esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Experimental Data and Results
### Probabilistic Model Development
#### 3.3.1 Bayesian Data Analysis  
A Bayesian regression approach was implemented to estimate the model parameters and error. This technique is used to estimate and update the posterior distribution of the unknown model parameters [28]. In the Bayesian inference, a prior probability distribution function (pdf) of the model parameters is combined with observed data (evidence) in the form of a likelihood function of an unknown parameter (\(\theta\)). The result is an updated state of knowledge in the form of the posterior joint distribution of the model parameters, \(f(\theta\,|\,x)\). The posterior pdf of the model parameter can be assessed as described according to the Bayes' Theorem [29] as:  
\[f(\theta|\,x)=\frac{f(x\,|\,\theta)}{f(x)}\propto f(x\,|\,\theta)f(\theta) \tag{5}\]  
where, \(f(\theta\,|\,x)\) is the posterior pdf of the vector of parameters (\(\theta\)) _with_\(\theta^{T}=[\alpha_{1},\alpha_{2},\sigma]\) given the observed data (\(x\)); \(f(x)\) is the marginal pdf of the random variable \(x\); \(f(\theta)\) is the prior pdf of the model parameters; and \(f(x\,|\,\theta)\) is the likelihood of the model and contains the available information provided by the observed data:  
\[f(\theta|\,x)=\prod_{i=1}^{n}f(x_{i}\,|\,\theta) \tag{6}\]  
The results of the analyzed experimental data were used to develop a probabilistic linear model for the estimation of the crack growth rate as the dependent variable, and using the AE count rate as the independent variable. So, based on the observed correlation, the unknown parameters of the linear model described in Equation (2) are updated. An error term was added later to assess the model error.  
After the first steps of analyzing the experimental data (crack growth measurement, AE data filtration, and AE count rate calculations) were completed, the results were used to estimate the marginal and joint distribution of the unknown parameters in Equations (2) and (3) using the Bayesian regression using Equations (5) and (6). The software package WinBUGS [28] was used to perform the Bayesian inference. In this Bayesian inference, the likelihood function in Equation (6) for the observed independent crack data points (\(x_{i},y_{i}\)) can be expressed as a normal distribution:  
\[p(D|\alpha_{1},\alpha_{2},s)=\prod_{i=1}^{n}\frac{1}{s\sqrt{2\pi}}exp\left(- \frac{1}{2}\left(\frac{y_{i}-(\alpha_{1}x_{i}+\alpha_{2})}{s}\right)^{2}\right) \tag{7}\]  
where \(p(.)\) is the likelihood of all data in form of \(x_{i}=log(dc/dN)_{i}\); \(y_{i}=log(da/dN)_{i}\), and \(D\) is the data set of all pairs (\(x_{i},y_{i}\)) for \(i=1\) to \(n\) of the tests.
## 2 Model of an Active Hydraulic Mount and Frequency Band Definition
### Dynamics of the Active Hydraulic Mount and Frequency Band Definition  
An AHM has active and passive dynamics. Distinct features of the dynamics were studied to identify the key parameters. The study of the distinct features of passive dynamics lays the foundation for model simplification and decoupling of key parameters based on active dynamics.  
The passive dynamics refer to frequency response functions (FRFs) with the chassis side fixed and the actuator powered off. The input of the FRFs is the engine-side displacement, and the outputs are the reaction force of the engine side and the force transmitted to the chassis side. They are called the drive- and cross-point dynamics by definition. Cross-point dynamics are the primary path-transfer functions in the control of an AHM. The active dynamics refer to FRFs with the engine and chassis sides fixed. The input is the actuator current, and the outputs are the reaction force of the engine side and the force transmitted to the chassis side. The active dynamics from the actuator current to the force transmitted to the chassis side are the secondary path transfer functions in the control of the AHM-IT-DM-OCA.  
