esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 3 The Proposed Adaptive Sparse Attention Network (ASAN)  
### Adaptive Sparse Attention Network  
The attention mechanism imposes a set of attention coefficients on the whole sequence. Among them, the attention coefficients imposed by the more important sequence segments are large, and the attention coefficients imposed by the unimportant sequence segments are small, thus enhancing the classification ability of the network. However, in the application of fault diagnosis, the attention coefficients are scattered to more sequence segments with less classification contribution. As a result, the attention model imposes more computing costs and focuses on more redundant information. To get around this issue, we propose an adaptive thinning attention method. Through a layer of nonlinear transformation layers,the adaptive learning attention threshold is used to filter out smaller attention coefficients to achieve the purpose of attention thinning. The process is as follows.  
The typical attention structure is shown in Figure 2a, and the proposed adaptive sparse attention structure is shown in Figure 2b. This module adds a Scale parameter layer and a Max attention layer after the attention layer. The Scale parameter layer takes the output \(z\) of the attention layer as the input and finally generates a scale parameter \(\alpha\). The calculation process is shown as follows:  
\[\alpha=sigmoid(W_{\alpha}z+b_{\alpha}) \tag{17}\]  
where \(W_{\alpha}\) and \(b_{\alpha}\) are the trainable weight matrix and bias terms, respectively.  
The Max attention layer takes the output \(z\) of the attention layer and the output \(\alpha\) of the Scale parameter layer as input, and an attention threshold is finally generated \(\tau\). The calculation process is shown as follows:  
\[\tau=\alpha\cdot max(z) \tag{18}\]  
Then the natural exponential function is used to construct the filter function. The calculation process is as follows:  
\[f(z)=\frac{1}{1+e^{-a(z-\tau)}} \tag{19}\]  
Here, when \(z<\tau\), the output result of \(f(z)\) tends to be 0. When \(z=\tau\), \(f(z)\) equals 0.5. When \(z>\tau\), \(f(z)\) tends to be 1.  
Last, the output of the threshold filter and the output of the attention layer are reconstructed with the softmax function to obtain the attention coefficient \(newz_{i}\). The procedure for calculating is as follows:  
\[newz_{i}=\frac{f(z_{i})e^{z_{i}}}{\Sigma_{i=1}^{N_{seg}}f(z_{i})e^{z_{i}}} \tag{20}\]  
where \(N_{seg}\) represents the number of sequences.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 5 Conclusions  
The adversarial robustness of the compressed CNNs is essential for deploying them to the real-world embedded systems. In this paper, we proposed a robust model compression framework for CNNs. Our framework used the knowledge distillation to improve the result of the existing adversarial pruning approach. In several experiments, our framework  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network (Dataset)** & **CompRate** & **Method** & **Org Acc (\%)** & **FGSM Adv Acc (\%)** & **CW \(\ell_{2}\) Adv Acc (\%)** \\ \hline \multirow{6}{*}{LeNet (MNIST)} & \multirow{2}{*}{\(\times 1.5\)} & AP & 98.91 & 97.62 & 70.23 \\ \cline{3-6}  & & APD & **99.18** & **98.27** & **91.01** \\ \cline{2-6}  & & AP & 98.79 & 97.48 & 69.77 \\ \cline{2-6}  & & APD & **99.17** & **98.28** & **88.19** \\ \cline{2-6}  & & AP & 98.68 & 97.38 & 77.11 \\ \cline{2-6}  & & APD & **99.04** & **98.15** & **93.30** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of AP and APD results against FGSM and CW \(\ell_{2}\) attacks on the MNIST dataset.  
showed a significant improvement in the trade-off between the compression rate and the adversarial robustness on the two datasets, MNIST and CIFAR10. We found that the amount of improvement of our framework tends to decrease in the high compression rate. We expect that this phenomenon is due to the large gap in the architectural capacity between the teacher network and the student network. We hope that this phenomenon will be mitigated through future works.  
