esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Stiffness Calculation
### Mesh Stiffness Calculation of Crack Gear
#### 2.2.1 The Diameter of the Root Circle Is Smaller than the Base Circle  
Figure 7 shows the crack schematic diagram of gear teeth when the root circle diameter is smaller than the base circle diameter. \(q_{1}\) is the initial crack length, which does not reach the centerline of the tooth, and \(q_{2}\) is that the crack size exceeds the centerline of the tooth. \(v\) is the angle between the crack line and the centerline of the tooth.  
Figure 6: Schematic diagram of a gear tooth crack.  
Figure 7: The force diagram of cracked gear when the number of teeth is less than 42.  
As in the case of healthy gear, the combined mesh stiffness consists of Hertz stiffness, bending stiffness, shear stiffness, and axial compression stiffness. The Hertz stiffness does not change when the gear teeth crack, but the bending stiffness, shear stiffness, and axial compression stiffness will change with the change of the height. In order to deduce these three kinds of stiffness, we need to consider four cases, case 1 and 2 are that the crack does not pass through the centerline of the tooth, and case 3 and 4 are that the crack passes through the centerline of the tooth.  
With the continuous expansion of cracks, the cross-sectional area \(Ax\) and moment of inertia \(I_{x}\) can be refined according to the specific situations. There are four situations. Each situation will be discussed in detail one after another.  
Situation 1: When \(h_{a}\geq h_{0}\) and \(\alpha_{1}\geq\alpha_{a}\)  
\[I_{x}=\left\{\begin{array}{ll}\frac{1}{12}(h_{a}+h_{x})^{3}L&x\leq d_{a}\\ \frac{1}{12}(2h_{x})^{3}L&x>d_{a}\end{array}\right. \tag{34}\]  
\[A_{x}=\left\{\begin{array}{ll}(h_{a}+h_{x})L&x\leq d_{a}\\ 2h_{x}L&x>d_{a}\end{array}\right. \tag{35}\]  
Situation 2: When \(h_{a}<h_{0}\) or when \(h_{a}\geq h_{0}\) and \(\alpha_{1}\leq\alpha_{a}\)  
\[I_{x}=\frac{1}{12}(h_{a}+h_{x})^{3}L \tag{36}\]  
\[A_{x}=(h_{a}+h_{x})L \tag{37}\]  
where \(h_{a}=R_{b}\sin\alpha_{2}-q_{1}\sin\
u\)  
Situation 3: When \(h_{c}<h_{0}\) or when \(h_{c}\geq h_{0}\) and \(\alpha_{1}\leq\alpha_{c}\)  
\[I_{x}=\frac{1}{12}(h_{x}-h_{c})^{3}L \tag{38}\]  
\[A_{x}=(h_{x}-h_{c})L \tag{39}\]  
Situation 4: When \(h_{c}<h_{0}\) and \(\alpha_{1}\geq\alpha_{c}\)  
\[I_{x}=\frac{1}{12}(h_{x}-h_{c})^{3}L \tag{40}\]  
\[A_{x}=(h_{x}-h_{c})L \tag{41}\]  
where \(h_{c}=q_{2}\sin\
u\), \(h_{x}=\left\{\begin{array}{ll}R\sin\varphi-\left(a_{1}/\sin\gamma+r_{p} \right)\cos(\gamma-\varphi)&x\leq d_{c}\\ R_{b}[(\alpha_{2}+\alpha)\cos\alpha-\sin\alpha]&x>d_{c}\end{array}\right..\)  
According to the derivation method of TVMS, when the root circle of the healthy gear tooth is smaller than the base circle, the TVMS value under crack state can be derived.
## 3 Experimental Results and Analysis
### Experimental Result and Comparative Analysis
#### 3.4.3 Detection Result  
Figure 9 shows the detection results of the proposed model RCDAM-Net. The detection pictures represent the types of foreign objects that are easy to be attached to common transmission tower lines, and this result is able to check the practical effectiveness of the model.
