esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Moreover, the results of the sensitivity analysis that was performed by [PERSON] et al. [12] on a PV-battery system demonstrated that only battery costs of 550 \(\epsilon\)/kWh, which are hardly available today, could lead to a profitable PV-battery system operation. [PERSON] et al. [59] showed the sensitivity of the Return On Investment (ROI) to storage prices and concluded that, when considering their assumption with a storage price of 500 \(\epsilon\)/kWh, the system is not profitable; profit may be only made with storage prices below 450 \(\epsilon\)/kWh. However, in the present study, the battery costs will significantly improve the profitability of the proposed system once they are used with initial battery costs of less than or equal to 1000 \(\epsilon\)/kWh.
## 4 Experimental Results and Discussions
### Experiment Comparison of BBO, OBBO, ROBBO and COOBBO  
Results of applying BBO, OBBO, ROBBO and COOBBO to solve 8 benchmark functions are given in Table 2, in which the better results for each case are highlighted in **boldface**. In order to eliminate the influence of subjective and objective conditions, large scale independent experiments are implemented for each algorithm in this paper. As we all know, the smaller the variance of results is, the more stable the algorithm performance is. From Table 2, we see that BBO has the smallest variance for almost all experiment results, which may clearly indicate that BBO is reliability and robustness. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Benchmark**} & \multirow{2}{*}{ \begin{tabular}{c} **Comparison** \\ **Criteria** \\ \end{tabular}
## 2 Materials and Methods
### Test Methods  
#### 2.3.1 Flexural Performance Test  
For the flexural performance test, beam specimens of 100 \(\times\) 100 \(\times\) 400 mm\({}^{3}\) suggested by ASTM C1609/C1609M [27] and the Korean regulations of KS F 2566 [28] were fabricated, and tests of all specimens were performed at the age of 28 days. Figure 3 shows the set-up of the test equipment installed by the third-point loading method for the flexural performance test. It is the test equipment of a universal testing machine (UTM) with a capacity of 2500 kN.  
Meanwhile, deflection and applied load were measured using two linear variable displacement transducers (LVDTs) set up in the center of both sides of the specimen. The loading rate was controlled at a constant speed of 0.2 mm/min until the specimen was destroyed at L/1500 per minute, and the flexural load was recorded by using a load cell with a capacity of 850 kN. The flexural strength (\(f_{r}\)) in the third-point loading method is calculated by Equation (1) at the maximum load [27,28].  
\[f_{r}=\frac{PL}{bh^{2}} \tag{1}\]  
where, \(P\) is the maximum load (N), \(L\) is the span length (mm), \(b\) is the width of the specimen (mm), and \(h\) is the height of the specimen (mm).  
Figure 3: View of specimens and set-up for flexural performance test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Designation of Mixtures**} & \multirow{2}{*}{**Fiber Type**} & \multicolumn{2}{c}{**Fiber Mix Proportion by**} & \multirow{2}{*}{**Total Volume**} & \multicolumn{3}{c}{**Mixture Proportions**} & \multirow{2}{*}{**SP**} \\  & & \multicolumn{2}{c}{**Volume (V\({}_{f}\))**} & & & \multicolumn{2}{c}{**(Unit Weight, kg/m\({}^{3}\))**} & \\ \cline{5-8}  & & **(\%)** & **(kg/m\({}^{3}\))** & & & **W** & **C** & **FA** & **(C \%)** \\ \hline SP0 (plain) & 0 & 0.0 & 0.0 & 0.0 & 297 & 645 & 1290 & - \\ SC1 & SF & 1.0 & 78.5 & & & & & \\ CF & 0.0 & 0.0 & & & & & & \\ SC2 & SF & 0.75 & 58.87 & & & & & \\ CF & 0.25 & 13.50 & & & & & & \\ SC3 & SF & 0.5 & 39.25 & & & & & \\ CF & 0.5 & 9.00 & & & & & & \\ SC4 & SF & 0.25 & 19.62 & & & & & \\ CF & 0.75 & 4.50 & & & & & & \\ CF & 0.0 & 0.0 & & & & & & \\ CF & 1.0 & 18.0 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mixture designation and used materials proportions.  
