esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 2 Methods and Data Preparation
### Machine Learning Model
#### 2.3.2 Random Forest  
In machine learning, RF is an ensemble learning method that creates multiple decision trees during training and produces the average output of individual trees for regression problems; it was initially introduced by [PERSON] et al. (1984) [PERSON] et al. (1984) [PERSON] et al. (1984). This method has a reliable accuracy, can estimate missing values, and can handle large datasets with higher dimensionality. Furthermore, an RF-trained model can identify the most influential input parameters from the output of the dataset by using feature score tracking methods. These scores can be specified by each feature's contribution to the model's overall predictive performance. The RF algorithm evaluates the impact of individual features by measuring the decrease in accuracy or impurity when a particular feature is included in the model. Consequently, higher feature importance scores indicate a more influential role in predicting the target variable. These results are presented in the following sections. In this investigation, the number of trees in the RF model is 200, and the random number generator is initialized with a seed value of 10.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 3 Our Methodology
### The 3D Noise Perturbation Module (NPM)  
The necessity for perturbation learning emerges when replicating the feature \(X_{local}^{*}\)\(r\) times, causing the reconstructed points to be either too close due to feature similarities or too distant due to feature variances. Essentially, noise is introduced to adjust the closeness of the spaced points, and move them away from or towards the point \(x_{i}\) as required. Adaptive perturbation learning is then applied by using feature distance and Gaussian noise, which assigns larger perturbations to \"similar\" points and smaller ones to \"dissimilar\" points.  
The embedding feature of the current point \(x_{i}\) is denoted as \(x_{local_{i}}^{*}\in X_{local}^{*}\) (\(x_{local_{i}}^{*}\in\mathbb{R}^{1\times C}\)), with the following adaptive perturbation learning steps:  
First, we compute the feature distance between the embedding feature \(x_{local_{i}}^{*}\) of point \(x_{i}\) and \(x_{local_{j}}^{*}\) of any other points \(x_{j}(j=1,2,\dots,N-1)\) according to Equation (3).  
\[\sigma_{ij}=||x_{local_{i}}^{*}-x_{local_{j}}^{*}|| \tag{3}\]  
where \(\sigma_{ij}\in[0,1]\).  
Second, the noise distribution, denoted as \(Q(\theta_{l})\), is parameterized by \(\theta_{l}\). The learning constraints of parameters in the loss function aim to generate a uniformly distributed point cloud with a standard normal distribution.  
\[Q(\theta_{l})=\theta_{l}\cdot\mathcal{N}(0,(1-\sigma_{ij})) \tag{4}\]  
Finally, Gaussian noise is added for the current point \(x_{i}\), and the feature representation after perturbation learning is obtained as follows:  
\[x_{noise_{i}}^{*}=h_{\theta}\Big{(}x_{local_{i}}^{*}+Q(\theta_{l})\Big{)} \tag{5}\]  
where \(x_{noise_{i}}^{*}\in\mathbb{R}^{1\times C}\), and \(h_{\theta}(\cdot)\) represents MLPs.  
In this paper, the 3D coordinates of the output point cloud are reconstructed from the perturbation-learned features by using a fully connected layer with a size of \(rN\times C\).
## 6 Methods of Microalgae Cultivation Monitoring
### Monitoring of pH Changes in the Culture Medium and CO\({}_{2}\) Content
In cultivation systems with controlled exhaust or flue gas flow, analysing the gas mixture composition allows indirect determination of the CO\({}_{2}\) concentration in the culture medium. Precise monitoring of the CO\({}_{2}\) concentration in the gases supplying the PBR can be carried out using various measurement methods. Non-dispersive infrared (NDIR) gas analysers make use of the fact that CO\({}_{2}\) molecules absorb infrared radiation at certain wavelengths [184]. Measuring the absorption intensity makes it possible to determinethe CO\({}_{2}\) concentration in a gas sample. This method is highly precise and is often used to monitor biotechnological processes. In gas chromatography, the components of a gas mixture are separated and analysed. The gas sample passes through a chromatographic column in which the individual components, including CO\({}_{2}\), are separated. Detectors such as thermal conductivity detectors (TCD) or flame ionisation detectors (FID) with methanisers enable quantitative CO\({}_{2}\) determination [185]. Electrochemical gas electrodes measure CO\({}_{2}\) by detecting the changes in electrochemical potential caused by the presence of CO\({}_{2}\). They are used in biotechnological monitoring but require regular calibration and are sensitive to variations in temperature and humidity [186]. Raman spectroscopy enables the analysis of gas composition by light scattering from molecules [187]. Although less common for routine analyses, it offers simultaneous monitoring of multiple gas components, including CO\({}_{2}\). Classical chemical methods, such as titration, involve CO\({}_{2}\) absorption in an alkaline solution and subsequent quantification of the absorbed gas [188]. Although they are less precise and more time-consuming than instrumental methods, they are still used in some laboratories. In biotechnological practice, especially in the cultivation of microalgae, NDIR analysers are frequently used due to their speed and precision [189]. In studies on the utilisation of microalgae biomass for biogas upgrading, for example, monitoring the CO\({}_{2}\) content in the exhaust gases is critical for evaluating process efficiency. In addition, controlling the CO\({}_{2}\) concentration in hybrid PBR systems, where CO\({}_{2}\)-enriched air is added to the culture medium, enables the optimisation of growth conditions for the microorganisms [190].
