esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Preprocessing
#### 3.2.1 Convolution-Based  
A convolution is a filter in which the neighborhood of each pixel is organized by weighted average, according to a matrix that we call the convolution kernel [52]. These kernels are variable in size and shape producing different effects on the image.  
Mathematically, the convolution operation can be represented as follows [53].  
Let \(I=I(i,j)_{j=1,\ldots,M}^{i=1,\ldots,N}\) be a grayscale image. Let \(M=M(k,l)_{l=1,\ldots,q}^{k=1,\ldots,p}\) be the convolution kernel matrix, where \(p=2n+1\), \(q=2m+1\) with \(n\), \(m\) non negative integers. The result of applying the convolution \(M\) to the image \(I\) is the image \(I^{\prime}=I^{\prime}(i,j)_{j=1,\ldots,M}^{i=1,\ldots,N}\) where for each \((x_{0},y_{0})\)  
\[I^{\prime}(x_{0},y_{0})=\sum_{a=-n}^{n}\sum_{b=-m}^{m}M(a,b)I(x_{0}+a,y_{0}+b)\]  
where \(1\leq x_{0}-n\), \(x_{0}+n\leq N\),\(1\leq y_{0}-m\), \(y_{0}+m\leq M\).  
In this paper we have used some non-parametric filters from the Python Pillow library [54]. Although they are different in size and the effect they have on the image to which they are applied, they are all based on convolution and the only thing that varies is the kernel. Table 2 shows the matrices of the filters used, which are as follows:  
1. **Blur.** Applies a convolution with a kernel of size \(5\times 5\) which will result in a slightly blurred image. Because of the shape of the kernel, the central pixels will be ignored, thus losing some information.
2. **Contour.** Convolute with a \(3\times 3\) filter. This filter detects the contours of shapes in images.
3. **Detail.** It applies a \(3\times 3\) size convolution, giving greater importance to the central pixel compared to its neighborhood, thus achieving an enhanced image.
4. **Edge Enhance.** Applies a convolution with a \(3\times 3\) kernel. It aims to improve the quality and definition of the edges of an image.
5. **Edge Enhance More.** As in the previous filter, a \(3\times 3\) convolution is applied to improve the definition of the edges. The only difference is that the central value is changed, making the edges better defined than with the previous filter.
6. **Sharpen.** This filter convolves a \(3\times 3\) kernel to generate a cleaner image. It is used to sharpen the edges to improve their quality. It even increases the contrast between the light and dark areas of the image to improve its characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Classes**} \\ \hline  & **Healthy (0)** & **Cancer (1)** & **Total** \\ \hline Train & 63,050 & 63,050 & 126,100 \\ \hline Validation & 7868 & 7868 & 15,736 \\ \hline Test & 7868 & 7868 & 15,736 \\ \hline Discarded & 119,952 & 0 & 119,952 \\ \hline Total & 198,738 & 78,786 & 277,524 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the number of breast cancer patches for each data split.
## 3 Methods
### Data Preprocessing
#### 3.1.3 Spectral Dimensionality Reduction  
Full-spectrum, near-infrared wavelengths contain a great deal of redundant information; however, spectral data dimensionality reduction [24] can preserve the effective information of the spectrum and thus improve the accuracy of the model prediction. Spectral downscaling includes two types of methods, feature selection, and feature extraction. Feature selection is the process of filtering feature wavelengths or feature bands using specific methods, which do not alter the nature of the original feature space but rather select key features to form a new low-dimensional space. In this paper, the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS) [25] method are used to select features. The objective of feature extraction is to transform the original high-dimensional spectral data into a low-dimensional space in which each dimension is independent of the others through a mapping relationship. The feature extraction methods used in this paper are Principal Component Analysis (PCA) and locally linear embedding (LLE) [26; 27]. In this paper, the combination of CARS for feature selection and LLE for feature extraction can remove redundant information to the greatest extent.
