esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Buoyancy-Driven Square Cavity
### Case \(Ra=10^{6}\), \(\epsilon=0.01\), Constant Viscosity  
In this section, the present solutions are analyzed and compared with the reference solutions [46; 52; 56], in particular, the Nusselt number along the hot vertical wall. [PERSON] [46] presents extrapolated values obtained with a finite-difference method for the streamfunction-vorticity formulation applied to the Boussinesq model with up to \(81\times 81\) nodes. [PERSON] [56] solves the same equations with a spectral Chebyshev Galerkin method (with up to \(72\times 72\) degrees of freedom per variable), and [PERSON] [52] uses a finite volume method to solve the full Navier-Stokes equations on a \(512\times 512\) stretched grid. [PERSON] and coworkers use a stabilized finite element method applied to the Boussinesq model with stabilization based on bubble functions and \(80\times 80\) grids.  
For the three methods and the HH stabilizing matrix, Figure 8 shows the convergence of the minimum, maximum and average Nusselt numbers along the vertical hot wall as a function of the nodes on the side \(\sqrt{n_{\rm mp}}\), where \(n_{\rm mp}\) is the number of nodal points. It can be seen that, for small \(\epsilon\), the full NS equations and the Boussinesq models give very similar solutions, as shown in many publications. The present methods for the given mesh resolution provide Nusselt numbers between those reported by [PERSON] and [PERSON].  
Table 2 compares the present results on a uniform mesh of \(140\times 140\) elements with various published reference solutions. The Nusselt numbers computed with full NS and the classic tau converge from below. The Boussinesq model computed with the HH and Polner taus give very similar Nusselt numbers.  
In summary, it can be concluded that both SUPG and SGS give very similar solutions, so there is not a favorite method. Regarding the stabilization matrices, in this regime, the incompressible HH and Polner taus are the best choices for all models, including the compressible Navier-Stokes equations, because they attain a faster convergence of the Nusselt numbers with respect to the number of degrees of freedom.  
Figure 8: Buoyancy-driven square cavity. Nusselt numbers along hot vertical wall for \(\epsilon=0.01\) and HH tau.
###### Abstract  
The purpose of this study was to investigate the way in which anthropometric and fitness measures vary by class year before and after a Division I female field hockey competitive season. Anthropometric (mass and percent body fat) and fitness measures (vertical jump and Yo-Yo Intermittent Recovery Test 1 (Yo-Yo IRI) distance) were collected on 16 athletes (mean \(\pm\) SD-age = 19.5 \(\pm\) 0.9 years) before and after a competitive season. Class year, based on the athlete's year in college, was used to group athletes in the study. Body mass, vertical jump height, and Yo-Yo IRI distance all increased pre to post season (\(p\) < 0.05). First-year athletes had a significantly lower body mass, vertical jump height and Yo-Yo IRI distance compared to other class years across the testing period (\(p\) < 0.05). Post season levels for first-years were lower than both pre and post season levels for sophomore or junior athletes. Team mean vertical jump increased 8.9%, with all but two athletes improving. Team mean Yo-Yo IRI distance increased 16.4%. Athlete training over the course of the study was designed to improve on field playing performance and training was not directly select to improve measures in the study; therefore, the potential success of integrating fitness, speed, and strength development into technical training sessions is indicated. Coaches should be aware that first-year athletes may not be at the same level of conditioning as sophomores and juniors. Coaches should work specifically with the first-year athletes during the season to monitor their fitness levels.  
Keywords:field sports; seasonal changes; Yo-Yo Intermittent Recovery Test 1; vertical jump; percent body fat; women +
Footnote †: journal: Medical Informatics
# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
###### Abstract  
The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual human-based teaching. It achieves this by extracting rhythmic and emotional information from music through audio analysis to generate corresponding dance sequences. The virtual human, functioning as a digital tutor, demonstrates dance movements in real time, enabling students to accurately learn and execute dance postures and rhythms. Analysis of the teaching outcomes, including effectiveness, naturalness, and fluidity, indicates that learning through the digital human results in enhanced user engagement and improved learning outcomes. Additionally, the diversity of dance movements is increased. This system enhances students' motivation and learning efficacy, offering a novel approach to innovating dance education.  
