esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Correlation between Semantic Pairs and Design Parameters  
Three semantic pairs had a significant correlation with design parameters in AE 1. The correlation coefficient of each pair was \(-0.513\) for \"Dark-Bright\" (\(p\) = 0.000), \(-0.294\) for \"Dry-Moist\" (\(p\) = 0.000), and 0.405 for \"Flat-Rugged\" (\(p\) = 0.000). In AE 2, four semantic pairs had a significant correlation with design parameters. The correlation coefficient of each pair was \(-0.381\) for \"Dark-Bright\" (\(p\) = 0.000), \(-0.174\) for \"Dry-Moist\" (\(p\) = 0.037), 0.381 for \"Flat-Rugged\" (\(p\) = 0.000), and 0.174 for \"Hard-Soft\" (\(p\) = 0.037). Finally, in RE, five semantic pairs had a significant correlation with design parameters. The correlation coefficient of each pair was \(-0.614\) for \"Dark-Bright\" (\(p\) = 0.000), \(-0.282\) for \"Dry-Moist\" (\(p\) = 0.001), 0.317 for \"Slippery-Sticky\" (\(p\) = 0.000), 0.343 for \"Flat-Rugged\" (\(p\) = 0.000), and \(-0.180\) for \"Soft-Hard\" (\(p\) = 0.031). Table 4 presents the correlation coefficients and p-values for each SD method.
## 3 Model and Algorithm Flow
### Mimu/GNSS/MC Integration Model  
When motion constraints  
Combining (1), the measurement equation composed of motion constraints is expressed as  
\[\mathbf{Z}_{2}=\left[\begin{array}{c}\delta v_{x}^{m}\\ \delta v_{z}^{m}\end{array}\right]=\left[\begin{array}{c}v_{x}^{m}-0\\ v_{z}^{m}-0\end{array}\right]=\mathbf{H}_{2}\mathbf{X}_{2}+\mathbf{V}_{v} \tag{25}\]  
where \(\mathbf{Z}_{2}\) denotes the measurement matrix, \(v_{x}^{m}\) and \(v_{z}^{m}\) are the speed of train in the \(m\) frame, \(\mathbf{V}_{v}\) is the noise matrix, and the measurement matrix \(\mathbf{H}_{2}\) is expressed as  
\[\mathbf{H}_{2}=\left[\begin{array}{cc}M_{1}(1,\times)&M_{2}(1,\times)&0_{2\times 9 }&0&M_{3}(1,3)\\ M_{1}(3,\times)&M_{2}(3,\times)&0_{2\times 9}&M_{3}(3,1)&0\end{array}\right] \tag{26}\]  
where \(\mathbf{M}_{i}(k,\times)\) is all elements in the \(k\)-th row of matrix \(\mathbf{M}_{i}\) and \(k=1,2,3\).  
Combining (19) and (25), the combined observation equation composed of GNSS and motion constraints is  
\[\mathbf{Z^{\prime}}_{2}=\left[\begin{array}{cc}\mathbf{H}_{1}&0_{2\times 2}\\ \mathbf{H}_{2}\end{array}\right]\mathbf{X}_{2}+\left[\begin{array}{c}\mathbf{V}_{GNSS} \\ \mathbf{V}_{v}\end{array}\right] \tag{27}\]  
The frequency of motion constraints is set to 1 Hz and synchronized with GNSS observation data. Through feedback correction, the installation angles can be calculated.
## 3 Results  
No statistically significant differences were found between the BLD generated in SiP vs. StP for the mean forces (\(p=0.155\)). On the other hand, differences were observed in the BLD of the peak force (\(p=0.007\)). In addition, a smaller BLD was observed in the movements of StP than SiP (Figure 4).  
Figure 4: Mean force (**A**) and peak force (**B**) (kg) obtained during the performance of the isometric elbow flexion movement, both bilaterally and unilaterally (dominant + nondominant) for sitting position (SiP) and the standing position (StP).  
