esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation
### Results and Discussion  
This article primarily focuses on the contrast enhancement techniques applied to coronary angiography images, with an emphasis on improving their quality by addressing issues such as low contrast. This study explores various techniques such as the Retinex Algorithm [30; 31], Contrast Stretching (CS) [32], Gamma Correction (GC) [33], Histogram equalization (HE) [34], Local Bright Contrast (LBC) [35], Local Transformation Histogram Equalization (LTHE), Optimized maximum contrast (OMC) [36], Piecewise Linear Transformation (PLT), Sigmoid, Adaptive Histogram Equalization (AHE) [37], Bi-Histogram Equalization (BHE), Brightness Bi-Histogram Equalization (BBHE) [38], Contrast Limited Adaptive Histogram Equalization (CLAHE) [37], Dualistic Sub Image Histogram Equalization (DSIHE) [39], Logarithmic Transform (LT), Multi Histogram Equalization (MHE), Multi-Scale Retinex with Color Restoration (MSRCR) [40], Global Transformation Histogram Equalization (GTHE) [41], and Fast Local Laplacian Filter (FLLF) [10], and assesses their efficacy in enhancing the images before further analysis or diagnosis. This section of the article discusses the experiments conducted to evaluate the performance of noise removal and low contrast identification. It is divided into three subsections: simulation setup, visual analysis, and statistical analysis.
## 2 The UKF Detection of PQDs
### Unscented Kalman Filter
Compared with the traditional KF, which needs to solve the Jacobian matrix, UKF does not need to solve the matrix, which makes UKF more efficient and can avoid numerical calculation errors. The flowchart of UKF is shown in Figure 1. In this flowchart, the UKF process proceeds through four steps in a loop. First, Sigma points are generated from the current state estimate and transformed through the Unscented Transform (UT) to obtain the predicted state variables. Then, these Sigma points are passed through the measurement equation to obtain the predicted measurement variables. Next, based on the predicted state and measurement, the Kalman gain is calculated and used to update the state estimate and covariance. Finally, the covariance between the state and measurement is updated based on the latest Sigma points. This process is repeated at each time step to continuously improve the state estimation of the system.
## 4 Compaction Quality Evaluation System and Its Indices
### Evaluation System for Compaction Quality
#### 4.2.1 On-Site Point-Test Compaction Measurement Indices  
(1) Physical compaction measurement indices  
The on-site physical indices can be obtained with the random spot test method. The expressions of \(K\) and \(n\) are  
\[\left\{\begin{array}{l}K=\frac{\rho_{m}}{\rho_{max}}\\ n=\frac{\frac{V_{0}-V}{V_{0}}}{V_{0}}=1-\frac{V}{V_{0}}=\left(1-\frac{\rho_{0} }{\rho}\right)\times 100\%\end{array}\right. \tag{1}\]  
where \(K\) is the compactness, \(n\) is the porosity (%), \(\rho_{m}\) is the wet density (kg/m\({}^{3}\)), and \(\rho_{max}\) is the maximum wet density (kg/m\({}^{3}\)), \(V_{0}\) is the material apparent volume (m\({}^{3}\)), \(V\) is the material solid volume (kg/m\({}^{3}\)), and \(\rho_{0}\) is the material apparent density (kg/m\({}^{3}\)).  
(2) Mechanical compaction measurement indices  
The on-site mechanical compaction indices here include \(E_{vd}\), \(E_{0}\) and \(K_{30}\). \(E_{vd}\) can be detected by light weight deflectometer (LWD) tests and can be expressed as  
\[E_{vd}=\frac{3\sigma}{2S} \tag{2}\]  
where \(E_{vd}\) is the dynamic elastic modulus (MPa), \(r\) is the radius of load plate (mm), \(\sigma\) is the impact load (MPa), \(S\) is the settlement of load plate (mm).  
\(E_{0}\) and \(K_{30}\) can be obtained by plate load (PL) tests and can be expressed as  
\[\left\{\begin{array}{l}E_{0}=I_{0}\Big{(}1-\mu^{2}\Big{)}\frac{P_{0}}{S}\\ K_{30}=\frac{P_{0}}{0.00125}\end{array}\right., \tag{3}\]  
where \(E_{0}\) is the deformation modulus (MPa), \(K_{30}\) is the subgrade modulus (MPa/m), \(I_{0}\) is the shape coefficient, usually 0.785 for circular plate and 0.886 for square plate, \(\mu\) is the material Poisson ratio, \(p\) is the imposed pressure (kPa), \(d\) is the diameter or side length of load plate (mm), and \(S\) is the settlement of load plate (mm), \(P_{0}\) denotes the load when the settlement of PL tests is 1.25 mm for a 300 mm diameter circular load plate. The testing process can refer to Refs. [28; 29].