## 4 Results
### Back-Calculated Results of Elastic Modulus
#### 4.2.1 High-Strength Geopolymer  
The back-calculated elastic modulus results for Section 1 after grouting treatment are shown in Figure 7a. For the untreated original pavement, the average elastic modulus of asphalt on the left side was 516.38 MPa with a standard deviation of 28.94 MPa. Following treatment with high-strength geopolymer with waterproofing agents, the average elastic modulus increased to 614.69 MPa. And the standard deviation decreased to 22.77 MPa. This represents a 19% improvement in elastic modulus, indicating enhanced material stiffness, while the reduced standard deviation suggests improved uniformity and consistency in performance.  
Figure 7b presents the results for Section 2. Following the application of high-strength geopolymer without waterproofing agents, the pavement's average elastic modulus of asphalt increased from 509.37 MPa to 682.98 MPa, with the standard deviation decreasing from 37.36 MPa to 25.30 MPa. The elastic modulus significantly increased by approximately  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Section** & **Vertical Stress** & **Vertical Strain** & **Longitudinal** & **Transverse** \\  & **(MPa)** & **(\(\mu\epsilon\))** & **Strain (\(\mu\epsilon\))** & **Strain (\(\mu\epsilon\))** \\ \hline Section 1 & 0.034 & \(-115\) & \(-2\) & \(-14\) \\ Section 2 & 0.071 & \(-125\) & \(-4\) & \(-24\) \\ Section 3 & 0.117 & \(-327\) & \(-6\  
34%, indicating enhanced overall material stiffness. Concurrently, the reduced standard deviation demonstrates improved consistency in elastic modulus measurements, reflecting greater performance stability.  
The comparison of Sections 1 and 2 shows that the high-strength geopolymer without waterproofing agents exhibited better short-term mechanical performance, achieving a higher average elastic modulus. The smaller modulus difference between the left and right lanes indicates more uniform structural stress distribution, particularly in load transfer equilibrium between lane edges and centers. This suggests that the non-waterproofed formulation provides superior interfacial bonding and stress redistribution in practical engineering applications. This phenomenon may be attributed to the interference of waterproofing agents in the alkali activation reaction of geopolymers, leading to incomplete polymerization during the early stages. Materials without waterproofing agents undergo more thorough reactions, forming a denser three-dimensional microstructure in the short term, thereby achieving a higher elastic modulus.
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 3 Materials and Method
### Emotion Data  
Four different English speech emotion datasets were used in this study. eNTERFACE05 is known for capturing spontaneous emotional expressions in unscripted scenarios, while the [PERSON] audio-visual database of emotional speech and song (RAVDESS) focuses on emotional speech and song data. The Surrey audio-visual expressed emotion (SAVEE) dataset collects posed emotional expressions, and the Toronto emotional speech set (TESS) specializes in scripted emotional speech. Each database has unique characteristics that suit different research needs in the domain of emotion analysis and recognition. Through these datasets, the pros and cons of the algorithms can be more comprehensively evaluated.  
