esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods
### Overall Model Architecture  
In this section, we present the overall network architecture of our ABC ensemble model for classifying cancer using the HAM10000 dataset. The ABCD rule [42] is a tool used in dermatology to identify skin lesions that may be potential malignancies. Asymmetry (A) evaluates whether the two halves of a lesion are symmetrical. Border (B) checks for irregular or uneven edges around the skin lesion, while Color (C) assesses whether the pigmentation is uneven, with a mix of different shades. Diameter (D) helps to determine if a lesion is larger than 6 mm, which may indicate malignancy [43]. However, diameter (D) is only meaningful when all of the images are captured at a consistent height and angle. In the HAM10000 dataset, as shown in Figure 3, some images are highly magnified, while others are taken from farther away, making it difficult to effectively utilize diameter information. As a result, diameter (D) is excluded from our study. Therefore, our ensemble model focuses on the ABC components rather than the ABCD, and is referred to as the ABC ensemble.  
Figure 4 illustrates the overall architecture of our model. First, the ROI of the lesion is extracted from the original image using U-Net by generating a segmentation mask. The details are described in Section 3.3. Next, five different classifiers are trained: two for general classification tasks (Block 1: Generality Training and Block 2: Upsize Training), and three classifiers specifically tailored to the ABCD rule (Block 3: Asymmetry Enhancement Training, Block 4: Border Enhancement Training, and Block 5: Color Enhancement Training). Finally, a weighted ensemble of the five trained classifiers is performed to classify the skin lesions. The details on each of the five classifiers are provided in Section 3.4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diagnostic Category** & **Number of Images** & **Percentage** \\ \hline akiec & 327 & 3.27\% \\ \hline bcc & 514 & 5.13\% \\ \hline bkl & 1099 & 10.97\% \\ \hline df & 115 & 1.15\% \\ \hline mel & 113 & 11.11\% \\ \hline nv & 6705 & 66.95\% \\ \hline vasc & 142 & 1.42\% \\ \hline
**Total** & **10**,**015** & **100\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number and ratio of images in the ’akiec’, ’bcc’, ’bkl’, ’df’, ’mel’, ’nv’, and ‘vasc’ classes in the HAM10000 dataset.  
Figure 2: The difference in image ratios between classes before and after data augmentation. The left figure (**a**) shows the image ratios for each class in the original HAM10000 dataset before oversampling, while the right figure (**b**) shows the image ratios after augmentation.
## 3 Results and Discussion
### Effect of pH Value on Adsorption  
The acid or base nature of a solution exerts influence not merely on the solubility of the adsorbent but also affects the speciation of ions within the solution. To investigate the effect of pH on adsorption, the pH of a 0.15 mmol/L BPA solution was adjusted using HCl or NaOH. This was conducted at an adsorption temperature of 25 \({}^{\circ}\)C and an adsorbent dosage of 0.2 g/L. As Figure 9a illustrates, the adsorption rate of BPA decreased with increasing pH. At acidic pH (pH = 3), the nanocomposite exhibited an adsorption rate of 68% towards a BPA solution with C\({}_{0}\) = 0.15 mmol/L. Under neutral and alkaline conditions, the adsorption rates were 56% and 50%, respectively. The maximum adsorption rate was observed at pH = 3, and the adsorption capacity of the SWCNTs/Fe\({}_{x}\)O\({}_{y}\) for BPA gradually diminished as the pH value increased. Therefore, for practical applications, wastewater acidification is often used as a pre-treatment step for adsorption methods. This process can break down complex polymeric organic matter into smaller molecular organic compounds, but the acidification of wastewater will also increase the cost and affect the biological treatment.  
In general, BPA is present in its molecular form in the solution. However, the deprotonation of the BPA single anion occurs at pH 8, and further ionization occurs when the pH exceeds 9.6. Additionally, we also identified the point zero charge (pH\({}_{\text{pzc}}\)) of the SWCNTs/Fe\({}_{x}\)O\({}_{y}\) in the study, which is about 2.9, so the nanocomposite is positively charged at a pH below 2.9 and negatively charged at a pH above 2.9. Therefore, BPA2\({}^{-}\) and negatively charged SWCNTs experienced electrostatic repulsion [32], which resulted in a decrease in the adsorption rate of BPA onto the SWCNTs/Fe\({}_{x}\)O\({}_{y}\) as pH increased. Moreover, it is evident that the electrostatic interaction between the BPA and SWCNTs/Fe\({}_{x}\)O\({}_{y}\) is not the primary factor controlling adsorption.  