On the other hand, statistically significant differences with a \"large\" effect size were observed for comparisons between dominance (\(p<0.001\); \(\omega^{2}=0.673\)) and mean force. Post hoc analysis using Bonferroni correction revealed that when comparing unilateral D movement to unilateral ND movement, there were no statistically significant differences (mean difference = 0.210; \(p=1.00\)). In contrast, this post hoc analysis revealed that the comparisons between dominance differed when comparing D + ND vs. BL (mean difference = 3.28; \(p<0.001\)) in favor of D + ND. Regarding peak force, statistically significant differences with a \"large\" effect size were observed for comparisons between dominance (\(p<0.001\); \(\omega^{2}=0.638\)), and statistically significant differences with a \"small\" effect size were observed for comparisons between Position*Dominance (\(p=0.02\)\(\omega^{2}=0.002\)). Post hoc analysis of dominance revealed that when comparing D with ND, there were no statistically significant differences (mean difference = 0.277; \(p=1.00\)).  
In contrast, this post hoc analysis revealed that comparisons between dominance differed when comparing D + ND vs. BL (mean difference = 3.53; \(p<0.001\)). Post hoc analysis of Position*Dominance comparisons shows that there is no statistically significant difference between SiP.D vs. StP.D (mean difference = 0.870; \(p=1.00\)); SiP.D vs. SiP.ND (mean difference = 0.642; \(p=1.00\)); SiP.D Vs StP.ND (mean difference = 0.782; \(p=1.00\)); StP.D Vs SiP.ND (mean difference = \(-0.288\); \(p=1.00\)); StP.D vs. StP.ND (mean difference = \(-0.088\); \(p=1.00\)); SiP.ND vs. StP.ND (mean difference = 0.140; \(p=1.00\)); SiP.D + ND vs. StP.D + ND (mean difference = 1.010; \(p=1.00\)); StP.D + ND vs. SiP.BL (mean difference = 3.377; \(p=0.064\)); StP.D + ND vs. StP.BL (mean difference = 2.672; \(p=0.093\)) and SiP.BL vs. StP.BL (mean difference = \(-0.705\); \(p=1.00\)). On the other hand, statistically significant differences were found when comparing SiP.D + ND vs. SiP.BL (mean difference = 4.387; \(p<0.001\)) and SiP.D + ND vs. StP.BL (mean difference = 3.683; \(p=0.026\)).  
For both mean and peak force, the post hoc analyses comparing unilateral movements (i.e., D or ND) vs. D + ND or BL was significantly associated with the latter (\(p<0.001\)) (Table 2).
# Correlation of Plastic Strain Energy and Acoustic Emission Energy in Reinforced Concrete Structures
###### Abstract  
This paper presents a comparison of the acoustic emission (AE) energy and the plastic strain energy released by some reinforced concrete (RC) specimens subjected to cyclic or seismic loadings. AE energy is calculated, after proper filtering procedures, using the signals recorded by several AE low frequency sensors (25-100 kHz) attached on the specimens. Plastic strain energy is obtained by integrating the load displacement curves drawn from the measurements recorded during the test. Presented are the results obtained for: (i) two beams (with and without an artificial notch) and a beam-column connection subjected to several cycles of imposed flexural deformations; (ii) a reinforced concrete slab supported by four steel columns, and a reinforced concrete frame structure, both of the latter are subjected to seismic simulations with a uniaxial shaking table. The main contribution of this paper, which is a review of some papers previously published by the authors, is to highlight that, in all cases, a very good correlation is found between AE energy and plastic strain energy, until the onset of yielding in the reinforcing steel. After yielding, the AE energy is consistently lower than the plastic strain energy. The reason is that the plastic strain energy is the sum of the contribution of concrete and steel, while the AE energy acquired with thresholds higher than 35 dB\({}_{\text{AE}}\) captures only the contribution of the concrete cracking, not the steel plastic deformation. This good correlation between the two energies before the yielding point also lends credibility to the use of AE energy as a parameter for concrete damage evaluation in the context of structural health monitoring.  