Equivalent flexural strength is defined as the average flexural strength at a given deflection (L/150) in the load-deflection curve obtained from the flexural performance test. The equivalent flexural strength in the third-point loading method is calculated as in Equation (2) [27; 28].  
\[f^{\prime}_{r}=\frac{A_{b}}{\delta_{tb}}\times\frac{L}{bh^{2}} \tag{2}\]  
where,\(f^{\prime}_{r}\) is the equivalent flexural strength (MPa), \(\delta_{tb}\) is the deflection (mm) of L/150, \(L\) is the span length (mm), \(b\) is the width of the specimen (mm), and \(h\) is the height (mm) of specimen, and \(A_{b}\) is the area (J, kN\(\cdot\)mm) from the load-deflection curve to \(\delta_{tb}\).
## 2 Materials and Methods
### Analytical Methods  
The moisture content of the samples was analyzed by drying them to a constant weight at 105 \({}^{\circ}\)C [10].  
The total saponin content was determined using the colorimetric absorbance method [11]. The extract (0.5 mL) was transferred into a test tube with 0.5 mL of vanillin solution in methanol (8%) and 5 mL of concentrated sulfuric acid solution (72%). The mixture was heated and remained at 70 \({}^{\circ}\)C for 10 min before being cooled rapidly to room temperature using ice water. The absorbance of the reacted solution was measured using a UV-Vis spectrophotometer at a wavelength of 550 nm. The total saponin content in the extract was calculated based on the standard absorbance curve of aescin at various concentrations. The recovered saponin content was expressed as mg aescin equivalents per gram dry weight (DW) of material (mg AE/g DW).  
The determination of the total polyphenol content was performed using the Folin-Ciocalteu method, as described by [PERSON] et al. (2009) [12], with some modifications. First, 0.5 mL of extract and 2.5 mL of Folin-Ciocalteu reagent (10%) were well mixed in a test tube. The mixture was then left at room temperature for 5 min and then 2 mL of 7.5% (\(w/v\)) Na\({}_{2}\)CO\({}_{3}\) was added. The test tube was kept in the dark for 90 min at room temperature for reaction. The absorbance of the solution was measured using a UV-Vis spectrophotometer at 765 nm. The total phenolic content was calculated and expressed as milligrams of gallic acid equivalents (mg GAE/g DW) based on the absorbance curve of a standard gallic acid solution.  
The assessment of the antioxidant activity in this study was conducted by determining the ability to scavenge free radicals using the DPPH (2,2-diphenyl-1-picrylhydrazyl) assay, following the method outlined by [PERSON] et al. (2006) [13], with some modifications. A volume of 0.5 mL of extract and 2.5 mL of DPPH working solution were well mixed in a test tube and left for reaction in the dark for 120 min. The absorbance of the sample was measured using a UV-Vis spectrophotometer at 515 nm. The antioxidant activity of each extract was calculated based on its absorbance and the standard curve of Trolox. The recovered antioxidant activity was expressed as \(\upmu\)M Trolox equivalents per gram dry weight of material (\(\upmu\)M TE/g DW).
## 3 Measured Results Analysis
### The Measured Time History of Acceleration  
As shown in Figure 5, the translation accelerometers were installed at the 6 th, 12 th, 18 th, 24 th, 30 th, and 32 th floors to obtain the translational acceleration along X and Y axes on different floors of the high-rise building under the influences of typhoon Sarika, by which the translational acceleration responses of six floors were recorded. The partial data of acceleration responses within 4.5 h after the landing of Sarika were selected. The time histories of the translational acceleration on six floors (6 th, 12 th, 18 th, 24 th, 30 th, and 32 th) are shown in Figure 8.  
Figure 7: Wind field characteristics of typhoon Sarika.  
It can be known from Figure 8 that the translational acceleration responses at different floors along both the X and Y axes grow as the height of floors increases. The maximum acceleration, which appears on the 32 th floor along the Y axis, was 0.079 m/s\({}^{2}\). Besides, the time histories of translational acceleration root mean square and their peak values on the 6 th, 12 th, 18 th, 24 th, 30 th, and 32 th floors were also acquired when the basic time duration was 10 min. The results show that the peak values of accelerations and their root mean square all grow with the increasing floor height, and their development trends were essentially in agreement with each other.  
