esa-sceva/satcom-chunk-collection huggingface dataset:
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
###### Abstract  
Task-oriented dialogue systems (TODs) enable users to complete specific goals and are widely used in practice. Although existing models have achieved delightful performance for single-domain dialogues, scalability to new domains is far from well explored. Traditional dialogue systems rely on domain-specific information like dialogue state and database (DB), which limits the scalability of such systems. In this paper, we propose a Scalable Task-Oriented Dialogue modeling framework (STOD). Instead of labeling multiple dialogue components, which have been adopted by previous work, we only predict structured API queries to interact with DB and generate responses based on the complete DB results. Further, we construct a new API-schema-based TOD dataset MultiWOZ-API with API query and DB result annotation based on MultiWOZ 2.1. We then propose MSTOD and CSTOD for multi-domain and cross-domain TOD systems, respectively. We perform extensive qualitative experiments to verify the effectiveness of our proposed framework. We find the following. (1) Scalability across multiple domains: MSTOD achieves 2% improvements than the previous state-of-the-art in the multi-domain TOD. (2) Scalability to new domains: our framework enables satisfying generalization capability to new domains, a significant margin of 10% to existing baselines.  
task-oriented dialogue systems; cross-domain; scalability +
Footnote †: journal: _Applied Sciences_
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
## 5 Test of Detection Ability of Composite Detection Probe
\right.. \tag{9}\]  
When there is a defect in the tested part, the circuit's equivalent impedance changes, which also changes the rate of change of \(u_{c}\). The rate of change in \(u_{c}\) can be determined by monitoring the voltage value of A before the negative pulse arrives, in order to gather information about the defect. The pulsed eddy current test data obtained are summarized in Table 2.  
The data in Table 2 show that the induction voltage before the arrival of the negative pulse provides a clearer reflection of defect information compared to the peak detection voltage value, especially for identifying defects within 4 mm of the upper surface. However, detecting defects deeper than 4 mm using pulsed eddy current signals proves challenging, necessitating the use of electromagnetic ultrasonic testing to supplement the measurements. A high-frequency excitation signal of 4 MHz was applied, and the results are depicted in Figure 18.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defect to Upper** & **Average Induced Peak** & **Average Peak** & **Induction Voltage** & **Change in Induced** \\
**Surface Distance (mm)** & **Voltage (V)** & **Induced Voltage** & **before Arrival of a** & **Voltage before Arrival** \\  & & **Change (\%)** & **Negative Pulse (mV)** & **of Negative Pulse (\%)** \\ \hline No defects & 1.1941 & & 69.79 & \\
1 & 1.2091 & 1.26 & 78.20 & 12.05 \\
2 & 1.2042 & 0.84 & 75.22 & 7.78 \\
3 & 1.2012 & 0.59 & 73.56 & 5.40 \\
4 & 1.1979 & 0.32 & 71.59 & 2.73 \\
5 & 1.1974 & 0.27 & 70.99 & 1.87 \\ \The figure illustrates that the electromagnetic ultrasonic detection signal varies with the defect's position within the composite detection probe, showing the voltage induced in the detection coil after ultrasonic reflection. As the distance from the defect to the upper surface decreases, the interval between echo voltage signals shortens. When the distance from the defect to the surface exceeds 4 mm, the echo signal becomes clearly distinguishable. This indicates that blind spots in pulsed eddy current testing can be effectively identified using electromagnetic ultrasonic signals. When the defect is 2 mm from the surface, the echo voltage signal begins to overlap. When the defect is within 1 mm of the material surface, the echo signal shows significant overlap, indicating a blind area for electromagnetic ultrasonic testing. The defect position can be estimated using the time interval of the echo voltage signal and the ultrasonic propagation speed, as detailed in Table 3.  
The table indicates that the error for defect position information obtained from the electromagnetic ultrasonic signal is approximately 1%, demonstrating that the limitations of pulsed eddy current testing can be compensated by this method.
## 3 Performance Analysis of Balance Actuator
### Effect on Rotor Dynamics  
When a balance actuator is designed, the mass should be far less than the mass of the rotor to minimize the effect of the additional mass on the dynamic characteristics of the rotor system. The actuator used in the following experiment was made from an aluminum alloy, and a finite element analysis was conducted to evaluate its effect on the rotor dynamics, as shown in Figure 6. From the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Working Speed**} & \multicolumn{3}{c}{**Required Balance Level**} \\ \cline{2-4}  & **G0.4** & **G1.0** & **G2.5** & **G6.3** \\ \hline \(n(\text{r}/\text{min})\) & \multicolumn{3}{c}{Permissible residual imbalance \(U_{per}\)(g.mm)} \\
15,000 & 4.20 & 10.50 & 26.26 & 66.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Permissible residual unbalance of the rotor system.  
results, we could conclude that the critical speed of the initial rotor system was 20,020 rpm; after installing the actuator, the value was reduced to 19,216 rpm, representing a 4% decrease. The working speed of the rotor system was below 16,000 rpm, so the design was reasonable and did not affect the normal operation of the rotor system.
