esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Methods
### Fractal Modeling Based on NMR Data  
Previous studies on shale reservoirs have suggested that the pore systems possess fractal properties [24; 25; 26]. Fractal theory is a powerful analytical tool for measuring the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Depth/m** & **Stratum** & **Lithology** & **Gas Content** & **TOC** & **Quartz** \\  & & & & **(m3/t)** & **(\%)** & **(\%)** \\ \hline Q0 & 4110.7 & Wufeng Fr. (Layer 1) & black carbonaceous & 0.96 & 1.11 & 45.0 \\ Q1 & 4105.0 & Bottom S11-1 & black carbonaceous & 2.33 & 4.45 & 70.1 \\ Q2 & 4101.2 & sub-layer (Layer 2) & shale & 2.65 & 4.76 & 66.9 \\ Q3 & 4096.1 & Lower S11-1 sub-layer & black argillaceous & 1.80 & 2.62 & 54.8 \\ Q4 & 4091.4 & (Layer 3) & shale & 1.87 & 2.41 & 48.8 \\ Q5 & 4086.4 & & (Layer 3) & shale & 2.01 & 3.66 & 65.5 \\ Q6 & 4081.2 & & & 1.87 & 1.96 & 23.5 \\ Q7 & 4076.1 & Middle S11-1 & black silty shale & 2.52 & 2.34 & 40.6 \\ Q8 & 4071.3 & Longmaxi Fr. & sub-layer (Layer 4) & & 1.58 & 2.07 & 38.6 \\ Q9 & 4066.2 & & & 1.27 & 1.13 & 42.0 \\ Q10 & 4061.7 & & & black silty shale & 1.34 & 0.77 & 40.5 \\ Q11 & 4056.7 & & (Layer 5) & muddy silstone & 1.04 & 0.23 & 48.7 \\ Q12 & 4051.9 & & & black silty shale & 1.10 & 0.84 & 41.3 \\ Q13 & 4046.2 & & & 1.87 & 1.96 & 23.5 \\ Q14 & 4042.2 & & & 1.87 & 1.96 & 23.5 \\ Q15 & 4036.5 & & (Layer 6) & muddy silstone & 1.52 & 0.79 & 38.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stratification and lithology of representative samples from the Z-3 well.  
complex and irregular pore structure of various types of rocks. Based on the \(T_{2}\) spectrum distribution of the samples obtained by NMR experiments, the fractal theory is introduced to analyze the fractal features of shale pores, and the complexity and non-intuitive characteristics of the pore structures are further investigated. The equation for calculating the fractal dimension using NMR is presented as follows [27]:  
\[\text{lg}(S)=(3-D)\text{lg}(T_{2})+(D-3)\text{lg}(T_{2 max}) \tag{3}\]  
In Formula (3), \(S\) denotes the ratio of accumulated pore volume with a relaxation time less than \(T_{2}\) to the overall pore volume. \(D\) represents the fractal dimension, and \(T_{2 max}\) is the maximum relaxation time.  
Given that the samples display self-similar pore structures and demonstrate fractal characteristics, the fractal dimension can be determined by evaluating the slope 'k' of Equation (4):  
\[\text{D}=3-\text{k} \tag{4}\]  
Assuming that pores with pore sizes of 1 nm-20 nm and 20 nm-200 nm correspond to bound fluids, and pores with sizes of 0.2 \(\upmu\)m-20 \(\upmu\)m correspond to movable fluids, a certain \(T_{2}\) value can be selected to divide movable and bound fluids based on the correlation between the \(T_{2}\) relaxation time distribution and pore size distribution, which is called the \(T_{2 cutoff}\) value.
# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview
### Sensing Matrix  
Ideally, the sensing matrix \(\mathbf{\Phi}\) has to fulfill the following constraints:  
* Optimal or near-optimal reconstruction performance: The measured data maintain the salient information of the signal for reconstruction purposes.
* Optimized sensing performance: Only a few measurements are required to obtain an optimal (near-optimal) recovery.
