esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 3 Pectin Extraction Methods
### Green methods for Pectin Extraction
#### 3.2.3 Ultrasonic-Assisted Extraction  
Ultrasounds (Us) have been widely used in the food industry for their chemical and/or physical effects. There are a large number of potential applications of Us in the food industry, without going into excessive detail, such as extraction emulsification, filtration, cutting, or food preservation [80]. Ultrasonic waves, ranging from 20 to 100 kHz, are often used in U-assisted extraction [81; 82]. Ultrasonic-assisted extraction is a method used to reduce extraction time and increase yield compared to conventional methods (hydrolysis in an acid medium). In this method, sound waves pass through a liquid medium, creating compression and expansion. This process leads to cavitation, that is, the production, growth, and collapse of bubbles [83]. This causes the formation of unstable microscopic bubbles which have high temperatures and pressures. Cavitation takes place near the surface of the target material. As a result, it affects the plant matrix and allows better penetration of the extractor solvents [49; 84] and, therefore, extraction efficiency is better.  
Significant benefits of U-assisted extraction are reduced extraction time, equipment size, and energy consumption, and improved extraction yield, and it is considered more environmentally friendly than the conventional method. In contrast, U-extracted polysaccharides have lower viscosity, molecular mass, and degree of esterification [34; 85].  
The main parameters of U treatment in the food industry involve frequency and intensity of sound waves, sonication time, solvent type, and temperature [86]. U frequency affects the extraction process since it affects microbubble size and resistance to mass transfer. An increase in U frequency leads to a reduction in the production and intensity of cavitation in liquid [81]. The intensity of Us is an important extraction factor. Increased intensity of Us was reported to enhance pectin extractability by disrupting plant cells, thereby improving pectin yield [49]. However, it was also pointed out that high power intensity might produce the opposite effect, i.e., a decrease in pectin yield. Therefore, optimization of ultrasonic variables is very important. The extraction efficiency of U in combination with other technologies such as microwave heating (UMAE and MUAE) can be significantly increased [87]. Table 2 summarizes the physico-chemical properties of pectins extracted by green methods, including UAE.
## 4 Algorithms
### Bonobo Optimizer
#### 4.1.3 Parameter Updating  
The BO's parameters are updated during the iterative process based on the best solution \(X_{B}^{\alpha}\) at each iteration, where if there is an improvement in the final solution compared to the previous iteration, the BO's parameters can be updated in the following way.  
The negative phase count is set to zero (\(NP_{\_}{\_}{cont}=0\)) and the positive phase count grows by increments of one (\(PP_{\_}{\_}{cont}=PP_{\_}{\_}{cont}+1\)). In addition, \(\rho_{\_}{xg}=\rho_{\_}{xg\_initial}\) and \(\rho_{\_}{ph}=0.5+Cp\) where \(Cp\) is the amount of the change in the phase, and can be calculated as \(Cp=\min(0.5,\ PP_{\_}{cont}\times rcp)\) where \(rcp\) is the rate of the change in the phase. Moreover \(\rho_{\_}{d}=\rho_{\_}{ph}\) and  
\[\varepsilon_{\_}{sub}=min\Big{(}\varepsilon_{\_}{sub\_}{max},\ \Big{(} \varepsilon_{\_}{sub\_}{initial}+PP_{\_}{cont}\times rcp^{2}\Big{)}\Big{)} \tag{35}\]  
where \(\varepsilon_{\_}{sub\_}{initial}=0.5*\varepsilon_{\_}{sub\_}{max}\).  
On the other hand, if there is no improvement, the BO's parameters are updated as follows:  
\[\begin{array}{c}NP_{\_}{cont}=NP_{\_}{cont}+1\ \text{and}\ PP_{\_}{cont}=0,\\ Cp=\min(0.5,\ NP_{\_}{cont}\times rcp),\\ \rho_{\_}{xg}=\rho_{\_}{xg\_initial}\min\Big{(}0.5,\ \rho_{\_}{xg\_}{initial}+ NP_{\_}{cont}\times rcp^{2}\Big{)},\\ \text{and}\\ \varepsilon_{\_}{sub}=min\Big{(}\varepsilon_{\_}{sub\_}{max},\ \Big{(} \varepsilon_{\_}{sub\_}{initial}-NP_{\_}{cont}\times rcp^{2}\Big{)}\Big{)}. \end{array}\]  
The overall steps of the BO algorithm are presented in Algorithm 1.  
