esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The current study aims to explore the hepatotoxicity mechanisms of garlic extract through in vivo and in vitro assays. The in vitro investigation of antioxidant and anti-inflammatory potential showed maximum 67.5% of free radical scavenging and 71.36% albumin denaturation inhibition by 600 \(\mathrm{\SIUnitSymbolMicro g}\)/mL garlic extract. To explore the hepatotoxicityective activity by in vivo experiments, the animals were orally intoxated with 150 \(\mathrm{\SIUnitSymbolMicro g}\) of CCl4 (1:1 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{\SIUnitSymbolMicro g}\) in olive oil) and treated with garlic extract (75 mg/kg b.w.) 3 times/week, for eight successive weeks. The administration of garlic extract significantly ameliorated CCl4 induced increment in amounts of serum Alanine aminotransferase (ALT), Alkaline phosphatase (ALP) and Aspartate transaminases (106.7, 116.3, 136.4 U/L) as compared to disease control which showed increased level (140.5, 156.2, 187.6 U/L). Besides, significant reduction of Superoxide dismutase (SOD), Glutathione peroxididases (GPx), and Glutathione (GSH) (29.3, 48.4, and 25.9 U/mg protein) was noticed in CCl4 induced animals, respectively. Likewise, garlic extract treatment facilitated a significant increment in all tested antioxidant enzymes levels (41.6, 63.3, and 32.5 U/mg protein), respectively. Additionally, Tumor necrosis factorx (TNF-a), C-reactive protein (CRP), Interleukin-19 (IL-16), Interleukin 6 (IL-6) and ICAM-1 (Intercellular Adhesion Molecule 1) level (63.79, 580.2, 18.3, 63.74 and 148.4 pg/mL) were increased significantly in CCl4-induced group, while garlic extract treatment decreased these pro inflammatory marker levels (40.24, 460.4, 15.4, 45.14, and 125.3 pg/mL). The animals exposed to CCl4 showed various types of alterations like lymphocytes infiltration, edema and congestion, while the animals treated with garlic extract plus CCl4 showed amelioration of the hepatocytes architectures. Thus, our finding advocates that the consumption of garlic can be a potential therapeutic remedy in the inhibition of liver ailments.  
garlic; free radicals; antioxidant; anti-inflammatory; liver function enzymes; hepatotoxicity effect +
Footnote †: journal: Applied Soft. 2020, 10, 6200; doi:10.3390/appj.2020
# Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon
## 4 Discussions
This would allow for the models to be extended to predicting the glass-forming ability of a-Si under various cooling and pressure conditions. There are several exciting avenues for future work. One promising direction is expanding the dataset to improve the reliability and robustness of the results. Additionally, this dataset could be enriched with further structural descriptors, such as local order parameters, coordination numbers, or bond angles, which would offer a more comprehensive representation of the material's structural characteristics. By capturing more nuanced relationships between the structure and the thermodynamic properties, these descriptors could enhance the models' predictive power. Furthermore, incorporating data-driven metrics and fitting theoretical models to observed trends could provide a more quantitative understanding of the influence of temperature and pressure on the thermodynamic behavior. Such approaches could significantly strengthen the scientific discussion and are identified as an important direction for future research. Moreover, potential correlations between variables should be carefully examined. Exploring more advanced machine learning techniques--such as neural networks, ensemble methods, and nonlinear models--could further improve the predictive accuracy, particularly when incorporating additional features. These methods may also better capture the complex relationships inherent in fundamental thermodynamic equations across a wider range of variables. Neural networks, for instance, have demonstrated strong capabilities in modeling complex, high-dimensional systems and could be especially effective for identifying patterns that Linear regression techniques may miss. Moreover, applying this hybrid approach--combining MD simulations with machine learning techniques--to other amorphous materials, such as metallic glasses, polymers, or glass-forming alloys, could significantly broaden our understanding of material behavior. Each of these materials exhibits unique thermodynamic and structural properties that can be effectively modeled using this approach, offering atomistic-level insights and access to extreme conditions that are often difficult, costly, or impractical to reproduce experimentally. Beyond descriptive modeling, machine learning holds strong potential for predictive tasks--such as estimating thermodynamic properties, identifying phase transition boundaries, or mapping material responses to external stimuli--enabling the fast screening of material candidates. Investigating phase transitions, such as the glass transition or crystallization, could be particularly fruitful, as the models may predict critical thermodynamic parameters governing material behavior under different processing conditions. In this way, the proposed hybrid methodology can complement experimental efforts and accelerate the design of new materials, particularly for high-performance applications where thermodynamic properties are key, such as in advanced electronics, coatings, or structural components.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 4. Results
### Generation of Fragility
#### 4.2.3 Fragility Surfaces  
Figure 11 shows the fragility surfaces of the multiple impacts of seismic actions and floods treated as statistically independent random variables. The fragility surfaces are generated using the parameters outlined in Table 5 and mathematically estimated according to Equation (4). The formation of the fragility surface occurs exclusively when there is slight damage. Conversely, in the remaining three DS, no changes in the \(\theta\) values of fragility curves are noted with increasing local scour depths induced by flood events. Consequently, the surfaces at these three DS extend linearly and represent the intensity measure of floods, following the respective fragility curves.  