* Universality: The sensing matrix maintains a low coherence with almost all sparsifying matrices.
* Low complexity, fast computation and structure-based processing: These features of the sensing matrix are desired for large-scale, real-time sensing applications.
* Hardware friendly: Easy and efficient implementation on hardware is necessary.  
The above-mentioned conditions present the ideal criteria for any sensing matrix. However, it is hard to find matrices that satisfy all these conditions. Thus, two relaxed conditions that can grant a high reconstruction quality have been established for designing the sensing matrix. First, a low coherence between the sensing matrix and the sparsifying matrix is recommended to enable the sensing matrix to capture the salient information of the original signal with the minimum number of projections [38]. Second, the sensing matrix should satisfy the restricted isometry property (RIP) to ensure that the compression preserves the signal information (\(k\)-RIP) and to ensure that mapping the signal \(\mathbf{x}\) from a high dimension space \(\mathbb{R}^{N}\) to a lower dimension one \(\mathbb{R}^{M}\) should be unique, i.e., it should preserve the distance between each pair of two distinct signals \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) (\(2k\)-RIP).
# Binomial Number System
## 3 Quantitative Characteristics of BNS
### BNS Property
**_The number of different code lengths of the binomial codes with conditions (2) and (3), \(d^{\prime}=n-k\), and the number of different code lengths given by the conditions (4) and (5), \(d^{\prime}=k\)._  
**Proof of Theorem 5. ** To find the number of code lengths of binomial codes, it is necessary to subtract the minimum code length \(r_{max}\) from its maximum code length \(r_{min}\), adding 1 to this difference. From Equation (2), it follows that \(r_{max}=n-1\) and \(r_{min}=k\), the number of different code lengths of binomial code combinations determined by conditions (2) and (3) is found from the expression: \(d^{\prime}=r_{max}-r_{min}+1=(n-1)-k+1=n-k\). For binomial codes corresponding to conditions (4) and (5), it follows from Theorem 4 that \(r_{max}=n-1\) and \(r_{min}=n-k\). Then, the number of different code lengths of binomial codes by conditions (4) and (5) determines from the expression \(d^{\prime\prime}=r_{max}-r_{min}+1=(n-1)-(n-k)+1=k\). \(\square\)  
**Theorem 6. **_The parameter \(k\) (the maximum number of units) in binomial codes is equal to the minimum value \(k_{min}=1\), and the maximum value is equal to \(k_{max}=n-1\)._  
**Proof of Theorem 6. ** From Equation (5): \(q\leq k-1\) and \(q\geq 0\). These inequalities follow that for \(q=0=k-1\), and parameter \(k\) reaches its minimum value \(k_{min}\), equal to 1. Since the code length of binomial codes is \(r=q+1\), for the system of conditions (2) and (3), \(q=k\), then \(r=k+1\). Substituting \(k+1\) into Equation (2):  
\[k\leq k+1\leq n-1.\]  
In this equation, \(k\) reaches its maximum value \(k_{max}\), when \(l\) comes the minimum value \(l_{min}\). As follows from Theorem 1, \(l=l_{\min}=0\). Substituting this value into the inequality obtained above, we obtain that, for \(l=0\), it is transformed into the form \(k\leq n-1\). Thus, the parameter \(k\) reaches the maximum value if \(k=n-1\), for \(k=k_{max}=n-1\). \(\square\)  
**Corollary 1. **_There are no binomial codes with parameter \(k=0\)._  
**Corollary 2. **_In the case when the parameter \(k=k_{min}=k_{max}=1\) in binomial codes, the value of the parameter \(n\) becomes minimal--\(n=n_{min}=2\). Thus, \(n=2\), \(3\),..._  
Obviously, for \(n=n_{min}=2\) only two non-uniform binomial code combinations with \(k=1\)--\(0\), \(1\) are possible; three for \(n=3\)--\(00\), \(01\), \(1\), four for \(n=4\)--\(000\), \(001\), \(01\), \(1\), etc. The corresponding uniform binomial codewords: \(0\), \(1\), \(00\), \(01\), \(10\); \(000\), \(001\), \(010\). In the case when \(k=n-1\), for \(n=2\) we have \(0.1\); for \(n=3\)--\(0\), \(10\), \(11\); for \(n=4\)--\(0\), \(10\), \(110\), \(111\). The corresponding uniform binomial codewords are \(0\), \(1\), \(00\), \(10\), \(11\); \(000\), \(100\), \(110\), \(111\).
