esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
A numerical method was proposed to study the lubrication and temperature characteristics of an intermediate gearbox with splash lubrication. The temperature distribution was obtained through CFD simulations and experiments. Further numerical computations were performed to examine the flow details and the lubrication and temperature characteristics. The major conclusions are as follows:  
* The maximum relative error between the experimental and simulation results of the wall temperature of the casing and end covers was 10.636%. The relative error between the experimental and simulation results of the oil temperature in the oil pool was 3.180%. The oil temperature satisfied the limit requirement (<110 \({}^{\circ}\)C), and the oil content met the engineering application standard. The results indicate that CFD simulations can accurately predict the temperature distribution of an intermediate gearbox with splash lubrication.  
Figure 19: Temperature characteristics of raceways: (**a**) convective heat transfer coefficient of inner ring; (**b**) temperature of inner ring; (**c**) convective heat transfer coefficient of outer ring; (**d**) temperature of outer ring.  
* In splash lubrication, large amounts of lubricating oil are splashed onto the tooth surface near the gear meshing area to lubricate and cool it, because the gear meshing area experiences the highest amount of frictional heat generation. A large convective heat transfer coefficient corresponds to a low gear tooth surface temperature. The tooth surface temperature of the driving gear is higher than that of the driven gear, because the heat output per unit area of the former is higher.
* The lubricating oil flows in the direction of rotation of the roller. Rollers with a large convective heat transfer coefficient have a lower temperature. The convective heat transfer coefficient of the roller wall is largely related to the lubrication environment of the roller, including the oil distribution inside the bearing cavity and the flow rate. Therefore, the convective heat transfer coefficient and temperature of the rollers are not determined solely by the oil volume fraction of the roller wall.  
This study can serve as a reference to improve lubrication and reduce the temperature rise in a gearbox with splash lubrication, such as an intermediate gearbox.  
Conceptualization, F.L.; methodology, F.L. and M.W.; simulation, M.W.; experiment, W.P. and W.G.; validation, F.L., M.W. and W.P.; data curation, F.L.; writing--original draft preparation, F.L. and M.W.; writing--review and editing, F.L. and M.W.; supervision, F.L. and H.B.; project administration, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52075241.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Photocatalysis for Congo Red Dye Removal
#### 3.2.6 Structural Changes in CR Molecule during Photocatalysis  
By analyzing the changes that occur in the UV-VIS spectra of the CR solution during the photocatalysis test, information can be obtained about the structural changes in the dye molecule following the oxidation process.  
Figure 9a shows the UV-VIS spectra of the 50 mg/L CR solution subjected to photocatalysis with 1 g/L material EthylAmAcid. The CR dye solution has three absorption bands in the UV-VIS spectrum. The absorption band with the maximum at 498 nm is due to the azo bond and the decrease in the intensity of this band corresponds to the break of this bond, so it corresponds to the discoloration of the solution. The other two absorption bands in the UV domain, with maxima at 340 nm and 235 nm, are due to the benzene and naphthalene rings, respectively. The decrease in intensity of these bands is due to the opening of the naphthalene and benzene aromatic rings, with the formation of intermediate products. During the photocatalysis process, the maxima of the absorption bands decrease in intensity, without any shifting taking place since probably the CR oxidation products do not absorb UV-VIS radiation. After 240 min, the spectrum of the CR solution became almost flat, because of the opening of the benzene and naphthalene rings and the breaking of the azo bonds from the dye molecules.  
By applying the pseudo-first order kinetic model for the three absorption peaks, information can be obtained on the rates of discoloration and the opening of naphthalene and benzene rings (Figure 9b). The fastest process is discoloration, the breaking of the azo bonds having the rate constant k\({}_{\text{app}}\) = 1.18 \(\times\) 10\({}^{-2}\) min\({}^{-1}\). Following this occurs the breaking of the naphthalene rings, with k\({}_{\text{app}}\) = 4.23 \(\times\) 10\({}^{-3}\) min\({}^{-1}\), then the breaking of the benzene rings, with the rate constant k\({}_{\text{app}}\) = 2.87 \(\times\) 10\({}^{-3}\) min\({}^{-1}\).
