esa-sceva/satcom-chunk-collection huggingface dataset:
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Hybrid Random Walk-Optimized Simulated Annealing for Tourism Sentiment Chain Modeling
#### 3.3.2 Tourism Sentiment Chain Generation Method
Structural integrity is preserved through constraint enforcement aligned with the MAN-SRT graph model. Temperature reduction follows an exponential decay trajectory. Mathematically, let the current path be represented as follows:  
\[P^{(k-1)}=[Attr_{0},Attr_{1},\cdots,Attr_{i},\cdots,Attr_{n_{c}}], \tag{19}\]  
where \(P^{(k-1)}\) is the path before the \(k\)-th iteration. \(Attr_{i}\) denotes the \(i\)-th visited attraction apart from the starting one. The path score \(Score(P)\) is defined as the sum of the CAI values between all adjacent attractions along the path:  
\[Score(P)=\sum_{i=1}^{n_{c}}CAI_{i-1,i}, \tag{20}\]  
To mitigate the risk of converging to local optima, we implement a multi-mode neighborhood search strategy that generates diverse solution variants at each iteration--such as Swap, Reverse, and Replace mutations--each embodying a distinct path-adjustment mechanism. This approach enhances both the diversity and adaptability of the evolutionary process. Additionally, path validity constraints are enforced to guarantee that all derived routes conform to the connectivity requirements of the MAN-SRT graph structure. \[\hat{P}=Strategy\Big{(}P^{(k-1)},\;i,\;j\Big{)},Strategy\in\{\text{Swap, Reverse,Replace}\}, \tag{21}\]  
where \(Strategy\) represents the mutation method at the \(k\)-th iteration, and \(i\) and \(j\) are the mutation positions. In terms of temperature control, OSA adopts an exponential attenuation method to reduce the temperature:  
\[T^{(k)}=T^{(k-1)}\cdot\eta, \tag{22}\]  
where \(T^{(k)}\) represents the current temperature after the \(k\)-th iteration. \(\eta\in(0,\;1)\) denotes the cooling rate. The acceptance probability of the new path is calculated based on the Metropolis criterion:  
\[acc_{pro}= \begin{cases}1\text{,if }Score(\hat{P})>Score\Big{(}P^{(k-1)} \Big{)}\\ exp\bigg{(}\frac{\Delta Score}{T}\bigg{)}\text{,otherwise}\end{cases} , \tag{23}\]  
where \(\Delta Score=Score(\hat{P})-Score\Big{(}P^{(k-1)}\Big{)}\). When the score is higher, it will be accepted unconditionally; that is \(P^{(k)}=\hat{P}\). Otherwise, accept the poorer solution with a certain probability to jump out of the local optimal region. The algorithm terminates upon reaching either a predefined minimum temperature \(\theta_{T}\) or the iteration limit. The highest-scoring itinerary is designated as the TSC, representing the final route recommendation. The TSC formulation simultaneously accounts for geographical accessibility, thematic relevance, and affective alignment with traveler sentiment. Leveraging the adaptive weight mechanism, the system supports real-time responsiveness to preference shifts, thereby enabling highly personalized and emotionally resonant travel experiences.
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 5 Parameter Refinement Phase  
In this phase, the optimal values of the parameters of the network are learned from the training dataset \(X\). Such parameters include the centers \(c_{j}=\left[c_{1,j}\cdots c_{n,j}\right]^{T}\) and deviations \(v_{j}=\left[v_{1,j}\cdots v_{n,j}\right]^{T}\), \(1\leq j\leq J\), associated with the basis functions in the hidden layer, and the weights \(w_{f,j}\), \(1\leq j\leq J\), \(1\leq f\leq m\), between the hidden layer and the output layer, and the biases and biases \(b_{f}\), \(1\leq f\leq m\), associated with the output nodes in the output layer. A hybrid learning process is applied iteratively. In each iteration, we first treat all the weights and biases as fixed and use the steepest descent backpropagation to update the centers and deviations associated with the basis functions. Then we treat all the centers and deviations associated with the basis functions as fixed and use the least squares method to update the weights and biases associated with the output layer. The process is iterated until convergence is reached.