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 4 Results and Discussion  
### Slump Test and Air Content Test of Fresh Concrete  
Slump and air content tests were conducted to evaluate the effect of waste fibers on the properties of fresh concrete, and the results are as follows: First, the results of slump according to the addition of waste fiber wool are shown in Figure 9. The design slump of the reference concrete was set to 150 mm, but a result after measuring the slump of the reference concrete was 135 mm. In Korea, the design slump of 80 mm or more has an allowable range of \(\pm\)25 mm, so it was found that the reference concrete satisfies the allowable range of the slump. The results of slump test when waste glass wool was added in concrete were 130, 130, and 100 mm for 0.5, 1.0, and 2.0%, and when waste mineral wool results were 135, 125, and 100 mm, respectively. This means that at the addition rate of 2.0% or more, a slump change appeared to affect the workability of fresh concrete. In addition, these results are similar to those of studies related to fiber-reinforced concrete, which consistently showed that generally show that workability decreases with higher fiber addition rates in fiber-reinforced concrete [42; 43; 44]. Therefore, the low fractions of waste fiber wool do not significantly affect the flow properties of concrete, so it can be applied without changes in the existing mix design, but at the addition rate of 2% or more, measures are needed to offset the increase in adhesion due to waste fiber wool addition. Figure 10 shows the result of air content according to the type and addition rate of waste fiber wool, and the air content of reference concrete added with air entraining agent was 6.0%. The result of air content for concrete with waste glass wool and waste mineral wool fiber addition rates of 0.5, 1.0, and 2.0% were showed 6.0, 5.5, and 6.0% (waste glass wool) and 6.5, 6.5, and 6.5% (waste mineral wool), respectively. There was no significant change in the total air content of concrete according to the addition rate of waste fiber wool, so the effect of the addition rate of waste fiber wool on the air content was found to be insignificant compared to the slump characteristics. These means that the two types of waste fibers used in this study did not affect the computing process of addition rate of air entraining agent.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Charge Passed (Coulombs)** & **Chloride Ion Penetrability** \\ \hline \(>\)4000 & High \\ 2000–4000 & Moderate \\ 1000–2000 & Low \\ 100–1000 & Very Low \\ \(<\)100 & Negligible \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard for chloride ion penetration resistance.  
Figure 8: Experimental foreground and schematic diagram.
# Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review
## 3 Abrasive Waterjet Cutting Process Parameters and Influences
### Abrasive Waterjet Cutting Input Process Parameters Functions and Influences
#### 3.1.2 Abrasive System
[93] have investigated the machining hybrid metal matrix which consists of Al 6063 reinforced with boron carbide (B\({}_{4}\)C) and zirconium silicate (ZrSiO\({}_{4}\)) in the form of particulates in the proportion of 5% B\({}_{4}\)C and 5% ZrSiO\({}_{4}\) using different type and mesh size of abrasives. They used aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) and garnet with varied mesh size numbers of 60, 80 and 100. They concluded that a higher rate of material removal was achieved by using an abrasive type aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) with a mesh size number of 80. Abrasives with all varied sizes showed the formation of striations in cut surfaces [93]. Moreover, [PERSON] et al. [35] have analysed the effects of applying varying mesh sizes of garnet, including size 80, 100, and 120 in cutting AA5083--H32, where garnet with size number 80 produced a higher depth of cut, low kerf taper angle and surface roughness. Notwithstanding these studies, the abrasive mesh number is directly proportional to MRR, and a higher mesh size leads to higher roughness which results in a lower quality of cut surface. Impacts of abrasive mass flow rate: In AWJ cutting operation, increasing the abrasive mass flow rate increases the erosion efficiency, containing a higher number of abrasives which relatively increases the depth of cut and decreases surface roughness value [36]. An increased rate of waterjet pressure denotes a parallel performance with abrasive flow rate [92]. [PERSON] et al. [94] have presented a study of AWJ cutting process parameters performance in minimizing the surface roughness and kerf angle of AISI 1018 mild steel. They have observed that increasing the level of waterjet pressure, alongside with abrasive mass flow rate reduced the kerf taper angle and surface roughness. This achieved the minimum kerf geometries and less striation surface. [PERSON] et al. [44] have applied a moderate rate of abrasive mass flow. Mainly, a higher AFR directly leads to a higher \begin{table}
\begin{tabular}{c c c} \hline
**Mesh Number \#** & **Mesh in Microns \(\upmu\)m** & **Grade** \\ \hline
40–60 & 250–400 & Coarse \\
80–100 & 180–210 & Medium coarse \\
120–150 & 90–105 & Medium fine \\
180–220 & 70–88 & Fine \\
240 upwards & \(\leq\)60 & Very fine \\ \hline \end{tabular}
\end{table} 
Table 6: Abrasive mesh size and grade [1, 68, 88, 92]. MRR and DOC of machined material; however, it provides a conditional opposite effect in surface roughness and kerf taper angle, depending on the type of material.
