esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 3 Results and Discussion
### Comparative Analysis of Model Application  
To test the application effect of the TOC content prediction model, the TOC content of the shale reservoir in the study area's X2 well was predicted. The application results are shown in Figure 9. A low degree of conformity was observed between the predicted value and the measured value with the worst performance. The predicted value of the BP neural network prediction model was low at 2335-2345 m. The predicted value was close to the measured value at 2345-2355 m. The overall trend of GBDT and XGBoost prediction models was the same, but the predicted value of the former was lower at 2335-2355 m, while the predicted value of the latter was closer to the measured value. In summary, the XGBoost prediction model was the most effective and suitable for predicting TOC content in the study area.
# Triaxial Experimental Study of Zinc Contaminated Red Clay under Different Temperature Conditions
## 3 Results and Discussion
#### 3.1.1 Effect of Temperature on Stress-Strain Curves of Zinc-Contaminated Red Clay
1.3 Effect of Enclosure Pressure on Stress-Strain Curves of Zn-Contaminated Red Clay at Different Temperature Conditions  
In order to study the deformation characteristics of Zn-contaminated red clay under different temperature conditions, the stress-strain curves of red clay with ambient temperatures of 8.3 \({}^{\circ}\)C and 40 \({}^{\circ}\)C and Zn-contaminated concentrations of 0.05% and 0.5% at different enclosure pressures were selected, as shown in Figure 4.  
In addition to temperature and contamination concentration, the circumferential pressure to which the red clay is subjected also has an effect on its stress-strain relationship. From Figure 4, it can be judged that the stress-strain relationship of the Zn-contaminated red clay under different circumferential pressures also belongs to the strain-hardening type when the test temperature and the Zn contamination concentration of the red clay are the same. In Figure 4a, when \(\varepsilon\) is 15%, the stress of the red clay with 0.05% zinc ion concentration increases from 134.8 kPa to 437.3 kPa as the increase of the enclosing pressure from 100 kPa to 400 kPa. The stress of the soil at each enclosing pressure increases by 86.87%, 174.26%, and 224.41%, respectively. In Figure 4b, the stress of red clay with 0.5% Zn ion concentration increased from 227.5 kPa to 463.7 kPa with the increase of the enclosing pressure. The stress of the soil under each enclosing pressure increased by 13.25%, 17.91%, and 26.42%, respectively. In addition, the deformation modulus of the red clay increased linearly during the initial stage of shear; at a certain critical point, it began to fall rapidly and subsequently decreased gradually. At the same time, the principal stress difference of the red clay grows as the surrounding pressure becomes larger, and the phase of linear increase of the deformation modulus is extended. It indicates that the surrounding pressure  
Figure 4: Stress–strain curves of zinc-contaminated red clay under different confining pressures. (**a**) The temperature is 8.3 \({}^{\circ}\)C, and the zinc ion concentration is 0.05%; (**b**) The temperature is 40 \({}^{\circ}\)C, and the zinc ion concentration is 0.5%. can have a significant effect on the mechanical strength of zinc-contaminated red clay. This is due to the fact as the surrounding pressure applied to the soil increases, more water is removed from the red clay specimen during the consolidation stage, and it is known from the principle of effective stress of soil that with the discharge of pore water, the pore pressure decreases and the effective stress borne by the soil skeleton increases. In the undrained shear stage, the water in the soil is not completely discharged. The residual water in the soil can bear part of the shear stress with the increase of the surrounding pressure, so that the soil can resist the external damage ability.
## 2 Framework Design of Gateway
### Gateway: Functional Requirements Analysis
#### 2.1.1 Ability to Communicate with Remote Server via Ethernet  
Conventionally, WSN coordinators physically connect to the server through USB (maximum communication range: 5 m), RS232 (15 m), and RS485 (1,219 m), for example. However, if the target area exceeds the communication range of these cables, then the monitoring system cannot be operated. If the military warehouse is equipped with Ethernet (which is the case in most military bases in Taiwan), then we could use this communication method to extend the range of remote monitoring.  
Figure 2: Operational schematic diagram.
## 3 Results and Discussion  
### Composition of Experimental Raw Materials  
The proximate components of the dried shell of _Portunus trituberculosisatus_ shell and _Illex argentines_ squid gladius are shown in Table 1. Protein content of squid gladius is significantly higher than that of crab shell, suggesting that gladius of _Illex argentines_ can be used as an excellent protein source (59.34%). Previous studies showed that the protein content of gladius in various squid species ranged from 36.5% to 74.6% [29; 30; 31]. Ash content of _Portunus trituberculosisatus_ shell (mainly calcium carbonate) was 58.68%, while _Illex argentines_ gladius only 0.86%. [PERSON] et al. [32] reported that the content of calcium carbonate in _Eriocheir sinensis_ was around 55%. Therefore, demineralization is necessary when chitosan obtained from marine crustacean shell. [PERSON] et al. [33] reported that gladius of _Illex argentines_ squid contain 1% ash, 2.3% fat, 64% protein and 31% chitin. Hence, compared with marine crustacean shell, chitosan extracted from squid gladius the demineralization can be avoided.  