acoustic emission; reinforced concrete structures; earthquakes; damage evaluation +
Footnote †: journal: Applied Physics Letters B
## 3 Results
### Validation Results
#### 3.2.2 Evaluating the Practicability of Data Augmentation  
In this study, 11 datasets were created to validate the augmented data (Figure 8). Four machine learning algorithms (i.e., boosting, random forest, support vector machine <radial-based function>, and support vector machine <linear function>) were evaluated on the 11 datasets (Figure 8). The data were split into the following three datasets when these algorithms were run: training, validation, and testing. Then, 20% of the total data were assigned as the test data. The remaining data were the training data. Meanwhile, the data of 20% of the training data were assigned as the validation data. The evaluation metrics were calculated based on the results using the test data. Table S5 describes the parameter setting for each machine learning algorithm, while Table S6 shows the results of these machine learning algorithms. To assess datasets in terms of their predictive performances and stabilities, the average and standard deviation values of the evaluation metrics across the machine learning algorithms were calculated. The evaluation metrics and confusion matrices are shown in Table 3 and Supplementary Table S7. For the synthesized data in the \"Nonaugmented\" dataset group, the data generated using the GAN-based synthesizers (CTGAN and CopulaGAN) outperformed those generated by IVAE in terms of predictive performance. These findings align with recent studies [26; 43; 44; 45]. Notably, the real dataset exhibited the lowest performance. Augmented datasets are ranked second or third. Augmenting the original dataset improved the evaluation metrics of predictive performance by an average of 8.16%. Augmenting the seed dataset improved the evaluation metrics of predictive performance an average of 9.64%. The evaluation of the predictive analysis indicated that the GAN-based datasets in the \"Nonaugmented\" and \"Augmented\" dataset groups exhibited higher performance levels than the other datasets. In particular, concerning the seed dataset in the \"Augmented\" dataset group, the combination of the seed and synthesized datasets generated by CTGAN and CopulaGAN improved performance across evaluation metrics by an average of 11.41% and 10.93%, respectively, although IVAE enhanced the performance by 6.58%. The augmented and synthesized datasets had almost the same standard deviations as the original datasets and lower standard deviations than the seed datasets. The TVAE-related datasets had relatively lower standard deviations compared to the other datasets. Figure 8: Sample sizes (number of rows) in each datum: the number in the black square expresses the sample size (number of rows).
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 2 Approaches to Modeling, Determining, and Mitigating Errors in MLS Point Clouds
### Quality Control  
In purely practical terms, the end user is most likely not interested in the cause of a distorted point cloud but in its orders of magnitude. Many methods exist for post-mission quality control of MLS point clouds [22]. Commonly, either artificial black and white, or natural control points provide quantitative accuracy analysis [7]. Ref. [23] exploited the high reflectance of road markings to define corner points as GCPs. The residuals of the MLS points fitted to a reference plane are also a valid quantitative measure, provided that the reference surfaces are sufficiently planar [15]. The same is true for fitting multiple lines to the scanned roadway at an intersection [24].  
Static terrestrial laser scans (TLS) have proven suitable for evaluating 3D accuracy. ICP algorithms are state-of-the-art for determining point correspondences and for estimating transformation parameters in the context of a least-squares fitting. Due to high data redundancy, this method provides precise results without making assumptions. The TLS point cloud should be an order of magnitude more accurate to serve as a reference, though. Therefore, data collection requires a lot of effort, as registration errors between multiple static scans would be attributed to the MLS point cloud. [PERSON]. [25] divided an MLS point cloud of an urban scene into short time segments before applying ICP to obtain a piecewise transformation for alignment to a georeferenced TLS point cloud. [PERSON]. [26] established a large test field for MLS systems based on static TLS data as part of an EU project. They utilized artificial features such as poles and building corners to evaluate elevation and planimetric errors. Ref. [27] took a similar approach by creating a large test field for MLS systems in a city. Here, georeferenced scans of a scanning total station serve as a reference. They determined the normal distance from all MLS points to their nearest TLS reference points and minimized these deviations using least-squares adjustment. Similar to [25], they also subdivided the point cloud into sections and computed translations in so-called anchor points.
## 4 Benchmark Databases and Performance Evaluation Metrics
### Dataset Used
#### 4.1.11 CICIDS2017  
This dataset closely resembles actual real-world data and includes several legitimate and recent attacks. It represents the outcome of network traffic studied using a CIC flow meter. Eight separate files made up the dataset, which covered five days of both normal and attack traffic created between 3 July and 7 July 2017. CICIDS2017 meets all critical criteria for an intrusion detection dataset [199]. It has prevalent attacks, including DoS, DDoS, brute force, XSS, SQL Injection, web infiltration, port scanning, and botnets. It contains labelled network flows in flow-based formats as well as entire packet payloads in packet-based formats for machine and deep learning [199]. In this dataset, there are a total of 225,745 records. There are 97,718 records classified as \"normal traffic\", which amounts to 43.3%, and 128,027 (56.7%) classified as \"attack traffic\". The dataset is publicly available for researchers and can be downloaded from [200] in both pcap and csv formats.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 4 Conclusions  
A new method using 2D image and 3D point cloud to estimate the inclination angle of a pole has been proposed. The method has the following features:  
1. The expanded bounding box and the attention mechanism are the main influencing factors for improving Mask R-CNN in pole detection. It effectively executes to locate the pole by reducing the searching space in the 3D point cloud.