The slight DS has notably small values of \(\theta\) of flood hazard. This suggests a minimal impact of the multiple impacts of seismic actions and local scour induced by floods.  
Figure 11: Fragility surface of bridge at slight DS.  
Figure 10: Fragility curves of bridge at the system level.  
The fragility analysis developed at the same damage conditions further supports this observation, demonstrating only small variations in bridge fragility characteristics.
# Automatic Evaluation of Piano Performances for STEAM Education
## 4 Methodology
### Long Short-Term Memory (LSTM) Approach  
LSTM (Long-Short Term Memory) is a type of recurrent neural network (RNN) consisting of an input, an output, and a forget gate [27]. This network addresses the problem of vanishing gradients of RNN by splitting into three inner-cell gates and building the so-called memory cells to store information in a long range [28]. This network is used for processing, making predictions, and classifying based on time series data; LSTM was first introduced in 1997 and was further developed as a successful architecture by [PERSON] et al. in 2014 [29].  
Figure 5: The piano performance assessment system using NB Approach.  
For LSTM classification, we use the \"Librosa python library for audio signal processing\" [30]. Piano recordings are converted from their waveform representation into the Mel-frequency cepstral coefficients (MFCCs), the same as the CNN approach from the previous. The proposed LSTM approach is shown in Figure 7. Here, we used MFCCs as the LSTM model input. We used 2-layers LSTM architecture, in which we used the dropout value for preventing the overfitting data. In this case, the dropout value was set at 0.5. Then, we used Dense layers of 64 neurons. Finally, we used a Fully Connected layer, transferring vector to SoftMax Activation.  
Figure 7 shows the two LSTM blocks. The current input size is 160 \(\times\) 1, which is the same as the CNN approach. The first LSTM block has 128 hidden units. Regarding the main LSTM block, the return sequences parameter is used in each input time step with hidden state output. The dropout and recurrent rates are 0.05 and 0.35, respectively. Furthermore, the second LSTM block with 32 hidden units is used without return sequences. The dropout and recurrent rates are set to 0.05 and 0.35, respectively. Next, the dropout value is set at 0.5. The process ends with a Fully Connected layer, transferring vector to the SoftMax Activation.  
Figure 6: Architecture of the 3-layers CNN.  
The four classification methods used in this study represent the prevalent techniques in machine learning applications. To be specific, the former two methods, SVM and NB, could be the representatives for the so-called shallow learning, and the latter two methods, CNN and LSTM, could be the representatives for the deep learning strategy. In addition, SVM, NB, and CNN could be regarded as static classifiers, which do not depend on time or sequential relationships, while LSTM could be regarded as a dynamic classifier, which captures the sequential information carried by data. Because each of the methods has its own pros and cons, we made a comparison for the performances of the four methods and indicated a suggested direction for the implementation of a piano performance assessment system.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.2 \(\gamma\): PLE Parameter Variable  
In this subsection, the results presented in Figure 4 show the estimation NRMSE by varying the value of \(\gamma\) while \(m=4\) and \(\sigma=4 dB\). Similarly, boxplots show the results of 100 runs conducted using the parameters listed in Table 4. They show how the data error is extended, and red lines show the median of the error. Low errors show the approach stability. The interval of NRMSE values ranges between 0.01 and 0.11.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters [Unit]** & **Values (s)** \\ \hline \(\gamma\): PLE & \((2.5;3;4;5;6)\) \\ \hline \(\sigma\): Lognormal shadowing standard deviation [dB] & 4 \\ \hline \(m\): Nakagami parameter & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variable parameter setup 2.  