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 4 Discussion
### Analysis of Changes in SUHI and CLHI Ellipsoids  
NDVI reflects the growth status and density of vegetation. During the growing season (e.g., spring and summer), when the vegetation is lush and the NDVI value is high, green spaces can provide a better cooling effect, thus reducing the intensity of SUHI and CLHI. At this time, the elliptical area of daytime SUHI and CLHI may be relatively small. In contrast, during the non-growing seasons (e.g., autumn and winter), when vegetation fades and NDVI values decrease, the cooling effect of green spaces is reduced, which may lead to an increase in the elliptical areas of SUHI and CLHI. Albedo is influenced by the type of ground cover, such as water bodies, vegetation, bare soil, and buildings. In the summer, high-albedo surfaces (e.g., bare soil and building roofs) may absorb more heat due to the higher intensity of solar radiation [41], resulting in higher surface temperatures, which may exacerbate SUHI and CLHI phenomena. This may lead to an increase in the elliptical area of SUHI and CLHI during daytime. In winter, on the other hand, solar radiation is weaker and the effect of albedo on surface temperature is relatively small, so the change in the elliptical area of SUHI and CLHI may not be as significant as in summer. The land use data reflect the functions and characteristics of different areas within the city. During the summer months, the SUHI and CLHI phenomena may be exacerbated by the extensive use of equipment such as air conditioners, which may generate more heat in commercial and residential areas. This may lead to an increase in the elliptical area of SUHI and CLHI during the daytime. In winter, industrial areas may become the main source of heat due to increased heating demand, leading to variations in the elliptical areas of SUHI and CLHI in different areas [42].  
The increase in the average annual area of the SUHI and CLHI indicates (as shown in Figure 8) that the hot areas in the city are expanding. This is mainly due to the urbanization process [43], where a large number of impermeable surfaces (e.g., buildings, roads, etc.)  
Figure 14: Scatter plots of surface temperature distribution versus (**a,b**) HHF and (**c,d**) NSR for June and July 2020.  
have replaced the original vegetation and soil, as shown in Figure 15, resulting in an increase in the reflectivity of the urban surface, which makes it less easy for heat to escape, thus creating a heat island effect.
## 4 Results and Discussion
### DQ3: Most Active Countries  
Based on the affiliation of all authors, we have been able to identify 37 different countries in the set of selected documents, located in the five continents. The three countries with the greatest representation are the United Kingdom, China and the United States. The rest of the countries do not reach a representation higher than 50% of the previous three. Figure 5 shows the number of authors whose affiliations belong to the ten countries with the highest number of mentions. The complete list of countries, showing a distribution of countries that offers a wide coverage of the five continents can be accessed as online complementary material [44].  
Figure 5: Most active countries (by authors’ affiliation).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Organization’s Name and Country** & **\# Mentions** \\ \hline UIIT-PMAS Grid Agriculture University Rawalpindi, Pakistan & 12 \\ \hline Norwegian University of Science and Technology, IDI/NTNU, & 11 \\ Trondheim, Norway & 10 \\ \hline Vienna University of Technology, Austria & 10 \\ \hline Zhejiang University, Hangzhou, China & 8 \\ \hline Novosibirsk State Technical University, Novosibirsk, Russia & 8 \\ \hline CRI-Paris 1 Sorbonne University Paris, France & 6 \\ \hline North Carolina State University, USA & 6 \\ \hline University of Murcia, Spain & 6 \\ \hline Concordia University, Montreal, Canada & 5 \\ \hline Malardalen University, Vasteras, Sweden & 5 \\ \hline Shinshu University, Nagano, Japan & 5 \\ \hline University of Leeds, U.K. & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Top 12 Active Organizations.
## 3 Results and Discussion
### Cyclic voltammetry  
Applying cyclic voltammetry at different phases of the formation of a bioanode and the metabolic activity of electroactive biofilm can provide important information on the mechanism of anodic electron transfer. Figure 4 shows the low scan rate cyclic voltammograms (CVs) recorded at the beginning of the experiment (Figure 4A) and when the current density was at its maximum (Figure 4B) for unmodified and modified electrodes. When the maximum current density was reached, the rate of electron transfer rose rapidly at a potential higher than \(-0.450\) V/SCE.  
