esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ice-Templating of Nanocellulose Suspensions
### Effect of the Suspension Formulation
#### 4.2.4 Polymer Additives  
The fabrication of ice-templated foams from CNF-water-polymer dispersions has first been done by [PERSON] et al. [123; 147] with the main objective of tuning the mechanical properties of foam cell walls. Several CNF-starch nanocomposite foams were thus prepared by varying the starch content from 30 to 100 wt%. The authors observed that the addition of solubilised starch had a significant effect not only on the mechanical performances of the foams but also on the pore morphologies. Smaller cells, larger anisotropy ratios and more closed cells were observed with higher starch contents [147]. It was assumed that the presence of starch in the CNF suspensions affect ice crystal growth. The effect of starch would be to (i) promote ice nucleation and (ii) slow down the average kinetic of ice crystals growth by decreasing the diffusion rate of water molecules to the growing crystal surface.  
More recently, [PERSON] et al. [148] used polymer additives to obtain CNC foams with tunable porous microstructures. The authors added hydrazide-functionalised poly(oligoethylene glycol methacrylate) (H-POEGMA) to CNC suspensions. In order to obtain a chemical cross-linking between CNCs and H-POEGMA, the surface hydroxyl groups of cellulose nanocrystals was also selectively oxidised to aldehyde groups [149]. In doing so, the authors showed that by controlling the composition of the CNC/H-POEGMA dispersions, foams with fibrillary, columnar, or lamellar pore morphologies were produced (Figure 15). However, the chemical, physical, and mechanical phenomena involved are still not fully elucidated.  
Figure 14: FE-SEM micrographs of the cryo-fractured surface of the CNF foams with a density \(\rho\) of (**a**) 7 kg m\({}^{-3}\) and (**b**) 61 kg m\({}^{-3}\). Note that these foams were prepared by direct quenching at a freezing temperature of \(-196\)\({}^{\circ}\)C. (Adapted from [PERSON] et al. [40] with permission from the Royal Society of Chemistry).
## 3 Background and Theory
### Statistical Features  
The initial feature extraction approach involves statistical features [26], which are classified as time-domain characteristics. Widely applied in EEG signal processing, these features play a significant role in brain-computer interface applications and seizure detection. The four core measures include mean, variance, skewness, and kurtosis.  
Mean (\(\overline{x}\)): This is the average value of the signal and is calculated using Equation (6):  
\[\overline{x}=\frac{1}{N}{\sum}_{i=1}^{N}x_{i} \tag{6}\]  
where \(N\) is the total number of samples, and \(x_{i}\) represents the \(i\)-th sample in the signal.  
Variance (\(\sigma^{2}\)): Variance measures how much the data points deviate from the mean, given by Equation (7):  
\[\sigma^{2}=\frac{1}{N}{\sum}_{i=1}^{N}(x_{i}-\overline{x})^{2} \tag{7}\]  
Skewness: This metric accesses the asymmetry of the probability distribution of a real-valued random variable in relation to its mean. It is calculated as shown in Equation (8):  
\[\mathit{Skewness}_{stat}=\frac{1}{N}{\sum}_{i=1}^{N}\bigg{(}\frac{x_{i}- \overline{x}}{\sigma}\bigg{)}^{3} \tag{8}\]where \(\sigma\) is the standard deviation of the signal.  
Kurtosis: Kurtosis evaluates the presence of extreme values (outliers) in the tails of a distribution, similar to skewness, in that it considers the probability distribution of a real-valued random variable. It is determined using Equation (9):  
\[Kurtosis_{stat}=\frac{1}{N}{\sum_{i=1}^{N}}{\left(\frac{x_{i}-\overline{x}}{ \sigma}\right)^{4}}-3 \tag{9}\]  
where the subtraction of 3 adjusts the kurtosis so that a normal distribution has a kurtosis value of zero.