Figure 8: Effect of adsorbent dosage (T = 25 \({}^{\circ}\)C, pH = 6, C\({}_{\text{BPA}}\) = 0.075 mmol/L).  
Figure 9: (**a**) Effect of pH value and (**b**) the point of zero charge of SWCNTs/Fe\({}_{x}\)O\({}_{y}\).
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 3 Methodology
### Confusion Matrix  
A confusion matrix is a popular measurement tool for classification models; it displays a matrix representation of predicted values in comparison to actual values. It is mainly beneficial to examine a model's precision and recall capacity, as well as its accuracy and Area Under the Curve [24]. To understand terms used in the confusion matrix such as the True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN), we use this research's case:  
* True Positives (TP) are cases in which the taxpayer is predicted to be a fraudster and is, in fact, a fraudster.
* False Positives (FP) are cases in which the taxpayer is predicted to be a fraudster and is actually not a fraudster. This is also known as Type I error.
* True Negatives (TN) are cases in which the taxpayer is predicted not to be a fraudster and is not a fraudster.
* False Negatives (FN) are cases in which the taxpayer is predicted not to be a fraudster and is actually a fraudster. This case is most known as the Type II error, and it is a very risky error especially in tax fraud detection.
* Accuracy is the ratio of observations that are correctly predicted to the total observations and the formula is shown in below equation [24; 26]. \[\text{Accuracy}\ =\frac{TP+TN}{TP+FP+TN+FN}\] (3)
* Precision highlight how accurate is the model on predicting the positive classes, i.e., how many of the predicted positive are actually positive [26; 31]. \[\text{Precision}\ =\frac{TP}{TP+FP}\] (4)
* Recall highlight how many from the actual positives are predicted as positives. although it sounds simple, this is a very crucial metric for the tax fraud issue because there is much more risk and cost in predicting a taxpayer non fraudulent while they are fraudulent [26; 31]. \[\text{Recall}=\frac{TP}{TP+FN}\] (5)F1 Score as a function of recall and precision, the F1 Score is usefully to examine how much of a balance is between the precision and recall [26; 31].  
\[\text{F1 Score}=\frac{2\times Recall\times Precision}{Recall+Precision} \tag{6}\]
## 4 Experiments
### Ablation Experiment  
In order to evaluate the contribution of the method modules in this work, this paper designs ablation studies on the models, including an IIA for intent detection and a SLE for slot filling. In this section, this paper removes the proposed method modules one by one and performs ablation experiments on the IMCS-21 dataset with the CAIS dataset.  
Firstly, this paper removes the IIA for intent detection and keeps the other components of the model unchanged. In this paper, it is named Ours-IIF, and the results are given in Tables 6 and 7. In this paper, it is found that the Acc of the IMCS-21 dataset and the CAIS dataset is significantly reduced, which proves that the proposed IIA can effectively identify the correct intent to facilitate Chinese intent detection. It can also be observed that the F1 and OAcc decrease by 0.1% and 0.89%, respectively, in the results of Table 7, which further validates the effectiveness of the IIA proposed in this paper.  
Secondly, this paper removes the SLE for slot filling and keeps the other components of the model unchanged. In this paper, it is named Ours-LE. The results are shown in Tables 6 and 7. In this paper, it can be observed that the F1 of the IMCS-21 dataset and the CAIS dataset have decreased by 0.34% and 0.07%, respectively. This paper attributes this to the fact, that the fusion of character and word information can help the model detect important labels, and the proposed SLE can effectively extract complete label information to improve slot filling.  