As shown in Table 1, protein and fat were not detected in prepared chitosan and CS, indicating high purity. Ash content of CSC and CSS was lower than the detection level, and the difference was statistically significant (\(p\) = 0.03) compared with CS, which may be related to the purity of samples. Chitosan samples have relatively low moisture content, which is linked to the drying process and the extent of exposure to the atmosphere during storage. Chitosan is hygroscopic, minimal water content chitosan often has good shelf stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Protein (\%)** & **Fat (\%)** & **Ash (\%)** & **Moisture (\%)** & **Yield (\%)** \\ \hline Shell & 18.36 & 3.22 & 58.68 & 2.32 & NA 1  \\ Gladius & 59.34 & 2.06 & 0.86 & 1.13 & NA 1  \\ CS & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1  & ND 1 [FOOTNOTE:]Footnote 1: NA—Not applicable; ND—None detected (below detection limits). 2
# 3D In Situ Stress Estimation by Inverse Analysis of Tectonic Strains
## 2 Inverse Analysis for In Situ Stress Determination  
Boundary conditions are crucial for a definite solution of the governing equation. For any problem, boundary conditions are required. The processing of boundary conditions directly affects the accuracy of calculation results.  
Stress or displacement boundary conditions around the region of interest can determine the engineering-scale in situ stress field. For traditional inverse analysis about an in situ stress field, the purpose is to find the best-fit boundary stress or displacement distribution of the zone of interest. Optimization algorithms are usually used to determine the best-fit boundary conditions. With respect to the objective function for an optimization algorithm, it can be established by the errors between the field test values and the calculated values in the limited testing points of the zone of interest. The calculated values can be obtained by given boundary conditions during the optimizing process. Therefore, the in situ stress field can be inversely analyzed by optimizing engineering-scale boundary conditions and determined through best-fit boundary conditions. In this section, two traditional inverse analysis approaches are introduced [11; 16; 21], and furthermore, in order to overcome the drawbacks of traditional approaches, a new approach is proposed and explained.
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 5 Results and Discussion
### Impact of Varying the Number of Sensor Nodes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Throughput(packets/s) Comparison with Varying**} & \multicolumn{3}{c}{**\% Improvement of OOR as Compared with Existing Schemes**} \\ \hline \multirow{2}{*}{**Nodes**} & \multirow{2}{*}{**AOR**} & \multirow{2}{*}{**Max-SNR**} & \multirow{2}{*}{**ESN**} & \multirow{2}{*}{**OOR**} & \multicolumn{2}{c}{**\% Gain of OOR**} & \multicolumn{2}{c}{**\% Improvement of**} \\  & & & & & **Compared with ESN** & **Compared with ESN** & **OOR Compared** \\ \hline
100 & 6000 & 7200 & 9000 & 10,500 & 14.29 & 31.43 & 42.86 \\
200 & 5500 & 6900 & 8220 & 9825 & 16.34 & 29.77 & 44.02 \\
300 & 5000 & 6200 & 7490 & 8810 & 14.98 & 29.63 & 43.25 \\
400 & 3820 & 5220 & 6512 & 7900 & 17.57 & 33.92 & 51.65 \\
500 & 3000 & 4150 & 5425 & 6950 & 21.94 & 40.29 & 56.83 \\ \hline \multicolumn{2}{c}{**Average \% Improvement \(\rightarrow\)**} & \multicolumn{3}{c}{**17.02**} & \multicolumn{2}{c}{**33.01**} & \multicolumn{2}{c}{**47.72**} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive observations of OOR performance in terms of throughput. Figure 8: Throughput comparison with varying number of nodes. In Figure 9 the five different columns in the first part of the figure show the outcomes of the throughput parameter in 100 scale with variation in the number of nodes for each scheme, respectively. For example, consider different columns of OOR scheme in the first part of the figure, the third column represents the value of throughput parameter as 88.1 (packets/s) at 300 nodes in 100 scale whereas the fifth column depicts the value of 69.5 (packets/s) at 500 nodes in 100 scale. The values can be verified in Table 3. We use the 100 scale for better representation of columns in the figure. In the second part of the figure, the five different columns show the percentage improvement, and the sixth column depicts the average percentage improvement.
