esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Properties  
The mechanical properties of matrix samples are shown in Figure 6. With the increase of nano-WC particles addition, the relative density and hardness fluctuate and do not have significant changes (Figure 6a,b). It should be note that the introduction of nano-WC has little effect on relative density and hardness.  
Bending strength of matrix samples shows a trend of first increase and then decrease (Figure 6c), and sample S5 has the best value. The positive influence on bending strength of nano-WC addition can be associate with Orowan strengthening effect. The nano-WC particles pin the crossing dislocation and promote dislocations bowing around the particles under external load [32; 33]. In addition, the mismatch in the coefficient of thermal expansion (CET) between WC (\(6\times 10^{-6}\) K\({}^{-1}\)) and bronze (\(\geq\)18 \(\times\)\(10^{-6}\) K\({}^{-1}\)) needs further consideration [7; 34]. During the cooling from processing temperature (980 \({}^{\circ}\)C), thermal stresses around the nano-WC particles lead to plastic deformation, especially in the interface area [33]. These stresses decrease quickly with increasing distance from the boundary, generating dislocation defects in the close vicinity of nano-WC. A large amount of nanoparticles is benefited to enhance dislocation density, resulting in an improvement of the deformation resistance. While concentration of nano-WC goes higher than 2.5 wt%, agglomeration phenomenon weakens the reinforcement effect.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **SD0** & **SD1** & **SD2** & **SD3** & **SD4** \\ \hline Width (\(\mu\)m) & 4.1 & 3.0 & 3.8 & 4.0 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average width of the crack between diamond and matrix of impregnated samples.  
Figure 6: Mechanical properties of samples. (**a**) The relative density and HRC values of matrix samples; (**b**) The relative density of impregnated diamond samples; (**c**) The bending strength of matrix samples; (**d**) The bending strength of impregnated diamond samples.  
As illustrated in Figure 6d, the bending strength of impregnated diamond sample SD1 shows the peak value and the value of SD2, SD3 and SD4 is close to SD0. Combined with fracture SEM observation (Figure 5) and average crack width (Table 3), the width of the crack decreases from 4.1 \(\upmu\)m to 3.0 \(\upmu\)m after adding 2.2 wt% nano-WC. This structural feature indicates matrix of SD1 has good diamond-holding capability, which is benefited to the stress transfer, enhancing the bending strength. Diamond grits act as weakening phase during bending strength test, so the holding strength at the interfaces between diamond grits and matrix is a factor influencing diamond composites bending strength and overall performance [3; 5]. The interface structure observed between diamond grits and matrix of sample SD2, SD3 and SD4 is similar to SD0, this is accordance with bending strength test results.
## 3 Methodology
### Diving into Pairing Approach  
In this section, we introduce two different pairing approaches aimed at improving the security of CAVs: intra-vehicle ECU pairing and vehicle-to-vehicle pairing. In our approach to ECU pairing, the emphasis is on linking individual ECUs within a vehicle. Establishing secure connections between ECUs helps to guarantee reliable and coordinated communication inside the vehicle network. On the other hand, the vehicle-to-vehicle pairing technique concentrates on linking whole vehicles, thereby allowing secure communication between them. Our pairing approaches are supposed to provide a whole, effective solution covering the security concerns CAVs come with in internal and external communication environments.
# Stability Index of Surrounding Rock during Deep Rock Excavation Considering Energy Release Speed
## 3 Engineering Example  
### Engineering Background  
The deep geological disposal method is widely used for nuclear waste disposal and is adopted by most countries with nuclear power. In Canada, the plutonic rocks stratum in the depth range of 500 m to 1000 m of the Canadian Shield is selected as the underground space for settlement. To support this, the Canadian Atomic Energy Corporation (AECL)built an Underground Research Laboratory (URL) for 'Mine-by' research on the Lac du Bonnet granite foundation. The site is located about 120 km north of Winnipeg, Manitoba. The first stage of the study involved excavating a tunnel to analyze the progressive failure process and the development of excavation-induced damage around the tunnel. The tunnel was excavated using a non-blasting method at the depth of 420 m, with a length of 46 m and a diameter of 3.5 m. During the excavation, brittle spalling failure of the tunnel wall continued to occur and developed into a typical 'V-shaped' rockburst pit. Taking the Lac du Bonnet granite at a location of 18 + 50m as an example, the final depth of the rockburst pit was about 1.3 times the radius of the tunnel. As shown in Figure 1, the failure area was almost symmetrical. However, due to the presence of waste rock and residue soil on the tunnel's bottom plate during excavation, their weight caused a slight difference in the distribution of the failure area at the bottom and top of the tunnel. For the rock mechanics parameters, experiments had been conducted by the Canadian Centre for Minerals and Energy Technology (CANMET), and the rock mechanics test results for the rock at the site are shown in Table 2.  
