esa-sceva/satcom-chunk-collection huggingface dataset:
# Locomotion of Self-Excited Vibrating and Rotating Objects in Granular Environments
## 3 Results and Analysis  
### Coupling Oscillatory Translation and Constant Rotation Speed  
We set the oscillation amplitude and frequency to \(A=0.03\) m and \(f=3\) Hz, and the rotation angular velocity to \(\omega=80\) rad/s, with \(\omega>0\) denoting henceforth a counter-clockwise rotation of the SO. We ran five types of simulations: only vibration, vibration and rotation about the \(x\)-, \(y\)-, \(z\)-, or \(xz\)-axis. In each run, we monitored the dynamics and tracked the trajectory of the SO. Observing that its movement in the \(y\) direction is relatively small, we focus on the displacement in \(x\) and \(z\) directions.  
Our observations, shown in Figure 2, can be summarized as follows.  
* The displacement in the \(x\) direction oscillates strongly at frequency \(f\) whenever \(\omega\) has a component about the \(z\)-axis, while otherwise it is relatively smooth.
* The \(x\) displacement is vanishingly small except when the rotation is about the \(y\)-axis, in which case the SO moves steadily in the \(-x\) direction.
* The displacement in the \(z\) direction oscillates strongly at frequency \(f\) when \(\omega\) has a component about the \(x\)-axis, otherwise it is relatively smooth.
* The displacement in the \(z\) direction is always upward, with rotation enhancing the rising rate.  
Figure 2: The dynamics of the SO with the coupling of vibration and rotation around different axes. (**a**) The movement of the SO in the \(x\) direction. (**b**) The movement of the SO in the \(z\) direction.  
To understand the dynamics under coupled vibration and rotation, consider, for example, rotation about the \(x\)-axis. As the SO moves in the \(+y\) direction, the pressure from the medium on its forward moving (\(+y\)) surface is larger than the pressure on the rear surface. The constant counter-clockwise rotation around the \(x\)-axis then induces a friction force in the \(-z\)-direction on the forward moving surface, as illustrated in Figure 3, and a lower friction force in the \(+z\) direction on the rear surface. Therefore, the combined action of vibration and rotation leads to an overall downward force during this one-quarter of the cycle. On changing direction, the pressure is higher on the surface facing the \(-y\) direction and, since the rotation direction is constant, the overall friction force is upwards. Thus, the friction force oscillates between up and down with the translational motion. Nevertheless, the displacement in the \(+z\) direction is larger than in the \(-z\) direction because the layer supporting the SO is slightly less fluid than the medium above it, being deeper in the medium. This leads to an overall rise with time, consistently with the displacement marked in the red line in Figure 2b.  
Since the displacements in the \(x\) direction are negligible compared to those in the \(y-z\) plane, shown in Figure 2, we focus in the following on the rising and sinking in the \(z\) direction.
## 4 Calculation and Verification of Dynamic Derivatives
### Extrapolation Performance of Dynamic Derivatives Prediction Model  
For a method that predicts dynamic derivatives through an intelligent model, testing the generalization ability is essential. The LSTM network constructed in this paper has good temporal memory, so it can effectively learn and store the historical features of the flow field contained in the training data. Due to the similarity of flow field characteristics when the nonlinear characteristics of the flow field are weak, this intelligent model has good generalization ability in theory. Next, the extrapolation performance of PSO-LSTM must be tested, using three sets of data, k = 0.05, 0.06, and 0.07, as training data, and  
Figure 8: Comparison of aerodynamic data obtained using PSO-LSTM and CFD (testing data: k = 0.07).  
k = 0.08 as testing data. The time series length of the input angle of attack N is taken as 10. It can be seen in Figure 10 and Table 3 that the predicted aerodynamic coefficients exhibit oscillations with respect to \(\alpha\) (angle of attack). Upon analysis, it becomes evident that this phenomenon arises due to the increased reduced frequency at k = 0.08. This increase leads to a higher instantaneous angular velocity in the wing's motion, resulting in more pronounced variations in the flow field. In certain localized regions, even stronger nonlinear characteristics emerge. It is conceivable that enhancing the model's performance may be achieved by introducing additional samples to encompass a broader range of motion conditions and nonlinear characteristics.  
