esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### IAQ Performance Indicators  
#### 3.2.1 Co\({}_{2}\)  
The variation in CO\({}_{2}\) concentration in BR1 during C1 and C2 is shown in (Figure 3). In BR1, increases in overnight CO\({}_{2}\) concentrations were due to metabolic emissions. The concentrations start increasing at 22:00 each day, reaching a plateau until a maximum value at 06:30 and decreasing after that, quite suddenly due to the opening of windows. The CO\({}_{2}\) concentration in BR1 was higher than in the other bedrooms because it was occupied by two persons, despite the higher ACR. CO\({}_{2}\) concentrations exceeded 2000 ppm, when the door was closed, in winter C1 while they did not reach 1650 ppm at any time during spring C2. Moreover, there was a second increase in concentrations after 06:00 because of a third person who joined the room on February 13 and 15 for one hour on each day. The CO\({}_{2}\) concentration at night was 30% lower than usual on February 8 because there was only one person in the room.  
Figure 4 illustrates the dispersion of CO\({}_{2}\) in each room during the two campaigns. The median CO\({}_{2}\) concentrations in all the rooms in the house are in the range of 588-705 ppm and 539-656 ppm for C1 and C2, respectively. All the median concentrations are lower than the threshold of 1000 ppm. Although during C2 the outdoor median concentration of CO\({}_{2}\) was only slightly higher than that during C1, the indoor median concentration of CO\({}_{2}\) was 10% higher during C1 than during C2. The average concentration of CO\({}_{2}\) in our two campaigns did not exceed 1100 ppm.  
Figure 4: CO\({}_{2}\) concentration for each room–occupancy period (+: mean; *: median; o: minimum and maximum values).  
Figure 3: CO\({}_{2}\) concentration in BR1 during (**a**) C1; and (**b**) C2.  
Table 8 summarizes the CO\({}_{2}\)-based indicators used in this study. The cumulative exposure exceeding 1000 ppm was higher during C1 compared to C2 for each room. The highest exposure was reached in BR1 where the cumulative exposure was 48% higher than the threshold (1.68 \(\times\) 10\({}^{5}\) ppm-h). This was due to the change in seasons (C1 in winter and C2 in spring) and the permanent occupancy by at least one person during C1, while an occupant was not always present during C2. Windows and doors were opened during C2 due to favorable weather conditions, playing a role in the cumulative excess exposure not exceeding the threshold.  
According to the ICONE indicator, there were non-stuffy conditions in all rooms, except in BR1 which had low-stuffy conditions during both campaigns (1.57 in C1; 1.06 in C2).
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 3 Cellular Traffic Prediction Model  
### Model Framework Introduction  
This section mainly introduces our proposed hybrid spatiotemporal network HSTNet, which is mainly based on STDenseNet. HSTNet consists of two input sections and three module sections. The three modules include the convolution module, the time-embedding module, and the attention module. The predicted value \(P^{(h,w)}\) of the model was combined with the output data of the three modules. The model framework is shown in Figure 5.  
The traffic of each time period in the datasets was counted by 100 \(\times\) 100 cells; that is, the traffic data of each time period could be composed of a 100 \(\times\) 100 traffic distribution matrix. Therefore, we could effectively extract the spatial correlation of cellular traffic through convolutional neural networks. The main prediction module of our model was also implemented by the improved solution of the convolutional neural network (DenseNet) [19]. The historical data included the last three time periods and the current time period of the previous three days. We considered that cellular traffic was not only highly correlated with the traffic distribution of the previous few hours, but also depended on the traffic distribution at the current moment of the previous few days. Therefore, we entered the two pieces of historical data into the model separately. For example, if we forecast the traffic at 12 o'clock on the 11 th of a certain month, the input data were the traffic data of 12 h on 8/9/10 of the month and the traffic data of 9/10/11 o'clock on the day. After the historical data were entered  
Figure 4: Spatial correlation analysis.  
into the model, they were processed by two modules: the convolution module and the attention module. The convolution module consisted of two DenseNets with deformable convolutions [20] that handled the historical data of the two parts. The output matrix was fused through a matrix of learnable parameters.  