### Engineering Background  
In order to illustrate the applicability of the energy release effectiveness index intuitively, FLAC3D software was adopted to simulate the energy evolution process of the surrounding rock during the excavation of the mine by a test tunnel of the URL. The model, shown in Figure 2, had a size of 30 m \(\times\) 30 m and an excavation radius of 1.75 m. To meet the calculation schedule, the minimum size of the model was 0.13 m and the maximum size was 1.67 m. The simulation utilized the cohesion-weakening-friction-strengthening model (CWFS), which is a variant of the strain-softening model. Its basic parameters are shown in Table 3, and the variation of cohesion and friction are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rock Types** & **Granite** & **Granoidorite** \\ \hline Density (kg/m\({}^{3}\)) & 2630 \(\pm\) 10 & 2660 \(\pm\) 20 \\ Uniaxial compressive strength (MPa) & 213 \(\pm\) 20 & 228 \(\pm\) 20 \\ Young’s modulus (GPa) & 65 \(\pm\) 5 & 66 \(\pm\) 5 \\ Poisson’s ratio & 0.25 \(\pm\) 0.05 & 0.25 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rock mechanics parameters provided by CANMET.  
Figure 1: The final shape of the brittle failure of the test tunnel: (**a**) photograph [26; 27]; (**b**) outline.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Generalization Evaluations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Param**} & \multicolumn{2}{c}{**Scene Flow**} & \multicolumn{2}{c}{**KITTI 2015**} & \multicolumn{4}{c}{**Middlebury Eval 3 1**} & \multicolumn{2}{c}{**ETH3D**} \\ \cline{3-13}  & & **(M)** & **Bad.10** & **EPE** & **D1-all** & **EPE** & **APE** & **Bad.20** & **EPE** & **APE** & **Bad.10** & **EPE** & **APE** \\  & & **(\%)** & **(px)** & **(\%)** & **(px)** & **(px)** & **(px)** & **(px)** & **(px)** & **(px)** & **(px)** & **(px)** & **(px)** \\ \hline (1) & Baseline fast [12] & 9.86 & 9.573 & 0.866 & 5.887 & **1.217** & - & 15.153 & 1.943 & - & 6.270 & 0.523 & - \\ (2) & IGEV fast [32] 2 & 12.38 & 8.283 & 0.676 & 5.880 & 1.218 & - & 12.448 & 1.665 & - & 6.629 & 0.568 & - \\ (3) & EAI fast [14] & 14.13 & 8.595 & 0.808 & **5.834** & 1.246 & - & 14.979 & 2.198 & - & 6.022 & 0.416 & - \\ (4) & MUIR fast & 14.00 & 7.769 & 0.711 & 6.291 & 1.244 & **0.812** & 12.235 & 1.688 & 1.087 & 3.801 & **0.312** & **0.174** \\ (5) & MUIR-GEV fast 2 & 13.39 & **7.101** & **0.625** & 6.439 & 1.292 & 0.833 & 11.496 & 1.611 & 0.966 & 5.980 & 0.587 & 0.281 \\ (6) & MUIR-EAI fast & 18.05 & 7.200 & 0.675 & 6.442 & 1.271 & 0.848 & **11.155** & **1.394** & **0.813** & **3.694** & 0.324 & 0.197 \\ \hline (7) & Baseline [12] & 11.11 & 7.225 & 0.690 & 5.843 & 1.402 & - & 10.099 & 1.286 & - & **2.922** & 0.295 & - \\ (8) & MUIR & 17.15 & **0.519** & **0.570** & **5.719** & **1.214** & 0.760 & **8.323** & **1.081** & 0.578 & 3.889 & **0.275** & 0.174 \\ \hline \hline \end{tabular}  
* 1 The evaluation in Middlebury Eval 3 is conducted on the half resolution. 2 The results of IGEV fast and MUIR-IGEV fast are measured with the maximum disparity set to 192 pixels, following [32].  
