esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Failure Criterions
### Linear Failure Criterion  
As it is difficult for the engineering design department to establish the failure criteria, the nonlinear criteria should be further simplified for the convenience of analysis. Therefore, a linear failure criterion can be established by curve fitting as below [29].  
Tensile meridian:  
\[\frac{\tau_{oct}^{t}}{f_{c}}=a_{2}+b_{2}\frac{\sigma_{oct}}{f_{c}},\ R_{AC}^{2 }=0.94;R_{SMA}^{2}=0.96 \tag{10}\]  
Compressive meridian:  
\[\frac{\tau_{oct}^{c}}{f_{c}}=k_{1}\bigg{(}a_{2}+b_{2}\frac{\sigma_{oct}}{f_{c }}\bigg{)},\ R_{AC}^{2}=0.94;R_{SMA}^{2}=0.98 \tag{11}\]  
Strength envelope curve:  
\[\tau_{oct}(\theta)=\tau_{oct}^{t}-(\tau_{oct}^{t}-\tau_{oct}^{c})3\theta/ \pi,\ R_{AC}^{2}=0.88;R_{SMA}^{2}=0.89 \tag{12}\]  
where \(a_{2}\), \(b_{2}\), and \(k_{1}\) are model parameters as shown in Table 6.  
Seeing from Figures 9 and 10, the nonlinear failure envelope curves of asphalt mixtures are transformed from the shape similar to a shield into a hexagon in the \(\sigma_{oct}-\tau_{oct}\) space due to linear fitting. With the increase of average stress, the hexagon strength envelope gradually expands along the linear tensile and compressive meridians as shown in Figure 11. Furthermore, the simplified failure criterion under complex stress states can be established by the uniaxial compressive, uniaxial tensile, and the ordinary triaxial tests. Although each of these tests cannot reflect the strength properties of asphalt mixtures under three-dimensional stress states, the synergistic failure effect of each stress component can be considered with the combination of these tests. Therefore, the engineering design department has the ability to complete the related tests.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mixture Type** & \(\mathbf{a_{2}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{k_{1}}\) \\ \hline AC-13 & 0.088 & \(-\)0.733 & 1.4 \\ \hline SMA-13 & 0.08 & \(-\)0.579 & 1.6 \\ \hline \end{tabular}
\end{table}
Table 6: Parameters of the linear failure criteria.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Mixture Type** & \(\mathbf{a_{1}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{c_{1}}\) & \(k\) & \(m\) \\ \hline AC-13 & 0.085 & \(-\)0.67 & \(-\)0.32 & 1.69 & 5 \\ SMA-13 & 0.085 & \(-\)0.664 & \(-\)0.426 & 1.63 & 7 \\ \hline \end{tabular}
\end{table}
Table 5: Parameters of the simplified nonlinear failure criteria.
# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 3 Materials and Methods
### Metrics for Evaluating Deep Learning Models  
The evaluation of the deep learning models CASE and KITP is performed using metrics such as Precision, Recall, Accuracy, Loss, and the AUROC score and curve. To implement these metrics, the following basic concepts are defined:  
True Positive (TP): Textual Content that belongs to the toxicity class and is predicted as belonging to the same class  
True Negative (TN): Textual content that does not belong to the toxicity class and is predicted as not belonging to the same class.  
False Positive (FP): Textual Content that does not belong to the toxicity class and is predicted as not belonging to the same class  
False Negative (FN): Textual Content that belongs to the toxicity class and is predicted as not belonging to the same class.  
The above defined concepts are used to compute the following metrics:  
1. Precision (or positive predictive value): The ability of the model to identify the possible textual content from the input. \[\text{P}=\left(\frac{\text{TP}}{\text{TP}+\text{FP}}\right)\] (12)
2. Recall (or sensitivity or true positive rate (TPR)): The ability of the model to identify all the relevant abusive textual content from the predicted possible textual content. \[\text{R}=\left(\frac{\text{TP}}{\text{TP}+\text{FN}}\right)\] (13)  
Figure 9: Graphical Representation of a Network of various cloud-level modules to detect the exchange of similar abusive content across a network or organization.  
