esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussions
### Comparative Results  
The performance of the proposed forecasting models is compared with the multiple linear regression (MLR) model. The multiple linear regression (MLR) method is a well-known statistical based estimation method that is commonly used as a benchmark model for comparing forecasting models. Multiple linear regression analysis is a kind of simple linear regression analysis. MLR is used to define the correlation between two or more independent variables and a single continuous dependent variable. MLR is also used to identify whether a confounding influence exists. It ensures a way of adjusting for potentially confounding variables that have been included in the model. Both the MLR and proposed forecasting models are trained and tested with same dataset. The statistical parameters and results of the proposed MLR model are given in Tables 7 and 8. MLR model has four independent variables: the last day of consumption (\(\text{L}_{\text{DC}}\)), the last week of consumption (\(\text{L}_{\text{WC}}\)), the weekly load trend (\(\text{L}_{\text{CAL}}\)), and the weekly air temperature trend (\(\text{T}_{\text{EFF}}\)). The proposed linear model is given in Equation 5. The proposed MLR model was trained with a training dataset, and after analysis, the coefficients of weights (\(\text{w}_{0}\) to \(\text{w}_{4}\)) were determined. The analysis results and coefficients that were used in forecasting hourly-based consumption are summarized in Table 9.  
The linear forecasting model (LM) is:  
\[\text{L}_{\text{dc}}\text{w}_{1}+\text{L}_{\text{wc}}\text{w}_{2}+\text{L}_{ \text{est}}\text{w}_{3}+\text{T}_{\text{eff}}\text{w}_{4}+\text{w}_{0} \tag{5}\]  
A performance evaluation of each forecasting model has been conducted using the same dataset for training and testing. MAPE values have been calculated and used to determine the monthly forecasting performance of each model. Each model was trained using data obtained during the same period. Trained forecasting models were tested using data obtained during the same period. Training and testing phases were performed using the same monthly data sets for different years. The obtained results of the proposed models for the same period, 29-30 January 2013, are presented in Tables 10 and 11. \begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Regression Statistics**} \\ \hline Multiple R & 0.990516 \\ R Square & 0.981121 \\ Adjusted R Square & 0.980977 \\ Standard Error & 569.2768 \\ Observations & 528 \\ \hline \end{tabular}
\end{table} 
Table 7: Regression summary and statistics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Summarize**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**SS**} & \multicolumn{1}{c}{**MS**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{**Significance F**} \\ \hline Regression & 4 & \(8.81\times 10^{9}\) & \(2.2\times 10^{9}\) & 6794.925 & 0 \\ Residual & 523 & \(1.69\times 10^{8}\) & 32,4076 & & \\ Total & 527 & \(8.98\times 10^{9}\) & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Regression table (ANOVA).
## 5 Vegetation Changes in Everglades and Selection of Vegetation Types  
To be aware of recent vegetation condition, we compared various available materials including Landsat TM time-series imagery, a RapidEye image, a vegetation map, and aerial photographs acquired by a helicopter survey. A visual comparison of Landsat TM time-series images from 1 April 1994, 23 April 2008, 25 December 2010, and 10 November 2011, showed that the general distribution of vegetation had hardly changed in the Tarpon Bay region (Figure 4). Most vegetation typically adapts to the prevailing environmental conditions, except for indicator species, which are very sensitive to environmental changes. We concluded that the distribution of coarse vegetation types in our study area appeared to be constant over the past two decades.  
We then compared Landsat TM data from 25 December 2010, with RapidEye imagery from 23 December 2010, by overlaying the vegetation map on both images. Although Landsat TM images have a limited resolution for identifying detailed vegetation species, the higher-resolution RapidEye image allowed us to visually interpret vegetation conditions in more detail. Even though the vegetation near the Tarpon Bay area seemed to be unchanged, we could not completely rely on the vegetation map. In addition, some vegetation species were not even detected by photographs acquired during the helicopter survey. We therefore decided to simplify the vegetation types in our classification to three classes: forest, scrub, and prairie.  
Figure 4: _Cont._  
For these three vegetation types, we set up 145 sample sites based on the RapidEye satellite image, where the characteristic features could be recognized by comparison to the reference vegetation map and photographs from the helicopter survey. These sites were then separated randomly into training (74 sites) and reference samples (71 sites) (Figure 5).  