However, due to the fundamental similarity in the overall flow field characteristics, the relative error in aerodynamic forces during extrapolation remains relatively low.  
Furthermore, we used the least-squares method to identify the dynamic derivatives based on the aerodynamic force prediction results of PSO-LSTM, and we used the integration method to calculate the dynamic derivatives based on the CFD simulation results. Table 4 shows the comparison of dynamic derivative calculation results, with a relative error of 0.9750%. This indicates that the intelligent model of PSO-LSTM has good generalization ability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dynamic Derivatives** & **Reduce Frequency** & **CFD** & **LSTM** & \(e\) \\ \hline \(C_{m\dot{a}}+C_{mq}\) & 0.08 & \(-1.17025\) & \(-1.18166\) & 0.9750\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation Results of Dynamic Derivatives.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aerodynamic Data** & \(e_{rms}\) & \(e\) \\ \hline \(C_{l}\) & 0.217\% & 1.231\% \\ \hline \(C_{m}\) & 0.00324\% & 5.321\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error of predicted data.  
Figure 9: Comparison of time consumption for dynamic derivative calculation.  
Figure 10: _Cont._
## 2 Materials and Methods  
### Engine Specifications and Operating Conditions  
The experiments were carried out in a single-cylinder optical spark ignition engine. Table 1 summarizes the specification of this engine and the selected operating condition for this study. The engine capacity is 500 cm\({}^{3}\) and has a compression ratio of 10.4 with a bore of 86 mm and stroke of 86 mm. Optical access into the combustion chamber of the engine is provided by replacing certain metal parts with quartz window: at the top of the piston (54 mm diameter) and in the pent-roof as shown in Figure 2a. The whole metal liner can be replaced by quartz for extended optical view into the chamber. To observe the bottom-view of the combustion chamber, a 45\({}^{\circ}\) mirror is placed in the hollow extended-piston. It also provides an optical-access path for laser for the observation of pent-roof area, which is the setup applied for this study. The experiments were carried out with the engine operating at 1200 rpm with port fuel injection and an absolute intake pressure of 60 kPa. The spark timing was set at \(-15^{\circ}\) Crank-Angle after Top Dead Center (CA aTDC). Due to the focus of this study, only mixture at stoichiometry will be considered. In-cylinder pressure are monitored and recorded using a pressure transducer (Kistler, 6052C) and the engine is fired continuously during the PIV capturing process for synchronization purpose.  
\begin{table}
\begin{tabular}{c c} \hline
**Engine Type** & **4 Stroke, Single Cylinder, PFI** \\ \hline Bore \(\times\) stroke & \(86\times 86\) mm \\ Displacement & 500 cm\({}^{3}\) \\ Compression ratio & 10.4 \\ Fuel injection & Port fuel injection \\ Fuel type & Regular gasoline \\ Injection pressure & 300 kPa \\ Engine speed & 1200 rpm \\ Equivalence ratio & 1 (AFR 14.7) \\ Intake pressure & 60 kPa \\ Spark timing & \(-15^{\circ}\) CA aTDC \\ \hline \end{tabular}
\end{table}
Table 1: Engine specifications and test conditions. PFI: port fuel injection; CA aTDC: Crank-Angle after Top Dead Center.