Accuracy: The ratio of correct predictions made by the model to the total number of predictions by the model. \[\alpha=\left(\frac{\text{TP }+\text{ TN}}{\text{TP }+\text{TN}+\text{FN}+\text{FP}}\right)\] (14)
4. Selectivity (or specificity or true negative rate): The ability of the model to identify all non-abusive textual content from the predicted non-abusive content. \[\text{S}=\left(\frac{\text{TN}}{\text{TN}+\text{FP}}\right)\]
5. AUROC: measures the ability of the model to distinguish between classes whereas the ROC curve is a probability curve where TPR (R) is plotted against the false positive rate (\(1-\text{S}\)). AUROC determines the accuracy of the model being able to predict the true values as true and false values as false. As the AUROC increases, the MaLang model becomes better at recognizing different types of toxic content.
###### Abstract  
To enhance the performance of polyurethane foams, fillers are often incorporated into the matrix. However, the interaction between the filler and the polyurethane matrix is crucial for achieving the desired property improvements. Therefore, surface modification of the fillers plays a vital role in optimizing this interaction. The current study aims to activate the surface of expanded vermiculite and perlite with dextrin to incorporate additional functional groups on the surface of the fillers via the ball-milling process, thereby improving the reaction with a polymer matrix. Applied surface activation with dextrin resulted in the formation of dextrin-Si-O-Si-dextrin linkages in the fillers, allowing for a maximum improvement of 11% and 9% in water absorption, as well as slightly positive changes in the water contact angle of polyurethane foam with dextrin-activated perlite and vermiculite, respectively, compared to non-activated fillers. It also resulted in noticeable differences in the foaming times and viscosity of the premixes, affecting the structure of rigid polyurethane foam composites. Compared to non-activated perlite and vermiculite filler polyurethane foam composites, the dynamic viscosity of polyurethane foam composites with dextrin-activated perlite and vermiculite reduced maximally 16 and 21 times, respectively. At the same time, the closed cell content increased, resulting in lower thermal conductivity values up to a 7.5 wt.% filler concentration. In addition, a rise in mechanical performance was also achieved. Compressive strength increased by a maximum of 61% and 71%, while tensile strength increased by a maximum of 36% and 20% for polyurethane foam composites with dextrin-activated perlite and vermiculite, respectively.  
polytechnae foam composites; dextrin; expanded vermiculite; expanded perlite; interface adhesion; surface activation Article
## 5 Numerical Results and Discussion
### Regression Results  
Time-series graphs (Figures 9-12) represent values up to the second decimal place using seven regression algorithms. The x-coordinate indicates the number (frame) of data used in the regression, while the y-coordinate shows the coordinate value and error of the recognized object for each data point. Among the three algorithms used, Extra-Trees, Gradient Boosting and Prediction Voting Regressor for Unfitted Estimators showed a single frame value with an average y-prediction value, in contrast to the other four algorithms.  
The performance of Xgboost (Figure 9) is 0.15, which implies that the screw-fastening position and timing are largely consistent. This paper compared the performance of regression algorithms using RMSE for the performance measure as  
\[RMSE = \sqrt{\frac{1}{total\_frame}\cdot\Sigma(y(predict)-y(GT))^{2}}. \tag{4}\]  
RMSE has the characteristics of preventing values from becoming negative and increasing the sensitivity of errors by squaring them (in this experiment, total_frame is 102).  
Figure 7: Learning result of Dataset.  
The results show that Xgboost (Figure 9) performed the best, with Adaboost (Figure 9) and Random Forest showing values of 84.47 and 191.23 (Figure 10) respectively. The graph clearly shows a significant difference between the predicted and ground-truth values for these two algorithms. In other cases, Bagging (Figure 10), Prediction Voting Regressor for Unfitted Estimators (Figure 11), Extra-Trees (Figure 11) and Gradient Boosting (Figure 12) produced results where the predicted values were close to 1 and the error values were opposite to the ground-truth values. As a result, the RMSE was the lowest for Bagging at 273.22 and the highest for Gradient Boosting at 714.78.  
As a result of the comparative analysis, it was found that Xgboost performed significantly better than other regression algorithms, producing the most ideal error values. Therefore, Xgboost was selected for regression in the subsequent experiments.  