Finally, this paper removes both the IIA and the SLE, which is named Ours-ALL in this paper, and the results are shown in Tables 6 and 7. In the results of Table 6, the IMCS-21 dataset decreases 1.06%, 1.48%, 0.59%, and 0.76% in the four metrics of P, R, F1, and Acc, respectively. In the results of Table 7, the CAIS dataset decreases by 0.83%, 0.37%, and 1.22% in the metrics of F1, Acc, and OAcc, respectively. This further confirms the feasibility of the second contribution point proposed in this paper. Therefore, in summary, both the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **P** & **R** & **F1** & **Acc** \\ \hline Ours-IIF & 77.85 & 79.92 & 79.08 & 82.18 \\ Ours-SLE & 77.82 & 80.72 & 78.77 & 82.38 \\ Ours-ALL & 77.17 & 79.38 & 78.52 & 81.63 \\ Ours & **78.23** & **80.86** & **79.11** & **82.39** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments on LICS-21 dataset.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **F1** & **Acc** & **OAcc** \\ \hline Ours-IIF & 91.49 & 95.43 & 88.67 \\ Ours-SLE & 91.76 & 95.46 & 89.17 \\ Ours-ALL & 91.10 & 95.16 & 88.34 \\ Ours & **91.93** & **95.53** & **89.56** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiments on CAIS dataset.  
IIA and the SLE have their own advantages, and the union of the two is more instructive and practically valuable.
# Functionalization Strategies and Fabrication of Solvent-Cast PLLA for Bioresorbable Stents
## 3 Results and Discussion  
The aim of this study was to evaluate solvent-casted PLLA as a biomaterial for stent fabrication by 3D printing, and, its functionalization to enhance ECs adhesion. To this end, the effect of solvent casting and functionalization processes onto PLLA's biocompatibility, degradation and mechanical properties were evaluated. The generation of functional groups at the surface, generally carboxyl (-COOH) or hydroxyl (-OH) groups, can be obtained by different routes as explained in Figure 1. Two wet-processes involving chemical hydrolysis by sodium hydroxide (SH) or enzymatic treatment (ET) produces scission of the ester bonds in the chain generating new hydroxyl and carboxyl groups on the surface. In contrast, the oxygen plasma (OP) treatment, produces excited species in the gaseous phase (i.e., O* radicals, O2- ions, etc.) with high reactivity that can graft new chemical groups to the polymer surface through a dry process. Therefore, the density of surface functional groups was measured to compare the effect of NaOH, oxygen plasma, and cutinase enzymes employed as surface activation treatments. Finally, as a proof of concept, 3D-printed PLLA stents were obtained by the solvent-cast direct-write technique.  
Figure 1: Strategies followed in this work for the generation of functional carboxyl (-COOH) or hydroxyl (-OH) onto poly(-lactic acid) (PLLA): Hydrolysis by sodium hydroxide etching (SH) or enzymatic treatment (ET) (top); and surface functionalization by oxygen plasma (OP) (bottom).
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 3 Design of Target Importance Rank Algorithm
### Identification of Cyber Target Materiality Criteria and Utilization Parameters  
The cyber target importance quantification method defined in Section 2.3.3 was derived by synthesizing the research cases in 2.4.  
Equation (1) calculates the connectivity of each page through parameters such as \(M(p_{i})\) and outputs the probability distribution using parameter \(d\). Therefore, we change the definition of the connectivity between pages to the connectivity between hosts. We then compute \(d\) as the \"asset criticality\" value and assign a cyber target importance using the PageRank algorithm.  
As the cyber target is defined as the host in Section 2.2.1, the host exposure in Equation (2) can be assumed to be the target exposure, and the exposure degree, which is the vulnerability of the target, can be quantitatively calculated using EPF.  
Finally, using the indicators in Table 6, the target criticality, which is the degree of influence when attacking the host, can be quantitatively calculated.  
Based on these research results, three criteria for the importance of cyber targets were defined as listed in Table 7. When deriving the scores for each standard item quantitatively, factors that can be utilized were identified with Table 8.  
Analyzing Table 8, Equation (1) has two target importance criteria. It is a suitable algorithm for synthesizing the elements of the research cases of this paper. In addition, if Equation (2), which can quantify the target exposure, is properly fused with Equation (1), the importance of the target can be calculated quantitatively.  
In the next section, Equations (1) and (2), and Table 6, are used to design the TIR algorithm to be used to achieve the purpose of this study.
