esa-sceva/satcom-chunk-collection huggingface dataset:
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 1 Introduction
The emotional polarity here is clearly negative. Therefore, combining aspect words with SSWs is beneficial for identifying the emotional polarity corresponding to certain aspect words. We can identify the SSWs corresponding to each aspect of a sentence by analyzing the parts of speech and applying fixed rules. This approach allows us to consider the grammatical and semantic information of emotive support words in the sentence. Based on this idea, this paper presents a sentiment-supported graph conventional network (SSGCN). First, we used an aspect perception attention mechanism and a self-attention mechanism to obtain attention scores; then, we input these scores into the GCN through a syntactic mask matrix to obtain sentence features that initially combined semantic and grammatical information. Then, the semantic and grammatical information of the SSW combination was obtained. Finally, this was combined with the semantic and grammatical information of the aspect words to obtain the enhanced information of the combination of syntactic and semantic information. In this way, a more complete combination of semantic and grammatical information could be obtained. When evaluated on three publicly avail  
Figure 1: An example sentence of aspect-level sentiment analysis. able datasets, the model presented in this paper demonstrated a greater efficiency than the eight comparison models. The main contributions of this paper are as follows:  
1. A method of combining semantics and grammar using sentiment support words is presented. This method effectively captures the evaluation information of aspect words in sentences and assists the model in extracting the correct feature information. 2. A further combination method is presented. This method integrates syntactic and semantic information based on aspect and sentiment support words. It enhances the comprehensiveness of the constructed combined features by incorporating semantic and grammatical information. 3. Experiments on three public datasets demonstrated that the SSGCN was more effective than the benchmark models. The remainder of this paper is organized as follows: Section 2 provides a review of the research associated with aspect-level sentiment analysis. Then, the proposed SSGCN model is introduced in detail in Section 3. The experiments and discussion are presented in Section 4. Finally, Section 5 provides our conclusions.
## 2 Background  
The need to reduce landslide risk and improve emergency responses has led to many studies on landslide susceptibility around the world. Different technologies and methodologies have been employed to monitor landslide risk, given the complexity of creating landslide susceptibility maps, influenced by factors such as heavy rainfall, vegetation, geological and lithological factors, and slope.  
To address these challenges, various monitoring technologies have been developed.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 2 Theory and Model
### Finite Element Method  
The propagation process of laser ultrasonic waves in Cu/Al bimetallic composite was studied by numerical analysis. The heat conduction equations and finite element equations [25; 26; 27] are given as following  
\[[K]\{T\}+[C]\Big{\{}\dot{T}\Big{\}}=\big{\{}R_{q}\big{\}}+\big{\{}R_{Q}\big{\}} \tag{6}\]  
\[[M]\Big{\{}\ddot{U}\Big{\}}+[K]\{U\}=\{R_{ext}\} \tag{7}\]  
where \([K]\) is the conductivity matrix, \([C]\) is the heat capacity matrix, \([M]\) is the mass matrix, \([T]\) is the temperature vector, \(\Big{\{}\dot{T}\Big{\}}\) is the temperature change rate vector, \(\{R_{q}\}\) is the heat flux, \(\{R_{Q}\}\) is the heat source vector, \(\{R_{ext}\}\) is the external force vector, \(\{U\}\) is the displacement vector, and \(\Big{\{}\ddot{U}\Big{\}}\) is the acceleration vector.  
Appropriate element size and iteration time step are very important to ensure the accuracy of finite element calculation results. The element size and iteration time are determined by the following two formulae [26; 27].  
\[\Delta t\leq\frac{1}{180f_{\text{max}}} \tag{8}\]  
\[L_{e}\leq\frac{\lambda_{\text{min}}}{20} \tag{9}\]  
where \(\Delta t\) is the integration time, \(f_{\text{max}}\) is the maximum center frequency of laser ultrasonic, \(L_{e}\) is the element size, and \(\lambda_{\text{min}}\) is the shortest wavelength of laser ultrasonic.
## 4 Correlation Analysis of Shield Tunneling Parameters
### Data Type Determination  
Given the relative consistency of driving parameters within the same ring, it is standard practice to utilize the average values of these parameters under normal conditions as representative for each ring. To enhance the reliability of the data, the box chart method is employed to filter out anomalous data points, resulting in a dataset of tunneling parameters for 555 rings. For the purpose of statistical analysis, the following parameters are selected as the foundational data based on the project's specific circumstances and prior analogous analyses: average mud pressure, grouting pressure, grouting amount, unearthed amount, total thrust, cutter torque, cutter speed and excavation speed [39; 40].  
