esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Limitation and Prospective Research  
This study suggests that the development of an emotion dictionary for the learning domain, together with the proposed hybrid SnowNLP+Jieba model, is effective for further improving the correct discrimination of learning emotions. By identifying the keywords that affect learning emotions, the researcher can design a more appropriate curriculum to enhance positive emotions, which in turn will increase engagement and improve learning effectiveness.  
Future studies may combine AI-based sentiment analysis with data from additional measures, such as physiological sensors or computer vision techniques, to provide a more holistic view of student engagement. Conducting longitudinal studies to assess the long-term impact of the AI-based sentiment analysis system on learning engagement and outcomes would offer more comprehensive insights. This study employs a binary classification approach to analyze learning engagement. Future research can utilize other deep learning models - such as Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), or large language models (LLMs) - to explore the relationships among text sentiment scores, learning outcomes, and levels of learning engagement, as well as to perform prediction or classification.  
In addition, teaching styles and methods can impact the effectiveness of an AI-based sentiment analysis system in STEAM education by shaping the quantity, quality, and emotional texture of the text data. Inquiry-based, project-based, and hands-on approaches tend to generate richer, more actionable data, thereby boosting the AI's ability to measure engagement accurately. However, lecture-based or less interactive methods might challenge the system with sparse or ambiguous inputs. For optimal performance, the AI must be trained on diverse, context-aware datasets tailored to STEAM's unique blend of creativity and problem-solving, and it should adapt to the nuances that each teaching approach brings to student expression and engagement.  
Conceptualization, C.-H.W. and K.-L.P.; Methodology, C.-H.W.; Investigation, C.-H.W. and K.-L.P.; Resources, C.-H.W.; Data curation, C.-H.W.; Writing - original draft, C.-H.W.; Writing - review & editing, K.-L.P.; Visualization, C.-H.W. and K.-L.P.; Supervision, K.-L.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and Technology Council (Taiwan) under Grant No. MOST 110-2511-H-142-008-MY2 and NSTC 113-2410-H-003-144-MY3; and City University of Macau.  
Not applicable.  
Data Availability Statement: The original data presented in the study are openly available in Sweslo17 at [39] and [ppzhenghua] at [41].  
The authors declare no conflict of interest.
# Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling
###### Abstract  
The finite-difference method is widely used in seismic wave numerical simulation, imaging, and waveform inversion. In the finite-difference method, the finite difference operator is used to replace the differential operator approximately, which can be obtained by truncating the spatial convolution series. The properties of the truncated window function, such as the main and side lobes of the window function's amplitude response, determine the accuracy of finite-difference, which subsequently affects the seismic imaging and inversion results significantly. Although numerical dispersion is inevitable in this process, it can be suppressed more effectively by using higher precision finite-difference operators. In this paper, we use the krill herd algorithm, in contrast with the standard PSO and CDPSO (a variant of PSO), to optimize the finite-difference operator. Numerical simulation results verify that the krill herd algorithm has good performance in improving the precision of the differential operator.  
Keywords: seismic numerical modeling; finite-difference scheme; nature-inspired optimization algorithms; particle swarm optimization; center-decenter particle swarm optimization; krill herd algorithm +
Footnote †: journal: _articles Journal of Applied Mechanics_
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Feature Extraction
#### 2.4.4 Optimal Feature Selection  
The use of excessive feature variables with redundant data may lead to an increase in the computational complexity of a model and reduce its classification accuracy. Therefore, the optimal feature variables must be selected from the primary feature dataset. RF algorithms can be used to calculate the importance of feature variables and have been widely used to select optimal feature variables. The basic concept of an RF algorithm is that when a decision tree is established, some remaining original sample data are not extracted in the sampling process, namely, the out-of-bag (OOB) data. These data are then used to evaluate the performance of the decision tree and calculate the prediction error rate of the model, that is, the OOB error. Then, the OOB error is calculated again after noise interference is added to all random features in the OOB dataset. If the accuracy of the OOB data is greatly reduced when random noise is added, the corresponding feature has a large impact on the prediction results of the sample, further indicating that the feature is relatively important, and vice versa. Thus, the importance of different feature variables can be determined according to the OOB error. Finally, importance ranking was performed according to the feature variable importance. Moreover, the former \(k\) (\(i\) =1, 2,..., 50) features were input to construct an RF classification model and assess the impact of the number of feature variables on the classification accuracy. We used the training samples (see Table 2) to perform the optimal feature selection analysis.
