esa-sceva/satcom-chunk-collection huggingface dataset:
# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
## 3 BoVW-Based Cattle Identification
### Feature Extraction  
#### 3.1.1 Speeded-Up Robust Features (SURF)  
The speeded-up robust features (SURF) descriptor [24] was developed as an alternative to the SIFT descriptor. Briefly, the SURF descriptor starts by constructing a square region around points of interest, which is oriented along its main orientation. The size of this square is 20s, where \(s\) is the scale at which the point of interest is detected. The region inside the square is divided into smaller \(4\times 4\) subregions and the Haar wavelet responses in the horizontal \(d_{x}\) and vertical \(d_{y}\) directions are computed for each subregion at \(5\times 5\) sampled points, as shown in Figure 1.  
To improve the robustness of the descriptor against localization errors and some geometric deformations, these responses are weighted with a Gaussian window. The wavelet responses \(d_{x}\) and \(d_{y}\) are summed for each subregion, which, with the sum of their absolute values, form entries of the feature vector \(F_{v}\); that is,  
\[F_{v}=(\sum d_{x},\sum|d_{x}|,\sum d_{y},\sum|d_{y}|) \tag{1}\]  
This procedure is repeated for all the \(4\times 4\) subregions, resulting in a feature descriptor of \(4\times 4\times 4=64\) dimensions. To reduce illumination effects and make the descriptor invariant to region size, the feature descriptor is normalized to a unit vector. Applying restrictions (e.g., the number of divisions inside the square region) to the regular descriptor (i.e., SURF-64) \(F_{v}\) results in several extended versions of SURF, such as SURF-36, SURF-128, and U-SURF [24; 41]. In this work, the SURF feature descriptor (regular descriptor of length 64) is adopted to describe the image patches due to its balance of computing efficiency and representation capacity. It uses a 64-dimensional feature vector to describe the local features; in contrast, SIFT uses a 128-dimensional feature vector. Additionally, the SURF feature descriptor is more robust to various image perturbations than the SIFT local feature descriptor.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 3 Methods
### Diffusion  
To transform the distribution of the original building point cloud into a Gaussian distribution suitable for modeling, we follow the process shown in Figure 8. Over time steps, we gradually add Gaussian noise with known parameters, dispersing the points from the original distribution into a disordered point set that approximates a Gaussian distribution.  
Figure 6: Building constraint contour diagram: (**a**) contour constraint polygon \(U^{(R)}\); (**b**) contour constraint polygon \(U^{(W)}\).  
Figure 7: The contour constraint possibility region \(C^{(Z_{a})}\) and the contour constraint necessity region \(M^{(Z_{a})}\).  
\(X^{(0)}=\left\{x_{i}^{(0)}\right\}_{i=1}^{N}\) make up the input-constructing point cloud, where each point is independently sampled from the point distribution. The process of adding random Gaussian noise at each time step is modeled as a Markov chain:  
\[q\Big{(}X^{(1:T)}\mid X^{(0)}\Big{)}=\prod_{t=1}^{T}q\Big{(}X^{(t)} \mid X^{(t-1)}\Big{)}, \tag{5}\] \[q\Big{(}X^{(t)}\mid X^{(t-1)}\Big{)}=\mathcal{N}\Big{(}X^{(t)} \mid\sqrt{1-\beta_{t}}X^{(t-1)},\beta_{t}I\Big{)}, \tag{6}\]  
where \(q\Big{(}X^{(t)}\mid X^{(t-1)}\Big{)}\) is the Markov diffusion kernel, \(\beta_{t}\) is the hyperparameter that controls the variance scheduling of the diffusion process, and \(t=\{1,\dots,T\}\).
## 9 Efficacy and Impact of Nanomaterials on Biomass  
According to [PERSON] et al., [116] nanoparticles synthesized through electrolysis exhibit antibacterial activities against both _Gram Positive bacteria_ and _Gram Negative bacteria_. Antibacterialativity of copper is enhanced by an alteration in surface area to volume ratio. Antibacterial activity of copper NPs synthesized by electrolysis is higher than copper NPs synthesized by using a chemical reduction method against _Escherichia coli_. When using electric power for the synthesis of copper NPs, antimicrobial activity is increased. Availability of the chemicals required for the synthesis of NPs is easily accessible, cheap and non-toxic. Minimum infrastructure is required for the technology implementation. It is experimentally proven that this material could be used in antibacterial packaging, water purification, air filtration, air quality management, etc. For biochemical conversion of biomass, microorganisms play a vital role.  
