esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 2 Related Work
### K-Same  
The algorithm family of k-same [21; 22; 23; 24; 25] that adopts the average face of similar faces in the whole image set to represent the resulting face provides a sequence of techniques with theoretical anonymity guarantees. In this regard, various k-same algorithms have been developed to address this challenge. [PERSON] [26] was the first to introduce the k-anonymity idea and successfully implemented it in a relational database. The idea of k-anonymity subsequently gave rise to a set of k-same algorithms. [PERSON] et al. [27] suggested a k-same algorithm that preserves the visual features of all images in a cluster by working directly in the pixel space. To deal with the problem that generated de-identified images suffer from ghosting effects, the k-Same-Model [22] was offered. As proposed by [PERSON] et al., the k-Same-Model adopts the idea of Active Appearance Models [28], which achieves better alignment between images and synchronized images appear more realistic. Proposed by [PERSON] et al., k-Diff-furthest [29] utilized a novel algorithm to address the issue that the generated de-identified facial image has minor differences. [PERSON] [30] proposed k-Same-furthest-FST. It completes the goal of identity protection by morphing the privacy-free face region and original background. What is more, this method has been proven to deliver substantial safeguarding of facial privacy within the context of the FERET database [31]. [PERSON] et al. [32] proposed the k-Same-Net scheme. By combining the principle of anonymity with GNN architectures, this approach achieves impressive visual outcomes. Reference [33] explores a novel model based on AAM. It divides the face space into two subspaces and uses the appropriate k-anonymity technique to process utility to achieve face de-identification. Although facial privacy is, to some extent, well protected by k-anonymity family algorithms, these algorithms come with substantial limitations, as each individual can only be represented once in the dataset. This may cause a decrease in identity diversity in the whole dataset.
## 2 Proposed Algorithm: DBSCAN-Based MFI and OSNR Estimation
### Flowchart of MFI and OSNR Estimation Based on DBSCAN Algorithm  
Figure 5 is the flowchart of MFI and OSNR estimation joint algorithm based on DBSCAN. First, the DBSCAN algorithm calculates the cluster number \(N\) and proportion of core objects \(R\) of input signal with two neighborhood parameters being set the same values for five MFs. The respective decision zone for cluster number is set to identify different MFs with considering that the noise (phase noise due to laser linewidth and ASE noise from optical amplifiers used in optical transmission link) may cause the cluster number to decrease. The 32 QAM and 64 QAM signals are identified with extra steps combining cluster number \(N\) and \(R\) value.  
Figure 4: The value of \(R\) varies with different patterns of QPSK signal.  
Figure 5: The flowchart of DBSCAN based MFI and OSNR estimation joint algorithm.  
After identifying the MF of input signal, the DBSCAN-based OSNR estimation algorithm is performed by using different neighborhood parameters on five MFs, respectively, with consideration of different Euclidean distances. In this stage, the \(R\) value is calculated and OSNR is estimated by the fitted fourth-order polynomial expressed by Equation (2).  
In our proposed joint method, the most computation intensive process is the DBSCAN block. So the total time complexity of our proposed method is on the order of \(O(n^{\star}log~{}n)\)[38]. Considering the proposed method does not need training large number of signal samples before performing MFI and OSNR estimation, we believe our method taking less time than the other two joint algorithms for MFI and OSNR estimation using deep neural network [29] and convolutional neural network [30].
