esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Damage Constitutive Model  
### Deduction of Damage Constitutive Model  
[PERSON] [41] proposed that the strain of nominal stress acting on damaged material is the same as the strain of effective stress acting on a non-damaged mixture during uniaxial compression. The expression is as follows:  
\[\sigma=E(1-D)\varepsilon \tag{1}\]  
In this formula: \(\varepsilon\) denotes strain; \(\sigma\) represents effective stress; D stands for the damage variable; and E indicates the initial elastic modulus.  
Given that the stress-strain curve of the AOD slag mixture under compression conforms to a Weibull distribution, it is inferred that the damage variable D also adheres to this distribution. Considering the inclusion of the damage threshold, a three-parameter Weibull distribution is adopted to describe the damage variable D, with the damage threshold designated as \(t\). The equation is as follows:  
\[D=\begin{cases}0&,x<t\\ 1-\exp[-(\frac{t-t}{a})^{b}]&,x\geq t\end{cases} \tag{2}\]  
In this formula, \(a\), \(b\), and \(t\) are all non-negative numbers.  
Figure 8: SEM micro-morphology of AOD slag with different dosages. (**a**,**b**) A-0; (**c**,**d**) A-3; and (**e**,**f**) A-12.  
Substituting Equation (2) into Equation (1) yields  
\[\sigma=\begin{cases}E\varepsilon&,x<t\\ E\varepsilon\exp[-(\frac{\varepsilon-t}{a})^{b}]&,x\geq t\end{cases} \tag{3}\]  
From the characteristics of the stress-strain curve, the following conditions can be derived: \(\langle\)1\(\varepsilon=0\), \(\sigma=0\); \(\langle\)2\(\varepsilon=0\), \(\mathrm{d}\sigma/\mathrm{d}\varepsilon=\mathrm{E}\); \(\langle\)3\(\varepsilon\)\(\varepsilon=\varepsilon_{\mathrm{c}}\), \(\sigma=\sigma_{\mathrm{c}}\); and \(\langle\)4\(\sigma=\sigma_{\mathrm{c}}\); \(\mathrm{d}\sigma/\mathrm{d}\varepsilon=0\). Among them, \(\varepsilon_{\mathrm{c}}\) and \(\sigma_{\mathrm{c}}\) correspond to the peak strain and stress values, respectively.  
Differentiating Equation (3) leads us to  
\[\frac{d\sigma}{d\varepsilon}=E\exp[-(\frac{\varepsilon-t}{a})^{b}][1-\frac{ be}{\varepsilon-t}(\frac{\varepsilon-t}{a})^{b}] \tag{4}\]  
Due to \(\mathrm{E}\
eq 0\), \(\exp[-(\frac{\varepsilon-t}{a})^{b}]\
eq 0\); then, \([1-\frac{b\varepsilon}{\varepsilon-t}(\frac{\varepsilon-t}{a})^{b}]=0\) can be used.  
\[\frac{be}{\varepsilon-t}(\frac{\varepsilon-t}{a})^{b}=1 \tag{5}\]  
By organizing this, it can be concluded that  
\[a=\frac{\varepsilon_{c}-t}{(\frac{\varepsilon_{c}-t}{b\varepsilon_{c}})^{ \frac{1}{b}}} \tag{6}\]  
By substituting Equation (6) back into Equation (3) and incorporating Condition 3, we derive  
\[b=\frac{\varepsilon_{c}-t}{\varepsilon_{c}}\frac{1}{\ln(\frac{\varepsilon_{c} }{\sigma_{c}})} \tag{7}\]
## 3 Results
### Summary of the Models  
Table 6 summarizes the statistics of the five models studied in this research. The R\({}^{2}\) of the models were relatively high (above 0.7) both for the year 2020 and for all the time series. In both cases, the highest RMSE occurred with the MODIS_GPP product and with the GPP_M1, while the lowest RMSE occurred with the Sentinel-2 models (GPP_S1 and GPP_S2).
