esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Datasets
#### 2.5.3 Cardiovascular Disease  
Two datasets are used in the field of cardiovascular disease. The first dataset, known as fiv-CardioDB, is a comprehensive collection of heart disease data from five different sources: Cleveland, Hungarian, Swiss, Long Beach VA, and the Statlog (Heart) Dataset. This dataset is from Liverpool John Moore's University (fiv-CardioDB dataset: [[https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive](https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive)]([https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive](https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive)), accessed on 15 December 2024) and contains 1190 instances with 11 features.  
The second dataset, known as Ind-CardioDB, was developed by [PERSON] et al. at Lincoln University College (Ind-CardioDB dataset: [[https://data.mendeley.com/datasets/dzz48](https://data.mendeley.com/datasets/dzz48) mvjht/1]([https://data.mendeley.com/datasets/dzz48](https://data.mendeley.com/datasets/dzz48) mvjht/1), accessed on 15 December 2024). This dataset consists of 1000 records with 12 characteristics collected from a hospital in India.  
Both datasets have 11 features in common, which facilitates comparative analysis and usability. However, the Ind-CardioDB dataset includes an additional feature, the number of major vessels (noofmajorvessels), which is not present in fiv-CardioDB.  
The common features include a variety of attributes, including general characteristics such as age and sex, as well as specific cardiovascular parameters. For example, chest pain type categorizes chest pain into four types: 1 for typical angina, 2 for atypical angina, 3 for non-anginal pain, and 4 for asymptomatic cases. Additional characteristics include resting blood pressure (measured in mm Hg), serum cholesterol (in mg/dL), and fasting blood glucose (considered true if \(>120\)mg/dL).  
Electrocardiographic data are collected by resting electrocardiogram results (restingrelectro), classified as 0 for normal, 1 for ST-T wave abnormality, and 2 for probable or definite left ventricular hypertrophy according to Estes criteria. The datasets also record maximum heart rate achieved (maxheartrate) and the binary attribute exercise-induced angina (exercise ganglia).  
Other attributes include oldpeak, which measures exercise-induced ST depression relative to rest, and slope of the peak exercise ST segment, categorized as 1--sloping, 2--flat, or 3--sloping. The target variable, class, indicates the presence or absence of heart disease.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
###### Abstract  
This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tapraulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications.  
radiometric calibration; TOA radiance; reflectance-based approach; the Baotou site; ZY3-02 satellite +
Footnote †: journal: Sensors  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor. _Sensors_**2022**, 22, 2066. [[https://doi.org/10.3390/s22052066](https://doi.org/10.3390/s22052066)]([https://doi.org/10.3390/s22052066](https://doi.org/10.3390/s22052066))  
Academic Editor: [PERSON]  
Received: 22 December 2021  
Accepted: 5 March 2022  
Published: 7 March 2022  
**Published:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Keywords:** radiometric calibration; TOA radiance; reflectance-based approach; the Baotou site; ZY3-02 satellite
## 2 Materials and Methods
### Machine Learning Pipeline
#### 2.9.1 PFI Analysis of 12 Physiological Features for Classifying Baseline and Painful States  
We used an RF classifier to perform PFI analysis on 12 physiological features to distinguish between the baseline (negative class) and painful states (positive class). We utilized the PFI to establish an interpretable ranking of features within a small dataset. PFI assesses the significance of each feature by quantifying the decline in model accuracy when its values are randomly permuted, thereby directly linking the importance to predictive performance. Unlike Gini importance, which may favor features with multiple categories [29], PFI provides more impartial rankings and mitigates the emphasis on misleading features that are sometimes found in methods, such as [PERSON]'s additive explanations [30]. These attributes help to reduce overfitting artifacts and ensure that the selected features reflect meaningful physiological insights rather than noise.  