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 2 Pinion Failure and Data Collection  
A single-stage helical reduction gearbox was employed in a kraft process plant to reduce the speed (8350 rpm) of a steam turbine to operate an alternator at a speed of 1500 rpm, such that electricity (3 MW) would be generated for use in the plant. This corresponds to pinion and gear speeds of 8350 rpm and 1500 rpm, respectively. The gearbox was intended to operate continuously for 20 years (excluding break periods for maintenance). The data pertaining to the pinion and gear were measured/collected at the site of gearbox failure, which are listed in Table 1. The photographic view of the helical gearbox is shown in Figure 1 along with the names of key components. However, Figure 2a shows the key dimensions of the pinion and gear of the helical gearbox. Moreover, the photographic view of pinion is also shown in Figure 2b for its geometrical visual to the readers. Because of the requirement of greater electricity generation in the plant, the reduction gearbox was operated at 3.75 MW (instead of 3 MW) with the permission of the gearbox designer. However, when the gearbox was operated at high load (3.75 MW), the pinion teeth became plastically deformed near the thrust disc. The photographic views of healthy and failed pinions are shown in Figure 3a,b, respectively. Thus, it was vital to examine the cause(s) of failure of the pinion teeth.  
Figure 1: (**a**) Photographic view of helical gearbox with names of vital components; (**b**) photographic view of pinion’s axial thrust sustaining arrangement.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**S. No.** & **Parameters** & **Pinion** & **Gear** \\ \hline
1 & Helix angle (\({}^{\circ}\)) & 15.6 & 15.6 \\
2 & Pressure angle (\({}^{\circ}\)) & 20 & 20 \\
3 & Face width (mm) & 190 & 190 \\
4 & No. of teeth & 20 & 111 \\
5 & Pitch circle diameter (mm) & 131.9 & 732 \\
6 & Dia. of dedendum circle (mm) & 116 & 716.1 \\
7 & Dia. of addendum circle (mm) & 144.6 & 744.7 \\
9 & Tooth thickness at root (mm) & 14 & 16 \\
10 & Tooth thickness at PCD (mm) & 12 & 14 \\
11 & Material of gears and shaft & 17 CrillMo\({}_{6}\) & 17 CrillMo\({}_{6}\) \\
12 & Hardness at flank and face (HRC) & 60 \(\pm\) 2 & 60 \(\pm\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration data of pinion and gear collected at the site of the gearbox failure.  
Figure 2: (**a**) Key dimensions of pinion and gear in schematic view; (**b**) photographic view of pinion.
## 6 Evaluation
### Experimental Results
For a typical configuration for autonomic tasks (with 7 services), the microkernel and a simple application (2,824 Kb), FamiWare uses 1.51% (8,920 Kb) HTC Desire mobile memory (576 Mb), so this percentage goes down to 1.51%. Summarizing, the resource consumption of FamiWare is minimal and therefore very well suited to the resource requirements. Now, we evaluate the scalability of FamiWare, regarding the delay produced in the reception of the monitored data as the number of monitoring services and topics increase. We have performed some experiments with the hypothesis that there is an overload in the context-awareness side (_i.e._, the subscriber) when the number of monitored topics increases (_i.e._, publishers). For the TinyOS version, the subscriber has to search the topic when an event arrives. Since the topics are stored in a table, the time to search a topic lineally increases with an increasing number of topics as shows Figure 11(a). For 195 topics, it takes 1.013 ms so the overload produced is not very significant compared to the time taken to send a packet through the network (e.g., 6.43 s for three intermediate nodes,). However, the tests are not successful with a number of topics larger than 195. This is because the system is out of memory and the stack overflows. Therefore, we can conclude that the boundary in the TinyOS version is 195 monitored topics, more than enough for an AmI system. The time consumption is hard to measure in Android or Sun SPOT versions, since as with any Java-based system, this strongly depends on the state and the timing of the garbage collector. As shown in Figure 11(b,c), increasing the number of topics does not produce overload in the subscriber side for Android and Sun SPOT versions. This is because topics are not stored in a table, as in TinyOS, and instead, a _Topic_ object is constructed from the topic name, using the reflective mechanism of Java and is notified to the corresponding _DataReaders_. Then, this time depends more on the notification time to the _DataReaders_ than on the number of topics, resulting in very variable times. Comparing the three implementations, the two Java-based versions are more scalable than the TinyOS one. Figure 11: Reception of monitored data when many topics published.
