esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Feature Representation of Text on the Performance of Document Classification
## Appendix A Complex Network Measures  
In this section, we define all the network measures used for the construction of the features in the document representation model based on language networks. A language network is a pair of sets \((V,E)\), where \(V\) is the set of nodes (vertices) representing the linguistic units and \(E\) is the set of edges (links) links representing the interactions between linguistic units.  
The average shortest path is defined as  
\[L=\frac{1}{N(N-1)}\sum_{i\
eq j}d_{ij} \tag{1}\]  
where \(d_{ij}\) is a shortest path between nodes \(i\) and \(j\), and \(N\) is the number of nodes.  
An efficiency measure was first defined in [76; 77] as a property quantifying the efficiency of information exchange over the network  
\[E_{glob}(G)=\frac{1}{N(N-1)}\sum_{i\
eq j\in G}\frac{1}{d_{ij}}. \tag{2}\]  
Local efficiency is defined as the average efficiency of the local subgraphs:  
\[E_{loc}=\frac{1}{N}\sum_{i\in G}E_{glob}(G_{i}),\qquad i\
otin G_{i} \tag{3}\]  
where \(G_{i}\) is the subgraph of the neighbors of \(i\).  
Next we calculate local measures: in-degree and out-degree of node \(i\), denoted by \(k_{i}^{in/out}\) which are the number of its ingoing and outgoing nearest neighbours, in-strength and out-strength \(s_{i}^{in/out}\) of the node \(i\) which are the sum of its ingoing and outgoing edge weights, average strength or selectivity as:  
\[e_{i}^{in/out}=\frac{s_{i}^{in/out}}{k_{i}^{in/out}}. \tag{4}\]  
Inverse participation ratio  
\[Y_{i}^{in/out}=\sum_{j=1}^{N}\left(\frac{a_{ij}^{in/out}}{s_{i}^{in/out}}\right) \tag{5}\]  
where \(s_{ij}^{in/out}\) indicate the sum of the weights of the edges incident upon node \(i\) and \(a_{i,j}^{in/out}\) is weight of the edge between node \(i\) and \(j\)[78].  
Transitivity is defined as  
\[T=\frac{\#triangles}{\#triads} \tag{6}\]  
where triads are two edges with a shared node.  
The clustering coefficient is a measure which defines the presence of loops of the order three and is defined as:  
\[C_{i}=\frac{e_{ij}}{k_{i}(k_{i}-1)} \tag{10}\]  
where \(e_{ij}\) represents the number of pairs of neighbours of \(i\) that are connected.  
Betweeness centrality (\(c_{B}\)) and closeness centrality (\(c_{C}\)) [79] are  
\[c_{B}(v)=\sum_{s\
eq v\
eq t\in V}\frac{\sigma_{st}(v)}{\sigma_{st}}, \tag{11}\]  
\[c_{C}(v)=\frac{1}{\sum_{t\in V}d_{G}(v,t)} \tag{12}\]  
where \(\sigma_{st}=\sigma_{ts}\) denotes the number of shortest paths from \(s\in V\) to \(t\in V\), and \(\sigma_{st}(v)\) denotes the number of shortest paths from \(s\) to \(t\) that some \(v\in V\) lies on, and \(d_{G}(s,t)\) is the distance between nodes \(s\) and \(t\).  
Page rank [80] of the node is based on the eigenvector centrality measure and implements the concept of 'voting'. The PageRank score of a node \(v\) is initialized to a default value and computed iteratively until convergence using the following equation:  
\[C_{PageRank}(v)=(1-d)+d\sum_{u\in N_{in}(v)}\frac{C_{PageRank}(u)}{|N_{out}(u)|} \tag{13}\]  
where \(d\) is the dumping factor set between 0 and 1 (usually 0.85).
## 2 Materials and Methods
### Quantitative Polymerase Chain Reaction (qPCR)  
Samples were processed with a commercial kit for DNA extraction (MoIYsis Complete5, Moleym Gmbh and Co. KG., Bremen, Germany) following manufacturer's instructions. Subgingival DNA samples were analyzed using multiplex qPCR to quantify the bacterial DNA of three putative periodontal pathogens (_A. actinomycetemontains_, _P. gingivalis_ and _T. forysythia_). The qPCR amplification was performed on Taqman Probes using specific primers targeted against 16S rRNA gene. For multiplex qPCR, the oligonucleotide probes of each bacterium were labeled with different fluorescent dyes, which have been selected on the bases of different fluoresce wavelengths [20].  