```
Initialize a set of random search bonobo \(X^{i}_{\_}{B}=\left(X^{1}_{\_}{B},\ X^{2}_{\_}{B},\dots,\ X^{N}_{\_}{B}\right)\) within the limits \(X^{i}_{\_}{min}\leq X^{i}_{\_}{B}\leq X^{i}_{\_}{max}\). Initialize the BO's parameters Evaluate the objective function for all bonobos Identify the alpha bonobo \(X^{\alpha}_{\_}{B}\) While (k \(<\ K_{\_}{max}\)) Determine the actual size of the temporary sub-group Choose a bonobo using fission-fusion society strategy Create a new bonobo \(X^{new}_{\_}{B}\) as follows: if\(r\leq\rho_{\_}{ph}\) Create new bonobo using promiscuous or restrictive mating strategy else \(r>\rho_{\_}{ph}\) Create new bonobo using consorship or extra-group mating strategy end if Calculate the objective function Update alpha bonobo \(X^{\alpha}_{\_}{B}\) and the BO's parameters. \(K=K+1\) end while Return the final best solution \(X^{\alpha}_{\_}{B}\)
```  
**Algorithm 1** BO
###### Abstract  
A novel type of multifunctional hybrid membranes combining modified chitosan, functionalized multi-walled carbon nanotubes (MWCNTs), and rare earth element ion-imprinted polymers (REEIIPs) were designed and characterized. The synthesized materials were characterized by thermogravimetric analysis (TGA), scanning electron microscopy (SEM), vibrating sample magnetometry (VSM), X-ray diffraction (XRD), X-ray micro-tomography, and Fourier transform infrared spectroscopy (FTIR). The hybrid membranes were also studied in terms of their mechanical and rheological properties. The key element of the proper preparation of hybrid membranes using the casting method in an external magnetic field was to synthesize membrane components with appropriate magnetic properties. It was found that they showed tunable weak ferromagnetic properties, and the increase in modified nanotube addition caused the rise in the membrane's saturation magnetization, which for Nd-selective hybrid membranes reached 0.44 emu/g. Also, the increase in thermovoidative stability was noted after introducing functionalized nanotubes into polymer matrices, which, in the case of Gd-selective membranes, were stable even up to 730 \({}^{\circ}\)C. The rise in the modified MWCNT addition and selection of appropriate REE ion-imprinted polymers improved mechanical (Rm and E values increase even twice) and rheological parameters (almost double growth of E\({}^{\prime}\) and E\({}^{\prime\prime}\) values) of the tested membranes. Synthesized hybrid membranes showed a high rejection of matrix components and an increase in retention ratio with rising MWCNT-REEIIP addition, ultimately reaching 94.35%, 92.12%, and 90.11% for Nd, Pr, and Gd, respectively. The performed analysis confirmed homogeneous dispersion, phase compatibility, network integration, formation of a complex 3D microstructure, and improved operational stability of created hybrid membranes, which is significant for their future applications in Nd, Pr, and Gd recovery from coal fly ash extracts.  
ion-imprinted biopolymers; functionalized MWCNTs; multifunctional hybrid membranes; magnetic properties; thermal stability; mechanical properties
## 4 Numerical Experiments
### 39-Bar Planar Truss Experiment  
The task of this experiment is the generation of a planar simply supported truss in the design domain, as shown in Figure 10. The details of specified essential nodes and material property settings are summarized in Tables 10 and 11. The constraint combination used in this experiment is \((g_{1},g_{2},g_{3},g_{4})\) and the parameters are given in Table 12. The purpose of this experiment is to find the truss layout of minimum weight under given constraints.  
Figure 8: Generated layouts for the 10-bar planar truss experiment. (**a**) maxp = 6, weight = 2153.87 kg; (**b**) maxp = 7, weight = 1968.72 kg; (**c**) maxp = 8, weight = 1852.46 kg; (**d**) maxp = 9, weight = 2122.05 kg.  
Figure 9: Multiple competitive truss layouts generated by the KR-UCT algorithm.  