The kinetic behavior of the bioelectrochemical oxidation reaction is clearly different between \(-0.450\) and \(-0.200\) V vs. SCE, on the one hand, and \(-0.200\) and \(+0.200\) V vs. SCE, on the other hand. Between \(-0.200\) and \(+0.200\) V vs. SCE, the kinetics of oxidation slowed down (weaker slope J/E) for bioanodes formed on unmodified and zeolite-modified carbon felt. This slowing down was not as remarkable in the case of the bioanode formed from the bentonite-modified carbon felt, probably because the quantity of bacterial cells ensuring the oxidation activity was greater on this electrode (more biofilm on the electrode where the mineral clay deposit is the highest).Above all, the CV curves showed that LTA zeolite and bentonite-modified carbon felt could provide at least \(50\) A/m\({}^{2}\) and \(35\) A/m\({}^{2}\), respectively, at a potential of \(+0.200\) V vs. SCE (Figure 4).  
Figure 3: Evolution of the current density on raw carbon felt and modified carbon felt electrodes polarized at \(-0.2\) V vs. SCE in tannery wastewater and \(20\) mM of acetate inoculated with acclimated activated sludge. The inset gives a closer look at the first six days of the experiments.
## 3 Methodology
### Reduction of Dimensionality  
A feature selection process aims to find a subset of attributes representing the data collected from an intrusion detection dataset [26]. These attributes ensure that the algorithm can interpret the data correctly. Unfortunately, many irrelevant and redundant attributes exist in modern intrusion detection datasets [27]. This study proposes a method that aims to reduce the dimensionality of the data and select the feature subset representative of the data collected from the dataset. It also aims to improve the accuracy of the classification process by implementing the PCA technique. The main idea of this method is to evaluate the relevance of the selected feature subset and the redundancy of the data in the given search space. It uses different PCAs with selected features. In PCA, original variables are transformed into k principal components, which capture data variance. Despite reducing the dimensionality of data and detecting patterns, PCA can have difficulty in interpreting the resulting principal components. Therefore, it is difficult to determine what features have been identified. A PCA is a technique that combines the results of multiple correlated variables into several uncorrelated ones. This method aims to transform these variables  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**NSL-KDD**} \\ \cline{2-4}  & **KDDTrain+** & **KDDTest+** & **KDDTest-21** \\ \hline Normal & 67,343 & 9711 & 2152 \\ \hline DoS & 45,927 & 7458 & 4342 \\ \hline PRB & 11,656 & 2421 & 2402 \\ \hline R2L & 995 & 2754 & 2754 \\ \hline U2R & 52 & 200 & 200 \\ \hline Attack & 58,630 & 12,833 & 9698 \\ \hline Total & 125,973 & 22,544 & 11,850 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the three sets of the NSL–KDD dataset.  
into several principal components. The number of principal components derived from the various correlated variables is usually less than or equal to the original number of variables. Therefore, PCA aims to reduce the number of initial variables with significant dimensionality while retaining as much variance as possible. Let us consider a set of connection vectors composed of \(\mathrm{v}_{1}\), \(\mathrm{v}_{2}\), \(\mathrm{v}_{3}\), \(\ldots\), \(\mathrm{v}_{\mathrm{M}}\). The following steps are used to calculate the PCAs of a dataset:  
1. Assume the entire dataset has been obtained;
2. For each dimension, calculate the mean vector;
3. For the entire dataset, calculate the covariance matrix;
4. Identify the eigenvectors (\(\mathrm{e}_{1}\), \(\mathrm{e}_{2}\), \(\mathrm{e}_{3}\), \(\ldots\)\(\mathrm{e}_{d}\)) and eigenvalues (\(\mathrm{v}_{1}\), \(\mathrm{v}_{2}\), \(\mathrm{v}_{3}\), \(\ldots\)\(\mathrm{v}_{d}\));
5. Select the eigenvector with the highest eigenvalues and sort the eigenvalues in decreasing order;
6. By using this M form, a new sample space can be created;