Figure 4: A time series of Landsat TM data rendered as false-color composite images overlaid with the vegetation map created in 1999. (**a**) 1 April 1994; (**b**) 23 April 2008; (**c**) 25 December 2010; (**d**) 10 November 2011. The intensive red color along the tidal channel corresponds well with the mangrove forest, and the color ranges are consistent across the time series.  
Figure 5: Field sample sites for training (red) and reference (yellow) shown on the RapidEye image (true-color combination using red, green, and blue visible bands).
## 3 Results and Discussion
### Optimization Process  
The ramp function graphs indicating the optimum values of input independent variables and output response for minimizing the \(F_{max}\), \(F_{a}\), and \(F_{d}\) and maximizing the \(SF\) with a desirability value of 0.938 are presented in Figure 9. The confirmatory experiments were used to validate this solution. The experimental \(F_{max}\), \(F_{a}\), \(F_{d}\), and \(SF\) values for a composite with a ply angle equal to 15, feed rate equal to 1200 mm/min, and rotational speed equal to 12,000 rev/min were 110.2 \(\pm\) 4.06, 1.2 \(\pm\) 0.05, 0.07 \(\pm\) 0.01, and 20.2 \(\pm\) 0.3, respectively. These results indicate the efficiency and suitability of the preparation techniques and the accuracy of the proposed models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **\(p\)-Value** & \\ \hline Model & 277.14 & 6 & 46.19 & 28.57 & \textless{}0.0001 & **significant** \\ A-Feed rate & 88.17 & 1 & 88.17 & 54.53 & \textless{}0.0001 & **significant** \\ B-Rotational speed & 0.2204 & 1 & 0.2204 & 0.1363 & 0.7139 & \\ C-Angle of layers & 0.0352 & 1 & 0.0352 & 0.0218 & 0.8834 & \\ AB & 52.20 & 1 & 52.20 & 32.28 & \textless{}0.0001 & \\ AC & 10.94 & 1 & 10.94 & 6.76 & 0.0129 & \\ BC & 125.58 & 1 & 125.58 & 77.67 & \textless{}0.0001 & \\ Residual & 66.30 & 41 & 1.62 & & & \\ Lack of Fit & 15.92 & 11 & 1.45 & 0.8617 & 0.5845 & **not significant** \\ Pure Error & 50.38 & 30 & 1.68 & & & \\ Cor Total & 343.44 & 47 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA for Fourth response (SF).  
Figure 9: Desirability ramp solutions for minimizing the \(F_{max}\), \(F_{a}\), and \(F_{d}\) and maximizing the \(SF\). The red or blue points in the figures shows the selected value of their parameter in optimized solution.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 5 Trajectory Generation by Piecewise Cubic Bezier Curve  
### Background  
Named for [PERSON], Bezier curves employ two endpoints called anchor points which defines the span of the line segment, which at least one additional point called control points--points used to give curviness to the original line segment. Bezier curve \(\mathbf{B}\) is a parametric curve that can be expressed as the polynomial function (9) of degree n based on n+1 control point.  
\[\mathbf{B}(u)=\sum_{i=0}^{n}\left(\begin{array}{c}n\\ i\end{array}\right)(1-u)^{n-i}u^{i}\mathbf{P}_{i} \tag{9}\]  
where, \(u\in[0,1]\) with \(\mathbf{P}_{0}\), \(\mathbf{P}_{n}\) is anchor points.  
Directly using the original Bezier curve to smooth robot trajectory turns out many shortcomings. First, generating an n-order Bezier curve in order to smooth the path based on n+1 significant point cause difficultly to control curviness. Second, as the order of the equation increases, the complexity, and therefore the processing time increase. Third, Bezier curves always pass through anchor points and contained in convex hull constructed by the control point set. Therefore, the curve rarely passes through points in the significant point set. This property causes a great difference between the curve and initial path set produced by A-heuristic algorithm.  