Various nanomaterials affect the performance of AD. The mechanism of interaction of these particles is very complex and the way they interact with the biomass, the process of conversion, overcoming the adverse effects and optimizing the positive effect, all need to be understood better. The rate of AD can be influenced by the particle size because it affects the surface area for biomass biodegradation. Regardless of the chemical constituents, all nanoparticles possess an extremely high surface area to volume ratio. Therefore, the atoms of the surface and capping agents dominate the physical properties of nanoparticles. For applications such as catalysis, a high surface area to volume ratio is vital. The greater the surface of the same material, the greater is the reactivity.  
Different nanoparticles stimulate different responses and interactions among microorganisms. Although a few studies concluded that copper NPs have significant potential as bactericidal agents, NPs such as iron oxide, silica and its oxides, gold and platinum have not shown a bactericidal effect in _Escherichia coli_ studies. By stimulating the bacterial growth, magnetite NPs (Fe\({}_{3}\)O\({}_{4}\) NPs) can enhance methane production. Another application of NPs is as a fuel catalyst for the reduction of harmful engine combustion emissions. Recent studies found that the AD process, biodegradation and nitrification are inhibited by NPs [107; 117]. The particle size, time and concentration determine whether there is enhancement, inhibition or adverse effects of energy conversion.  
Biomass, such as municipal solid waste and agriculture, still possesses a huge potential scope in relation to nanomaterials. It is very important to find out the best possible use of nanoparticles in bioenergy systems. This review could serve as an important tool for the future in order to carry out further research on 'nanotechnology in bioenergy'.
## 3 Method
### Frequency-Adaptive Fusion Module (FAF)  
The frequency-adaptive fusion (FAF) module is one of the core innovations of UMFNet. It addresses the limitations of static frequency processing in low-light object detection. Traditional multi-scale fusion methods [19] apply fixed filtering operations regardless of the input conditions, resulting in poor performance under complex illumination conditions. FAF overcomes this limitation through steerable spectral decomposition and attention-driven recombination. The FAF module is shown in the Figure 2.
# Steel Surface Defect Classification Based on Small Sample Learning
## 2 Principles and Methodology
### Feature Extraction Network (FEN)
#### 2.1.2 Mobile Net  
In order to solve the problem of a sharp increase in the number of parameters caused by the deepening of the model, [PERSON] [21] proposed a lightweight network with low latency and high response, named MobileNet. It mainly reduces the amount of model parameters by converting the standard convolution to depthwise separable convolution.  
Figure 1: Residual block.  
Depthwise separable convolution can be divided into two smaller convolutions, namely, depthwise convolution and pointwise convolution.  
As shown in Figure 2, the convolution kernel of standard convolution acts on all input channels. However, every convolution kernel of the depthwise convolution corresponds to one input channel. There is little difference between pointwise convolution and standard convolution, except for a \(1\times 1\) convolution kernel of pointwise convolution.  
Assume that the size of the input feature image is \(D_{F}\cdot D_{F}\cdot M\) (where \(D_{F}\) represents the width and height of the feature image and \(M\) is the number of channels), the size of the output feature image is \(D_{F}\cdot D_{F}\cdot N\) (where \(N\) is the number of convolution kernels) and the convolution kernel size is \(D_{K}\cdot D_{K}\).  
Then the computational cost of the standard convolution is as follows:  
\[N_{std}=D_{K}\cdot D_{K}\cdot M\cdot N\cdot D_{F}\cdot D_{F}, \tag{2}\]  
The computational cost of the depthwise convolution is as follows:  
\[N_{dw}=D_{K}\cdot D_{K}\cdot M\cdot D_{F}\cdot D_{F}, \tag{3}\]  
Figure 2: (**a**) Standard convolution kernel; (**b**) Depthwise Convolutional Filters; (**c**) Pointwise Convolutional Filters.  
The computational cost of pointwise convolution is as follows:  
\[N_{pw}=M\cdot N\cdot D_{F}\cdot D_{F}, \tag{4}\]  
The total computational cost of depthwise separable convolution is as follows:  
\[N_{ds}=N_{dw}+N_{pw}=D_{K}\cdot D_{K}\cdot M\cdot D_{F}\cdot D_{F}+M\cdot N\cdot D _{F}\cdot D_{F}, \tag{5}\]  
The ratio of the computational cost of the depthwise separable convolution to the computational cost of the standard convolution is as follows:  
\[\frac{N_{ds}}{N_{std}}\frac{D_{K}\cdot D_{K}\cdot M\cdot D_{F}\cdot D_{F}+M \cdot N\cdot D_{F}\cdot D_{F}}{D_{K}\cdot D_{K}\cdot M\cdot N\cdot D_{F}\cdot D _{F}}=\frac{1}{N}+\frac{1}{D_{K}^{2}}, \tag{6}\]  
In this paper, the standard Mobile Net structure is adopted, and the size of the convolution kernel is \(3\times 3\). Theoretically, this method can reduce the computational cost compared with standard convolution, which is beneficial to the small sample learning of the neural network.