\end{table} 
Table 3: Quantitative zero-shot generalization evaluation on the KITTI 2015 [24], Middlebury Eval 3 [25] and ETH3D [26] training sets. Rows (1–6) and rows (7 and 8) show models’ performance at \(1/8\) and \(1/4\) resolution, respectively. The best results in each category are bolded. the MUIR-EAI fast also achieves 25.5% and 38.7% outlier proportion reduction. It should be noted that, compared to the corresponding baselines, the D1-all of MUIR, MUIR-IGEV and MUIR-EAI are increased by 6.9%, 9.5% and 10.4%, respectively, due to the excessive domain gap in KITTI datasets. The visualization of partial experimental results is shown in Figure 8. The generalization experimental results show that the proposed MUIR can be applied to multiple RAFT-based models and provide performance improvements especially in some ill-posed regions.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology
### Computational Complexity Analysis
#### 4.5.2 Space Complexity  
The space complexity of AH-EAT is determined by the storage requirements for model components and intermediate results:  
* Node Embedding Storage: \(O(|V|\cdot h)\), where \(|V|\) is the number of nodes.
* Edge Feature Matrix: The edge feature matrix requires \(O(|E|\cdot d_{e})\) space.  
* Adversarial Perturbation Cache: Storing adversarial perturbation vectors for edges incurs a space complexity of \(O(|E|\cdot d_{e})\), which is the same as the edge feature matrix.
## 3 Technical Overview
### BL Prediction Algorithm  
The auditory excitation matrix of each input signal is inserted in the \(BL\) prediction algorithm. To predict the \(BL\) from the auditory excitation matrix, the following methodology was implemented. The excitation matrix of the signal of the pseudo-acoustic environment and the signal of the virtual environment was introduced in the \(BL\) calculation algorithm (Figure 7). Then the excitation values are converted to \(BL\) values according to the acoustic energy conversion table. The algorithm searches for the nearest \(BL\) value corresponding to the specific auditory excitation energy value for each predefined frequency band. The results for each predefined zone compose the \(BL\) matrix for each environment. The \(BL\) matrices represent the perceptual characteristics according to which the adapted mix works.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Filter Band Middle Frequency** & **Filter Band** \\ \hline
250 Hz & 0–500 Hz \\
500 Hz & 250–1000 Hz \\
1000 Hz & 625–1750 Hz \\
2000 Hz & 1250–3500 Hz \\
4000 Hz & 2500–7000 Hz \\
8000 Hz & 5000–14,000 Hz \\
16,000 Hz & 8000–24,000 Hz \\ \hline \end{tabular}
\end{table}
Table 1: This table presents the frequency bands of filters.  
Figure 6: This figure presents the frequency bands energy estimation controller.
## 2 Methodology  
### Sample Compositions and Material Properties  
Four different small-scale wall assembly samples with 200 \(\times\) 500-mm surface areas (Figure 1) were used for the tests. These samples corresponded to the timber-frame lightweight panels used as internal walls in buildings. Assembly A and Assembly B represent 8.5-cm thick walls with an inner air gap of 20 mm. Assembly A differed from Assembly B only in the type of internal insulation used. In the case of Assembly A, this was mineral wool (4.12), while in the case of Assembly B the insulation was made of wood board (3.3.2). Assemblies C and D represent 12.2-cm thick walls, differing in the composition of the inner layers. In Assembly C, the internal insulation was mineral wool (4.12) with a 20-mm air gap, while in Assembly D, the inner layer was wood fibreboard (3.3.2) with a thickness of approximately 60 mm, with no air gap. The coating used in Assemblies C and D, designated as material 3.1, was an earthen adhesive layer, whose purpose was to strengthen the top layers of the wall assemblies, made of earth plaster with straw (2.2.4). All the materials used to prepare the samples are commercially available, and the thicknesses are representative of the products (Table 1). However, the basic hygothermal properties necessary for the simulations, such as density, sorption isotherm (water content as a function of RH), thermal conductivity and water-vapour permeability, were measured in the laboratory using steady-state methods. Some physical (porosity) and thermal (heat capacity) properties were taken from the WUFI software database [23]. The material properties of the assemblies are shown in Table 1.  