1. eNTERFACE05: eNTERFACE05 is a well-known European research project and dataset that focuses on the development of technologies for human-computer interaction, particularly in the fields of facial and emotional expression recognition [21]. The eNTERFACE05 dataset contains emotional expressions such as happiness, sadness, anger, fear, disgust, and surprise;
2. Ryerson audio-visual database of emotional speech and song: RAVDESS is a valuable resource for studying emotions in speech and music because it includes both acted and natural emotional expressions performed by professional actors [22]. RAVDESS encompasses a wide range of emotions such as calm, happiness, sadness, anger, fear, surprise, and disgust;
3. Surrey audio-visual expressed emotion: SAVEE is a dataset containing audio and video recordings that display emotional expressions by English native speakers [23]. This dataset covers various emotions including happiness, anger, disgust, sadness, fear, and neutral;
4. Toronto emotional speech set: TESS consists of professionally acted and recorded speech segments spoken by North American English speakers [24]. TESS includes expressions of anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral.
## 3 Results
Figure 2: Biomass produced under different concentrations of leachate. The concentration of the different metabolites analyzed are presented in Figure 5. In the case of carbohydrates, different concentrations of NaHCO\({}_{3}\), and Na\({}_{2}\)CO\({}_{3}\) increased the final concentration in _Chlorella_ sp. Most studies using leachate as a source of nutrients for algal production mainly report the concentration of carbohydrates and lipids. However, other metabolites such as proteins and carotenoids must be measured and reported. To the best of the author's knowledge, this is the first study that reports the effect of leachate on the concentration of carbohydrates, lipids, proteins, and carotenoids. The highest concentration of carbohydrates (28% \(w/w\)) was achieved using 1.2 g\(\cdot\)dm\({}^{-3}\) of NaHCO\({}_{3}\). In the case of _Scenedesmus_ sp., only NaHCO\({}_{3}\) enhanced the concentration of carbohydrates (27% \(w/w\)) over the control (23% \(w/w\)). In the synthesis of total proteins, both NaHCO\({}_{3}\) and Na\({}_{2}\)CO\({}_{3}\) reduced their concentration in _Chlorella_ sp. On the other hand, 1.6 g\(\cdot\)dm\({}^{-3}\) of NaHCO\({}_{3}\) increased the concentration of up to 46% \(w/w\) of proteins in _Scenedesmus_ sp. In the case of lipids, 1.2 g\(\cdot\)dm\({}^{-3}\) of Na\({}_{2}\)CO\({}_{3}\) increased its concentration (8%) in _Chlorella_ sp. in comparison to the control. In _Scenedesmus_ sp. 1.2 g\(\cdot\)dm\({}^{-3}\) of NaHCO\({}_{3}\) increased the concentration by up to 46% \(w/w\) of proteins in _Scenedesmus_ sp. In the case of lipids, 1.2 g\(\cdot\)dm\({}^{-3}\) of Na\({}_{2}\)CO\({}_{3}\) increased its concentration (8%) in _  
to 8% \(w/w\). The concentration of total carotenoids was increased up to 8% (\(w/w\)) in _Chlorella_ sp. (0.8 g.dm\({}^{-3}\) of Na\({}_{2}\)CO\({}_{3}\)); in contrast, none of the evaluated carbon sources enhanced the concentration of total carotenoids in _Scenedesmus_ sp. Finally, the concentration of total ashes increased from 11 to 15% \(w/w\) in all the treatments for both algae.
## 2 Two Models for Calculating the Satellite Repeat Period
### Simplest Fraction Method  
Similarly, the simplest fraction method is also necessary to calculate the satellite operation period, which is expressed as \(T_{SOP}\). The calculation method is same as the description in Section 2.1. Then, based on the simplest fraction method, the simplest fractions, \(N\) and \(D\), can satisfy the following equation:  
\[NT_{SOP}=DT_{e}, \tag{4}\]  
which means that, after \(D\) solar days, the satellite operates \(N\) laps, and the satellite orbital ground trajectory begins to repeat. \(T_{e}\) is the time interval of the orbit revolving around the Earth one time, which is named the sidereal day. Since \(T_{SOP}\) can be calculated by the broadcast ephemeris, the concrete values of \(N\) and \(D\) can be determined by the search and test process [23]. Similarly, \(D\) also means the approximate days of the satellite repeat period united in solar days.