We combined PFI with a repeated nested CV strategy involving subject-wise splitting to achieve stable rankings despite data scarcity. Participants' data were grouped to prevent an overlap between the training and test sets. In each iteration, the dataset was randomly divided into five folds at the subject level; one set was designated as the outer test set, and the remaining four were designated as the outer training set. Then, optimal hyperparameters (scored by binary F1, Table S1) were identified using a nested 4-fold CV within the outer training set. Subsequently, the model was retrained on the complete outer training set and evaluated using an outer test fold. This process was repeated 20 times using random seeds. We explored the following RF hyperparameters: the number of decision trees (30 or 50), the minimum number of samples required to split an internal node (15 or 20), and the minimum number of samples at a leaf node (6 or 12). The maximum depth of the tree was set to three, and the maximum number of features considered for splitting was set to \"sqrt.\".  
After selecting the hyperparameters, PFI was calculated for each outer test fold. First, we recorded the binary F1 score of the model under normal conditions (no permutations). Subsequently, each feature was permuted sequentially, with other features held constant, and the F1 score was recalculated to assess performance decline. Each permutation test was repeated 10 times per feature, and the results were averaged across folds and repeats to reduce variance from the limited sample size. This cross-validated, repeated methodology yields consistent estimates of feature importance, enhancing confidence that the top-ranked features are genuinely influential in distinguishing painful and baseline states.
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 2 Design of the Numerical Simulation Scheme  
In foam slurry, bentonite particles and bubbles are dispersed, making the Discrete Element Method (DEM), which is based on [PERSON]'s second law, highly suitable for simulating the migration and accumulation process of soil particles and bubbles in the slurry. Furthermore, compared to traditional experimental methods [19; 20], the DEM can better represent the complex interactions between soil particles in the slurry and the dynamic characteristics of bubble expansion and contraction. Therefore, this study treats bentonite particles and bubbles as discrete entities and uses the CFD-DEM, employing PFC3D to establish a one-dimensional fluid-solid coupling model for foam slurry infiltration in soil, with the CFD module calculating the fluid forces.  
The solid phase in the computational model includes the soil, bentonite particles, and bubbles in the slurry. The region containing the soil is represented as a grid region, while the generation area of the soil particles and bubbles in the slurry is represented as a non-grid region, as shown in Figure 1.  
Figure 1: Model computational domain discretization.  
The fluid phase in the model consists of the liquid phase in the slurry, with fluid grids generated only in the soil region. To accurately capture the expansion and contraction of bubbles during migration due to pressure changes, as shown in Figure 2, a bubble expansion-deformation calculation function was written using FISH language, as presented in Equations (1) and (2) [21].  
\[r_{i}=\sqrt[3]{\frac{C}{p_{0}+\frac{i(p_{c}-p_{0})}{1000}}} \tag{1}\]  
\[f=\sqrt[3]{\frac{(i-1)+(1001-i)\frac{p_{0}}{p_{c}}}{i+(1000-i)\frac{p_{0}}{p_{ c}}}} \tag{2}\]  
In this study, both soil particles and foam bubbles are represented as spherical particles (balls) within the PFC3D framework. Soil particles are modeled as rigid, non-deformable entities, while foam bubbles are assigned lower stiffness values to allow for deformation. The interactions between particles are governed by the built-in linear contact model provided by PFC3D. As a foam bubble encounters a constriction between soil particles, it deforms to navigate through the pore space and subsequently regains its original shape upon exiting the constriction. This modeling approach effectively captures the dynamic processes involved in mud cake formation.
# Seismic Ground Response Prediction Based on Multilayer Perceptron
## 4 Conclusions  
This study proposes an MLP-based model to evaluate the seismic response of the surface based on the ground motion at bedrock (or 100 m depth) level, and compares its performance with that of a conventional physical model SHAKE2000. A total of 6 sites in Japan were selected, and 100 ground motions at each site were used for the models. The acceleration response spectra were calculated from the predicted and measured (baseline) acceleration histories for comparison.  
The proposed MLP model predicted the magnitudes of response and the natural periods where the response amplifies closely with the measured ground motions (baseline). The proposed model did not perform as well for earthquakes whose response spectra exceed about 2 g due to a deficiency in large earthquake measurements in the training datasets.  