Figure 8: Performance-analysis results by class.  
Figure 9: Regression results of the Xgboost and Adaboost.  
Figure 11: Regression results of the Prediction Voting and Extra Trees.  
Figure 12: Regression results of the Gradient Boosting.  
Figure 10: Regression results of the Random Forest and Bagging.
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 1 Introduction
They have investigated the impact of different reservoir characteristics, such as confining pressure, rock type, and the degree of fracturing on rock sample permeability. As a result, several theoretical models have been established, including the binomial model, power function model, and exponential model. Common theoretical equations for calculating the relationship between reservoir permeability and effective stress include the following:  
\[K=K_{ref}*(\sigma_{re}/\sigma_{ref})^{-\alpha} \tag{1}\]  
\[K=K_{ref}*e^{-\beta*(\sigma_{re}-\sigma_{ref})} \tag{2}\]  
\[K=m*\sigma_{re}^{2}+g*\sigma_{re}+z \tag{3}\]  
where, \(\sigma_{re}\) is the effective stress, in MPa; \(\sigma_{ref}\) is the reference effective stress, in MPa; K is the permeability, in mD; K\({}_{ref}\) is the permeability under the reference effective stress (\(\sigma_{ref}\)), in mD; \(\alpha\) is the stress sensitivity coefficient of the power law model; \(\beta\) is the stress sensitivity coefficient of the exponential model; and g and z are fitting coefficients. Most scholars currently research the stress sensitivity of reservoirs based on conventional sandstone reservoirs or theoretical discussions without specific geological conditions. However, research on the stress sensitivity of ultra-deep carbonate reservoirs is relatively less in-depth. Therefore, this study aims to quantitatively study the stress sensitivity of fracture-porosity reservoirs in ultra-deep carbonate reservoirs. This study will be based on numerical simulations and the rock mechanic characteristics of carbonate reservoirs to calculate the relationship between reservoir permeability and effective stress in the Shunbei oilifield fault zone of the Tarim Basin. This research will provide crucial guidance for developing and adjusting oilfield development plans.
## 2 Results and Discussions
### Elemental Profile Composition  
A total of 12 elements (Ca, K, Mg, Na, Zn, Fe, Se, Ag, Ba, Ni, Mn, and Cu) were monitored by AAS determination in samples of ALF and RCV seeds and sprouts during the 5-day germination process. The determination of macro- and trace elements in ALF and RCV seeds and sprouts is reported in Tables 1 and 2, as mean and standard deviation of two independent replicates. Elemental data were examined according to the day of germination of ALF and RCV sprouts using one-way ANOVA. The averages in the same row followed by the same lowercase letter were not significantly different according to the [PERSON]'s multiple range test at \(p\leq 0.05\). The ANOVA results showed that the content of Ca, Mg, Na, K, Fe, Zn, Ni, and Mn were significantly different (\(p<0.05\)) depending on the day of germination for both ALF and RCV sprouts, while the content of Cu and Se were not significantly different (\(p>0.05\)) during germination. In the studied samples, K was the most abundant element, followed by elements like Na, Mg, Ca, Fe, and Zn. Ba and Ag weren't found in the analyzed samples. The amounts of Mg and K measured in ALF sprouts was comparable with RCV sprouts, whereas the Ca and Na amounts were higher, reaching a level about 4.6-fold higher in ALF sprouts than in RCV for Ca, and 1.56-fold higher for Na, respectively. A 3.5-fold higher concentration of Fe in ALF sprouts than in RCV sprouts was measured in the third day of germination, and also a higher amount of Zn, Mn, Ni, and Se. The sum of the elements in ALF exceeded the RCV from the first day of germination, with the same trend maintained throughout the germination process. Regarding the dynamic of the elemental profile, almost symmetrical patterns were observed during germination for most of the measured elements, shown as relative change in quantity per day compared with raw seeds (Figure 2 and Supplementary Figure S1). While the amount of Zn, Cu, Se, and Ni trace elements slowly increased during the 5 days of germination, substantial increases were observed for Ca, Mg, Na, and K macro-elements. The total amount of minerals in RCV seeds was above the value in the ALF seeds; however, the increase in the total values was notably higher in ALF. Although the sum of the measured elements exhibited the same trend, in particular a significant increase starting from the second day of germination, differences between the plants species were observed with respect to the dynamics of some elements (Figure 3).
