esa-sceva/satcom-chunk-collection huggingface dataset:
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 6 Conclusions  
In order to completely utilize the incidence relations and shared resources between the two modules by only relying on present joint models, and exploring the value of knowledge base to these modules, this paper proposes a joint model for intent detection and slot filling based on MTL with a knowledge base, which makes full use of the external knowledge, and a high-quality relationship information between intents and slots. Firstly, we obtained the shared parameters and features between two modules based on the neural networks of LSTM and CNN. Secondly, the knowledge base was introduced into the model to improve its performance. Finally, a weighted-loss function was built to optimize the whole joint model. Experiments are based on the ATIS and Snips dataset to evaluate the performance of the proposed method. The experimental results show that the accuracy of the intent detection was 98.83% and the F value of slot filling was 97.06%, both on the ATIS dataset. On the Snips dataset, the accuracy of the intent detection and the F value of slot filling are 98.79% and 97.31% respectively. Furthermore, we also analyzed the keys to the success of our joint model, i.e., sharing representation, the BERT coding method, the knowledge base and learning-weighted-loss function, which can fully determine the relationship between these two tasks and external knowledge. The results demonstrate that our model is feasible and superior to the related baseline model. Certainly, the proposed method still has some limitations. For example, our method is evaluated on the small-scale datasets, which may limit the number of observation and behavioral characteristics. In the future researches, the method proposed in this paper can be extended to more complex and diversified datasets, or trying something new with dialogue systems.  
Conceptualization, X.X. and T.H.; methodology, X.X. and Y.W.; software, Y.W. and X.X.; validation, T.H. and H.W.; formal analysis, J.C.; investigation, X.X; resources, T.H.; data curation, X.X.; writing--original draft preparation, Y.W. and T.H.; writing--review and editing, X.X. and T.H.; visualization, X.X.; supervision, T.H.; project administration, T.H. and H.W.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China National Social Science Fund (19 RXW110).  
Figure 4: Loss function change.
## 2 Materials and Methods
### Intrinsic Feature Extraction Based on Time-Frequency Image
#### 2.2.2 Time-Frequency Analysis of AE Signal Based on Short-Time Fourier Transform (STFT)  
The signal can be roughly decomposed to different scales by discrete wavelet decomposition. In order to further characterize the time-frequency characteristics of the signal, it is necessary to decompose the time-domain waveform of the signal into two-dimensional time-frequency plane through an effective time-frequency analysis method. Among many time-frequency analysis methods, STFT can not only suppress energy leakage but also  
Figure 2: Multiresolution analysis of acoustic emission signals based on Discrete Wavelet Transform (DWT).  
perform well in time resolution and computational efficiency. Therefore, this paper uses STFT for analysis of cutting AE signals. The mathematical expression for the STFT is:  
\[STFTs(t_{s},f)=\int_{-\infty}^{+\infty}[x(\tau)r^{*}(\tau-t)]e^{-j2\pi ft}dt \tag{2}\]  
where \(s(t)\) is a non-stationary original signal, \(r^{*}(\tau-t)\) is the window function.  
The time-frequency diagram of the AE signal can reflect the non-stationarity of the cutting process to a certain extent, but it is clearly affected by the acoustic emission energy. In this paper, the time-frequency image is normalized to remove the factors of energy changing with time from the time-frequency diagram and obtain the time-varying information that only reflects the frequency component in the cutting state. Assuming the discrete signal sequence \(s(n)\) passes STFT, the time-frequency matrix obtained is \(P[n,k]\). For column \(i\) of \(P[n,k]\), \(P[n,k]\) is normalized according to the following method:  
\[P[n,k]=\frac{(P[n,k]-P_{min}[n,k])\times 2}{P_{max}[n,k]-P_{min}[n,k]}-1 \tag{3}\]  
All columns of \(P[n,k]\) are normalized to obtain normalized time-frequency matrix \(P[n,k]\).  
