esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Experiment 1: POM-PSASIDEA Algorithm Resolves MSIMPLTS Effectiveness
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline T & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 \\ Sat & 5 & 6 & 1 & 7 & 2 & 3 & 8 & 7 & 4 & 5 & 4 & 6 & 8 & 2 & 6 & 3 & 1 & 3 & 7 & 6 \\ E & 8 & 7 & 6 & 4 & 8 & 9 & 8 & 8 & 6 & 5 & 6 & 6 & 9 & 7 & 9 & 5 & 8 & 6 & 5 & 9 \\ \hline T & 21 & 22 & 23 & 24 & 25 & 26 & 27 & 28 & 29 & 30 & 31 & 32 & 33 & 34 & 35 & 36 & 37 & 38 & 39 & 40 \\ Sat & 1 & 7 & 5 & 3 & 4 & 2 & 7 & 1 & 8 & 2 & 3 & 8 & 5 & 7 & 6 & 1 & 7 & 4 & 2 & 6 \\ E & 5 & 4 & 7 & 5 & 6 & 7 & 6 & 6 & 5 & 5 & 7 & 9 & 8 & 5 & 4 & 5 & 3 & 6 & 4 & 8 \\ \hline T & 41 & 42 & 43 & 44 & 45 & 46 & 47 & 48 & 49 & 50 & 51 & 52 & 53 & 54 & 55 & 56 & 57 & 58 & 59 & 60 \\ Sat & 7 & 1 & 2 & 5 & 3 & 4 & 6 & 1 & 8 & 7 & 8 & 1 & 2 & 5 & 8 & 3 & 7 & 4 & 6 & 8 \\ E & 8 & 3 & 9 & 7 & 6 & 3 & 4 & 8 & 9 & 7 & 9 & 5 & 6 & 7 & 4 & 8 & 7 & 4 & 6 & 8 \\ \hline T & 61 & 62 & 63 & 64 & 65 & 66 & 67 & 68 & 69 & 70 & 71 & 72 & 73 & 74 & 75 & 76 & 77 & 78 & 79 & 80 \\ Sat & 1 & 7 & 6 & 3 & 4 & 2 & 6 & 8 & 5 & 2 & 5 & 4 & 7 & 1 & 3 & 5 & 8 & 4 & 6 & 8 \\ E & 4 & 5 & 8 & 9 & 9 & 7 & 7 & 8 & 6 & 5 & 3 & 5 & 4 & 5 & 5 & 9 & 5 & 5 & 7 & 9 \\ \hline Target & 81 & 82 & 83 & 84 & 85 & 86 & 87 & 88 & 89 & 90 & 91 & 92 & 93 & 94 & 95 & 96 & 97 & 98 & 99 & 100 \\ Sat & 4 & 3 & 2 & 5 & 8 & 7 & 4 & 6 & 1 & 7 & 2 & 3 & 5 & 2 & 6 & 8 & 4 & 5 & 1 & 3 \\ E & 7 & 9 & 6 & 7 & 4 & 9 & 9 & 5 & 7 & 4 & 5 & 8 & 5 & 4 & 4 & 6 & 8 & 5 & 9 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated experimental results of the allocation optimization phase. Figure 7: Initial task planning scheme. task scenarios, and the use of the allocation optimization, time window optimization, and global optimization phases of the progressive optimization mechanism can generate the final mission plan effectively for large-scale target task scenarios. Therefore, the POM-PSASIDEA algorithm solution has MSIMPLTS effectiveness.