The MLP model outperformed the conventional model for seismic ground response analysis, with a few exceptions. It is noted that the seismic ground response analysis by  
Figure 15: The global errors in the earthquake predictions for all sites.  
SHAKE2000 was conducted based on the dynamic soil properties widely used in practice. The results of conventional seismic ground response rely greatly on the input properties.  
The proposed MLP model incorporates the input and output seismic motions with no soil properties, and therefore it is only applicable for certain sites where the earthquake measurements are available. As a subsequent development, authors aim to implement information of soil layers into the deep learning model so that it can also evaluate the surface ground motion for sites where only the soil information is known.  
Conceptualization and methodology, J.A.; investigation and formal analysis, J.Y., S.H.; validation, J.Y. and J.A.; writing--original draft preparation, J.Y., S.H. and J.A.; review and editing, J.Y., S.H. and J.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Advancement Research Program by the Ministry of Land, Infrastructure, and Transport of the Korean government, grant number 2 ICTAP-C152100-03.  
Data Availability StatementData available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kyoshin.bosai.go.jp](https://www.kyoshin.bosai.go.jp)]([https://www.kyoshin.bosai.go.jp](https://www.kyoshin.bosai.go.jp)), accessed on 22 February 2021.  
The authors declare no conflict of interest.
# Stability Analysis of Mixed Convection Flow towards a Moving Thin Needle in Nanofluid
## Problem Formulation
### Stability Analysis  
It has been discovered in [PERSON] et al. [31] that the lower branch solutions for the forced convection flow passing through a permeable flat plate are unstable and not physically realizable. Meanwhile, the upper branch solutions are stable and physically realizable. To test these features, we need to consider the unsteady Equations (2) and (3) by introducing the new dimensionless time variable, \(\tau=2 Ut/x\). Note that \(\tau\) is associated with an initial value problem that is consistent with the solution that will be obtained in practice (physically realizable). From Equation (6), the new similarity solutions in terms of \(\eta\) and \(\tau\) are:  
\[\psi=\
u xf(\eta,\tau),\hskip 14.226378 pt\eta=\frac{Ur^{2}}{\
u x},\hskip 14.226378 pt \theta(\eta,\tau)=\frac{T-T_{\infty}}{T_{w}-T_{\infty}},\hskip 14.226378 pt\tau= \frac{2 Ut}{x} \tag{14}\]  
Substituting Equation (14) into Equations (2) and (3) yields:  
\[\frac{2}{\left(1-\phi\right)^{2.5}\left[\left(1-\phi\right)+ \phi\left(\rho_{s}/\rho_{f}\right)\right]}\left(\eta\frac{\partial^{2}f}{ \partial\eta^{2}}\right)^{\prime}+f\frac{\partial^{2}f}{\partial\eta^{2}}+ \frac{\left(1-\phi\right)+\phi(\rho\beta)_{s}/\left(\rho\beta\right)_{f}}{ \left(1-\phi\right)+\phi(\rho_{s}/\rho_{f})}\frac{\lambda\theta}{4}-\frac{ \partial^{2}f}{\partial\eta\partial\tau} \tag{15}\] \[+\tau\frac{\partial f}{\partial\eta}\frac{\partial^{2}f}{ \partial\eta\partial\tau}=0,\]  
\[\frac{2}{\left(1-\phi\right)+\phi\left(\rho C_{p}\right)_{s}/\left(\rho C_{ p}\right)_{f}}\frac{k_{nf}/k_{f}}{Pr}\left(\eta\frac{\partial\theta}{ \partial\eta}\right)^{\prime}+f\frac{\partial\theta}{\partial\eta}-\frac{ \partial\theta}{\partial\tau}+\tau\frac{\partial f}{\partial\eta}\frac{ \partial\theta}{\partial\tau}=0, \tag{16}\]  
subject to the boundary conditions given below:  
\[f(c,\tau)=\frac{\varepsilon}{2}c,\hskip 14.226378 pt\frac{\partial f}{\partial \eta}(c,\tau)=\frac{\varepsilon}{2},\hskip 14.226378 pt\theta(c,\tau)=1,\]  
\[\frac{\partial f}{\partial\eta}(\eta,\tau)\rightarrow\frac{1}{2}(1-\varepsilon ),\hskip 14.226378 pt\theta(\eta,\tau)\to 0\hskip 14.226378 pt\mbox{ as }\hskip 14.226378 pt\eta\rightarrow\infty, \tag{17}\]  
Furthermore, to identify the stability of the steady flow solution \(f=f_{0}(\eta)\) and \(\theta=\theta_{0}(\eta)\) satisfying the boundary value problem (15)-(17), we assume (see [31]):  
\[f(\eta,\tau)=f_{0}(\eta)+e^{-\gamma\tau}F(\eta),\hskip 14.226378 pt\theta(\eta, \tau)=\theta_{0}(\eta)+e^{-\gamma\tau}G(\eta) \tag{18}\]  
where \(\gamma\) is a small disturbance of growth (or decay) and functions \(F(\eta)\) and \(G(\eta)\) are smaller relative to \(f_{0}(\eta)\) and \(\theta_{0}(\eta)\), respectively.