###### Abstract  
Geophysical logging is an essential measurement tool in the oil/gas exploration and development field. In practice, predicting missing well logs is an effective way to reduce the exploration expenses. Because of the complexity and heterogeneity of the reservoir, there must be strong nonlinear correlations between the well logs. To improve the accuracy and stability of the missing well logs prediction, a method based on a Bayesian optimized hybrid kernel extreme learning machine (BO-HKELM) algorithm is proposed. Firstly, the LightGBM algorithm is applied to screen out important features related to the missing well logs and reduce the input dimension of the prediction model. Secondly, the hybrid kernel extreme learning machine (HKELM) algorithm is applied to construct the missing well logs prediction model, and the hyperparameters \((C_{0},C_{1},d,\sigma,C)\) of the model are optimized by the Bayesian algorithm. Finally, the BO-HKELM model is applied to the prediction of the missing well logs in a block of the Ordos Basin in China. The results show that the RMSE, MAE, and R-square predicted by the BO-HKELM model are 0.0767, 0.0613, and 0.9029, respectively. It can be found that the BO-HKELM model has better regression accuracy and generalization ability, and can estimate missing logs more accurately than the traditional machine learning methods, which provides a promised method for missing well logs estimation.  
logging curve prediction; hybrid kernel function; extreme learning machine; Bayesian optimization
## 1 Introduction
These differences also cause complex multicollinearity issues among spectral bands [31; 32; 33]. Therefore, targeted complex feature extraction is necessary. This study employs a combination of three widely used denoising methods and nine mathematical transformation methods for preprocessing. We then conduct modeling using SVM, PLSR, BPNN, and CNN to compare their performance and determine the optimal preprocessing method for the spectral data in the study area. In recent years, research has found that integrating different types of modeling methods provides richer feature representations, significantly enhancing model performance and developing many application scenarios [34; 35]. Combining CNN component construction with hyperparameter tuning can significantly advance the application of CNN in soil spectral modeling [15; 36]. Based on these points, this study proposes a CNN-SVM prediction model optimized by the Black Kite algorithm. This model leverages the powerful feature extraction capabilities of CNN and the classification and regression abilities of SVM, combined with the strong optimization ability of BKA, aiming to develop a new method for measuring SOM content in forest soils in Guangxi and similar regions.
## 2 Materials and Methods
### rRNA 16S Analysis  
For 16S rRNA amplification, the primers 616F (5\({}^{\prime}\)-AGAGTTTGATYMTGGCTCAG-3\({}^{\prime}\)) and 1510R (5\({}^{\prime}\)-GCTTACCTTGTTACGACTT-3\({}^{\prime}\)) were used [27,28]. Polymerase chain reaction was performed in a BioRad iCycler thermal cycler (BioRad, Hercules, CA, USA) using the following cycling parameters: 2 min of initial denaturing step at 95 \({}^{\circ}\)C, followed by 35 cycles of denaturing (95 \({}^{\circ}\)C for 1 min), annealing (55 \({}^{\circ}\)C for 1 min) and extension (72 \({}^{\circ}\)C for 2 min), with an additional extension step at 72 \({}^{\circ}\)C for 10 min at the end. PCR products were sequenced by Macrogen Inc. (Amsterdam, The Netherlands). The curated and assembled sequence was compared to the EZBioCloud database [29]. The phylogenetic analysis was carried out after 16S rRNA sequences alignment and comparison of strain L5B5 with corresponding sequences of members of the genus _Pseudomonas_. Alignments were created using the multiple sequence alignment program MUSCLE [30]. A phylogenetic tree was created using MEGA X [31]. The evolutionary relationship among bacteria was deduced using the maximum-likelihood method, Tamura-Nei model with a discrete Gamma distribution and invariant sites [32].
# A Study on Karst Cave Collapse Based on Improved Terzaghi Theory and Upper Limit Analysis
## 3 Case Study
### Stability Analysis of the Karst Cave  
**(1)** **[PERSON]'s pressure arch theory**  
Substituting Equation (1) results in  
\[H_{\text{min}}=\frac{0.828d\left[1+\tan\left(45^{\circ}-\frac{\varphi^{\prime} }{2}\right)\right]}{f_{k}}=13.2\text{m} \tag{27}\]  
In practice, the overburden clay thickness at the karst cave was 11 m, which was less than the critical overburden height. This indicates that the karst cave was potentially unstable with the possibility of collapse.  