Conceptualization, J.L. and S.L.; methodology, J.L. and S.L.; validation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, S.L.; supervision, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07051383), and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2020-0-01749) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).  
The data presented in this study are openly available: MNIST ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/))) and CIFAR10 ([[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html))).  
The authors declare no conflict of interest.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (3): \(R=0.7778\)  
In this case, the cosine wave half period is chosen to be greater than the array length, \(H>L_{h}\) and equals \(H=6.345\)\(\lambda\), hence \(R=4.935\lambda/6.345\lambda=0.7778\). Accordingly, after applying the EPPs of the synthesized array elements for \(A_{c}\) changing from positive and the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=1.25\lambda\), the array main beam tilted from the broadside direction of \(\theta^{\circ}=90^{\circ}\) to \(\theta^{\circ}=62.36^{\circ}\) with minimal changes in the HPBW that is increased by \(0.23^{\circ}\) at \(\theta^{\circ}=62.36^{\circ}\) compared to the broadside direction, while the SLL is increased from \(-29.79\) dB to \(-19.1\) dB as the main beam is titled away from the broadside direction to \(\theta^{\circ}=62.36^{\circ}\). The achieved maximum scanning range is \(-27.64^{\circ}\leq\theta_{r}\leq 27.64^{\circ}\) around the broadside direction. The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range for the eight-element ULAA at a compression ratio \(R=0.7778\) are listed in Table 11. The polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.7778\) and \(A_{c}=1.25\)\(\lambda\) is shown in Figure 32.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cosine Wave Amplitude \(A_{c}\)** & **Main Beam Direction \(\theta_{o}\), in \({}^{\circ}\)** & **SLL (dB)** & **HPBW in \({}^{\circ}\)** & **Maximum Scanning Range** \\ \hline \(0\) & \(90^{\circ}\) & \(-29.79\) dB & \(12.8^{\circ}\) & \\ \hline \(0.25\,\lambda\) & \(84.02^{\circ}\) & \(-28.9\) dB & \(12.78^{\circ}\) & \\ \hline \(0.5\,\lambda\) & \(78.35^{\circ}\) & \(-27.14\) dB & \(12.88^{\circ}\) & \\ \hline \(0.75\,\lambda\) & \(72.67^{\circ}\) & \(-24.59\) dB & \(12.98^{\circ}\) & \\ \hline \(\lambda\) & \(67.52^{\circ}\) & \(-21.74\) dB & \(13.06^{\circ}\) & \\ \hline \(1.25\,\lambda\) & \(62.36^{\circ}\) & \(-19.1\) dB & \(13.03^{\circ}\) & \\ \hline \(1.5\,\lambda\) & & & & \\ \hline \(1.75\,\lambda\) & & & & \\ \hline \(2\,\lambda\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range \(\theta_{r}\) for 8-element ULAA at compression ratio \(R=0.7778\).  
Figure 31: Polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.875\) and \(A_{c}=1.25\,\lambda\).
# Comparison of Multi-Tracked Running Gears in Terms of Obstacle Negotiation Capabilities
## 4 Simulation Results
### Curb Negotiation  
The results of the first test of curb overcoming showed similar tendencies to those observed in the case of ditch negotiation. Almost all the multi-tracked modules enabled the overcoming of obstacles in less advantageous ground conditions than the single-track module solution A (Figure 13). The exception was solution B, which required the highest ground strength to overcome the obstacle. The platform came too close to the obstacle lifting its first module vertically upwards, losing contact with the obstacle surface. It would be beneficial in this case to implement an appropriate angle restriction or active control of the track module's position. The most advantageous solution E allowed for a 65% reduction in the tg\(\mathcal{O}\) value required to overcome the obstacle, compared to the reference solution A.  