# Unsaturated Hydraulic Conductivity in Composite Porous Media
## 3 Results and Discussion
It would be prudent to carry out an analysis of the correlation that exists between the permeability values obtained and the physical variables of the composite materials. Figure 6 above shows different contents of diatom (_Aulacoseira Granulata_ species); the highest values of \(k_{u}\) are recorded because the particle size distribution in the diatom (Figure 4) reveals sizes relatively larger than iron oxide and calcium carbonate. Including diatomaceous soil in a more significant proportion, although it decreases the value of hydraulic conductivity, creates a double effect that acts combined when suction is imposed on the sample. Lower values are observed in iron oxide and carbonate, strongly related to smaller PSD sizes. Figure 6: Results of unsaturated permeability coefficient for different suction values. These materials have particles smaller than 0.002 mm, a greater quantity than mixtures with diatomaceous soils. It is not only essential to observe the response of the unsaturated hydraulic conductivity as a function of suction. For a complete analysis of the research, it is vital to focus on aspects such as the dependence of permeability concerning the combinations of proposed composite materials. In Figure 7, it is possible to analyze the behavior of the hydraulic conductivities for different suction levels related to the dosage of the inclusions. The curve for zero suction (\(k_{s}\)) behaves as an envelope, limiting the unsaturated permeability states (\(k_{u}\)). Below the zero suction curve, the curves for different suction levels are located in relation to the proportion of diatom inclusions, Fe\({}_{2}\)O\({}_{3}\) and CaCO\({}_{3}\). This graph also shows the \(k_{u}\) reduction as a function of suction and the addition of different materials. It is important to analyze that for high rates of additions, the permeability values tend to be very low and to be very similar for all the curves. Although there are few studies to date where the results obtained in this study can be objectively contrasted to validate the permeability coefficients in an unsaturated situation (\(k_{u}\)), [68; 69] show results for similar materials that can corroborate the values recorded in this study. In the investigations consulted, methodologies similar to those used in the current study were used.
## 1 Introduction
[PERSON] and [PERSON] [15] compared the results obtained by using [PERSON]'s non-linear \(k\)-\(\varepsilon\) model and the standard \(k\)-\(\varepsilon\) model [16], respectively. They found that the non-linear [PERSON]'s \(k\)-\(\varepsilon\) model provides a better prediction, and shows a good agreement with the experimental results. [PERSON] and [PERSON] [17] investigated the performance of four different subgrid-scale (SGS) models to predict a turbulent flow over two-dimensional (2-D) steep ridges, using two kinds of surface roughness. By comparing the results with that by [PERSON] et al. [18], they found that the results attained via hybrid SGS model match well with the experimental data in case of 2-D rough ridge, but cannot predict the flow well in the wake of a 2-D smooth ridge. [PERSON] et al. [19] studied the wind field around a 2-D periodical hill, with consideration of different Reynolds numbers over a wide range, through wind tunnel tests and numerical simulations. [PERSON] and [PERSON] [20] studied the turbulent flow over a 2-D steep ridge and a three-dimensional (3-D) steep hill having rough and smooth surfaces, in which a delayed detached-eddy simulation (DDES) method was used. [PERSON] et al. [21] studied the mean and turbulent quantities on the surfaces on an isolated hill by large-eddy simulations (LES), and found a spiral-shaped structure in both the lateral and vertical directions. [PERSON] et al. [22] also studied the turbulent flow over a 2-D hill and a 3-D ridge with a smooth ground by using LES. In the wake it was found that the spectra of fluid are very sensitive to the oncoming turbulence in case of 2-D ridge. Most of the above studies focused on the streamwise mean wind speed; however, few studies have been conducted on the vertical wind and the wind fluctuations. To date, the relevant terms in the codesused to determine the characteristics of the hilly terrain wind field are not sufficient for wind engineering. It is worth noting that the formulae in these codes are mainly for two-dimensional situations. In this paper, a wind tunnel test was conducted to study the wind fields of streamwise wind and vertical wind for the flow over a typical 3-D hill possessing a cosine-squared cross section. The mean wind speed, along with the turbulence intensity at the typical positions on the surface of the hill, was measured. Furthermore, the speed-up along the streamwise direction was compared with that specified in the codes. The contour maps of the mean wind speed, as well as the turbulence intensity in the directions of streamwise and vertical, are presented, which provides a reference for the wind resistant design of the structures under the hilly terrain wind field, and also enriches the database of experimental study on speed-up ratio. Finally, an example of the windage yaw of the jumper cable that is frequently utilized in the transmission lines is given to show the usage of the presented hilly terrain wind field in the analysis of wind-induced response of a structure stationed on top of a hill.