## 3 Numerical Modeling
[25], the stress-based composite failure criteria, and the orthotropic energy-based softening [26]. For the equation of state, the pressure is defined by  
\[p=p(\varepsilon_{vol},e)-1/3(C_{11}+C_{21}+C_{31})\varepsilon_{11}^{d}-1/3(C_{ 12}+C_{22}+C_{32})\varepsilon_{22}^{d}-1/3(C_{13}+C_{23}+C_{33})\varepsilon_{3 3}^{d} \tag{5}\]  
where \(C\) are coefficients of the stiffness matrix and \(\varepsilon_{11,22,33}^{d}\) are the deviatoric strains in the principal directions. The pressure contribution from the volumetric strain is expressed as  
\[p(\varepsilon_{vol},e)=p_{r}(v)+\Gamma(v)/v[e-e_{r}(v)] \tag{6}\]  
where \(v\) is the volume, \(e\) is the internal energy and \(\Gamma(v)\) is the Gruneisen coefficient. \(p_{r}(v)\) and \(e_{r}(v)\) are the reference pressure and the internal energy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Values** \\ \hline Density (g/cm\({}^{3}\)) & \(\rho_{0z}\) & 2.508 \\ Bulk modulus (GPa) & \(K_{1}\) & 233 \\ Pressure coefficient (GPa) & \(K_{2}\) & \(-\)593 \\ Pressure coefficient (GPa) & \(K_{3}\) & 2800 \\ Shear modulus (GPa) & \(G\) & 197 \\ Hugoniot elastic limit (HEL) (GPa) & \(HEL\) & 19.0 \\ Intact strength coefficient & \(A\) & 0.9637 \\ Intact strength exponent & \(N\) & 0.67 \\ Strain rate coefficient & \(C\) & 0.005 \\ Fracture strength coefficient & \(B\) & 0.7311 \\ Fracture strength exponent & \(M\) & 0.85 \\ Maximum fracture strength & \(S_{f\text{max}}\) & 0.2045 \\ Damage coefficient & \(D_{1}\) & 0.001 \\ Damage exponent & \(D_{2}\) & 0.5 \\ Bulking factor & \(\beta\) & 1.0 \\ Tensile strength (GPa) & \(\sigma_{hyd}\) & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters for boron carbide used in Johnson–Holmquist’s model [19]. The quadratic yield surface proposed by [PERSON] et al. (2015) is used as the strength model. \[ \begin{array}{ll}f(\sigma_{ij})&=a_{11}\sigma_{11}^{2}+a_{22}\sigma_{22}^{2}+a _{33}\sigma_{33}^{2}+2a_{12}\sigma_{11}\sigma_{22}+2a_{23}\sigma_{22}\sigma_{33} +2a_{13}\sigma_{11}\sigma_{33}\\ &+2a_{44}\sigma_{23}^{2}+2a_{55}\sigma_{31}^{2}+2a_{66}\sigma_{12}^{2}=k\\ \end{array}  \tag{7}\]  
where \(a_{ij}\) are the plasticity coefficients of the material, \(\sigma_{ij}\) are stresses in the principal directions and \(k\) is a state variable. The failure model is based on a combined stress criterion and is initiated when  
\[\left(\frac{\sigma_{ii}}{S_{ii}(1-D_{ii})}\right)^{2}+\left(\frac{\sigma_{ij}}{ S_{ij}(1-D_{ij})}\right)^{2}+\left(\frac{\sigma_{ki}}{S_{ki}(1-D_{ki})} \right)^{2}\geq 1;i,j,k=1,2,3 \tag{8}\]  
where \(S_{ii}\) is the failure strength. The damage parameter \(D_{ii}\) is defined by  
\[D_{ii}=\frac{L\sigma_{ii,f}\varepsilon_{cr}}{2G_{ii,f}} \tag{9}\]  
where \(L\) is the characteristic cell length, \(\varepsilon_{cr}\) is the crack strain and \(G_{ii,f}\) is the fracture energy. All of the UHMW-PE composite material parameters are given in Table 4. (20)
## 2 Communication Architecture
The service layer is responsible for transmitting specific service requirements to the platform layer for processing the uploaded load information and sending control instructions to the intelligent perception devices at the terminal layer. Key Technologies Supporting the Communication Network Architecture of Data Center Management Systems
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Copper Ion Removal Effect Examination Index  
The adsorption effect of RSAC particles on copper ions is mainly reflected in two aspects, namely the copper ion removal rate and the adsorption amount. Removal rate (\(\eta\)) and adsorption amount (\(\Gamma\)) are used in this paper to investigate the copper ion removal effect and the adsorption performance of RSAC particles, respectively.  