PCR amplification was performed in a total reaction mixture volume of 10 mL, which included 5 mL of 2 \(\times\) TaqMan master mixture (LC 480 Probes Master, Roche Diagnostic GmbH, Mannheim, Germany), optimal concentrations of primers and hydrolysis probe (300, 300 and 100 nM for _A. actinomycetemontains_; 300, 300 and 300 nM for _P. gingivalis_ and 300, 300 and 200 nM for _T. forysythia_) and 2.5 mL of DNA from the samples. Each plate featureured a standard curve using DNA from the target bacteria for internal calibration, as well as a no-template control (NTC). The samples underwent an initial amplification step at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles consisting of 15 s at 95 \({}^{\circ}\)C and 1 min at 60 \({}^{\circ}\)C, using the LightCycler(r) 480 II thermocycler (Roche Diagnostic GmbH, Mannheim, Germany). The assays were designed to have a linear quantitative detection range with a slope between 3.3 and 3.6 cycles per log decade, an r\({}^{2}\) value greater than 0.997 and efficiency ranging from 1.9 to 2.0.
## 4 Enhancement of Brillouin Stokes Signal (I): Induction of Stimulated Scattering
### Summary  
The SBS in a PFGI-POF was successfully observed at 1.55 \(\upmu\)m with pump-probe technique. We detected the BGS with extremely high S/N ratio, even with a 1-m PFGI-POF, scrambled polarization state, and no averaging. We also investigated the BGS dependence on probe power, which proved that the measured BGS was caused not by SpBS but by SBS. Besides, we measured the BGS dependence on temperature, showing that BGS in a PFGI-POF observed with this technique can be applied to temperature sensing with high accuracy. We are sure that use of SBS with large Stokes signal will be indispensable in developing Brillouin-based distributed temperature/strain sensing systems using PFGI-POFs.
## 3 Materials and Methods
### Experimental Settings  
The feasibility study was carried out in Sigal Dental Clinic, Riyadh, Saudi Arabia. The study was conducted in accordance with ethical principles and standards set forth by the Research Ethical Committee in King Saud University. Prior to conducting the study, written informed consent was obtained from the parents or legal guardians of all child participants, and their confidentiality and privacy were protected throughout the study.  
The functionality and acceptability of the proposed system were examined with children who had or had not had prior dental exposure. Children were randomly assigned to one of the two groups: the VR group, which used the proposed systems, and the control group, which used conventional distraction techniques such as the tell-show-do (TSD) technique to assist children in adapting to the dental setting.  
A total of 16 children, 6 boys and 10 girls, ranging in age from 7 to 10 with an average age of 8.3 \(\pm\) 1.0, were randomly allocated to the VR group (\(n\) = 8) and the control group (\(n\) = 8).  
The Statistical Package for Social Science (SPSS) version 25 was used to analyze all data. The significance threshold was set at 5% (\(p\) < 0.05). The T test was used to evaluate the differences between the groups. [PERSON]'s exact test was used to compare the distributions of gender and age between the groups. Due to the possibility of sample bias and varying levels of anxiety, all the children's clinical consultations were conducted by a single dentist.  
Dental anxiety levels in children and adults have been measured for many years through validated questionnaires in the literature [46; 47]. The Children's Fear Survey Schedule--Dental Subscale (CFSS-DS) [46] is the most used questionnaire to assess dental anxiety in children over the age of six years, and it was chosen for use in this research.
## 3 Results and Discussion
### Biodegradability  
After the test, we tried to dig up the samples of foam. However, the packaging had completely degraded and adhered to the earth, exhibiting no residual material capable of being weighed (as shown in Figure 1).  
The result of the loss of mass as a function of the time during which the package remained buried has been shown in Figure 2. The figure also shows the standard deviation (SD), which indicates that the data is more dispersed during days 1-14, after which it exhibits little variation.  
Figure 1: Samples of foam developed from the potato by-products on the first and twenty-first days of the biodegradability analysis.  