7. A principal component is determined from the samples obtained.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Types** & **Component** & **Model** & **MAE (\%)** & **RMSE (\%)** & \(\mathbf{R^{2}}\) **(\%)** \\ \hline \multirow{6}{*}{Office Appliance} & Water & LSTM & 6.47 & 13.22 & 58.61 \\  & Dispenser & GRU & 5.33 & 10.42 & 64.34 \\  & & Hybrid model & **3.78** & **8.15** & **82.04** \\ \cline{2-6}  & Air & LSTM & 3.1 & 6.2 & 95.21 \\  & Conditioner & GRU & 2.38 & 5.39 & 92.5 \\  & & Hybrid model & **2.20** & **4.91** & **96.06** \\ \cline{2-6}  & & LSTM & 6.89 & 11.66 & 80.01 \\  & & GRU & 8.24 & 13.84 & 71.64 \\  & & Hybrid model & **2.12** & **7.77** & **93.69** \\ \hline \multirow{6}{*}{Weather Data} & Temperature & LSTM & 8.66 & 15.05 & 47.08 \\  & & GRU & 9.59 & 15.85 & 39.01 \\ \cline{1-1}  & & Hybrid model & **0.09** & **0.22** & **99.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics for different models on office appliances and weather data. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **LSTM** & **GRU** & **Hybrid Model** \\ \hline Time (ms/step) & 7 & 9 & **5** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational time comparison. Figure 9: Comparison of actual and predicted values for (**a**) AC, (**b**) refrigerator, (**c**) water dispenser, (**d**) temperature, and (**e**) humidity.
## 4 Experiment Results and Analysis
### Ablation Experiments and Results Analysis  
To further validate the effectiveness and necessity of the improvements in MDD-YOLOv8, we designed and conducted a series of ablation experiments. Metrics such as precision (P), recall (R), mAP@50, and F1 score were selected to evaluate the efficiency of each module. The results are presented in Table 6. The first row lists the results of the baseline model, YOLOv8n. The second to fourth rows show the results when modifying a single module of the base model, while the fifth to seventh rows display the results of changing two modules simultaneously. Finally, the last row presents the overall results of MDD-YOLOv8.  
From the second line, it is evident that the inclusion of the DynamicConv module increases precision from 79.6% to 80.4%. This demonstrates that the DynamicConv module effectively enhances the accuracy of object detection. However, the DLKA module shows an even greater impact, improving precision to 84.8%, which is 4.4% higher than the DynamicConv module alone. Additionally, the seventh line reveals that combining the DynamicConv and M-FPN-PAN modules results in significantly greater improvements compared to the baseline model. This indicates that the DynamicConv module plays a critical role, as it increases the network's flexibility and compatibility with the M-FPN-PAN module, which enhances both precision and recall, as shown in the third line. Furthermore, the fifth line demonstrates that the combination of DynamicConv and DLKA modules results in better performance than using each module individually.  
Ultimately, our model achieves 87.7% precision, 76.1% recall, 78.9% mAP@50, and an F1 score of 0.81. These values represent the highest precision, recall, and F1 score among all configurations, although the mAP@50 is 0.1% lower than the seventh line. However, the comprehensive evaluation of MDD-YOLOv8 demonstrates its superiority,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**YOLOv8n** & **M-FPN-PAN** & **DynamicConv** & **DLKA** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **F1** \\ \hline \(\surd\) & & & & 79.6 & 70.1 & 72.0 & 0.74 \\ \(\surd\) & & \(\surd\) & & 80.4 & 69.9 & 73.0 &  
with improvements of 8.1%, 6.0%, 6.9%, and 0.07 in precision, recall, mAP@50, and F1 score, respectively, compared to YOLOv8n.
Sensors_**2014**, _14_, 20188-20216; doi:10.3390/s141120188  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Electrical Engineering, Graduate Institute of Communication Engineering,  
National Chung Hsing University, Taichung 402, Taiwan;  
E-Mails: [EMAIL_ADDRESS] (C.-C.H.); [EMAIL_ADDRESS] (Y.-H.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-4-2285-1549 (ext. 811); Fax: +886-4-2285-1410.  
External Editor: [PERSON]  
_Received: 21 June 2014; in revised form: 9 October 2014 / Accepted: 17 October 2014 / Published: 27 October 2014_  
In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs.  
