Discussion
### Interference Experiments  
The influence of foreign substances, such as sugars, metal ions, amino acids, vitamins, and reductive acids was tested in the 4 \(\upmu\)g/mL VC solution. The tolerance ratios of the maximum concentrations of the foreign substances that caused an approximately \(\pm\)5% relative error in the VC concentration are listed in Table 2.  
The interference experiment demonstrated that light metal ions, sugars, \(Fe^{2+}\), and \(NH_{4}^{+}\) could be allowed at very high concentrations, while some substances, such as those with reducibility and heavy metal ions, could be allowed only at low concentrations. However, the common plants generally contain little or no heavy metal ions, and thus do not affect the measurement. In addition, in the study combining the voltammetric technique with iodometry (of which the main chemical process was also the redox between \(I_{2}\) and VC), [PERSON] et al. treated natural samples with ascorbate oxidase and carried out iodometric titration [26].  
Compared with the average value of untreated samples (113 mg/100 g), the mean value of the treated samples was only 1.5 mg/100 g. This result proves that, although there are other reducing substances, the degree of reaction was very low, accounting for only 1.3% of the total reaction. Therefore, the interference of the reducing substances could be ignored.
## 4 Results
### Evaluation Metrics  
The evaluation of our aircraft recognition results relies on commonly used pixel-based metrics, similar to those employed in studies involving building recognition. Specifically, we utilize the pixel accuracy and mean IoU scores to assess the model's performance, as shown in Formulas (1) and (2). The terms True Negative (TN), False Positive (FP), True Positive (TP), and False Negative (FN) are used to assess the performance of our model. TP represents the count of pixels that have been correctly predicted as aircraft when the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Methods** & **Value** \\ \hline Horizontal flip & True \\ Height shift & 0.05 \\ Width shift & 0.05 \\ Image zoom & 0.05 \\ Image shear & 0.05 \\ Image rotation & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data augmentation method applied during training phase.  
actual labeled pixels are also aircraft. FP represents the count of pixels that have been incorrectly predicted as aircraft when the actual labeled pixels represent the background. The term \"TN\" refers to pixels that have been appropriately identified as not falling inside the relevant class, for example, the backdrop or non-aircraft pixels. Last but not least, FN stands for the number of pixels that have been wrongly classified as background when they really represent an airplane. These words are crucial for determining if the model correctly identifies airplanes in the semantic segmentation outputs. The mean IoU quantifies the intersection over union for each class and computes their mean, while the pixel accuracy assesses the proportion of properly predicted pixels to all pixels. These metrics aid in assessing the precision and potency of our model's ability to identify airplanes from the semantic segmentation outcomes.  
A key criterion for assessing the precision of semantic aircraft picture segmentation is the mean IoU score. It calculates the accuracy by dividing the union area, which includes both the ground truth and the detected aircraft, by the intersection area between the ground truth and the detected aircraft masks. The IoU's \"mean\" refers to the average score determined over all classes, which are normally \"aircraft\" and \"background\". This measure reveals how successfully the model recognizes and segments aircraft in the photos.  
\[\text{Pixel}-\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+ \text{TN}+\text{FN}} \tag{1}\]  
\[\text{Mean}-\text{IoU}\quad=\sum_{\text{c}\in(\text{Aircraft},\text{Background })}\frac{\text{target}(\text{c})/\text{prediction}(\text{c})}{\text{target}( \text{c})\cup\text{prediction}(\text{c})} \tag{2}\]  
\[=\sum_{\text{c}\in(\text{Aircraft},\text{Background})}\frac{\text{TP}(\text{ c})}{\text{TP}(\text{c})+\text{FP}(\text{c})+\text{FN}(\text{c})}\]
### Requirements of Underwater Communication  
The harshness of the ocean makes it even more difficult for researchers to design efficient protocols for UWSN, in this section, we will discuss the staple requirements of underwater acoustic communication. In [13], the authors have pointed out the following five major requirements of routing protocols for UWSN.