dance instruction; diffusion model; digital human; music driven +
Footnote †: journal: Applied Sciences
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
## 3 Finite Element Analysis of Piezoelectric Impedance
The peak frequencies in the admittance signature can reflect the local dynamic characteristics of the host structure with the piezoelectric patch. After comparative analysis, the piezoelectric admittance around some resonant frequencies is simulated and analyzed with different crack sizes. The scanning frequency bands are selected around 19, 62 and 174 kHz, which is near the coupled structure resonance frequencies. The detailed frequency bands of 19.2-19.3, 62.5-63 and 174.5-175 kHz, covering from relatively low to relatively high frequencies, are chosen as scanning frequency bands for the finite element harmonic response analysis. Then the piezoelectric impedance is computed. The piezoelectric admittance spectra and peak frequencies in the admittance spectrum with different cracks lengths (0 mm crack length means primitive state) on the beam are shown from Figures 4-6, the vertical coordinate is an imaginary part of the piezoelectric admittance spectrum, whose unit is siemens (S). From Figures 4a, 5a and 6a, it is clear that as the crack appears and grows, in both selected frequency bands, the peak frequencies in the piezoelectric admittance spectra shift to the left. It means that the peak frequencies are different from those of the piezoelectric modes, which are not affected by the piezoelectric modes. Figure 5: Numerical simulation results (**a**) piezoelectric admittance spectrum and (**b**) peak frequency tendencies in 62.6 kHz–62.8 kHz with different crack sizes. Figure 6: Numerical simulation results (**a**) piezoelectric admittance spectrum and (**b**) peak frequency tendencies in 174.5 kHz–175 kHz with different crack sizes. that the peak frequency decreases when the crack grows, as shown in Figures 4b, 5b and 6b. Based on the above theoretical analysis, the local stiffness of the host structure around the crack decreases when the crack occurs and propagates. Once the local stiffness decreases, the resonant frequency of the host structure will decrease, shifting the peak frequency to left in the piezoelectric admittance spectrum. The numerical simulation results are consistent with the theoretical analysis. Based on the above findings, the peak frequency in the piezoelectric admittance is chosen as the feature parameter to characterize the crack propagation. The peak frequency shift between crack-free and 6 mm crack is defined as \(\Delta f_{1}\), while the peak frequency offset between crack-free and 12 mm crack is defined as \(\Delta f_{2}\). The peak frequency shifts at different frequency bands with different crack sizes are extracted as shown in Table 3. It shows that the higher the scanning frequency band, the greater the peak frequency shift, thus the higher the frequency band, the higher the sensitivity of the proposed method.
# Effect of Temperatures on Polyphenols during Extraction
## 2 Thermal Degradation
### Extraction Conditions  
The duration of heating, storage conditions prior to extraction and exposure to oxygen are all factors that play an important role in determining the effect of temperature on the polyphenols [63, 65]. PLE and traditional extraction are performed in very different environments. PLE is performed inside a cell with no exposure to light, mostly in the absence of oxygen (usually the cell is purged with nitrogen) for a shorter period compared with traditional extraction performed for long hours in a beaker. Long extraction times leave the partially extracted polyphenols exposed to light and oxygen, which has been reported to degrade the extractants [36]. This is further supported by studies conducted on the impact of various methods used for drying the plant material before extraction. [PERSON] et al. [66] found that sun-drying finger leaves caused significant loss in polyphenol yield compared with other drying methods that do not expose the leaves to light. Hot air or conventional drying has been shown to significantly decrease polyphenol yield compared with drying in a vacuum or nitrogen [30, 67, 68]. [PERSON] et al. [37] performed a PLE extraction of polyphenols from dried rosmears leaves at 100, 150 and 200 \({}^{\circ}\)C. The study also found that when the temperature increased, the TPC increased. As the TPC and AOA were maximum at 200 \({}^{\circ}\)C, the authors suggested that thermal decomposition did not occur. As deoxygenated water was used and no exposure to air occurred during the process, it was suggested that a low level of oxidation occurred. Carnosic acid is said to be more sensitive to the presence of oxygen than temperature, which could explain why a PLE at 200 \({}^{\circ}\)C showed the highest yield of carnosic acid. [PERSON] et al. [36] studied the stability of phenolic compounds, specifically the impact of temperature and other conditions on individual polyphenol standards (gallic acid, cis-coumaric acid, caftaric acid, catechchin, epicatechin and epigallocatechin gallate). The samples were subject to a superheated methanol treatment at 40, 50, 100 and 150 \({}^{\circ}\)C, while methanol extraction at 65 \({}^{\circ}\)C (boiling point of methanol) for 45 min was conducted as a control. The recovery rates of individual polyphenols were calculated using HPLC.