## 3 Analysis and Results
### Performance Analysis of Machine Learning Models
#### 3.2.3 Machine Learning Model Performance for Tridem Axle Stress Prediction  
The Table 12 demonstrates the performance of various regression models based on significant metrics. Gradient boosting is demonstrated as the best performer, with the lowest testing MSE (0.003260), MAE (0.037326), and RMSE (0.057093), as well as an almost perfect R\({}^{2}\) value of 0.998295, suggesting superior precision and generalization. Random forest also performs well, with a modest variation between training and testing results (MSE: 0.001837 vs. 0.013046, R\({}^{2}\): 0.998324 vs. 0.993176), showing low overfitting. On the other hand, decision tree exhibits obvious overfitting, obtaining perfect training results (MSE: 0, R\({}^{2}\): 1) but poorer testing performance (MSE: 0.014616, R\({}^{2}\): 0.992354). Both linear regression and ridge regression perform consistently, with high R\({}^{2}\) values and low errors, albeit ridge marginally trails behind linear regression. Lasso regression faces significant challenges with severe underfitting, as shown by its high testing error (MSE: 1.913,948) and a negative R\({}^{2}\) value (\(-\)0.001202). While LightGBM and CatBoost perform well during training, their higher testing errors (MSE: 0.114631 and 0.115462, respectively) and lower R\({}^{2}\) values (0.940036 and 0.939601) suggest they struggle to generalize effectively to unseen data. To further test the efficiency of gradient boosting, diagnostic plots were employed to verify its accuracy, as shown in Figure 10. The normal probability plot (Q-Q Plot) demonstrates that the residuals follow a normal distribution with acceptable deviations. However, the actual vs. predicted values plot reveals significant alignment, showing good predictive accuracy. The externally studentized residuals plot verifies no bias or systematic mistakes. Meanwhile, the residuals vs. run number plot illustrates stable, random swings, demonstrating the model's robustness and independence from data sequence. The SHAP value investigation displayed in Figure 11 demonstrates that \"Thickness\" exerts the most significant influence on model predictions, as indicated by its elevated average SHAP value. This means that modifications in thickness result in considerable changes in outcomes. On the other hand, X spacing and Y spacing exhibit lesser impacts, with Y spacing suggesting a more stable effect.  
Figure 9: SHAP value analysis for tandem axle model features.  
Figure 10: Residuals plots analyzing gradient boosting regression model for tridem axle.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 4 Results and Discussion
### Cross-Validation Results  
* R\({}^{2}\) and Adjusted R\({}^{2}\): The Random Forest algorithm has improved performance on average over cross-validation folds; however, it displays significantly greater variability than on the test set. Linear Regression exhibits marginally superior performance compared to Lasso in cross-validation, although the disparity is negligible.
* MSE, RMSE, and MAE: Random Forest consistently surpasses both linear and Lasso regressions in terms of MSE, RMSE, and MAE throughout cross-validation folds, exhibiting a narrower error range. Linear Regression exhibits marginally superior cross-validation performance compared to Lasso; yet, both models demonstrate considerable stability with minimal discrepancies in error.
## 5 III-V Lasers Grown on InP-SOI  
One of the key advantages of the integration platform by InP seed-bonding and regrowth as compared to direct epitaxy on Si is the possibility to use a thin seed layer enabling light-coupling through photonic components underneath the SOI wafer. Therefore, this platform not only allows us to benefit from the multi-regrowth technique applied in the III-V monolithic platform by means of the regrowth technique but also to the Si-photonics circuitry offered by the Si photonics platform [43].  
In this section, we show our work on the exploitation of evanescent light-coupling from III-V vertical lasers grown on heterogeneous InP-SOI wafer to Si photonics passive circuitry. To do so, a 2.5 \(\upmu\)m-thick III-V laser structure composed of an AlGaInAs-based MQW active region surrounded by two n- and p-doped InP layers was grown on an InP-SOI wafer. After growth, shallow-ridge lasers were fabricated. Two silicon cavity designs were tested: the first design is a FP laser for which cavities are provided by simple cleaved facets and the second design which is III-V laser coupled to DBR cavities made from SiO\({}_{2}\)/Si Bragg gratings and vertical couplers to extract light.
## 3 Case Analysis
### Prediction Accuracy with Factor Ablation
#### 3.2.5 Test Site GPP without LSWI  
In this experiment, all LSWI data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 13. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy of the AR site improves significantly and is only lower than that after removing NDVI. The DXST, DXSW, NMC, and ZF sites have similar prediction accuracy as in the previous ablation experiments, with a large decrease in accuracy, and the prediction accuracy of the ZF site is only higher than that after removing EVI. The GL site shows a slight decrease in accuracy, while the HBKO, HBSH, and HBSW sites show no obvious changes in prediction accuracy.