## 2 Dynamic Contact Model of SNBSP
### Calculation of the Frictional Force of SNBSP and Other Related Parameters  
#### 2.3.1 The Calculation of Ball and Raceway Contact Pair Frictional Force  
As demonstrated in Figure 3, the frictional force between the ball and the screw and nut can be decomposed into two directional components in \(\text{CSO}^{\prime\prime}_{\varsigma}x^{\prime\prime}_{\varsigma}y^{\prime\prime }_{\varsigma}z^{\prime\prime}_{\varsigma}\): the \(x^{\prime\prime}_{\varsigma}\) direction (transverse) frictional force and the \(y^{\prime\prime}_{\varsigma}\) direction (longitudinal) frictional force. The formula can be expressed as [7]:  
\[F_{x^{\prime\prime}\varsigma}=a_{\varsigma}b_{\varsigma}\int_{-1}^{1}\int_{- \sqrt{1-{t^{\prime\prime}}^{2}}}^{\sqrt{1-{t^{\prime\prime}}^{2}}}\tau_{x^{ \prime\prime}\varsigma}dt^{\prime\prime}dt^{\prime\prime} \tag{35a}\] \[F_{y^{\prime\prime}\varsigma}=a_{\varsigma}b_{\varsigma}\int_{-1}^{1}\int_{- \sqrt{1-{q^{\prime\prime}}^{2}}}^{\sqrt{1-{q^{\prime\prime}}^{2}}}\tau_{y^{ \prime\prime}\varsigma}dt^{\prime\prime}dq^{\prime\prime} \tag{35b}\]  
where \(a_{\varsigma}\) and \(b_{\varsigma}\) denote the semi-major and semi-minor axis of contact ellipse, respectively, the detailed calculation method can be detailed in the reference [16,19]. \(q^{\prime\prime}\) denotes the dimensionless coefficient in the direction of \(x^{\prime\prime}_{\varsigma}\) axis, \(q^{\prime\prime}=x^{\prime\prime}/a_{\varsigma}\). \(t^{\prime\prime}\) denotes the dimensionless coefficient in the direction of \(y^{\prime\prime}_{\varsigma}\) axis, \(t^{\prime\prime}=y^{\prime\prime}/b_{\varsigma}\). \(\tau_{X^{\prime\prime}\varsigma}\) and \(\tau_{Y^{\prime\prime}\varsigma}\) denote the component of the shear stress of the ball and screw or nut in the direction of the \(x^{\prime\prime}_{\varsigma}\) axis and \(y^{\prime\prime}_{\varsigma}\) axis, respectively.  
\[\tau_{x^{\prime\prime}\varsigma}=\tau_{\varsigma}\cos(\psi_{\varsigma}) \tag{36a}\] \[\tau_{y^{\prime\prime}\varsigma}=\tau_{\varsigma}\sin(\psi_{\varsigma}) \tag{36b}\]  
where \(\tau_{\varsigma}\) denotes the shear stresses of the ball and screw or nut. \(\psi_{\varsigma}\) denotes the slip angle, which are described in Section 2.3.3.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 2 PFA-Reinforced Rockfill Materials Technology
### Construction Process  
In current engineering applications, clear construction specifications and processes for grouting reinforcement of PFA-reinforced rockfill materials in dam crest areas are lacking. Consequently, there is a need to consolidate the construction technology for grouting reinforcement of earth-rock dam crests using polymer cementitious materials. This consolidation should draw from engineering applications of polymer grouting for pavement settlement repairs [12] and integrate findings from existing research [13] (Figure 1).  
The specific construction requirements and steps are outlined as follows:  
1. Preparation of polymer cementitious materials: The polymer cementitious material is primarily divided into two parts. Before grouting, it is blended and subjected to tests for density, viscosity, gel time, coagulation time, foaming rate, and compressive strength to determine the optimal material ratio.  
Figure 1: Schematic diagram of polymer grouting reinforcement.  
Preparation before grouting: Per the seismic design requirements of earth-rock dams, seismic reinforcement measures are implemented for rockfill materials in the upper portion of the dam body (within the dam height range of 3/4-4/5). For new earth-rock dams, polymer grouting reinforcement is conducted concurrently with the layer-wise filling of the dam body. To minimize construction period disruption, reinforcement construction and dam filling construction are carried out separately in different areas. For earthquake-damaged earth-rock dams, visual inspection, leveling measurement, core drilling, and ultrasonic tomography are utilized to identify dam cracks, subsidence, and landslide instability.