## 3 Results and Discussion
### Pollutant Disappearance Experiments
#### 3.1.1 Polycyclic Aromatic Hydrocarbons (PAHs)
The involvement of these metabolites to basic metabolism and their subsequent mineralization to CO\({}_{2}\) are possible, and another possibility is that they can be the end metabolites of PAH degradation; their accumulation was found in _Phanerochaete chrysosporium_, _B. alusta_, and _Pycaporus sanguineus_. In our experiments, some differences were also found. The end metabolites of the degradation of PAHs by _S. rugosonnulata_ were quinone derivatives: 9,10-anthraquinone, phenanthrene-9,10-quinone, and 9-fluorenone. During PAH degradation by both _Trametes_ strains, the traces of the corresponding quinones were found. Similar metabolites were detected during cultivation of _P. ostreatus_ strains, _S. commune_, and _B. alusta_. They disappeared when the cultivation time was extended. This indicated that they were utilized further. Phthalic acid was the metabolite resulting from \"deep\" degradation of anthracene and fluorene by _P. ostreatus_ strains. The 2,2\({}^{\prime}\)-diphenic and phthalal acids were produced by \"deep\" phenanthrene degradation by these fungi. These metabolites can be included in the basal metabolism of the fungi. The proposed metabolic pathways of three-ring PAH degradation by studied fungi are shown in Figure 1.
## 3 Results
### CG vs. EG Post-Intervention Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **CG Post (n = 35)** & **EG Post (n = 35)** & \(p\)**-Value** \\ \hline
**Aerobic endurance (m)** & 398.71 \(\pm\) 81.98 & 480.82 \(\pm\) 73.78 & 0.036 \({}^{*}\) \\ \hline
**Lower body strength (Number of repetitions)** & 11.85 \(\pm\) 2.93 & 14.74 \(\pm\) 2.63 & 0.031 \({}^{*}\) \\ \hline
**Upper body strength (Number of repetitions)** & 14.42 \(\pm\) 3.52 & 18.82 \(\pm\) 4.55 & 0.068 \\ \hline
**Lower body flexibility (cm)** & \(-\)6.20 \(\pm\) 12.09 & 1.57 \(\pm\) 5.98 & 0.017 \({}^{*}\) \\ \hline
**Upper body flexibility (cm)** & \(-\)11.97 \(\pm\) 9.99 & \(-\)9.80 \(\pm\) 9.63 & 0.094 \\ \hline
**Dynamic balance (seconds)** & 7.34 \(\pm\) 2.11 & 6.45 \(\pm\) 1.55 & 0.158 \\ \hline
**Hand pressure strength right (kg)** & 54.85 \(\pm\) 13.95 & 55.85 \(\pm\) 17.88 & 0.237 \\ \hline
**Hand pressure strength left (kg)** & 51.71 \(\pm\) 13.05 & 54.00 \(\pm\) 16.66 & 0.779 \\ \hline \hline \end{tabular}  
* Note: \({}^{*}\)\(p\) < 0.05.  
\end{table} 
Table 6: Post-intervention results on the Senior Fitness Test battery variables of the control and experimental groups. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & & **EG Pre (n = 35)** & **EG Post (n = 35)** & \(p\)**-Value** \\ \hline
**Aerobic endurance (m)** & women & 435.46 \(\pm\) 64.01 & 470.85 \(\pm\) 68.75 & \textless{}0.001 \({}^{**}\) \\  & men & 455.57 \(\pm\) 70.51 & 520.71 \(\pm\) 85.21 & \textless{}0.001 \({}^{**}\) \\ \hline
**Lower body strength (Number of repetitions)** & women & 13.35 \(\pm\) 2.43 & 14.57 \(\pm\) 2.79 & \textless{}0.001 \({}^{**}\) \\  & men & 14.28 \(\pm\) 2.05 & 15.42 \(\pm\) 1.90 & 0.016 \({}^{*}\) \\ \hline
**Upper body strength (Number of repetitions)** & women & 15.57 \(\pm\) 5.30 & 18.82 \(\pm\) 4.70 & \textless{}0.001 \({}^{**}\) \\  & men & 15.85 \(\pm\) 7.03 & 18.85 \(\pm\) 4.25 & 0.004 \({}^{*}\) \\ \hline
**Lower body flexibility (cm)** & women & 0.39 \(\pm\) 5.82 & 2.67 \(\pm\) 5.72 & 0.010 \({}^{*}\) \\  & men & \(-\)5.28 \(\pm\) 8.45 & \(-\)2.85 \(\pm\) 5.14 & 0.165 \\ \hline
**Upper body flexibility (cm)** & women & \(-\)11.28 \(\pm\) 10.65 & \(-\)8.21 \(\pm\) 9.60 & \textless{}0.001 \({}^{**}\) \\  & men & \(-\)19.42 \(\pm\) 7.54 & \(-\)16.14 \(\pm\) 7.24 & 0.009 \({}^{*}\) \\ \hline
**Dynamic balance (seconds)** & women & 7.00 \(\pm\) 1.49 & 6.71 \(\pm\) 1.51 & 0.041 \({}^{*}\) \\  & men & 6.14 \(\pm\) 1.46 & 5.42 \(\pm\) 1.39 & 0.011 \({}^{*}\) \\ \hline
**Hand pressure strength right (kg)** & women & 45.82 \(\pm\) 10.50 & 49.46 \(\pm\) 9.26 & \textless{}0.001 \({}^{**}\) \\  & men & 77.28 \(\pm\) 18.91 & 81.42 \(\pm\) 21.73 & 0.009 \({}^{*}\) \\ \hline
**Hand pressure strength left (kg)** & women & 45.60 \(\pm\) 8.63 & 48.39 \(\pm\) 8.92 & 0.002 \({}^{*}\) \\  & men & 74.14 \(\pm\) 21.33 & 76.42 \(\pm\) 21.93 & 0.197 \\ \hline \hline \end{tabular}  
* Note: \({}^{*}\)\(p\) < 0.05; \({}^{**}\)\(p\) < 0.001.  
