esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Feature Selection Methods  
Although we have defined various terminal nodes, not all of them are equally useful for GP in designing new DRs. The problem with including unnecessary terminal nodes is that the solution space in which GP searches for a suitable PF increases exponentially. Therefore, it becomes increasingly difficult for GP to converge to good solutions. Therefore, it is not sufficient to define only a set of terminal nodes that represent all features of the problem; it is also crucial to determine whether these features are relevant for GP.  
Although various algorithms and methods for feature selection have been defined in the literature [59; 60; 61], we use two simple methods: backward sequential feature selection (BSFS) and frequency-based selection (FBS). Although simple, the BSFS method is commonly used in the machine learning community to select appropriate features. The reason we select only the above-mentioned methods is that our goal is to determine the best feature set for the problem under consideration rather than compare different feature selection methods.  
BSFS starts with the complete feature set and removes one feature from the feature set in each iteration. GP is then executed with this reduced feature set, and the developed PFs are evaluated. The reduced set that results in the best performance for the generated DRs is selected, and the procedure is repeated until all features are removed. So, in each iteration, this method tests the effect of removing each feature on the results and removes the feature that has the least negative effect. This process requires a quadratic number of experiments to determine the appropriate terminal set.  
The FBS method also starts with the complete feature set to generate a certain number of DRs. Based on the best DRs, the frequency of occurrence of each terminal node is determined. The node that occurs the least frequently is removed, and the process is repeated until all terminal nodes are removed from the terminal set. This method, therefore, removes the node that occurs least frequently in the generated PFs in each iteration. Compared to the BSFS method, this method only requires a linear number of experiments in relation to the terminal nodes. This is because only one experiment is performed in each iteration, on the basis of which the frequency of occurrence of terminal nodes can be determined to decide which node should be removed next.
# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 4 Experiments
### Evaluation Metrics  
In this paper, the proposed scheme and all baselines are evaluated in terms of classification accuracy on the test dataset, i.e., test accuracy (TA), which is defined as Equation (16).  
\[\text{TA}=\frac{\left|\left(x_{i}^{t},y_{i}^{t}\right)\in D_{test}:f\left(x_{i} ^{t}\right)=y_{i}^{t}\right|}{D_{test}} \tag{16}\]where \(D_{test}\) represents the test set, \(x_{i}^{t}\) represents the features of the test instance, \(y_{i}^{t}\) represents the ground-truth label of each sample, and \(f\left(x_{i}^{t}\right)\) represents the output of the classifier trained by each scheme. If two classifiers are trained in some scheme, e.g., co-teaching and our proposed, \(f\left(x_{i}^{t}\right)\) is simply set as the average of both classifiers' output. Note that, intuitively, under the same percentage of noise, the larger the TA is, the more robust the classifier is.  
Another evaluation indicator is Macro F1, which is defined as Equation (17).  
\[\text{F1}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{17}\]  
\[\text{Macro F1}=\frac{\sum\text{F1}\ score\ for\ each\ class}{\text{The\ number\ of\ classes}} \tag{18}\]  
The definitions of precision (P) and recall (R) are given in Equations (19) and (20).  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{19}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{20}\]  
The meanings of TP, FP, FN and TN are illustrated as follows:  
* TP (true positive): the number of samples that are actually positive and predicted to be positive;
* FP (false positive): the number of samples that are actually negative but predicted to be positive;
* FN (false negative): the number of samples that are actually positive but predicted to be negative;
* TN (true negative): the number of samples that are actually negative and predicted to be negative.
## 3 Results and Discussion
### Preprocessing of Gas Monitoring Data
#### 3.3.3 Mathematical Model of Harmful Gas Concentration Release Characteristics  
In order to further quantify the release law of harmful gases during the putrid process of _Limnoperna fortunei_, the characteristic equation of NH\({}_{3}\) and H\({}_{2}\)S concentrations changing with the density of _Limnoperna fortunei_ and time out of water was fitted (Equations (34) and (35)). Among them, the gas concentration, the mass density of _Limnoperna fortunei_, and the time out of water are quite different in magnitude. In order to avoid the influence of the above situation on the experimental analysis results, this part of the study has carried out the normalization of the concentration, density, and time data. The complex correlation index R\({}^{2}\) of NH\({}_{3}\) concentration release characteristic equation is 98.14%, and the root mean square error RMSE value is 0.05143. The complex correlation index R\({}^{2}\) of the H\({}_{2}\)S concentration release characteristic equation is 98.71%, and the root mean square error RMSE is 0.03043. The above model tables all have suitable fitting ability and can be used to estimate the release of NH\({}_{3}\) and H\({}_{2}\)S concentration caused by the corruption of _Limnoperna fortunei_ based on the distribution rule of _Limnoperna fortunei_ and drainage maintenance time in the water pipeline.  