There are many ways to tackle this problem, divide and conquer is the basic approach. Robot trajectory is disjointed to many curve segments. The number of curve segments is based on the number of points of significant point set. By adding k control points to each curve segment, it becomes k+1 order Bezier curve that can be presented in Equation (10). Each curve segment passes through 2 anchor points which are points in significant point set. The new curve is the piecewise (k+1) order Bezier curve and passes through all points of significant point set. The second anchor point (endpoint) of the vth curve shares the first anchor point (start point) of the (v+1)th curve (Equations (11) and (12)). This reason makes any piecewise Bezier curve becomes the continuous function over its domain.  
\[\mathbf{B}(u,v)=(1-u)^{k+1}\mathbf{P}_{A}(v)+\sum_{j=1}^{k}\left(\begin{array} []{c}k+1\\ 1\end{array}\right)(1-u)^{k-j+1}u^{i}\mathbf{C}_{j}(i)+u^{k+1}\mathbf{P}_{A}(v+1) \tag{10}\]  
\[u\in[0,1],v\in\{0,1,\ldots,n-1\}\]  
\[\mathbf{B}(0,v)=\mathbf{P}_{A}(v) \tag{11}\]  
\[\mathbf{B}(1,v)=\mathbf{B}(0,v+1) \tag{12}\]
## 4 Results
### Dynamic Responses of the Multi-Layered Soils
#### 4.2.2 Dynamic Responses along with Depth  
The vibration responses of pore water pressure, effective stress, total stress, and displacement without and with piles are shown in Figure 13. When the piles are not considered, the pore water pressure in case 1 was higher than that in case 2 and case 3 when the depth of ground was lower than 7 m, as shown in Figure 13a. In case 3, the pore water pressure was much higher in subgrade, but it rapidly decreased to a small constant value in the ground. When the piles were taken into consideration, the pore water pressures of ground soils in case 1, case 2, and case 3 became lower than the values in natural ground, as shown in Figure 13a,b.  
Figure 13: _Cont._  
The effective stress in case 1 still exhibited the tendency of increasing firstly and then decreasing along with depth in natural ground, but when the subgrade is considered in case 2 and case 3, this phenomenon could not be observed. The effective stress in ground had no obvious difference between case 2 and case 3 no matter if the subgrade was dry or saturated. However, when the subgrade and ground were both saturated (case 3), the effective stress decreased rapidly from the subgrade to ground, indicating the subgrade had an excellent vibration attenuation effect. In the pile-reinforced ground, the effective stresses in case 1, case 2, and case 3 were lower than those in natural ground, as shown in Figure 13c,d.  
The total stresses in ground in case 2, case 3, and case 4 were much lower than those in case 1. Besides, the total stress exhibited no significant differences between case 2, case 3, and case 4, indicating that the total stress was less influenced by the dry or saturated condition. When the piles were considered, the total stress in subgrade had no obvious difference, but the total stress in ground was reduced, as shown in Figure 13e,f.  
The displacement in saturated soils was smaller than that in dry soils by comparing the results from case 2 and case 3, as shown in Figure 13g. Note that the maximum displacement of ground occurred in case 1 because of the lack of subgrade. The piles could also significantly reduce the displacement, as shown in Figure 13h.
# Evaluating Biosedimentation for Strength Improvement in Acidic Soil
## 2 Materials and Methods
### Biosedimentation Procedure  
The physical model dimensions were set in terms of seepage velocity through the targeted soil sample for treatment and longitudinal elongation of the sample to investigate the treatment response with distance and to facilitate the samples' collection. The proposed electrode cell positions were designed to allow the treatment response to be evaluated along the flow direction as well as surrounding the electrode containers.  
Figure 4 shows the dimensions of the used container. Acrylic glass was used to construct the set-up, which contained two specimens measuring 450 mm and 600 mm in length. In addition, two chambers of acrylic glass were attached within the container, allowing it to function as an electrolyte solution holder. Each chamber had a perforated wall with two fixed graphite bars on one side. The flow between the two compartments was appropriately allowed. Moreover, to regulate the pH value, a pH controller system was added in the cathode chamber and was adjusted using a Tris buffer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties (Unit)** & **Value** \\ \hline Specific gravity Gs & 2.57 \\ Liquid limit (\%) & 63.66 \\ Plastic limit (\%) & 32.57 \\ Plasticity index (\%) & 31.09 \\ Hydraulic conductivity (cm/s) & \(1.88\times 10^{-7}\) \\ \(\gamma_{\mathrm{d}}\) (kg/cm\({}^{3}\)) & 945 \\ pH & 3.69 \\ Clay (\%) & 47.7 \\ Mineral composition & Quartz, kaolinite and illite \\ Undrained shear strength (kPa) & 3.65 \\ Water content (\%) & 67 \\ Organic content & 2.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engineering characteristics of the soil samples.  