## 4 Experimental Results
### Dataset Description  
Five datasets have been used to evaluate the performance of OCATC. These datasets, including DatasetA, which was created by [PERSON] et al. [39], contain 2700 documents distributed over nine categories. In our experiments, we used the same distribution for DatasetA as in [44]. DatasetB, collected by [PERSON] et al. [38] and known as CNN dataset, contains 5070 documents and six main categories. DatasetC, also known as the BBC dataset and created by [PERSON] et al. [38], contains 4763 documents and seven main categories. DatasetD (Alj-News dataset), collected by [PERSON] et al. [42], contains 1500 documents distributed across five categories. DatasetE (Alarabiya dataset) was collected by [PERSON] et al. [47] and we selected the same distribution of [1], in which every category contains 3700 documents distributed into five categories. In addition, we manually collected 1000 documents from different newspaper websites, and classified them into six categories to construct DatasetF. It is worth noting that DatasetA, DatasetD, and DatasetF are balanced datasets where DatasetA, DatasetD contain 300 documents, and DatasetF contains 3700 documents. Other datasets' document distributions are shown in Figure 2. Furthermore, we set the maximum number of features to be extracted from each dataset using TF-IDF to 1000 to avoid the existence of too many rare words.
## 4 Proposed Framework
### Spatial Graph Convolution
To summarize, we use this graph convolution layer to extract spatial features from the detector arrival waveforms. The proposed Spatial GC module consists of 3 stacked graph convolution plus Global Additive Pooling (GAP) layers. The feature map after each GAP layer is aggregated to obtain the final output of Spatial GC block (as shown in Figure 3). The weights of Spatial GC block are shared across all the timesteps, the output for each time step is concatenated across temporal dimension which in turn is fed into the EDTAM module.
## 4 Experiments
### Method Analysis Results and Discussion
#### 4.3.4 Comparison with Complex Mechanisms  
The computational complexity of our PCC method primarily stems from the simple fully connected layers and element-wise tensor manipulations in the differential operations. Both of these can be accelerated by parallel computing on GPUs, resulting in lower computational overhead compared to more complex structures such as self-attention mechanisms. We conducted a comparison of performance and computational cost between the proposed PCC module and some complex mechanisms, again using SegRNN as the backbone model. Table 7 shows the results. The methods for comparison included the following:  
1. SAMP + SC: Replaces the MCC module with SAMP [46], a self-attention based method for capturing variable correlation in time series.
2. LIFT + SC: Replaces the MCC module with LIFT [47], a channel dependence correction method for time series forecasting using linear structures with complex calculations such as Fourier transforms.
3. saPCC: Replaces the MLP structure in both PCC submodules with a multihead self-attention mechanism. The implementation details of these three methods are described in Appendix E. The results show that PCC achieves superior performance with its simple linear structure while maintaining significantly lower computational complexity, making it more suitable for resource-sensitive practical scenarios. We attribute this to the temporal independence in the design of the MCC module, which avoids the negative effects of temporal variance, making it more effective in capturing physical correlation patterns.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### Seawater Effects on Multiple Low-Velocity Impact Bending Performance of FRPSS  
#### 4.3.1 Load-Time Multiple Impact Behaviour  
The evolution of the load response of FRPSSs when subjected to multiple impacts with an energy level of 0.52 J is presented in Figure 8. This energy level was chosen through several preliminary tests performed to establish the optimal number of impacts required to cause fractures in both exposed and unexposed samples. As in the single-impact scenario, all samples displayed a notable decrease in damage resistance after the seawater exposure. Among the samples, GSV showed the greatest reduction in impact resistance with an increasing number of impacts, ranging from 48.7% to 54%, while GSH exhibited the smallest reduction, with a range of 6.3% to 10.9% in its load-bearing capacity. Table 4 provides a detailed breakdown of the performance variations across samples with the number of impacts. The impact of moisture varied, showing inconsistent effects across the samples, likely due to the degradation of adhesive layers through plasticisation and weakened interface cohesion among the FRPSS components. Additionally, while this general trend was observed in all the cases, the thicker core in GSH samples contributed to greater damage resistance at low-impact energies compared to that of GSP and GSV specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Impact No.**} & \multicolumn{2}{c}{**GSP**} & \multicolumn{2}{c}{**GSV**} & \multicolumn{2}{c}{**GSH**} \\ \cline{2-7}  & **Force (N)** & **Diff (\%)** & **Force (N)** & **Diff (\%)** & **Force (N)** & **Diff (\%)** \\ \hline
1 & 103.42 (77.43) & 25.1 & 119.54 (56.33) & 52.9 & 144.57 (128.84) & 10.9 \\
2 & 97.18 (81.56) & 16.1 & 120.06 (59.73) & 50.2 & 144.86 (131.25) & 9.4 \\
3 & 92.48 (69.64) & 24.7 & 109.08 (55.92) & 48.7 & 142.73 (132.07) & 7.5 \\
4 & 91.17 (60.97) & 33.1 & 106.58 (49.02) & 54.0 & 139.79 (130.96) & 6.3 \\
5 & 85.69 (58.47) & 31.8 & 107.44 (39.89) & 62.9 & 135.25 (131.49) & 2.8 \\ \hline \hline \end{tabular} Note: exposed impact force values are in brackets.  