## 4 Numerical Examples
### Engineering Cases
#### 4.4.2 Case 2: Double Slope, Multilayered, Three Random Variables  
The cross section and state variables of case 2 are shown in Figure 12 and Table 7, respectively [38]. The cohesion strength of layer 1 and cohesion and angle of friction of layer 2 are considered random, while the bulk density is considered constant, making the problem three-dimensional. Performance function evaluation was realized by the slip surface stress method. The FEM model contains 3428 elements as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & & **IGD\_S** & **EOR** \\ \hline \multirow{2}{*}{NMMSO} & mean & 4.14 & 0.16 \\  & std & (0.78)+ & (0.02)+ \\ \multirow{2}{*}{Multi\_AMO} & mean & 5.56 & 0.19 \\  & std & (1.94)+ & (0.05)+ \\ \multirow{2}{*}{DP-MSCC-ES} & mean & 4.65 & 0.26 \\  & std & (1.48)+ & (0.03)+ \\ \multirow{2}{*}{ RSCSAESII} & mean & 7.22 & 0.07 \\  & std & (4.79)+ & (0.02)+ \\ \multirow{2}{*}{NSPSO} & mean & 0.46 & 1.00 \\  & std & 0.05 & 0.00 \\  & +/\(-\)/\(-\) & & 4/0/0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical results of _IGD_S_ and _EOR_ of different algorithms for engineer case 1 over 20 runs.  
Figure 11: Resulting optimal points for Engineer case 1: (**a**) NMMSO. (**b**) Multi_AMO. (**c**) DP-MSCC-ES. (**d**) RSCSAESII. (**e**) NSPSO.  
In conclusion, through the comparative analysis of the explicit performance functions and the engineering cases, it can be concluded that NSPSO shows the best capability in the sampling process for SDOEs. The proposed method can provide candidate samples that cover every detail of the LSF and generate accurate. However, when the LSF is highly nonlinear, the solution may have a rather inferior quality (for example in the four corners in Figure 9b). This limitation is hopefully be addressed in our future study with the help of grid strategy. Nevertheless, the present study still provides a novel and useful sampling method for active learning reliability analysis.
## 4 Experiment and Analysis
### Skewing Resolution and LED-ID Recognition Analysis  
A modern color camera system is based on Bayer filter, where the color information recorded by the camera is estimated according to certain rules [24], so there is always some error when we take the skewing of the images. When the phase difference of the input signal at the transmitter is too small, it will seriously affect the feature detection and recognition. This section tests the impact of different phase different resolution on the LED-ID recognition rate. For ease of understanding, instead of using the skewing coefficient as the transverse axis, we use the phase difference of the transmitter signal as the transverse axis. The experimental results are given in Figure 17.  
It can be seen from Figure 17 that the recognition rate of ANN and SVM decreased greatly when the phase difference resolution was less than 20\({}^{\circ}\).Because \(\frac{360^{\circ}}{20^{\circ}}=18\), there are A\({}_{18}^{3}=4698\) different combinations of species caused by phase differences of the transmitter signal.  
Through experiments 4.1-4.3, and the conclusion of experiment 3.1-3.2 in our previous work in Ref. [17], which illustrates that the detection accuracy of the bright stripe's duty-ratio would not affect the training of classifiers, we can know the proposed scheme of LED recognition. With a modulation bandwidth of 0.1 to 8 kHz, frequency resolution of 60 Hz, and nine different duty-ratios in each frequency, this could offer roughly \(\frac{8000-1000}{50}\times 9\times\mathrm{A}_{18}^{3}=6,680,556\) unique LED-ID and systems with different transmission frequencies for three channels can offer roughly \(\mathrm{A}_{185}^{3}=\) 3,869,736 (\(\frac{8000-100}{50}=158\)) different unique LED-IDs. The total is about \(1.05\times 10^{7}\). This amount is far greater than our previous work in Ref. [17]. It can meet the quantity requirement of a complement technology of QR code,  
Figure 16: The recognition rate with different frequency resolution.  
and with the development of camera technology, the proposed method would provide more channels for LED recognition.
## 3 Result
### Feature Importance and ROC Analysis  
Figure 6 shows the importance of features for the ANN model used to detect adulterated milk samples with starch. The graph shows the importance of features at 16 different wavelengths from 940 nm to 410 nm.  