Before conducting correlation analysis on the shield tunneling parameters, it is essential to assess whether the data adhere to a normal distribution [41]. Various methods, including numerical techniques like the S-W method and K-S method, as well as graphical approaches such as histograms, P-P diagrams, and Q-Q diagrams, are commonly employed for this purpose. The S-W method is suitable for small sample sizes (typically less than 50), and the Q-Q diagram assesses the data distribution similarity. In alignment with the shield tunneling data, a normal test is performed using the K-S numerical method and P-P graphical method. The normality is established if the \(p\)-value from the K-S test exceeds 0.05. The results of the normal distribution test are presented in Table 10.
## 7 Simulation and Evaluation
### Performance Metrics
#### 7.1.1 Average End-to-End Latency  
Latency measures the time it takes for packets to travel from the source to the destination, indicating the responsiveness of the network.  
The Figure 9 demonstrates that MMS-DSR protocol provides a significant advantage in managing latency due that it increases from 20 ms at a density of 10 vehicles/km2 to only 60 ms at 100 vehicles/km2. This increase is notably lower compared to other protocols, such as SOL-DSR, which jumps from 25 ms to 75 ms, and Enhanced OLSR, which escalates from 30 ms to 85 ms under similar conditions. This remarkable performance by MMS-DSR can be primarily attributed to the integration of Long Short-Term Memory networks, which empower the protocol to proactively adjust routes in response to potential congestion and vehicle mobility, thereby avoiding common causes of delay.  
Figure 8: Throughput comparison across varying vehicle speeds.  
Figure 9: Latency comparison in function of vehicle density.  
The trend illustrated in Figure 10 confirms that as vehicle density increases, MMS-DSR effectively uses its LSTM models to foresee and navigate around these potential bottlenecks. This proactive routing ensures that even in scenarios where vehicle density significantly increases, the latency remains optimally low. For example, when vehicle density increases from 50 to 100 vehicles/km\({}^{2}\), MMS-DSR exhibits only a minimal increase in latency from 40 ms to 60 ms, demonstrating an exceptional ability to manage and adapt to increased traffic without a corresponding steep rise in delay. In a dense urban environment characterized by frequent stop-and-go traffic and variable vehicle speeds, MMS-DSR's latency remains around 60 ms at 100 vehicles/km\({}^{2}\), significantly lower than the 75 ms for SOL-DSR and 85 ms for Enhanced OLSR.
## 2 Design of the 3.7 kW WCS
### Design of the Input and Output Electrical Modules
#### 2.1.4 Design of Filter  
The battery has a requirement for the ripple of charging voltage and current, so the charging current must be filtered before charging the battery. A simple LC filter is selected, which has a wide applicability and small simultaneous impact. The inductor of the LC filter needs to not make the output current deteriorate. The inductor \(L_{o}\) and capacitor \(C_{o}\) can be selected according to the following formula [2]:  
\[L_{o}=\frac{U_{o}^{2}}{2P_{o}f_{o}}(1-D_{out}), \tag{4}\]  
\[C_{o}=\frac{L_{o}l_{o}^{2}}{U_{\text{max}}^{2}-U_{o}^{2}}, \tag{5}\]  
where \(D_{out}\) is the duty ratio and \(U_{max}\) is the maximum output voltage.  