## 2 The Computational Principle of the Particle Flow Model
### The Equation for Fluid Forces  
Within the CFD module, the interaction between particles and fluid is incorporated through the inclusion of additional forces. As represented by Equations (7) and (8) [39].  
\[\frac{\partial\overrightarrow{u}}{\partial t}=\frac{\overrightarrow{f_{mch} }+\overrightarrow{f_{fluid}}}{m}+\overrightarrow{g} \tag{7}\]  
\[\frac{\partial\overrightarrow{w}}{\partial t}=\frac{\overrightarrow{M}}{I} \tag{8}\]  
Figure 2: Contact model.  
In the given equations: \(\overrightarrow{u}\) denotes the velocity of the particles; \(m\) represents the mass of the particles; \(\overrightarrow{f_{fluid}}\) signifies the total force exerted on the particles by the fluid; \(\overrightarrow{f_{mech}}\) represents the external forces acting on the particles; \(g\) denotes the acceleration due to gravity; \(w\) represents the angular velocity of the particles; \(I\) denotes the moment of inertia; and \(M\) signifies the total torque acting on the particles.  
\(\overrightarrow{f_{fluid}}\) is composed of two components: the drag force and the fluid pressure gradient force. This is represented by Equation (9) as follows [40]:  
\[\overrightarrow{f_{fluid}}=\overrightarrow{f_{drag}}+\frac{4}{3}\pi r^{3}( \overrightarrow{\
abla}p-\rho_{f}\overrightarrow{g}) \tag{9}\]  
The drag force is determined based on the fluid elements containing the particle. Throughout the computation, the fluid-particle interaction is always applied at the center of the particle, thus inducing no bending moment on the particle. The drag force is represented by \(\overrightarrow{f_{drag}}\) as follows [40]:  
\[\overrightarrow{f_{drag}}=\overrightarrow{f_{0}}\varepsilon^{-\chi} \tag{10}\]  
In the equations, \(\overrightarrow{f_{0}}\) denotes the drag force exerted on a single particle, \(\varepsilon\) represents the porosity of the fluid element in which the particle is situated, and \(\varepsilon^{-\chi}\) is an empirical coefficient considering the local porosity.  
The drag force \(\overrightarrow{f_{0}}\) acting on a single particle can be expressed as follows [40]:  
\[\overrightarrow{f_{0}}=\frac{1}{2}C_{d}\rho_{f}\pi r^{2}\left|\overrightarrow {u}-\overrightarrow{v}\right|(\overrightarrow{u}-\overrightarrow{v}) \tag{11}\]  
The empirical coefficient \(\chi\) is defined as follows [40]:  
\[\chi=3.7-0.65\exp\left[-\frac{1}{2}(1.5-\log_{10}R_{ep})^{2}\right] \tag{12}\]  
In the given equations, \(C_{d}\) denotes the drag force coefficient, \(\rho_{f}\) represents the fluid density, \(r\) is the particle radius, \(\mu\) stands for the particle velocity, and \(v\) denotes the fluid velocity.  
The drag coefficient \(C_{d}\) is defined as follows [39]:  
\[C_{d}=(0.63+\frac{4.8}{\sqrt{R_{ep}}})^{2} \tag{13}\]  
The Reynolds number \(R_{ep}\) is calculated using the following formula [39]:  
\[R_{ep}=\frac{2\rho_{f}r\left|\overrightarrow{\mu}-\overrightarrow{v}\right| }{\mu_{f}} \tag{14}\]  
\(\mu_{f}\) denotes the dynamic viscosity of the fluid.
## 2 Materials and Methods  
### Participants  
Utilizing G*Power 3.1.9 software and in alignment with previous studies [9] as well as the statistical methods employed in this research, the required sample size was estimated. The power, Type I error rate (\(\alpha\)), and effect size were set to 0.95, 0.05, and 0.4, respectively. Calculations indicated a sample size of 24 individuals, with an additional 20% to account for potential invalid samples, resulting in a minimum sample size of 29 individuals. Following the selection criteria established by the International Ankle Consortium [19], 15 participants with unilateral FAI were recruited for the experimental group (FAI group), comprising eight males and seven females, through scores on the Cumberland Ankle Instability Tool (CAIT)and in conjunction with anterior drawer test methods. A control group of 15 individuals with no history of ankle injuries was matched based on gender, morphological characteristics, and athletic background to the FAI group participants, and the corresponding unaffected side was chosen for matching (Table 1). Prior to the experiment, all participants were informed of the testing procedures and signed informed consent forms. The study was approved by the Ethics Committee of Soochow University (Ethics Approval Number: SUDA0221005H03).  