The measured vibration modes of high-rise buildings can be obtained by synchronous measurement of acceleration response of different floors or moving measuring point method. In this measurement, sensors are arranged in four floors, and the angular acceleration synchronous response of four floors (8 th, 16 th, 24 th and 32 th) can be obtained. However, in order to obtain more floor vibration mode vectors, the moving measuring point method is adopted to identify the vibration modes of high-rise buildings, The moving measuring point method only needs two acceleration sensors (such as the acceleration sensors of 24 th and 32 th floors) to obtain the measured vibration modes of high-rise buildings. Therefore, the measured position of the sensor on the 32 th floor can be set to be fixed, and the measured position of the other sensor can be continuously moved to obtain the synchronously measured acceleration of the 8 th, 12 th, 16 th, 20 th, 24 th and 28 th floors relative to the 32 th floor respectively. The time histories of the angular accelerations on the 24 th and 32 th floors during Typhoon Sarika are presented in Figure 9, the maximum torsional angular acceleration is 0.031 rad/s\({}^{2}\).  
Figure 8: Translational acceleration time histories of Typhoon Sarika. (**a**) 6 th-X axis; (**b**) 6 th-Yaxis; (**c**) 12 th-X axis; (**d**) 12 th-Yaxis; (**e**) 18 th-X axis; (**f**) 18 th-Yaxis; (**g**) 24 th-X axis; (**h**) 24 th-Yaxis; (**i**) 30 th-X axis; (**j**) 30 th-Yaxis; (**k**) 32 th-X axis; (**l**) 32 th-Yaxis.
## 3 Results

Table 3: Multilevel linear regression model with three levels of organization for implant MBL (continuous variable in mm) (n = 58 individuals, 113 implants, 226 sites). Table 4 shows a multilevel Poisson regression model for the statistical association between implant MBL and the variables of interest, considering a dichotomous outcome (bone loss <0.2 mm and \(\geq\)0.2 mm). The final model showed that only a torque of above 40 N had an influence on the outcome [Risk Ratio 1.53 (95% CI 1.03-2.26); \(p=0.033\)]. Table 4. Multilevel Poisson regression model with three levels of organization for implant MBL (dichotomous outcome <0.2 mm and \(\geq\)0.2 mm) (n = 58 individuals, 113 implants, 226 sites). \begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Crude**} & \multicolumn{4}{c}{**Adjusted**} \\ \hline
**Variables** & **RR (CI 95\%)** & \(p\)**-Value** & **RR (CI 95\%)** & \(p\)**-Value** \\ \hline Individual level & & & & \\ Age (continuous variable) & 0.98 (0.97-0.99) & 0.046 & - & - \\ Sex (ref = female) & & & & \\ Male & 1.29 (0.93-1.78) & 0.122 & - & - \\ Diabetes (ref = no) & & & & \\ Yes & 1.03 (0.65-1.64) & 0.889 & - & - \\ Hypertension (ref = no) & & & & \\ Yes & 1.00 (0.70-1.44) & 0.976 & - & - \\ Antihypertensive use (ref = no) & & & & \\ Yes & 1.10 (0.75-1.60) & 0.637 & - & - \\ Implants level & & & & \\ Arch (ref = maxilla) & & & & \\ Mandible & 1.02 (0.73-1.40) & 0.916 & - & - \\ Position (ref = anterior) & & & & \\ Posterior & 0.99 (0.69-1.43) & 0.982 & - & - \\ Brand (ref = neeodent) & & & & \\ Conexão & 1.19 (0.82-1.70) & 0.359 & - & - \\ Protection (ref = cover screw) & & & & \\ Healing Abutment & 0.91 (0.60-1.39) & 0.668 & - & - \\ Provisional & 1.49 (0.55-4.02) & 0.435 & - & - \\ Torque (ref = 21-40) & & & & \\
0–20 & 1.54 (0.96-2.47) & 0.071 & 1.54 (0.96-2.47) & 0.071 \\ Above 40 & 1.53 (1.03-2.26) & 0.033 & 1.53 (1.03-2.26) & 0.033 \\ Time (ref = up to 6 months) & & & & \\ Above 6 months & 1.30 (0.94-1.80) & 0.108 & - & - \\ \hline \hline \end{tabular}   
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Crude**} & \multicolumn{4}{c}{**Adjusted**} \\ \hline
**Variable** & **log Beta (CI 95\%)** & \(p\)**-Value** & **log Beta (CI 95\%)** & \(p\)**-Value** \\ \hline Implant level & & & & \\ Arch (ref = maxilla) & & & & \\ Mandible & \(-\)0.02 (\(-\)0.39–0.35) & 0.907 & - & - \\ Position (ref = anterior) & & & & \\ Posterior & \(-\)0.05 (\(-\)0.46–0.36) & 0.811 & - & - \\ Brand (ref = neeodent) & & & & \\ Conexão & 0.21 (\(-\)0.24–0.67) & 0.352 & - & - \\ Protection (ref = cover screw) & & & & \\ Healing Abutment & \(-\)0.12 (\(-\)0.63–0.40) & 0.661 & - & - \\ Provisional & 0.61 (\(-\)0.70–1.93) & 0.365 & - & - \\ Torque (ref = 21–40) & & & & \\
0–20 & 0.49 (0.04–0.93) & 0.032 & 0.94 (0.44–1.44) & \(<\)0.001 \\ Above 40 & 0.53 (0.15–0.90) & 0.006 & 0.93 (0.52–1.33) & \(<\)0.001 \\ Time (ref = up to 6 months) & & & & \\ Above 6 months & 0.48(0.12–0.85) & 0.009 & 0.46 (0.10–0.81) & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
###### Abstract  
Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive-moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc.  
