esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Partial Least Squares (PLS) Analysis
In particular, there is evidence that it potentiates osteogenic differentiation of human dental pulp stem cells and promotes the proliferation of intestinal stem cells and epithelial regeneration [50]. At the same time, rutin has been shown to have excellent medicinal properties, since it is indicated for the treatment of various heart diseases [51]. Lastly, apigenin also exhibits remarkable therapeutic properties. Depending on the dosage, apigenin offers muscle relaxation and sedation [52]. Additionally, it could represent a new tool for delaying the onset of [PERSON]'s disease or slowing its progression [53]. Taking everything into account, a beneficial extract can be produced from the watercress leaves, with a multitude of health-promoting properties. \begin{table}
\begin{tabular}{c c c c} \hline
**A/A** & **Polyphenolic Compound** &
\begin{tabular}{c} **Optimal Extract** \\ **(mg/g dw)** \\ \end{tabular} & **Quantity (\%)** \\ \hline
1. & Chlorogenic acid & 3.13 \(\pm\) 0.23 & 17.1 \\
2. & Caffeic acid & 0.17 \(\pm\) 0.01 & 1.0 \\
3. & Syringic acid & 0.16 \(\pm\) 0.01 & 0.9 \\
4. & \(p\)-Coumaric acid & 0.55 \(\pm\) 0.04 & 3.0 \\
5. & Ferulic acid & 0.19 \(\pm\) 0.01 & 1.0 \\
6. & Rutin & 2.54 \(\pm\) 0.18 & 13.9 \\
7. & Quercetin 3-\(\beta\)-D-glucose & 0.95 \(\pm\) 0.03 & 5.2 \\
8. & Luteolin-7-glucose & 1.37 \(\pm\) 0.10 & 7.5 \\
9. & Narirutin & 0.96 \(\pm\) 0.05 & 5.3 \\
10. & Kaempfer-3-glucose & 1.72 \(\pm\) 0.04 & 9.4 \\
11. & Apigenin-7-O-glucose & 2.55 \(\pm\) 0.13 & 13.9 \\
12. & Myricetin & 0.59 \(\pm\) 0.04 & 3.2 \\
13. & Rosmarinic acid & 3.42 \(\pm\) 0.09 & 18.7 \\  & Total identified & 18.3 \(\pm\) 0.97 & \\ \hline \end{tabular}
\end{table} 
Table 7: The polyphenic compounds under the optimal extraction conditions (\(X_{1}\): 4, \(X_{2}\): 3, \(X_{3}\): 1, and \(X_{4}\): 5). The percentages (%) of total identified polyphenols in watercress plant are also displayed.
## 5 Evaluation
More specifically, SC combined with MIC improves the average F-measure by 0.129, 0.026, and 0.140 compared to SC combined with CS, IG, and ReF, respectively. g-mean results: As shown in Table 7, our proposed SCM feature selection framework, combined with the MIC-based feature ranking strategy, achieves the best g-mean on six of the seven projects and the best average g-mean across all projects, compared with SCM combined with the other three feature ranking strategies. More specifically, SC combined with MIC improves the average F-measure by 0.103, 0.025, and 0.120 compared to SC combined with SC, IG, and ReF, respectively. MCC results: As shown in Table 8, our proposed SCM feature selection framework, combined with the MIC-based feature ranking strategy, achieves the best MCC on five of the seven and the best average MCC across all projects, compared with SCM combined with the other three feature ranking strategies. More specifically, SC combined with MIC improves the average F-measure by 0.161, 0.030, and 0.164 compared to SC combined with SC, IG, and ReF, respectively. AUC results: As shown in Table 9, our proposed SCM feature selection framework, combined with the MIC-based feature ranking strategy, achieves the best AUC on six of the seven projects and the best average AUC across all projects, compared with SCM combined with the other three feature ranking strategies. More specifically, SC combined with MIC improves the average F-measure by 0.086, 0.022, and 0.093 compared to SC combined with SC, IG, and ReF, respectively. **Answer**: The proposed hybrid feature selection framework combining SC-based feature clustering with MIC-based feature ranking is more effective than the ones combining SC with other three feature ranking strategies. This implies that the reserved features, based on their MIC values, are more representative for the task of predicting residence of crashing faults.