\end{table} 
Table 5: Pre-post-intervention comparison on the Senior Fitness Test battery variables of the EG according to sex.
## 4 Analysis of Ice Shedding Response of Overhead Conductors under Strong Winds
### Influence of Ice Morphology on the Ice Shedding Response of Transmission Lines  
In order to analyze the ice shedding response of transmission lines with different ice shapes under strong winds, three consecutive transmission line spans were established, with a distance of each span of 400 m. The middle span was an ice shedding section, with a 100% ice shedding rate and the insulator height difference not being taken into account. The wind speed was taken to be 10 m/s, and the equivalent thickness of the ice cover was 15 mm (with the ice loading being 20 N). The displacement curves of the middle location of the ice shedding span with different icing shapes are shown in Figure 5.  
Figure 5: Effect of different icing morphologies on ice-shedding bouncing process of transmission lines, and the red and yellow lines represent the projections of curves on different planes, respectively. (**a**) Round-shaped; (**b**) elliptical-shaped; (**c**) crescent-shaped; (**d**) fan-shaped; (**e**) D-shaped.  
The curves of the elliptical, circular, and crescent shaped iced conductor were smooth, and the lateral deviation distance generated by wind force was less than 1 m. For circular, elliptical, and crescent shaped icing samples, the outer surface of the icing was approximately streamlined, with a smaller lateral displacement at the initial position at a 180\({}^{\circ}\) wind attack angle. After ice shedding, the lateral amplitude was 1 m, slightly larger than that of the circular case. The longitudinal amplitude of the ice shedding bounce for the three icing morphologies was about 10 m, which was much greater than the transverse amplitude. Therefore, it can be concluded that the risk of ice shedding condition still mainly lay in the vertical direction, including discharge faults and structural faults.  
For fan-shaped and D-shaped iced conductors, the airflow resistance increased significantly at a 180\({}^{\circ}\) wind attack angle. The initial positions of the midpoint of the ice shedding span were 5.58 m and 8.82 m, respectively. The lateral amplitudes after ice shedding were 6.65 m and 11.71 m, while the longitudinal amplitudes were 9.35 m and 0.79 m, respectively. Therefore, in addition to the vertical direction, attention should also be paid to the lateral swing of the D-shaped iced conductor. Furthermore, the actual displacement of the fan-shaped iced conductor was greater than the other four types, and its risk is more complex.