With respect to biodegradability, it is observed that hydrolysis may have been the main mechanism of foam degradation. Other factors that could have influenced biodegradability include biological activity (especially the activity of enzymes released by microorganisms present in the soil) and abiotic degradation, such as photodegradation [36].  
The biodegradation of starch is achieved by hydrolysis of the acetal bond by enzymes (present in the experiment). The amylases attack the \(\alpha\)-1,4 bond while the glucosidases attack the \(\alpha\)-1,6 bond. It should be noted that the degradation products do not have any toxic characteristics. As shown in Figure 2, there was an increase in mass in the first seven days. A similar result was reported by [PERSON] et al. [37], with their samples showing visible swelling after seven days of soil burial testing. The authors claim that water could have reacted with the hydroxyl groups of the starch molecules: the hydrogen bond and the molecular interaction among the starch molecules were potentially destroyed, thereby increasing the swelling.  
The use of sodium benzoate as a preserving agent does not appear to have impacted biodegradability. This is because the compound is readily biodegradable, with 90-93% of the biodegradation occurring in seven days [38].  
[PERSON] et al. [39] verified the decomposition of films of cassava starch, glycerol, and natural polyphenols extracted from rosmenary leaves and observed that the decomposition occurred almost entirely after 14 days of testing. During this period, the integrity of the films was better preserved, indicating that biodegradation was delayed by the presence of rosmenary extract.  
These findings can be applied to future work: other agro-industrial residues with high production can be tested to replace oats, such as those from the brewing industry, and rice husks. The proportion of components can be optimized through factorial planning, aiding in the search for suitable packaging material. The design of portable equipment capable of the temperature and pressure control necessary to mold the dough in the desired format is crucial for standardization of the process; such equipment would also facilitate tests on an industrial scale.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## Appendix A The Structural Similarity Index (SSIM)
### The Gradient Magnitude Similarity Deviation (GMSD)  
The gradient is generally calculated by convolving an image with a linear filter, usually a Prewitt filter, along both the horizontal direction, \(h_{h}\), and the vertical direction, \(h_{v}\). By performing the convolution of \(h_{v}\) and \(h_{h}\) with the reference image and the distorted image, the horizontal and vertical gradient images of \(X\) (reference image) and \(Y\) (distorted image) are obtained as follows:  
\[\begin{array}{c}m_{X}(i)=\sqrt{\left(X*h_{v}\right)^{2}(i)+\left(X*h_{h} \right)^{2}(i)}\\ m_{Y}(i)=\sqrt{\left(Y*h_{v}\right)^{2}(i)+\left(Y*h_{h}\right)^{2}(i)}\ \ \ \ \ i\in[1,\ N],\end{array}\] (A4)  
where \(i\) represents any pixel of the image and \(N\) is the total number of pixels in the image. With the gradient images, we calculate the gradient magnitude similarity (\(GMS\)),  
\[GMS(i)=\frac{2m_{X}(i)m_{Y}(i)+c}{m_{X}^{2}(i)+m_{Y}^{2}(i)+c},\] (A5)  
where c is a positive constant that provides numerical stability and controls the contrast response in low gradient areas. In this work, we used a value of \(c=0.0026\), obtained by [PERSON] et al. [11] provided that 8-bit images are considered and with normalized luminance in the range \([0,1]\). If \(m_{X}(i)\) and \(m_{Y}(i)\) are the same, \(GMS(i)\) reaches the maximum value, 1. The \(GMS\) map serves as the quality map of the distorted image and reflects the local quality of each small area in the distorted image. The most used way to calculate the quality of a distorted image is to obtain the mean of the elements of the \(GMS\) map, \(GMSM\),  
\[GMSM=\frac{1}{N}\sum_{i=1}^{N}GMS(i)\] (A6)  
Based on the idea that the overall variation of the degradation of local image quality may reflect its overall quality, [PERSON] et al. proposed a metric that evaluates the standard deviation of the \(GMS\) map. This metric, gradient magnitude similarity deviation is denoted by \(GMSD\),  
\[GMSD=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(GMS(i)-GMSM)^{2}}\] (A7)  
The value of this metric is null if there is no distortion. Although the metric does not have higher bounds, we can observe that, for very high values of distortion (normalized difference mean opinion score, \(DMOS\), close to unity), the value of this metric is approximately 0.35.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 3 Dry Methods for Black Silicon Fabrication
### Plasma Etching  
The developed BSi using a novel plasma etching technique known as Clear, Oxidize, Remove, and Etch (CORE) has diverged from traditional methods by replacing the fluorocarbon inhibitor in the RIE process with O2[65]. This adaptation leverages the self-regulating characteristics of the oxidation phase, thereby improving the synthesis and regulation of BSi compared to fluorocarbon-based methods. Their study thoroughly investigates the impact of procedure parameters on the formation of masks and BSi creation, demonstrating that the CORE process allows for the precise manipulation of the removal and isotropic etch steps. This technique enables \"BSi on demand\", where the formation of BSi can be selectively controlled, offering improved uniformity across various structural dimensions and independence from the aspect ratio of the etched features. The versatility of the CORE process thus opens new avenues for applications requiring finely tuned optical and surface properties.  