\end{table}
Table 4: Reduction in load-bearing performance of FRPSS subjected to seawater exposure after multiple impacts.  
Figure 7: Energy–time behaviour for single impact.
## 4 Conclusions  
In this study, a three-dimensional numerical model is established and validated to investigate basement-tunnel interaction. Then, the influences of basement geometry and tunnel location relative to the basement on the vertical deformation of the tunnel are studied. The following conclusions can be drawn:  
1. The vertical deformation of the tunnel is greatly affected by the basement length, the basement width, the excavation depth, the tunnel cover depth, and the distance between the tunnel axis and the basement center, but is little affected by the tunnel diameter. When the ratio of basement length or width to tunnel cover depth is less than 3, the maximum vertical deformation of the tunnel increases rapidly with increasing basement length or width.
2. The vertical deformation of the tunnel increases linearly with the unloading ratio, which describes the degree of excavation depth above the tunnel. But there exists a critical unloading ratio of 0.6, beyond which the vertical deformation of the tunnel increases significantly. On this basis, an empirical model is proposed to predict the vertical deformation of the tunnel considering the unloading ratio.
3. The vertical deformation decreases with an increasing offset ratio, which describes the degree of tunnel offset from the basement center. But there exists a critical offset ratio of 0.4, below which the maximum vertical deformation of the tunnel decreases slightly. The maximum reduction in deformation is about 30%.  
Based on the empirical models proposed in this study, the vertical deformation of the existing tunnel induced by overlying basement excavation in sand can be estimated.  
Figure 21: Relationship between deformation discount ratio and offset ratio.  
However, it should be pointed out that these conclusions were drawn without considering similar cases in clay. Thus, subsequent research should be carried out to investigate basement-tunnel interaction in clay using coupled mechanical and hydraulic modeling.  
Conceptualization, J.L.; methodology, J.L. and W.M.; software, X.H.; validation, J.L., K.L. and W.M.; formal analysis, J.L., X.H. and W.M.; investigation, X.H.; resources, J.L. and W.M.; data curation, X.H.; writing--original draft preparation, J.L. and X.H.; writing--review and editing, K.L. and Y.D.; visualization, X.H., K.L. and W.M.; supervision, J.L.; project administration, J.L., K.L. and W.M.; funding acquisition, J.L. and K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Natural Science Research Project of Anhui Province (Nos. 2022 AH030035, 2022 AH020029, and KJ2021 ZD0066), the National Natural Science Foundation of China (Nos. 51608005 and 12172001), and the Anhui Provincial Natural Science Foundation (Nos. 2208085Y01 and 2008085 QA23).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Lanthanum Selenide Films  
Lanthanum selenide (La\({}_{2}\)Se\({}_{3}\)) thin films were prepared on substrates [122] and could be employed in energy storage applications. Experimental results showed the highest specific capacitance was 363 F/g (0.8 M LiClO\({}_{4}\)/PC electrolyte) when the scan rate was 5 mV/s (at 1.3 V/saturated calomel electrode (SCE) potential range). Moreover, researchers concluded that the specific capacitive retention was 83% over 1000 cyclic voltammetry investigations. Further, fast ion diffusion could be reached when the power density was 2.5 kW/kg and high energy (80 Wh/kg).
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
###### Abstract  
Up-to-date predictive rubber friction models require viscoelastic modulus information; thus, the accurate representation of storage and loss modulus components is fundamental. This study presents two separate empirical formulations for the complex moduli of viscoelastic materials such as rubber. The majority of complex modulus models found in the literature are based on tabulated dynamic testing data. A wide range of experimentally obtained rubber moduli are used in this study, such as SBR (styrene-butadiene rubber), reinforced SBR with filler particles and typical passenger car type rubber. The proposed formulations offer significantly faster computation times compared to tabulated/interpolated data and an accurate reconstruction of the viscoelastic frequency response. They also link the model coefficients with critical sections of the data, such as the gradient of the slope in the storage modulus, or the peak values in loss tangent and loss modulus. One of the models is based on piecewise polynomial fitting and offers versatility by increasing the number of polynomial functions used to achieve better fitting, but with additional pre-processing time. The other model uses a pair of logistic-bell functions and provides a robust fitting capability and the fastest identification, as it requires a reduced number of parameters. Both models offer good correlations with measured data, and their computational efficiency was demonstrated via implementation in [PERSON]'s friction model.  
vicoelastic modulus; rubber; friction; empirical modeling +
Footnote †: journal: Applied Sciences