**Keywords:** telematics; vehicular sensor networks; topology control
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 5 Overall Algorithm  
We summarize the algorithm presented in the previous section and perform alternating iterations. In this algorithm, the results from the previous iteration are used as the starting estimate for the succeeding iteration, and the pseudo-code for the alternating iteration of the three sub-problems is provided in Algorithm 1. In the forthcoming convergence analysis, we will demonstrate that the average achievable rate obtained in each iteration of Algorithm 1 for solving problem \(\mathrm{P}(1)\) will not decrease compared to the previous iteration, meaning it will not decrease as the number of iterations increases. The algorithm flow chart is shown in Appendix A.  
```
1:Initialize: set the number of iterations \(i=0\), give the initial value \(P_{1}^{[0]},P_{2}^{[0]},\mathcal{Q}^{[0]},\Phi_{1}^{[0]},\Phi_{2}^{[0]},\mu_{1}^ {[0]},\mu_{2}^{[0]},\
u_{1}^{[0]}\), and then solve the initial \(\overline{R}^{[0]}\) by (17) through the above initial value.
2:whileture
3:Set the number of iterations \(i=i+1\);
4:After fixation of \(P_{1}^{[i-1]},P_{2}^{[i-1]},\Phi_{1}^{[i-1]}\) and \(\Phi_{2}^{[i-1]}\), update \((Q^{[i]},u_{1}^{[i]},u_{2}^{[i]},\
u_{1}^{[i]})\) by addressing problem \(\mathrm{P}(3)\);
5:Update \(\[PERSON]_{1}^{[i]}\) and \(\[PERSON]_{2}^{[i]}\) by addressing Formulas (20) and (22);
6:Using the \(\mathcal{Q}^{[i]},\Phi_{1}^{[i]}\) and \(\Phi_{2}^{[i]}\) of the previous iteration, update \(P_{1}^{[i]}\) and \(P_{2}^{[i]}\) by addressing problem \(\mathrm{P}(5)\);
7:Combined with \((P_{1}^{[i]},P_{2}^{[i]},\[PERSON]_{1}^{[i]},\[PERSON]_{2}^{[i]},\mathcal{Q}^{[i]},u_{1} ^{[i]},u_{2}^{[i]},\
u_{1}^{[i]})\), calculate \(\overline{R}^{[i]}\) according to (17);
8:if\(\frac{\overline{R}^{[i]}-\overline{R}^{[i-1]}}{\overline{R}^{[i]}}<\epsilon\)then
9:break;
10:endif
11:return the optimal solutions \(P_{1}^{[i]},P_{2}^{[i]},\Phi_{1}^{[i]},\Phi_{2}^{[i]},\mathcal{Q}^{[i]}\).
```  
**Algorithm 1** Solve problem \(\mathrm{P}(1)\) by alternating iterative algorithm.
## Appendix B Details on the Data Screening Methods for Cognitive Attention Pilot Studies
### Data Screening Method 2: HbO Increasing (after Removing Outliers)  
As an alternative second screening method, we applied \"HbO increasing\" processing after removing the outlier participants. As before, the first step was to exclude the outlier participants, leaving a set of 17 participants to work from (9 blocks of data per participant). Next, out of the nine blocks, the blocks were selected for \"HbO increasing\" when, during the attention test, the HbO level was higher than that of the resting state. The table in Figure 18 illustrates the data selection. The first column is the participant number, and the second column is the number of data blocks (out of nine) that had increasing HbO values. For example, out of the total of 9 blocks \(\times\) 17 participants = 153 data blocks, 72 data blocks (72/153 = 47%) had increasing HbO values during the attention test. Since this was the expected behavior, with the \"HbO increasing\" criteria, we only used these 72 data blocks. Finally, the third column in Figure 18 shows that out of the 72 data blocks with increasing HbO values, many of the data blocks showed the redox state of CCO decreasing. In particular, 57/72 = 79.2% of the accepted data blocks showed that CCO decreased whilethe HbO increased during the attention test. For the \"HbO increasing\" data selection method, Figure 19 shows the change in HbO, HbR, and the redox state of CCO during the attention test.