Figure 6 shows that the 940 nm and 410 nm wavelengths are the most important features. These wavelengths are the features that most influence the model's predictions. The 435 nm, 705 nm, and 730 nm wavelengths are also important features, but not as important as the 940 nm and 410 nm wavelengths. Other wavelengths have a less significant impact on the model's predictions. The 485 nm wavelength is the least important feature. This information can be used to improve the performance of the model in future studies.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Baseline Model** & **Proposed ANN Model** \\ \hline Accuracy & 95.6\% & 100\% \\ Precision & 94.2\% & 100\% \\ Sensitivity (Recall) & 93.5\% & 100\% \\ F1-Score & 93.8\% & 100\% \\ AUC & 0.92 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Performance Metrics.  
Figure 6: Feature importance.  
Figure 7 shows the performance of the ANN model used to detect adulterated milk samples with starch. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) for a model.  
The area under the curve (AUC) is a measure of the overall performance of a model. The closer the AUC value is to 1, the better the performance of the model. In this graph, the AUC value is 0.99. This shows that the model is very good at detecting adulterated milk samples with starch. The area under the ROC curve shows the probability that the model will correctly identify true positives (adulterated samples) as true positives. The closer the AUC value is to 1, the higher the probability that the model will correctly identify true positives as true positives. In this graph, the ROC curve reaches a TPR of 0.99 at an FPR of 0.01. This shows that the model can correctly identify adulterated samples with 99% accuracy.  
The FPR shows the probability that the model will incorrectly identify false positives as true positives. The closer the FPR value is to 0, the lower the probability that the model will incorrectly identify false positives as true positives.  
In this graph, the ROC curve reaches a TPR of 0.99 at an FPR of 0.01. This shows that the model will incorrectly identify false positives as true positives with 1% probability.
## 3 Simulation Results and Discussion  
### Validation of the Simulation of Faceted Growth by CA  
In order to verify the correctness of the CA model for faceted growth, this paper simulates the faceted growth for strong anisotropy of kinetic coefficient and compares the simulation results with the literature and experimental observations.  
Initially, a small circular solid nucleus is placed in an undercooled liquid. The temperature is initially set as \(T=1683\) K in the nucleus and \(T=1680\) K in the surrounding undercooled liquid. Adiabatic boundary conditions are applied to temperature field. As shown in Figure 2, a facet is formed having a normal vector in the \(45^{\circ}\) direction for \(q(\phi)\) with \(\lambda=3\) and \(\delta=0.01\). The facet forms from an initially circular crystal, and the shape is maintained while growing.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Melting temperature, \(T_{mel}\) (K) & 1683 \\ Density of silicon solid, \(\rho_{s}\) (\(\,\mathrm{kg}\cdot\mathrm{m}^{-3}\)) & \(2330-2.19\times 10^{-2}T\) \\ Density of silicon melt, \(\rho_{m}\) (\(\,\mathrm{kg}\cdot\mathrm{m}^{-3}\)) & \(2330-2.19\times 10^{-2}T-1.21\times 10^{-5}T^{2}\) \\ Solute diffusion coefficient, \(D\) (\(\mathrm{m}^{2}/\mathrm{s}\)) & \(3\times 10^{-8}\) \\ Thermal diffusivity, \(\alpha\) (\(\,\mathrm{m}^{2}/\mathrm{s}\)) & \(2.53\times 10^{-5}\) \\ Molar liquid heat capacity, \(C_{p}\) (\(\,\mathrm{J}/\mathrm{mol}\cdot\mathrm{K}\)) & 26.6 \\ Molar melting entropy, \(\Delta S_{\mathrm{f}}\) (\(\,\mathrm{J}/\mathrm{mol}\cdot\mathrm{K}\)) & 29.8 \\ Free interfacial energy, \(\gamma_{\mathrm{SI}}\) (\(\,\mathrm{J}/\mathrm{m}^{2}\)) & 0.438 \\ Anisotropy strength, \(\epsilon\) & 0.15 (Estimated) \\ Kinetic coefficient, \(\overline{\mu}\) (\(\,\mathrm{m}/\mathrm{K}\cdot\mathrm{s}\)) & \(2.4\times 10^{-4}\) (Estimated) \\ Molar latent heat, \(\Delta H_{\mathrm{f}}\) (\(\,\mathrm{J}/\mathrm{mol}\)) & 4.444 \(\times 10^{4}\) \\ Liquidus slope, \(m\) (\(\,\mathrm{K}/\mathrm{mass}\%\)) & \(-0.7637\) \\ Partition coefficient, \(k\) & 0.07 \\ Standard deviation of nucleation at silicon-crucible & 0.1 \\ interface, \(\Delta T_{s,\sigma}\) (\(\mathrm{K}\)) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties and calculation parameters used in the present model.  