\[\eta=\frac{\text{C}_{0}-\text{C}_{\text{e}}}{\text{C}_{0}}\times 100\% \tag{2}\]  
\[\Gamma=\frac{\left(\text{C}_{0}-\text{C}_{\text{e}}\right)\text{V}}{\text{m}} \tag{3}\]  
In Equations (2) and (3), \(\eta\) is the removal rate of copper ions (%), C\({}_{0}\) is the initial copper ion concentration of the solution (mg/L), C\({}_{\text{e}}\) is the concentration of copper ions in solution at equilibrium (mg/L), \(\Gamma\) is the amount of copper adsorbed per unit mass of adsorbent (mg/g), V is the volume of the solution (L), and m is the mass of adsorbent (g).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Absorbance A** & **0.019** & **0.020** & **0.374** & **0.563** & **0.751** \\ \hline Copper ion concentration (mg/L) & 0 & 2 & 4 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Measurement results for the standard series.
## 2 Model Tests of Fault Dislocation
### Monitoring System Deployment and Testing Process  
In order to quantitatively analyze the deformation of the foundation and subgrade induced by the fault dislocation, as shown in Figure 3, a steel bracket is set to fix an industrial camera on the beam above the bracket. The MV-CS200-10 UC (Hikrobot, Hangzhou, China) is selected as the industrial camera, with a 5472 \(\times\) 3648 px resolution. During the test, the industrial camera takes photos at an interval of 0.5 s to record the fault dislocation deformation process. After the test, a PIV analysis of the photos is performed. PIV (particle image velocimetry) [19; 20; 21] is an image-processing technology that can accurately and quantitatively analyze the displacement of soil particles. In geological engineering, PIV has been widely used in the physical model test of fault zone dislocation sandboxes [22], but it is rarely used in the model test of the influence of fault dislocations on subgrades. By dividing a large number of pixels in the photo taken at time t into several interpretation areas, recording the location of soil particles in each interpretation area, and searching for the location of soil particles in the surrounding area of the same interpretation area in the photo taken at a particular time (t + \(\Delta\)t), the displacement vectors of soil particles at different positions in the model can be quantitatively revealed.  
Before starting the test, the motor speed should be set on the motor control panel. Photos should be taken to record the soil deformation after the tests start. The test should be stopped when the displacement of the fault dislocation plate is 40 mm. All the test faults in this paper are sinistral strike-slip faults. The fault dislocation rate is 0.15 mm/s.  
Figure 2: Test conditions: (a) no subgrade; (b) \(\alpha=90^{\circ}\); (c) \(\alpha=45^{\circ}\).
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Analysis
#### 2.3.1 RGB Imagery  
RGB images were processed using a custom script in MATLAB(r)[27], as follows:  
1. The reference length (obtained from the ruler) in each image was used to scale the image pixels, creating a calibration value in cm\({}^{2}\).
2. Manual selection of the region of interest (ROI) containing the relevant bunch/bunches was undertaken. ROIs were strategically digitised so as to capture minimal background.
3. The masked RGB images were then converted to the HSV (hue, saturation, value) colour space and segmented using MATLAB's [27]_Colour Thresholder_ app, part of the _Image Processing Toolbox(tm)_. It was visually evident when selecting the threshold values that the lighting conditions influenced the values. Separate threshold values were therefore determined for the respective experiments. Threshold values were computed using a random training sample from the specific dataset; equivalent to 25% of the experiment's dataset.
4. After the image segmentation process, the number of segmented pixels was determined (adaption of the pixel count metric [9]) and converted into a pixel area (cm\({}^{2}\)) using the calibration value.  
Figure 6 illustrates the image segmentation process for experiment one at bunch-level (Figure 6a,b), and experiment three at plant-level (Figure 6c,d).  