The AHM-IT-DM-OCA involves the resonance of two subsystems: the fluid channel and the mover. Experimental studies have shown that the natural frequency \(f_{\rm n3}\) of an actuator mover in the fluid-filled state is typically greater than 200 Hz, which is much higher than the natural frequency \(f_{\rm n2}\) of the fluid channel (typically less than 20 Hz). Research on \(f_{\rm n2}\) has been thoroughly conducted and can be expressed as Equation (2). For the forced vibrations of a 1-DOF system, the natural frequency \(f_{\rm n3}\) of the actuator mover in the fluid-filled state can be expressed using Equation (3). Since the two natural frequencies are far apart, the analytical bands are divided into low-, mid-, and high-frequency bands to simplify the inertia and damping forces in different frequency bands, as shown in Figure 3.  
\[f_{\rm n3}=\frac{1}{2\pi}\sqrt{\frac{A_{3}^{2}(K_{1}+K_{3})}{m_{3}}} \tag{3}\]  
where \(K_{\rm u1}\) is the bulk stiffness of the upper chamber surrounded by the main rubber spring and decoupler membrane and can be expressed as Equation (4) [22; 23]. \(\rho\) denotes the density of the fluid in the hydraulic mount.  
\[\frac{1}{K_{\rm u1}}=\frac{1}{K_{1}}+\frac{1}{K_{3}} \tag{4}\]  
Mathematical models were established and simplified for different frequency bands for active and passive dynamics. Nonlinear and linear mathematical models for mid- and low-frequency bands were constructed to investigate the distinct features of the dynamics, which can be expressed by the following equation:  
\[k^{*}=k(\cos\varphi+j\sin\varphi)=k^{\prime}+jk^{\prime\prime}=k^{\prime}+j \omega c \tag{5}\]  
where \(k^{*}\) is the dynamic stiffness, \(k\) is the dynamic stiffness modulus, \(\varphi\) is the loss angle, \(k^{\prime}\) is the in-phase dynamic stiffness, \(k^{\prime\prime}\) is the out-of-phase dynamic stiffness, and \(c\) is the viscous damping.
# Seismic Response and Mitigation Analysis of a Subway Station in the Site with Weak Interlayers
## 1 Introduction
However, in comparison to rubber bearing, friction pendulum bearing (FPB) is cost-effective, highly durable, and possess self-centering capabilities. In 1987, [PERSON] et al. [34] introduced FPB at the University of California, Berkeley. The bearing operates on the principle of a pendulum, extending the structural natural period by the swaying motion of the upper support and slider. The swaying motion can effectively dissipate seismic energy. In practical engineering applications, FPB have become widely employed. [PERSON] et al. [35] explored the application of FPB in bridge structures and found that the bearing effectively reduces deformation and damage to bridge structures under seismic forces. [PERSON] et al. [36] studied the mitigation seismic performance of FPB under near-fault motion and derived the optimal range for the friction coefficient of the bearing. [PERSON] [37] conducted seismic analyses on reinforced concrete frames fitted with FPB and observed that a lower friction coefficient leads to greater story rotations and residual displacements. However, many studies on seismic mitigation mechanisms have been focused on above-ground structures and the research of seismic mitigation of underground structure started relatively late. [PERSON] et al. [38] delved into the effectiveness of FPB in underground structures by drawing inspiration from the accomplishments in seismic mitigation research for above-ground structures. Based on the research findings of several scholars, it is evident that weak interlayers have detrimental effects on subway station structure deformations and structural forces. The seismic mitigation effects of the FPB in subway station structure situated within the weak interlayer site is not clear and needs further study. This paper proposes a seismic mitigation scheme with FPB on the top of the central column for the subway station structure situated within the weak interlayer site. This study focuses on a two-storey two-span subway station structure, employing the finite element software ABAQUS to establish a two-dimensional model of the soil and subway station structure. The impact of weak interlayers on the seismic response of the subway station structure and the effectiveness of FPB in improving the seismic performance of the subway station structure within the weak interlayer site is studied by time-history analysis.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 2 Numerical Simulation Programme  
