esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Operational Stability of Immobilized Laccase  
Storage stability and reusability are also important characteristics and are prerequisites for the practical application of immobilized enzymes. Storage is particularly difficult because the conformation of an enzyme changes over time and gradually loses its activity in solution. As shown in Figure 4c, it can be observed that with the increase in storage time, the difference between the free and immobilized increase activities became significant. The relative activity of the free lactose was less than 50% after 15 days, while PIL (2)-NH\({}_{2}\)-GA-Lac was still able to maintain more than 80%. After 30 days, the relative activity of PIL (2)-NH\({}_{2}\)-GA-Lac was twice that of free lactose. The storage stability of PIL (1)-NH\({}_{2}\)-GA-Lac fell between that of PIL (2)-NH\({}_{2}\)-GA-Lac and free lactose. This shows that immobilization significantly improved the storage stability of the lactose. One possible reason for this is that the lactose was in a confined space provided by PIL-NH\({}_{2}\) and that the spatial conformation had little or no tendency to change [48].  
The reusability test performed over 10 successive catalytic cycles showed that the immobilized lactose exhibited superior circularity. Despite its loss of activity in cycling, the immobilized lactose (i.e., PIL (2)-NH\({}_{2}\)-GA-Lac) exhibited good reusability, retaining 80% of its initial activity after 10 cycles (Figure 4d). It should be noted that the amount of immobilized lactose in PIL (2)-NH\({}_{2}\)-GA-Lac was about two-fold higher than that in PIL (1)-NH\({}_{2}\)-GA-Lac under theoretical conditions; however, the relative activity of PIL (2)-NH\({}_{2}\)-GA-Lac was three-fold higher than that of PIL (1)-NH\({}_{2}\)-GA-Lac after 10 cycles. This can be attributed to the higher specific surface area and stronger binding force of PIL (2)-NH\({}_{2}\), by which the lactose activity loss of PIL (2)-NH\({}_{2}\)-GA-Lac was smaller under the same experimental conditions.
## 3 Results and Discussion
### Modelling and Analysis of Variance(ANOVA)  
The data obtained from the CCD experimental matrix (Table 1) was statistically modelled using a multifunctional selection at a 95% confidence level and \(p\)-values of <0.05 [44]. This was done to graphically determine the influential and interactive effect of the input variables on the responses. The model equations, in terms of the actual terms expressed in (1)-(3), are a reduced form of a quadratic model obtained after the rejection of insignificant terms with \(p\)-values > 0.05. The negative and positive signs before the model terms, respectively, represent the antagonistic and synergistic effects on the responses. Here, the interaction (AB) effective between the nanoparticle load (A) and biochar load (B) had a significant impact on all the responses (pH, biogas production, and COD removal) and the model's predictability concerning the responses for each given level of the input factors for the experimental runs (Figure 3). It was observed that the regression coefficients  
Figure 1: pH response per CCD experimental run.  
were highly significant and scaled to accommodate each unit factor intercept. Further, the closeness of the model-predicted results to the diagonal line illustrated the precision and accuracy of the models [45; 46; 47].  
\[\mathrm{pH}=6+6.325\mathrm{A}-0.19\mathrm{B}-3.875\mathrm{AB}-8.6 \mathrm{A}^{2}+0.89\mathrm{B}^{2}+8.87\mathrm{A}^{2}\mathrm{B}-1.85\mathrm{AB}^ {2} \tag{1}\] \[\mathrm{Biogas\ production}=-51.97+95.31\mathrm{A}+108.08\mathrm{B}-23.85 \mathrm{AB}-73.34\mathrm{A}^{2}-49\mathrm{B}^{2}\] (2) \[\mathrm{COD\ removal}=84.93+36.75\mathrm{A}-35.06\mathrm{B}+4.9 \mathrm{AB}-48.12\mathrm{A}^{2}+12.1\mathrm{B}^{2} \tag{3}\]
# Detecting Inverse Boundaries by Weighted High-Order Gradient Collocation Method
## 3 Weighted High-Order Gradient Reproducing Kernel Collocation Method  
### Reproducing Kernel Approximation  
To approximate an unknown function \(u(x)\) by using the collocation method, the reproducing kernel (RK) shape function evaluated at \(N_{s}\) source points can be used in conjunction with the generalized coefficients \(a_{I}\) given by  