## 3 Results  
### Suspension Characterization  
The optimal level of energy needed to print the samples was determined via the cure depth of the photocurable suspension. The result of the cure depth test can be seen in Figure 6a. Due to the high level of absorption and scattering of light by the silicon nitride particles, the cure depth is significantly lower than that of other ceramic particles such as alumina or zirconia. The binder system, which is a light-sensitive organic matrix, is based on an acrylate-based monomer system and contains some additives, such as solvents and a photoinitiator [26; 30]. According to [PERSON] et al., the curing depth should be approximately three times thicker than the printed layers to avoid the cracks during the thermal post-processing. Based on these results, all the parts generated in this study were printed using a CeraFab 7500 system (Lithoz GmbH, Vienna, Austria) with a layer thickness of 20 \(\upmu\)m and an energy dose of 350 mJ cm\({}^{-2}\). In addition to the cure depth, the lateral over-polymerization was measured with an aid of a light microscope (see Figure 6b; E = 350 mJ/cm\({}^{2}\)). As it is a deviation from the target dimensions, lateral over-polymerization should be avoided in order to print precise and accurate structures [40]. Due to the pixel arrangement, lateral over-polymerization does not affect tragically in that case because in that range the surface first begins to round itself.  
Figure 7 depicts the rheological behavior of the suspension at the relevant shear rates between 5 and 50 s\({}^{-1}\). Stability of the suspension was also tested by means of rheological measurements. Fresh suspension was tested (Week 0; blue dashed line) and the same suspension was also measured 8 th  
Figure 6: Cure depth (C\({}_{d}\)) with different energy doses (**a**); lateral over-polymerization measurements (**b**) for silicon nitride-based ceramic suspension.  
weeks after production, both before (Week 8; red dotted dashed line) and after (Week 8*; red solid line) re-homogenization with the aid of a centrifugal mixer. Although 8-week-old suspension has a dissimilar rheological behavior to fresh suspension, centrifugal mixing helps to achieve very similar properties to the fresh suspension and can still be printed using the very same process parameters. In addition to this, all the measured viscosities have a standard deviation of less than 0.04 Pa.s.  
Finally, in terms of material efficiency, tests were performed for the three different printing cycles resulting in a variety of geometries such as test bars (\(5\times 5\times 25\) mm) and discs (\(\O\) 22 mm with a thickness of 2.5 mm). The amount of suspension, which was converted into shaped green parts was measured to be 89% of the LithaNit 720 suspension that was fed into the printer, which means that only approximately 13% of suspension was lost during the manufacturing of the samples (mainly due to cleaning of the green parts). This high material efficiency sets LCM distinctly apart from any other stereolithography process.