## 1 Introduction
[PERSON] et al. studied the feasibility of using active control systems to improve the performance of indoor barriers based on 2D numerical simulations. The simulation results showed that an extra IL of about 10 dB can be obtained below 500 Hz with the ideal error sensing strategy. In their investigation, the additional source is located on the top of the barrier [22]. The directivity of the additional source is also a factor that affects the performance of the active noise barrier. [PERSON] et al. proposed a type of unidirectional additional source that consists of two close loudspeakers. They indicated that the noise reduction performance can be improved remarkably by replacing monopoles with the unidirectional sources when the secondary sound field of the unidirectional sources is adjusted to maximally match with the primary sound field in the shadow zone behind the barrier [23]. In the studies mentioned above, most of the research is focused on improving the performance in local spaces or some desired positions, such as the shadow zone. However, for traffic noise control, [PERSON] et al. suggested that attention should be paid not only to the shadow zone of the barrier but also to the space beyond the shadow zone [24]. This paper aims to improve the noise reduction effect in a large space behind a barrier by optimizing the arrangement of the additional source. The relationship between the location of the additional source and the noise reduction effect in a large space is explored by the method of FEM. Section 2 introduces the concept of an active noise barrier. In Section 3, the simulation method is explained and the sound field at the top of the barrier is calculated under different conditions to explore the effect of the additional source's location on the directivity of sound waves. Section 4 investigates the effect of the primary source's location on the arrangement of the additional source. An approximate model for the optimal location of the additional source is proposed in Section 5. Finally, Section 6 presents the results of experiments conducted to verify the conclusions. The preliminary research helps to explore the new layout of an active noise barrier system, which is meaningful for improving the performance of active noise barriers in a large space.
## 5 Results and Discussions
### Detection and Annotation of Severe Weather Events
Since the video datasets of different events have temporal correlations and semantic similarities, the high accuracy of 97% is obtained. Figure 23 shows the live detection and annotation of severe weather events using the VGG 19 CNN model. Ninety-seven percent accuracy is achieved after fine-tuning the last five layers. Figure 22: Cyclical learning rate using the triangular method.
## 3 Results and Analysis
### Results and Analysis of Modified Bitumen Mixture Test  
#### 3.3.1 Marshall Stability Test  
The results are shown in Table 6.  
Table 6 shows: 1 The Marshall stability of the bitumen mixture with modifiers has different degrees of improvement compared to the virgin bitumen. Among the three warm-mix modified bitumen mixtures, compared to the Marshall stability of the virgin bitumen mixture, the Marshall stability of the Sasobit-modified bitumen mixture has the largest MS with an increase of 3.2 KN and the Evothem and XT-W3-modified bitumen mixture increased by 2.34 KN and 0.74 KN, respectively, indicating that the warm-mix modifier improved the compressive capacity of bitumen, with Sasobit-modified bitumen mixture having the strongest compressive capacity. 2 The flow values of the Sasobit-modified and XT-W3-modified bitumen mixtures are lower than those of the virgin bitumen mixture, indicating that the incorporation of Sasobit and XT-W3 modifiers enhanced the resistance to deformation of the bitumen mixture. However, the flow values became larger than those of the virgin bitumen mixture after incorporating the Evotherm modifier, indicating that the Evotherm modifier harms the resistance to deformation of the bitumen mixture but  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bitumen Mixture** & **MS (KN)** & **FL (mm)** \\ \hline Virgin Bitumen & 13.52 & 2.34 \\
3\% Sasobit & 16.72 & 2.25 \\
5\% XT-W3 & 14.26 & 2.31 \\
0.6\% Evothem & 15.86 & 3.15 \\ SBS & 14.73 & 2.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the Marshall stability test.  