In the second section, we analyzed the time correlation of cellular traffic data. There were strong correlations with cellular traffic at different times of the day and whether it was a holiday. The input data of the time embedding module included the hour value of the predicted time and the attribute of the date (working day or holiday). The matrix generated by the time embedding module would be added to the matrix output by the convolution module.  
The attention module received the input of two historical data items and performed integration operations. Then, a weight matrix based on historical data was output. The matrix adjusted the weight of the output of the first two modules and output \(O^{(h,w)}\). Finally, the final prediction matrix \(P^{t(h,w)}\) was output through the sigmoid function and compared with the real value \(P^{(h,w)}\).
## 2 Materials and Methods
### 5a-Reductase Inhibitory Activity  
Rats were sacrificed by injecting Zoletil\({}^{\text{0}}\) 100 (tiletamine HCl and zolazepum HCl with 200 mg/kg body weight) intravenously, and the liver was collected. The liver was rinsed with saline and minced into small pieces in 0.32 M sucrose and 1 mM dithiothreitol. The homogenous solution was centrifuged at 9000\(\times\)\(g\) at 0 \({}^{\circ}\)C for 30 min. The supernatant acts as an enzyme source. The inhibition of 5-reductase was determined as previously described [11]. Briefly, the reaction mixture was prepared with 0.2 mL of finasteride or samples, 1.0 mL of phosphate buffer (0.02 M with pH 6.5), 0.3 mL of testosterone (0.5 mg/mL dissolved in methanol), and 0.7 mL of rat microsomal suspension. The reaction was initiated by adding 0.5 mL of NADPH (0.77 mg/mL dissolved in phosphate buffer) and incubated at 37 \({}^{\circ}\)C for 30 min. After incubation, the reaction was stopped by adding 5.0 mL of dichloromethane, and 0.5 mL of propyl p-hydroxybenzoate (0.1 mg/mL dissolved in methanol) was added, which acts as an internal standard. Organic phases were collected and evaporated. The leftovers were redissolved in 5.0 mL of methanol. The sample was injected into the HPLC system using Shodex C18 columns (5 um; 250 \(\times\) 4.6 mm). Methanol and deionized water were mixed at a ratio of 65:35 and used as the mobile phase. The system was performed with a flow rate of 1.0 mL/min, and an ultraviolet detector at 242 nm was enabled. The results were represented as mg finasteride equivalents per g of extract (mg FE/g of extract) [11].
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 4 Experimental Validation
### Data Processing  
Firstly, 400 sample data of each of the seven states were selected, respectively, to obtain a total of 2800 samples, and the sample of experimental data is shown in Table 2.  
Secondly, the CEEMDAN algorithm is used to decompose the seven labelled samples into the corresponding IMF components, and the decomposition results are shown in Figure 5, taking one sample with label 4 as an example.  
The correlation coefficient indicates the magnitude of correlation between the component and the original signal; using the correlation coefficient to filter the component and reconstruct the signal can effectively reduce noise interference. The formula of the correlation coefficient is as follows:  
\[r=\frac{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{\sqrt {\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})}^{2}}\sqrt{\sum\limits_{i=1}^{n}{ (Y_{i}-\overline{Y})}^{2}}} \tag{17}\]  
where: \(X_{i}\) is the original signal, \(Y_{i}\) is the IMF component, \(\overline{X}\) and \(\overline{Y}\) are the average values of the corresponding signals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing Condition** &
\begin{tabular}{c} **Fault Diameter** \\ **(mm)** \\ \end{tabular} & **Sample Length** & **Sample Size** & **Label** \\ \hline Normal state & 0 & 1024 & 400 & 1 \\ \hline Rolling body failure & 0.2 & 1024 & 400 & 2 \\  & 0.5 & 1024 & 400 & 3 \\ \hline Inner ring failure & 0.2 & 1024 & 400 & 4 \\  & 0.5 & 1024 & 400 & 5 \\ \hline Outer ring failure & 0.2 & 1024 & 400 & 6 \\  & 0.5 & 1024 & 400 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental data.  
Figure 4: Data acquisition platform.  