A plasma etching technique to fabricate BSi surfaces caped with nanoneedles altering in length, areal density, and sharpness has been employed by [PERSON] et al. [66]. These nanostructured surfaces were subsequently enveloped in a conformal and homogenous layer of diamond via hot filament chemical vapor deposition, thereby facilitating the formation of black diamond surfaces.
## 4 Conclusions  
Numerical simulation research on the operation-condition monitoring device of a high-speed train was conducted. The calculation accuracy was increased by using the IDDES method. By conducting an analysis of the aerodynamic force, surface flow pattern, and space wake structure, the following conclusions were obtained:  
1. The windward side of the detection device experienced positive pressure, and the sideline and leeward sides experienced negative pressure. Increasing the filled radius of the sideline can appropriately reduce the aerodynamic resistance.
2. The shaping of the downstream flow in the leeward area can effectively reduce the resistance of the monitoring device. The resistance of configurations 1 and 2 in the reverse direction was about \(1/2\) of that in the forward direction, and the resistance of configurations 3 in the forward and reverse directions was the same. The streamlined upwind surface was conducive to reducing the scope of the leeward separation area and the amplitude of the pressure fluctuation in the leeward area and thus reduced the resistance;
3. The monitoring device had a certain height and formed a vortex on its leeward side when the train was running at high speed. From the results of the analysis on the pressure monitoring, we found that the separated airflow did not have an obvious dominant frequency and energy peak, and the possibility of resonance damage occurring to the following parts, such as the top of the train, was small.  
Methodology, B.L., X.W. and N.X.; Software, B.L. and X.W.; Validation, X.W. and N.X.; Formal analysis, Y.T. and N.X.; Investigation, B.L. and J.W.; Resources, J.W.; Data curation, J.W.; Writing--original draft, Y.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China, grant number 11372337.  
The authors declare no conflict of interest.  
Figure 17: Comparison of pressure and Mach number distribution on symmetry plane under different operating conditions; (**a**) Pressure distribution during positive operation; (**b**) Pressure distribution during negative operation; (**c**) Mach number during positive operation (**d**) Mach number during negative operation.
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Classification Models
In our study, GoogleNet was applied to classify five datasets. The confusion matrices derived from these classification results are displayed in Figure 11.  