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 2 Materials and Methods
### Fourier Neural Operator Networks
Choosing the number of Fourier modes to retain largely depends on the smoothness of the PDE's solution. The maximum number of modes is defined by the size of the computational grid. PDE solvers using Fourier methods assume a periodic boundary condition. Although the FNO network heavily uses the Fourier transform, it is not limited by this assumption and can be applied to solve PDEs with non-periodic boundary conditions, such as Burgers' equation and the Navier-Stokes equation [31]. This is important because the photoacoustic wave equation also has non-periodic boundary conditions. The photoacoustic wave equation can be solved using either a 2D or 3D FNO architectural implementation. In the 2D architecture, the FNO network performs 2D convolutions in space and finds a solution for some fixed interval length \(\Delta t\). The solution is then recurrently propagated in time and used to solve for the next interval length. In the 3D architecture, the FNO network performs 3D convolutions in space-time and can directly output the full time series solution with any time discretization. Both implementations were demonstrated to have similar performance. In this work, the 3D FNO network was used because it was found to be more expressive and easier to train [31].
## 1 Introduction  
Seminal quality is a good predictor of the male potential fertility and causes 25% or more of the infertility problem [1]. Predicting the results of the semen analysis based on the data such as environmental factors or life habits is very useful in early diagnosis of seminal disordered patients, selection of semen donors candidates and the prioritization of further infertility treatment [2; 3; 4; 5].  
To get a quick estimate of the seminal profile of the patients based on data of life style or personal habits, machine learning and data mining tools have been applied in decision support systems in biomedical engineering [4; 5]. In the pioneering work by [PERSON] _et al._[4], three kinds of machine learning tools including decision trees (DT), multilayer perceptron (MLP) artificial neural network and support vector machines (SVM) were compared. In a most recent work [5], a neural network approach based on MLP was further proposed to estimate semen parameters.  
As is common in bioinformatics [6] and medical diagnosis [7], most available seminal quality prediction data also have the class imbalance problem, _i.e._, the number of negative (majority) observations far outnumbers the number of positive (minority) observations. Because of this skewed distribution in data, many standard learning algorithms often perform poorly on the minority class [8]. Due to complexity of the problem itself, the data imbalance and its impact on prediction accuracy in seminal quality need further study.  
In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests (CBDF), to tackle unbalanced class learning problem in seminal quality prediction. In the proposed algorithm, to alleviate the class imbalance problem, the borderline majority data called Tomek Links [9] observations are first removed from the training data. Then, the remaining majority training data will be further clustered into a number of subsets of majority samples. We use each majority subset in turn and a bagging [10] version of the minority data to train each base classifier within the ensemble. To further achieve ensemble diversity, random subspace [11] technique is then applied to the new training data to obtain diverse training sets for the base learners in the ensemble. Different from random forests [12], base learners (decision trees) within CBDF are aggregated using a weighted majority voting scheme. All these together improve the prediction accuracy in the final ensemble. This is the major contribution of this paper.  
Experiment results on the fertility diagnosis dataset [4] have shown that in terms of AUC (Area Under the ROC curve), the proposed algorithm outperforms DT, SVM, RF, MLP and LR by a noticeable margin.  
As a second contribution, we provide a measure to identify variable importance based on mean Gini decrease in building decision trees, which may help the interpretation of the model results.
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### Electric Devices  
To replace traditional power sources and achieve low-carbon targets, an important application of high-speed motors is driving devices, most of which are applied in electric vehicles and high-speed trains [9; 10]. In electric devices, their rated speed requirement is not very high, but their power is often larger [11]. Adequate cooling is therefore essential for stability. [PERSON] et al. [12] designed air-cooling for the high-speed motors of distributed traction high-speed trains, as shown in Figure 1. This high-speed motor is rated at 380 kW with a peak speed of 6000 rpm. Because of its low rotational speed, the motor adopts air-cooling as its form of heat dissipation. [PERSON] et al. [13] developed a water-cooling method for high-speed motors of mangle wheel systems. Excellent heat evacuation is achieved by water-cooling the conductive plates, and a slight boost in propulsion force is produced at low speeds.