## 7 Concluding Remarks  
Wireless sensor networks (WSNs), which are one of the attractive topics in academia, are penetrating the physical world through different application fields that are impacting our daily lives. Thus, there are many opportunities to develop real applications for WSNs. Data aggregation techniques increase the lifetime of sensor nodes providing continuous services to users because they reduce the number of message transmissions. On the other hand, many applications require the privacy of sensor data during their collection in the sink. In this paper, we first explored the design principles, issues and challenges for PPDA protocols. Then, we investigated different protocols to achieve privacy-preserving data aggregation in WSNs and classified them into _homogeneous_ and _heterogeneous protocols_ on the basis of the types of data aggregators. The former uses regular sensor nodes as data aggregators whereas the latter uses special nodes - different from regular sensor nodes in terms of available resources - as data aggregators. We further categorized these protocols into four groups on the basis of the techniques applied to achieve data privacy. They are _perturbation, shuffling, PH and hybrid_. After that, we presented a comprehensive comparison of the existing PPDA protocols in terms of many meaningful metrics, such as supporting type of privacy and energy consumption. Our comparison result provides an insight into the strengths and weaknesses of the existing PPDA protocols for WSNs. Finally, we provided some future research directions in the field of privacy-preserving data aggregation for WSNs. We believe that our comparison result along with future research directions will definitely encourage many researchers improving the existing protocols and designing new PPDA protocols for achieving privacy-preserving data aggregations in WSNs, efficiently.  
This research was financially supported by the Ministry of Education, Science Technology (MEST) and Korea Institute for Advancement of Technology (KIAT) through the Human Resource Training Project for Regional Innovation. This work was also supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No. 2010-0000202).
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods
### Metrics and Algorithms
#### 2.3.2 Linear Regression  
Within Python, the _sklearn_ ordinary least squares (OLS) linear regression algorithm was applied as it is a common algorithm for regression problems. OLS linear regression is a statistical method used to model the relationship between the predictors (channels) and the dependent variable (quantum phase) by fitting a linear equation to the data. The algorithm minimizes the sum of squared differences between the observed and predicted values of the dependent variable. Other forms of linear regression, such as ridge regression or lasso regression, were not used as the dataset did not suffer from multi-collinearity or have need for feature selection [15]. Within the _sklearn_ OLS algorithm, the default Moore-Penrose pseudo-inverse solver was used, and all features were used without interaction terms.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 1 Introduction
Moreover, we aim to jointly optimize the receive power of the legacy and IoT transmissions by maximizing the sum rate of the IoT users under the condition that the legacy satellite system can meet its performance requirement. To solve the formulated problem, we prove that the sum rate of the IoT users is quasi-concave over the legacy satellite user's receive power. Based on such characteristics, we derive the optimal receive powers for these two systems. With the designed power allocation scheme, IoT users can achieve information transmission on the basis of ensuring the performance of the primary system. Finally, extensive simulation results are provided to validate the effectiveness of the designed power allocation scheme, which shows that IoT transmission can achieve information transmission on the basis of ensuring the performance of the primary system. The rest of this paper is organized as follows. In Section 2, we build up the cognitive LEO satellite communication system model. In Section 3, we derive the asymptotic SINR for the legacy and IoT systems. In Section 4, the resource allocation problem is formulated and solved. Section 5 presents extensive simulation results which verify our theoretical analysis and validate the effectiveness of the proposed scheme. Finally, the paper is concluded in Section 7. The notations used in this paper are listed as follows. The lowercase, boldface lowercase, and boldface uppercase letters \(x,\mathbf{x}\), and \(\mathbf{X}\) denote a scalar variable (or constant), vector, and matrix, respectively. \(\mathcal{CN}(\mu,\Sigma)\) denotes the complex Gaussian distribution with mean \(\mu\) and variance \(\Sigma\). Notations \(\mathbf{X}^{T}\) and \(\mathbf{X}^{H}\) denote the transpose and conjugate transpose of matrix \(\mathbf{X}\), respectively. The notation \(\mathbf{X}^{*}\) denotes the optimal value of variable \(\mathbf{X}\). \(\mathbf{I}_{N}\) denotes the \(N\)-dimensional identity matrix. Notation \(\mathbb{E}(\cdot)\) denotes the statistical expectation. The notation \(\mathbf{A}\circ\mathbf{B}\) denotes the Hadamard (element-wise) product.