## 3 Results and Discussion  
### Weld Morphology  
Figure 3 showed the surface, cross-section morphologies and X-ray nondestructive test results. Compared with the non-oscillating weld, when \(f\) = 50 Hz, which increased to a small extent, the effect of the laser beam oscillation was not obvious. As a result, there was little change in the weld morphology, which had undercut and uneven fish scale defects. As \(f\) increased, the weld surface quality continuously improved. When \(f\) increased to 350 Hz, there were no undercut or concave defects, and the fish scales were uniform. The angle between the lock step and fusion line increased with \(f\), and the porosity showed the opposite trend. In detail, with \(r\) fixed as 0.6 mm, the state of pores changed from close-packed chain-shaped to dispersed distribution until they were fully eliminated when \(f\) was in the range of less than 200 Hz, 200-350 Hz and more than 350 Hz, respectively. Notably, when \(f\) = 50 Hz, the porosity increased compared with the non-oscillating weld. The pores of non-oscillation and low oscillating frequency (\(f\) = 50 Hz) welds are located at the center line of welds, but the former are located above the lock step, and the latter are close to the bottom of molten pool (below the lock step), which is related to the cause of pores formation. The pores in non-oscillation welds are due to the turbulent flow formed by the confluence of the upper and lower vortexes [28]. The depth of the molten pool where the confluence located is similar to the height of the lock step. When \(f\) = 50 Hz, the keyhole stability is poor, resulting in the collapse of the keyhole root and pore formation. When \(f\) continues to increase, the keyhole stability is improved, and the pores can be captured. Therefore, the number and size of pores are reduced, and the position is not fixed. The threshold of \(f\) for pore suppression was 350 Hz, which was consistent with the results of bead-on-plate welding revealed in Ref. [19].  
Figure 2: Schematic diagram of tensile specimens.
## 3 Results
### Performance Comparison (Early vs. Late Fusion)
Metrics are displayed in Figure 4, with Late Fusion outperforming Early Fusion in recall, accuracy, F1 score, ROC-AUC, Balanced Accuracy, Negative Predictive Values, Cross-Validation Balanced Accuracy, and Cross-Validation ROC-AUC score. The Cross-Validation Metrics were calculated during the outer folds inside the 5 Folds \(\times\) 5 Repeats Nested CV before the application of the best hyperparameter tuning. These are a reliable metric of the model's classification ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **TN** & **FP** & **FN** & **TP** \\ \hline
**Early Fusion** & 74 & 14 & 18 & 81 \\ \hline
**Late Fusion** & 88 & 16 & 7 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix comparison. Figure 3: Confusion matrix comparison between Early and Late Fusion meta-classifiers.
## 3 Wireless Localization Technology
### Comparison  
Wireless localization technologies can be classified into range-based technologies and range-free technologies, depending on whether or not they use physical factors of communication signals. Table 2 summarizes the advantages and disadvantages of these two technologies.  
The range-based technology estimates the location in real-time by measuring physical factors, does not require prior work, and is accurate but has the characteristic of being resource-intensive and the geometric calculation process is complex. On the other hand, the range-free technology does not measure the physical factor of the signal but requires prior work and has the advantage of being a resource-efficient and simple comparison process.  
In more detail, the advantages and disadvantages of range-based technologies can be compared as shown in Table 3. RSS-based technology requires simple hardware implementation, leading to low costs, but it also comes with lower accuracy. Its accuracy is compromised by factors such as multipath and reflection. ToA and TDoA technologies have lower attenuation and cost, but they are influenced by hardware resolution and LoS environments. AOA technology offers high accuracy but comes with a higher cost by employing complex antenna arrays. Additionally, it is susceptible to environmental conditions such as multipath, scattering, and NLoS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison Aspects** & **Range-Based Techniques** & **Range-Free Techniques** \\ \hline Accuracy & High & Moderate \\ \hline Power Consumption & High & Low \\ \hline Deployment & Hard & Easy \\ \hline Additional Hardware & Required & Not required \\ \hline Preparation Process & Not required & Required \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of range-based techniques and range-free techniques.  
Figure 8: An illustration of the concept of fingerprinting.  
Similarly, range-free technologies can be compared as shown in Table 4. Proximity technology enables moderate accurate positioning with inexpensive implementation, leading to high energy efficiency. The accuracy and cost of DR technology depend on the measurement device, and estimating movement distance and direction in DR technology may result in some energy consumption. Fingerprinting technology's accuracy relies heavily on the precision of the radio map. Although creating a precise radio map can incur high costs and may have relatively low energy efficiency, it provides exceptionally high accuracy.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 4 Detection and Verification Method
### Radial Error Motion Measurement  
The experiment to measure the radial error motion of the rotating shaft is presented in this section. ISO 230-7: 2015 [15] specifies the mounting of a precision test sphere or other suitable artefact in the machine spindle, using a displacement sensor to measure the error motions of the spindle. The influence of installation eccentricity is introduced. Many previous studies have proposed other methods to measure the error motions of the spindle [17; 18; 19; 20]. We used the contact measurement method based on the features of the rotation joint.  