3. Grouting hole setting: Before excavating grouting holes, the construction site is cleaned to ensure debris-free, clean positions for the holes. For new earth-rock dams, grouting holes are arranged in square or triangular patterns, with adjacent filling layer holes staggered. Hole spacing within each layer is set at 4 m, with a minimum hole diameter of 40 mm. Hole depth should be a multiple of the filling height of each layer, with specific construction adjusted based on actual conditions. For earthquake-damaged earth-rock dams, adjustments are made based on the location, extent, and slurry diffusion radius of the damage.
4. Grouting: Necessary polymer and grouting equipment is prepared, and grouting pipes are inserted into boreholes, ensuring tight connection with equipment pre-loaded with mixed polymer. Grouting is conducted by injecting the material components into the holes under pressure. Grouting duration is determined by porosity and slurry diffusion radius. Grouting ceases upon reaching the preset time, ensuring each hole is grouted in one session.
5. Hole detection and sealing post-grouting: Upon completion of grouting, grouting effectiveness is assessed via coring. Once testing confirms satisfactory results, holes are sealed with thin mortar or a protective coating layer.
## 3 Results  
The two-year data analysis demonstrated that the interaction between soil tillage and organic fertilization had only a substantial effect on the crude fat content of amelina oilseed cake (Table 3). Moreover, the year \(\times\) soil tillage interaction was also found to be statistically significant for the above-mentioned trait. The main effect of soil tillage was significant on the crude protein content, crude fat content, cis-11-eicosenoic acid (C20:1), erucic acid (C22:1), and total monounsaturated fatty acids (MUFA) of amelina oilseed cake. With the exception of seed cake extraction rate, crude ash content, stearic acid (C18:0), and omega-6 to omega-3 fatty acids ratio (_n-6/n-3_), the main effect of organic fertilization regimes was substantial on seed yield, seed cake extraction rate, and yield, together with nutritional components and fatty acids composition of oilseed cake. In addition, the main effect of the year presented a substantial effect on seed yield, seed cake content and yield, dry matter content, crude fat content, total carbohydrate (CHO) content, residual oil content of oilseed cake, as well as on its fatty acids composition and their combinations, excepting cis-11-eicosenoic acid (C20:1), erucic acid (C22:1), \(\alpha\)-Linolenic acid (C18:3 _n-_3), total polyunsaturated fatty acids (PUFA), and _n-6/n-3_ ratio.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Source of** \\ **Variance** \\ \end{tabular} & **Df** & \begin{tabular}{c} **Seed Cake** \\ **Extraction** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Seed** \\ **Cake Yield** \\ \end{tabular} & \
\begin{tabular}{c} **Dry** \\ **Matter** \\ **Content** \\ \end{tabular} &
\begin{tabular}{c} **Crude** \\ **Protein** \\ \end{tabular} & **Crude Fat** \\ \hline Year (Y) & 1 & 30.917 *** & 15.827 ** & 18.111 ** & 14.915 ** & 1.544
## 3 Methodology and Data Collection
### Data Collection  
For the purposes of the research, it was necessary to use varied data. All collected data concerns the Czech Republic. The analysis data were based on traffic measurements which involved, in particular, the measurement and averaging of speed of passing road segments arising from the merger of FCD and other traffic sensor data. The measurement was closely connected to additional data on the individual road segments set forth in OpenStreetMap (e.g., public transport stops, traffic lights), and information officially issued on short-term and long-term traffic restrictions (both planned and unplanned) on the roads in the Czech Republic. Meteorological forecast values were collected for each road segment. We must add that calendar data containing information on the monitored days per year were used only during the design of the prediction model. Summary information on the individual datasets (before its pre-processing) is set forth in Table 1. The 4 main datasets were analyzed in Section 4.  