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 2 Related Work  
### Brain Tumor Segmentation  
In recent years, research on brain tumor segmentation has been continuously developed. [PERSON] et al. [17] proposed the UNet, whose biggest feature was the U-shaped structure and the skip connection. The skip connection combined low-resolution information and high-resolution information; low-resolution information helped the category identification of objects, and high-resolution features provided accurate positions for segmentation. This network was perfectly suitable for medical image segmentation as the parameters reached 39.4 M, which also made it suitable for playing the teacher network. After UNet was proposed, a series of upgraded networks appeared, but most of them made some changes based on UNet. For example, [PERSON] et al. [18] reported an improved UNet for brain tumor segmentation that successfully avoided overfitting by using the Dice loss and data expansion. In [19], a comprehensive scheme of a long connection and a short connection was proposed. The parameter of the model was only 9.2 M, but the precision was low, so it was suitable to be a student network. [PERSON] et al. proposed DeepResUNet [20], pointing out that it was important to combine low-level semantic information with high-level semantic information using a skip connection; however, it was difficult to train such a deep neural network with limited medical training samples. The residual units were proposed to simplify the deep network, thus the model parameters were reduced to 32.6 M; however, the same problem of accuracy reduction still existed, so it was suitable to act as a student network in our experiments. [PERSON] et al. [21] indicated that there was a lot of redundant information in the map extracted from the encoder. Soft attention was proposed to add at the end of the skip connection to suppress the activation of irrelevant regions, in order to segment the medical images better; however, at the same time, it increased the computational complexity, causing the parameters to have values up to 39.7 M. This network was also suitable for playing a teacher network. Other methods, such as [22; 23; 24], also enhanced UNet to segment brain tumors better. In addition, some researchers have recognized the importance of capturing spatial continuity information, confirming that 3D brain tumor segmentation is more effective than 2D brain tumor segmentation [25; 26; 27].
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
###### Abstract
At the same time, based on the traces obtained by distributed tracing, for each business scenario, the numbers of records with the same operation on the same database are aggregated. Finally, we define operators for four basic database operations (creating, retrieving, updating and deleting) to formalize the task of database contribution assessment as a nonlinear programming problem. By solving this nonlinear programming problem, the contributions of databases in the microservice system can be gained. It should be noted that each database in the system could contain one or more tables, and the granularity of our method is the database. Our main contributions are summarized as follows:  
* To the best of our knowledge, this paper is the first research to assess the contributions of the databases to business scenarios for microservice systems based on the information observed by distributed tracing. * The method proposed in this paper considers the importance weight of business scenarios and defines operators for the four basic database operations (creating, retrieving, updating, and deleting) so as to skillfully formulate this task as a nonlinear programming problem. * The experimental results on two open-source benchmark microservice systems (i.e., Sock-Shop [15] and TrainTicket [16]) demonstrate the effectiveness and rationality of our proposed method. The rest of this paper is structured as follows. Section 2 introduces the related work about distributed tracing and contribution assessment. Section 3 defines the task and details our proposed method. Section 4 introduces the experimental settings and analyzes the experimental results in detail. Section 5 discusses some issues about the method and experiments in this paper. Section 6 provides the conclusion and future work of our study.
## 3 Results
### Passive EQ-Lumped Elements, Synthesis
#### 3.1.2 RL Eq  
A passive EQ with RL series connected in parallel to the line is next analyzed both in frequency and time domains. The values can be either determined via optimization process in MWO or can be computed in the synthesis process. The resistor is used mainly for matching purposes at the ports and the series inductor has to compensate the high frequency loss. The high level peak is at 1.066 V, so the circuit still has overshoots and undershoots in the waveform representation in Figure 16. Connecting the EQ at both ends of the line, the resulting eye diagram has a smaller peak compared to the other two variants (pre- or post-EQ for RL type).  
Figure 14: RC EQ: (**a**) schematic, (**b**) pre (**c**) post-, (**d**) pre- and post-, and, (**e**) overlaid results obtained from synthesis process. (**f**,**g**) result from selection of R1 and C1 values.  
Figure 15: Insertion (**a**) and return loss (**b**) in the RC EQ: pre- (light green), post- (dark green), pre- and post- (red), and overlaid for the synthesis results.  
The return and insertion loss for the present circuit shows in Figure 17 a resonance at 10 GHz of \(-\)19.08 dB.  
Reflection loss presents good response for combination of pre\(-\) and post\(-\)EQ placed at both ends of the microstrip line as in Figure 17.
## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT Images Using Deep Learning Models
A hierarchical deep CNN (HD-CNNs) model was proposed by embedding deep CNNs into a two-level category hierarchy, where the easily distinguishable classes were classified using a coarse-category classifier and difficult classes were classified using fine-category classifiers. Hierarchical classification models have a noticeable impact on reducing classification errors. Despite their advantages in classifying COVID-19 due to the similarity in symptoms and physical features shared with many other diseases (e.g., viral pneumonia, bacterial pneumonia, tuberculosis), only one study by [PERSON] et al. [69] has used a deep hierarchical model for the classification of COVID-19. The study leveraged deep learning with a pre-trained CNN model to classify 1144 CXR images for 7 flat labels and 14 hierarchical labels of multi-class pneumonia classification, including COVID-19 and healthy lungs. To reduce classification bias, the dataset was randomly distributed into training and validation sets of 70% and 30%, respectively. To reduce the effect of imbalanced data on the final performance of the classifier, different resampling algorithms were employed (i.e., ADASYN, SMOTE, SMOTE-B1, SMOTE-B2, Al-IKNN, ENN, RENN, TomekLinks (TL), and SMOTE + TL). In the feature extraction phase, different hand-crafted features were used, including oriented basic image features (oBIFs), locally encoded transform feature histograms (LETRISTs), and local directional numbers (LDNs), using local phase quantization (LPQ) and deep learning (Inception-v3) methods. Both early and late fusion techniques were employed, followed by data resampling and multi-class and hierarchical classifiers. For multi-class classification, different traditional ML methods were employed, namely, support-vector machines, multilayer perceptrons, random forests, and decision trees. For the hierarchical classification, Clus-HMC--an unsupervised predictive clustering algorithm--was selected as the hierarchical classifier. The experiment proved that the hierarchical structure for the classification of COVID-19, which was tested on the RYDLS-20 dataset, achieved a higher F1-score (89%) with early fusion and BSIF, EQP, and LPQ features, compared to a flat structure for classification (83%). However, these findings are limited due to the way in which the authors classified pneumonia, as well as the extraction of features from a single modality, which were applied on a small sample size.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 3 Improved CEEMD-WPT Noise Reduction Algorithm
### Improve the Algorithm Steps of CEEMD-WPT  
The detailed steps of this method are shown in Figure 1.  
1. Add n white noise to the collected signal \(x(t)\) to obtain a noisy signal group.
2. Process the noisy signal group with WPT first, and then with EMD to obtain the \(n\)_inf_s components and residual \(r(t)\).
3. Calculate the average value of _imf1_ as _IMFj_, and subtract _IMFj_ from the original signal as \(R(t)\).
4. If \(R(t)\) can be decomposed or the number of decompositions is lower than the upper limit, then cycle steps (2-4).
5. Eliminate the residual \(r(t)\), and obtain the _IMFs_ of improved CEEMD.
6. Calculate the correlation coefficient between each component in _IMFs_ and the original signal, and distinguish the strong and weak correlation components according to the threshold value.
7. Conduct WPT noise reduction for weakly correlated components to remove noise components.
8. Reconstruct the weakly correlated component and the strongly correlated component with EMD, and finally obtain the signal \(x^{\prime}(t)\) after noise reduction.
## 6 Results and Discussion
### Comparison of CART and Modified CART Algorithms  
The CART decision tree algorithm use all 16 features while incorporating no feature selection other than the one performed by CART. The average performance is measured after splitting the data randomly into training and testing sets in a 80:20 ratio, respectively. Selecting different data partitions results in slight changes in the performance. Therefore, average performance was considered instead of the performance of a single partition of 80:20. The simple CART algorithm was applied, which showed better results compared to the approach proposed by [PERSON] et al. [1] The summary of the performance of applying the CART decision tree algorithm is given in Table 6. The modified CART algorithm was also implemented by iteratively adding features one by one to the training dataset and making a DT in each step. The algorithm stops when the accuracy starts going down. Afterwards, the selected features are used for the prediction of DDoS attack. Table 6 also shows the results achieved by this approach while using Monte Carlo validation. As observed from the table, both CART and modified CART produced results of almost the same quality with respect to different performance measures, but better than that of [PERSON] et al. [1] However, the obvious advantage of the modified CART algorithm was with respect to the number of features utilized. In comparison to CART, which used 16 features, the modified CART used 10.1 (rounded off to 10) features on average (based on 10 runs using Monte Carlo), thus making modified CART a better performer.  