Two precision test spheres are fixed on the top and bottom of the rotating shaft. The max surface roughness is 0.014 \(\upmu\)m, which is negligible compared with radial error motions. As shown in Figure 7, the inductive probes (lever type, TESA Company, model 32.10802, with max repeatability 0.1 \(\upmu\)m, permissible error for deviations in linearity is \(0.2+50\times\mathrm{L}^{2}\)\(\upmu\)m for the measuring range of \(\pm 0.3\) mm) are adjusted to make contact with the precision test spheres. One \(2\uppi\) period sinusoidal signal generated by the installation eccentricity of the precision test sphere superimposes on the inductive probe outputs.  
Figure 6: Rotary encoder works in actual condition.  
Figure 7: Experimental setup for radial error motion measurement.  
The data of inductive probes \(d(\theta)\) are expanded to 10 order Fourier series, shown as Equation (6):  
\[d(\theta)=a_{0}+\sum_{i=1}^{10}\left(a_{i}\sin(i\theta)+b_{i}cos(i\theta)\right), \tag{6}\]  
the Fourier coefficients \(a_{0},a_{1},b_{1},\ldots,a_{10},b_{10}\) are calculated by using the least-square fitting method. Then the radial error motion of the precision test sphere \(\delta(\theta)\) is acquired by subtracting the zero order term caused by the position of inductive probes and the first order term caused by installation eccentricity of precision test sphere, as expressed by Equation (7):  
\[\delta(\theta)=\sum_{i=2}^{10}\left(a_{i}\sin(i\theta)+b_{i}\cos(i\theta)\right). \tag{7}\]  
As shown in Figure 8, the tilt error motion of the shaft can be calculated by Equation (8):  
\[\varepsilon_{\mathrm{X}}(\theta)=\frac{\delta_{\mathrm{Y,L_{1}}}(\theta)- \delta_{\mathrm{Y,L_{2}}}(\theta)}{\mathrm{L_{2}}}. \tag{8}\]  
The radial error motion of the grating disk is calculated by the following equation:  
\[\delta_{\mathrm{Y}}(\theta)=\delta_{\mathrm{Y,L_{1}}}(\theta)-\varepsilon_{ \mathrm{X}}(\theta)\times\mathrm{L_{1}}=\frac{\mathrm{L_{2}-L_{1}}}{\mathrm{ L_{2}}}\delta_{\mathrm{Y,L_{1}}}(\theta)+\frac{\mathrm{L_{1}}}{\mathrm{L_{2}}} \delta_{\mathrm{Y,L_{2}}}(\theta), \tag{9}\]  
where \(\mathrm{L_{1}}\) is the distance between the center of the sphere on the top of the rotating shaft and the grating disk, and \(\mathrm{L_{2}}\) is the distance between the center of two spheres.
# A Next-Generation Core Network Architecture for Mobile Networks
## 6 Re-Direction
### Scenario with eeNodeB  
Figure 17 shows the high-level architecture of a CNN when using an eeNodeB. Similarly to the previous scenario, the SIP proxy server behaves as a Registrar and Redirect server for the SN, while it behaves as a B2 BUA for a VCN. The EPC, on the other hand, has now collapsed into a simple Internet gateway.  
As mentioned in Section 3.3, we do not modify the \(Uu\) interface between UE and eeNodeB. This means that, when a redirection takes place, it will not involve the UE. The UE will be unaware of any change taking place while eeNodeB and SN will make sure traffic is redirected accordingly. In particular, as shown in Figure 7a,b and as described in Section 3.3, the eeNodeB is an MPLS router on the user plane and supports P2P protocols on the control plane in order to be able to talk to the P2P overlay of the core network (e.g., RELOAD). When a redirect for a UE needs to take place, the SN behaves as an SDN controller for that UE and it updates the MPLS flow tables accordingly so as to route the UE traffic to its VCN. This can be accomplished by leveraging existing protocols such as OpenFlow and RSVP-TE. At the application layer, the SN updates the information in the P2 PSIP DHT in order to point the UE's AoR to the newly created VCN. In this case, the UE is completely unaware of this change taking place. As before, the UE's IP address, the SIP Proxy server's IP address and the Internet gateway's IP address do not change. The underlying MPLS network takes care of re-routing the traffic from the SN to the VCN.