Figure 3: Inter-node distance estimation NRMSE for variable \(m\)-Nakagami parameter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters [Unit]** & **Values (s)** \\ \hline \(\gamma\): PLE & \(3\) \\ \hline \(\sigma\): Lognormal shadowing standard deviation [dB] & 4 \\ \hline \(m\): Nakagami parameter & \((1;2;3;4;5;6)\) \\ \hline \end{tabular}
\end{table}
Table 3: Variable parameter setup 1.  
Another relationship is identified in this case between the PLE value \(\gamma\) and the localization error. Remarkably, the results showed that as \(\gamma\) increases from 2.5 to 6, the median localization error progressively decreases from 9% to 1% of the range. This enhancement is explained by the fact that smaller PLE values translate into higher average-range values. Thus, the number of neighbors within range, yet at more than a single-hop distance, gets increasingly larger, thus biasing more seriously the estimate in (17).
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 4 Simulation Analysis
### Performance Results Analysis
This enhancement stems fundamentally from TPC-CQF's three-queue dynamic scheduling architecture and its integrated priority management, conflict resolution, and redundant slot utilization strategies, which effectively address the challenges of LEO satellite network link dynamics and onboard resource constraints.
## 3 Three-Point Bending Tests  
The tensile failure of brittle materials depends on the microstructure in terms of the flaw density and failure stress distribution [23]. A series of three-point bending tests were conducted to characterise the ultimate tensile strength of the rock under static loading and to identify the parameters for the DFH model (Weibull parameters). This test was performed with an Instron press 100 kN, which has a level of accuracy of 0.5% (Figure 3). The test is conducted in a set-up with a loading pin that can tilt in two directions and with two fixed supports. The test is displacement-driven, and for this reason it is possible to investigate the crack evolution. The velocity is assigned to V = 3.6 \(\upmu\)m/min and it is sufficiently slow to avoid any dynamic effect, and the results are captured each 0.1 s. The sample has a loaded volume \(b\times h\times L\) = 20 \(\times\) 40 \(\times\) 120 mm\({}^{3}\) and is positioned in a way to guarantee at least 20 mm out of the pins support. Several specimens presented different pre-existing cracks before the test. For this reason, the tensile surface was chosen in order to avoid their presence on the tensile surface. The tensile strength is calculated based on the nominal stress given by three-point bend test theory according to:  
\[\sigma_{max}=\frac{3F_{max}L}{2 bh^{2}} \tag{5}\]  
where \(F_{max}\) is the maximum applied load. From the experimental results, it is possible to observe that the maximum load varies from 121 N to 605 N, providing tensile strengths ranging from 0.673 MPa to 2.98 MPa. The reason behind the large difference in the results is most certainly due to the presence of structural defects and pre-existing cracks. In a large part of experiments, a failure crack develops near the middle support, so in the region where the maximum tensile stress is reached. However, it is observed that when pre-existing cracks are close to the middle support, they can drive the specimen to failure (cf. Figure 4). The bending tests results are listed in Table 1. It should be clearly mentioned that when structural defects and pre-existing cracks are present, Equation (5) only gives a qualitative measure on the tensile strength.  
Figure 3: Set-up used for bending tests.  
The obtained Weibull parameters are the following:  
Weibull modulus: \(m\) = 2.77  
Mean strength: \(\sigma_{w}\) = 2.01 MPa  
Effective volume: \(V_{\mathit{eff}}\) = 3377 mm\({}^{3}\)  
The low Weibull modulus corresponds to the strong scatter in the obtained failure stresses.