Figure 11 depicts the optimal truss layout generated for this experiment. In this experiment, the third step of KR-UCT can be simplified because this problem is controlled by stress constraints. Hence, the structural optimization criterion, i.e., the full stress criterion [29], can help the agent to make the selection of cross-sectional area faster and better. The third step of KR-UCT is no longer to adjust the cross-section of bars but to give all bars the cross-section directly according to the fully stressed design criterion.  
The optimal solution of KR-UCT is compared with that from other literature, as shown in Table 13. The optimal solution found in this study weighs 82.14 kg, which is about 2.56% lighter than the IPVS algorithm [7]. It should be noted that by introducing symmetry, the number of decision steps required to generate this truss structure is reduced from 49 to 26, which reduces the total decision length by nearly half. Thus, the decision process can be significantly shortened by introducing symmetry into the actions of MDP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Essential Node** & **Node Location (mm)** & **Node Label** \\ \hline \((1)\) & \((0,\ 0)\) & Pinned support \\ \((2)\) & \((12,192,\ 0)\) & Sliding support \\ \((3)\) & \((3048,\ 0)\) & Loading \((0,-88,964\ \mathrm{N})\) \\ \((4)\) & \((9144,\ 0)\) & Loading \((0,-88,964\ \mathrm{N})\) \\ \((5)\) & \((6096,\ 0)\) & Loading \((0,-88,964\ \mathrm{N})\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Essential node coordinates in the 39-bar experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Materials Properties** & **Settings** \\ \hline Young\({}^{\prime}\)s modulus & \(68,950\ \mathrm{MPa}\) \\ Density & \(2767.99\ \mathrm{kg/m^{3}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Material property settings in the 39-bar experiment.  
Figure 10: Design domain of the 39-bar planar truss.
## 3 Results and Discussion
### Quantification of Light Interception in the Growth Chamber Environment  
The distributions of intercepted light at different planting densities are visually described by a color gradient along with simulation results in Figure 6. Overall, light interception was evidently heterogeneous at each part of the canopy. In particular, in a leaf, light interception was dramatically decreased at marginal areas due to its convex curvature. In many studies using plant models and optical simulation, most structural elements, such as the angle, size, and geometrical dimensions of plant organs, were generally well considered [13; 14; 15; 29], but detailed morphological features, such as leaf curvature, are often overlooked, which are hard to measure and digitize. However, because the exclusion of some morphological features can lead to misestimations [18], the importance of reflecting morphological details on plant models should be emphasized.  
When the planting density was changed from HD to LD, the light interception of the central plant was increased at the middle canopy height, but those of the outer plants decreased at the high canopy height by receding from the center of the light source (Figure 7A). Consequently, the average light interception increased by approximately 18.7% at the central plant but decreased by approximately 5.5% at the outer plants; thus, the total light interception was larger for HD by approximately 2.2% (Figure 7B).  
Under natural light, planting density and canopy arrangement are the main considerations for efficient light interception because natural light is assumed to be a surface light source, uniform in illuminating areas, and uncontrollable. However, under electrical lights, changing the canopy arrangement affects not only the mutual shading between plants but also the incident light environment  
Figure 5: Comparison between measured and estimated canopy photosynthetic rates (P\({}_{n}\)) in the growth chamber. The PPFD was set to 100, 200, and 300 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), and the CO\({}_{2}\) concentration was set to 500, 600, and 700 \(\upmu\)mol mol\({}^{-1}\). Nine lettuce plants at DAT 21 were used.  
Figure 6: Simulation results of light distribution on the lettuce canopy for HD (**A**) and LD (**B**). HD and LD indicate high planting density (25 plant m\({}^{-2}\)) and low planting density (16 plant m\({}^{-2}\)), respectively.  
due to the physical light distribution [30]. When the planting density was changed from HD to LD, light interception of the central plant was evidently expected to be increased by reduction of mutual shading, but those of the outer plants were unpredictable. In this case, the decrement in light interception by receding from the center of the light source was larger than the increment by diminished mutual shading for border plants, and consequently, whole canopy light interception was decreased for LD.