## 2 Materials and Methods
### Model Development  
PLS and SVM techniques were chosen for the main algorithms used to develop the FW-component detection models. PLS is a multivariate analysis method that extracts new latent variables or factors from raw spectra. These factors indicate the maximum covariance between the reference and spectral information [13]. Thus, the PLS method can resolve the multi-collinearity problem using the latent variables of the independent and dependent variables. Unlike the PLS technique, the LS-SVM method can optimize separation of hyperspectral matrixes that minimize the misclassification rate for groups and can solve non-linear problems [31; 32]. By comparing the results of both techniques, the linear and non-linear features of the OF FW component evaluation could be compared and analyzed. Of the 275 samples, 205 and 70 were used for model development and prediction, respectively.  
To enhance the forecasting capacity and robustness of calibration of the PLS and LS-SVM models, it is important to derive the effective wavelength (EW) region and PLS factors [33]. The EW regions were selected using the intermediate PLS (iPLS) method, which can prevent overlap and weak absorption intensity in the obtained spectral data. The effective PLS factors were determined from the minimum root mean square error of the validation (RMSE) value and the EW regions were extracted using a baseline lower than the average RMSE values. The RMSE values were calculated from the following equation [33]:  
\[\text{RMSE}\ =\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i,\ actual}-y_{i,\ predict} \right)^{2}}{n}} \tag{2}\]  
where \(y_{i,actual}\) and \(y_{i,predicted}\) are the actual and estimated values for the developed PLS model, respectively, and \(n\) indicates the actual/predicted samples. To accomplish this, the total wavelength was divided by 50-nm intervals and the regions lower than the average of the entire subinterval were selected as the EW regions.  
To improve the performance of both models, five spectral preprocessing methods were applied to the hyperspectral images of the samples: multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, the Savitzky-Golay derivative (SGD), smoothing, and normalization methods. These processes simplified the obtained spectrum such that the spectral peaks (corresponding to specific chemical characteristics) were emphasized through removal of noise signals such as scattering effects generated by the irregular surfaces of the samples and irregular changes in the illumination paths and intensity [22; 33]. Among them, a preprocessing method with a low standard error of calibration (SEC), a low standard error of prediction (SEP), and a high coefficient of determination (\(R^{2}\)) was selected to realize the optimal model imaging.
## 4 Local Updates to Handle Mobility of Sensors for Geocasting
### Parent Invalidation for Child Nodes of MN  
If a child node of MN does not receive another beacon packet from its mobile parent after the beacon interval defined in the previous MN_BEACON packet, it concludes that it is not in the communication range of its parent anymore. Therefore, it searches for another parent. For this purpose, it broadcasts a PARENT_REQUEST message. The child node C2 does not receive another beacon from [PERSON] and at time t19 it starts to search for another parent by sending a PARENT_REQUEST message in Figure 5. All nodes receiving a PARENT_REQUEST message reply a PARENT_REPLY message back to the initiator of the parent request message. After the node receives PARENT_REPLY messages, it chooses the node which has the smallest HCL to the sink (at time t22 in the figure). Then, the child node sendsa HELLO_TO_PARENT message to its new parent to inform about its convex hull (at time t\({}_{23}\) in the figure). The new parent receiving a HELLO_TO_PARENT combines its convex hull with its new child node's convex hull and propagates its new convex hull to upper nodes in the tree if the aggregated convex hull is changed. After the new parent selection, if the current HCL of the child node is different than its previous HCL, it also propagates its new HCL to its child nodes (at time t\({}_{24}\) in the figure) and the whole branch down in the tree.  
Several factors contribute to the fact that wireless sensor networks often do not work as expected when deployed in a real-world setting. Some of them are environmental influences which may lead to non-deterministic behavior of radio transmission, and therefore, packet loss. In case of packet loss, our local updating mechanism can still function. If a beacon packet (_i.e._, MN_BEACON, ACK_CHILD) from a child node is lost, the convex hull of this child node can be removed from the parent temporarily. However, in the next beaconing the child node is reinforced in the parent node. If a beacon packet (_i.e._, MN_BEACON, ACK_BEACON) from a parent node is lost, the child node can connect itself to another node from its neighborhood. Therefore, packet loss does not have much influence on basic functions (_i.e._, query and data routing) of the network although there might be temporary child-parent node disconnections. The performance evaluation metrics, which are used in the next section, are independent of imperfect conditions of wireless transmission such as packet losses.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 5 Conclusions  
This work proposed an uplink GF-NOMA message-passing detector for UAV-assisted communications. The BG-MC model was first developed to accurately characterize the slow change feature of the AS of all UEs under a GF-NOMA system. Based on the HMP rule, this work designed the GAMP-BG-MC algorithm and tested it with the two scenarios. Numerical results demonstrated that the proposed algorithm not only has a faster convergence speed but also achieves a better SER performance.  