The aim of this vast data collection was to detect any data which could have a direct or indirect effect on road traffic flow. However, it was neither possible nor suitable to use all data with respect to the targets of this article, see Sections 3.2 and 5. The stated data are described in more detail below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset Name** & **Source** & **Collection Period** & **Number of Records** \\ \hline Road segments & \begin{tabular}{c} OpenStreetMap Foundation, \\ United Kingdom \\ \end{tabular} & status as at 7 January 2020 & 20,504 \\ \begin{tabular}{c} Traffic \\ measurement \\ \end{tabular} & \begin{tabular}{c} Road and Motorway Directorate \\ of the Czech Republic \\ \end{tabular} & 7 January–29 June 2020 & 176,795,419 \\ \begin{tabular}{c} Weather forecast \\ \end{tabular} & \begin{tabular}{c} Meteorologicalisk institutt, Norway \\ National Traffic Information \\ Center, Czech Republic \\ \end{tabular} & 7 January–29 June 2020 & 309,628,693 \\ \
\begin{tabular}{c} Traffic restrictions \\ \end{tabular} &
\begin{tabular}{c} National Traffic Information \\ Center, Czech Republic \\ \end{tabular} & 18 May–29 June 2020 & 48,722 \\ \hline \end{tabular}
\end{table}
Table 1: Basic description of four main datasets prior to pre-processing. For the period of 7 January–29 June 2020, calendar data were also acquired but were used only during the design of the model.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 3 Improved Adaptive Federated Kalman Filter (IAFKF)
### Procedure of Improved AFKF  
The procedure is shown below:  
Step 1: Set the initial values of the filter, including the initial state value \(\mathbf{x}_{0}\), the system noise covariance \(\mathbf{Q}_{0}\), and the measurement noise covariance \(\mathbf{R}_{0}\).  
Step 2: Time update: the sub-filter performs a time update as per Equations (10) and (11).  
Step 3: Measurement update: the sub-filter performs a measurement update as per Equations (12)-(14).  
Step 4: Fault detection: using the residual chi-square and sliding-window averaging method for fault detection, the estimated value of the faulty sub-filter is replaced with the estimated value of the main filter as per Equations (21)-(28).  
Step 5: Information fusion is carried out as per Equations (15) and (16).  
Step 6: Adaptive information sharing factor is calculated as per Equations (29)-(31).  
Step 7: Information sharing process is carried out as per Equations (17)-(20).  
Step 8: Steps 2 to 7 are repeated in a loop.
## 2 LSDF-Net and Structural Optimization of the Network  
A lightweight sequential-differential-frame-based network (LSDF-Net) for infrared dim small target detection suitable for detection under a sky cloud background and the optimization methods of the network are proposed in this section. The architecture of the network is shown in Figure 1, which includes the input, differential, intermediate, and output layers. Sequential images are used in the input layer, and the images are differentiated. The middle layer is the main part of the network, which uses a down-sampling pyramid structure to extract the features of the input image. A false-alarm-object learning strategy and multi-anchor box assignment strategy are adopted to improve the detection performance. The output layer contains the regression of the target classification and location, which completes the target detection task.
## 3 Analysis and Results  
### Numerical Analysis of the Anchoring Performance of DBS Slips  
#### 3.1.1 Finite Element Model of DBS Slips  
Two predominant slip configurations employed in fracturing operations are the slip nail and slip block designs. This study conducts a numerical analysis of their respective anchoring performance characteristics. The DBS assembly was computationally simplified based on its fundamental seating and anchoring mechanics, with particular focus on structural influences on anchoring efficacy. Consequently, non-essential components such as sealing elements were omitted while the core structural assemblies were retained: the sliding body, slip units, and casing interface.  
Given the axi-symmetric nature of the dissolvable ball seat slip arrangement, a single-slip unit was modeled to optimize computational efficiency. Mesh generation employed an intelligent structured grid algorithm. The resulting mesh configuration is presented in Figure 4.  
The material of the DBS sliding body and slip base is dissolvable aluminum alloy, and the material of the slip block is cemented carbide. The mechanical and chemical properties of these materials, including dissolution rates under downhole conditions, are systematically presented in Table 1.  
According to the working principle of DBS slip, the side of the casing and the outer surface were set as a fixed constraint, and the axial displacement of the lower surface of the slip base is 0 mm. The downward displacement is applied to the upper end surface of the sliding body, and the normal displacement of the contact surface between the slip base and the base is 0 mm.  
Figure 4: Meshing of two kinds of DBS slip. (**a**) Nail-type slip. (**b**) Block-type slip.  