\[C_{NH_{3}}=f(m,t)=\sum_{i=1}^{6}\sum_{j=1}^{6}a_{ij}m^{i-1}t^{j-1}\]  
\[a_{ij}=\left[\begin{array}{cccccc}-0.0156&0.9349&-7.0490&18.8100&-20.5900&7.9130\\ -0.0315&-0.6988&2.7800&-5.8740&3.2910&0\\ 0.7443&3.3820&0.6363&1.5850&0&0\\ -2.2930&-4.1990&-2.6180&0&0&0\\ 3.7020&2.6230&0&0&0&0\\ -1.9990&0&0&0&0&0\end{array}\right] \tag{34}\]  
where \(C_{NH_{3}}\) is the \(NH_{3}\) concentration, dimensionless; \(m\) is mass density of _Limnoperna fortunei_, dimensionless; \(t\) is time out of water, dimensionless.  
\[C_{H_{2}S}=f(m,t)=\sum_{i=1}^{6}\sum_{j=1}^{6}a_{ij}m^{i-1}t^{j-1}\]  
\[a_{ij}=\left[\begin{array}{cccccc}0.0200&-0.8871&7.3950&-22.2400&27.2000&- 11.5800\\ -0.0260&-1.4920&3.5600&-1.1250&0.2000&0\\ 1.2360&6.0510&-3.6330&1.6100&0&0\\ -4.3470&-7.3830&-0.0841&0&0&0\\ 5.5550&3.4290&0&0&0&0\\ -2.4410&0&0&0&0&0\end{array}\right] \tag{35}\]  
Figure 12: The change of H\({}_{2}\)S concentration under different densities of _Limnoperna fortunei_.  
where \(C_{H_{2}S}\) is the \(H_{2}S\) concentration, dimensionless; \(m\) is mass density of _Limnoperna fortunei_, dimensionless; \(t\) is time out of water, dimensionless.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Hydrodynamics in Submerged HF Membranes
#### 5.2.4 Aeration Modes  
Armed with the knowledge that the shear stress induced by bubbles is the dominant mechanism in controlling particle back-transport from membrane surfaces, the bubbling mode (e.g., continuous, alternating, pulsed) can be optimized to minimize the energy cost while achieving an optimum shear to improve system performance [213; 214]. [PERSON] et al. [215] carried out a study of the frequency or duration of cycling between filtration and bubbling phases and showed that intermittent aeration is effective for fouling control in a denitrification MBR. [PERSON] et al. [216] studied the positioning of aeration ports and reported that the injection of air in different zones around the fiber bundles greatly improved the overall system performance. [PERSON] and [PERSON] [188] studied the effectiveness of continuous, alternating, and pulsed bubbling modes and found that, even though the volume of gas used by pulse sparging was half of that used by the other sparging conditions, relatively similar induced shear stress was observed for all three bubbling modes. Similarly, [PERSON] et al. [217] observed that semi-continuous aeration could suppress the membrane fouling at the same level as at continuous aeration.
## 4 Firm Financing  
The literature suggests that firms can provide financing options when customers have budget constraints. In practice, firm financing exists in various forms such as installment payments. We model such a financing strategy with a constraint on Friend R's budget that is more flexible than budget constraints without a financing strategy.  
Denote \(m\) as the reduced cash constraint. Thus, the budget constraint becomes \(p-m\leq c\), while the customer's surplus constraint remains \(v+I-p\geq 0\). Customer S's estimation regarding the likelihood of Friend R's purchase becomes \(Pr(\delta=1)=\ Pr(p-m\leq c)\cdot\chi>0\).  