The normalized time-frequency image is shown in Figure 3. In the time-frequency image, the AEs' energy at 2000 kHz in the cutting in process is smaller than that in the cutting out process. On the contrary, in the normalized time-frequency image, the AEs' relative energy at 2000 kHz in the cutting in process is larger than that in the cutting out process. Therefore, it can be seen that the normalized time spectrum is not affected by the energy of acoustic emission signal and can directly reflect the change I the instantaneous frequency component of the acoustic emission signal with time.  
Figure 3: Comparison of the normalized time–frequency image and the time–frequency image.
## 3 Database and Models
### Proposed ANN Model  
The ANN models proposed in this paper have three layers: an input layer, a hidden layer, and an output layer (in Figure 1). The number of input and output nodes are determined by the parameters of the research problems. However, because of reliable mathematical formulas for calculating the number of hidden layer nodes are not available, the initial number of hidden layer nodes should be estimated by trials. The Log-Sigmoid activation function is used in the hidden layer, and the linear activation function is used in the output layer.  
To develop the ANN model for predicting the flexural strength and the compressive strength of UHPFRC, a series of trials were carried out in order to determine the number of layers and other parameters of the ANN models by the minimum mean square error (\(MSE\)) of the training data. The length (\(L\)), the diameter (\(D\)), the aspect ratio (\(AR\)), and the volume fraction (\(VF\)) of steel fibers and the flexural strength (\(PFS\)) of UHPFRC without fibers are investigated as inputs, and the flexural strength (\(FS\)) of UHPFRC is as output, meanwhile, the length (\(L\)), the diameter (\(D\)), the aspect ratio (AR), and the volume fraction (\(VF\)) of steel fibers, the water to binder ratio (\(W/B\)) and the compressive strength (\(PCS\)) of UHPFRC without fibers are investigated as inputs, and the compressive strength (\(CS\)) of UHPFRC is as output. The parameters of the ANN models for predicting the flexural strength and the compressive strength of UHPFRC are shown in Table 4 and the structures of models are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Flexural Strength Model** & **Compressive Strength Model** \\ \hline Number of input layer nodes & 5 & 6 \\ Number of hidden layers & 1 & 1 \\ Number of hidden layer nodes & 15 & 20 \\ Number of output layer nodes & 1 & 1 \\ Momentum factor & 0.8 & 0.6 \\ Learning rate & 0.3 & 0.3 \\ Target error & 0.00001 & 0.00001 \\ Learning cycle & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used in the ANN models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Compressive Strength**} & \multicolumn{2}{c}{**Flexural Strength**} \\ \cline{2-5}  & **Minimum** & **Maximum** & **Minimum** & **Maximum** \\ \hline \(W/B\) & 0.13 & 0.30 & — & — \\ \(D/\)mm & 0.15 & 0.65 & 0.16 & 0.65 \\ \(L/\)mm & 6 & 30 & 9 & 30 \\ \(AR\) & 100 & 30 & 32.5 & 100 \\ \(VF/\)\% & 0.00 & 5.00 & 0.00 & 5.00 \\ \(PCS/\)MPa & 72.15 & 220.98 & 8.23 & 26.88 \\ \(CS/\)MPa & 72.15 & 249.68 & 8.23 & 73.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranges of Parameters in Compressive Strength and Flexural Strength Data base.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Contrast Experiment  
In order to further verify the effectiveness of the fusion algorithm, we compared with the literature [33]. Since the fusion algorithm and literature [33] are based on the seven-element stereo microphone array model, it is feasible to use the time-delay data measured by the same array shown in Figure 8 for comparative analysis.  
To explore the extent to which the sound source is at an extreme angle to the negative impact of sound source localization performance, the sound source we selected is at (1.6 m, 2.7 m, 1 m), which matches the literature [33]. In addition, the spacing of the array elements was set to 0.4 m to match the experiment conducted in the literature [33] on 2 April 2018. At this time, the theoretical elevation angle of the sound source reaches \(17.67^{\circ}\), which is an extreme angle and can verify the effectiveness of the fusion algorithm. The measured results are shown in Table 9.  