## 3 Experimental Results
### Characteristics of Fracture Propagation
#### 3.1.3. Tortuosity of the Longitudinal Fractures  
In order to study the main fractures quantitatively, a parameter called tortuosity is introduced by [PERSON] et al. [26], which is defined as the total length of a fracture divided by the shortest length of the two ends of the fracture along the surface, and it has been used by [PERSON] et al. [42] and [PERSON] et al. [43]. In this section, tortuosity of the longitudinal fractures on the vertical surfaces of the specimens in group A, B and D is calculated by following equation [26; 42; 43]:  
\[T=\frac{L_{f}}{L} \tag{1}\]  
\(T\) (dimensionless) is fracture tortuosity, \(L_{f}\) (m) is the sum of total fracture lengths on the vertical surface; \(L\) (m) is the sum of linear lengths between the two ends of the fracture's pathway. The \(L_{f}\) and \(L\) are calculated by the following equation:  
\[L_{f}=\sum_{i=1}^{n}L_{fi} \tag{2}\]  
\[L=\sum_{i=1}^{n}L_{i} \tag{3}\]  
\(L_{fi}\) (m) is the trace length of the \(i\)-th fracture; \(L_{i}\) (m) is the linear length between the two ends of the \(i\)-th fracture pathway. Vertical surfaces of specimens are digitized (see Figure 8) to calculate the fracture lengths (black lines) and the linear lengths (red lines).  
Figure 7: The digital images of fracture branches on the surfaces. Three types of fracture branches were summarized: type-I, initiating from a main fracture at a low angle and then converging to the main fracture; type-II, connecting two main fractures; type-III, initiating from a main fracture and connecting no fractures.  
The characteristics of transverse and oblique fractures cannot be measured by tortuosity, so only the longitudinal fractures are evaluated quantitatively in this part. The calculated tortuosity of longitudinal fractures on vertical surfaces of the specimens in group A (hard rocks), B (soft rocks) and D (soft-hard rocks) is shown in Figure 9. Generally, the effect of injection pressure on the tortuosity of longitudinal fractures is not obvious. The tortuosity of longitudinal fractures in soft rocks (group B) is apparently the highest, indicating that the longitudinal fractures in soft rocks are the most complicated among the three groups.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
###### Abstract  
Defects in welds can affect the structural safety and reliability of workpieces. Currently, the method of using phased array ultrasonic inspection technology for non-destructive testing of weld structures with high detection efficiency, good sensitivity, and good visualization of the results is widely used. However, the defective A-scan data collected by the ultrasonic phased array detector inevitably contain noise data, including the test piece material structure noise, equipment noise, and environmental noise, which undoubtedly affects the analysis of the A-scan signal. In addition, when defects are interpreted, the presence of noise also interferes with the process, which affects the accuracy of the interpretation. Therefore, to enhance the accuracy of defect identification based on phased array ultrasonic inspection technology, we must prevent the series of consequences caused by misjudgments. In this study, ultrasonic phased array inspection experiments were carried out, and the specific process flow of ultrasonic phased array inspection of flat plate butt welds was summarized. Utilizing pre-fabricated flat plate butt specimen blocks containing five types of typical defects, defect A-sweep signals based on ultrasonic phased array inspection were obtained. Combining the sparrow optimization algorithm (SSA), variational mode decomposition (VMD), and wavelet packet decomposition (WPD), a defect signal noise reduction method based on parameter optimization was studied. A noise reduction study was carried out using the noise-added simulated signal, and the results indicated that the noise reduction method proposed in this paper had a better noise reduction effect and the proposed method could effectively retain the detailed features of the ultrasonic phased array defective A-scan signal and realize the noise reduction processing of the defective A-scan signal.  