# Sensitivity Estimation for Differentially Private Query Processing
## 5 Experiments
### Summary for Experimental Results  
The experimental results are summarized as follows:  
* Sampling-SE and Sketch-SE both have higher efficiency than residual sensitivity for join queries with large-scale datasets;
* With an appropriate sample rate, Sampling-SE has an equal level of accuracy to residual sensitivity while keeping a lower time overhead.
* Sketch-SE has the same level of efficiency as elastic sensitivity but results in a relatively lower value of sensitivity, which leads to higher accuracy.
# Probabilistic Sensitivity Analysis of Wear Property for MEMS Gas Bearing
## 1 Introduction
[PERSON] (2017) used the Pareto optimal method to study and design flat pad aerostatic bearings and proposed a method for considering roughness sensitivity. The research shows that a multi-orifice aerostatic flat pad bearing was very sensitive to surface contour changes. [PERSON] (2018) investigated the impact of misalignment and wear on radial bearings, and the results showed that the orifice compensator bearing was more sensitive to wear depth changes compared to the capillary compensated bearing. [PERSON] et al. (2019, 2020) studied the influence of surface waviness on gas bearing and found that the surface waviness in different directions had different effects on the static and dynamic characteristics of the bearing. [PERSON] (2018) studied the performance of micro spherical spiral groove gas bearings with surface roughness considering temperature effects. The results showed that the gas temperature of the bearing increased significantly with increased wall roughness. [PERSON] (2019) investigated the influence of the shape error of the bearing surface on the stability of the bearing using a semi-implicit format. The results showed that the shape errors of the bearing's inner surface and journal surface had a different degree of effect on bearing performance, and the shape errors of the rotating shaft had more obvious effects. In the literature (2013, 2014, 2015, 2016), detailed analysis of the bearing performance considering surface friction was also carried out. Because the structure scale of MEMS gas bearings are much smaller than that of traditional gas bearings, much more attention should be paid to the wear of the bearing's inner wall. Previous investigations on wall friction or wear of bearings mostly focus on thrust gas bearings and traditional radial gas bearings, however few of them focus on micro radial gas bearings. Moreover, the description of bearing wear and friction is mostly based on the single roughness and wear numerical model, which cannot describe the randomness property of the wear and surface roughness very well. Therefore, this paper proposes a mathematical model for describing bearing wear and roughness properties using a statistical method and then investigates the static and dynamic characteristics of MEMS gas bearings under specific wall wear and roughness from the perspective of probability. Finally, the probabilistic sensitivity of bearing performance is studied with wear at different axial and radial positions, and then some practical methods are proposed to prevent wear.
## 3 Methodology
### Collaborative Clustering
#### 3.2.7 Generate and Adapt Collaborative Members  
As explained earlier, the majority of clustering algorithms are unable to process mixed data, so specialised clustering algorithms for each data type, selected according to the previously described method limitations, have been used.  
We show the clustering algorithms used in our proposed model and how they are adapted to our collaborative approach.  
* Gaussian and Bernoulli Mixture Models  
Figure 3: Collaborative clustering for reinforcement scenario.  
The EM algorithm to estimate the parameters of a mixture model from data makes essential use of these posterior probabilities [39]. In this paper, both Gaussian Mixture Models (GMMs) and BMMs were employed in order to model the observed data set. As the qualitative data are binary, a superior fit is obtained from the Bernoulli distribution. Given that a Gaussian distribution operates on the binary data, i.e., 1 and 0, this was utilised to model the qualitative data after converting them into binary data in the pre-processing phase.  
* K-Means Clustering  
The k-means approach to solving the problem is referred to as EM. The E-step maps the data points to the nearest cluster and the M-step calculates the centroid of each cluster using a k-means algorithm based on Euclidean similarity. A k-means algorithm was also employed, in which the similarity calculation equation was changed to the cosine similarity measure. The k-means algorithm is not probabilistic, but it can be considered to be a degraded case of the GMM algorithm.  
* K-modes clustering  
A K-modes clustering algorithm was proposed by the author in [40]. This is an extension of the k-means algorithm, which can handle categorical features. It inherits the characteristics of the KMC algorithm and is efficient and easy to implement. It is therefore widely used in various fields. Since the k-modes algorithm is suitable for clustering categorical data, it was utilised to process the qualitative heart disease data features. However, it can only be used in the reinforcement scenario as it was not adapted for use with the collaborative method. In this paper, the collaborative method presented can only be applied to GMMs and BMMs. However, adaption of the technique is possible with respect to algorithms based on alternative probabilistic models.