Overcoming small objects, such as a curb, generates a significant pitching of the track modules (Figure 14). The simulation results of the second test with the ground parameters tg\(\mathcal{O}\) = 0.9 C = 3 kPa are presented in Table 4. Particularly high pitch angle values were noted for solutions B and D. In the case of solution D, it also caused a high angular velocity when the first track module fell down. It could be dangerous in terms of platform stability or while operating sensors and transporting equipment. A larger pitch angle caused higher resistance to overcoming the slope, which increased the maximum slip value of the loaded track module and extended the time of overcoming the obstacle. This can be clearly seen in the solution B results.  
Figure 13: Ground shear strength characteristics required for overcoming the curb for each (**a**–**e**) solution.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Ad-Hoc
#### 3.2.3 Breast Cancer  
DeepMiner was proposed by [136] in 2018. Their framework attempts to discover explainable representations and build explanations using these representations for medical purposes, specifically breast cancer. An advantage to this approach is there is no need to train on full medical reports. They attempt to analyze the individual units rather than train a network to output a medical report. Their network is composed of three phase. First, they crop patches of the mammogram image. They then train a network using these cropped patches. Second, they have human experts annotate the units from this network. Third, they generate explanations by ranking the annotated units. The utilize transfer learning and obtain a AUC for 0.838 for normal patches, 0.802 for benign patches, and 0.872 for malignant patches. They test the explanation quality by using the Greedy Matching Score to compare the generated explanations with the explanations in the ground truth resulting in a score of 0.627.  
In general, ad-hoc techniques provide more advantageous explanations compared to post-hoc techniques due to their inherently explainable design. One can argue that this makes the explanations more trustworthy. The disadvantage of ad-hoc techniques is their accuracy and scalability. When altering deep learning architectures to be inherently explainable there is usually an amount of accuracy lost. Also, the scalability of ad-hoc methods comes into question. In general, there are claims of post-hoc techniques not being as trustworthy since they are attempting to explain inherently unexplainable models. However, they do not suffer from the accuracy and scalability problems the ad-hoc methods do. See Table 2 for an overview of both post-hoc and ad-hoc methods.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Intra-Subject Experiments  
The main objective of the present contribution was to develop and test a robust subject-independent classification approach for a P300 speller. The results of the proposed approach are presented later; however, in this subsection, the results for the intra-subject experiments are presented, where the models were trained and tested from the same subject. In other words, SST training was applied using five ALS patients and five healthy subjects. 80% of the data from one subject was used for training and 20% for testing.  
The averaged metrics were obtained by summing the results from each subject and dividing by the number of subjects. The experiments were done for eight-channel data, four-channel data, and single-channel data. The obtained averaged F-score is presented for each model in Table 1.  
It can be observed from Table 1 that there was no significant difference between the performance when using eight data channels and four data channels. Single-channel data provided inaccurate results, achieving about an 83% average F-score for all subjects. Thus, it can be concluded that single-channel data is a poor choice for intra-subject classification. It is further seen in Section 4.5 that single-channel usage did not provide high performance in generic training either.  
The usage of CNN in LDA-SVM-\(k\)NN-CNN did not significantly decrease the performance in the eight-channel data experiment, reaching a 98.75% F-score. However, it dropped to 93.56% when using four-channel data. All of the other ensemble voting models provided quite stable results during the experiments on eight-channel and four-channel data.  
When trained and tested for each subject separately, the models achieved higher performance, compared to the proposed subject-independent training results, presented in Sections 4.3-4.5. However, it should be noticed, that this approach is not a good option  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**F-Score (\%)**} \\ \hline  & **8-Channel Data** & **4-Channel Data** & **Single-Channel Data** \\ \hline Gradient Boosting & 99.53 & 99.45 & 84.01 \\ XGBoost & 99.70 & 99.75 & 85.52 \\ \hline LDA & 98.78 & 98.84 & 83.35 \\ kNN & 98.51 & 98.54 & 82.29 \\ SVM & 98.80 & 98.81 & 82.64 \\ CNN & 98.45 & 92.18 & - \\ \hline LDA-kNN & 98.78 & 98.95 & 83.35 \\ LDA-SVM-kNN & 98.85 & 99.23 & 83.28 \\ LDA-SVM-CNN & 98.75 & 93.56 & - \\ W-LDA-kNN-SVM & 99.45 & 99.37 & 83.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intra-Subject Test Results.  