## 4 Discussion
### Limitations  
This study has some limitations. The small sample size, focus on only mild to moderate intellectual disabilities, and narrow age range limit the broader applicability of the findings. Expanding the sample size in future studies would not only strengthen the statistical analysis but also allow for a closer examination of subgroups, such as differences by age, level of intellectual disability, or sensory processing abilities, leading to more reliable and nuanced conclusions. While the study highlights the impact of auditory environments on postural balance, it does not fully explore the underlying mechanisms or control for potential confounding variables, such as the time of day, fatigue, and environmental factors, that could influence the outcomes. Future studies should aim to investigate these mechanisms in more depth and account for additional variables that may affect postural stability. In addition, we recognize that our study did not directly investigate the biological or neural mechanisms behind these effects. Future research could use tools like electroencephalogram to explore which brain regions are involved when participants are exposed to different auditory environments. Additionally, measuring physiological markers like cortisol or heart rate variability could help explain how auditory stimuli influence stress levels, balance, and cognitive performance. Moreover, the absence of a control group and the potential for cumulative or learning effects over the course of the sessions are important limitations of this study. Without a control group, it is difficult to isolate the specific effects of nature sounds from other variables that may have influenced participants' performance, such as practice effects or learning over time. To address this limitation, we propose that future studies incorporate a randomized controlled design with multiple baseline measurements and include a control group to better evaluate the effects of nature sounds. Furthermore, the potential for cumulative or learning effects suggests that repeated exposure to the same tasks could have influenced participants' performance, independent of the auditory stimuli. Future studies could include different task orders to account for the learning effects and assess their potential impact on the results. Furthermore, this is a cross-sectional study, and the lack of longitudinal data obstructs our understanding of how postural balance changes over time in response to different auditory environments. Future studies using longitudinal designs could provide information into the dynamic nature of these relationships. Finally,in future studies, it would be interesting to include a wider range of auditory stimuli as well as different surface types. This approach could give us a better understanding of how auditory cues and environmental conditions work together to affect postural balance.
## 3 Results and Discussion
### FMT Validation
The statistical indices of the FMT are in good agreement with the simulation results of the low deflector installation, which is shown in Figure 20.  
Qualitatively speaking, the FMT results are consistent with the simulation results of the large-sized and extra-large-sized particles. The corresponding distributions of the accumulated particles are similar. Meanwhile, it provides an indication that the optimized triangular deflector is an effective approach in reducing the accumulation of the ash particles with relatively large sizes. According to the experimental results, the low installation of the triangular deflector shows the best performance. It proves that the optimal installing position is in accordance with the simulation results, which is in the high-speed area of the vertical duct. Besides, it is revealed that the installation of the optimized deflector won't bring any drastic impact on the flow field of the SCR-deNO\({}_{x}\) facility. On the other hand, there are still drawbacks for the implementation of the FMT. In this study, since the diameter range of the crushed millet is larger than \(8.8\times 10^{-2}\)-0.15 mm, it is unable to represent the ash particles perfectly and can only provide qualitative analysis. Moreover, the distributions of the particles smaller than \(8.8\times 10^{-2}\) mm and the comparisons among the four classes of particles cannot be validated via the FMT experiments. Further study is needed and a more precise evaluation on the accumulated particles may be presented.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel with Assistance
#### 3.3.3 Use of Non-Coherent Accumulation with Serial Implementation  
The implementation is exactly the same as when there is no assistance, shown in Figure 9. The timing diagram is also the same, shown in Figure 7, the only difference is that the part that repeats is repeated \(N_{\rm SCB}\) times instead of \(N_{\rm S}\) times, as in Section 3.3.1. Therefore, the processing time for one frequency bin is given in Equation (20).