The budget constraint is  
\[\Pr(p-m\leq c)=\begin{cases}0,p-m>H\\ q,L<p-m\leq H\\ 1,p-m<L\end{cases} \tag{9}\]  
Customer S's surplus is \(u_{S}=\Pr(\delta=1)\cdot B-\beta I\). Customer S's problem is to solve the optimal \(I\) from maximizing \(E(u_{S})\). Finally, the firm sets the price \(p\) and referral reward \(B\), in addition to setting the financing program \(m\) to maximize the expected profit. The literature indicates that, when a firm provides financing, it is optimal to extend credit to the customers at an interest rate that is lower than the firm's cost of funds [1]. In our study, we assume \(\alpha\) to be the cost coefficient associated with the financing program, which might be incurred by realizing a time value of money or by hiring an external financing agency for the program. In addition, we assume \(\alpha+\beta>1\) because the cost of referring and selling the financing is small and the company can intuitively take both strategies if \(\alpha+\beta\leq 1\). The firm's expected profit is \(E(\pi)=Pr(\delta=1|I^{*}(p,B,m))(p-B-\alpha m)\).  
\[\max_{p,B,m} Pr(\delta=1|I^{*}(p,B,m))(p-B-\alpha m) \tag{10}\] \[\text{s.t. }p\geq 0,\,B\geq 0,\,m\geq 0 \tag{11}\]  
The results are illustrated in Figure 4.  
As shown in Figure 4, Cases 1-3 represent the regions where RRPs remain optimal, whereas the other regions are optimal for firm financing. Figure 4 implies that these two strategies are perfectly substitutable, and it is unnecessary for the firm to adopt both of them.  
**Proposition 3**.: Firm financing is optimal when \(H-\frac{qH-L}{\beta}\leq\
u<\frac{qH-\beta H-\alpha L}{1-\alpha-\beta}\) and \(\beta<q<\beta+(1-\beta)\frac{L}{H}\) hold simultaneously.  
Proposition 3 can be illustrated in Figure 4 as the parameters move from S2 to S4. Proposition 3 shows that, when both \(\
u\) and \(q\) are at a medium level, firm financing brings more profit to the firm than the RRPs do. When Friend R's initial valuation is very low (\(\
u<L\)), there is no need for the firm to provide a financing option because the budget constraint is redundant. When Friend R's initial valuation is very high (\(\
u\geq H\)), it is intuitive that firm financing can generate more profit for the firm.
# Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method
## 3 Model Description
### Simulation of the Source and the Propagation Medium  
The assembly was modeled by three layers corresponding to the steel substrate, the composite plate and the epoxy joint. The behavior of each layer was assumed to be linearly elastic. The excitation was simulated by an imposed displacement equal to 0.01 mm at one of the mesh nodes corresponding to the emitting sensor (T) center location (Figure 2b). The calculation was performed in two steps. During the first step, an input signal was generated at the emitting sensor location, then in the second step the resulting acoustic wave propagated in the assembly. The input signal was defined as a 150 KHz sinusoidal signal consisting of 3 periods, with a total duration of 2.00 \(\times\) 10\({}^{-5}\) s. This signal is idealized because it is not experimentally straightforward to control the signal emitted by a sensor (S9204), where sensitivity in emission is expected to influence the output signal. Therefore, it is difficult to achieve a quantitative comparison of numerical simulations with experimental results. Nevertheless, working with an idealized sinusoidal source at emission enables the influence of the receiver sensor type and location to be studied, as well as the presence of a defect, for a given signal. For the wave propagation simulation, a total time period of 2.80 \(\times\) 10\({}^{-4}\) s and a time step of 3.30 \(\times\) 10\({}^{-7}\) s were used. The material damping was described by the Rayleigh coefficients \(\alpha\) and \(\beta\)[39, 40, 41, 42, 43]. Their values for epoxy and carbon fiber composite were taken from the literature [42, 43]. For steel, we calculated the damping coefficients \(\alpha\) and \(\beta\) using Equation (1) in order to obtain the known damping coefficient (\(\xi\)) at a given frequency.  
\[\xi=\frac{\alpha}{2\omega}+\frac{\beta\omega}{2} \tag{1}\]  
where \(\omega\) is the pulsation.  
Table 2 summarizes the values of \(\alpha\) and \(\beta\) for the studied materials. Linear 3D elements with 8 nodes were selected. In order to determine the mesh size, a convergence study was performed. Based on this study, a 2 mm mesh size was selected. It was ensured that 20 elements existed per wavelength. Acoustic excitation at 150 kHz was sent by the emitting sensor through the assembly, and the propagated signals were determined by calculating the out-of-plane displacements at specific locations on the assembly surface.  