As can be seen from Table 9, the distance error rate of the data measured by the fusion algorithm was far less than that measured by [PERSON] et al. [33], and the error rate of the elevation angle was also slightly less than that measured by [PERSON] et al. [33]. Although the error rate of the horizontal angle was slightly larger than that measured by [PERSON] et al. [33], on the whole, the ranging and direction-finding performance of the fusion algorithm is better than that of the method proposed by [PERSON] et al. [33].  
From the perspective of commonality, both algorithms can locate the sound source; similarly, the difference between the two is also obvious. From Equations (20), (23), and (30) in the study of [PERSON] et al. [33], it can be seen that the method proposed by [PERSON] et al. [33] is affected by the sound source at an extreme angle. For example, when the elevation angle is close to \(90^{\circ}\), the estimation error of the horizontal angle in Equation (20) is close to infinity, which is undoubtedly not expected. Assuming that the theoretical elevation angle of the contrast experiment is less than \(10^{\circ}\), the error rate of data measured by [PERSON] et al. [33] will rise sharply. On the contrary, the introduction of fusion algorithm can better solve this problem.
# An Eight-Zonal Piezoelectric Tube-Type Thread Ultrasonic Motor Based on Second-Order Bending Mode
## Structure, Design Principle, and Working Mechanism  
### Structure of the Threaded Actuator  
The structure of the piezoelectric tube-type threaded ultrasonic motor we proposed is illustrated in Figure 1a. A fabricated prototype is shown in Figure 1b. The motor is combined with a customized piezoelectric ceramic tube (from PI Co., Ltd, Germany) acting as a stator, two nuts (outer diameter 6.35 mm, inner diameter 3 mm, length 1.5 mm), and a precise screw rod (diameter 3 mm, length 50 mm) acting as a rotor. The nut is screwed with the precise screw rod through the internal thread. The stator is composed of a piezoelectric ceramic tube and two nuts bonded with both ends. The dimension of the customized piezoelectric ceramic tube is \(\phi 6.35\times\phi 5.35\times 30\ mm\). The inner and outer surfaces of the tube are both plated with silver electrodes, but the electrode on the outer surface is divided into eight parts as shown in Figure 1a to excite two second-order bending (B2) modes, which could then be used to produce a \"hula hoop\" wobbling motion [10]. Finally, through the thread drive mechanism, the motor directly transforms the wobbling motion of the stator into linear movement along the screw rod without additional mechanical conversion.  
Figure 1: (**a**) Structure of the threaded motor. (**b**) Photo of the fabricated motor.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 2 Synchronized Optimization Model for Network and Scheduling  
### Model Assumptions  
Due to the multifaceted nature of factors influencing the bus system, the following assumptions are made to accurately define the bus network optimization problem addressed in this study:  
(1) Passengers choose the shortest travel time route to ride; when multiple routes can satisfy the shortest travel time, passengers choose based on the proportion of the total number of trips taken by the vehicles on that OD route. (Shortest Time)  
(2) The waiting cost for passengers is assumed to be half of the average waiting time for the selected route, which is equal to half of the departure interval of the vehicles on the chosen route.  
(3) The vehicles on each route are assumed to be identical except for the number of trips. Differences in factors such as road traffic, passenger load, and driving speed that may affect passenger route selection are ignored.  
(4) The total travel time for passengers includes waiting time, time spent on the vehicle, walking time from the origin to the bus stop, and walking time from the bus stop to the destination.  
(5) Passenger transfers are limited to the same bus stop. If there is no direct route between the origin and destination, passengers need to get off at a stop on the initial route and board another bus passing through that stop to reach the destination, only considering transfers at the same stop.  
(6) It is assumed that passengers can board the next available vehicle on the desired route after arriving at the stop, without any capacity restrictions on the vehicles.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 5 Conclusions  
This article provides a comparative analysis of the protocols used in V2V communications. These communications play a crucial role in autonomous driving, offering solutions to emerging challenges in new usage scenarios. Such usage scenarios include V2V Cooperative Awareness (e.g., Forward Collision Warning), V2V Unicast Exchange (e.g., Pre-Crash Sensing), V2V Decentralized Environmental Notification (e.g., danger signaling devices), and others. A comparison was made between 802.11p, 802.11 bd, LTE-V2X and NR-V2X protocols in terms of performance. The aim was to identify which of these protocols offers the best support for URLLC services in the scenario of autonomous driving in rural line-of-sight (LOS) scenarios.  