Thermal conductivity values \(\lambda\) (W-m\({}^{-1}\)-K\({}^{-1}\)) were measured according to the EN 12664 standard [24]. Samples of area 300 mm \(\times\) 300 mm and thickness, according to the thickness of each type of material used in the wall assemblies, were prepared. They were conditioned at various relative humidity values (20%, 50% and 80%), at 23 \({}^{\circ}\)C. Their thermal conductivities were then determined using a Fox 314 HFM apparatus (TA instruments, Hullhorst, Germany) which measures the steady-state heat transfer through flat materials. The samples were placed between two flat plates controlled to a specified constant temperature. Thermocouples fixed in the plates measured the temperature drop across the specimen and wireless thermal flux meters (HFMs) embedded in each plate measured the heat flow through the specimen. The thermal flux meters were located in the centre of the plates. The thermal conductivity of the specimen \(\lambda\)(W-m\({}^{-1}\)-K\({}^{-1}\)) was then calculated by measuring the heat flux \(q\) (W/m\({}^{2}\)), the temperature difference across the specimen \(\Delta T\) (K) and the thickness of the specimen \(s\) (m) in steady state. \[\lambda=\frac{s\cdot q}{\Delta T^{\prime}}, \tag{1}\]  
The adsorption isotherm was tested in accordance with the ISO 12571 [25] standard. The samples of dimensions 100 mm \(\times\) 100 mm, with a thickness of 40 to 80 mm, were placed in environments
# Less Is More: Influence of Cross-Linking Agent Concentration on PFOS Adsorption in Chitosan
## 2 Materials and Methods
### Preparation of Chitosan  
The synthesis protocol for chitosan gel synthesis was slightly adapted from [PERSON] et al. (2018). In short, 1.5 g chitosan flakes were dissolved in 50 mL of 2% (_v/v_) acetic acid over 2 h with occasional stirring. The viscous mixture was transferred into a 50 mL syringe equipped with a shortened cannula (1 cm) with 0.9 mm diameter at the top (care must be taken while shortening the cannula so as not to clog the cannula). Subsequently, the mixture was quickly squeezed into a 500 mL NaOH (0.5 M) solution stirred with a 3 cm stir bar at 300 rpm and was left stirring for 2 h. Afterwards, the precipitated chitosan was washed three times with deionized water, and the stir bar was exchanged for a smaller one with max 2 cm length. To cross-link the chitosan, 300 mL of (0, 1, 2, or 4% (_v/v_)) epichlorohydrin (ECH) solution was added to the chitosan. Consequently, the pH of the mixture turned alkaline (-pH 11) due to the excess NaOH in the chitosan polymer. After that, the mixture was stirred at 40 \({}^{\circ}\)C for 18 h at 200 rpm. Please note that faster stirring and using a bigger stir bar can lead to milled chitosan instead. The cross-linked chitosan is then thoroughly washed with 150 mL deionized water in the beaker at least 4 times till the pH value of the washing solution is below 8 after contact with the chitosan for at least a minute.  
To increase the reactive surface, \"milled\" chitosan was produced. Therefore, the stir rate of the suspension after the cross-linking step was increased to 1000 rpm with a 3 cm stir bar for 18 h to produce a milky white suspension, which slowly precipitated after stirring was stopped. For storage, the chitosan gel was left under water to prevent drying. Before the chitosan gel was weighed, the supernatant water was decanted, and the gel was put between two paper towels with gentle pressure to remove excess water.  
To produce molecular imprinted chitosan polymer, the above-described method was changed in the following way: the non-branched chitosan was shaken for 3 d with a PFOS (0.4 mmol/L) solution to which then later ECH was added to reach a 2% ECH solution during the crosslinking step. The washing step following the cross-linking was done twelve times with 80 mL 0.1 mmol/L NaOH/Acetone (1:1, _v/v_) to remove all PFOS from the polymer and afterward six times with 80 mL deionized water. The non-imprinted polymer for the control experiment was treated the same but without the addition of PFOS to the solution.
## 2 Materials and Methods
### Biological Activity of Kaempfer on Spodoptera frugiperda  
To determine the insecticidal and insecticatic activity of kaempfer, concentrations of 1000, 800, 500, 300, and 100 ppm of kaempfer 3-\(\beta\)-D-glucopyranoside were evaluated. This compound was of analytical grade and obtained from Sigma Aldrich (St. Louis, MO, USA), following the same methodology described in the bioassay of methanolic extracts. A completely randomized experimental design was used, comprising 4 replicates with 5 experimental units (EUs) each, placing each second instar larvae in a PRIMO brand #0 polyethylene cup (Ecatepec, Mexico) with a 1 cm\({}^{3}\) diet cube, under the same conditions as those of the breeding stock. This bioassay was reviewed every 24 h until the larvae reached the sixth instar; at this point, the dependent variables (larval mortality, pupal mortality, cumulative mortality, and mean lethal concentration--LC\({}_{50}\)) were evaluated.  