###### Abstract
PWD shares similarities with GHPD in that it utilizes a sequential subtraction strategy but progressively decomposes a waveform, starting with the maximum peak, in the order of signal intensity rather than time. Although the sequential subtracting decomposition techniques are fast and flexible, they may inherently involve the risk of overestimating components decomposed earlier and overlooking ambiguous peaks. Linearly approximated iterative Gaussian decomposition (LAIGD) linearly estimates the degree of overlap between adjacent Gaussian components and performs Gaussian modeling according to the deformation impact rank by overlap [30]. Another flexible Gaussian decomposition method utilizes a genetic algorithm to estimate overlapping peaks that are not detected in the initial peak detection [31]. These flexible Gaussian decomposition approaches offer a general applicability regardless of waveform types or characteristics, facilitating the extraction of physical properties (waveform features) from each decomposed component. However, the techniques generally rely on the residual between the waveform and approximate model as a termination criterion for progressive processes. This can potentially lead to the omission of weak reflections (such as those of deep seafloors) in ABL waveforms. In this study, we aimed to propose a novel flexible Gaussian decomposition approach that can be generally applied to practical ABL waveform data. The primary objectives of the method are as follows:  
* Decompose ABL waveforms of various types and shapes;
* Improve the extractability of seafloor points by detecting elusive seafloor signals. The proposed approach decomposes an ABL waveform to multiple Gaussian models by progressively estimating the potential components that are not sensed during peak detection. The decomposition performance is improved by utilizing a termination criterion based on the time difference between the originally detected and estimated peaks as well as the residuals between the original waveform and the approximated model. The approach was demonstrated through experiments with the waveform data acquired by Seahawk, an ABL system [32]. The remainder of this paper is organized as follows. In Section 2, the test site and data employed in the experiments are described. Section 3 provides the theoretical foundation and details of the proposed approach. Experimental results and evaluations are presented in Section 4. Section 5 presents the discussion, and Section 6 concludes with the summary and final remarks.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Matching Results  
#### 3.2.1 Comparison with Single-Modality Image Matching Methods  
In this section, a group of matching results on infrared and visible images is given out to show the performance comparison of DDCE against single-modality image matching methods. The single-modality image matching methods used for comparison include SIFT, ORB, LIFT, and SuperPoint. Introductions of these methods are listed in Table 2. This section will present the visual results and performance analyses of DDCE and the matching methods listed in Table 2.  
Figure 4 shows the matching results of SIFT, ORB, LIFT, SuperPoint, and DDCE on a pair of infrared and visible images successively. In Figure 4, red lines indicate the wrong matches and green lines indicate correct matches. To make the matching results easier to see, the number and the percentage of correct matches of each method shown in Figure 4 are listed in Table 3.  
As shown in Table 3, DDCE achieves the largest number and the highest percentage of correct matches. Due to the difference of pixel intensities and gradients between infrared and visible images, SIFT and ORB can only obtain 1 and 0 correct matches, respectively. SIFT and ORB build feature descriptions by gradient orientation and pixel comparison, respectively. Because of the nonlinear intensity variation, SIFT and ORB descriptions for infrared and visible images are dissimilar. LIFT only obtains one correct match. Because the training data of LIFT is obtained from image 3D reconstruction, LIFT aims to tackle the matching of wide baseline visible images. As a result, LIFT cannot handle nonlinear intensity variations between infrared and visible images as well. SuperPoint achieves better matching performance than other single-modality image matching methods. SuperPoint is trained for matching the objects with regular shapes like buildings [16]. Because consistent edge structures reduce the effect of nonlinear intensity variations between infrared and visible images, DDCE achieves the best performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Type** \\ \hline SIFT & Hand Craft Based Method \\ ORB & Hand Craft Based Method \\ LIFT & Deep Learning Based Method \\ SuperPoint & Deep Learning Based Method \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single-modality image matching methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **The Number of** & **The Percentage of** \\  & **Correct Matches** & **Correct Matches** \\ \hline SIFT & 1 & 0.011 \\ ORB & 0 & 0.000 \\ LIFT & 1 & 0.002 \\ SuperPoint & 56 & 0.144 \\ DDCE & 107 & 0.177 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number and percentage of correct matches of single-modality matching methods.  
Figure 4: Matching result comparison with single-modality image matching methods. (**a**) SIFT, (**b**) ORB, (**c**) LIFT, (**d**) SuperPoint, and (**e**) DDCE. Red lines indicate the wrong matches and green lines indicate correct matches.