long-range dependence (LRD) model; particle filter (PF); equivalent vibration severity (EVI); kurtosis; Hurst exponent; rotating machinery +
Footnote †: journal:  
doi:10.3390/a11070089
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
## 2 Numerical Design of the Circular Disk with a Vibroacoustic Metamaterial
### Numerical Design of Vibroacoustic Metamaterial Compound  
The functionality of the designed VAMM array integrated into the circular disk is verified via an FE model. In this model, 68 resonators are integrated into the disk in such a way that there is free space at the circumference and close to the central bore. With this arrangement, the disk can be measured with a stationary laser Doppler vibrometer in these regions without the resonators influencing the measurement. The resonators are rotated by 90\({}^{\circ}\) in each quadrant of the circular disk. This ensures that the spring element of the resonators always points in the direction of rotation when the disk rotates clockwise. The model of the disk with incorporated resonators can be seen in Figure 7a.  
The resonators are positioned in such a way that there are diameters that are not interfered with by resonators so that a measurement of the vibrations of the disk with a stationary observer is possible. In particular, a portion at the outer diameter of the disk is kept free of resonators so that the disk can be excited there with a stationary impulse hammer without hitting a resonator.  
The FE model consists of 17,657 hexahedral elements of type SOLSH190. The material parameters and boundary conditions are the same as for the model of the plain disk. The results of the simulation of the circular disk with and without the VAMM are shown in Figure 7b. Depicted are the transfer functions in terms of accelerance averaged over the 12 evaluation points as indicated in Figure 7a.  
Figure 6: Unit cell in its first natural frequency with Brillouin zone indicated with arrows (**left**). Dispersion curve of the unit cell along the Brillouin zone (**right**). The solid lines indicate out-of-plane vibrations, and the dotted lines that intersect the stop band correspond to in-plane vibrations.  
Reduced vibration amplitudes are visible within the predicted stop band frequency range of 2045 Hz to 2967 Hz as predicted by the unit cell modelling. The stop band is interrupted by three distinct modes at 2448 Hz, 2690 Hz and 2790 Hz. These frequencies are related to mode shapes with deflections at the circumference of the circular disk. Since no resonators are placed in this area, the vibrations cannot be reduced with the VAMM. Figure 8 depicts these modes.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 2 Materials and Methods
### Constructing the Pi-GRU Model
It adopts gradient descent as its learning rule and continuously adjusts the network weights and thresholds through BP to minimize the sum of the squared errors. The procedure of the BP network primarily consists of two stages [22]. The first stage involves forward signal propagation: the input signals travel from the input layer, undergo processing in the hidden layer, and finally reach the output layer to produce the output result. The error between the actual output and the expected output is subsequently calculated. The second stage is backward error propagation: the error signal propagates backward from the output layer to the hidden layer and ultimately to the input layer. During this process, on the basis of the error, the weight coefficients and biases of each layer are adjusted sequentially to optimize the performance of the network, ensuring that the output aligns more closely with the expected result. The structure of the BP network is illustrated in Figure 6 below. Figure 7 shows a schematic diagram of the Pi-GRU model architecture. The Pi-GRU model is constructed by combining a GRU model and a BP model in parallel, integrating the advantages of both approaches. In this model, the GRU network is responsible for processing time series data, whereas non-time series data, such as lithology types, are first processed using one-hot encoding to transform the discrete categorical information into binary vectors for category identification purposes. However, this encoding method often results in high-dimensional sparse data. To address this issue, a word embedding algorithm is further introduced to reduce the dimensionality of the data, converting them into low-dimensional dense vectors, which are then used as inputs for the BP neural network. After the GRU network and BP neural network independently complete the training process  
on their respective input data, the outputs acquired from both networks are fed into a fully connected layer for integration. Within the fully connected layer, the data obtained from different sources are fused, effectively consolidating the information contained in both the time series and non-time series data. Finally, the integrated data are passed to the output unit, where they undergo a series of computations and processes to generate the final predictive output. This enables the Pi-GRU model to precisely predict drilling trajectory parameters and other targets.