For experiment three, segmentation produced a segmented bunch area image (Figure 6d) for a single side of the vine, with a similar image for the reverse side of the vine. To obtain a single area per vine, the _Total Bunch Area of Vine (TBAV)_ was calculated, as follows:  
\[TBAV=(Ae+Aw)/2 \tag{1}\]  
where \(Ae\) was the area of the east-facing side of the vine, and \(Aw\) represented the vine's west-facing area. The \(TBAV\) was in cm\({}^{2}\). Segmentation success was assessed on the testing subset (25% of the total dataset). A confusion matrix was computed for each experiment, using the predicted values from the segmented binary image versus the actual values of the original RGB image. F1-score and accuracy metrics calculated from the confusion matrix evaluated the segmentation accuracy.  
Figure 5: Experiment three data examples at plant-level. RGB imagery of FC (**a**) and LR (**b**) treatments. RGB-D (Kinect point cloud) of FC (**c**) and LR (**d**) treatments.
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 3 Results and Discussion
### Influence of Irradiation Treatment on Structural Properties of Miscanthus Biomass
#### 3.3.5 1H and 13C NMR Analysis  
1H and 13C NMR analyses were carried out to evaluate the chemical structural changes of _Miscanthus_ biomass after irradiation treatment, and the data are shown in Figure 5. For the untreated sample (0 kGy), the chemical shift at 5 ppm of 1H NMR profiles was ascribed to the H+ proton signal peak of hemicellulose, whereas in the cases of irradiated samples, the 1H proton signal peak was apparently enlarged and shifted to 5.2 ppm with the increase in absorbed doses from 400 to 1200 kGy. From the 13C NMR profiles, the peaks intensity of the glucosyl unit at 21.3, 74.6, and 88.7 ppm reduced with the increase in absorbed doses. However, the intensity of the peak at 56.6 ppm increased with the increase in the absorbed dose. These small variations in carbon chemical shifts of irradiated samples may lead to intermolecular C-C bond cleavage in backbone structure. In combination with the FTIR, and 1H and 13C-NMR profiles, we concluded that inter-molecular hydrogen bonds and carbon-carbon bond cleavage of biomass are caused by high absorbed doses [13; 19].
###### Abstract  
The precoder obtained using the traditional singular value decomposition (SVD) method for legitimate user's channel, while achieving the highest spectral efficiency for the legitimate user, cannot defend against eavesdropping attacks, thus posing a security vulnerability. This paper investigates the millimeter wave (mmWave) secure beam hybrid precoding technology and proposes a generalized singular value decomposition (GSVD)-based secure beam hybrid precoding algorithm, termed GSVD-Sparsity, leveraging the sparsity of the mmWave beamspace channel. The algorithm selects the most powerful paths from the legitimate user's beamspace channel representation and utilizes their corresponding angle information to construct a radio frequency (RF) precoder. It then constructs a hybrid precoder that closely approximates the optimal digital precoder derived from the GSVD-based scheme in a fully digital system. The simulation results indicate that, compared to the SVD-based scheme that focuses on spectral efficiency, the GSVD-based precoding scheme can form secure beams in a fully digital system. Under the condition that the legitimate user experiences a certain loss in the received signal-to-noise ratio (SNR), the eavesdropper is unable to correctly reconstruct the original constellation diagram, ensuring the scheme has strong anti-eavesdropping capabilities. In a hybrid precoding system, the low-complexity GSVD-Sparsity algorithm can achieve a spectral efficiency close to that of the GSVD-based scheme in a fully digital system while maintaining anti-eavesdropping capabilities.  
hybrid precoding; secure beam; beamspace channel; sparsity; GSVD
## 2 Related Work
Classic text region detection models [11; 12] tend to divide text region detection into multiple stages, separately detecting sub-sections of the whole region before merging, which is insufficiently accurate and highly time-consuming. In contrast, the EAST model proposed by [PERSON] et al. [18] simplifies the intermediate process steps and achieves end-to-end text region detection, which greatly improves text detection accuracy and speed, and can detect quadrilateral regions with a large tilt angle in street signs. However, the EAST model has a small receptive field and is not effective in detecting long text regions. Therefore, this paper presents an improved EAST model, modified from the original EAST model in the following aspects: an improved shrinking algorithm for the bounding box while analyzing text region shape features, an added feature enhancement module (FEM) to increase the model's receptive field and ability to extract features for long text regions, and a Focal loss function to address imbalances between numbers of positive and negative samples and hard versus easy samples for text region detection. The effect of the improved EAST model is then discussed on the public ICDAR2015 and the SSTD datasets.