For each model's performance, we have conducted a detailed comparison based on the results from the aforementioned confusion matrices, as shown in Tables 5-9. These tables include the performance of each model in each dataset, with parameters such as accuracy, precision, recall, and F1 score. These measures were chosen for their comprehensive and detailed reflection of model performance. Firstly, accuracy, as the most direct performance indicator, provides the proportion of correct predictions in the overall sample and is fundamental for assessing the overall efficacy of the model. However, relying solely on accuracy might overlook the impact of class imbalance. To address this, the introduction of precision and recall becomes necessary. Precision measures the ratio of true positives  
Figure 10: Resnet50 confusion matrix. ((**a**-**e**) represent the confusion matrices obtained from the classification tasks performed by ResNet50 on datasets HAM10000, Diabetic Retinopathy Arranged, Breast Ultrasound Images Dataset, Brain Tumor Classification (MRI), and Chest X-ray Images, respectively). among the predicted positives, essential in situations where false positives carry a high cost. Conversely, recall quantifies the percentage of actual positives accurately identified, vital in contexts where minimizing false negatives is paramount. Lastly, the F1 score, calculated as the harmonic mean of precision and recall, provides a balanced metric between these two measures, especially suitable for scenarios that require reducing both false positives and negatives. Figure 11: GoogleNet confusion matrix. ((**a**–**e**) represent the confusion matrices obtained from the classification tasks performed by GoogleNet on datasets HAM10000, Diabetic Retinopathy, Arranged, Breast Ultrasound Images Dataset, Brain Tumor Classification (MRI), and Chest X-ray Images, respectively). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline normal & DenseNet121 & 0.97 & 0.99 & 0.92 & 0.95 \\ pneumonia & DenseNet121 & 0.97 & 0.96 & 0.99 & 0.97 \\ AVERAGE & DenseNet121 & **0.97** & **0.975** & **0.955** & **0.96** \\ normal & EfficientNet\_b3 & 0.95 & 0.98 & 0.89 & 0.93 \\ pneumonia & EfficientNet\_b3 & 0.95 & 0.94 & 0.99 & 0.96 \\ AVERAGE & EfficientNet\_b3 & **0.95** & **0.96** & **0.94** & **0.95** \\ normal & GoogleNet & 0.96 & 0.99 & 0.9 & 0.94 \\ pneumonia & GoogleNet & 0.96 & 0.94 & 0.99 & 0.97 \\ AVERAGE & GoogleNet & **0.96** & **0.97** & **0.95** & **0.95** \\ normal & ResNet50 & 0.97 & 0.97 & 0.96 & 0.96 \\ pneumonia & ResNet50 & 0.97 & 0.97 & 0.98 & 0.98 \\ AVERAGE & ResNet50 & **0.97** & **0.97** & **0.97** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of model performance of chest X-ray dataset.
## 1 Introduction
Other studies adopt image-enhancement techniques for semantic segmentation to distinguish the delaminated from sound portions of the structure [13; 14]. In studies conducted by [PERSON] et al. [24] for automatic delamination segmentation, the authors adopted an encoder-decoder architecture. The developed model was trained on augmented laboratory data, validated, and tested on non-augmented data. The model falsely detected defects, resulting in lower precision and recall. To the best of the authors' knowledge, very few or no studies have been carried out extensively on evaluating delamination detection for in-service bridges using publicly available IRT datasets for training, validating, and testing DCNN models. Most DCNN applications for delamination detection are conducted on mostly laboratory-developed datasets. These datasets are usually collected in a controlled environment. In this study, we have used a publicly available dataset [13; 14; 25; 26] to develop a DCNN model for delamination classification and prediction. We further evaluated the effect of different preprocessing and annotated datasets on the model's output. We have adopted an encoder-decoder UNET DCNN model in our study. This architecture had been successful for biomedical image segmentation [27] but had not been effectively deployed to evaluate delamination in bridge decks. The architecture has also been adopted in evaluating three classes of bridge structural conditions: delamination, rebar exposure, and non-damage [28]. Therefore, the aim of this study centers on deploying the UNET encoder-decoder DCNNarchitecture to train the publicly available SDNET2021 dataset for pixel-wise semantic segmentation in bridge decks. The following objectives are highlighted in achieving the goal of this study:  
1. Adopted publicly available IRT dataset of in-service bridge deck for training, validating, and testing DCNN model;
2. Considered different levels of preprocessing on the performance of the model;
3. Compared the DCNN model's performance with the image-based method adopted on the same dataset.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 4 Results and Discussion
### Simulation Results
#### 4.4.7 Scalability of the Proposed Scheme (Type-II Attack)  
To assess the scalability of the proposed scheme, different numbers of nodes are used. The selfish population is fixed at 20% and the speed is kept at 15 m/s. Figure 10 shows the end-to-end delay versus the number of nodes. It is evident that our proposed algorithm performs similarly regardless of the number of nodes and reports less delay as compared to the benchmark scheme. It is worth noting that the selfish nodes continuously change their position (location), but our scheme can effectively locate these nodes and isolate them from the network quickly. As a result, the nodes can quickly reconstruct the route and communicate within a controlled delay. In contrast, when nodes in OCEAN change location, every time they appear as new nodes they will utilize network resources until they are detected. As a result, it can be concluded that the proposed scheme can scale to networks with a large number of users.