Figure 6: Results of the dynamic shear rheological test.  
meets the requirements of the corresponding technical specifications. 3 The Marshall stabilities of the Sasobit-modified and Evotherm-modified bitumen mixtures were higher than those of the SBS-modified bitumen mixture, but the flow value was higher than that of the SBS-modified bitumen mixture. The Marshall stability of the XT-W3-modified bitumen mixture was lower than that of the SBS-modified bitumen mixture, but the difference was insignificant. This indicates that the compressive performance of the Sasobit-modified and Evotherm-modified bitumen mixtures is stronger than that of the SBS-modified bitumen mixture; however, the resistance to deformation is lower than that of the SBS-modified bitumen mixture. This conclusion corresponds to the results of the needle penetration test of the bitumen.
## 5 Discussion
### Case Study for Zero-Day Attack  
In today's rapidly evolving cyber threat landscape, zero-day attacks pose a significant challenge to network security. These attacks exploit unknown vulnerabilities, often bypassing traditional security measures. To address this challenge, this case study examines the application of an advanced deep learning model, specifically a Transformer-CNN for effective zero-day attack detection. In the realm of zero-day attack detection, our Transformer-CNN model has proven to be highly effective, especially in the context of the \"Reconnnaissance\" category within the NF-UNSW-NB15-v2 dataset. To rigorously test  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Benign & 98.57\% & 99.94\% & 98.57\% & 99.25\% \\ \hline PortScan & 99.89\% & 96.78\% & 99.89\% & 98.31\% \\ \hline DDoS & 99.90\% & 99.47\% & 99.90\% & 99.69\% \\ \hline DoS Hulk & 99.96\% & 99.38\% & 99.96\% & 99.67\% \\ \hline DoS GoldenEye & 100\% & 98.97\% & 100\% & 99.48\% \\ \hline FTP-Patator & 99.22\% & 96.23\% & 99.22\% & 97.70\% \\ \hline SSH-Patator & 98.25\% & 96.55\% & 98.25\% & 97.39\% \\ \hline DoS slowloris & 100\% & 97.39\% & 100\% & 98.68\% \\ \hline DoS Slowhttpttest & 100\% & 100\% & 100\% & 100\% \\ \hline Bot & 97.20\% & 77.04\% & 97.20\% & 85.95\% \\ \hline Web Attack–Brute Force & 100\% & 75.82\% & 100\% & 86.25\% \\ \hline Web Attack–XSS & 96.49\% & 80.88\% & 96.49\% & 88\% \\ \hline Infiltration & 60\% & 37.50\% & 60\% & 46.15\% \\ \hline Web Attack–Sql Injection & 100\% & 40\% & 100\% & 57.14\% \\ \hline Heartbleed & 100\% & 57.14\% & 100\% & 72.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 37: Performance metrics for Transformer-CNN across several classes in multi-class classification on CICIDS2017 dataset.  
the model's ability to detect previously unseen threats, we deliberately omitted this attack class from the training dataset, reserving it solely for evaluation during the testing phase. Remarkably, the model was able to accurately identify 293 out of the 299 instances of this attack, as illustrated in Figure 9. This outcome highlights the model's strong capacity for generalization, allowing it to recognize and respond to novel attack patterns it had not encountered before. The model's success in handling such sophisticated and unknown attack vectors underscores its robustness and positions it as a powerful asset in real-world cyber security defense mechanisms.