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 5 Experiments
### Results and Analysis
#### 5.4.2 Main Performance Evaluation
Furthermore, the \(t\)-test results reveal no significant differences in metrics between \(X^{Y}\) and \(X\) within this dataset. However, the relatively higher RCT indicates that SS-SCE may employ a more lenient criterion when extracting sentiment cues on the Yelp dataset. Regarding the IMDb dataset, the results with \(X^{Y}\) as input show the highest decrease in accuracy and precision among the three datasets, while the impact on recall is the opposite, even surpassing the performance using \(X\) as input. This phenomenon could be attributed to longer texts containing more distracting information, which our SS-SCE method is adept at effectively filtering out. The relatively lower RCT value among the three datasets corroborates this observation. Furthermore, the higher recall rate for \(X^{Y}\) suggests that SS-SCE effectively extracts sentiment cues from \(X\), improving the model's ability to identify relevant sentiment information. The \(p\)-value of the \(t\)-test being less than 0.01 indicates a significant difference in the sentiment classification results between \(X\) and \(X^{Y}\). Coupled with the increase in recall, we consider this impact positive. On the ChnSentiCorp dataset, the RCT is 0.3148, indicating that 31.48% of tokens in \(X\) were extracted as sentiment cues. In this context, the loss in recall is minimal, only 0.0198, suggesting that SS-SCE likely captures the majority of sentiment cues. However, compared to the IMDb dataset, the performance metrics on ChnSentiCorp are noticeably poorer. This indicates that our SS-SCE method may have certain limitations when processing Chinese data. This could be due to BERT's character-level processing of Chinese, whereas Chinese semantics are typically conveyed at the word level. Therefore, during the sampling process, words might be segmented into characters that fail to express complete semantics, thereby affecting the model's performance. In summary, the experimental results prove that our SS-SCE method achieves good results on English datasets, especially on datasets with longer text lengths, where the extraction of sentiment cues is more effective. However, there are clear deficiencies in the Chinese dataset. In future research, we will consider addressing the issues encountered in the Chinese dataset.
## 2 Structure Design
### Design of Limit Structure of Rotation Angle  
According to the Euler transformation of the fixed-point rotation of a rigid body, the Euler angle has no limit. However, in the coarse-fine composite structure of the two-axis four-gimbal electro-optical pod, the rotation angle of each gimbal is limited due to the external dimension, load weight, and the center of mass imbalance of the gimbal.  
As shown in Figure 7a, in order to ensure the normal operation of the UAV's electro-optical pod in a safe range, a limit stopper is used to limit the rotation angle of each gimbal structure. A balancing weight is used to allocate the overall mass to prevent the occurrence of center of mass imbalance. As shown in Figure 7a,b, the rotation angle of inner pitch gimbal limiting stopper is \(+7^{\circ}\)--\(-7^{\circ}\) (a total of \(14^{\circ}\)), and the rotation angle of inner roll gimbal limiting stopper is \(+12^{\circ}\)--\(12^{\circ}\) (a total of \(28^{\circ}\)). In this angle range, the operation of the UAV's electro-optical pod is normal and safe.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Max** & **Min** \\ \hline
**Stress** & \(2.02\times 10^{7}N/m^{2}\) & \(1.13\times 10^{4}N/m^{2}\) \\
**Strain** & \(3.49\times 10^{-4}\) & \(2.79\times 10^{-7}\) \\
**Displacement** & \(3.42\times 10^{-1}mm\) & \(1.00\times 10^{-30}mm\) \\ \hline
**The deformation ratio** & & 49.3719 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Finite element analysis data obtained by Solidworks software.  
Figure 7: The diagram of rotation angle of each gimbal of the electro-optical pod. (**a**) The electro-optical stopper; (**b**) the rotation angle of the inner pitch gimbal limiting stopper; (**c**) the rotation angle of the inner roll gimbal limiting stopper.