## 3 Results
### Classification Performance Comparison over the Testing Data Set
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **AHI** & \(TPR\) **(\%)** & \(TNR\) **(\%)** & \(PPV\) **(\%)** & \(ACC\) **(\%)** & \(F_{1}\) **(\%)** & \(AUC\) **(\%)** \\ \hline \multirow{4}{*}{OSA} & All & 92.7 & 80.6 & 25.3 & 81.4 & 39.8 & 86.7 \\ \cline{2-8}  & Severe & 97.2 & 77.5 & 59.8 & 82.5 & 74.0 & 87.4 \\  & Moderate & 92.8 & 84.8 & 31.3 & 85.4 & 46.8 & 88.8 \\  & Mild & 87.6 & 71.8 & 1.9 & 71.9 & 3.7 & 79.7 \\  & Normal & 0.0 & 93.3 & 0.0 & 93.2 & NA & 46.6 \\ \hline \multirow{4}{*}{CSA} & All & 88.6 & 80.6 & 41.8 & 81.7 & 56.8 & 84.6 \\ \cline{2-8}  & Severe & 95.6 & 77.5 & 52.8 & 81.3 & 68.0 & 86.6 \\ \cline{1-1}  & Moderate & 85.9 & 84.8 & 23.9 & 84.9 & 37.4 & 85.4 \\ \cline{1-1}  & Mild & 90.1 & 71.8 & 1.7 & 71.9 & 3.3 & 80.9 \\ \cline{1-1}  & Normal & 7.6 & 93.3 & 1.1 & 92.4 & 1.9 & 50.4 \\ \hline \multirow{4}{*}{MSA} & All & 97.2 & 80.6 & 9.9 & 80.9 & 18.0 & 88.8 \\ \cline{1-1}  & Severe & 98.1 & 77.5 & 32.6 & 79.5 & 48.9 & 87.8 \\ \cline{1-1}  & Moderate & 100 & 84.8 & 12.2 & 85.2 & 21.7 & 92.4 \\ \cline{1-1}  & Mild & 27.3 & 71.8 & 0.1 & 71.8 & 0.0 & 49.5 \\ \cline{1-1}  & Normal & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed test performance of AICPV with GMM classifier (AICPVwGMM) over different Apnea–Hypopnea Index (AHI) severities per apnea class. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**AICPVwTH** & & \(TPR\) **(\%)** & \(TNR\) **(\%)** & \(PPV\) **(\%)** & \(ACC\) **(\%)** & \(F_{1}\) **(\%)** & \(AUC\) **(\%)** \\ \hline \multirow{4}{*}{Type of Apnea} & MSA & 97.5 & 81.1 & 7.8 & 81.4 & 14.4 & 89.3 \\  & CSA & 86.8 & 81.1 & 17.1 & 81.4 & 28.6 & 83.4 \\  & OSA & 88.4 & 81.1 & 20.6 & 81.6 & 33.4 & 85.6 \\ \hline \multirow{4}{*}{AHI Severity} & Severe & 92.4 & 90.3 & 82.5 & 91.0 & 87.2 & 91.4 \\  & Moderate & 79.5 & 80.7 & 32.8 & 80.6 & 46.4 & 80.1 \\ \cline{1-1}  & Mild & 76.6 & 76.1 & 3.7 & 76.1 & 7.1 & 76.3 \\ \cline{1-1}  & None/Minimal & 6.3 & 97.0 & 2.0 & 96.1 & 3.0 & 51.7 \\ \hline All & & 88.4 & 79.4 & 28.2 & 80.2 & 61.1 & 83.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test performance of AICPV with threshold classifier over different types of apnea and different apnea severities.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Privacy-Preserving Transferable Learning  
A privacy-preserving semi-supervised transfer and multi-task learning problem has been recently addressed in [22] by means of variational membership mappings. The method, as suggested in [22], involves the following steps:
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 2 Related Works and Background
### Hash Table-Based Name Lookup
#### 2.3.1. Descending Order from the Longest Name  
In CCNx, the search begins with the key corresponding to the length of the longest prefix in the FIB or the length of the full content name [27]. If the search fails, the search key length decrements and the search continues iteratively. Although the search stops upon finding the first match, in the worst-case scenario, up to \(O\) (min(\(k\), \(n\))) searches may be needed, where \(k\) is the longest prefix length in the FIB and \(n\) is the length of the given name. In this context, the prefix length is defined as the deepest component level rather than the length of the character string.  