It was viewed that the IEEE 802.11 bd protocol tends to perform better than IEEE 802.11p because it has the characteristic of having higher diversity gains for frequency-selective channels and due to the use of midambles for channel estimation.  
In the analysis between NR-V2X and LTE-V2X, it was viewed that NR-V2X tends to outperform LTE-V2X in several parameters. This is because NR-V2X uses advanced interference cancellation techniques such as beamforming or advanced MIMO systems and LDPC codes.
# Study on Seismic Attenuation Based on Wave-Induced Pore Fluid Dissolution and Its Application
## 5 Discussion  
The Squirt model and the Pride model are not suitable for predicting the attenuation of rocks containing micro-pores at the seismic frequency band. The energy loss caused by fluid flow is neglectable, and gas dissolution and exsolution become the dominant attenuation mechanisms.  
Although the conventional constant Q model is widely accepted, it will not predict accurate velocity dispersion when the corner frequency occurs in the seismic frequency band. For the same rock properties, the velocity range differs from that of the pore fluid dissolution model. The Backus averaging method almost results in a similar velocity range to that of real data. The size of the window chosen will not improve accuracy of velocity. Neither of these two methods are suitable for upscaling. The SLS model is suitable for upscaling the pore fluid dissolution model. The velocity dispersion and attenuation curve can be well modeled by the SLS model for different porosity and gas saturation cases.  
Figure 10: (**a**) Comparison of Q\({}^{-1}\) results by the WIGED model using logging data and data from core measurements. (**b**) Well-to-seismic calibration before and after using Q\({}^{-1}\) results from the WIGED method as a guidance.  
Synthetic seismic data generated by using velocities calculated by these methods show obvious difference. Due to similar velocity range from the Backus averaging method, the events in the synthetic seismic data show little stretch. The constant Q model underestimates the velocity dispersion, and thus, the synthetic seismic data show little stretch until 0.4 s. The attenuation becomes observable because of the accumulation of the attenuation effect. The SLS model reasonably predicts the attenuation, and the synthetic seismic data show obvious difference from the traces generated by using logging velocity. When choosing a rock physical model for well-to-seismic calibration, one needs to consider the geology, geochemistry, and core data analysis report, and then select the appropriate rock physics model and update the parameters in the model.
## 2 Holistic Model of the SLM Process and Main Algorithm  
### Holistic Model and Algorithm  
The holistic model was developed after an analysis of SLM at three levels: technological, associated physical processes and phenomena, and associated mathematical models (Figure 1) [39]. The latter, with appropriate connections or interfaces, is the content of the holistic model. On the first technological level, SLM can be schematically represented as a cyclic process consisting of the sequence: powder deposition, pause, laser beam treatment, pause, and powder removal. This sequence can be repeated many times until the product is finished. The following six basic processes are considered at the second level of analysis and can be consequently modeled: powder deposition, laser beam heat treatment, melting, liquid flow (free flow of melted material), solidification, and powder removal. The defined processes and phenomena associated with SLM are modeled using the following five sub-models: a powder deposition model, a heat transfer model with laser treatment, a phase transition model, a fluid flow model with a free surface, and a powder removal model. The work to develop the model required some adjustments to the structure of the holistic model and its components. The main algorithm, the structure, and the functioning of the model can be represented as a block diagram (Figure 2) [39]. The algorithm has two circuits. The first external circuit contains the powder bed generation (PBG) model, cycle initialization, and its realization (represented by the internal circuit--LBM calculation module), and the powder removal (PR) model.  