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 4 Results and Discussion
### Model Interpretations  
An enhanced model analysis was conducted through a SHAP summary assessment. The calculated quantitative value that sums the Shapley values by each severity class and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Binary-Classes**} \\ \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline XGBoost & 0.94 & 0.94 & 0.94 & 0.94 & 0.98 \\ Random Forest & 0.9 & 0.91 & 0.9 & 0.9 & 0.97 \\ Logistic Regression & 0.65 & 0.65 & 0.65 & 0.65 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Models’ binary-class performances.  
Figure 6: Binary-class model confusion matrix and ROC curves: (**a**) XGBoost model; (**b**) random forest model; (**c**) classifier accuracies by 10-fold binary-class.  
represents the contribution of each variable to the model can be seen in Figure 7. The input variables are arranged vertically according to their influence, beginning with the most influential variable. In this figure, the horizontal axis represents the SHAP value, and the color scales indicate the significance level of each variable, with blue indicating low significance and pinkish-red indicating high significance. Data points within a range of SHAP values with more data points show a stronger correlation between input variables and injury severity. Figure 7a is the summary plot of the PDO class, which identifies that PDO accidents were mainly classified depending on the consequences and the number of vehicles involved. Moreover, the main cause of the accident is a factor in the higher severity level. Figure 7b identifies the most important parameters for classifying the injury class: main cause followed by road type and lighting condition. The most severe class (i.e., death class) is affected mostly by temporal parameters: month and day of the week, and, again, road type (Figure 7c). Based on the summary plots in Figure 7, it can be seen that a feature's value directly correlates with its impact on prediction. However, only SHAP dependence plots can reveal the exact shape of the relationships.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 3 Compression Experiment of Shipborne Vision Sensor Video  
According to the analysis of the ship vision sensor video characteristics, it is known that the redundant data of ship vision sensor video mainly contain intra-frame spatial domain redundancy and inter-frame time domain redundancy, etc. In this section, in order to test the actual control performance of HEVC video compression technology on the redundant data within the ship vision sensor video, a large number of HD perception video sequences captured from a real ship experiment and the measured compression effect are analyzed.  
Figure 4: Shipborne infrared camera video and grayscale histogram. (**a**) Shipborne infrared camera video. (**b**) Grayscale histogram.  
Figure 5: Intra pixel difference statistics of shipborne infrared camera video: (**a**) level pixel difference probability density and (**b**) vertical pixel difference probability density.  
Figure 6: Inter pixel difference statistics of shipborne infrared camera video.
# Extraction of Bioactive Compounds from _Ulva lactuca_
## 1 Introduction
Ethanol, water, and their mixtures are ideal solvents for the production of extracts with high antioxidant capacity, while they are generally recognized as safe (GRAS) and have a lower environmental impact in comparison to other solvents [13]. Regarding _U. lactuca_, experimental studies have been focused on the extraction and characterization of polysaccharides (ulvan) and proteins [3; 14; 15; 16; 17; 18; 19; 20; 21], while some others have been focused on the study of _U. lactuca_ extracts' content in antioxidant chemical compounds (phenolics, carotenoids, tocopherols, etc.) and of their antioxidant capacity [8; 22; 23]. In this study, a complete biomass characterization of _U. lactuca_ (total lipids, proteins, polysaccharides, and mineral ash contents) was performed. Moreover, the effectiveness of four nontoxic solvents (water, ethanol, ethyl acetate, and ethanol/water mixture 70:30 _v/v_) on the total carotenoids and total phenolics content, was studied. The ethanol/water mixture was determined as the most efficient among the four for extracting the targeted bioactive compounds from _U. lactuca_, thus it was employed for further investigation. The aim of the present study is the acquisition of _U. lactuca_ extracts rich in carotenoid and phenolic compounds that exhibit potent antioxidant activity, appropriate for use in a variety of cosmetics, pharmaceutical, and food products. To that end, using the ethanolic solvent, the effects of extraction parameters, namely time, temperature, and the ratio of biomass to solvent on the carotenoid and phenolic compounds content, antioxidant activity, and extraction yield, were investigated in order to identify the optimum extraction conditions. To date, there has not been documented a study for _U. lactuca_ targeting the investigation of the individual and combined effects of important extraction parameters regarding the carotenoid and phenolic content, as well as the antioxidant activity.