\[u(\mathbf{x})\approx v(\mathbf{x})=\sum_{I=1}^{N_{s}}\Psi_{I}(\mathbf{x})\;a_{I} \tag{5}\]  
where the RK shape function \(\Psi_{I}(\mathbf{x})\) has the following form:  
\[\Psi_{I}(\mathbf{x})=\mathbf{H}^{\mathrm{T}}(\mathbf{x}-\mathbf{x}_{I}) \mathbf{b}(\mathbf{x})\varphi_{a}(\mathbf{x}-\mathbf{x}_{I}) \tag{6}\]  
which consists of a monomial \(\mathbf{H}(\mathbf{x}-\mathbf{x}_{I})\), coefficient vector \(\mathbf{b}(\mathbf{x})\), and kernel function \(\varphi_{a}(\mathbf{x}-\mathbf{x}_{I})\). The influence of kernel function is related to the RK support size as denoted by the subscript \(a\). A general form of a two-dimensional monomial basis is expressed as  
\[\mathbf{H}^{\mathrm{T}}(\mathbf{x}-\mathbf{x}_{I})=\Big{\{}(\mathbf{x}- \mathbf{x}_{I})^{\alpha}\Big{\}}_{|\alpha|\leq p} \tag{7}\]  
where the multi-index \(\alpha\) is defined by \(\alpha=(\alpha_{1},\alpha_{2})\) with length defined as \(|\alpha|=\alpha_{1}+\alpha_{2}\); the superscript \(p\) denotes the order of approximation to reproduce a polynomial. As the inverse Cauchy problems considered in this work are governed by the second-order differential equations, a quintic B-spline kernel function is adopted and is given in the previous work [12]. With regard to the coefficient vector, it is determined by the following reproducing conditions:  
\[\sum_{I=1}^{N_{s}}\Psi_{I}(\mathbf{x})\mathbf{x}_{I}^{\alpha}=\mathbf{x}^{ \alpha},|\alpha|\leq p \tag{8}\]  
Figure 1: Schematic diagram of a multiply connected inverse problem.  
from which the coefficient vector is found to be  
\[\mathbf{b}(\mathbf{x})=\mathbf{M}^{-1}(\mathbf{x})\mathbf{H}(0) \tag{9}\]  
where \(\mathbf{M}(\mathbf{x})\) is the moment matrix given by  
\[\mathbf{M}(\mathbf{x})=\sum_{I=1}^{N_{s}}\mathbf{H}(\mathbf{x}-\mathbf{x}_{I}) \mathbf{H}^{\mathrm{T}}(\mathbf{x}-\mathbf{x}_{I})\varphi_{a}(\mathbf{x}- \mathbf{x}_{I}) \tag{10}\]  
By substituting Equation (9) into Equation (6), the RK shape function reaches the following form:  
\[\Psi_{I}(\mathbf{x})=\mathbf{H}^{\mathrm{T}}(0)\mathbf{M}^{-1}(\mathbf{x}) \mathbf{H}(\mathbf{x}-\mathbf{x}_{I})\varphi_{a}(\mathbf{x}-\mathbf{x}_{I}) \tag{11}\]
## 2 Spectral Wave Equation for Uniform Cross-section Waveguides
### GW Frequency Response Function (FRF)  
Using the modal expansion technique, and applying the orthogonality relations of Equation (20), the response in the frequency-wavenumber domain \(\hat{\mathbf{U}}\) for a given frequency \(\omega\) can be written as:  
\[\overline{\mathbf{U}}=\sum_{n=1}^{2M}\frac{\left(\xi^{n}\overline{\mathbf{U}}_ {L}^{m}\right)^{\!\!\mathrm{T}}\overline{\mathbf{F}}}{D_{nm}\left(\xi^{m}- \xi\right)}\overline{\mathbf{U}}_{R}^{m} \tag{25}\]  
Next, the frequency response at an abscissa \(z=z_{{}_{R}}\) due to an excitation \(\overline{\mathbf{F}}\), where the subscript \(R\) stands for the receiver location, can be evaluated as spatial inverse Fourier transform of Equation (25):  
\[\overline{\mathbf{U}}(z_{{}_{R}})=\frac{1}{2\pi}\int\limits_{-\infty}^{+ \infty}\overline{\mathbf{U}}\;e^{i\xi z_{{}_{R}}}d\xi \tag{26}\]  
Since we consider the force located at \(z=z_{{}_{S}}\), at the calculation frequency the force vector can be expressed as \(\overline{\mathbf{F}}(z)=\overline{\mathbf{F}}\delta(z-z_{{}_{S}})\), where \(\delta(\cdots)\) is the Dirac delta, that in force of Equation (16) reads:  
\[\overline{\mathbf{F}}=\int\limits_{-\infty}^{\infty}\overline{\mathbf{F}} \delta(z-z_{{}_{S}})e^{-i\xi z}dz=\overline{\mathbf{F}}e^{-i\xi z_{{}_{S}}} \tag{27}\]
## 3 Proposed Methodology
### Hyperparameter and Training  
A three-step process is involved in building any statistical classification model. Firstly, the model development phase involves choosing hyperparameters such as batch size, activation function, learning rate, number of iterations, etc. that influence how well the model is built and trained. Adequate variation and a sufficient quantity of data are necessary for this phase. Secondly, model training and validation are carried out, with the training set being used to select hyperparameters, and the validation set to evaluate performance. In this particular instance, the training hyperparameters were carefully chosen. They consisted of a learning rate of \(1\times 10^{-3}\), 100 epochs, and a batch size of 128. To ensure efficient learning, a callback monitor was utilized to adjust the learning rate, reducing it by 75% if no progress was made for ten successive epochs. The training process incorporated data shuffling by randomizing the order of the data before the beginning of each epoch, introducing diversity. The hyperparameters were determined through an iterative process of experimentation and refinement, aiming to achieve the highest level of accuracy.  