## 4 Residual Properties Analysis
### Stiffness Degradation  
The law of secant stiffness degradation of specimens is shown in Figure 14. And secant stiffness is the secant modulus at 0.4 Np. As seen from Figure 14a-e, the laws of stiffness degradation of the specimens are similar to that of bearing capacity. But the degradation of stiffness is larger than that of bearing capacity. The bending stiffness of the beam deteriorated most slowly. Bending stiffness degradation rate is about 49%, when \(T\) is 600 degrees centigrade. The axial compressive stiffness of the prism test block deteriorated most rapidly. The axial compressive stiffness degradation rate is about 70% when \(T\) is 600 degrees centigrade. And the axial compressive stiffness degradation rate is about 88% when \(T\) is 800 degrees centigrade. According to the degradation trend, the stiffness degradation rate of SRRAC components under axial compression is larger than that of the flexural components after exposure to elevated temperature. Stiffness degradation coefficient (\(\beta_{\text{K}}\)) of compression components is about 5-21% less than that of the flexural member when \(T\) is at 200 degrees centigrade to 600 degrees centigrade.  
As seen in Figure 14f, the stiffness of each component after exposure to elevated temperature is an insignificantly affected by the replacement percentage. As replacement percentage increases, the variation ranges of the axial compression stiffness of prism test block and column, flexural rigidity and shear rigidity of beams are \(-5\)-\(8\)%, \(-7\)--\(3\)%, \(-3\)-\(2\)% and \(-14\)--\(2\)%, respectively. In general, stiffness of a component has a slight descent trend after exposure to elevated temperature with replacement percentage increases. The cause of stiffness degradation of RAC and SRRAC is similar to that of  
Figure 14: Stiffness degradation. (**a**) rectangular prisms specimens; (**b**) columns; (**c**) bending beams; (**d**) shear beams; (**e**) The influence of temperature; (**f**) The influence of replacement percentage.  
bearing capacity degradation. Both are caused by the evaporation of free water in RAC and bond failure between the cement slurry and aggregate. And the failure mechanism of the RAC after exposure to elevated temperature is basically similar to that of ordinary concrete.
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 2 Related Work
### Work Based on Unbalanced Datasets  
[PERSON] et al., combined text-based and content-based email features, utilizing ensemble learning methods to classify emails [23]. One of the highlights of this paper is that it considers the actual situation and the development trend of phishing emails, constructing a more practical and closer to real-world scenario imbalanced dataset containing 32,051 benign emails and 3460 phishing emails. [PERSON] et al. collected 10,500 benign emails and 10,500 phishing emails as an initial dataset [31]. They then adjusted the ratio of benign emails to phishing emails from 1:1 to 1:10 to create an imbalanced test set and achieved an F1-score of 99.6% on an extremely unbalanced (1:10) ratio. [PERSON] et al. [32] developed an adversarial dataset using various legitimate text attack techniques. The dataset comprised 5092 legitimate emails and 568 phishing emails. It retrained phishing email detection models using this adversarial dataset. Results showed that the accuracy and F1-score of the models improved with subsequent attacks. It can be observed that although these experimental designs used imbalanced datasets that are closer to real-world scenarios, no measures were taken to reduce the impact of the imbalance on the classification performance.