## 5 Experimental Validation
### Experimental Validation of the Current Reference Tracking
Finally, the experimental HESS is stable in all the operating points and power flows, thus ensuring a safe operation and validating the previous simulation results. Figure 27: Dynamic performance of current control (**a**) PI, (**b**) LQG, and (**c**) LQG-FF with \(v_{sc}=15\) V and \(v_{dc}=12\) V (buck mode). Magenta: converter current (\(i_{sz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)). Figure 28: Performance of the current controller LQG-FF with \(v_{sc}=9\) V and \(v_{dc}=12\) V (boost mode). Magenta: converter current (\(i_{sz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Test Conditions**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Error Comparison**} \\ \hline \(v_{b}\) (\(V\)) & \(v_{sc}\) (\(V\)) & **PI** & **LQG** & **LQG-FF** & **LQG-FF/PI** & **LQG-FF/LQG** \\ \hline \multirow{8}{*}{12} & 7 & 0.1645 & 0.1173 & 0.0909 & 55.3\% & 77.5\% \\  & 8 & 0.1513 & 0.1115 & 0.0655 & 43.3\% & 58.7\% \\  & 9 & 0.1242
## 3 Results and Discussion  
### Experiment Results  
The initial experiment achieved a validation mean square error (MSE) of about 0.02, accompanied by a validation R\({}^{2}\) of 82.38%. To enhance these outcomes, a decision was made to supplement the data by carrying out a subsequent experiment. Increased dataset sizes typically result in improved performance, enhancing the reliability of artificial neural networks for prediction tasks in diverse applications.  
Research conducted by [PERSON] showed that using larger datasets improves the ability of artificial neural networks to make accurate predictions [35]. In addition, [PERSON] achieved significant improvement in the model's R\({}^{2}\), increasing it from 83% to 92% by transitioning from the Taguchi technique to full factorial in the design of experiments (DoE) [36].  
By expanding the span of each parameter to add observation points, a second full factorial design was created, as presented in Table 6 below. Consequently, the entire dataset consisted of 486 data points. Basic information for the entire dataset can be seen in Table 6 below.
## 2 Materials and Methods
### Test Setup and Instrumentation  
Figure 3a shows the test setup with the wall foundation beam which was anchored to the floor using 30-mm diameter threaded rods and a horizontally mounted hydraulic actuator providing a horizontal shear force connected to a steel plate fixed at a concrete loading beam. Lateral stability was provided at loading beam for all walls and also at the midbeam when available, without preventing the free movement in the plane of walls. Stability was provided by transverse rollers, two on each side of the wall at the loading beam and midbeam, which were connected to separate steel frames anchored to the strong floor. The transverse rollers were adjusted until they were just free from contact with the loading beam and midbeam, thus providing lateral stability.  
Strain gages, displacement potentiometers, a load cell and a dial gauge were used to monitor and acquire data while the walls were under testing. The locations of the displacement potentiometers, labeled from 1 to 18, are shown in Figure 3b. The locations of some of the displacement potentiometers varied when the aspect ratio of the walls changed. The displacement potentiometers were used to measure the vertical displacements, sliding and shear displacements at several locations on each wall specimen. The locations of the strain gages at the critical locations of masonry wall are shown in Figure 3c.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Density (kg/m\({}^{3}\))**} & **Thermal Conductivity** & **Specific Heat** & **Young Modulus** & **Yield Strength** \\  & **(w/m K)** & **Capacity (J/kg\({}^{\circ}\)C)** & **(kN/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline
30 & 0.04 & 1300 & 3.6 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mechanical and thermal properties of EPS.  
Figure 2: Self-insulating concrete masonry unit (SCMU) configurations: (**a**) block; and (**b**) half-block units [15]. EPS: expanded polystyrene.
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
## 1 Introduction
[PERSON] [24] unintentionally took into account the shell or carrier of accelerometer during error estimation process by placing the sensor at six different tilt angles. Similarly, [PERSON] [25], [PERSON] [26], and [PERSON] [27] drew on a combination of the ellipsoid fitting method and the multi-position method to solve the problem. However, all the multi-position methods require an extra device to provide a reference position and are unsuited to the MWD instrument. In addition, both [PERSON] [28] and [PERSON] [29] found that introducing the relationship between the gravitational acceleration and the geomagnetic field may help to eliminate the problem. [PERSON] [30; 31] and [PERSON] [32] exploited the relationship and proposed a cross product method and a dot product method, respectively. However, they still do not take a carrier platform into the calibration approach for a multi-sensor model. To fill these gaps, our study should address the following two questions:  
1. How to align the coordinate frames of all the sensor triplets with the coordinate frame of a carrier platform in the MWD instrument? 2. Can the new calibration approach be effectively used to eliminate the measuring errors without the use of prior knowledge about the vector field? Our study designed a calibration approach in which three underlying algorithms--the ellipsoidal fitting (EF) method, the planar fitting (PF) method and the inner product invariance (IPI) method were combined as a new method to develop the measuring accuracy and eliminate the systemic errors in the MWD instrument. Specifically, in our calibration approach, the traditional EF method and PF method were optimized to cut down the size of sample data. This new calibration approach was simulated to compare the accuracy of the data under different conditions in MATLAB. This new approach was also applied actually to calibrate the self-designed MWD prototype. The parameters obtained from the calibration approach were verified by a tri-axial non-magnetic turntable.