## 2 Design Principle
### Shim Plate  
Figure 4a shows the size of the robot (110 mm wide and 99 mm tall) relative to the ferromagnetic pipes of different sizes (5 to 9-inch or 127 to 228 mm diameters). When the size of the pipe changes, the distance between the magnet and the inner wall of the pipe also changes. For the magnetic adhesive force to have the desired value for different pipe diameters, a shim plate with a specific thickness is required to be mounted on the front and rear modules, as shown in Figure 4b. The suitable thickness of the shim plate for the various pipe sizes can be calculated from Equations (1) and (2) based on the parameters shown in Figure 5.  
\[w=r_{t}-\sqrt{{r_{t}}^{2}-{d_{c}}^{2}} \tag{1}\]  
\[c=w+u-h_{m}-g_{h} \tag{2}\]  
where \(c\) is the thickness of the shim plate, \(r_{t}\) is the radius of the pipe, \(d_{c}\) is the distance between the center of the robot and the center of the wheel (45 mm), \(u\) is the distance between the base of the robot wheel and the lowest end of the robot body (17.4 mm), \(w\) is the distance between the base of the robot wheel and the inner wall of the pipe below the robot, \(h_{m}\) is the thickness of the permanent magnet (13.5 mm), and \(g_{h}\) is the distance between the lowest end of the magnet and the inner wall of the pipe below the robot.  
After calculation, the suitable thickness of the shim plate for various pipe sizes is presented in Table 1. To keep the distance between the lowest end of the magnet and the inner wall of the pipe below the robot (\(g_{h}\)) equal to 4 mm (see Section 2.4), a 5-inch (127 mm) diameter pipe requires an 18-mm shim plate. Table 1 also shows that larger pipe diameters require thinner shim plates to satisfy the requirement of the adhesive force.  
Figure 4: (a) The size of the robot relative to the pipes of different sizes. (b) The shim plate installation.  
Figure 5: The parameters for the shim plate thickness calculation.
## 2 System Model
### Differential Game Model
#### 2.4.1 Framework  
The differential game (\(\mathcal{G}\)) framework comprises five sets: \(\mathcal{G}=\{\mathcal{K},\mathcal{E},\mathcal{S},\mathcal{V},\mathcal{C}\}\). In this setting, the group of players \(\mathcal{K}=\{1,\ldots,K\}\) denotes deployed nodes, with each node functioning as a rational decision maker. The action space \(\mathcal{E}=[E_{\mathtt{min}},E_{\mathtt{max}}]\) defines potential transmit energy levels. The state space \(\mathcal{S}\) is characterized by \(\gamma\) and \(E^{\tau}_{k}\), where the state of a node is denoted as \(s_{k}\in\mathcal{S}\) and \(s_{k}(t)=[E^{\tau}_{k}(t),\gamma_{k}(t)]\). Both states' evolution is presumed to adhere to the Ornstein-Uhlenbeck dynamics, a mathematical framework offering insight into real-world dynamic phenomena [46]. The cost function \(c_{k}(t)\in\mathcal{C}\) for a node at any given time considers both receiver performance and transmit energy, and is defined as [47]  
\[c_{k}(t)=(\gamma_{k}(t)-\gamma_{\mathtt{th}}(t))^{2}+\mu E_{k}(t), \tag{9}\]  
where the term \((\gamma_{k}-\gamma_{\mathtt{th}})^{2}\) signifies the performance-related term, and \(\mu E_{k}(t)\) denotes the transmit energy-related term. \(\mu\) is a weighting factor that balances the importance of receiver performance against energy consumption.  
To determine the value of \(E_{k}\) that will minimize \(c_{k}(t)\) in this dynamic system, each node formulates an optimal control policy \(v^{*}_{k}\in\mathcal{V}\)[48] defined as  
\[v^{*}_{k}(0\to T) =\arg\min_{E_{k}(0\to T)}\mathbb{E}\bigg{[}\int_{0}^{T}c_{k}(t )dt+c_{k}(T)\bigg{]},\forall k\] \[\mathtt{subject\ to:}s_{k}(t)=\{E^{\tau}_{k}(t),\gamma_{k}(t)\},\forall k \tag{10}\]  
where \(t\in[0,T]\) and \(c_{k}(T)\) represents the final condition which characterizes the system's cost at final time \(T\).