In order to assess the efficacy of the proposed model, we utilized two publicly accessible datasets, CSI-HAR and StanWiFi. Since these datasets did not have predefined training and testing sets, we adopted the five-fold cross-validation technique [36] to assess the model's performance. This technique involved randomly dividing the complete dataset into ten equally sized subsets that were mutually exclusive. The model fitting process followed an iterative procedure, where nine subsets were used for training, while the remaining subset was used for evaluating the performance. This testing and training process was repeated ten times to ensure that each subset underwent a precise testing phase. The overall performance of the model was evaluated by computing the mean value of the outcomes obtained from all iterations.  
The Adam optimizer [35] played a crucial role in our methodology by updating the weights of our model. Moreover, we employed the cross-entropy loss function to quantify the error or loss during the training phase.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 3 Three-Dimensional Analysis of Square Silo
### Effect of Wall Thickness  
The objective of modifying the wall thickness is to study the effect of wall flexibility on lateral wall pressure. This study may be conducted in a different manner by adding more stiff structural elements to the wall to enhance its stiffness. Ring beams and stiffeners are the names given to these structural elements. That approach, however, will not be used in this research.  
In order to investigate the effect of wall thickness, a series of tests of unstiffened plates were carried out using the Lahlouh silo geometry [25] filled with sand, but with an increased thickness value of t. The model had a 6 mm wall thickness. The model was tested with wall thicknesses of 10, 15, and 20 mm, and the results are illustrated in Figure 17.  
The lateral pressure distribution is shown in Figure 17 at 0.5 m above the bottom surface. The distribution of lateral wall pressure has been observed to be substantially dependent on wall thickness. When the wall is extremely stiff (i.e., the wall thickness assumed numerically to be \"t\" = 200 mm), pressures on the wall tend to be uniform and equal to Janssen values that can be calculated by Equation (1). When the thickness of a wall  
Figure 16: Vertical load distributions inside rigid wall silos for various slenderness ratios.  
Figure 17: Effect of wall thickness on lateral wall pressure.  
is decreased, a large transfer of pressure occurs from the center of the silo to its corners in the horizontal direction.  
Two silo models were selected to study the influence of wall thickness on lateral wall pressure distribution, as shown in Figure 18. In terms of slenderness ratio, both models vary since the first model is considered flat, while the second is regarded as slender.  
Figure 18 illustrates various behaviors for flat and slender silo walls based on thickness. Both models behaved similarly when their wall thickness was varied, and yielded the same result. If the wall thickness is minimal, the wall will acquire flexible behavior, producing a non-uniform lateral pressure distribution. On the other hand, if the wall thickness is thick, it will behave rigidly, resulting in almost uniform lateral pressure distribution. In other words, wall flexibility can have a significant impact on wall pressure regimes and drastically change horizontal pressure distributions throughout the wall. As a result, designers need to give greater attention to wall flexibility during the design phase.