# Line-Detection Based on the Sum of Gradient Angle Differences
## 3 Proposed Line-Detection Method
### Classification of Line Pixels into Ridge and Valley, and Suppression of Non-Maxima  
The pixels classified as line pixels in the previous step are further classified into the groups ridge and valley. A ridge is a line feature with an intensity greater than that of other features in its neighborhood, and a valley is a line feature with an intensity lower than that of the other features in its neighborhood. The aforementioned classification is based on the sum of the convolutions of the gradients with the following two kernels. The first kernel is defined as follows:  
Figure 3: Pairings for calculating gradient angle difference.  
\[\mathbf{N_{c}}=\begin{bmatrix}-1&0&1\\ -1&0&1\\ -1&0&1\end{bmatrix}. \tag{13}\]  
The second kernel is defined as follows:  
\[\mathbf{N_{r}}=\begin{bmatrix}-1&-1&-1\\ 0&0&0\\ 1&1&1\end{bmatrix}. \tag{14}\]  
\(N_{c}\) in (13) gathers gradients outgoing from (or incoming to) the center of the kernel in the column direction. Similarly, \(N_{r}\) in (14) gathers gradients outgoing from (or incoming to) the center of the kernel in the row direction.  
The measure that indicates whether a line pixel at (\(r\),\(c\)) is a ridge or a valley is calculated as follows:  
\[\mathrm{RV}(r,c)=-\big{[}(\mathbf{g_{r}}*\mathbf{N_{r}})+(\mathbf{g_{c}}* \mathbf{N_{c}})\big{]}(r,c), \tag{15}\]  
where \(\mathbf{g_{r}}\) and \(\mathbf{g_{c}}\) denote images containing the gradients in the row and the column directions, respectively. If \(\mathrm{RV}(r,c)\) is positive, the pixel at (\(r\),\(c\)) is classified as ridge. Else, the pixel is classified as valley.  
Then, a non-maxima suppression process is employed to eliminate the detected line pixels whose absolute SGAD values are non-maxima as compared to those of their neighboring pixels. The suppression process is executed using only the neighboring pixels of the same line class, with either ridge or valley as the center pixel. In addition, the suppression process employs the maximum curvature direction, which is determined from the eigenvector corresponding to the maximum eigenvalue of the Hessian matrix [18].
## 3 Results
### Eutrophication Pressure  
The estimated eutrophication pressure level was significantly correlated with most of the metrics studied, in accordance with the expected trend. Positive correlations were found with several nutrient metrics, such as ammonium (Figure 5a), nitrite, orthophosphate or silica concentrations (\(p\)-value < 0.001). A strong positive correlation was found with dissolved oxygen, as a measure of the increased photosynthetic activity, linked to increased trophic levels (Figure 5b). Chl-\(a\) concentration was also significantly correlated with the eutrophication pressure (Figure 5c). Finally, even with a low number of samples (\(n\)), a negative correlation was found for the benthic invertebrates EQR, demonstrating the negative response of this ecological quality indicator to eutrophication (Figure 5d). In line with the correlations, the linear regressions were also significant, although, as previously mentioned, with a relatively low explained variance in most cases.  
Figure 5: Relationship between the eutrophication pressure level and (**a**) ammonium concentration, (**b**) dissolved oxygen concentration, (**c**) chl-\(a\) concentration, and (**d**) benthic invertebrate index, as EQR. Spearman coefficient (with the significance of the correlation), as well as linear regression (with the R\({}^{2}\) and significance of the regression model) are shown.