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 1 Introduction
* To mitigate negative impacts on global model accuracy by low-quality data owners, we propose a model quality detection module to update their quality indicator and eliminate malicious data owners. Moreover, to predict each data owner's actual training time, we propose reinforcement learning-based methods to estimate their actual CPU frequency at the current round, in order to achieve faster convergence. * Through extensive evaluations and rigorous theoretical analysis, QuoTa improves model accuracy over the state-of-the-art and achieves all desired economic properties like individual rationality, truthfulness, and budget balance.
# The Optimization of the Geometry of the Centrifugal Fan at Different Design Points
## 2 Materials and Methods
#### Optimization Problem Definition
[PERSON] et al. [32] and [PERSON] et al. [33] investigated the optimization of centrifugal fan blades to enhance aerodynamic and aeroacoustic performance. [PERSON] et al. [32] demonstrated that optimizing blade geometry can significantly improve fan efficiency, while [PERSON] et al. [33] achieved a reduction in noise levels through aeroacoustic optimization. Both studies highlight the importance of considering various design parameters and employing computational fluid dynamics simulations for the development of efficient and low-noise fans. [PERSON] et al. [34] compared numerical models for fluid-viscous dampers and used genetic algorithms to evaluate damper performance. Their results indicate that the proposed nonlinear Bingham model outperforms traditional linear and nonlinear models in terms of prediction accuracy. This study emphasizes the significance of assessing different numerical models and implementing advanced optimization techniques for effective damper design and analysis. [PERSON] et al. [35] conducted a review of deep learning applications in structural health monitoring, illustrating the potential of data-driven approaches for the monitoring and maintenance of structural integrity. They presented several case studies demonstrating the successful implementation of deep learning techniques, such as convolutional neural networks and recurrent neural networks, in detecting and classifying structural damage. This research underscores the importance of leveraging interdisciplinary methodologies and emerging technologies to address engineering challenges. Aerodynamic optimization of a multi-blade centrifugal fan based on an extreme learning machine surrogate model and a particle swarm optimization algorithm was presented by [PERSON] et al. [36]. In this study, the authors employed an Extreme Learning Machine (ELM) surrogate model and a Particle Swarm Optimization (PSO) algorithm to explore various blade designs and identify optimal configurations. The ELM surrogate model was used to approximate the objective functions, while the PSO algorithm facilitated the optimization process. This research contributes to the growing body of work on advanced computational techniques for optimizing the aerodynamic performance of centrifugal fans. By showcasing the potential of combining surrogate modeling and optimization algorithms, the authors demonstrated a promising approach for improving fan efficiency and performance.
## 4 Implementation and Results
### System Implementation
#### 4.3.2 GPS Manager  
The GPS manager extracts location data coming in through serial communication from the GPS module to store it into the database. Figure 17 shows the location information manager interface and the location information values stored in the database.
## 4 Results and Discussion
The sequence averages were also calculated (Figure 12), and it was verified that the symmetrical method (SM) sequence deformed around 9.8% less than the single-pass (SP) sequence and 12.0% less than the backward method (BM). In order to make multiple comparisons and verify between which sequences would exist meaningful differences, the Tuckey test was used. According to Table 10, it is possible to conclude that there are significant differences between the distortions that occurred when comparing the sequences SS \(\times\) SR and the sequences SS \(\times\) SC, obtaining a p-value of 0.012 and 0.033, respectively. On the other hand, for the interaction between BM \(\times\) SP, the Tukey test showed that there are no significant differences between the averages of the displacements of the sequences, with a \(p\)-value equal to 0.625 (\(p\) > 0.05). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**(I) Sequence**} & \multirow{2}{*}{**(J) Sequence**} & \multirow{2}{*}{**Average Difference (I-J)**} & \multirow{2}{*}{**P**} & **Inferior Limit** & **Superior Limit** \\ \cline{3-5} \multirow{2}{*}{SM} & BM & \(-\)26.901 * & & \(-\)43.032 & \(-\)7.770 \\ \cline{2-5}  & SP & \(-\)211.202 * & & \(-\)40.333 & \(-\)2.071 \\ \hline \multirow{3}{*}{BM} & SM & 26.901 * & & \(-\)7.770 & 46.032 \\ \cline{2-5}  & SP & 5.699 & & \(-\)13.432 & 24.830 \\ \hline \multirow{3}{*}{SP} & SM & 21.202 * & & \(-\)0.033 & 2.071 & 40.333 \\ \cline{2-5}  & BM & \(-\)5.699 & & \(-\)6.652 & \(-\)24.830 & 13.432 \\ \hline \hline \end{tabular}  
* The average difference is significant at the level 0.05.  