In order to observe the morphological transformation from planar to zigzag facets in detail we attempted to use the computational domain size of 613 \(\upmu\)m \(\times\) 500 \(\upmu\)m. Figure 3 shows the shape of the Si crystal-melt interfaces with \(\lambda=3\), \(\delta=0.01\) and \(\varepsilon=0.15\). A wavy perturbation is introduced into a planar crystal-melt interface, and the perturbation is amplified and results in zigzag facets. The simulation results and phenomena are consistent with the literature of [PERSON] [24] and experimental observations [34; 3; 35; 36]. Through the comparison with the literature and experimental observations, the CA simulation can be validated.
## 3 Results and Discussion
### Fractography
#### 3.3.2 Weld Metal Fractography  
The macroscopic morphology of the WM samples changes in a similar way to the BM metal samples. Ductile cup-cone fracture changes to a flatter fracture surface in samples tested in a hydrogen rather than a nitrogen environment. The difference between the two metals is that the weld specimen fracture surfaces undergo a change from HE related QC fracture toward ductile shear failure during the fracture propagation process. This is shown in Figure 10a. The inside of the specimen fracture surface behaves like the BM samples, and these regions show the QC fracture mode. Near the outside, shear failure takes over and microvoids can be observed. Larger voids can also be observed, which might originate as lack of fusion defects in the weld before testing. Furthermore, a transition zone exists on the surfaces of all weld specimens in between the QC and MVC regions that has a morphology of mixed features such as shown in Figure 10b. The quasi-cleavage mode also contains voids in the weld specimens that originate from ductile failure mechanisms. The size of the QC fracture zone does not change significantly between specimens tested at different H\({}_{2}\) pressures, which supports the similar findings for the %RA values at different test pressures.  
Figure 9: SEM Fractographic images of notched base metal samples. (**a**) Tested in 100 barg N\({}_{2}\). (**b**) Tested in 100 barg H\({}_{2}\). (**c**) Higher magnification image of smooth base metal samples tested without gas. (**d**) Higher magnification image of smooth base metal samples tested in 100 barg H\({}_{2}\). Secondary cracks parallel to the fracture surface are present on the inside surface of the sample in (**b**), as indicated by the arrow.
## 2 Material and Method
### Taguchi Analysis  
In many engineering problems, full factorial and fractional factorial design techniques are frequently preferred [20]. However, as the number of factors increases, the statistical process becomes complex and time-consuming, foremost. To simplify this laborious process of examining the entire space, describing the process, and finding the best combination of parameters, the Taguchi experimental design method proposes the use of an orthogonal array. In this way, the Taguchi approach is able to overcome the problem by using a minimum number of experiments to obtain information such as the main and interaction effects of these parameters. For this reason, the Taguchi approach is considered one of the most effective methods for evaluating the effects of process variables needed to address challenges in engineering fields [20; 21]. In the Taguchi technique, a loss function is utilized to measure the target value change and interpret the performance characteristics, and then this function value is expressed as the signal-to-noise (S/N) ratio [22]. These S/N ratios have three categories: \"higher is better\" (HiB), \"nominal is best\" (NiB), and \"lower is better\" (LiB), depending on the nature of the problem during the analysis or experiment performed [23].  