The parameters of each component of system can be shown in Table 1:
###### Abstract  
A three-dimensional thermal simulation investigation for the thermal management of GaN-on-SiC monolithic microwave integrated circuits (MMICs) of consisting multi-fingers (HEMTs) is presented. The purpose of this work is to demonstrate the utility and efficiency of the three-dimensional Transmission Line Matrix method (3D-TLM) in a thermal analysis of high power AlGaN/GaN heterostructures single gate and multi-fingers HEMT SSPA (solid state power amplifiers). The self-heating effects induce thermal cross-talk between individual fingers in multi-finger AlGaN/GaN that affect device performance and reliability. Gate-finger temperature differences only arise after a transient state, due to the beginning of thermal crosstalk which is attributed to the finite rate of heat diffusion between gate fingers. The TLM method accounts for the real geometrical structure and the non-linear thermal conductivities of GaN and SiC in order to improve the realistic calculations accuracy heat dissipation and thermal behavior of the device. In addition, two types of heat sources located on the top of GaN layer are considered in thermal simulations: Nano-scale hotspot as a pulsed wave heat source under gate and micro-scale hotspot as a continuous wave heat source, between gate and drain. Heat diffusion however, occurs not only between individual gate fingers (inter-finger) in a multi-finger HEMT, but also within each gate finger (intra-finger). To compare results, a Micro-Raman Spectroscopy experience is conducted to obtain a detailed and accurate temperature distribution. Good agreement between the microscopic spectral measurement and TLM simulation results is observed by accepting an error less than 2.2% relative to a maximum temperature. Results show that the 3D-TLM method is suitable for understanding heat management in particular for microwave devices AlGaN/GaN HEMTs SSPA amplifier. TLM method helps to select and locates the expected hot spots and to highlight the need of thermal study pre-design in order to minimize the system-level thermal dissipation and lead therefore to higher reliability.  
AlGaN/GaN HEMTs; SSPA; heat transfer; self-heating; 3D TLM method; Nano-scale hot spots; Raman spectroscopy +
Footnote †: journal: Low Power Electronics and Applications
## 4 Experiment and Results
### Networking Measures
#### 4.2.3 PDR  
The other metric that was calculated was the packet delivery ratio (PDR) for the solutions generated from our developed method and the two benchmarks. As shown in Figure 14, SC-MOPSO generated the highest PDR, reaching a value of 100% compared with a PDR of around 90% for NSGA-II and of around 40% for NSGA-III.  
Figure 12: Time series of E2E generated from our developed algorithm and the benchmarks.  
Figure 13: Time series of energy generated from our developed algorithm and the benchmarks.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 3 Evaluation Criteria Analysis for De-Speckling Performance
### De-Noising Process to Improve the Smoothness  
In order to measure the extent of the de-noising, some indices have been proposed, such as VMR [31], Speckle Suppression Index (SSI) [48], Speckle Suppression and Mean Preservation Index (SMPI) [49], and Mean Preservation Speckle Suppression Index (MPSSI) [50]. The SSI is a filtering evaluation index derived from the VMR, which is similar to the normalization processing of VMR. The SMPI and MPSSI are two indices modified from the VMR and SSI, which focus on solving the problem that the filter overestimates the mean value [49] in real applications. However, we choose the VMR as a general measure of the parameter to perform evaluation without considering more complicated situations. The VMR is defined as  
\[\text{VMR}=\frac{1}{\text{MR}} \tag{34}\]  
where \(\text{MR}=\left(\frac{\sigma}{\mu}\right)^{2}\), \(\mu\) and \(\sigma\) are the mean and standard deviation of an image, respectively.  
Obviously, for a homogeneous region where the mean value is certain, larger VMR indicates a higher degree of smoothing because better filter will get a smaller variance in the homogeneous region. In practical applications, the normalized parameter can be expressed as  
\[\begin{array}{l}\text{MR}=\left(\frac{\hat{\phi}}{\hat{\mu}}\right)^{2}/ \left(\frac{\sigma}{\mu}\right)^{2}\\ \text{VMR}~{}=~{}\frac{1}{\text{MR}}\end{array} \tag{35}\]where \(\overset{\wedge}{\mu}\) and \(\overset{\wedge}{\sigma}\) are the mean and standard deviation of the filtered image, respectively; and \(\mu\) and \(\sigma\) are those of original image. Usually, in order to avoid the edge information eroding accuracy of VMR, we only calculate the VMR in the homogeneous area of image.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 6 Discussion of Results  