### Project Overview  
The Yutianbao coal mining subsidence area is located in Wansheng Economic Development Zone in the southwest of Chongqing Municipality, which is situated in the hilly area on the upper reaches of the Yangtze River at the junction of Sichuan Basin and Yunnan-Guizhou. The terrain is complex and dominated by mountains, hills, and valleys, with an average elevation of 650 m. The rock formations in the area include tuff, coal beds, and marl, with a rock dip angle of 29\({}^{\circ}\) and a rich coal resource. The strata include the Longtan Formation of the Permian system, as well as the Changxing Formation, Feixianguan Formation, and Jialingjiang Formation of the Triassic system. Yutianbao coal mine was put into production in 1958 and was shut down in 2016 due to the structural adjustment of national industrial enterprises to resolve overcapacity. The mining depth was from 240 m to \(-\)330 m, with a mining thickness of 2 m, and underground mining was adopted. The mined-out mining area was 3.69 km\({}^{2}\), the mining area was 10.74 km\({}^{2}\), and the proportion of the mined-out mining area was 34.35%. It was a small-scale coal mine. Coal mining destroyed the stress balance, which led to subsidence of the overlying rocks, destroyed the rock structure, and produced ground collapse and cracks. The cracks are perpendicular to the ground surface with an underexposed bottom, presenting an upper narrow and lower wide shape. The subsidence is extensive and is mainly distributed in the mining area and its surrounding areas. Ground elevation is generally lowered, with localized waterlogging in some areas. The surface water level is normal, and the water is weakly alkaline and slightly corrosive. Groundwater resources are abundant and are mainly derived from precipitation and surface water infiltration, and the water level is relatively high. Coal mining has led to surface subsidence, aquifer destruction, and water waste, affecting groundwater flow and storage. Nine subsidence monitoring points are arranged within the region, located respectively in residential clusters at lower elevations within the subsidence-affected area, as shown in Figure 1.
## 5 Simulation Results
### Simulation of SUUIL Model
At this time, the shadow fading has a major influence on the UAV channel. In long-distance communication, that is, when the signal-noise ratio is 0 dB, the system uncertainty value is the smallest, and the influence of shadow on BER can be ignored, but the influence of small-scale fading needs to be considered. Therefore, it is not difficult to find that the channel characteristics change as the communication distance changes. That is, when the altitude changes from ultra-low altitude to low altitude, different channel models should be established for the change of channel characteristics to improve channel performance.
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 2 Related Work
### Summary  
With the development of deep learning, facial expression recognition technology has achieved excellent results. However, it still faces the issue of insufficient robustness in complex environments, and its reliance on large annotated datasets limits its generalization ability. The OFER method reduces the impact of occlusions on recognition accuracy through robust feature extraction and sub-region analysis. However, many methods rely on manually designed features or region divisions, making it difficult to adapt to complex occlusion scenarios.  
Our work builds upon the strengths of both [14; 15]. Specifically, we adopt the RB-loss proposed in [15] to optimize attention weights, enabling the fusion of key regional features with fine-grained features to enrich feature representations. Additionally, we employ the con-feature loss to encourage mutual guidance between fused features and global features, helping the network learn more consistent representations and thereby improving recognition accuracy. Based on these insights, we propose a method that incorporates multi-scale and fine-grained branches to extract global, regional, and fine-grained features from input images. This design facilitates a more comprehensive and expressive feature representation.