## 4 Sparse Cost Functions  
In this section, we take a look at sparsely structured cost functions, for which the Sinkhorn algorithm becomes much faster and we overcome the curse of dimensionality. Let \(G=(\mathcal{V},\mathcal{E})\) be an undirected graph with vertices \(\mathcal{V}\) and edges \(\mathcal{E}\). We say that the MOT\({}_{\eta}\) problem has the graph \(G\) structure if \(\mathcal{V}=[K]\) and the cost matrix (3) decouples according to  
\[\mathbf{C_{i}}=\sum_{\{k_{1},k_{2}\}\in\mathcal{E}}\left\|x_{i_{1}}^{k_{1}}-x_{i_{ 2}}^{k_{2}}\right\|_{2}^{2},\quad\mathbf{i}=(i_{1},\ldots,i_{K})\in\mathbf{I}.\]  
This implies that the kernel \(\mathbf{\mathcal{K}}\in\mathbb{R}_{\geq 0}^{\mathbf{\mu}}\) satisfies  
\[\mathbf{\mathcal{K}}_{i}=\exp\Bigg{(}-\frac{\mathbf{C_{i}}}{\eta}\Bigg{)}=\prod_{\{k_{ 1},k_{2}\}\in\mathcal{E}}\mathbf{\mathcal{K}}_{i_{1},i_{k_{2}}}^{(k_{1},k_{2})}, \quad i\in\mathbf{I}, \tag{7}\]  
where the kernel matrix \(\mathbf{\mathcal{K}}^{(k_{1},k_{2})}\in\mathbb{R}_{\geq 0}^{m_{k_{1}}\times n_{k_{2}}}\) for \(\{k_{1},k_{2}\}\in\mathcal{E}\) is given by  
\[\mathbf{\mathcal{K}}_{i_{k_{1}},i_{k_{2}}}^{(k_{1},k_{2})}\coloneqq\exp\Bigg{(}- \frac{1}{\eta}\Big{\|}x_{i_{1}}^{k_{1}}-x_{i_{2}}^{k_{2}}\Big{\|}_{2}^{2}\Bigg{)}. \tag{8}\]  
We use the indices \(k\in\mathcal{V}=[K]\) to identify the marginal measures \(\mu^{k}\) in the rest of the paper.  
The discrete, dual formulation (6) of the MOT\({}_{\eta}\) problem has the same form independently of the structure of the graph \(G\); only the marginals \(P_{k}(\mathbf{\mathcal{K}}\odot\mathbf{\Phi})\) differ. If the graph \(G\) is complete, i.e., each of the two vertices in \(\mathcal{V}\) are connected with an edge, then the computational complexity of the Sinkhorn Algorithm 1 depends exponentially on the number \(K\) of marginal measures (vertices). For larger values of \(K\), it is practically impossible to numerically compute an optimal plan for the MOT\({}_{\eta}\) problem. We consider two sparsity assumptions of the MOT\({}_{\eta}\) problem, each of them yielding that the Sinkhorn algorithm has a linear complexity in the number \(K\) of the nodes. It was shown in [25] that MOT\({}_{\eta}\) problems with graphically structured cost functions of constant tree-width can be implemented in polynomial time. This is the case for the tree and circle, whose tree-widths are \(1\) and \(2\), respectively. In the following, we give an explicit scheme to efficiently compute the Sinkhorn iterations for the tree and circle structures.
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
## 2 Related Work  
### Password Hash Algorithm  
Password hashing algorithms are important functions used in cryptography that take an input (or 'plaintext') and return a fixed-size string of bytes, typically a 'digest' that is unique for each unique input. Hash functions are deterministic, meaning that they always return the same output for the same input. Password hashing algorithms play a crucial role in maintaining the security of stored passwords. Instead of storing a user's actual password, a system stores the hash of that password. During the login process, when a user enters their password, the system applies the hash function to this password and compares the result with the stored hashed password. If the two hashes match, the user is granted access. Well-designed password hashing algorithms possess several important characteristics. Firstly, it should be computationally difficult to compute the original input given only the hash output, a property known as 'pre-image resistance'. Secondly, given a specific input and its hash, it should be computationally difficult to find a different input with the same hash, a property known as'second pre-image resistance'. Thirdly, it should be difficult to find two different inputs that produce the same hash output, a property known as 'collision resistance'. Lastly, the algorithm should be able to use random data known as'salt' as additional input, which prevents identical passwords from producing identical hash outputs and protects against attacks using pre-computed tables of hash outputs (rainbow tables [7]). Examples of password hashing algorithms include bcrypt, scrypt, Argon2, and PBKDF2 [6; 8; 9; 10; 11]. Among these, bcrypt and Argon2 are widely recommended because they include built-in salting and their computational cost makes brute-force attacks slower. Notably, Argon2 was the winner of the Password Hashing Competition in July 2015 and is considered the state-of-the-art technology.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 3 Results and Discussion