## 3 Proposed Td-MPPT Method
Because of this, the maximum overshooting time is not fixed, and under certain circumstances, a direct control MPPT cannot not show underdamped behavior. It must be noted that the Td-MPPT algorithm would avoid drift even under these conditions because of the principle behind algorithms that perform additional measurements as [19; 20]. Furthermore, it would operate as a regular P&O under steady-state irradiance, performing two perturbations in the same direction around the MPP since the additional measurement would not provide additional information about the PV-curve. In this research, the natural oscillation frequency of the DC-DC converter will be used to approximate \(T_{x}\). An autotune stage can be added at system startup to detect \(T_{x}\) for a specific system. The proposed algorithm was designed to not require additional sensors compared to the traditional P&O. The added computational burden remains marginal as it only requires two extra multiplications compared to the conventional P&O algorithm if the condition specified in equation 4 substitutes the division to calculate \(dp_{1}\) and \(dp_{2}\). As specified in [34], P&O can be implemented in a low-cost embedded board such as an STM32F103 Blue pill board. A system based on this low-cost microcontroller requires only one clock cycle per  
Figure 2.7 Flowchart of the proposed Td-MPPT algorithm. Figure 2.8 Test platform for simulations. multiplication [34], meaning that would only need two additional clock cycles compared to the implementation of P&O. For this reason, a system designed to operate using the classical P&O could be reprogramed to run Td-MPPT at no extra cost. Selecting a microcontroller with at least two hardware analog to digital converters (ADC) is crucial for obtaining the maximum tracking speed as it would allow converting two analog signals simultaneously. The three algorithms in this work would benefit from this feature as they use the same two sensors to operate. However, this is even more important for the Td-MPPT as it samples a rapid change transient response. Since the Td-MPPT algorithm is based on a conventional approach, it is unable to guarantee tracking of the global maximum power point (GMPP) under nonuniform irradiance. A hybrid algorithm between a GMPP search technique and the proposed algorithm could be implemented to solve this shortcoming. These hybrid algorithms have shown promising results in tracking GMPP by using conventional hill-climbing MPPT hybrid algorithms [17; 25; 26; 27; 28; 29]. Nonetheless, these techniques involve a higher computational burden and would require a more powerful microcontroller due to the high number of divisions, multiplication, summation of series of numbers, and power calculations used in their structure [34]. However, some are more affordable techniques than others [35]; thus, a combination of a relatively inexpensive GMPP approach and Td-MPPT could be implemented at a lower cost than algorithms with equivalent efficiency.
## 5 Performance Analysis
### Analytical Model
A developed Matlab code is used to run all the detailed balance equations and calculate the probabilities of being in any defined state. After finding the probability of each feasible state, the transition matrix Q is derived. This transition matrix contains the transition rates between every two-system state. Having the entire steady-state probabilities \(\pi_{\text{ij,k,m,n}}\) and the transition rates between every two states, the steady-state probability vector \(\pi\) is derived.
## 2 Preliminaries
### Decision Trees  
#### 2.2.1 Basic Decision Trees  
Decision trees are supervised learning methods that are primarily used for classification tasks, and a tree structure is created while learning from data (Figure 3). When predicting the label \(y\) of \(\mathbf{x}\), the process begins from the root of the tree, and the corresponding leaf is searched for while referring to each feature of \(\mathbf{x}\). Finally, through this referral process, \(y\) is predicted.  
The label determined by the leaf is derived from the dataset \(D\) used to generate the tree structure. In other words, after the tree structure is created, for each element \((\mathbf{x}_{i},y_{i})\) in dataset \(D\), the corresponding leaf leaf is found, and the value of \(y_{i}\) is stored. If \(y_{i}\in Y=\{0,1\}\), then in each leaf, the number of \(y_{i}\) labeled 0 and the number of \(y_{i}\) labeled 1 are preserved. More precisely, \([n(\texttt{leaf}^{(1)}),n(\texttt{leaf}^{(0)})]\) are preserved for each leaf leaf, where \(n(\texttt{leaf}^{(0)})\) and \(n(\texttt{leaf}^{(1)})\) represent the numbers of data points with label \(y\) that have values of 0 and 1, respectively. Table 4 shows the notations used in the paper.  
For a given prediction \(\mathbf{x}\), we first search for the corresponding leaf, and it may be denoted as 1 if \(\frac{n(\texttt{leaf}^{(1)})}{n(\texttt{leaf}^{(0)})+n(\texttt{leaf}^{(1)})}> \frac{1}{2}\), and 0 otherwise. Here, the threshold 1/2 can be set flexibly depending on where the decision tree is applied, and when providing the learned  
Figure 3: An example of a decision tree concerning a dataset with two labels, e.g., 1 and 0. The vectors in the leaves show the number of data points being classified, in which the two terms of each vector correspond to the number of data points labeled 1 and 0, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zip** & **Age** & **Nationality** & **Disease** \\ \hline
130 ** & \(<\)30 & \(\ast\) & Heart \\
130 ** & \(<\)30 & \(\ast\) & Flu \\
1485 * & \(<\)30 & \(\ast\) & Cancer \\
1485 * & \(>\)40 & \(\ast\) & Heart \\
1485 * & \(>\)40 & \(\ast\) & Flu \\
130 ** & 30–40 & \(\ast\) & Cancer \\
130 ** & 30–40 & \(\ast\) & Fluor \\ \hline \hline \end{tabular} The * and ** in the Zip column mean that the last one and two digits of the original data are hidden, respectively; while the * in the Nationality column means no partitioned for the corresponding attribute.  