## 2 Materials and Methods
### Characterization
To prepare the SBF solution, NaCl, NaHCO\({}_{3}\), KCl, K\({}_{2}\)HPO\({}_{4}\).3H\({}_{2}\)O, MgCl\({}_{2}\).6H\({}_{2}\)O, CaCl\({}_{2}\), Na\({}_{2}\)SO\({}_{4}\), and (CH\({}_{2}\)OH)\({}_{3}\)CNH\({}_{2}\) were successively dissolved in distilled water and stabilized at pH 7.4 using 1N HCl at 37 \({}^{\circ}\)C according to [27]. The solution's ion concentration was nearly identical to that of human blood plasma. To establish balanced open circuit potential, the samples were submerged in naturally aerated SBF (80 mL) at 37 \({}^{\circ}\)C for 3300 s before the potentiodynamic measurements. Potentiodynamic polarization curves were assessed at a velocity of 1 V/s, starting at a potential of \(-\)0.5 V and ending at 1.5 V versus OPC. The corrosion potential (E\({}_{\rm corr}\)) and corrosion current density (I\({}_{\rm corr}\)) were estimated using Tafel linear extrapolation. A determination of the polarization resistance (R\({}_{\rm p}\)) was made using the Stern-Geary formula. Equation (6) was employed to determine the protection efficacy of the coatings against corrosion [28]:  
\[\rm P.E.(\%)=\frac{I_{\rm corr}^{0}-I_{\rm corr}^{c}}{I_{\rm corr}^{0}}\times 100 \tag{6}\]  
where the corrosion current densities of the bare and coated states are denoted by the symbols I\({}_{\rm corr}^{0}\) and I\({}_{\rm corr}^{c}\), respectively.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 2 Problem Formulation and Preliminaries
### Itzfic
#### 2.2.2 The Principle of FLC Realizes DHTC Technology  
According to the introduction in Part 2 of this article, FLC can solve the two problems of DHTC.  
For Question 1: As shown in Figure 3, the input of FLC is a range. By setting a reasonable membership function, a weight can be set for each precise value in the input range. We take the e and \(\Delta\)e of the photoelectric tracking system as the input of the FLC, set up reasonable fuzzy rules, and let the FLC determine the timing of switching.  
For Question 2: Use the output value of FLC as the gain of the integrator. In the same way, by setting a reasonable membership function, the output value of the fuzzy controller is set to a gradual change, and the traditional \"0\" and \"1\" switch structure is changed to an integrator gain gradual change structure, which can avoid the shock caused by hard switching. When the fuzzy output is 0, the integrator is disconnected to realize the dynamic switching type.
## 4 Fire and Firefighter Monitoring
### Firefighter Monitoring
#### 4.2.1 Training-Based RSSI Localization
The measurements from distant anchor nodes provide less useful information and are more affected by noise. We performed experimental tests to determine \(p\). The setting was 21 TelosB static nodes deployed in a regular square grid. Distance between neighbor nodes was 5 m. The mobile node was put at different locations and the proposed localization method was applied with different values of \(p\). The number of anchor nodes, \(m\), used in the localization method was also analyzed. Figure 5 shows the mean localization errors obtained. A minimum of three anchor nodes were necessary to obtain reasonable localization errors. The results also revealed that taking into account distant anchor nodes frequently perturbs the localization performance. As above discussed, measurements from distant anchor nodes often include low information and high noise. In fact, with high values of \(m\), mean localization error decreases as \(p\) increases since high \(p\) penalizes contribution of distant nodes. This experiment also analyzed the influence of \(p\). The lowest localization errors were obtained with \(m=4\) and \(p=4\) and with \(m=5\) and \(p=5\). These errors were significantly lower--15%--than with \(p=1\). Although the result depends on the setting, the trends and conclusions were observed in the different settings analyzed. In the experiments shown in Section 6, where dense WSN are assumed, we selected \(p=4\).
## 3 Experiment
### Quantitative Comparison between Real and Generated Digital Cores
#### 3.6.2 Porosity Distribution Analysis  
In this section, we extend the analytical purview to encompass the porosity distribution within the digital rock samples. We synthesized a cohort of 1000 digital rock samples and computed their porosity, juxtaposing these findings with those derived from an equivalent number of original samples. The histogram in Figure 12 portrays the comparative porosity distribution between the real and generated digital rock samples. This visual representation elucidates that the distribution of porosity is congruent across both sets of samples, with no instances of anomalously low or high porosity values detected within the generated subset.  
The histogram reveals that the fidelity of the synthetic generation process is of a high caliber, capturing the intrinsic porosity characteristics with commendable accuracy. It is noteworthy that the porosity values of the generated samples did not deviate significantly from the natural samples, suggesting the absence of overfitting or underfitting phenomena within the generative model employed.  
The distribution curves manifest a slight deviation in the tails, yet these differences are statistically insignificant. Such minor discrepancies do not detract from the overall conclusion that the generative model can reliably replicate the porosity distribution of natural digital rock samples. Thus, we posit that the generative model exhibits a high degree of validity, rendering it a potent tool for the simulation of digital rock porosity which could be indispensable for predictive modeling and reservoir characterization.  
In conclusion, the visual and quantitative assessments jointly affirm the quality and authenticity of our generated digital cores, marking a significant stride in the domain of digital rock physics.  
Figure 12: Overlay of porosity distribution histograms for real and generated digital rock samples.