### Performance Analysis of Deep Learning Acceleration Core Inference
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Literature** & **Literature** & **Literature** & **Literature** & **Literature** & **Literature** & **Literature** & **This Paper** \\  & **[30]** & **[31]** & **[29]** & **[32]** & **[33]** & **[34]** & **[35]** & **This Paper** \\ \hline Hardware Platform & VC707 & VC707 & \begin{tabular}{c} Strati-V \\ GSD8 \\ \end{tabular} & \begin{tabular}{c} XC7/2020 \\ \end{tabular} & \begin{tabular}{c} XC7/2045 \\ \end{tabular} & \begin{tabular}{c} XC7/2045 \\ \end{tabular} & \
\begin{tabular}{c} XC7/2045 \\ \end{tabular} &
\begin{tabular}{c} ZU15 EG \\ \end{tabular} \\ \hline Clock frequency & 100 MHz & 100 MHz & 120 MHz & 214 MHz & 225 MHz & 200 MHz & 125 MHz & 300 MHz \\ \hline DSP/Quantity & 2800 & 2800 & 1963 & 220 & 576 & 900 & 900 & 209 \\ \hline Performance/GOPS & 85.2 & 61.62 & 72.4 & 84.3 & 45.13 & 107 & 124 & 153.60 \\ \hline DSP efficiency/ & & & & & & & & \\ (GOPS/Quantity) & 0.030 & 0.022 & 0.037 & 0.38 & 0.078 & 0.12 & 0.14 & 0.734 \\ \hline Power & 7.6 W & 6.6 W & 10.4 W & 3.5 W & 5.7 W & 9.6 W & 4.8 W & 5.3 W \\ \hline Energy Efficiency/ & & & & & & & & \\ (GOPS/W) & 11.2 & 9.336 & 6.96 & 24.08 & 7.91 & 11.14 & 25.8 & 28.98 \\ \hline Model & KNN & LSTM & CNN & CNN & VGG16 & U-Net & VGG & TinyYOLO \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different literature methods. Figure 18: The actual output of the bridge detection in the aerial video using TinyYolo inference with the acceleration core.
## 1 Introduction
The main objective of the present research is developing novel leak detection methods based on the inherent properties of the measured signals, rather than methods based on a comparison between vibration levels, thus being possibly independent from the local boundary conditions of the monitored pipe location. In particular, a technique based on the shape of the normalized autocorrelation function, whose trend is described by a single scalar parameter (namely the kurtosis), is presented and tested through experimental measurements. This work focuses on the detection of burst leaks occurring in high-density polyethylene (HDPE) pipes of small-diameter, which are being increasingly adopted for service connections in water supply networks with respect to metal pipes [18; 19; 20]. Such leaks are often harder to detect, due to the low flow rates typically exhibited and the higher signal attenuation [4; 5; 18; 19; 20; 21], hence resulting in longer total runtime. Since each water district generally includes thousands of service pipes, the overall losses generated by these leaks may be extremely high [1; 22]. Therefore, efficiently managing such leaks is essential as well. An experimental campaign is conducted in a test facility in order to assess the effectiveness of the proposed leak detection approach based on signal autocorrelation. In particular, leaks are artificially generated on a HDPE small-diameter pipe that closely mimics a service pipe of typical water distribution networks. The vibro-acoustic signals related to the active leaks and the non-leaking condition are measured by using a hydrophone (fluid-borne pressure waves) and two accelerometers (radial and longitudinal pipe wall vibrations). Two datasets are gathered in different periods of the year to verify the influence of seasonal modifications in the conditions of the surrounding medium. The detection performances provided by the different transducers are evaluated and compared.
## 5 RF Feature Extractors
### Error Vector Magnitude (EVM)  
The error vector magnitude is a time-domain transform that measures how far the estimated symbols at the receiver side may deviate from the true symbols. I/Q imbalance, thermal noise, in- and out-of-band leakage, and phase noise are all causes that can degrade the EVM metric, thus EVM has the potential to be a good feature-extractor transform to capture hardware impairments from the received signals.  
In general, EVM is applied in the context of demodulated signals, as follows: let us assume that a symbol \(\mathbf{x}\) is transmitted, and that at the receiver, a symbol \(\mathbf{y}\) is received. The receiver estimates (e.g., via decoding process) the symbol \(\mathbf{\hat{x}}\). Therefore, the estimation error \(\epsilon\) is: \(\epsilon=\mathbf{x}-\mathbf{\hat{x}}\), as depicted in Figure 9. The EVM of the symbol \(\mathbf{x}\) is defined as  
\[\text{EVM}_{\mathbf{x}}\triangleq\frac{\left\lVert\epsilon\right\rVert_{2}}{ \left\lVert\mathbf{x}\right\rVert_{2}} \tag{17}\]  
where \(\left\lVert\cdot\right\rVert_{2}\) is the Euclidian norm.  