Figure 3: (**a**) Location of Malacca on the Peninsula Malaysia map and (**b**) location of the sampling site for marine clay soil at Malacca River in Malaysia.  
For a consistent fluid level inside the system, the surplus fluids created by the electro-osmotic flow were manually evacuated. As a result, the specimen did not produce the hydraulic gradient effect. Electro-osmotic flow usually occurs from the anode to the cathode chamber.  
A power source with a direct current of 35-40 V was used in the system. A voltameter was also used to detect the change in voltage potential along the specimen. Vane blades were employed to estimate the shear strengths across the soil sample after 7 treatment days, in line with BSI1377-7-3 [45]. A thermometer was added to the cathode chamber to track temperature changes during the electrokinetic biosedimentation operation. In addition, the water content of each part was measured, and the CaCO\({}_{3}\) percent was determined using the EDX test.  
As illustrated in Figure 3, to eliminate any ambiguities, two treatment lengths between electrode cells of 600 mm (D60) and 450 mm (D45) were investigated using two physical models under the same conditions for the EK effect, treatment period and solutions. As shown in Table 3, the soil samples were analyzed at the end of the treatment period to investigate any micromechanical changes. The experimental work design is shown in Figure 5.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 6 Conclusions  
In the present study, the impacts of vehicle shape on the hypersonic plasma sheath were investigated numerically. Based on the 0.14 THz array antenna and plasma sheath coupling model, the influence of vehicle shape on antenna radiation performance was explored. The result shows that different vehicle shapes will lead to different plasma sheath distributions and result in quite different antenna performances. The following conclusions are drawn:  
1. The plasma sheath distribution of different vehicle shapes is quite different and radiation performance is seriously affected by the plasma sheath;  
2. For a coned vehicle, as the vehicle nose becomes sharper, the plasma sheath thickness becomes smaller, while the peak electron density becomes higher. In the same working conditions, the antenna performance improves as the vehicle nose becomes sharper.  
3. The blunt-coned vehicle plasma sheath will form a lens-like structure and refract the mainlobe of an antenna mounted on the vehicle sidewall. The phenomenon does not exist in the situation of the sharp-coned vehicle plasma sheath.  
4. With the condition that the onboard antenna operation frequency be higher than the plasma sheath cut-off frequency, the shape of the vehicle should be designed to be as sharp as possible to guarantee antenna performance.  
Conceptualization, Z.Z., B.B., K.Y. and R.T.; methodology, Z.Z. and K.Y.; validation, Z.Z., J.X. and K.W.; formal analysis, Z.Z.; investigation, Z.Z.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, K.Y. and R.T.; visualization, Z.Z.; supervision, K.Y. and R.T.; project administration, Z.Z.; funding acquisition, K.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41727804, 41974195 and 61861031), and the Interdisciplinary Innovation Fund of Natural Science from Nanchang University under grant 9166-27060003-YB14.  
Not applicable.  
The IMAGE Data was obtained from NASA and are available at [[https://spdf.gsfc.nasa.gov/pub/data/image/](https://spdf.gsfc.nasa.gov/pub/data/image/)]([https://spdf.gsfc.nasa.gov/pub/data/image/](https://spdf.gsfc.nasa.gov/pub/data/image/)) (accessed on 3 January 2022).  