when talking about online training and practical usage. As stated earlier, the aim of this research is to develop a subject-independent classifier, which can be used by ALS patients without the necessity to train. So, despite the fact that by using SST training the models were able to reach a 99% F-score, inter-subject results are more important for a user's comfort and are detailed in the following subsections.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 3 Results
### Inference
#### 3.2.3 Errors Management  
In Figure 7 the effect of the wrong detections on the classification performance is shown. In this simulation all detections are injected in the network but \(k\) of them are assumed to be completely wrong. For increasing number of wrong variables, we compute all the possible variables' combinations and, for each combination, we insert 5 random detections for each variable using the smooth deltas. We let the messages flow in the network and average the obtained metrics: classification accuracy, Jensen-Shannon Divergence and Conditional Entropy. In Figure 7d we show also the number of completely uncertain classification. The graph shows how the system performance does not decrease too much for one wrong detection, but it decreases dramatically when more errors are inserted.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{_Predicted_} & \\ \cline{2-9}  & **fc** & **id** & **pa** & **Other** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{_Actual_} & **fc** & 78.9\% & 0 & 0 & 21.1\% & 0.8333 & 0.7895 & 0.8108 \\ \cline{2-9}  & **id** & 0 & 54.5\% & 36.4\% & 9.1\% & 0.5455 & 0.5455 & 0.5455 \\ \cline{2-9}  & **pa** & 2.1\% & 20.8\% & 77.1\% & 0 & 0.7872 & 0.7708 & 0.7789 \\ \cline{2-9}  & **other** & 6.1\% & 0 & 6.1\% & 87.8\% & 0.8286 & 0.8788 & 0.8529 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion Matrix, per class precision, recall and F1-Score including missing values.  
Figure 7: **(a)** Classification accuracy, **(b)** Jensen-Shannon divergence on Class Variable, **(c)** Conditional Entropy of Class Variable and **(d)** the number of completely uncertain classifications varying the number of wrong values.
## 2 Data
### VIIRS Active Fire  
The VIIRS 750 m daily level 2 active fire product ([[https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/)]([https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/))) provides fire detection time, fire location (longitude, latitude, sample, and line), detection confidence, fire radiative power, view zenith angle, and the logical criteria used for fire selection in each fire pixel [40]. The product also includes a fire mask (FM) containing nine different pixel classes: not processed, bowtie detection, sun glint, water, cloud, land, unclassified, and three fire-affected classes along with their detection confidence [41]. We used the locations of actively burning fires to get the fire density map [42]. We also used the water class from VIIRS fire mask to identify ocean areas because sun glints over oceans and green waves along ocean coasts are easily confused with smoke.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VIIRS Band** & **Center Wavelength (\(\mu\)m)** & **Spectral Range (\(\mu\)m)** \\ \hline M3 & 0.488 & 0.478–0.498 \\ M4 & 0.555 & 0.545–0.565 \\ M5 & 0.672 & 0.662–0.682 \\ M7 & 0.865 & 0.846–0.885 \\ M8 & 1.240 & 1.230–1.250 \\ M10 & 1.610 & 1.580–1.640 \\ M11 & 2.250 & 2.225–2.275 \\ M16 & 12.013 & 11.538–12.488 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The S-NPP VIIRS bands (750 m) used in the SSDA.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 5 Comparison of Transmission Efficiency of Different Structures  
After obtaining the transmission efficiency model, the design parameters of the cycloid gear are introduced to investigate the effect of contact characteristics on lubrication characteristics. The simulations are conducted with the data in Table 2. The load moment is the main input parameter, mainly referring to the RV-E series manufactured by Nabtsco. The allowable torque range for the different types of E series products is 196 N m to 44,100 N m, while the rated torque range is 58 N m to 14,700 N m. Therefore, 100 N m was selected as the input parameter in this range.