When the input to the EVM transform is the I/Q sampled data, one can apply the EVM as follows: \(\mathbf{x}\) is a complex-valued sequence of an ideal GNSS signal (i.e., without any distortions); it can be generated, for example, via a GNSS signal generator; \(\mathbf{\hat{x}}\) is the received signal (genuine or spoofer) at the I/Q level. Then, the EVM based on pre-correlation data measures the discrepancy between an ideal GNSS signal and the received signal. Under the hypothesis that the spoofer transmitter non-idealities will be further away from the  
Figure 9: The illustration of EVM principle. The blue arrow denotes the transmitted symbol, the yellow arrow denotes the received symbol, the blue arrow denotes the estimate, and the crimson arrow denotes the estimation error.  
ideal case \(\mathbf{x}\) than the GNSS transmitter non-idealities, then the EVM of a genuine GNSS signal is expected to be smaller than the EVM of a spoofer.  
Figure 9(a),b show two illustrative examples of EVM outputs for genuine GNSS transmitter and spoofer, respectively (both using Galileo E1 signal specifications and based on a software simulator built by us). The EVM results for the genuine Galileo E1 transmitter and spoofer have visible differences, with EVM values for the spoofer being, on average, slightly higher than those for the Galileo signal, as predicted by the theory. The examples in Figure 9(a),b are based on a very high CNR of 100 dB-Hz, for illustrative purposes. At lower CNRs, such differences are no longer visible to the naked eye, but they still have some potential to be captured by a machine learning algorithm, for example.
# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
## 4 Experiments
### ChestX-ray14 Dataset  
The ChestX-ray14 dataset is used for multi-label classification. It contains 112,120 frontal chest X-rays of 30,805 unique patients, each of which is marked with one or more common chest diseases out of a total of 14. In order to make a fair comparison with the previous methods, we divided them into a training set, a verification set, and a test set with the proportions 70%, 10%, and 20%. They were a total of 20 rounds of training, and the slope factor T was set to 10. Before training, we adjusted each image in the dataset to the size of 384 \(\times\) 384 and normalized it using the statistical data collected from the ImageNet dataset. As this dataset is much larger than ISIC 2018 dataset, DenseNet169 pre-trained on ImageNet was adopted as our network backbone. Referring to the previous work [15], we only used AUC as our model evaluation index.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{2-7}  & **Labeled** & **Unlabeled** & **AUC** & **Sensitivity** & **Accuracy** & **Specificity** \\ \hline Upper bound & 100\% & 0 & 95.43 & 75.20 & 95.10 & 94.94 \\ \hline SRC-MT & 5\% & 95\% & 87.61 & 62.04 & 88.77 & 89.36 \\ Ours & 5\% & 95\% & 89.56 & 64.32 & 88.56 & 88.96 \\ \hline SRC-MT & 10\% & 90\% & 90.31 & 66.29 & 89.30 & 90.47 \\ Ours & 10\% & 90\% & 91.24 & 67.56 & 89.56 & 90.16 \\ \hline SRC-MT & 20\% & 80\% & 93.58 & 71.47 & 92.54 & 92.72 \\ Ours & 20\% & 80\% & 94.02 & 72.03 & 92.61 & 91.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of our methods on the ISIC 2018 dataset with different percentages of labeled data.  
Figure 4: AUC performance of each category for the ISIC 2018 dataset.  
Table 3 compares the performance of the previous method with ours under the different percentages of labeled data. The observation shows that GraphX\({}^{\text{NET}}\) performs particularly well with 20% labeled data, but its fluctuations with labeled data are very large, indicating that it largely depends on the size of labeled samples. SRC-MT is not only superior to the GraphX\({}^{\text{NET}}\) method but also more \"stable\" given the same scale change of labeled data. However, our method has better AUC performance than SRC-MT in each percentage marking stage, and its stability has not decreased.  
Table 4 compares our method with MT and SRC-MT to analyze the impact on multi-label classification tasks. As we have observed, with 20% labeled data, our method achieves 79.74% AUC, which is better than that of MT 78.83% and SRC-MT 79.23%. Among 14 kinds of chest diseases, there may be some differences among the categories due to the imbalance of sample classification, but they are generally good, which can prove the effectiveness of our proposed method for multi-label classification.