## 3 Results
### Simulated Case Study
#### 3.1.5 Sensitivity to Measurement Noise  
Permanent sensor installation in multiple buildings requires the use of affordable sensors, which come with the drawback of reduced precision and higher noise floors. In Figure 15, the influence of changing noise levels on the variability of OMA-based DSFs and the related differentiation between linear and nonlinear model responses is assessed. As observed for all three DSFs (\(\Delta MSC\), \(PAAC\), and \(TAC\)), a signal-to-noise ratio of at least 5 is required to avoid large variability in the results. Given that the amplitude of AVs typically ranges between \(10^{-3}\) and \(10^{-1}\) mm/s\({}^{2}\), this requires sensors with resolution around \(10^{-4}\) mm/s\({}^{2}\). For DSFs derived from GMs, given the higher amplitude of shaking, lack of precision only initiates at noise levels of 5 cm/s\({}^{2}\), which is above the typical range of modern--even low-cost--sensors.  
Figure 14: Correlation of six DSFs with respect to the Park and Ang DI (\(DI_{PA}\)) for three nonlinear models that are defined by changing the stiffness degradation parameter (_delta_). Two DSFs are derived from post-earthquake AVs: \(\Delta f\) (**top left**) and \(TAC^{amb}\) (**top center**). Another two DSFs are derived with short moving time windows: \(TAC\) (**top right**) and \(WLC\) (**bottom left**). The final two DSFs are derived from the entire GM signal: \(CE\) (**bottom center**) and \(CohAC\) (**bottom right**). All DSFs are derived for the first mode and for an input sensor at the ground and an output sensor at \(M_{5}\), as defined in Figure 3.  
The DSFs evaluate structural behavior through an input-output sensor pair. For such a setup to be successful, good synchronization needs to be guaranteed between sensor nodes.
## 5 Implementation and Evaluation  
This section discusses the implementation of the proposed methodology and performance evaluation of various feature selection method on the dataset identified from the literature. The present work uses raw CSI traces of the SignFi dataset [29] where the gesture movements refer to American Sign Language, ASL-LEX database [58], and the authors manually added labels for gestures that are not present in the database.
## 3 Performance Analysis of the PSI Technique
### Signal Spectrum Leakage
It can be seen that when NRB is increased, the length of IFFT is also increased (for instance, when NNRB = 6, IFFT/FFT length = 128, and bandwidth is 1.4). The PSI technique was tested with all the varieties of signal, and as seen in the Table 5, the spectrum leakage remained small in all cases. It should be noted that here, we consider spectrum leakage very close to the main spectrum in both cases, with and without re-sampling process. The worst case shows about 9.29 dB leakage when NRB is 75. It should be noted that by optimising parameters of the PSI algorithm, such as SR, PR, and NS, the leakage is minimised, as shown in Figure 11(a). However, here in order to have clear picture, worse scenarios were considered. As the variation is comparably small, and close to the main spectrum, this comparison was performed; however, in many works, when the variation and spectrum reshaping is noticeably high, mathematical calculations for adjacent channel power ratio (ACPR) or adjacent channel leakage ratio (ACLR) must be measured. ACPR or ACLR is a measure that shows a ratio between the power of adjacent channel to the main channel's power. In this paper, the comparison of the modified signal with the original signal without PSI applied are shown with red and blue colours in Figures 12 and 13. It should be noted that all the tests were performed based on LTE toolbox and QPSK, or OQPSK modulation schemes; however, according to LTE standard documents [28], if test model 3.1 or test model 3.2 are employed, the waveform is slightly different, since the modulation scheme in test model 3.1 is \(64 QAM\) and the modulation scheme in test model 3.2 is \(16 QAM\). PSI was tested with the aforementioned test model signals; by running one test with each, it was found that the original signal with test model 3.1 experiences \(12.1859\) dB PAPR, and test model 3.2 experiences \(16 QAM\) PAPR. The PSI technique was able to reduce this PAPR to about 8 dB, which is very similar to QPSK and OQPSK schemes. However, further analysis for EVM for different test models can be performed in future studies.