A comparison with previous harmonic tags can be conducted from different points of view that evaluate different characteristics of tags. We introduce four different metric keys: type of antenna, number of antennas used in the tag, use of dual polarization, and the conversion losses. The type of antenna is important because the tag must be insensitive to the object on which it is positioned. To satisfy this requirement, groundless antennas can be used only in specific applications where the tag is in the air; in other cases, it is preferable to have a tag with a ground plane. The number of antennas (many harmonic tags use two antennas) can affect the compactness of the tag. The use of dual polarization is preferable to facilitate the distinction of the back-transmitted signal from the clutter. Conversion losses, clearly, allow us to evaluate the quality of the transponder. From the comparison reported in Table 2, it is clear that our tag model has the advantages of having a ground plane, of being sufficiently compact since it is made up of a single antenna, and of using dual polarization. From the point of view of conversion loss it is placed in a median position of the ranking and is in line with the results obtained by other authors. It is interesting to analyze the reasons why the conversion losses of the proposed transponder are 14 dB greater than those of the best in the ranking [23]. Conversion losses can depend on three factors (at least): the type of diode, the material of the substrate on which the transponder is made, and the manufacturing method. From Figure 4 in article [23], we observe that this reference uses the diode SMS7630, which has at least 5 dB lower losses than the HSMS2850. The substrate used in [23] is Rogers RT/duroid 6002 with a dissipation factor of 0.0012, 20 mil thickness, and 18-\(\upmu\)m copper cladding. Our transponder uses a 1.5 mm thick Teflon slab with a dissipation factor of 0.0027 and 35 \(\upmu\)m thick adhesive copper, whose glue adds  
Figure 21: Received power vs. tag distance for different values of the transmitted power. Curves: theoretical prediction of harmonic radar equation, including the insertion loss of the measurement setup; asterisks: measurement results for transmitted power of 9 dBm.  
further losses. Further losses may be due to the manufacturing process, which, in our case, is quite rough, as it was carried out by hand with transmission lines not perfectly stretched over the dielectric slab. Certainly, an improvement in the conversion losses can be obtained by considering a better realization of the transponder using photolithographic techniques and materials with lower losses.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{
\begin{tabular}{c} **Run** \\ **Order** \\ \end{tabular} } & \multicolumn{2}{c}{**Coded Value**} & \multicolumn{2}{c}{**N [rpm]**} & \multicolumn{2}{c}{**Clearance**} & \multicolumn{2}{c}{**D\({}_{\text{screw}}\) [mm]**} & \multicolumn{2}{c}{**D\({}_{\text{shaft}}\) [mm]**} & \multicolumn{1}{c}{**Pitch [mm]**} \\  & & **A B C D E** & & **[150–250]** & **[2–10]** & **[70–85]** & **[2–35]** & **[20–35]** & **[75–90]** \\ \hline
19 & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(1\) & 185.8 & 4.9 & 75.4 & 25.4 & 84.6 \\
20 & \(1\) & \(1\) & \(-1\) & \(-1\) & \(1\) & 214.2 & 7.1 & 75.4 & 25.4 & 84.6 \\
21 & \(1\) & \(-1\) & \(1\) & \(-1\) & \(1\) & 214.2 & 4.9 & 79.6 & 25.4 & 84.6 \\
22 & \(-1\) & \(1\) & \(1\) & \(-1\) & \(1\) & 185.8 & 7.1 & 79.6 & 25.4 & 84.6 \\
23 & \(1\) & \(-1\) & \(-1\) & \(1\) & \(1\) & 214.2 & 4.9 & 75.4 & 29.6 & 84.6 \\
24 & \(-1\) & \(1\) & \(-1\) & \(1\) & \(1\) & 185.8 & 7.1 & 75.4 & 29.6 & 84.6 \\
25 & \(-1\) & \(-1\) & \(1\) & \(1\) & \(1\) & 185.8 & 4.9 & 79.6 & 29.6 & 84.6 \\
26 & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & 214.2 & 7.1 & 79.6 & 29.6 & 84.6 \\
27 & \(0\) & \(0\) & \(0\) & \(0\) & \(2\) & 200.0 & 6.0 & 77.5 & 27.5 & 90.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._translated to a decrease in space and an increase in geometry volume inside the housing. Mass flow increases with a larger clearance due to the expanded volume between the blade and housing which results in higher Mass flow. Considering the coarse-graining employed in our study, it is important to conduct further investigations into the impact of particle size. This is crucial as coarse-graining may yield misleading results, and a better examination of actual particle size could provide a more accurate understanding of the system's behaviour. Moving to Figure 8, a Pareto Chart reveals that the main effect on power consumption seems to be rotation velocity, with significant rates also observed in terms of clearance and shaft diameter. Concerning shear intensity, stresses, and strains (Figures 9-11), clearance emerges as having the most significant effect. At a particular particle size, wear drastically arises because of the \"particle entrapment\". Thus, it's critical to note that coarse-graining may yield misleading results,
## 4 Discussion
### Normal Stress  
The large normal stress leads to a small stiffness ratio and a greater tendency to unstable slip. The value of shear stiffness increases with the normal stress when the normal stress is low, and it remains constant when the normal stress is large.  