## 4 Experiments and Results
### Results and Discussion
#### 4.2.2 Results on Ablation Experiments on our QAAPA Method  
As introduced in Section 4.1.3, the coding lengths are obtained by summing the information entropy of all data fields. Figure 7 shows the separate information entropy of examples data fields. As we can see from it, with our tested dataset, the linear regression predictor is able to provide shorter coding lengths than the CNN predictor for most of the data fields, which indicates that a simple algorithm is sufficient to provide satisfactory compression effectiveness for most cases. However, there are still data fields that can achieve shorter coding lengths using more complicated CNN models, which proves the  
Figure 6: The estimated information entropy with different number of data.  
necessity to combine different algorithms. As expected, our attention-averaging-based algorithm can achieve the shortest coding lengths by assigning suitable weights for both algorithms for all data fields.  
Based on the above-introduced summation method, we can obtain the average coding lengths of using different data compression methods shown in Table 16. As we can see, Quantization alone reduced the average coding length to approximately 40% of the original data size. Incorporating arithmetic coding further halved the average coding length. Introducing predictive coding enhanced compression, with linear-regression-based prediction outperforming CNN-based prediction for most ship status data fields. The complete QAAPA method achieved the most substantial compression, reducing the original data size to less than 10%. These results collectively demonstrate the effectiveness of each step within the QAAPA approach and highlight the superior performance of the complete method in compressing real-time ship status data.
## 4 Experiment Setup and Results
### Tested Movements  
We have chosen four representative arm movements and investigated how much energy can be saved compared to baseline movements made by the movej commands, which produce linear curves in the joint space blended by a set acceleration of nearby endpoints. The chosen movements represent four categories of typical motions:  
* A--manipulation from side to side (Figure 7(a)) represented by a left-to-right motion with the rotation angle of 90\({}^{\circ}\) and the tool centre point (TCP) staying at approximately the same height,
* B--manipulation from front to back (Figure 7(b)) represented by a left-to-right motion with the rotation angle of 180\({}^{\circ}\) close to the robot base,
* C--manipulation from bottom to top (Figure 7(c)) represented by a movement between the horizontal position and the upright position, and
* D--manipulation from top to bottom (Figure 7(c)) represented by a movement between the upright position and the horizontal position.  
The duration of all the movements is approximately 3 s, so that the results can be reasonably compared by the energy consumption.  
As already mentioned above, we have not implemented a collision avoidance mechanism in our setup--our testing conditions provide enough free space around the robotic arm and the purpose of the work is just the verification of the proposed general principle. In cases when one would have to deal with a limited working area with a danger of collisions, we recommend setting the objective function (energy used for the PSO proposed movement) to a high value of energy for the case of a self-collision or a collision with the environment. This approach would also give a successful optimization.
## 5 Case Studies  
To further analyze the MPPT practicability of TRL under PSC, it was compared with that of INC [11], GA [15], PSO [16], ABC [17], CSA [18], and TLBO [19], respectively. Four case studies are carried out in this section. Here, each meta-heuristic algorithm shares the same optimization cycle, which is chosen as 0.01 \(s\). Meanwhile, the TRL parameters are given in Table 1.  
\begin{table}
\begin{tabular}{|c|c|c|} \hline
**Parameter** & **Range** & **Value** \\ \hline \(J\) & \(J>1\) & 10 \\ \hline \(c\) & \(c>1\) & 4 \\ \hline \(\alpha\) & \(0<\alpha<1\) & 0.01 \\ \hline \(\gamma\) & \(0<\gamma<1\) & 0.0001 \\ \hline \(\epsilon\) & \(0<\epsilon<1\) & 0.9 \\ \hline \(k_{\text{max}}\) & \(k_{\text{max}}>1\) & 5 \\ \hline \(M\) & \(M>1\) & 5 \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of TRL. TRL: Transfer Reinforcement Learning.  
Figure 4: Overall flow diagram of TRL for MPPT. PSC: Partial Shading Conditions.  
For MPPT under PSC, a buck-boost converter is employed, due to its advantages described in reference [33]. Table 2 demonstrates the parameters of the PV system. In addition, the rated values of environment temperature and solar irradiation are set as 25 \({}^{\circ}\)C and 1000 W/m\({}^{2}\), respectively.