## 7 Results and Analysis
### Comprehensive Security Evaluation
#### 7.9.3 Sensitivity Analysis  
Figure 18 reveals our framework's robustness to weight variations:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Weight (\(w_{i}\))** & **Normalization Formula** \\ \hline Attack Success Rate (ASR) & 0.25 & \(1-\text{ASR}\) \\ \hline Classification Loss Rate (CLR) & 0.20 & \(\text{CLR}/100\) \\ \hline Adversarial Detection Precision (ADP) & 0.20 & F1-score \\ \hline Packet Loss Rate (PLR) & 0.15 & \(1-\text{PLR}\) \\ \hline Response Time (RT) & 0.10 & \(1-(\text{RT}/500)\) \\ \hline Model-Level Accuracy (MLA) & 0.10 & Accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 16: AHP-Derived metric weights.  
Figure 18: CSS metric weight sensitivity analysis. Radar plot shows weight variations (\(\pm 20\%\)) across six security dimensions.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Framework** & **CSS** & **Key Strengths** & **Key Weaknesses** \\ \hline Proposed ZTA + FL & 89.7 & High CLR (85), ADP (94\%) & Compute overhead 8\% \\ \hline ZTORAN [45] & 78.2 & Blockchain trust & Low MLA (81\%) \\ \hline RBAC [64] & 54.1 & Low latency & High ASR (87\%) \\ \hline DP-FL [62] & 63.5 & PLR = 9\% & Poor CLR \\ \hline \hline \end{tabular}
\end{table}
Table 17: Composite security score comparison.  
7.9.4. Security-Performance Trade-Offs  
Our framework achieves a CSS of 89.7, surpassing ZTORAN by 11.5 points through three key innovations:  
* Cross-layer resilience (CLR = 85) via synergistic ZTA-FL integration.
* Rapid threat recovery (RT = 200 ms) through automated quarantine.
* Privacy-preserving optimization (PLR = 12.3%) with adaptive DP.  
Table 18 provides a security-performance trade-off analysis, showing how varying token refresh intervals affects CSS scores and latency.  
While our CSS provides standardized security assessment, two limitations merit attention:  
* Weight calibration requires expert input--future work will automate this via RL.
* Current metrics focus on 3 GPP networks--extension to non-3 GPP RAN needs investigation.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 8 Future Research Direction
### Enhancing Dataset Diversity and Realism  
This review emphasizes a strong reliance on benchmark datasets such as BoT-IoT and TON_IoT, which may not be sufficient to capture the diversity and variability of real-world IoT traffic. This focus should be directed in future towards developing and distributing large-scale actual-world datasets that may mitigate the issue of heterogeneous IoT devices and evolving attack patterns. Furthermore, synthetic data generation methods such as Generative Adversarial Networks (GANs) could be used to simulate rare attack patterns and improve dataset diversity. The above should enhance the capability of ML models to generalize across multiple datasets rather than fine-tuning against a single dataset. Future research could also find ways to adapt models so they work well with different datasets.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 4 Results and Discussion
### Comparison Experiments  
#### 4.3.1 Next Activity Prediction  
The following methods are selected as the comparison methods in the next execution activity prediction task.  
* LSTM [10]: next activity prediction using Long Short-Term Memory (LSTM).
* GRU [11]: next activity prediction using Gated Recurrent Unit (GRU).
* CNN [21]: next activity prediction using Convolutional Neural Networks (CNN).
* DNC [22]: a memory unit is added to the LSTM model to constitute a differential neural computer (DNC) to implement the next activity prediction.
* Transformer [16]: the next activity prediction is predicted using the traditional Transformer directly.  
Table 2 shows the accuracy of the above five comparison methods and the method in this paper (HiP-Transformer) on the next execution activity prediction task. Overall, the HiP-Transformer model achieves the best prediction results on five of the seven datasets, especially compared to the prediction methods using the traditional Transformer directly on all datasets. The HiP-Transformer model did not achieve the highest accuracy on the BPI2012 dataset, because the business process instances in the dataset have only activity information in each event. The lack of attribute information makes the HiP-Transformer model unable to fuse various attributes, which is not conducive to improving prediction results. The Sepsis dataset is a log of sepsis patients' visit information, and the patient visit process changes with its state. Different business process cases have large differences, so it is more difficult to predict the task of the next execution activity, and the accuracy of all methods is relatively low.