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 3 Methodology
### Dataset Partitioning
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Set** & **Partition Ratio** & & **MSE1 (\(\mathbf{\Omega\times 10^{-3}}\))** & **SAD\(\downarrow\)** & **Grad\(\downarrow\)** & **Conn\(\downarrow\)** \\ \hline \multirow{4}{*}{Composition-1k} & \multirow{2}{*}{1/32} & SupOnly & 33.9 & 83.5 & 41.6 & 63.4 \\  & & Tri-SSL & **26.4** & **68.2** & **38.1** & **54.5** \\ \cline{2-6}  & & \multirow{2}{*}{1/16} & SupOnly & 13.8 & **46.7** & 20.3 & 39.9 \\  & & Tri-SSL & **9.8** & **39.8** & **17.1** & **33.7** \\ \cline{2-6}  & & \multirow{2}{*}{1/8} & SupOnly & 7.4 & 34.2 & 14.7 & 29.1 \\  & & Tri-SSL & **7.1** & **33.3** & **13.7** & **28.2** \\ \hline \multirow{4}{*}{Distinction-646} & \multirow{2}{*}{1/32} & SupOnly & 42.9 & 63.6 & 44.6 & 37.9 \\  & & Tri-SSL & **35.4** & **58.4** & **36.7** & 39.7 \\ \cline{1-1} \cline{2-6}  & & \multirow{2}{*}{1/16} & SupOnly & 17.6 & 38.1 & 19.4 & 29.8 \\  & & Tri-SSL & **15.4** & **36.5** & **18.4** & **29.6** \\ \cline{1-1} \cline{2-6}  & & \multirow{2}{*}{1/8} & SupOnly & 12.4 & 31.3 & 16.3 & 25.0 \\  & & Tri-SSL & 13.0 & 33.3 & **15.9** & 26.5 \\ \hline \multirow{4}{*}{AIM-500} & \multirow{2}{*}{1/32} & SupOnly & 22.8 & **24.5** & 15.9 & 13.6 \\  & & Tri-SSL & **21.6** & **23.9** & 17.6 & **12.7** \\ \cline{1-1} \cline{2-6}  & & \multirow{2}{*}{1/16} & SupOnly & 22.6 & 25.4 & 17.5 & 16.0 \\  & & Tri-SSL & **15.5** & **18.8** & **14.2** & **12.8** \\ \cline{1-1} \cline{2-6}  & & \multirow{2}{*}{1/8} & SupOnly & 15.8 & 19.7 & 15.4 & 11.8 \\  & & Tri-SSL & **14.6** & **18.4** & **14.4** & 11.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The quantitative results of dataset partitioning experiments based on the Matterformer on various test sets. The \(\downarrow\) indicates that the lower the value, the better. The _SupOnly_ indicates supervised training using only labeled data.
# Incorporating Word Significance into Aspect-Level Sentiment Analysis
## 3 Proposed Model
### Contextual Layer  
We feed the vectors from the previous layer into two bidirectional LSTMs to capture temporal interactions in the context and aspects words respectively. Each Bi-LSTM is created using two stacked LSTMs to learn long term dependencies and avoid vanishing gradient to produce hidden states  
Figure 1: The architecture of the proposed ISAN model.  
\(\overrightarrow{h}_{s}\in R^{n\times d}\) where d is the hidden states dimension. Given embedding x at each time step t, the update process of the forward LSTM can be formalized as follows:  
\[i_{t}=\sigma(\overrightarrow{W_{i}}\cdot[\overrightarrow{h}_{t-1}, \overrightarrow{x}_{t}]+\overrightarrow{b_{i}}) \tag{1}\]  
\[f_{t}=\sigma(\overrightarrow{W}_{f}\cdot[\overrightarrow{h}_{t-1}, \overrightarrow{x}_{t}]+\overrightarrow{b}_{f}) \tag{2}\]  
\[o_{t}=\sigma(\overrightarrow{W_{o}}\cdot[\overrightarrow{h}_{t-1}, \overrightarrow{x}_{t}]+\overrightarrow{b_{o}}) \tag{3}\]  
\[g_{t}=tanh(\overrightarrow{g}_{g}\cdot[\overrightarrow{h}_{t-1}, \overrightarrow{x}_{t}]+\overrightarrow{b_{g}}) \tag{4}\]  
\[\overrightarrow{c}_{t}=f_{t}*\overrightarrow{c}_{t-1}+i_{t}*g_{t} \tag{5}\]  
\[\overrightarrow{h}_{t}=o_{t}*\tanh(\overrightarrow{c}_{t}) \tag{6}\]  
where \(\sigma\) is the sigmoid activation function to control the outflow of irrelevant information and \(i_{t},f_{t}\) and \(o_{t}\) are the input forget and output gates respectively to control the inflow of information. \(\overrightarrow{W_{i}},\overrightarrow{W_{f}},\overrightarrow{W_{o}}, \overrightarrow{W_{g}}\in R^{d\times 2d}\) are weights \(\overrightarrow{b_{i}}\), \(\overrightarrow{b_{f}}\), \(\overrightarrow{b_{o}}\), \(\overrightarrow{b_{g}}\in R^{d}\) are biases, where d is the hidden dimension size. The symbol \"*\" is element-wise multiplication, the symbol \".\" is matrix multiplication. The backward LSTM performs a similar process to produce \(\overleftarrow{h_{s}}\), resulting in \(h_{s}=[\overrightarrow{h_{s}},\overleftarrow{h_{s}}]\in R^{n\times 2d}\) as the output of the BiLSTM. Given sentence s and aspect word a, we separately perform two BiLSTMs for the sentence output as \(h_{s}\in R^{n\times 2d}\) and aspect output as \(h_{a}\in R^{m\times 2d}\).