After the boundary conditions are imposed, the support reaction force at the upper surface of the sliding body is obtained for different displacements of the sliding body. The stress, strain, and contact pressure between the block/nail and the casing wall are obtained when the seating force is not greater than 9t, i.e., the seating force on a slip block is not greater than 1.6 tons.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 3 Simulations
### Simple Simulation for the Study of COT Analysis after Time-Domain Filtering  
In the FOSCK, the COT must be used after the envelope demodulation analysis. In order to explain this and determine the influence of COT on impulse feature extraction, a simple outer race simulated signal that has a single resonant frequency and consists of a series of pure impulses is shown in Figure 3. The simulation signal parameters in the model are given in Table 1, where \(f_{s}\) is sampling frequency  
The rotating frequency is given by  
\[f(t)=10+5\times\sin(10\pi t) \tag{16}\]  
and illustrated in Figure 3a. The corresponding time-domain signal and angle-domain resampling signal are shown in Figure 3b,c, respectively. It is clear that the intervals of adjacent impulse responses  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**N (s)** & **B\({}_{\mathbf{n}}\)** & **\(\boldsymbol{\psi_{m}}\)** & **\(f\) (Hz)** & **\(f_{\mathbf{o}}\)** & **\(f_{\mathbf{s}}\) (kHz)** & **\(f_{\mathbf{r}}\) (kHz)** & **\(\boldsymbol{\beta}\) (kHz)** & **\(\boldsymbol{\tau_{n}}\)** \\ \hline
3 & 1 & 0 & 5–15 & 4.5 & 20 & 5 & 1.2 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation model.  
change synchronously with the rotating frequency in the time-domain signal; the rotating frequency is larger, and the adjacent impulse intervals are smaller. Unlike that of the time-domain signal, the pulse interval is unchanged in the angle-domain signal.  
The short-time Fourier transforms (STFTs) of the time domain and angle domain impulse responses are shown in Figure 4a,b, respectively. The carrier frequencies of the time-domain signal are concentrated around the resonant frequency, while the carrier orders of the angle-domain signal spread to a wider order scope. Therefore, it can be concluded that the COT procedure causes distortion of the signal resonance band, which is very important in the resonance demodulation analysis.  
In addition to the above defects, as stated by [PERSON] in [27], a simple filter with a fixed cutoff order cannot deal with the angle-domain signal, whose carrier orders vary over time. The envelopes obtained by a low pass filter with a fixed cutoff frequency in the time domain and a fixed cutoff order in the angle domain, as shown in Figure 5a,b, respectively. The amplitude of the filtered signal in the angle domain is distorted. Therefore, an envelope demodulation analysis must first be carried out in the time domain, then the COT method is performed on the envelope.  
Figure 4: STFTs of the simulation: (**a**) STFT of the time-domain signal and (**b**) STFT of the angular-domain signal.  
Figure 3: Simple simulation: (**a**) the shaft rotational frequency, (**b**) the impulse signal in the time domain and (**c**) the resampling signal in the angle domain.
## 4 Results and Discussion
### Influence Analysis of Pipeline Parameters  
#### 4.3.1 Pipeline Diameter  
Figure 11a-c successively show the cloud diagram of vertical deformation, horizontal deformation, stress and axial force of the pipeline when the pipeline diameter was set to d = 400 mm, d = 600 mm and d = 1000 mm. The maximum vertical deformation under three conditions were 17.5, 17.3, and 14.8 mm, respectively, the maximum horizontal deformation was 4.13, 4.14, and 3.66 mm, respectively, the maximum stress was 0.98, 1.11, and 1.41 MPa, respectively. The maximum axial force was 48.7, 54.8, and 70.1 kN, respectively. The maximum stress and axial force of the pipeline increased with the increase of pipeline diameter. With the increase of pipeline diameter, the maximum vertical deformation caused by tunnel excavation decreased, while the maximum horizontal deformation decreased slightly. The distribution of pipeline deformation, stress and axial force were basically unchanged.  
Figure 11: _Cont._Take the settlement of the top of pipeline 1 above the central axis of the tunnel to analyze the influence of pipeline diameter on pipeline settlement, as shown in Figure 12. Under the same other factors, the larger the pipeline diameter, the smaller the pipeline settlement caused by tunnel excavation. The vertical deformation of the pipeline with a diameter of 400 mm increased by 2.97 mm at most compared with that of the pipeline with a diameter of 1000 mm. The pipeline diameter of the project varied from 400 mm to 1000 mm, the diameter of sewage pipe was 400 mm, the diameter of gas pipe and rainwater pipe was 500 mm, and the diameter of water supply pipe was 600 mm. Therefore, the monitoring of the deformation of small diameter pipelines and the stress change of large diameter pipelines should be strengthened to prevent the deformation of small diameter pipelines from exceeding the standard value and the stress of large diameter pipelines from exceeding the allowable stress.