Figure 2 illustrates how the trie in Figure 1 is represented as a hash table. Given a content name /a/b/c/k for lookup, searches for each of its sub-prefixes--/a/b/c/k, /a/b/c, /a/b, and /a--may be conducted sequentially in the hash table until a matching prefix is found. In this case, the search stops at /a/b, which is the longest matching prefix, and the lookup returns 10 as the face value. The hash table can be constructed using only prefix nodes (P\({}_{1}\) to P\({}_{4}\)), while non-prefix nodes (N\({}_{1}\) to N\({}_{5}\)) can be omitted. However, as shown in Figure 2, the number of hash table accesses can be reduced if each non-prefix node inherits and retains the face value of the nearest ancestor prefix. Under this configuration, a lookup for /a/b/c successfully finds a match in the hash table and returns the correct face value. There is a trade-off between the number of hash table accesses and the increase in collisions as hash table entries grow.
## 3 Our Approach
### Graph-STNN
#### 3.3.1 Spatial Encoder  
The spatial encoder is designed to encode the structural information around the frontier waypoints. As GCN has achieved great success in graph structure data processing, we use GCN to deal with the spatial interaction. Specifically, we use a two-layer GCN to construct the spatial encoder. We denote the \(l\)-layer node features as \(\mathbf{F}^{(l)}=[\mathbf{f}_{1}^{(l)},\cdots,\mathbf{f}_{N}^{(l)}]^{\mathsf{T}}\), the node aggregation at layer \(l\) is then calculated as:  
\[\mathbf{F}^{(l+1)}=\sigma_{s}(\mathbf{D}^{-\tfrac{1}{2}}\tilde{ \mathbf{A}}\mathbf{D}^{-\tfrac{1}{2}}\mathbf{F}^{(l)}\mathbf{W}_{S}^{(l)}), \tag{8}\]  
where \(\tilde{\mathbf{A}}=\mathbf{A}+\mathbf{I}\), \(\mathbf{I}\) is the unit matrix, \(\mathbf{A}\) the adjacency matrix of exploration graph, \(\mathbf{D}\) the degree matrix, \(\mathbf{W}_{S}^{(l)}\) the learnable weight matrix of layer \(l\), and \(\sigma_{s}(\cdot)\) the ReLU function. The input of the encoder is \(\mathbf{F}^{(0)}=\mathbf{F}=[\mathbf{f}_{1},\cdots,\mathbf{f}_{N}]^{\mathsf{ T}}\). Then, we use a two-layer GCN. The features of adjacent layers are combined by using the attention mechanism. The output spatial feature of node \(i\) can be represented as:  
\[\mathbf{f}_{\text{Spatial}}^{i} =\alpha_{i}^{1}*\mathbf{f}_{i}^{(1)}+\alpha_{i}^{2}*\mathbf{f}_{ i}^{(2)}, \tag{9}\]  
where  
\[\alpha_{i}^{l}=\frac{\exp(\mathbf{W}_{1}\mathbf{f}_{i}^{(l)}+ \mathbf{b}_{1})}{\sum_{l=1}^{2}\exp(\mathbf{W}_{1}\mathbf{f}_{i}^{(l)}+\mathbf{ b}_{1})}, \tag{10}\]  
Figure 3: The structure of Spatiotemporal Neural Network on Graph (Graph-STNN). The current state is represented as the exploration graph \(s_{T}\). All frontier waypoints can be represented as \(C_{1}\)\(\sim\)\(C_{st}\), in which \(st\) is the number of frontier waypoints in the current state. The inputs of the network are an adjacency matrix \(\mathbf{A}\) and an original node feature matrix \(\mathbf{X}\) of the exploration graph. First, the original features encoded by a multi-layer perceptron (MLP), the spatial features extracted by the spatial encoder, and the temporal features extracted by the temporal encoder are concatenated together. Then, the concatenated feature is input to the state encoder to obtain the state feature, which is the same for each node. Finally, the state feature combined with the original features and spatial features is input to a liner layer after dropout, and the output is the value of each frontier waypoint. The frontier waypoint with the largest value is the local target point.  