# Scan Matching by Cross-Correlation and Differential Evolution
## 2 Related Work
Another work used differential evolution as part of a feature-based scan matching algorithm that extracted lines, planes and spheres from laser range scans [40]. Other nature-inspired methods such as the harmonic search algorithm were used for differential scan matching as well [41]. This work proposes a novel scan matching algorithm based on a combination of two recently introduced approaches. The cross-correlation approach [42; 43] is adopted for the assessment of laser range scans' similarity and the differential evolution is used for the search of accurate laser range scan transformation parameters [44]. The evaluation of the proposed scan matching strategy is performed in a series of simulation experiments with the help of a software framework introduced in [45].
# Dog Identification Method Based on Muzzle Pattern Image
###### Abstract  
Currently, invasive and external radio frequency identification (RFID) devices and pet tags are widely used for dog identification. However, social problems such as abandoning and losing dogs are constantly increasing. A more effective alternative to the existing identification method is required and the biometrics can be the alternative. This paper proposes an effective dog muzzle recognition method to identify individual dogs. The proposed method consists of preprocessing, feature extraction, matching, and postprocessing. For preprocessing, proposed resize and histogram equalization are used. For feature extraction algorithm, Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scaling Keypoints (BRISK) and Oriented FAST, and Rotated BRIEF (ORB) are applied and compared. For matching, Fast Library for Approximate Nearest Neighbors (FLANN) is used for SIFT and SURF, and hamming distance are used for BRISK and ORB. For postprocessing, two techniques to reduce incorrect matches are proposed. The proposed method was evaluated with 55 dog muzzle pattern images acquired from 11 dogs and 990 images augmented by the image deformation (i.e., angle, illumination, noise, affine transform). The best Equal Error Rate (EER) of the proposed method was 0.35%, and ORB was the most appropriate for the dog muzzle pattern recognition.  
dog biometrics; muzzle pattern; identification; feature extraction; image processing +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Image Processing  
#### 2.4.1 Initial Processing  
With inclusion of the PME, three sub-images are observed in every B-scan, each corresponding to three different bulk scattering angles. To align the images for analysis, the first sub-image (Pathlength 1) is used as a reference. Sub-images are selected manually, each with the same height (typically ~150 pixels). Then, Pathlengths 2 and 3 are shifted in the z-direction (axially) to align with Pathlength 1. The MATLAB function \"_inregister_\" is set to perform only axial shifting for precise alignment. Simple axial shifting, in contrast to other more complex methods, is appropriate for this system because it  
Figure 8: A dual axis MEMS mirror located at the focal plane raster scans through a range of bulk backscattering angles. The grayscale beam is the incident beam, and the green beam is an example reflected beam. The green beam, reflected with some angular deviation due to the dual-axis MEMS mirror, depicts more light travelling through the glass of the PME than the incident beam had. One would expect that the resultant images would have higher intensity in Pathlengths 2 and 3 (H\({}_{2}\) and H\({}_{3}\)) and lower intensity in Pathlength 1 (L) compared to a beam that is reflected with no angular deviation.  