# Classification of Valvular Regurgitation Using Echocardiography
## 2 Related Works  
The heart is a sophisticated structure that requires a trained cardiologist for the proper diagnosis of any abnormalities. Some of the works that deal with the heart perspective related to our work are described below.  
[PERSON] [8] worked with valvular regurgitation using color Doppler images. Two types of regurgitation were considered, namely MR and AR, whereby the images were first converted into YCbCr space. Two filters were used for noise removal, namely the Wiener filter and Gaussian filter. After filtering using Fuzzy K Means and anisotropic diffusion, segmentation was carried out, and Proximal Isovelocity Surface Area Method (PISA) was used for quantification, which classifies regurgitation into mild mitral regurgitation, moderate aortic regurgitation, and severe aortic regurgitation. [PERSON] et al. [12] used 2D echo and proposed an approach for information extraction in apical view. The procedure takes the information of patients and image for classification into volume and label. For finding intensity and label, Joint Independent Component Analysis (JICA) was used. The approach classified moderate MR with an accuracy of 82%.  
[PERSON] and [PERSON] [24] used an approach for segmentation of echo from videos. A closed curve was used to detect heart boundary and Gaussian Mixture Model (GMM)clustering for segmentation. [PERSON] et al. [3] worked on view classification using echo. Two features were used namely histogram features and statistical features to classify of four standard views, namely, parasternal short axis (PSAX), parasternal long axis (PLAX), apical two chambers (A2C), and apical four chambers (A4C), was carried out. For 200 images, an accuracy of 87.5% was achieved. Another work by [PERSON] et al. in [25], similar to that in [3], on view classification was done. Mathematical morphology was used before segmentation using Connected Components Labeling (CCL). Classification of three standard cardiac views, namely parasternal short axis (PSAX), apical two chambers (A2C), and four-chamber (A4C) views, was carried out with an accuracy of 94.56%. [PERSON] [16] has worked on view classification using a Bayesian classifier. [PERSON] et al. worked on preprocessing using median filter and Ramponi filter and segmentation using active contours. [PERSON] [26] dealt with the left ventricle (LV) segmentation by detecting LV boundary using fuzzy logic and watershed algorithm along with hidden Markov model-based scheme. [PERSON] et al. [17] used a level set method for LV boundary detection. [PERSON] et al. [18] also worked with LV boundary segmentation and reviewed using level set, active contour, and active shape models. In [27], heart valve disease for aortic stenosis (AS) was assessed. A review on machine learning techniques for heart disease prediction can be seen in [28], and work based on heartbeat counts for the classification of heart diseases could be observed in [29].  
In the next section, a discussion on the different existing methods and the proposed approach used for the classification of regurgitation into AR, MR and TR can be observed.
## 3 Nanocellulose
### Bacterial Nanocellulose (BNC)  
Contrary to CNC and CNF which are produced from cellulose extracted from plant biomass, BNC, as the name suggests, is nanocellulose produced by bacteria. It is made from the fermentation of glucose or other carbohydrate feedstocks by bacterial processes, and is also known as microbial cellulose or bio cellulose [31; 35]. It is mainly produced by _Gluconacetobacter xylinus_ (_Acetobacter xylinum_) [45]; however, genera such as _Acetobacter_,  
Figure 2: Production of cellulose nanocrystals and cellulose nanofibrils from a cellulose chain.  
_Achromobacter_, _ Bacillus_, _Sarcina_, _Aerobacter_, _Agrobacteriumrium_, _Escherichia_, _Azotobacter_, _Rhizobium_, _Enterobacter_, _Klebsiella_ and _Salmonella_ also produce cellulose. The synthesis of bacterial cellulose typically takes place in four stages: in the first stage, monosaccharides are activated through sugar nucleotide development; in the second stage, polymerization occurs, which leads to the formation of assembly of repeating sugar units; the third stage is characterized by the in-time addition of any acyl group if present; and in the last stage, extracellular secretion of BNC in the form of three-dimensional microfibril and nanofibril network takes place [46]. Enzyme cellulose synthase plays the role in the synthesis of BNC and the BNC produced by _Gluconacetobacter xylinus_ that possesses a chemical structure identical to plant cellulose. Since BNC is produced from bacteria and not from plants, it does not contain impurities such as lignin, pectin, hemicellulose, wax, etc. [17; 46; 47; 48]. BNC is also traditionally used to produce a common dessert in Southeast Asia, called 'Nata de coco', through the fermentation of coconut water. Currently, many agricultural wastes have been reported that can be fermented for the production of bacterial cellulose, such as orange peel [49] and pineapple peel [50]. BNC has a twisted ribbon topology and usually has a diameter of 20 to 100 nm, length up to 100 \(\upmu\)m and a large surface area per unit. The morphology of BNC is dependent on factors such as cultivation mode and cultural parameters [17; 35]. A high porosity, high elastic modulus, high degree of polymerization (up to 8000), high water-holding capacity and high crystallinity (up to 84-89%) are only a few of the distinctive physical and mechanical attributes of BNC. BNC may possess a promising potential application in various fields such as biomedical applications, electronic paper, fuel cell membranes and reinforcement in nanocomposites [41; 43]. The properties of CNC, CNF and BNC are summarized in Table 1.