Identification and Quantification of Kaempfer in the Methanolic and Hydrolyzed Methanolic Extracts of Aerial Parts of R. communis  
The identification and quantification of kaempfer in the hydrolyzed methanolic and methanolic extracts of _R. communis_ were carried out through high-resolution liquid chromatography (HPLC) using Waters Alliance model equipment (Milford, MA, USA), composed of a model e2695 quaternary pump and a model 2998 diode array detector (DAD). Data acquisition and processing were carried out using Empower 3 software. In addition, a C18 column (5 \(\upmu\)m, 150 \(\times\) 4.5 mm) was used. Acetic acid at a concentration of 12.5 mM and acetonitrile (CH\({}_{3}\)CN) were used as the mobile phase, using the gradient described in Table 2.  
The flow of the mobile phase was 1.0 mL min\({}^{-1}\), the wavelength used for detection was 363 nm, and an injection volume of 10 \(\upmu\)L and an analysis time of 35 min were used. The retention time for the kaempfer standard was 20.3 min. Analyses for kaempfer quantification were performed in triplicate.
# On Nash Equilibria in Non-Cooperative All-Optical Networks +
###### Abstract  
We consider the problem of determining a routing in all-optical networks, in which some couples of nodes want to communicate. In particular, we study this problem from the point of view of a network provider that has to design suitable payment functions for non-cooperative agents, corresponding to the couples of nodes wishing to communicate. The network provider aims at inducing stable routings (i.e., routings corresponding to Nash equilibria) using a low number of wavelengths. We consider three different kinds of local knowledge that agents may exploit to compute their payments, leading to three corresponding information levels. Under complete information, the network provider can design a payment function, inducing the agents to reach a Nash equilibrium mirroring any desired routing. If the price to an agent is computed only as a function of the wavelengths used along connecting paths (minimal level) or edges (intermediate level), the most reasonable functions either do not admit Nash equilibria or admit very inefficient ones, i.e., with the largest possible price of anarchy. However, by suitably restricting the network topology, a constant price of anarchy for chains and rings and a logarithmic one for trees can be obtained under the minimal and intermediate levels, respectively.  
Keywords:optical networks; WDM; pricing of optical network services; selfish routing; nash equilibria; price of anarchy; algorithmic game theory +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Datasets
#### 4.1.1 TILDA Dataset  
The TILDA textile defect dataset is specifically designed for surface defect detection in textiles. It contains 896 images with a resolution of 416 \(\times\) 416 pixels, covering four different categories of textile defects: holes, foreign objects, oil stains, and misaligned threads, as shown in Figure 6.
## 2 Related Works
### Support Vector Machine
The objective is to minimize the classification error and maximize the margin. This is typically done using quadratic programming techniques. The decision function of an SVM can be represented as:  
\[f(x)=sign(\sum_{i=1}^{N}\alpha_{i}y_{i}K(x_{i},x)+b) \tag{1}\]  
where \(f(x)\) is the predicted class label for a new data point \(x\), \(\alpha_{i}\) and \(y_{i}\) are the Lagrange multipliers and class labels for the support vectors, \(K(x_{i},x)\) is the kernel function that measures the similarity between the support vectors and the new data point, and \(b\) is the bias term. During the learning process, the SVM determines the optimal values of \(\alpha_{i}\), \(y_{i}\), and \(b\) by solving the optimization problem, typically using optimization algorithms like Sequential Minimal Optimization (SMO). Once the SVM model is trained, it can be used to make predictions on new, unseen data points by evaluating the sign of the decision function. In summary, the learning process of Support Vector Machines involves mapping the data to a higher-dimensional feature space, maximizing the margin between support vectors, optimizing the decision function parameters, and making predictions based on the learned model. Please note that the above explanation provides a general understanding of SVM learning. SVM have various extensions and variations, such as kernel selection, handling imbalanced data, and handling multi-class problems, which can be explored in more detail for a comprehensive understanding of SVM.