## 3 Design Principles of the Helical Phased Array
### Beamformer Performance Evaluation  
To evaluate the beamformer performance as a standalone unit, laboratory measurements were conducted by replacing the helical antennas with the signal generator AGILENT N9310A. Since the two helical antennas were placed side by side \(\lambda/8\) apart, the phase delay between them at the input ports of the beamformer was achieved using a 50 \(\Omega\), 45\({}^{\circ}\) delay line. The two LNAs were integral parts of the active helical antennas and were therefore included in the test to identify any phase and amplitude imbalances between them when interacting with the beamformer, as illustrated in Figure 22a. The test setup is depicted in Figure 22b.  
After numerous trials, the optimum front-to-back ratio response was achieved at 23 MHz, diverging from the original target of 25 MHz. As depicted in Figure 23 below, the phase difference between the two modeled helical antennas (blue curve at the bottom) operating within the 20-30 MHz frequency range, when \(\Delta\Phi\) is fixed, exhibits a positive slope in the forward direction. This problem arises due to the phase error caused by the distance between the two helical antennas as a function of the wavelength. In contrast, the phase difference in the backward direction of the array (grey graph at the bottom) exhibits roughly a linear behavior, with a phase error of approximately +2.5% due to the linearization which is attributed to the integration of a high-pass phase progressive network into the beamformer. Figure 24a shows the amplitude response of the array in both forward (depicted by the blue graph at the top) and backward (depicted by the orange graph at the bottom) directions. Furthermore, Figure 24b illustrates the beamformer front-to-back ratio under ideal conditions, ranging from a minimum value of 30 dB at 30 MHz to a maximum value of 37 dB at 20 MHz.  
Figure 21: The simulation of the entire system at 25 MHz.  
The average front-to-back ratio response within 20-30 MHz is at 33 dB. Despite minor amplitude imbalances between the two LNAs regarding the input ports of the beamformer,  
Figure 23: The phase difference in the two helical antennas under ideal conditions.  
Figure 22: (**a**) Testing arrangement. (**b**) Instrumentation during testing.  
the front-to-back ratio achieved under ideal laboratory conditions is considered very satisfactory [34]. The following sections focus on the performance evaluation of the helical phased array tested under real conditions.
## 3 Results and Discussion
### Material Characterization
#### 3.1.3 BET Analysis  
To elucidate the pore size distribution and specific surface area of SiPMANs, a BET analysis was performed. The N\({}_{2}\) adsorption-desorption isotherms of SiPMANs are shown in Figure 4a. The presence of a type IV hysteresis loop in the adsorption curve indicates that SiPMANs had a mesoporous structure [35]. The pore size of SiPMA-TETA did not differ significantly from those of SiPMA-TETA and SiPMA-DETA, presumably because the polymerization of organic monomers did not fully penetrate the pore channels of SiO\({}_{2}\). However, SiPMA-TETA still exhibited excellent adsorption performance. As shown in Table 1, the specific surface area, pore volume, and pore diameter of SiPMANs were all lower than those of SiO\({}_{2}\). This indicates that the organic polymer occupied space within the SiO\({}_{2}\) pores.  
Figure 3: TG–DSC analysis of SiPMA (**a**), SiPMA-DETA (**b**), SiPMA-TETA (**c**), and SiPMA-TETA (**d**).