\(\exp(\cdot)\) is the expedition function, and \(\mathbf{W}_{1}\) and \(\mathbf{b}_{1}\) are the parameters of attention mechanism. The spatial feature matrix is \(\mathbf{F}_{\text{Spatial}}=[\mathbf{f}_{\text{Spatial}}^{l},\cdots,\mathbf{f }_{\text{Spatial}}^{N}]^{\mathsf{T}}\), which consists of all nodes' spatial feature.
# Robust Template-Based Watermarking for DIBR 3D Images
## 4 Experimental Results
### Robustness against DIBR  
Table 5 shows the results of a DIBR attack. All three methods have low BER against the DIBR attack. Since [PERSON]'s method inserts multiple watermarks, the BER is slightly higher on the right image. The previously inserted watermark (right watermark) was disturbed by the later inserted watermarks (left and center watermarks).  
The major advantage of the DIBR process is that DIBR configurations can be adjusted to suit a user's needs. As described in Section 2.1, the user can preprocess the depth image to increase the rendered image quality. Table 6 shows robustness results for synthesized images with a preprocessed depth image. Unlike other methods, [PERSON]'s method raised the BER. This result shows that [PERSON]'s method is susceptible to the shift distance of the pixel, because the left and right watermarks were inserted by predicting the shift distance of the pixels.  
Baseline distance is another DIBR configuration aspect. Various viewpoints of an image can be synthesized depending on the baseline distance change. Figure 12 shows average BER where the baseline distance is adjusted from 1-10%. [PERSON]'s method increases BER for the same reason as in depth image preprocessing, whereas [PERSON]'s and the proposed method do not increase BER in the baseline distance adjustment.  
For additive noise, [PERSON]'s method has the best performance, and the proposed method has a slightly higher BER than [PERSON]'s method. However, additive Gaussian noise with a variance of 2000 is a very severe attack, and such a large amount of noise barely occurs. Considering this, the proposed method is robust enough to additive noise.  
For JPEG compression, the proposed method has a slightly higher BER than [PERSON]'s method. However, since the proposed method exhibits error <0.1 for a JPEG quality factor of 30, which is very large compression, it can be considered sufficiently strong against a JPEG attack.  
Meanwhile, [PERSON]'s method is more vulnerable to signal distortion than the other methods, since the quantized DT-CWT coefficients are greatly affected by the signal distortion.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 4 Results
### Multiple Pallet Recognition  
The multiple pallet recognition results are shown in Figure 8, where red rectangles indicate the ROI of pallets marked by the algorithm. The precision and recall of the proposed method in this paper are 0.989, 0.926. Seventy-five pallets were recognized from eighty-one pallets. The precision and recall of the yolo v5 are 0.964, 0.986.
###### Abstract  
The collision cross sections (CCS), momentum transfer cross sections (MTCS), or scattering cross sections (SCS) of an electron-neutral pair are important components for computing the electric conductivity of a plasma gas. Larger collision cross sections for electrons moving freely within neutral particles (molecules or atoms) cause more scattering of these electrons by the neutral particles, which leads to degraded electron mobility, and thus reduced electric conductivity of the plasma gas that consists of electrons, neutral particles, and ions. The present work aimed to identify the level of disagreement between four different methods for describing how electron-neutral collision cross sections vary when they are treated as a function of electron temperature alone. These four methods are based on data or models previously reported in the literature. The analysis covered six selected gaseous species that are relevant to combustion plasma, which are as follows: carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), molecular hydrogen (H\({}_{2}\)), water vapor (H\({}_{2}\)O), potassium vapor (K), and molecular oxygen (O\({}_{2}\)). The temperature dependence of the collision cross sections for these species was investigated in the range from 2000 K to 3000 K, which is suitable for both conventional air-fuel combustion and elevated-temperature oxygen-fuel (oxy-fuel) combustion. The findings of the present study suggest that linear functions are enough to describe the variations in the collision cross sections of the considered species in the temperature range of interest for combustion plasma. Also, the values of the coefficient of variation (defined as the sample standard deviation divided by the mean) in the collision cross sections using the four methods were approximately 27% for CO, 42% for CO\({}_{2}\), 13% for H\({}_{2}\), 39% for H\({}_{2}\)O, 44% for K, and 19% for O\({}_{2}\). The information provided herein can assist in simulating magnetohydrodynamic (MHD) power generators using computational fluid dynamics (CFD) models for combustion plasma flows.  
collision cross section; CCS; momentum transfer cross section; MTCS; electron-neutral scattering; plasma; MHD; magnetohydrodynamic Article