In future work, it will be interesting to investigate the impact of channel fading on the multi-user symbol detection in UAV-based GF-NOMA systems, while considering a comprehensive channel model for UAV communication and developing robust detection algorithms that can adapt to varying channel conditions in UAV communication scenarios.  
Conceptualization, P.S. and Z.W.; methodology, Y.S.; software, Y.Z.; validation, Y.S., Y.Z. and X.L.; formal analysis, P.S.; investigation, Z.W.; writing--original draft preparation, Y.S. and K.C.-H.; writing--review and editing, K.C.-H.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Project of Henan Province under Grants 242102211107; the Natural Science Foundation of China, Grant/Award Number: 61901417; the Natural Science Foundation of Henan, Grant/Award Number: 222300420504; Academic Degrees Graduate Education Reform Project of Henan Province, Grant/Award Number: 2021 SJGLX262Y and 2023 SJGLX370Y; Postgraduate Education Reform and Quality Improvement Project of Henan Province, Grant/Award Number: YJS2024 AL141; Foundation and frontier project of Nanyang, Grant/Award Number: JCQY008.  
Not applicable.  
Figure 4: SER vs. SNR with \(p_{a}=0.3\).  
Figure 5: SER vs. \(p_{a}\) with SNR \(=9\) dB.  
Data Availability StatementThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
# Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay
## 4 Conclusions  
In this study, LCC analysis was conducted to examine the applicability of asphalt and concrete overlays for the maintenance of deteriorated concrete pavements on Korean expressways. In addition, construction and user costs were examined with and without a traffic operation measure that uses the road shoulder to maintain the number of lanes under the complete blocking of one lane for overlay application. The conclusions are as follows:  
1. The user cost tended to increase for increasing traffic volume and repair length. The cost increase rate was higher with respect to traffic volume than with respect to repair length, indicating that the application of traffic operation measures is essential for minimizing the dissatisfaction of road users and reduces the cost.
2. When the shoulder was used for smooth traffic flow, the total construction cost tended to decrease. It was reduced by up to 19.7 and 24.8% for asphalt and concrete overlays, respectively. The economic efficiency is expected to improve through increasing the number of lanes by using the shoulder to distribute the concentrated traffic, rather than utilizing only the unblocked lanes after completely blocking a lane.
3. When the shoulder was utilized, the LCC analysis results showed cost reductions in both asphalt and concrete overlay. The analysis results before and after the use of the shoulder indicate that the economic efficiency of concrete overlay is higher than that of asphalt overlay.
4. The initial construction cost of asphalt overlay was found to be lower than that of concrete overlay. However, in the long-term, for a 20-year cycle, the economic efficiency of concrete overlay was found to be higher than that of asphalt overlay. Concrete overlay is expected to be more favorable than asphalt overlay from an economic perspective for increasing repair length.
5. The LCC analysis results for these overlay methods showed that the overlay method (asphalt or concrete) ought to be selected according to the target service life after repairing the deteriorated target sections of concrete pavements for beneficial economic effect.  
Conceptualization, H.J., S.K. and C.P.; methodology, H.J., S.K., T.O., K.Y. and C.P.; writing--original draft preparation, H.J., T.O., J.N. and C.P.; writing--review and editing, H.J., J.N., K.Y., S.K. and C.P. All authors have read and agreed to the published version of the manuscript.  
This study was conducted under research project \"Development of High-Performance Concrete Pavement Maintenance Technology to Extend Roadway Life (Project No: 22 POQW-B146691-05).J funded by the Ministry of Land, Infrastructure and Transport (MOLIT) and the Korea Agency for Infrastructure Technology Advancement (KAIA). The authors would like to thank the members of the research team, MOLIT, and KAIA for their guidance and support throughout the project.  
Not applicable.  