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 6 Transfer Learning between Datasets  
To understand the universality of these models, it is informative to attempt transferring a learned model between the datasets. As discussed above, although both datasets could be described as from a \"shallow water\" environment, they vary quite substantially in terms of physical environment and the data observed reflects that. This makes them a useful pair of datasets for evaluating the transferability of these models.
## 5 Experimental Results
### Visual Servo Control of MMMS  
To further verify the effectiveness of the real time joint backlash compensation, we subsequently validated the visual servo algorithm on the planar MMMS test-bed. Comparing with the previous test-bed of FBMS, the flexible base is replaced by a prototypical manipulator of CMM [38], and the EMM is joined together through the mechanical interface on a larger air-bearing table. The power supply and communication is rebuilt in the \"Macro-Micro Mode\", where the 1553B bus of the two manipulators are connected, and the manipulators are commanded through the operating consoles.  
The experimental scenarios were designed in three trajectories to move between the \"coarse\" and \"fine\" position, where the coarse position is 200 mm ahead of the fine position. There are initial errors of \(-\)528 mm, \(-\)21 mm, and 2.3 deg in the X, Y, and Rz, respectively. The visual servo results shown in Figure 14 are tested without payload. It can be seen that the controller is stable in different trajectories, and the final berth accuracy is less than (3 mm, 3 mm, 0.3 deg). One can notice that the gradual deceleration works when the manipulator approaches the target; at the same time, the max overshoot of 12 mm in the Y-direction is apparent, which gradually converges to zero and no widespread fluctuations are generated.
# Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD
## 6 Conclusions  
To extract the fault information in the weak fault signal effectively and realize the fault diagnosis, a bearing incipient fault feature enhancement method based on CEEMDAN and MCKD is proposed, and the main conclusions are as follows:  
1. The fault signal is decomposed using the CEEMDAN algorithm to obtain a set of IMF components, and the sensitive IMF components are selected according to the kurtosis-correlation coefficient criterion and the signal is reconstructed, which can effectively remove the noise from the fault signal.
2. In comparison to the MED method, the MCKD algorithm may emphasize continuous shock pulses in rotating equipment. PSO-MCKD then processes the signal to efficiently improve the signal's weak shock components after noise reduction.
3. Envelope analysis is performed on the signal processed by feature enhancement to realize the extraction of bearing incipient fault features. The bearing inner ring faults under constant working conditions and the bearing outer ring faults under varied working conditions are used to demonstrate the method's usefulness. When compared to the PSO-MCKD and CEEMDAN-MED approaches, it exhibits better feasibility and generality.  
Data curation, X.C., P.L. and J.D.; Writing--original draft, L.Z. and X.C.; Writing--review & editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of P.R. China under Grants (No. 62073051), the Science and Technology Research Project of the Chongqing Municipal Education Commission of P.R. China under Grants (No. KJZD-K 201900704).  
Not applicable.  
The authors would like to thank the Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis of China and Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System for providing the dataset of the rolling bearings.  
The authors declare no conflict of interest.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 6 Discussion
### Classification Results on Augmented Test Data  
We applied 20 transformations using rotation and displacement of each image in the test dataset to evaluate the accuracy and robustness of the proposed method. In addition, the performance of 24 CNN models was evaluated on both the cross-validation and test sets (Table 5). The top five models were then selected to classify the augmented test data for comparison with the proposed method.  
Figure 8: Statistics for Sperm Head Pose Correction Network results.  