It is obvious that the Taguchi method can be preferred as a design of experiment (DoE) due to the many advantages mentioned before, especially since determining the most appropriate parameters is of critical importance in applications such as the autofrettage process where mechanical behavior in high-pressure cylinders needs to be improved. In addition, with this method, it is possible to systematically evaluate the effects of various process parameters and their different levels, and with this approach, the computational cost will also be reduced with fewer simulations. Additionally, this method enables a systematic analysis of the effects of the various process parameters and their different levels, thereby reducing computational costs through fewer simulations. As a consequence, this research employed the Taguchi method to enhance product quality and process efficiency by identifying the optimal parameter combinations for the autofrettage process, both with and without machining. Since the safety factor was taken as the control parameter in  
Figure 4: Main effects plots for S/N ratios of the safety factor of the autofrettagged cylinder without machining under working pressure of 400 MPa.  
order to evaluate the efficiency in the analyses, HiB was preferred for the S/N ratio. The signal-to-noise (S/N) ratio for the HiB case is described as follows:  
\[\frac{S}{N}=10 log\frac{1}{N}{\sum_{i=1}^{n}{\frac{1}{{{y_{i}}^{2}}}}} \tag{18}\]  
In this work, [PERSON]'s \(L_{9}\) orthogonal array method was employed due to its recognized utility and user-friendly nature. This method facilitates a reduction in computational time by conducting a limited number of analyses while allowing for the evaluation of parameters and their cross-effects. The factors and their levels for the Taguchi \(L_{9}\) orthogonal array are summarized in Table 1.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
###### Abstract  
To solve the problem of \"under-maintenance\" and \"over-maintenance\" in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information of the equipment is extracted by the Hilbert-full-vector spectrum as the input of fault prediction. Compared with the traditional spectrum, this spectrum information fuses the signals of two sensors in the same section of the device, which can reflect the actual operational state of the device more comprehensively. Then, the temporal convolutional network is used to predict the amplitudes of different feature frequencies, and the double-layer attention mechanism is introduced to mine the correlation between the corresponding amplitudes of different feature frequencies and between the data at different historical moments, to highlight the more important influencing factors. In this way, the prediction accuracy of the model for the amplitude corresponding to the feature frequency of concern is improved. Finally, experimental verification is carried out on the XJTU-SY dataset. The results show that the TCDAN model proposed in this paper is significantly superior to TCN, GRU, BiLSTM, and LSTM, which can provide a more effective decision-making basis for the predictive maintenance of equipment.  
fault prediction; Hilbert transform; full-vector spectrum; temporal convolutional network; attention mechanism +
Footnote †: journal: Applied sciences
# CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
## 4 Experiments and Discussions
### Experiment Results and Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{3}{c}{**Performance (F\({}_{1}\))**} \\ \cline{2-4}  & **Precision** & **Recall** & **F\({}_{1}\)** \\ \hline Power demand & 100 & 96.12 & 98.02 \\ UCR & 100 & 95.335 & 97.57 \\ ECG & 100 & 96.825 & 98.35 \\
2D-Gesture & 100 & 99.86 & 99.93 \\ PSM & 92.94 & 98.73 & 95.74 \\ SMD & 90.975 & 77.61 & 81.835 \\ MSL & 74.07 & 97.08 & 84.03 \\ SWaT & 71.37 & 97.84 & 82.53 \\ WADI & 88.00 & 22.43 & 39.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performances of our method on the benchmark datasets. of the training datasets as shown in Table 5. We tested our method on both versions, resulting in similar performances across both. The experiments on the nine benchmark datasets showed that the CL-TAD outperformed other compared methods on five datasets, and achieved competitive performance on two datasets. It did not achieve excellent performance on high-dimensional multivariate time series data like WADI. The CL-TAD produces effective representations on a feature space by incorporating the transformation module and the TCN-based encoder into its encoding process and making use of the contrastive-learning-based training. For high-dimensional time series data like WADI of dimension 127, it appears to struggle in deriving appropriate representations for anomaly detection.
# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 2 Related Work
### Squeeze Networks  
Most CNN architectures are resource-hungry to achieve good accuracy for a particular dataset. The smaller architectures with equivalent accuracy require less bandwidth, are easily deployable on limited-capacity hardware, and offer benefits in distributed training. The model weights for most of the CNN architectures range from 100 MB (e.g., ResNet) to 553 MB (e.g., VGG). The weights for AlexNet sit in the middle at 249 MB. Recently, in SqueezeNet [30], the author proposed an architecture with a comparable accuracy to AlexNet but with fewer parameters and with an incredibly lower weight of 4.9 MB. The weights were further reduced to 0.5 MB by applying compression techniques such as weight pruning and sparsifying the model.  
The basic _Fire Module_ is employed in the SqueezeNet architecture with a clever combination of \(1\times 1\) and \(3\times 3\) filters. The module consists of a two-phase operation of \"squeeze\" and \"expand\". The squeeze phase applies a smaller number of \(1\times 1\) filters than the input volume, thus reducing the dimensions of the output feature map. Before feeding to the expanding phase, ReLU activation is applied to the output of the squeeze phase. During the expanding phase, a combination of \(1\times 1\) and \(3\times 3\) filters is used to capture the spatial relationship and extract more complex features, as shown in Figure 8.
## 5 Results and Discussion
### Limitations and Future Work  
While our methods have shown promising results on a subset of the Huatuo-26M dataset, several limitations exist. Firstly, although models like BERT and RoBERTa capture deep semantic meanings, they are not specifically tailored for the mental and psychological health domain, potentially limiting precision. Future work includes fine-tuning these models with larger domain-specific datasets to enhance accuracy. Secondly, the use of the Cross-Encoder structure with RoBERTa incurs high computational costs and poses challenges in parallelization. Exploring more efficient network architectures is planned to reduce computational overheads. Thirdly, the dataset and the mental health-knowledge graph used are not comprehensive. Expanding these resources and testing with more diverse datasets are future objectives.  
Importantly, our methods have not yet been validated in real clinical settings, which may affect clinician adoption due to concerns about clinical validity. To address this, future work will focus on the following:  
* Clinical Validation: Collaborating with mental health professionals to deploy our tools in clinical environments and compare their performance against standard clinical practices.
* Feedback Collection: Gathering insights from clinicians and patients on usability and practical utility.
* Evaluating Key Metrics: Assessing improvements in assessment time, diagnostic accuracy, and patient satisfaction.  
Figure 21: Mania knowledge generation.  
Figure 20: Depression knowledge generation.  
By undertaking these steps, we aim to demonstrate the practical applicability of our work, addressing concerns about clinical validity and encouraging adoption in real-world settings.
## 8 Numerical Simulation and Experimental Testing
### Simulation of Data Fusion Algorithm Based on TCPF
Figure 4 shows the results of 30 RSME and Std tests of five algorithms against three types of models (\(f_{1},f_{2},f_{3}\)). From the figure, it can be seen that when the noise variance was small, the RMSE and Std values of the TCPF data fusion algorithm were smaller than the values of the other four algorithms in each independent simulation experiment. When the noise variance increased, the RMSE and Std values of the five algorithms increased, which indicates that the filtering performance of the algorithms decreased, but the estimation accuracy of TCPF was still significantly higher than those of the other four algorithms. The test results in Figure 4 also verify that the filtering optimization performance of the TCPF data fusion algorithm was the best, and the filtering accuracy was the highest. Figure 4: _Cont_.