## 4 Experiments and Results
### Experimental Settings  
In this paper, we used a Pytorch, Intel(R) Xeon(R) E5-2680 v4 central processing unit (CPU) with 2.4 GHz and an NVIDIA GeForce GTX 1080 ti graphics processing unit (GPU) as the experimental platform. The learning rate was set to 0.003 and the batch size was set to 128. We applied the batch norm technique at the output of each convolutional layer, and, in order to avoid overfitting, L2 regularization was applied; we also used a dropout technique with a threshold of 0.5 at the fully connected layer. The optimizer chosen was Adam, and \(\beta_{1}\) and \(\beta_{2}\) were set at respectively 0.9 and 0.999. The loss function used was cross-entropy. The formula for cross entropy is as follows:  
\[L=-[y\log\hat{y}+(1-y)\log(1-\hat{y})], \tag{3}\]  
where \(y\) is labeled with 1 or 0 (i.e., 1 denotes nodule and 0 denotes non-nodule), and \(\hat{y}\) indicates the probability that the model predicts real nodules.  
In the data preprocessing stage, we extracted \(40\times 40\times 40\), \(30\times 30\times 30\), and \(20\times 20\times 20\) blocks for each sample via the central coordinates of the candidate nodules and used the nearest neighbor interpolation to transform them all into blocks. We used minimum-maximum normalization to ensure the algorithm converged faster by adjusting the data distribution of the sample. The intensities of the blocks were cut to \([-1000,400]\) Hounsfield units (HU)\({}^{2}\) and normalized to \([0,1]\).  
We used a five-fold cross-validation method to evaluate the algorithm, randomly dividing the CT scans into five parts based on patients, such that four parts were used for training and one was used for testing. To deal with the class imbalance between nodules and non-nodules and to avoid overfitting, we used data augmentation for nodule samples. Specifically, we rotated the nodules by \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) on a transverse plane and shifted the center along each axis by 1, 2 pixels; we also added Gaussian noise with a mean of 0 and variance of 0.4 to those added data. The number of nodules and non-nodules in the five-fold cross-validation is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Candidates**} \\ \cline{2-3}  & **Nodule** & **Non-Nodule** \\ \hline V1 & 1351 (1120) & 549,714 \\ V2 & 1557 (1166) & 753,418 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the dataset in the Lung Nodule Analysis 2016 (LUNA16) challenge. The number in parentheses indicates the number of nodule-labeled matches with 1186 real nodules. V—version.
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment
## 2 Materials and Methods
### Additional Data to Claims
#### 2.2.1 HURDAT2  
Atlantic HURDAT2 comprises the best track data available from the National Oceanic and Atmospheric Administration National Weather Service National Hurricane Center (NOAA NWS NHC; available at [[http://www.nhc.noaa.gov/data/#hurdat](http://www.nhc.noaa.gov/data/#hurdat)]([http://www.nhc.noaa.gov/data/#hurdat](http://www.nhc.noaa.gov/data/#hurdat)), 11 February 2022). This dataset has a comma-delimited text format with six-hourly information on the location, maximum winds, central pressure, and size of all known tropical cyclones and subtropical cyclones. It is updated at least once per year to include the previous year's best tracks and/or historical reanalysis tracks [11].  
Since many of the NFIP claims are due to tropical storms or hurricanes, HURDAT2 might be the best resource for matching historical storms to loss claims properly in Florida. The date and time of the storm track positions can be compared to the claim location and time of loss using spatial and temporal metrics to determine possible storm influence. Additional information concerning the storm intensity level (depression, tropical storm, or hurricane) can be added to the provided claims as well.