Figure 2: (a) Schematic representation of the composite/metal assemblies with the location of the defect and sensors (transmitter, sensor 2 and sensor 1 denote the positions of the transmitter and receivers during the tests). (b) Simulation of the AU test indicating the locations of the virtual transmitter and receiver sensors (T denotes transmitter), where each virtual receiver is separated by about 28 mm.
# A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model
## 3 Experiments
### Experimental Settings
#### 3.1.2 Out-of-Bounds Simulation Experimental Settings  
In the out-of-bounds simulation experiments, three performance indicators are involved, namely the response time of the admittance model, trajectory of the moving point in the end-effector, and response time for out-of-bounds behaviors. The proposed safe admittance boundary algorithm is used to impose virtual constraints on the robot workspace to effectively and feasibly improve the HRI safety.  
HRI safety testing is part of the experiment. A non-powered linkage mechanism with 4 DOFs and low damping and a six-dimensional force sensor (Force Torque Sensor, FT-300, Canada ROBOTIQ, Levis, QC, Canada) are used to simulate the robot admittance model with low damping and no stiffness. Twenty groups of output force data of the wrist are collected from five experimenters during the active control of the motion of the shoulder and elbow. The space accessible by the five experimenters is measured. Compared with the space accessible by the upper arm in DAS, the former is larger. Therefore, the ability of the system to make an effective response when the moving point in the end-effector touches the safe admittance boundary could be reliably verified by the experiments.  
Figure 8: Test logic behind the space verification experiments.  
Figure 7: Experimental workflow.  
For each collection, the subject's wrist is attached to the six-dimensional force sensor by the hook and loop fasteners (as shown in Figure 9a). The subjects are asked to actively control the motion of the shoulder and elbow, with the upper arm making a linear reciprocating motion along the x-axis. The admittance model is configured based on the damping factor and stiffness coefficient of the linkage mechanism. The collected data are imported into the robotic simulation system (as shown in Figure 9b) for motion simulation and analysis.  
The computer hardware for building the simulation system consists of the following components: Windows 10 operating system, Intel CPU i5-12400F, NVIDIA GPU RTX 3060, and 64 GB random access memory. The software environment consists of Matlab 2022b and Simulink 2022b. The side length \(r_{1}=r_{2}=0.06\) m of the voxel grid for voxel center filtering is configured in the experiment so that the response time of the variable stiffness admittance strategy is ensured. The sampling rate is set to 20 ms for the safe admittance boundary in the simulation, and K\({}_{\text{low}}\) is 0 N/m. The values of these parameters ensures that the response time requirements are met during rehabilitation training and that the motion features of the linkage mechanism used for data collection are fully considered. In the admittance model, M is set to 0.01 kg, B to 1 Ns/m, K\({}_{\text{mid}}\) to 5 N/m, and K\({}_{\text{high}}\) to 10 N/m. The expected position, a fixed point, lies at the center of the safe space.
## 2 Results and Discussion
### General workflow
#### 2.1.5 Feature Selection Using GA  
Feature selection facilitates data visualization and data understanding, reduces the measurement and storage requirements, reduces training and utilization time, and improves prediction performance. Feature selection methods can be classified into two categories. If the feature selection process does not involve a learning algorithm, it is a filter approach; otherwise it is a wrapper approach. Filter methods such as variable ranking are widely used because of their simplicity, scalability, and good empirical success. Sophisticated wrapper methods improve predictor performance compared to simpler variable ranking methods. Wrapper methods are remarkably simple and universal by utilizing the learning machine of interest as a black box to score subsets of variable according to their predictive power. Search strategies include best-first, branch-and-bound, simulated annealing, and genetic algorithms, using a validation set or cross-validation to assess performance. GA is a good feature selection algorithm because it automatically selects a small number of feature metabolites during evolutionary learning process, and it easily constructs an optimal prediction model with a small number of feature metabolites [33]. In addition, GA calculates the network connections among featured components, makes metabolic pathway analysis possible. A general GALGO pipeline has four major stages:  
1. GA procedure initially creates chromosomes that are subsets of variables;
2. Fitness of each subset was defined as its ability to predict the group membership of each sample in the dataset, and the GA assigns a score to each subset;
3. GA procedure selects the fittest subset; this fittest subset generates more numerous offspring;
4. Two randomly selected parent subsets are used to create two new subsets mimicking biological crossover and mutation mechanisms; the process is repeated from stage 2 until an accurate subset is obtained.  