## 2 Problem Formulation
The relative distance (\(\rho_{ij}\)) and angle (\(\lambda_{ij}\)) between the \(i\)th USV and the leader (\(j\)) are expressed as  
\[\rho_{ij} =\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{9}\] \[\lambda_{ij} =\text{atan2}(y_{j}-y_{i},x_{j}-x_{i}). \tag{10}\]  
The differential equations of \(\rho_{ij}\) and \(\lambda_{ij}\) are given by:  
\[\dot{\rho}_{ij} =\frac{(x_{j}-x_{i})(\dot{x}_{j}-\dot{x}_{i})+(y_{j}-y_{i})(\dot{ y}_{j}-\dot{y}_{i})}{\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}}\] \[\quad=\frac{(x_{j}-x_{i})(\dot{x}_{j}-\dot{x}_{i})+(y_{j}-y_{i})( \dot{y}_{j}-\dot{y}_{i})}{\rho_{ij}}, \tag{11}\] \[\dot{\lambda}_{ij} =\frac{1}{1+\frac{(y_{j}-y_{i})^{2}}{(x_{j}-x_{i})^{2}}}\frac{( \dot{y}_{j}-\dot{y}_{i})(x_{j}-x_{i})-(\dot{x}_{j}-\dot{x}_{i})(y_{j}-y_{i})}{ (x_{j}-x_{i})^{2}}. \tag{12}\]  
According to \(x_{j}-x_{i}=\rho_{ij}\cos(\lambda_{ij})\) and \(y_{j}-y_{i}=\rho_{ij}\sin(\lambda_{ij})\),  
\[\dot{\rho}_{ij} =[u_{j}\cos(\psi_{j})-v_{j}\sin(\psi_{j})-u_{i}\cos(\psi_{i})+v_{ i}\sin(\psi_{i})]\cos(\lambda_{ij})\] \[+[u_{j}\sin(\psi_{j})+v_{j}\cos(\psi_{j})-u_{i}\sin(\psi_{i})+v_{ i}\cos(\psi_{i})]\sin(\lambda_{ij})\] \[=-u_{i}\cos(\psi_{i}-\lambda_{ij})+u_{j}\cos(\psi_{j}-\lambda_{ij})\] \[+v_{i}\sin(\psi_{i}-\lambda_{ij})-v_{j}\sin(\psi_{j}-\lambda_{ij}) \tag{13}\] \[\rho_{ij} =\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{9}\]0 \[\rho_{ij} =\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{9}\]1 \[\rho_{ij} =\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{9}\]2  
The control objective is to design an event-triggered control law for USVs to track the leader with a desired distance (\(\rho_{ij,d}\)) and angle (\(\lambda_{ij,d}\)). **Remark 1**. : _In the controller design process, the \(i\)th USV tracks the the \(j\)th USV using the \(x_{j}\), \(y_{j}\) and \(\psi_{j}\) information. This means that communication costs are not saved between multiple USVs._
# Cosine-Based Embedding for Completing Lightweight Schematic Knowledge in DL-Lite\({}_{core}\)+
## 5 Experiments and Results
### The Results of Transitivity and Symmetry
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**YAGO-On**} & \multicolumn{6}{c}{**FMA**} \\ \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**MRR**} & \multicolumn{2}{c}{**Hits@N(\%)**} & \multicolumn{2}{c}{**MRR**} & \multicolumn{2}{c}{**Hits@N(\%)**} & \multirow{2}{*}{} \\  & **Raw** & **Filter** & **10** & **3** & **1** & **Raw** & **Filter** & **10** & **3** & **1** \\ \hline TransE [13] & 0.120 \(\dagger\) & 0.627 \(\dagger\) & 0.846 \(\dagger\) & 0.693 \(\dagger\) & 0.507 \(\dagger\) & **0.122 \(\dagger\)** & 0.639 \(\dagger\) & 0.927 \(\dagger\) & 0.741 \(\dagger\) & 0.491 \(\dagger\) \\ TransH [14] & 0.010 \(\dagger\) & 0.014 \(\dagger\) & 0.220 \(\dagger\) & 0.010 \(\dagger\) & 0.003 \(\dagger\) & 0.005 \(\dagger\) & 0.006 \(\dagger\) & 0.002 \(\dagger\) & 0.001 \(\dagger\) & 0.001 \(\dagger\) \\ TransR [15] & **0.132 \(\dagger\)** & 0.792 \(\dagger\) & 0.974 \(\dagger\) & 0.848 \(\dagger\) & 0.710 \(\dagger\) & 0.010 \(\dagger\) & 0.010 \(\dagger\) & 0.050 \(\dagger\) & 0.050 \(\dagger\) & 0.050 \(\dagger\) \\ TransD [15] & 0.066 \(\dagger\) & 0.774 \(\dagger\) & 0.906 \(\dagger\) & 0.621 \(\dagger\) & 0.000 \(\dagger\) & 0.066 \(\dagger\) & 0.292 \(\dagger\) & 0.873 \(\dagger\) & 0.488 \(\dagger\) & 0.000 \(\dagger\) \\ RESCAL [16] & 0.100 \(\dagger\) & 0.640 \(\dagger\) & 0.920 \(\dagger\) & 0.720 \(\dagger\) & 0.500 \(\dagger\) & 0.094 \(\dagger\) & 0.640 \(\dagger\) & 0.940 \(\dagger\) & 0.750 \(\dagger\) & 0.480 \(\dagger\) \\ HolE [18] & 0.084 & 0.237 & 0.611 & 0.278 \(\dagger\) & 0.072 \(\dagger\) & 0.078 \(\dagger\) & 0.224 & 0.622 & 0.240 \(\dagger\) & 0.065 \\ ComplEx [19] & 0.066 \(\dagger\) & 0.470 \(\dagger\) & 0.970 \(\dagger\) & 0.820 \(\dagger\) & 0.110 \(\dagger\) & 0.003 \(\dagger\) & 0.003 \(\dagger\) & 0.010 \(\dagger\) & 0.000 \(\dagger\) & 0.000 \(\dagger\) \\ Analogy [47] & 0.074 \(\dagger\) & 0.598 \(\dagger\) & 0.988 \(\dagger\) & 0.854 \(\dagger\) & 0.317 \(\dagger\) & 0.069 \(\dagger\) & 0.557 \(\dagger\) & 0.979 \(\dagger\) & 0.823 \(\dagger\) & 0.264 \(\dagger\) \\ RotatERotatE [23] & 0.001 & 0.001 & 0.000 & 0.000 & 0.000 & 0.001 & 0.001 & 0.000 \(\dagger\) & 0.000 \(\dagger\) & 0.000 \(\dagger\) \\ EL Embedding [24] & 0.016 & 0.016 & 0.010 & 0.001 & 0.001 & 0.000 & 0.028 & 0.028 & 0.037 & 0.002 \(\dagger\) & 0.001 \\ \hline CosE\({}^{-}\) & 0.066 & 0.723 & 0.994 & 0.824 & 0.764 & 0.058 & 0.644 & 0.962 & 0.617 & 0.512 \\ CosE & 0.097 & **0.917** & **0.996** & **0.970** & **0.860** & 0.090 & **0.870** & **0.990** & **0.950** & **0.780** \\ \hline \hline \end{tabular} \(\dagger\) shows that the results are taken from the work [49]. Other results are obtained by their source codes.  
\end{table} 
Table 6: The results of YAGO-On and FMA on link prediction about \(disjointWith\) axioms.