## 3 WRF-CFD Wind Field Downscaling Technology
### Simulation of Typhoon Field by WRF Model
#### 3.1.3 Typhoon Wind Field Characteristics
After the typhoon passed and advanced to the northwest, the surrounding airflow continued to converge toward the center of the typhoon. The sea and land characteristics of Hainan Island separated the vortex structure and the two regions showed a trend of chaotic wind speeds and irregular weakening. From the simulation results of the wind field, it can be seen that the WRF model can effectively simulate the landing and weakening of Typhoon Rammasun. It also has a certain ability to simulate the movement of weak low-pressure circulation on land after landing. Figure 5: Simulation results of Typhoon Rammasun by WRF model. (**a**) Sea level pressure. (**b**) Wind speed. (**c**) Equivalent potential temperature. (**d**) Absolute vorticity. Figure 6: _Cont._  
Figure 7a shows the wind speed profiles near the center of the typhoon at different times. It can be seen that when the typhoon landed on 18 July, as a result of the influence of ground roughness, the closer to the ground the greater the change in wind speed. After some hours, the near-surface wind speed decreased by 33%. To compare the difference between the wind field of the normal wind and the typhoon, the speed profile of class A landform (GBT 50102-2014 2014) is studied with the same method, setting the wind speed of the normal wind at 300 m equivalent to that of the typhoon wind field. Based on this, the inflow profile boundary conditions for the typhoon and the normal wind of the subsequent small-scale CFD model were extracted. Figure 6b shows a comparison diagram of the average wind speed profile between the typhoon and the normal wind, which was obtained by the nonlinear Least-Squares Method (LSM). The near-surface wind speed of the typhoon increases with altitude faster than that of normal wind. In the expression of the standard wind speed profile, the ground roughness index of normal wind is 0.12, the fitted value of the typhoon ground roughness index is 0.087, while the basic wind speed at the reference height of the normal wind and the typhoon was 14.6 m/s and 16.59 m/s, respectively.
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Range Signal Tracking
#### 2.4.3 Coherent Integration Time  
The coherent integral gain \(G_{c}\) is calculated from the change of noise bandwidth before and after the integrator. Although there is no change before and after integral in signal power, noise power spectrum density and carrier to noise ratio, the noise bandwidth \(B_{L}\) has changed by the integrator  
Figure 8: Comparison of PLL discriminators.  
from \(B_{pd}\) to \(1/T_{c}\). Since the integral gain value is defined as the multiple of the signal-to-noise ratio increase, the integral gain value is equal to the reduction factor of the noise bandwidth and can be denote as:  
\[G_{c}=10 log(\frac{B_{pd}}{1/T_{c}})=10 log(B_{pd}T_{c})\]  
Because there is quantization loss in the process of digital signal processing, which may bring loss to the coherence integral, the actual noise-to-noise ratio will be reduced, generally no more than 2 dB. Next we will analyze the loss of the coherent integral due to the frequency error.  
The coherence integral in the Figure 7 can be denoted as:  
\[\mathbf{r}_{p}(n)=I_{p}(n)+jQ_{p}(n)=a\cdot\text{sinc}(f_{e}T_{c})e^{i\left[2 \pi f_{e}(t_{1}+\frac{T_{c}}{2})+\theta_{e}\right]}\]  
where \(a\) is the amplitude of IF signal, \(f_{e}\) represents frequency error of carrier loop, \(T_{c}\) is the coherent integration time, \(\theta_{e}\) is the initial phase error and \(t_{1}\) is the moment for integration to start. The coherent integral loss caused by frequency error is shown in Figure 9, and we can see from Figure 9 that the 3 dB coherent integral loss is \(0.44/T_{c}\), which is also the maximum frequency error \(f_{e}\) allowed by the tracking loop.