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 2 Related Work
In reference [15], the authors utilized the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. In our discussion, we focus on the first two datasets, as they were also used in our own research. They proposed a memory-augmented deep autoencoder to address the overgeneralization issue of autoencoders. On the NSL-KDD dataset, they achieved an accuracy of 89.5%, while on the UNSW-NB15 dataset, they achieved an accuracy of 85.3%. Our proposed model performed better on the UNSW-NB15 dataset, while our research achieved comparable results on the NSL-KDD dataset. This research also relied on the feature compression ability of the deep learning models and did not make any attempt to reduce the number of input features using statistical or machine learning techniques. Reference [16] tested their proposed method on the UNSW-NB15 dataset. They divided the dataset based on the TCP, UDP, and OTHER protocol categories. For each protocol category, they performed feature selection using the chi-square method, which reduced the number of features by approximately half. For classification, they used a one-dimensional Convolutional Neural Network (CNN). They presented their results for each protocol category separately as well as an overall result. The authors reported an accuracy of 76.3% for the overall categories, which was lower than what we achieved on the same dataset. In [17], the UNSW-NB15 dataset and variants of the NSL-KDD (KDDTest+ and KDDTest-21) were used. This paper utilized a supervised variational autoencoder, where the latent vector was computed using Wasserstein GAN. It effectively addressed the class imbalance issue in the dataset by synthesizing low-frequency attacks. The proposed approach achieved an accuracy of 93.0% on the UNSW-NB15, 89.3% on the KDDTest+, and 80.3% on the KDDTest-21 datasets. Although the authors did not perform feature reduction separately, their results demonstrated a comparable accuracy metric to ours due to the removal of the class imbalance issue. The authors of [18] used the UNSW-NB15 and NSL-KDD datasets for their experiments. Initially, the researchers addressed the issue of class imbalance by generating synthetic records via the application of a Generative Adversarial Network (GAN). Subsequently, they employed a multiclass support vector machine classifier to carry out the classification task. Additionally, the parameters of the classifier were optimized using Bayesian hyperparameter optimization. The authors achieved classification accuracies of 99.5% on the NSL-KDD dataset and 85.38% on the UNSW-NB15 dataset. The reported accuracy in this research outperformed our own findings on the NSL-KDD dataset due to removing the class imbalance issue in the dataset.
## 3 Results  
### Diatom Growth and Ecotoxicological Parameters (IC\({}_{50}\))  
Data on the exposure concentration and toxicity of five contaminants that inhibit the growth of the marine diatom _P. tricornutum_ were obtained from previous studies [15; 22; 23; 24; 25] and are presented in Table 1. According to the IC\({}_{50}\) values, which refer to the exposure concentration capable to inhibit half of the maximum growth in this marine diatom, SDS and fluoxetine are the most toxic compounds tested in _P. tricornutum_, with the lowest IC\({}_{50}\) values of the tested cultures (<50 \(\mu\)g L\({}^{-1}\)). In opposition, ibuprofen appears as the least toxic compound tested, with the highest dose (ca. 350 \(\mu\)g L\({}^{-1}\)) needed to reduce to half the growth of _P. tricornutum_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **Concentrations** & **Inhibition (\%)** & **IC\({}_{50}\) (\(\mu\)g L\({}^{-1}\))** & **Reference** \\ \cline{3-5}  & **Tested (\(\mu\)g L\({}^{-1}\))** & **Inhibition (\%)** & **IC\({}_{50}\) (\(\mu\)g L\({}^{-1}\))** & **Reference** \\ \hline \multirow{4}{*}{Propranololol} & 0.3 & 0.15 & & [22] \\  & 8 & 4.11 & & \\  & 80 & 41.11 & & \\  & 150 & 77.08 & & \\  & 300 & 154.16 & & \\ \hline \multirow{4}{*}{Fluoxetine} & 0.3 & 0.63 & & [23] \\  & 0.6 & 1.27 & & \\  & 20 & 42.26 & 47.3 & \\  & 40 & 84.52 & & \\  & 80 & 169.04 & & \\ \hline \multirow{4}{*}{Ibuprofen} & 0.8 & 0.23 & & \\  & 3 & 0.86 & & [24] \\ \cline{1-1}  & 40 & 11.41 & 350.6 & \\ \cline{1-1}  & 100 & 28.52 & & \\ \cline{1-1}  & 300 & 85.57 & & \\ \hline \multirow{4}{*}{Glyphosate} & 10 & 4.43 & & \\  & 50 & 22.13 & & \\ \cline{1-1}  & 100 & 44.27 & 225.9 & \\ \cline{1-1}  & 250 & 110.67 & & \\ \cline{1-1}  & 500 & 221.34 & & \\ \hline \multirow{4}{*}{SDS} & 0.1 & 15.5 & & \\ \cline{1-1}  & 1 & 21.1 & & \\ \cline{1-1}  & 3 & 24.6 & 11.4 & \\ \cline{1-1}  & 10 & 43.8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exposure compounds concentrations, _Phaeodactyllum tricornutum_ relative growth inhibition and calculated IC\({}_{50}\).