Figure 9: A sample of typical results for the Sperm Head Pose Correction Network is shown, where the left side shows the original sperm head and the right side shows the corrected sperm head.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Cross Validation**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Macrø–F1 (\%)**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Macrø–F1 (\%)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Mean**} & & & & \\ \hline VGG11 & 91.7 & 0.029 & 91.4 & 0.03 & 80.0 & 79.7 \\ VGG13 & 91.1 & 0.043 & 91.1 & 0.041 & 92.5 & 92.6 \\ VGG16 & 92.3 & 0.064 & 92.1 & 0.066 & 82.5 & 81.8 \\ VGG19 & 88.8 & 0.034 & 88.4 & 0.036 & 80.0 & 79.7 \\ ResNet18 & 92.3 & 0.024 & 92.4 & 0.024 & 85.0 & 84.8 \\ ResNet34 & 90.6 & 0.015 & 90.5 & 0.015 & 90.0 & 89.6 \\ ResNet50 & 89.3 & 0.042 & 89.4 & 0.041 & 82.5 & 81.9 \\ ResNet101 & 92.9 & 0.031 & 93.1 & 0.03 & 87.5 & 87.4 \\ ResNet152 & 91.7 & 0.034 & 92.0 & 0.034 & 82.5 & 82.3 \\
* Squeeze2 exl1\_0 & 94.7 & 0.035 & 94.5 & 0.037 & 87.5 & 87.2 \\ SqueezeNet1\_1 & 90.9 & 0.023 & 90.3 & 0.023 & 90.0 & 90.1 \\ DenseNet121 & 92.9 & 0.023 & 92.8 & 0.024 & 82.5 & 82.6 \\ DenseNet161 & 91.7 & 0.012 & 91.6 & 0.012 & 85.0 & 84.3 \\ DenseNet169 & 91.7 & 0.054 & 91.6 & 0.054 & 85.0 & 85.1 \\ DenseNet201 & 92.9 & 0.015 & 92.9 & 0.014 & 87.5 & 87.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of validation and test results for CNNs and our method.  
Table 6 and Figure 10 provide a comparative analysis of performance metrics, specifically accuracy, precision, recall, F1 score, and confusion matrix, for five CNN models (VGG13, SqueezeNet1_1, ResNet34, ShuffleNet_V2_x1_0, MobileNet_V3_small) as well as our proposed method. Our method demonstrates superior performance across all metrics, achieving an accuracy of 95.8%, precision of 95.9%, recall of 96.1%, and F1 score of 95.7%. These results represent a substantial improvement over the next-highest performing model, ShuffleNet_V2_x1_0, indicating that the proposed method offers enhanced classification accuracy and robustness. The higher precision and recall values also suggest a strong capacity to minimize both false positives and false negatives. Figure 11 shows representative misclassified images.
## 4 Experiment
### Analysis of Experimental Results
The proposed GNNet network structure is based on HRNet, and the GCTblock is used to model the channels more efficiently, thus enabling the Gateblock module and the Gateneck module extracts channel features more accurately, and improves the fusion module between different resolution subnets of each stage by using the Non-local block. This paper adds the Non-local block before the fusion of different resolution representations, which can extract more useful spatial feature information from different resolution representations, thus enabling better information fusion between multi-resolution representations. This table shows the performance comparison of several different methods on the COCO VAL 2017 dataset for human pose estimation. The input size of all the methods is 256 * 192, and the GFLOPS (floating point operations per second) metric is used to measure the computational efficiency of each method. The evaluation metrics used are AP (average precision) at IoU thresholds of 0.5 and 0.75, mAP (mean average precision), and AR (average recall) at a fixed false positive rate. Among the methods, CPN and CPN+OHKM do not have reported results for AP at either threshold, while HRNet-32, SimpleBaseline-50, SimpleBaseline-101, and SimpleBaseline-152 achieve AP values ranging from 88.6 percent to 89.5 percent at IoU threshold of 0.5, and from 78.3 percent to 79.8 percent at IoU threshold of 0.75. Our method outperforms all the other methods with an AP of 91.2 percent at IoU threshold of 0.5 and 81.5 percent at IoU threshold of 0.75, achieving the highest mAP of 74.3 percent and the highest AR of 79.3 percent. Compared with several other recent research methods, the method used in this paper has a better performance in AP50, AP75, mAP and AR metrics, which are generally better than other methods, and is also better represented in publicly available \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Input Size** & **GFLOPS** & \(\mathbf{AP^{50}}\) & \(\mathbf{AP^{75}}\) & **mAP** & **AR** \\ \hline CPN [50] & 256 * 192 & 6.20 & - & - & 68.6 & - \\ CPN + OHKM [50] & 256 * 192 & 6.20 & - & - & 69.4 & - \\ HRNet-32 [51] & 256 * 192 & 7.10 & 89.5 & 80.7 & 73.4 & 78.9 \\ SimpleBaseline-50 [52] & 256 * 192 & 8.90 & 88.6 & 78.3 & 70.4 & 76.3 \\ SimpleBaseline-101 [52] & 256 * 192 & 12.40 & 89.3 & 79.3 & 71.4 & 77.1 \\ SimpleBaseline-152 [52] & 256 * 192 & 15.70 & 89.5 & 79.8 & 72.0 & 77.8 \\
**Ours** & 256 * 192 & 8.1 & **91.2** & **81.5** & **74.3** & **79.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results on COCO VAL 2017. Figure 9: Model application effect chart and video frame rate comparison chart. datasets, which can show that the proposed method based on graph convolutional neural network and single body pose estimation has a better application in human pose estimation.