When the normal stress is 3 MPa, the shear stiffness of faults is small, and the shear stiffness of faults is greater than the critical stiffness, so faults maintain stable slip. Both the shear stiffness and critical stiffness of faults increase with increasing normal stress, and the increase in critical stiffness is larger. This results in the shear stiffness at a normal stress of 5 MPa and 7 MPa being larger than that at 3 MPa, but unstable slip still occurs when the slip displacement is larger. The shear stiffness remains constant as the normal stress increases from 5 MPa to 7 MPa, but the critical stiffness continues to increase.  
The interaction between asperities and the lubrication of fault gouge may be the main reason for the variation in shear stiffness. As shown in Figure 9, when fault block slip, the climbing mode and the fracture mode occur simultaneously between the asperities on fault surfaces. When the normal stress is low, the climbing mode is the dominant form of asperities interaction, resulting in low shear stiffness. When the normal stress is high, the interlocking between the surface asperities is stronger, resulting in the fracture mode accounting for the main form during the shearing process and an increase in shear stiffness. As the normal stress continues to increase, the interaction mode between asperities does not change. Higher normal stress would lead to strong asperity interlocking, thereby increasing shear stiffness, and the enhanced interaction between asperities causes more asperities to fracture, producing more fault gouge particles. The fault gouge could participate in the shearing process of the fault as a lubricant reducing the shear stiffness. A combination of two factors results in a constant shear stiffness under high normal stress.  
In summary, when the normal stress is low, both the shear stiffness and the critical stiffness increase with the normal stress, but the critical stiffness increases faster. As the normal stress increases, the fault shear stiffness remains constant, and the critical stiffness  
Figure 8: Test results with different slip displacements. The average value of the repeated test results is each data point, and the standard deviation is the length of error bar. (**a**) Trend of the average shear stiffness with the slip displacement under different normal stresses. (**b**) Trend of the average shear stiffness with the slip displacement under different roughness of the fault surfaces.  
continues to increase when the normal stress is high, which causes faults to be more prone to unstable slip.
## 1 Introduction
However, these stimulation-based interventions have three main limitations: (1) short-lasting effects that lead to adaptation-related fatigue; (2) difficulty in controlling stimulation intensity, with significant individual variability in responses; and (3) potential for frequent stimuli to provoke annoyance or emotional fluctuations, thereby interfering with normal driving behavior. As a result, there is an urgent need for a more flexible, dynamic, and personalized intervention approach to enhance effectiveness. In recent years, voice interaction has emerged as a natural and convenient means of communication, widely adopted in intelligent driving scenarios. Studies have shown that, compared to single-turn dialogues, users in driving contexts prefer multi-turn interactions, pointing toward the need for more interactive in-vehicle voice assistants [20]. Moreover, due to its eyes-free and hands-free nature, voice interaction holds unique potential for fatigue intervention by providing cognitive stimulation to enhance driver alertness [21; 22]. While existing research has made progress in developing voice assistant functions and exploring their role in emotional regulation [23; 24], significant gaps remain. On the one hand, most systems still rely on templated, one-way interactions and lack dynamic perception of the driver's fatigue level, cognitive load, and interaction preferences. On the other hand, current studies primarily focus on system performance or emotional expression itself, with limited exploration of users' subjective experiences and behavioral response mechanisms. To address these gaps, this study focuses on fatigue-driving intervention scenarios and proposes a dialogue-awakening mechanism centered on the driver's fatigue state. Unlike conventional system-oriented approaches, this research combines Grounded Theory with the Stimulus-Organism-Response (SOR) framework to deeply analyze how drivers' preferences for dialogue content, tone, frequency, and style vary under different levels of fatigue. Through in-depth interviews and coding analysis of representative driver groups, we identify heterogeneous demands for voice assistant functions. Based on these findings, we design a driving simulation experiment to validate the interview results, thereby providing both theoretical grounding and empirical evidence for the development of context-aware and personalized in-vehicle voice interaction systems.