## 4 Materials and Methods
### Differential Expression Analysis and Functional Annotations  
In order to obtain the clean reads, the raw reads were filtered by removing the adaptor, low-quality reads (Qphred \(\leq\) 20 bases) and unknown sequences. Then, clean reads were mapped to apple reference genome ([[https://iris.angers.inra.fr/gddh13](https://iris.angers.inra.fr/gddh13)]([https://iris.angers.inra.fr/gddh13](https://iris.angers.inra.fr/gddh13)), accessed on 1 July 2017). Quantification of the gene expression levels was estimated as fragments per kilobase of transcript per million mapped reads (FPKM). Cufflinks (version 2.2.1) software was used in the process of quantifying gene expression, and the counts were then normalized to the FPKM values. DESeq R package (version 1.18.0) was used to identify the differentially expressed genes (DEGs) between the two apple varieties. A fold change (FC) value of \(\geq\)2 and a false discovery rate (FDR) of <0.05 were used as criteria for selecting DEGs. G0 seq R package was used to analyze Gene Ontology (GO) enrichment. GO terms with corrected \(p\)-values < 0.05 were assigned as significantly enriched by DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs was conducted using the KOBAS software (version 3.0).
## 2 Materials and Methods
### Probabilistic Neural Network (PNN)  
The most common and popular neural network type used by the researchers in different applications is the ordinary feed-forward neural network (FNN). The FNN is more suitable in value prediction, and as this study aimed at directional prediction which leads to classification problem, probabilistic neural network (PNN) is more suitable than ordinary FNN due to the following reasons. If FNN is used for classification, there is no proper way of deciding the threshold or the adjustment, and such a requirement is not involved in PNN. Further, PNN provides global optimal solutions, which leads to more accurate results.  
In 1966, Probabilistic Neural Network (PNN) was introduced by [PERSON] for classification problems. PNN is a feedforward neural network with four layers: input layer, pattern layer, summation layer and output layer, which are illustrated in Figure 1.  
Probabilistic neural network is based on the Bayesian method of classification and can classify a sample with the maximum probability of success. The principle of a Bayesian classifier rests on the selection of class C with the largest product term in the Bayesian classification theorem:  
\[Max_{c}[P_{c}L_{c}f_{c}(x)] \tag{7}\]  
where \(P_{c}\) is a priori probability for class C, \(L_{c}\) is the loss incurred by misclassifying a sample which actually belongs to class C, X = (x\({}_{1}\), x\({}_{2}\),..., x\({}_{k}\)), is the input vector to be classified and \(f_{c}(x)\) is the probability of X given the density function of class C [18; 23].  
In general, the distribution of the vector X assumed to be Gaussian:  
\[f_{c}(x)=\frac{1}{{(2\pi)^{k/2}\sigma_{c}k_{n}}}\sum_{k=1}^{n_{c}}exp\frac{-(X -Y_{ic})^{\prime}(X-Y_{ic})}{2{\sigma_{c}}^{2}} \tag{8}\]  
where \(k\) is the number of elements in X, \(n_{c}\) is the number of training samples belong to class C, \(Y_{ic}\) is the \(i\)th training sample in class C, and \(\sigma_{c}\) is the smoothing parameter. However, past studies depict that the distribution of the vector X may take other possible forms of distributions [17; 23; 37; 38; 39; 40; 41].  
Figure 1: Architecture of probabilistic neural network.
## 2 System Structure  
The Prony algorithm is a mathematical model that employs a linear combination of exponential functions to fit the sample data. It has the characteristics of high-frequency resolution and high-frequency estimation accuracy. However, the Prony algorithm is very sensitive to noise, which leads to suboptimal detection accuracy. Moreover, there are many false components in the fitting results of the Prony algorithm, and the amplitude of false components is higher than that of non-false components, which interferes with data screening. In light of these drawbacks, this paper proposes a novel harmonic and interharmonic detection method based on enhanced SVD and the Prony algorithm, whose process is illustrated in Figure 1.  
Targeting Prony algorithm's sensitivity to noise, this method first applies the SVD denoising algorithm to denoise the matrix constructed by the electric energy signal. Since the choice of the characteristic matrix's order in the SVD denoising process affects the signal denoising effect, the \"adaptive order determination\" algorithm is proposed to determine the optimal order of the characteristic matrix for enhanced denoising effect of SVD. The Prony algorithm is then carried out on the reconstructed one-dimensional electric energy signal after denoising and the fitting result of the signal is obtained. Finally, a method based on the dominant factor is used to filter the fitting results and find out the non-false components representing harmonic and interharmonic parameters. The non-false component is composed of amplitude, frequency and phase, which is the detection result of harmonic and interharmonic signal parameters in the signal.