From the results of the modified CART algorithm, it was observed that even though [PERSON] et al. [1] gave a logical explanation of the relevance of the features, not all of them are relevant. There is redundancy in the features as the number of features selected by modified CART is lower. Therefore, it was implied that a feature selection technique, if incorporated before the CART algorithm, might result in a higher accuracy. Accordingly, the simple greedy algorithm in conjunction with CART was tried on the dataset, the details of which follow in the next section.
# Interface Behavior Analysis of Different Geomembrane Liner Systems Based on PIV Techniques
## 4 Conclusions  
1. It can be seen from the comparative analysis of working conditions that the settlement of the ribbed geomembrane was significantly less than that of the smooth geomembrane. It was proved that the ribs enhanced the stability of the liner system.
2. According to the analysis of the test results, there was an optimum value of 60 mm for the rib spacing and 4.5 mm for the rib height. When the value was less than the optimal value, the ribbed geomembrane could not effectively improve the stability of the liner system. When the value was more than the optimal value, the reinforcing effect of the ribbed geomembrane on the sand gradually decreased.
3. By analyzing the additional stress of the sand, it can be concluded that the ribbed geomembrane was beneficial to the stress redistribution, thereby improving the overall stability of the liner system.
4. According to the PIV image analysis, the ribbed geomembrane can form an indirect influence area with the sand within a specific range to restrain the displacement of nearby sand particles. The indirect influence area was conducive to the stability of the liner system.
## 3 Approximate Inverse Kinematics Solution for Imposed End-Effector State
### Segment's Bending Angles \(\theta_{n}\) Calculation  
Computation of an approximate spine curve in Section 3.1 allows for further estimation of each segment's bending angle. First of all, spine curve parametric equations \(\hat{x}_{\text{ec}}(t),\hat{y}_{\text{ec}}(t),\hat{z}_{\text{ec}}(t)\), as in Equations (6)-(8), have first and second function derivatives. Appendix A gives detailed expressions of the computation formulas for \(\hat{x}_{\text{ec}}\), \(\hat{y}_{\text{ec}}\), \(\hat{z}_{\text{ec}}\), \(\hat{x}_{\text{ec}}\), \(\hat{y}_{\text{ec}}\), and \(\hat{z}_{\text{ec}}\). According to [37], Equations (A1)-(A6) are necessary for the evaluation of arc  
Figure 5: Approximate bent robot spine curve that reaches goal point \(G\) and is in vector \(\mathbf{o}_{r}\) orientation: (**a**) Preliminary bent robot spine curve first segment; (**b**) preliminary bent robot spine curve first and second segments; (**c**) preliminary bent robot spine curve—first, second, and third segments.  
curvature \(k\). Equation (13) provides an expression of arc curvature \(k\) for a parametrically defined space curve in three dimensions (Cartesian coordinates):  
\[k=\frac{\sqrt{\left(\tilde{z}_{\text{ec}}\dot{\tilde{y}}_{\text{ec}}-\ddot{\tilde {y}}_{\text{ec}}\dot{\tilde{z}}_{\text{ec}}\right)^{2}+\left(\ddot{\tilde{x}}_{ \text{ec}}\dot{\tilde{z}}_{\text{ec}}-\ddot{\tilde{z}}_{\text{ec}}\dot{\tilde{x}} _{\text{ec}}\right)^{2}+\left(\ddot{\tilde{y}}_{\text{ec}}\dot{\tilde{x}}_{ \text{ec}}-\ddot{\tilde{x}}_{\text{ec}}\dot{\tilde{y}}_{\text{ec}}\right)^{2}}}{ \left(\dot{\tilde{x}}_{\text{ec}}^{2}+\dot{\tilde{y}}_{\text{ec}}^{2}+\dot{ \tilde{z}}_{\text{ec}}^{2}\right)^{\frac{3}{2}}}. \tag{13}\]  
Inserting Equation (13) into the expression of arc \(\theta_{n}=kL_{n}\) returns average bending angles \(\theta_{n}\) for any segment based on curvature representations:  
\[\theta_{n}=\frac{\int_{0}^{L}\theta(t)}{t_{g}}\ \frac{\theta_{n}\gets kL_{n}}{ \Leftrightarrow}\int_{0}^{L_{n}}\ kdt. \tag{14}\]  
The average bending angle \(\theta_{n}\) of the \(n\)-th segment is one of the main parameters represented in Figure 3a. The following section will present the approach for finding the remaining parameters of the robot segments.
# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 5 Discussion
### A Critical Point Is How Appropriate Triggers Are Generated
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Full-Shot Setting**} \\ \cline{2-5}
**Method** & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **F1** & **F1\({}_{c}\)** & **F1\({}_{c}\)** & **F1\({}_{c}\)** \\ \hline BERT\({}_{s}\)\({}^{\star}\) & 61.81 & 56.86 & 59.94 & 55.29 \\ \hline + _GenTr_ (T5) & 63.07 (+1.26) & 57.60 (+0.74) & 59.80 (\(-\)0.14) & 55.48 (+0.19) \\ + _GenTr_ (BART) & 62.58 (+0.77) & 57.07 (+0.21) & 60.16 (+0.22) & 55.83 (+0.54) \\ \hline + _GenTr_ (w/rel) & 75.21 (+13.4) & 67.47 (+10.61) & 73.36 (+13.42) & 65.63 (+10.34) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: This table shows the comparison in performance based on the generative model type of generated triggers. BART and T5 models were used for the trigger generation task. _GenTr_ (w/rel) model refers to a case where relation class information is provided as an input feature when generating a trigger. The values in parentheses indicate the change in performance compared to the baseline model. The \({}^{\star}\) mark indicates the re-implemented version. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Few-Shot Setting**} \\ \cline{2-7}
**Method** & \multicolumn{2}{c}{**K = 8**} & \multicolumn{2}{c}{**K = 16**} & \multicolumn{2}{c}{**K = 32**} \\ \cline{2-7}  & **F1** & **F1\({}_{c}\)** & **F1\({}_{c}\)** & **F1\({}_{c}\)** & **F1\({}_{c}\)** \\ \hline BERT\({}_{s}\)\({}^{\star}\) & 27.67 & 26.49 & 42.84 & 40.11 & 48.71 & 45.55 \\ \hline + _GenTr_ (T5) & 31.71 & 30.47 & 44.83 & 41.46 & 47.87 & 43.99 \\ (+.04) & (+3.98) & (+1.99) & (+1.35) & (+0.84) & (\(-\)1.56) \\ + _GenTr_ (BART) & 28.69 & 29.54 & 43.64 & 40.94 & 48.73 & 45.07 \\ (+1.02) & (+3.05) & (+0.80) & (+0.83) & (+0.02) & (\(-\)0.48) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: This table demonstrates the performance comparison between T5 and BART models in the few-shot setting. K indicates the number of shots, and results from the test set are provided. The values in parentheses indicate the change in performance compared to the baseline model. The \({}^{\star}\) mark indicates the re-implemented version. significant features for improving the trigger generation procedure even without relation classes will be our future work.
## 3 The Radiative Transfer Equation
### RTE Analytical Solutions with Simplification
#### 3.3.1 Beer-Lambert's Law  
As we stated, BLL provides a simple exponential relationship between the attenuation of light intensity and the medium's optical properties (absorption coefficient, scattering coefficient, and path length). While it is not a complete solution to the RTE, it is a fundamental concept widely used to describe light attenuation in water [93; 94; 95].  
This approach is on the easy side of the RTE solution challenge. The third term on the RHS of Equation (43) is set to zero to obtain such a simplified solution. This means that \(c(\lambda)\) should be equal to \(a(\lambda)\) only in Equation (2). Hence, the light attenuation will be due to absorption only. Since there will be no scattered photons, there will be no feed to \(S(\theta,\varnothing)\), allowing for a setting \(S(\theta,\varnothing)\) equal to zero. Consequently, Equation (43) will be reduced to the following:  
\[\cos\theta\frac{dL(\theta,\Phi)}{c\,dz}=L(\theta,\Phi) \tag{44}\]  
Equation (5) will be the analytical solution of Equation (44), meaning that BLL is the simplest analytical benchmark. The BLL scenario can be considered when (1) the re-scattered light can be ignored for systems that do not rely on the temporal clarity of an optical signal at a very low bit rate and (2) when the transmission occurs in a region without sunlight.