Details of the development, testing, verification and validation of the 2D and 3D submodels and the entire holistic model of the SLM process, as well as several examples of simulations of separated phenomena by submodels and the entire process, can be found in previous papers [33; 34; 35; 36; 37; 38; 39; 40]. The paper [39] presented the main algorithm, but it was only partially implemented. The external circuit was not closed, and the results of the simulations performed by the LBM calculation module were not transferred to the PBG model for further deposition of the next powder layer. The LBM calculation module allows modeling of only one stage of the SLM with several laser tracks on a distributed level of the powder. A file with a structure after the stage was prepared but could not be used by the PBG model. This can be explained by the logic of developing a holistic model. Until the internal circuit has been developed and verified, the external circuit was not worked on and the PBG model was simplified and could perform only the necessary functions. Closing the external circuit required the development of a new version of the PBG model that provides feedback communication from the LBM calculation module.
## 4 Result
### Quantitative Evaluation of Generated Images
#### 4.4.2 Classified Evaluation Indicators  
In addition to the image evaluation metrics, which is a quantitative evaluation method, classification index offers an alternative perspective to assess the enhancements introduced by the improved DCGAN model. The VGGNet, AlexNet and ResNet classifiers are used to train the three data augmentation methods (T1: traditional data augmentation, T2: improved DCGAN, T3: traditional data augmentation \(+\) improved DCGAN), respectively, and Table 5 presents the training results for the three classifiers.  
The T1 datasets exhibited the lowest performance, with accuracies of 78.23%, 72.90%, and 71.14% for the three classifiers. The corresponding G-mean values were 53.81%, 55.62%, and 66.25%, while the F-score were 70.12%, 66.86%, and 68.80%. In comparison, the T2 datasets showed improvements in accuracy by 3.03%, 4.09%, and 3.05% over T1. G-mean increased by 14.29%, 12.78%, and 1.64%, and F-score improved by 5.8%, 5.55%, and 2.14%. Notably, the T3 datasets achieved the highest accuracy across all three classifiers, with increases of 2.07%, 6.09%, and 2.63% over T2, reaching 83.33%, 83.08%, and 76.82%, respectively. G-mean improved by 8.71%, 5.79%, and 5.74% over T2, reaching 76.81%, 74.19%, and 73.63%, respectively. F-score increased by 4.42%, 5.74%, and 3.59% over T2,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **DCGAN** & \begin{tabular}{c} **DCGAN-** \\ **TTUR** \\ \end{tabular} & **SA-TTUR** & **SN-TTUR** & \
\begin{tabular}{c} **MiniBatch-** \\ **TTUR** \\ \end{tabular} &
\begin{tabular}{c} **Improved** \\ **DCGAN** \\ \end{tabular} \\ \hline FID & 234.868 & 228.962 & 198.519 & 219.143 & 205.735 & 184.267 \\ SSIM & 0.325 & 0.406 & 0.414 & 0.478 & 0.370 & 0.601 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FID and SSIM scores of the generated images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Data Augmentation Methods** & **Accuracy (\%)** & **G-Mean (\%)** & **F-Score (\%)** \\ \hline VGGNet & T1 & 78.23 & 53.81 & 70.12 \\  & T2 & 81.26 & 68.10 & 75.92 \\  & T3 & 83.33 & 76.81 & 80.34 \\ \hline AlexNet & T1 & 72.90 & 55.62 & 66.86 \\  & T2 & 76.99 & 68.40 & 72.41 \\  & T3 & 83.08 & 74.19 & 78.15 \\ \hline ResNet & T1 & 71.14 & 66.25 & 68.80 \\  & T2 & 74.19 & 67.89 & 70.94 \\  & T3 & 76.82 & 73.63 & 74.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification Results.  
reaching 80.34%, 78.15%, and 74.53%, respectively. Bar charts are plotted according to Table 5 for a more intuitive view of the performance of the improved DCGAN, as shown in Figure 10.  
As shown in the figure, after data augmentation using the improved DCGAN, all metrics significantly improved, with T3 achieving the highest accuracy. This demonstrates that the hybrid datasets substantially enhances classifier performance, further validating the effectiveness of the generated gas PE pipe defective images.