\end{table} 
Table 10: Tukey Test—Multiple comparisons between the sequences. Figure 12: Graph of the displacement averages of ST, BM, and SP sequences.
## 3 Results
### Channel-Preserving vs. Channel-Averaging  
The compositions with preserved channels (i.e., spatially patterned composition) exhibited a better performance across all channels than those that were averaged. Compared with all-channels-averaging compositions (FC-7, 8, and 9), MCC\({}_{\text{RS}}\) values in all-channels-preserved compositions (FC-1, 2, and 3) were higher by 14.98% (paired t-test, \(p\) < 0.00005) and detection latency was shortened by 18.01% (\(p\) = 0.11) CNR-channels (Table 2). With the CNR-selected channels, channel-preserved compositions (FC-4, 5, and 6) raised MCC\({}_{\text{RS}}\) values by 11.30% (\(p\) < 0.000005) and reduced detection latency by 36.52% (\(p\) < 0.005) more than channel-averaging compositions (FC-10, 11, and12).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \
\begin{tabular}{c} **Channel** \\ **Use** \\ \end{tabular} &
\begin{tabular}{c} **Channel** \\ **Selection** \\ \end{tabular} & **[HbO] \& [HbR]** & **[HbR]** & **Mean** \\ \hline \multirow{4}{*}{\(\text{MCC}_{\text{RS}}\)} & \multirow{2}{*}{Preserving} & All & 0.7586 & 0.7207 & 0.6669 & 0.7154 \\  & & CNR-sel. & 0.7196 & 0.7023 & 0.6532 & 0.6917 \\ \cline{2-7}  & \multirow{2}{*}{Averaging} & All & 0.6617 & 0.6316 & 0.5732 & 0.6222 \\  & & CNR-sel. & 0.6554 & 0.6406 & 0.5685 & 0.6215 \\ \cline{2-7}  & \multicolumn{2}{c}{Mean} & 0.6988 & 0.6738 & 0.6154 & - \\ \hline \multirow{4}{*}{\(\text{Detection}\)} & \multirow{2}{*}{Preserving} & All & 2.4015 & 2.9464 & 3.2066 & 2.8515 \\  & & CNR-sel. & 2.9184 & 3.3637 & 3.4257 & 3.2359 \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Averaging} & All & 3.7349 & 3.4492 & 3.2491 & 3.4778 \\ \cline{1-1}  & & CNR-sel. & 4.2665 & 4.4143 & 6.6126 & 5.0978 \\ \cline{1-1} \cline{2-7}  & \multicolumn{2}{c}{Mean} & 3.3303 & 3.5434 & 4.1235 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Results.  
Figure 3: Classification Performance according D\({}_{\text{HR}}\) and L\({}_{\text{FW}}\): (**a**) The Matthews Correlation Coefficient (MCC)\({}_{\text{RS}}\) map according to D\({}_{\text{HR}}\) (s) and L\({}_{\text{FW}}\) (s); (**b**) The map of detection latency according to D\({}_{\text{HR}}\) and L\({}_{\text{FW}}\). The pixel values of the detection latency map were calculated on the basis of assumed delay in hemodynamic response. Thus, for true detection latency values, the corresponding D\({}_{\text{HR}}\) must be added to each pixel value. Note that each pixel is the average from the seven participants. The pixel values for both maps are given in Table S1 (Supplementary Materials).