Inclusion Criteria for the FAI Group: 1 At least one instance of a unilateral ankle sprain, with the initial sprain occurring at least 12 months prior to the trial and presenting with inflammatory symptoms (pain, swelling, etc.) that interrupted physical activity for at least one day; 2 Participants without a history of severe lower-limb injuries, except for ankle sprains [20]; 3 CAIT score \(\leq\) 24 [21]; 4 FAI present in only one ankle.  
Exclusion Criteria for the FAI Group: 1 History of lower-limb surgery [20]; 2 Acute musculoskeletal injury to other joints in the lower limb within the month prior to the formal trial; 3 Bilateral FAI [20]; 4 Congenital joint deformity; 5 Positive anterior drawer test result or talar tilt; 6 CAIT score \(>\) 24.  
Inclusion Criteria for the Control Group: 1 CAIT score \(\geq\) 28 [21]; 2 Gender, morphological characteristics, and athletic background matched with the FAI group participants.  
Exclusion Criteria for the Control Group: 1 CAIT score \(<\) 28; 2 History of musculoskeletal surgery on the lower limb; 3 Acute joint injury within the month prior to the trial; 4 Inability to complete assessments as required or missing data during the trial.
## 3 Method Design
### Dependency Information Fusion Attention Graph Convolutional Network  
In this paper, we propose a graph convolutional network architecture incorporating dependency information. We call this model Dependency Information Fusion Attention Graph Convolutional Network, abbreviated as DIF-A-GCN. The model is divided into: input layer, BERT encoding layer, graph convolution layer, attention mechanism, and classification layer.  
Figure 7 shows the model diagram of this paper. The input sentence dependency tree is pruned by the pruning operation in Section 3.1, and then the sentence is encoded by  
Figure 5: Illustration of L+G Tree pruning.  
Figure 6: Illustration of the construction of the dependency type matrix.  
BERT. The obtained word vectors enter the graph convolution module in the blue dashed box on the left. The Adjacency Matrix(A) in the figure is obtained based on the nodes and edges of the dependency connections after the pruning of the sentence dependency tree and is used in the graph convolution model. The Dependency Type Matrix(D) in the figure refers to the matrix obtained in Figure 6, which serves to incorporate the external dependency information of the sentence into the word embedding so as to perform graph convolution for feature extraction and obtain a more complete representation of entity pairs. The blue dashed box module on the right is used to calculate the attention weight of each dependency connection to obtain the third matrix Attention Matrix, which guides the model to learn features better by incorporating attention mechanism. All three matrices are used in the graph convolution module, which improves the traditional GCN. Finally, the relation between two given entities is predicted by sentence representation and entity pair representation.
###### Abstract  
Recent advances in the production and development of two-dimensional transition metal dichalcogenides (2D TMDs) allow applications of these materials, with a structure similar to that of graphene, in a series of devices as promising technologies for optoelectronic applications. In this work, molybdenum disulfide (MoS\({}_{2}\)) nanostructures were grown directly on paper substrates through a microwave-assisted hydrothermal synthesis. The synthesized samples were subjected to morphological, structural, and optical analysis, using techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman. The variation of synthesis parameters, as temperature and synthesis time, allowed the manipulation of these nanostructures during the growth process, with alteration of the metallic (1T) and semiconductor (2H) phases. By using this synthesis method, two-dimensional MoS\({}_{2}\) nanostructures were directly grown on paper substrates. The MoS\({}_{2}\) nanostructures were used as the active layer, to produce low-cost near-infrared photodetectors. The set of results indicates that the interdigital MoS\({}_{2}\) photodetector with the best characteristics (responsivity of 290 mA/W, detectivity of \(1.8\times 10^{9}\) [PERSON] and external quantum efficiency of 37%) was obtained using photoactive MoS2 nanosheets synthesized at 200 \({}^{\circ}\)C for 120 min.  
MoS\({}_{2}\); microwave-assisted hydrothermal synthesis; low-cost photosensors +
Footnote †: journal: Applied Sciences