The authors declare no conflict of interest.
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 4 Conclusions  
In this paper, a hierarchical resource allocation scheme is proposed to realize fair and effective resource allocation in VANET. In the first layer, the random movement of vehicles brings difficulty for the central cloud to allocate resources to RSUs. Therefore, we use Nash bargaining game to achieve fair and efficient resources allocation between RSUs. In the second layer, considering the selfishness of VUEs and the difference of services requested by VUEs, we construct the matching degree index between RSUs and VUEs. Then, we establish the Nash bargaining game model to achieve fair and effective resources allocation to VUEs. In order to reduce the computation complexity, we transform the original problem into two sub-problems: power allocation and slot allocation. Simulation results show that the proposed hierarchical resource allocation scheme can realize the fairness and effectiveness of resource allocation in VANET.  
**Author Contributions**: All authors contributed to the development and completion of this paper. Conceptualization and Data curation, [PERSON] ([PERSON]); Formal analysis, [PERSON] ([PERSON]) and Y.L.; Funding acquisition and Investigation, [PERSON] ([PERSON]); Validation, [PERSON] ([PERSON]); Review & Editing, [PERSON] ([PERSON]) and Y.L. All authors conducted the revisions and approved the publication.  
**Funding**: This work was supported by the National Natural Science Foundation of China (Grant No. 61873353), the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2018 zzts535).  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 2 Materials and Methods
### Driving Intention Characteristics Analysis  
Based on the experimental platform, EEG data under the same driving intention video stimuli for subjects 1, 2, and 3 were selected for analysis. For each of the four driving intention states, 3 s EEG data in the 4-30 Hz frequency band were extracted for analysis. Changes in these bands are believed to be closely related to the driver's alertness, attention level, and cognitive decision making while performing tasks. The average value of each channel was calculated to quantify the EEG activity for each channel, and EEG topographic maps were drawn based on these data, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject/Driver** & **Left** & **Right** & **Normal** & **Avoidance** & **Scale (\(\mu\)V)** \\ \hline Subject 1 & & & & & \\ Subject 2 & & & & & \\ Subject 3 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average EEG topographies of 4–30 Hz under different driving intention scenarios for some subjects.  
Table 1 reflects the EEG topographic maps showing the average values of the 3 s 4-30 Hz frequency band EEG data for subjects 1, 2, and 3 under the same driving intention video stimuli. By analyzing these topographic maps, significant differences in the spatial distribution patterns of EEG activity can be observed across different driving intention states, reflecting changes in the driver's cognitive load, alertness, and attention allocation when performing different driving tasks. In various driving intention scenarios, the 4-30 Hz frequency band EEG topographic maps exhibit different characteristics, effectively reflecting the driver's cognitive and decision-making processes under different driving intentions. Driving intention is essentially a decision-making process over a period of time. When facing different driving scenarios, drivers need to continuously make judgments and adjustments based on road conditions, traffic flow, and their own operational intentions. In relatively simple and stable driving situations such as normal driving, the driver's attention load is lighter, typically presenting a relaxed yet alert state. At this time, the driver's cognitive load is lower, primarily focusing on maintaining basic vehicle control operations, and the EEG activity shows a steady and consistent pattern. For driving situations such as obstacle avoidance, the driver needs to make quick responses, and the EEG topographic map typically shows extensive changes, especially in brain areas related to perception and decision making, such as the prefrontal and parietal regions, with significant increases in activity. Therefore, the 4-30 Hz frequency band EEG data can be used to assess the driver's current driving intention, and the differences in EEG features provide an effective basis for driving intention recognition based on EEG signals.
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 5 Pricing Optimality Analysis
### Example 2  
This example compares the algorithm with an equilibrium method (Case I in Section 4). Recall that Case I uses the profit maximization scheme with a single service level. The main comparison targets are the price vector and the expected system objective value. The comparison results are summarized in Tables 6 and 7. Three system objective values, namely \(\mathcal{R}_{theory}\), \(\mathcal{R}\) of the equilibrium method, and \(\mathcal{R}\) of the heuristic search, are compared.  
Figure 11: System behavior in the not-pruned zone.  
Figure 10: System behavior in the pruned zone.  