## 2 Material and Method
### Finite Element Model
#### 2.2.2 Modal Analysis  
Modal analysis is conducted to explore the vibration characteristics, including natural frequencies and mode shapes, of a mechanical structure or component. It provides insight into how different parts of the structure move under dynamic loading conditions. This analysis presents various modes along with their corresponding natural frequencies, allowing for the determination of vibration frequencies. To evaluate these frequencies, modal analysis was carried out using the subspace iteration method, which is based on the Rayleigh-Ritz method and the power method. This approach has proven to be particularly effective for resolving large-scale structures with a limited number of vibrations and modes [31; 32] ([PERSON] et al., 2011; [PERSON] et al., 2020).  
Modal analysis is widely used to understand and predict the behavior of structures. However, since these methods are based on idealized assumptions, the results obtained analytically must be verified with experimental data. In this context, [PERSON] et al. used experimental modal analysis in their study to demonstrate the accuracy of the modal analysis of the reinforced concrete beam. The beam length, cross-section, and reinforcement placement are shown in Figure 3 ([PERSON] et al., 200).  
The tests are prepared and conducted in the laboratory of the Civil Engineering Department of the K.U. Leuven. During the dynamic tests of the beam, they suspended  
Figure 3: Views of the validated beam: (**a**) isometric, (**b**) cross-section, and (**c**) reinforcement layout.  
Figure 2: SOLID65 geometry (NSAYS, 2025).  
it by means of elastic springs in order to simulate the free boundary conditions. They placed these springs at the theoretical nodes of the basic (first) bending mode of the beams. In the analytical model, the experimental model was taken into account; the concrete was modeled using the SOLID65 element and the reinforcement was modeled using the LINK180 element. The experimental and analytical model analysis results are given in Table 2. It was determined that the mode shapes in the study conducted by [33] [PERSON] et al. were similar to the analytical mode shapes. The concrete mode shape and the reinforcement mode shape are given in Figure 4. It was observed that the concrete and reinforcement worked together.
## 5 Numerical Examples
### Experimental Runs Results
#### 5.2.2 Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**First DSPOMDP Model**} & \multicolumn{3}{c}{**Second DSPOMDP Model**} \\ \hline \multicolumn{3}{c}{**POMCP**} & \multicolumn{3}{c}{**POMCP**} \\ \hline \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) & \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) \\ \hline
1 & Inhibitor & 4 & 4 & \(-2000\) & 2 & Do-nothing & 2 & 2 & \(-500\) \\
4 & Replace & 1 & 1 & \(-2250\) & 2 & Inhibitor & 2 & 2 & \(-350\) \\
1 & Do-nothing & 5 & 5 & \(-200\) & 2 & Do-nothing & 2 & 3 & \(-500\) \\
5 & Replace & 1 & 2 & \(-250\) & 2 & Pigging & 5 & 5 & \(-3500\) \\
1 & Do-nothing & 1 & 2 & \(-200\) & 5 & Replace & 1 & 1 & \(-250\) \\ \hline \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-4500.47\)} & \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-4488.19\)} \\ \hline \multicolumn{3}{c}{**ARDESPOT**} & \multicolumn{3}{c}{**ARDESPOT**} \\ \hline \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) & \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) \\ \hline
1 & Do-nothing & 3 & 3 & \(-200\) & 2 & Do-nothing & 2 & 2 & \(-500\) \\
3 & Inhibitor & 4 & 3 & \(-300\) & 2 & Inhibitor & 2 & 2 & \(-350\) \\
4 & Do-nothing & 4 & 5 & \(-2000\) & 2 & Inhibitor & 2 & 3 & \(-350\) \\
4 & Replace & 1 & 2 & \(-2250\) & 2 & Inhibitor & 3 & 3 & \(-350\) \\
1 & Do-nothing & 3 & 4 & \(-200\) & 3 & Inhibitor & 5 & 5 & \(-300\) \\ \hline \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-3861.47\)} & \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-1692.81\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Example of CSPOMDP model run result using ARDESPOT. (Figure 5b). This result showed a major difference between the two solvers. However, only the ARDESPOT significantly varied when compared to the CS model.