2. It can segment high-quality pole data from the raw point cloud by introducing the pole mask feature and depth map fusion.
3. It can estimate the inclination angle of the pole point cloud by fitting the central axis of the cylinder-like objects, even in scenarios of noise or missing point cloud.  
The method could be improved by:  
1. As the quality of the pole visible light (RGB) image could be insufficient in variable light, such as strong sunlight or rain, the improved Mask R-CNN might not accurately detect the pole. The preprocessing step of enhancing the RGB image is necessary for the automatic detection method.
2. As the unmatched 2D image is captured from a visible light camera and the 3D point cloud is captured from LiDAR or a depth camera, the reduced point cloud searching space in the new method might not able to locate the pole.  
In the future, the present study will be extended to optimize the algorithm running speed on edge computing devices for large-scale pole inclination angle estimations in a city.  
Conceptualization, L.C. and J.C.; methodology, J.C. and J.X; software, Z.Y. and J.C.; validation, L.C. and J.C.; formal analysis, J.X. and Z.Y.; investigation, J.C., L.C. and J.X.; resources, J.X., L.C and J.C.; data curation, Z.Y., J.C. and J.X.; writing--original draft preparation, J.C. and J.X.; writing--review and editing, J.C. and L.C.; supervision, J.X.; project administration, J.X. and L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Inclination Angle**} & \multicolumn{8}{c}{**Rotation Angle**} & \multirow{2}{*}{**Average Error**} & \multirow{2}{*}{**Variance**} \\ \cline{2-2} \cline{5-10}  & & & & & & & & & \\ \hline
0 & 0.91 & 0.35 & 0.82 & 0.45 & 0.65 & 1.15 & 0.32 & 1.14 & 0.72 & 0.11 \\
2 & 2.35 & 2.34 & 2.65 & 2.42 & 2.85 & 2.32 & 3.35 & 2.92 & 0.65 & 0.14 \\
4 & 4.33 & 4.27 & 4.53 & 5.15 & 4.71 & 4.53 & 5.12 & 5.07 & 0.71 & 0.13 \\
6 & 6.56 & 6.69 & 6.05 & 6.74 & 7.32 & 6.15 & 7.13 & 6.32 & 0.62 & 0.20 \\
8 & 8.35 & 8.86 & 8.44 & 9.13 & 8.62 & 8.81 & 8.06 & 8.78 & 0.63 & 0.11 \\
10 & 10.53 & 10.36 & 10.94 & 10.43 & 10.52 & 11.11 & 10.84 & 10.13 & 0.61 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pole angle distribution.
## 6 Global Genome Repair (GGR)
### Dissociation of UvrA from DNA and Formation of the Pre-Incision Complex
crosslinked UvrB to DNA on the 5' side of the lesion; release of the crosslink exposed the 5'\(\to\)3' translocation activity, which led to stalling of UvrB at the lesion [47]. Second, [PERSON] et al. used substrates with two lesions, variously spaced, to show that only the lesion closest to the 5' end was incised; this finding implies that the single processed lesion stalls translocation by UvrB [20]. Although both studies confirm that UvrB translocates in the 5' direction and is stalled by the lesion during NER, they do not reveal the initial site of UvrB binding relative to the lesion. Although considerable evidence has been developed to support the idea that two UvrB molecules are loaded by UvrA [99, 101, 102], possibly on distinct strands [21], this finding must be reconciled with experiments that show that a single UvrB molecule is bound to the UvrC nuclease in the incision complex [101]. An important question associated with the transition from two to one UvrB molecules in the incision complex centers on which DNA strand, damaged or undamaged, is bound under the \(\beta\)-hairpin of UvrB. Two independent studies definitively showed that it is the damaged strand that is under the \(\beta\)-hairpin of UvrB for presentation to the UvrC nuclease [20,47]. Furthermore, a crystal structure of UvrB bound to mismatch containing DNA lays the foundation for understanding the precise geometry of the cuts made by the dual nuclease UvrC, eight positions 5' of the lesion and five positions 3' of the lesion [47].