\(\mathcal{R}_{theory}\) is the system objective value (i.e., the system profit) achievable at the optimal equilibrium state as follows (assuming the system converges on the equilibrium state):  
\[\mathcal{R}_{theory}\ =\ \lambda^{*}P_{1}-\mu P_{B},\]  
where \(\lambda^{*}\) is the job arrival rate at the equilibrium. \(\mathcal{R}\) is the system objective value computed from the SAM result using Equation (16), without assuming equilibrium.  
Table 6 presents the two observations. First, \(\mathcal{R}\) of the equilibrium method matches \(\mathcal{R}_{theory}\) well. This is expected because I have already shown in Section 4 that the equilibrium method accurately models the actual system behavior in large capacity networks with frequent arrival rate updates. Second, \(\mathcal{R}\) of the algorithm is also very close to \(\mathcal{R}_{theory}\). These results validate the accuracy of the proposed algorithm.  
Table 7 shows very different results. First, \(\mathcal{R}\) of the equilibrium method is lower than \(\mathcal{R}_{theory}\). This is because the system fails to converge to a steady state and the oscillation drops the overall system utilization (i.e., fewer jobs are submitted than the equilibrium model estimated), which decreases the system profit for the same price. Second, the price derived from the algorithm is higher than the price derived from the equilibrium method, which is due to the lack of accurate knowledge of the current system state from the user side. As the system oscillates, it broadcasts a low arrival rate and a high arrival rate. The low arrival rate makes the users underestimate the job completion delay, and the users are willing to submit their jobs even for a higher price. When the job arrival rate rises as a result of users' decisions, the expected job completion delay increases, and the users decide not to submit their jobs. Third, from the system profit viewpoint, the gain from high price is more than enough to compensate the loss from the lower overall system utilization. Therefore, the \(\mathcal{R}\) of the algorithm is higher than that of the equilibrium. In essence, the profit-maximizing network takes advantage of the users who do not have up-to-date knowledge of the system status.
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were used to summarize the data, calculating means and standard deviations for the highest vertical jump measurements recorded for each participant across all the methods. The Shapiro-Wilk test was used to evaluate the data distribution normality, while [PERSON]'s test assessed the homogeneity of the variances. Reliability was assessed through the Intraclass Correlation Coefficient (ICC), which measures the consistency or reproducibility of quantitative measurements by different observers assessing the same quantity [47]. Following the recommendations of [PERSON] and [PERSON], the intra-rater reliability ICC was used to illustrate absolute agreement across multiple measurements. Reliability was categorized as poor (ICC \(<\) 0.5), moderate (0.5 \(\leq\) ICC \(<\) 0.75), good (0.75 \(\leq\) ICC \(<\) 0.9), or excellent (ICC \(\geq\) 0.9) [48].  
Agreement between the Enode Sensor, My Jump 3, and the Force Plate was assessed with Bland-Altman analysis, which provides a visual representation of the differences between methods. This analysis calculates the mean bias (the average difference between methods) and limits of agreement (mean difference \(\pm\) 1.96 times the standard deviation of the differences) [49].  
Correlation analysis was conducted to evaluate the strength and direction of relationships between measurements from different methods, with both [PERSON]'s and [PERSON]'s correlation coefficients calculated). Linear regression analysis was also performed to assess how well measurements from the Enode Sensor and My Jump 3 could predict Force Plate measurements. The coefficient of determination (R\({}^{2}\)) was used to quantify the proportion of the variance in Force Plate measurements explained by the other methods [50].  
This comprehensive data analysis approach involved multiple statistical methods to rigorously evaluate the reliability and validity of the Enode Sensor and My Jump 3 against those of the Force Plate. Combining descriptive statistics, reliability analysis, Bland-Altman analysis, correlation analysis, and linear regression allowed for a robust understanding of the performances of these tools. These methods provided a detailed evaluation of CMJ performance data, summarizing trends (descriptive statistics), assessing consistency (reliability analysis), evaluating agreement (Bland-Altman analysis), examining relationships (correlation analysis), and predicting outcomes (linear regression). Statistical analyses were conducted using Jamovi (version 2.3.28) [51; 52; 53; 54] and SPSS (version 29.0.2.0) [55].