## 3 Results and Discussion
### Changes in the Dynamic Pressure of Mudflow  
To obtain detailed information regarding the dynamic pressure of mudflow surging from a tailings dam-break during randomly selected experimental runs, four pressure sensors were used, and the dynamic pressure was taken. The dynamic pressure at four specific locations \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) (1.5 m, 3.0 m, 4.5 m, and 6.0 m downstream of the gate) were analyzed, and the dynamic pressure of mudflow was plotted in Figure 6 corresponding to three different types of tailing particle sizes. Figure 6 shows the dynamic pressure curve at locations \(S_{2}\) and \(S_{4}\) (3.0 m and 6.0 m downstream from the gate) for tests with \(H\) = 30 cm as the initial filling height. The dynamic pressure curve consisted of three stages: rapid increase (Stage A), rapid reduction (Stage B), and slow reduction (Stage C).  
Figure 4: Mudflow flood hydrograph at locations \(S_{2}\), and \(S_{4}\) (3.0 m and 6.0 m downstream of the gate) for tests with \(H\) = 30 cm as the initial filling height. The hydrograph comprises two peaks related to a first water pulse and a second tailings flow. Stage (A): rapid increase; Stage (B): rapid reduction; Stage (C): slow reduction. Note: The time t = 0 in Figure 4a,b are the starting time of mudflow reaching station \(S_{2}\) and \(S_{4}\).  
Figure 5: The depth of mudflow at the section of \(S_{2}\) (3.0 m downstream of the gate).  
As shown in Figure 7, along with an increase in the article size of the tailings, the peak of dynamic pressure also gradually increased in value. An analysis of the peak value at the location of \(S_{2}\), the peak values of dynamic pressure were 4.1 kPa, 5.6 kPa, and 6.5 kPa for fine tailings, medium tailings, and coarse tailings, respectively. Relative to the maximum pressure of fine tailings-mudflow, the medium tailings-mudflow and the coarse tailings-mudflow increased by 26.7% and 13.8%, respectively. The grain size significantly affected the maximum pressure of tailings-mudflow.  
It has been pointed out in Section 3.1 that the smaller the particle size, the smaller the mudflow velocity. The experimental results are explained. According to the relationship between mudflow velocity and impact force in literature [26], the pressure of mudflow is positively correlated with velocity. Therefore, the smaller the particle size, the smaller the velocity, and the smaller the pressure.
## 6 Conclusions  
In this paper, we have developed a parametric method aiming at multipath estimation in the frequency domain for arbitrary BOC signals as well as BPSK signal. It takes advantage of the processes of segmentation and averaging to deal with the imperfections of conventional frequency domain technique--high computation complexity and vulnerability to the noise. These changes almost halve the computation amount and meanwhile significantly improve the feasibility and robustness.  
The multipath suppression performance was well evaluated in three simulation scenes: (1) single-path channel; (2) three-path channel; and (3) high-order BOC signals. Results show that the number of reflected path has very limited effect on the proposed method, but significant impact on the CCRW and MEDLL method. When C/N\({}_{0}\) is 40 dB-Hz, the RMSE of aFFT method (\(L\) = 64) is almost smaller than CCRW and MEDLL in a three-path channel by 60 m and 50 m, respectively. Besides, when the incoming signal is a high-order BOC signal, such as BOC(14,2) signal, for a C/N\({}_{0}\) of 30 dB-Hz, the RMSE of the aFFT method (\(L\) = 64) is smaller than CCRW by 37 m. Thus, the proposed method has good robustness in severe multipath environments with lower computational load for both low-order and high-order BOC signals and its performance becomes better as the value of the window length \(L\) decreases.  
The work was partly supported by the National Natural Science Foundation of China (Grant No. 91438116) as well as the Doctorial Innovation Fund of Beihang University. The work of [PERSON] was also supported by the China Scholarship Council with No. 201606020020.  
[PERSON], [PERSON], [PERSON] conceived the study idea and designed the experiments. [PERSON] and [PERSON] developed the simulation platform and performed the experiments. [PERSON] analyzed the experiment results and wrote the manuscript. [PERSON] helped to conduct the experiment and revised the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.