ultrasonic phased array; a-sweep signal; weld defects; signal noise reduction
###### Abstract  
Biochar (BC), which can be produced from several feedstocks, has been widely studied. However, the BC derived from highly pyrolytic shrubs, such as broom and gorse, has been less frequently used and only partially characterized. These wastes, when used for the preparation of biochar, can fix carbon and contribute to environmental conservation, helping to achieve sustainable development objectives. Eight biochars from broom and gorse were produced and fully analyzed, providing a more complete and novel description, with new insights for assessing their utilization. The aims of this study were to elucidate the effects of feedstock, pyrolysis temperature, and residence time on biochar properties and to assess the adequacy of these biochars as fuel. Elemental and proximate analyses and estimations of the lower and higher heating values were performed, and physical and chemical properties, as well as several other related energy indices, were determined. The experimental results showed that the temperature was a key factor in the properties of the biochars, while residence time was less important. The BCs obtained from the two feedstocks did not show important effects on the properties, which is consistent with the fact that they are woody legumes. These biochars had a high carbon content and were thermally stable. The BCs also had a high calorite value and suitable energetic properties. Additionally, their PAH contents were low, indicating that the use of these biochars would be safe. In conclusion, broom- and gorse-derived biochars can be considered as renewable fuels for green energy production.  
biochars; broom; gorse; waste; valorization Article
# Evaluation of Urban Transportation Resilience under Extreme Weather Events
## 5 Results  
Transportation resilience in the Cangshan, Gulou, and Jinan subareas of Fuzhou during yellow and red rainstorm warnings was quantitatively evaluated using the aforementioned indicator and method in this case study. The curves of the transportation performance in each subarea during the yellow and red rainstorm warning are shown in Figure 11. The results of this quantitative evaluation are shown in Table 2. The results are discussed and interpreted from three perspectives, including a comparison between different subareas under the yellow rainstorm warning, a comparison between different subareas under the red rainstorm warning, and a comparison between different rainstorm levels in the same subareas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Yellow Rainstorm Warning**} & \multicolumn{3}{c}{**Red Rainstorm Warning**} \\ \cline{2-7}  & **Cangshan** & **Gulou** & **Jinan** & **Cangshan** & **Gulou** & **Jinan** \\ \hline Beginning of rainstorms & & & & & & & \\ (\(\mathbf{t_{0}}\)’) & & 23:00, 2 May 2018 & & & & & \\ \hline Beginning of performance & 0:30, & 23:30, & 0:05, & 17:05, & 18:30, & 19:00, \\ perturbation (\(\mathbf{t_{0}}\)) & 3 May 2018 & 2 May 2018 & 3 May 2018 & 20 June 2018 & 20 June 2018 & 20 June 2018 \\ \hline The lowest point of PI (\(\mathbf{t_{1}}\)) & 5:00, & 9:45, & 1:15, & & 20:00, \\
3 May 2018 & 3 May 2018 & 3 May 2018 & 3 May 2018 & & 20 June 2018 & \\ \hline End of performance & 10:05, & 11:15, & 11:30, & 22:30, & 23:00, & 21:00, \\ perturbation (\(\mathbf{t_{2}}\)) & 3 May 2018 & 3 May 2018 & 3 May 2018 & 20 June 2018 & 20 June 2018 & 20 June 2018 \\ \hline Disruption phase & 4 h 30 min & 10 h 15 min & 1 h 10 min & 2 h 55 min & 1 h 30 min & 1 h \\ \hline Slope of the disruption & \(-\)0.04 & \(-\)0.013 & \(-\)0.36 & \(-\)0.154 & \(-\)0.187 & \(-\)0.23 \\ phase (\(\mathbf{k_{d}}\)) & & & & & & \\ \hline Recovery phase & 5 h 5 min & 1 h 30 min & 10 h 15 min & 2 h 30 min & 3 h & 1 h \\ \hline Slope of the recovery & 0.035 & 0.087 & 0.041 & 0.18 & 0.093 & 0.23 \\ phase (\(\mathbf{k_{r}}\)) & & & & & & \\ \hline Duration of performance & 9 h 35 min & 11 h 45 min & 11 h 25 min & 5 h 25 min & 4 h 30 min & 2 h \\ perturbation & & & & & & \\ \hline The lowest PI value & 0.77 & 0.82 & 0.53 & 0.5 & 0.67 & 0.72 \\ \hline R value & 0.93 & 0.95 & 0.86 & 0.69 & 0.82 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of evaluation results.