The authors declare no conflict of interest.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
###### Abstract  
This paper presents an ANN-based rotational speed control to avoid the stalling behavior in Oscillating Water Columns composed of a Doubly Fed Induction Generator driven by a Wells turbine. This control strategy uses rotational speed reference provided by an ANN-based Maximum Power Point Tracking. The ANN-based MPPT predicts the optimal rotational speed reference from wave amplitude and period. The neural network has been trained and uses wave surface elevation measurements gathered by an acoustic Doppler current profiler. The implemented ANN-based rotational speed control has been tested with two different wave conditions and results prove the effectiveness of avoiding the stall effect which improved the power generation.  
acoustic doppler current profiler; artificial neural network; back-to-back converter; oscillating water column; rotational speed control; stalling behavior; wave energy; wells turbine +
Footnote †: journal: Applied sciences
## 4 3D Horn Failure Model  
To assess the slope stability, many researchers have proposed various failure mechanisms, such as the log spiral failure model and cylindrical failure model [1,3,4]. It should be noted that these failure models were all plan-strain failure mechanisms. However, recent published literatures showed that the practical collapse of slopes in the critical state is a three-dimensional failure, and the plan-strain failure model will definitely result in conservative (unsafe) estimations for three-dimensional slope stability issues. To solve this shortcoming, [PERSON] developed a 3D rotational failure mechanism that had the shape of a curvilinear cone with an apex angle (see Figures 3 and 4) [6]. Figure 3 presented the longitudinal section of the 3D horn failure model. Figure 4 presented the cross-section of the 3D horn failure model. It can be seen in Figure 4 that the 3D horn failure model was composed of two parts: (1) the plane-strain insert; and (2) the 3D horn section. The plane-strain insert had the geometry of the logarithmic spiral and connected the two 3D horn sections, which allowed a transition to the plane-strain case if there is no limitation to the slope width. The 3D horn section was the end part of the failure mechanism. The 3D horn failure model was bounded by two log-spirals (see Figure 3) by respecting normality rule:  
\[r=r_{0}e^{(\theta-\theta_{0})\tan\varphi_{t}}, \tag{7}\]  
\[r=r^{\prime}_{0}e^{-(\theta-\theta_{0})\tan\varphi_{t}}, \tag{8}\]  
where, \(r_{0}=OA\) and \(r^{\prime}_{0}=O^{\prime}A^{\prime}\) (see Figure 3). A detailed description of the 3D horn failure model could be found in [PERSON]'s study. In the light of the study, the 3D horn failure model had been repetitively adopted in the estimation of the slope stability. The effects of pore water pressure, inhomogeneous soils, pile reinforcement and seismic loads on the slope stability in the linear Mohr-Coulomb failure criterion were investigated, respectively [8, 9, 10]. However, rare research had estimated critical seismic acceleration of rock slopes using the 3D horn failure model in the MHB failure criterion. Consequently, this study will fill this gap.  
Figure 3: Longitudinal section of the 3D horn failure model.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 3 Results and Analysis
### Combined Models of GFO-KBR and FPG  
More representative results can be demonstrated by GFO-KBR & FPG combined models. In Figure 5, we plot the true HIS signal (black curve) of January 2006, as well as error curves (in colored) of four GFO-KBR & FPG models in terms of DGHE in this month. Basically, the results are comparable with those in Figure 4, that is, the retrograde FPCs generally outperform prograde ones for their lower DGHE. Table 4 records the ERR of GFO-KBR & FPG combined models with respect to the GFO-KBR standalone model in terms of cumulative geoid height error (CGHE) in Equation (13), which is calculated by setting \(q_{i}=CGHE_{GFO-KBR}\) & \({}_{FP}\) and \(q_{j}=CGHE_{GFO-KBR}\) in Equation (16). It shows that the retrograde FPCs start highlighting their advantages after degree 50.  
To observe the error patterns in spatial domain, we draw the global spatial differences plots (SDP), introduced in Section 2.4, of above four GFO-KBR & FPG combined models. As shown in Figure 6, obviously fewer stripes can be found in the retrograde-orbit based GFO-KBR & FPG combined models, comparing subfigure (a) GFO-KBR & FPG-87 with (c) GFO-KBR & FPG-93 or (b) GFO-KBR & FPG-89 with (d) GFO-KBR & FPG-91. These typical north-south stripes are, to a large extent, the product of temporal aliasing errors, which are caused by errors in both ocean tide model and Atmosphere and Ocean De-Aliasing (AOD) product [25; 26]. It seems that the retrograde FPGAs, in combination with GFO, are more effective on reducing the temporal aliasing errors than prograde ones, especially in low-latitude areas within \((30^{\circ}S,30^{\circ}N)\). However, only marginal improvement can be observed over high-latitude regions in Figure 6.