## 2 Materials and Methods
### Characterisation and Chemical Analysis
#### 2.3.5 Dehydrogenase Activity (DHA)  
Dehydrogenase activity (DHA) is a measure for microbial activity and thus may reflect toxic effects. The four best-performing surfactants, Triton-X and the sophorolipids A, C and H were tested according to [33]. A dilution series of 250 mg/L, 500 mg/L, 1000 mg/L and 2000 mg/L was prepared for each of the selected surfactants. In total, 18 50-mL tubes were prepared (4 surfactants at 4 different concentrations, and 2 blanks (distilled water)). Dry, sieved garden soil from the vicinity of the VSCHT campus (2 g) was weighed and transferred to each of the tubes. Then, 1 mL of the surfactant dilutions or distilled water were transferred to the tubes. To each tube, 1 mL of 2% TTC (2,3,5-triphenyl tetrazolium chloride) was added. The tubes were wrapped in foil paper and kept overnight. Microbial production of the enzyme dehydrogenase converts TTC to TPF (1,3,5-triphenyl formazan), which causes a red colour that is determined by photometry. After 24 h, 10 mL methanol was added to each of the tubes, shaken and left undisturbed for 1 min. The soil suspensions were then filtered using Whatman's filter paper, and absorbance at 482 nm was measured using a spectrophotometer (Cintra 101, GBC Scientific Equipment, Melbourne, Australia). Before measurement, the spectrophotometer was calibrated bymeasuring the absorbance of methanol against a blank. All subsequent measurements were taken with the blank as reference. The DHA was calculated as:  
\[a=\frac{\left(c_{sample}-c_{blank}\right)V}{m}\times\frac{100}{DM\%}\times\frac{1} {t} \tag{1}\]  
where \(a\) is dehydrogenase activity (mg/L), \(c_{sample}\) is the concentration of TPF in the sample (mg/L), \(c_{blank}\) is the concentration of TPF in the blank (mg/L), \(V\) is the total volume of solution (= volume of surfactant + volume of TTC+ volume of methanol) (mL), \(m\) is the mass of soil (g), \(DM\%\) is the dry matter content of the soil (%, mass based) and \(t\) is the time of incubation, 24 h. The DHA was expressed in mg/(g h).
## 5 Conclusions  
The first four orders of normal frequencies in a fluid-filled PE pipeline were calculated, and the distributions of sound in the axial and radial directions were analyzed. The acoustic propagation characteristics of such a pipeline were also studied in an experimental system.  
Both the theoretical and experimental investigations have revealed the following:  
1. Sound in a fluid-filled PE pipeline propagates through the pipeline with the normal frequency at each order.
2. Sound above a certain cutoff frequency can propagate in the axial direction of the pipeline for long distances, whereas sound below the cutoff frequency is attenuated exponentially in the axial direction and cannot propagate over long distances.
3. In the fluid in the pipeline, the sound power is highest at the axial center and decreases with radial distance from the axial center according to a Bessel function dependence \(J_{0}(k_{r}r)\).
4. Sound above the cutoff frequency is propagated mainly through the fluid, while sound below the cutoff frequency propagates in the form of vibrations along the pipe wall.
5. Controlling and reducing the vibration of the pipe wall is the most effective way to reduce low-frequency noise in a fluid-filled pipeline system.  
Conceptualization, Q.L. and J.S.; methodology, Q.L.; software, J.S.; data validation, J.S.; formal analysis, D.S.; writing--original draft preparation, J.S.; writing--review and editing J.S. and D.S.; supervision and project administration, Q.L.  
This research was funded by Acoustic Science and Technology Laboratory, Harbin Engineering University (SSJSWDZC2018010) and by the National Science Foundation of China (11874131).  
The authors declare no conflict of interest.  
Figure 16: Mechanical excitation at a single frequency of 5.2 kHz. Measurement results from vibration sensors at different distances from the excitation point. (**a**) 1.3 m; (**b**) 5.1 m.
# Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System
###### Abstract  
This paper proposes an effective management system for stand-alone solar photovoltaic (PV) using real-time data with Hybrid Energy Storage System (HESS). The abrupt movement of fleeting clouds often gives rise to PV power output fluctuations which in turn affect the power quality and system stability due to scattered solar radiation reception. These variations can limit through a ramp-limit controller and employing a DC link controller to maintain the stable DC link voltage. The battery is used in the system for continuous power application and the sudden variations in charging and discharging of battery power can create stress on the battery. These sudden changes in a battery will be removed by the super-capacitor (SC) unit and achieves a fast DC link voltage regulation. Hence, the high energy and power density devices such as battery and SC units will deliver more stable power into the system. The control scheme is tested in Matlab/Simulink and validated by Real-Time Hardware-in-Loop (HIL) simulator using periodic one-minute data for one year from the solar PV power plant from real-time.  
solar PV smoothening; fluctuations; ramp-limit controller; power management; HESS +
Footnote †: journal: Applied Sciences