\end{table}
Table 3: \(\ell\)-diversity (\(\ell=2\)) after \(k\)-anonymity of the dataset shown in Table 1.  
decision tree to a third party, it is possible to pass \(n(\texttt{leaf}^{(0)})\) and \(n(\texttt{leaf}^{(1)})\) together for each leaf leaf. In this paper, we considered the security of decision trees in such situations.  
Generally, the deeper the tree structure, the more likely it is to overfit; thus, we frequently prune the tree, and this technique was employed to preserve data privacy within this paper.
## 3 Results
### Initial Sticking Coefficient, So  
Equation (5) was used to compute the initial sticking coefficient from a half coverage length of the substrate [17]. The initial sticking coefficient (S\({}_{0}\)) governs the slope of the leading front of the thickness profile. The slope was found to be 0.035. It yields an initial sticking coefficient of 4.257 \(\times\) 10\({}^{-3}\). The relation at the slop normalized to be \(\widetilde{z}=zL_{x}\) (1D, one dimensional approach) at the point of half coverage \(\theta=\frac{1}{2}\) along the \(x\)-direction, Equation (6).  
\[\left|\frac{\partial\theta}{\partial\widetilde{z}}\right|_{\theta=1/2}\approx \sqrt{s_{0}/13.9} \tag{5}\]  
\[\left|\frac{\partial\theta}{\partial\widetilde{x}}\right|_{\theta=1/2}\approx \sqrt{s_{0}/13.9} \tag{6}\]  
The initial sticking coefficients of TMA found from other methods and substrate structures are listed in Table 2. This study found an initial sticking coefficient \(S_{0}\) = 4.257 \(\times\) 10\({}^{-3}\) that fits in the range of (2-7) \(\times\) 10\({}^{-3}\) found by [PERSON] et al. [17]. The method using a slope at a half the surface coverage is the simplest way of computing the sticking coefficient.  
Figure 5: **(a)** Energy minimization of the system and (b) energy distribution after the first cycle along the growth length (\(z\)-direction).  
Figure 6: Surface after relaxation indicating -OH surface hydroxyls **(a)** -OH species attach to the surface of Al and strengthen its coordination to it **(b)** surface coverage across the substrate along the \(x\)-direction.  
Therefore, the \(S_{0}\) found using an MD relaxation approach fits well with the same method from the literature like the high-aspect-ratio of trenches that was utilized by [PERSON] et al. [17].
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### The Triggering Phase  
The flow table saturation attack starts with generating flow rules. For the default fixed timeout setting, the typical attack method is effective, because it can trigger flow rules with just one packet. Since the timeouts of all flow rules are identical, we omit the process here. When the controller sets different timeout values, the attacker needs to filter the flow rules. If the timeout value is large enough, the required rate of attack traffic can be very low. Thus, the primary purpose of this phase is to generate flow rules with large idle timeout values based on the timeout strategies.
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### SUID Dataset Evaluation  
In this section, we present and conduct quantitative experiments on 30 randomly selected pairs of images, choosing generated underwater images with different color shifts, turbidity, and low light in comparison to standard images in a subjectivity assessment of the 30 images.  
As illustrated in Figure 6, UMMC and MLLE had the best overall performance in terms of visual representation in the SUID dataset. Compared to MLLE, UMMC handles the foggy and turbid images with average results, but the images are more detailed. EUICCCLF shows unwanted color distortion. PCDE makes the images too dark overall. UIESS images show non-yellow tones overall. BRUIE removes the fog better, with noise and localized over-darkness in some of the images. The UW-AAE enhancement is mediocre and does not effectively deal with color casts and only slightly improves them. UWCNN effectively improved the blue-green bias, but new color problems arose and it did not defog well.  
Table 2 shows the evaluation of the SUID synthetic dataset, and the results for both data demonstrate the high visual quality of the processing effect of our method. It is also verified that in the subjective assessment, our enhancement results in richer details and a reduction in the loss of structural information compared to other images.  
Figure 5: A subjective comparison of the UIEB dataset. From left to right are the input image, as well as UW-AEE, EUICCCLF, MLLE, PCDE, UWCNN, UIESS, BRUIE, the proposed UMMC and the reference image.