## 5 Result and Discussion
The controllers' performance in their respective motions is summarized in Table 3, and the percentage improvement is outlined in Table 4 below. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller** & **State** & **Roll** & **Pitch** & **Yaw** & **Altitude** \\ \hline \multirow{3}{*}{PD} & Proportional gain, \(K_{p}\) & 0.935 & 0.935 & 1.4066 & 127.4166 \\  & Derivative gain, \(K_{D}\) & 0.4361 & 0.4361 & 0.5517 & 48.6953 \\ \hline \multirow{3}{*}{PID} & Proportional gain, \(K_{p}\) & 2.0566 & 2.0566 & 2.0392 & 12.3688 \\  & Integral gain, \(K_{l}\) & 0.0423 & 0.0423 & 0.0479 & \(-\)0.0629 \\  & Derivative gain, \(K_{D}\) & 0.4684 & 0.4684 & 0.4027 & 6.3616 \\ \hline \multirow{3}{*}{LQR} & Optimal LQR gain, \(K\) & [1 1.0505] & [1 1.0505] & [1 1.0129] & [1 1.8000] \\  & State weighting matrix, \(Q\) & [\(I_{2\times 2}\)] & [\(I_{2\times 2}\)] & [\(I_{2\times 2}\)] & [\(I_{2\times 2}\)] \\  & Control weighting matrix, \(R\) & [1] & [1] & [1] & [1] \\ \hline \(\mathrm{P}-\)LQR & Proportional gain, \(K_{p}\) & \(-\)1.0084 & \(-\)1.0084 & \(-\)1.0105 & \(-\)1.0061 \\ \hline \multirow{2}{*}{PD\(-\)LQR} & Proportional gain, \(K_{p}\) & \(-\)1.0067 & \(-\)1.0067 & \(-\)1.0062 & \(-\)1.1653 \\  & Derivative gain, \(K_{D}\) & \(-\)0.3446 & \(-\)0.3446 & \(-\)0.3633 & 16.2935 \\ \hline \multirow{3}{*}{PD2\(-\)LQR} & Proportional gain, \(K_{p}\) & \(-\)1.0073 & \(-\)1.0073 & \(-\)1.0070 & \(-\)0.9609 \\  & Derivative gain 1, \(K_{D1}\) & \(-\)0.1948 & \(-\)0.1948 & \(-\)0.1991 & 1.71 \\  & Derivative gain 2, \(K_{D2}\) & \(-\)0.1948 & \(-\)0.1948 & \(-\)0.1991 & 1.6958 \\ \hline \multirow{3}{*}{GWO PD2\(-\)LQR} & Proportional gain, \(K_{p}\) & 0.5215 & 0.5215 & \(-\)0.7398 & \(-\)0.0365 \\  & Derivative gain 1, \(K_{D1}\) & 2.5482 & 2.5482 & 3.8812 & 16.8081 \\  & Derivative gain 1, \(K_{D2}\) & 2.6989 & 2.6989 & 3.4329 & 9.4481 \\ \hline \multirow{3}{*}{IGWO PD2\(-\)LQR} & Proportional gain, \(K_{p}\) & \(-\)0.6122 & \(-\)0.6122 & \(-\)0.5639 & \(-\)0.7704 \\  & Derivative gain 1, \(K_{D1}\) & \(-\)2.9517 & \(-\)2.9517 & 2.2259 & 33.2581 \\ \cline{1-1}  & Derivative gain 1, \(K_{D2}\) & 7.8656 & 7.8656 & 0.7634 & \(-\)6.5025 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of the controller (Model 13). By carefully observing the step response in Figure 4 and the average values in Table 3 we can see that IGWO PD2-LQR controller achieves much better performance than the conventional controllers in term of the rise time and settling time in all four motions. The percentage overshoot is dominated by the PD controller in roll and pitch motions, while in yaw and altitude motions it is dominated by the IGWO PD2-LQR controller.