The GA parameters were optimized: the nearest centroid classification method was applied, the maximum solutions value was set as 2,000 to stabilize top 20 feature components, the maximum generations value as 500 because thousands of generations would end up in over fitting, the goal fitness as 1.0, the subset (chromosome) size as 5, the population size as 20 plus an additional unit per each 400 variables, the mutation rate as 1 mutation per subset (chromosome), and the crossover value as all subsets (chromosomes) in exchange.
###### Abstract  
The present study was designed to determine the changes in the chemical composition of kefir enriched with encapsulated volatile oils by enzymatic methods and the antimicrobial activity of volatile oils. Using encapsulated volatile oils (fennel, mint, and lavender) and cow's milk, we created three different forms of kefir. To highlight the antiseptic capacity of the volatile oils, we performed their antimicrobial analysis on three types of molds (_Gotrichum candidum_, _Pencilium expansum_, _Apsergillus niger_) and one Gram-negative bacterium (_Escherichia coli_). The technique used to determine antimicrobial activity was Kirby-Bauer. The changes in the chemical composition of kefir samples with encapsulated volatile oils were analyzed using enzymatic methods and were compared with a control sample of kefir. The main analyzed chemical compounds were lactose, D-glucose, D-galactose, acetic acid, ethanol, L-lactic acid, and L-glutamic acid. The kefir samples enriched with volatile oils obtained superior results compared to the control sample during the whole analysis period. The main advantage of using encapsulation is that the bioactive compounds of the volatile oils are gradually released in the kefir sample due to the protection provided by sodium alginate. As a result, products with high nutritional values were obtained that are beneficial to the consumer's health and have a longer shelf life thanks to the volatile oils' antimicrobial properties.  
kefir; volatile oil; antimicrobial; enzymatic analysis; bioactive compounds +
Footnote †: journal: Applied Sciences
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 3 Results
### Exponential Distribution of Index  
For the graded-index AR coating with exponential index distribution [18], the index distribution can be written as [26; 27]  
\[n(z)=n_{\text{max}}\exp(\frac{1}{2}\ln(\frac{n_{\text{max}}}{n_{\text{min}}}) \times\{\sin[\pi(\frac{x}{x_{tot}})+\frac{\pi}{2}]-\sin\frac{\pi}{2}\}), \tag{4}\]  
where, \(x=\int_{0}^{z}n(zt)dzt\), \(x_{tot}=\int_{0}^{d}n(zt)dzt\) are the optical distances from the substrate and total optical thickness, respectively.  
Figure 2a shows transmittance of exponential index AR coatings at different thickness \(d\) of divided thin layers. We find that the best thickness of thin layer is \(d\) = 0.928 nm as the number of divided layers is 450. The transmittance of wavelengths from 200 nm to 2000 nm at best length \(d\) are shown in Figure 2b. From Figure 2b, it can be found that the values of the transmittance are almost equal to 1 at wavelengths from 200 nm to 700 nm. However, the transmittance is quickly down as wavelength is larger than 700 nm. The value of the transmittance at wavelength of 1200 nm is about 99%. The distributions of standing wave field at wavelengths of 355 nm, 510 nm and 1064 nm are presented in Figure 2c. From Figure 2c, we can find that the intensity of standing wave field in exponential index coating decreases periodically from surface to substrate and the period is proportional to wavelength. The standing wave field is similar to the case of linear distribution of index, but the oscillation range is much larger than linear distribution.  
Figure 2d is an example for real fabrication. In this re-division, exponential index distribution is divided into 41 layers and the minimum thickness of one layer is about 6 nm. The index contrasts between adjacent layers is above 0.01. The comparison of transmittance between division with best thickness \(d\) and the re-division with 41 layers is shown in Figure 2e. From Figure 2e, we can find that the transmittance curves for both divisions are quite close.  
Figure 1: **(a)** Transmittance of graded-index AR coatings with linear index distribution at different searching thickness \(d\) of divided thin layers, the black line presents \(d\) = 0.3 nm, the red line presents \(d\) = 0.5 nm and the blue line shows \(d\) = 0.6 nm; **(b)** The transmittance at best thickness \(d\) (\(d\) = 0.925 nm); **(c)** The intensity of standing wave field for optimum matching thickness of coating at wavelengths of 355 nm, 510 nm and 1064 nm; **(d)** Refractive index distributions for the division with best thickness \(d\) (green line) and the re-division with 45 layers (black bars); **(e)** Comparison of transmittance between division with best thickness \(d\) and the re-division with 45 layers.  
Figure 2: _Cont._