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 5 Methodology for Using Positive CUSUM  
This research article presents the use of Positive CUSUM of the NEI value for monitoring the severity of faults. The steps for implementing Positive CUSUM are as follows:  
1. Extract the DGA data of nine gases from the online measurement device, remove non-numeric values, and separate it into 12 months.
2. Calculate the logarithm of the total dissolved combustible gas (TDCG).
3. Find the values of NEIHC and NEICO for each month.
4. Create a scatter plot between log(TDCG) and NEI.
5. A linear regression of log(TDCG) against NEI will identify the month exhibiting the strongest correlation (highest R\({}^{2}\)), which will then serve as the baseline for a linear equation: \[NEI_{predict}=m\times log(TDCG)+c\] (3)
6. Calculate the forecast NEI values (NEI\({}_{\text{Predict}}\)) of each data point with the base month equation.
7. Compute the discrepancy (Diff) between measured NEI (NEI\({}_{\text{Data}}\)) and predicted NEI (NEI\({}_{\text{Predict}}\)).  
\[Diff=NEI_{Data}-NEI_{predict} \tag{4}\]
8. Calculate the Positive CUSUM (Pos. CUSUM).  
\[Pos.\ CUSUM_{0}=0 \tag{5}\]  
\[Pos.\ CUSUM_{i}=Pos.\ CUSUM_{i-1}+Diff_{i} \tag{6}\]  
\[IF\ Pos.\ CUSUM_{i}<0\ THEN\ Pos.\ CUSUM_{i}=0\]  
\[ELSE\ Pos.\ CUSUM_{i} \tag{7}\]
9. Create a Positive CUSUM chart.
10. Generate a graphical representation comparing NEI values, Positive CUSUM, NEI score, and cumulative NEI.  
Positive CUSUM's straightforward calculation makes it ideal for online DGA devices that are continuously monitoring critical transformers in production lines. Positive CUSUM is sensitive to changes, thus yielding results that clearly show differences. The following section will illustrate the application of Positive CUSUM using data from online dissolved gas analysis (DGA) devices from two steel mills.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model
### Multiple Preprocessing  
Although the level set model is highly robust, it is also easier to obtain highly accurate segmentation results from an image with less noise and higher contrast. Furthermore, the proposed HLSM requires an initial value that can exclude PPA regions and bright noise. Therefore, multiple preprocessing includes the detection of multiple feature-based regions of interest (ROI), quantitative-analysis-based channel selection, morphological-based blood vessel removal, frequency domain-based noise removal, and initial-value detection with adaptive threshold-based ellipse fitting.
# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches  
The Convolutional Neural Networks (CNNs) and their extensions, the Graph Neural Networks (GNNs), are also used in community detection, and they are a powerful tool used to extract the spatial localization. CNNs can only operate on regular Euclidean structured data like images (2D grids) while their extension, GNNs, can be used in cases where the numbers of nodes connections vary [46], and the nodes are not ordered (irregular on non-Euclidean structured data). Thus, the latter approach seems to be more favorable recently. The majority of CNN/GNN community detection methods are semi-supervised or supervised. However, there are approaches that combine them with auto-encoders, to implement unsupervised approaches ([24] is an example). In the remainder of this subsection, we present the most recent CNN and GNN approaches to community detection.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 2 Lattice and Code-Based Post Quantum Cryptography  
In 2019, the number of candidates of the NIST standardization effort was narrowed to 26, as part of phase two of the program [6]. In July 2020, NIST announced the selection of seven likely finalists for phase three of the program [7]: CRYSTALS-Kyber, CRYSTALS-Dilithium, SABER, NTRU, and FALCON with lattice cryptography [8; 9; 10; 11; 12]; RAINBOW with multivariate cryptography [13], and Classic McEliece with code-based cryptography [14; 15]. The software developed is mainly targeting DSA applications, as well as KEM. Lattice cryptography is relatively mature, well documented, and is most likely to become mainstream for cybersecurity. Lattice-based algorithms exploit hardness to resolve problems such as the closest vector problem (CVP), learning with error (LWE), and learning with rounding (LWR) algorithms, and share similarities with the knapsack cryptographic problem.