**(2)** **Improved Terzaghi theory**  
Equation (15) is employed to calculate residual collapse resistance, as follows:  
\[\begin{array}{l}R=\frac{2K_{0}\gamma^{\prime}\tan\varphi^{\prime}}{d}z^{2}- \frac{d\gamma^{\prime}-4\epsilon^{\prime}+4K_{0}\tan\varphi^{\prime}z_{1}( \gamma^{\prime}-\gamma)}{d}z-M\\ =-48\text{kPa}\end{array} \tag{28}\]  
The calculation demonstrated that \(T<0\), which indicates that the karst cave was in an unstable state. According to Figure 7, the vertical stress of the karst cave boundary is approximately \(-50\) kPa, demonstrating the accuracy of the improved Terzaghi theory.  
**(3)** **Limit analysis method**  
According to the collapse profile (Figure 5), the angle between perpendicular lines of the surface and failure boundary is minor (\(5^{\circ}\), namely \(\alpha=\beta=5^{\circ}\)), thus substituting Equation (26) to obtain  
\[K=\frac{P_{i}}{P_{g}+P_{i}}=0.96 \tag{29}\]  
The calculation demonstrated that \(K<1\), which suggested that the karst cave was in an unstable state, as in the case of the improved Terzaghi theory.  
To sum up, the three analytical methods have the same conclusion and are consistent with reality, which proves the accuracy of the two new methods. Compared with [PERSON]'s pressure arch theory, the improved Terzaghi theory and limit analysis take into account more factors and can be closer to the actual situation.
# A Graph-Cut-Based Approach to Community Detection in Networks
## 3 Methods  
The community detection method proposed in this paper is based on the idea that connections between vertices of the same community tend to be denser than connections between vertices of different communities. Since a minimum cut in graph theory finds as few connections to a cut as possible, applying a minimum cut to a network may give us boundaries between different communities. In other words, a minimum cut might result in a cut-set that contains more inter-connections between communities than intra-connections within communities.  
The proposed method of this paper iteratively applies the minimum cut solution to a network to generate a community structure. The method first finds two temporary source and sink nodes that are expected to be of different communities, and computes a minimum cut between the two nodes. As a result of the cut, the network is divided into two groups. Then, the same procedure is applied to each group, and quality measures of the resulting community structures of the entire network are compared to select the better result between the two. The same procedure is repeated until no further refinement of the quality of community structure is obtained. A precise description of the proposed algorithm is presented later in this section.
## 3 Results
### Colocalization Experiments  
The formation of the lipid bilayer on the surface of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs was confirmed through wide-field fluorescence microscopy colocalization experiments (Figure 4).  
Two different mass ratios (1.5:5 and 1:5) were tested to achieve the best ratio of NPs:Phospholipids. The obtained results confirm a successful colocalization between the phospholipids and a-TiO\({}_{2}\) NPs for both ratios (Figure 4). The best result was achieved for 1:5 mass ratio with \(88.5\pm 13.8\%\) of colocalization (Table 1) between the red spots (coming from the ATTO 550 dye labeling the titania nanoparticles) and the green ones (deriving for the DiO dye coupled with the lipid bilayers) and thus this was the selected ratio to proceed with further experiments (EPR and cavitation).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **a-TiO\({}_{2}\)-NH\({}_{2}\)//DOPC-DOTAP** & **a-TiO\({}_{2}\)-NH\({}_{2}\)//DOPC-DOTAP** \\  & **1:5 (m/m)** & **1.5:5 (m/m)** \\ \hline
**Colocalization (\%)** & \(88.54\pm 13.75\) & \(75.0\pm 35.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conjugation yield in percentage obtained for both mass ratio tested of Nanoparticles (NPs):Phospholipids (1:5 and 1:5:5).  
Figure 4: Wide-field fluorescence image of (**a**) a-TiO\({}_{2}\) NPs labeled with ATTO 550-NH2 ester; (**b**) lipid shell labeled with 1% of DiOC\({}_{18}\); (**c**) the merged channels showing complete colocalization. Scale bar: 1 \(\upmu\)m and mass ratio NPs:Lipids (1:5).  
Figure 3: Z-potentials and hydrodynamic diameters of pristine a-TiO\({}_{2}\) NPs, a-TiO\({}_{2}\)-NH\({}_{2}\) NPs, and a-TiO\({}_{2}\)-NH\({}_{2}\)//DOPC-DOTAP NPs suspended in milliQ water: (**a**) Z-potential measurements; (**b**) dynamic light scattering measurements in number (%).