# Understanding of Customer Decision-Making Behaviors Depending on Online Reviews
## 2 Background
### Studies on Helpfulness of Reviews  
It is important to understand the helpfulness of reviews to allow consumers to be exposed to more holistic and better summarized review information, despite the excess of review information in the modern age [26]. The helpfulness of reviews can be identified by analyzing which characteristics of reviews are essential to consumers [27; 28; 29]. Quantitative characteristics, such as the numerical rating of reviews or the length of a review [30], or qualitative characteristics, such as sentiment or review complexity [31], affect the ability to infer the quality of a consumer product. In addition, factors affecting review helpfulness include reviewer-related factors (e.g., writer reputation), reader-related factors (e.g., reader identification), and environmental factors (e.g., website reputation) [27]. Through techniques such as natural language processing (NLP), machine learning, and deep learning, review helpfulness is regressed or classified by the factors mentioned above [32; 33; 34; 35; 36]. According to past studies, negative reviews tend to be more impactful than positive reviews [37; 38] and reputation deterioration caused by negative reviews has an adverse effect on profits [39]. For both the product providers and consumers, it is necessary to present negative reviews, positive reviews, and an overall picture of consumer reviews all together, appropriately sorted.  
[PERSON] et al. studied a method of rating sorting and observed a user's choice when presented with two different rating scenarios [40]. Rating scenarios were a combination of a thumbs-up/thumbs-down ratio and a sample size. For example, there are comparisons where one distribution has a higher positive ratio but a lower number of total votes. To compare the two distributions, they calculated inter-rater reliability, a percentage value obtained by dividing the absolute value of the difference in the number of selections between two combinations by the total number of selections. Additionally, by measuring the time-to-rank, the time taken to decide which combination is more preferred, observations were made on the difficulty of ranking these scenarios.  
[PERSON] et al. developed Review Spotlight which summarizes text reviews and presents them as adjective-noun pairs [41]. Review Spotlight uses sentiment analysis to select adjacent adjectives and nouns in the texts and matches them as pairs. The pairs shown to the user have different colors depending on the three cases, where their sentiment is interpreted as positive, neutral, or negative. If the user clicks on the adjective-noun pair, the original text review of the pair is shown.
# DPS Clustering: New Results
## 3 Results: Iterative DPS  
The first DPS-stage of the \(\text{DPS}(P,\beta,r)\) algorithm carves a maximal perfect subset \(X(\beta)\), \(\beta\)-extremely \(P\)-dense in the background of \(X\) at each of its points, from the space \(X\).  
Let us turn to Figure 3b: it shows the result \(X(\beta)\) of the DPS algorithm on array \(X\) (Figure 3a) in red when \(\beta=0.02\). It is easy to see that not all noteworthy condensations from \(X\) were included. The reason for their non-inclusion in the result \(X(\beta)\) is explained by the contradiction between the \(r\)-local character of the view of \(X\) and the global approach to determine the level \(\alpha(\beta)\) of density \(P\) by its extremality level \(\beta\) based on the whole image \(P_{X}(X)\) (7) and (8): the density level of worthy condensations in the complement \(\overline{X(\beta)}\) appears to be below \(\alpha(\beta)\).  
To include them in the final result, we need to lower the extremum level \(\beta\): \(\beta\Rightarrow\bar{\beta}<\beta\) and switch from the \(\text{DPS}(P,\beta,r)\) to the \(\text{DPS}(P,\bar{\beta},r)\) algorithm. In the control example, the first level of \(\overline{\beta}\), for which the result \(X(\bar{\beta})\) will include all worthy condensations, will be \(\overline{\beta}=-0.25\). The result is shown in red in Figure 3c: we see that the result \(X(\bar{\beta})\) of the algorithm \(\text{DPS}(P,\bar{\beta},r)\), along with \(X(\beta)\) and worthy condensations from \(\overline{X(\beta)}\), included weak points of the \(r\)-halo of the set \(X(\beta)\), and it helped them in this.  
It can be done otherwise: keep the extremum level \(\beta\), but change the original space by transition from \(X\) to \(\overline{X(\beta)}\). The result of \(\text{DPS}(P,\beta,r)\) on \(\overline{X(\beta)}\) is shown in green in Figure 3b. If we make another similar transition, a blue cluster appears in Figure 3b.  
Such way of using the DPS scheme is called the \"iterative DPS\" algorithm.