is known a priori that each A-scan location contains the exact same retinal cross section in the three sub-images. Any image registration that goes beyond simple axial shifting risks mixing A-scans across different locations and corrupting the scattering angle analysis. After the sub-images are aligned, each pathlength sub-image is saved as a tiff stack. This results in three image stacks with size 150 (height) \(\times\) 512 (width) \(\times\) 4096 (depth). These image stacks are stored without any image compression to maintain signal fidelity.  
This first initial processing step outputs three aligned image stacks. With these three stacks, intensity processing, speckle processing, and segmentation may be performed before the four subsequent analysis steps for feature extraction.
## 3 Results and Discussion  
### XRD-Analysis  
The atomic arrangement, lattice characteristics and crystalline size can all be determined using the crucial method of X-ray diffraction [25].  
Figure 2 shows the XRD pattern of the synthesis of selenium nanoparticles (Se NPs); the XRD pattern shows multi peaks with different intensities at 23.4\({}^{\circ}\), 29.6\({}^{\circ}\), 41.2\({}^{\circ}\), 43.5\({}^{\circ}\), 45.3\({}^{\circ}\), 51.6\({}^{\circ}\), 55.8\({}^{\circ}\), 61.5\({}^{\circ}\) and 65.1\({}^{\circ}\), which correspond to planes \(\left(100\right)\), \(\left(101\right)\), \(\left(110\right)\), \(\left(102\right)\), \(\left(111\right)\), \(\left(201\right)\), \(\left(003\right)\), \(\left(022\right)\) and \(\left(210\right)\), respectively. This behavior refers to Se NPs and full agreement with JCPDS (01-073-0465); our result is in good agreement with the published result by [PERSON] et al. in 2021 [26]. The high crystallinity is very clear, and the phase structure of nanoparticles is hexagonal.  
The average crystalline size (D) of Se NPs was calculated from Bragg reflections of full width half maximum (FWHM) by Debye-Scherrer equation [27].  
\[\mathrm{D}=\frac{0.9\lambda}{\beta\cos\theta} \tag{2}\]  
where \(\lambda=1.5406\) is the wavelength of the X-ray and \(\beta\) is the full width half maximum (FWHM) of the peak in radians, and \(\theta\) is the Bragg angle.  
The average crystallite size of nanoparticles (D) is equal to 24.97 nm. The crystallite size of Se NPs is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Sample & **20 (\({}^{\circ}\))** & **hdl** & **FWHM(\({}^{\circ}\))** & **20 (Rad)** & **FWHM (Rad)** & **D (nm)** \\ \hline \multirow{8}{*}{Se NPs} & 23.4289 & (100) & 0.2362 & 0.204 & 0.004 & 34.336 \\ \cline{2-6}  & 29.6254 & (101) & 0.1968 & 0.259 & 0.003 & 41.739 \\ \cline{2-6}  & 41.2558 & (110) & 0.3149 & 0.360 & 0.005 & 26.946 \\ \cline{2-6}  & 43.5642 & (102) & 0.2362 & 0.380 & 0.004 & 36.206 \\ \cline{2-6}  & 45.3221 & (111) & 0.3936 & 0.396 & 0.007 & 21.864 \\ \cline{2-6}  & 51.6654 & (201) & 0.3149 & 0.451 & 0.005 & 28.018 \\ \cline{2-6}  & 55.8639 & (003) & 0.9446 & 0.488 & 0.016 & 9.515 \\ \cline{2-6}  & 61.5571 & (022) & 0.9446 & 0.537 & 0.016 & 9.785 \\ \cline{2-6}  & 65.1915 & (210) & 0.576 & 0.569 & 0.010 & 16.364 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The crystallite size of Se NPs.