The correlation coefficient plots of each IMF component for the seven labels are shown in Figure 6. From the figure, it can be learnt that the correlation coefficient value decreases with the increase in the number of layers of decomposition and the first five IMF components have high correlation coefficient values. Therefore, the first five IMF components are selected for signal reconstruction to obtain the signal after noise reduction.  
Figure 5: CEEMDAN decomposition diagram.  
Figure 6: Plot of correlation coefficients.  
After obtaining the reconstructed vibration signal, it needs to be converted into a 2D grey-scale map. The grey-scale map is an effective visualisation tool for displaying and analysing the time domain characteristics of the signal. The specific steps of grey-scale map conversion are as follows: Firstly, the signal is normalised, and the normalised signal values are mapped to [0, 255]. Secondly, the normalised signal is reconstructed into a two-dimensional matrix. Finally, the values in the 2D matrix are converted to their corresponding grey values and a grey-scale map is generated. In this paper, this method is used to convert a one-dimensional signal with a length of 1024 into a 32 \(\times\) 32 two-dimensional matrix and then visualise it as a grey-scale map.
## 3 Results and Discussions
### Comparison of Forming Behavior of Steel
#### 3.2.1 Comparison of Forming Response: Effective Stress  
The comparison of forming response-effective stress (s\({}_{\text{eff.}}\)) for varying billet size ratios of different AISI grade steels obtained using ANN models with low, middle, and high-level combinations of \(a\), \(h\), and \(r\) is depicted in Figure 8. For any given combinational values of \(a\), \(h\), \(r\), and \(z\), the effective stress was significantly higher for AISI 1045, as compared to either AISI 1010 or AISI 1018. The nature of stress variations with respect to \(z\) was almost the same in all three materials for all combinational values of \(a\), \(h\), and \(r\). The effective stress almost linearly increased with an increase in \(z\) up to 0.6, whereas for \(z>0.6\), the change in stress was minimum. It was also observed that the value of \(z\) at which maximum stress occurs shifts towards the origin with an increase in carbon content (max. stress for  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Maximum Absolute Prediction Error (\%)**} \\ \cline{2-13}  & & \multicolumn{3}{c}{**AISI 1010**} & \multicolumn{3}{c}{**AISI 1018**} & \multicolumn{3}{c}{**AISI 1045**} \\ \cline{2-13}  & s\({}_{\text{eff.}}\) & \(\in_{\text{eff.}}\) & \(\in_{\text{Frate.}}\) & F & s\({}_{\text{eff.}}\) & \(\in_{\text{Feff.}}\) & \(\in_{\text{Frate.}}\) & F & s\({}_{\text{eff.}}\) & \(\in_{\text{Feff.}}\) & \(\in_{\text{Frate.}}\) & F \\ \hline Training patterns (226) & 2.22 & 9.98 & 30.15 & 8.53 & 1.89 & 10.81 & 29.08 & 6.18 & 5.24 & 11.99 & 29.16 & 7.75 \\ \hline Testing patterns (30) & 3.30 & 13.72 & 29.05 & 5.36 & 11.42 & 8.88 & 24.09 & 4.88 & 1.05 & 8.94 & 27.16 & 4.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANN training performance.  
Figure 7: Billet as machined, annealed, MoS\({}_{2}\) coated forged, and sectioned part.  
AISI 1045 occurred at a lower z value, whereas the max. stress for AISI 1010 occurred at slightly higher \(z\) value). The effective stress was almost insensitive to variations in z beyond 0.6. Hence, the higher billet volume did not contribute to a rise in stress level.  
The plots of Figure 8 eventually revealed that the increase in the carbon content of steel lowered the formability and, hence, more stress developed in the AISI 1045 steel compared to AISI 1010 and 1045. For the fixed diameter of the billet (30 mm), the punch diameter was 25 mm for \(r=0.3\) and 19 mm for \(r=0.6\); therefore, a decline in the effective stress was observed for lower to higher combinational values of \(a,h\), and \(r\). An increase in the volume of billet (\(z>0.6\)) has a cushioning effect during deformation; therefore, the stress values remained unaffected or sometimes decreased for any specified combinations of \(a,h\), and \(r\) for all three steel. However, with a lower billet length (\(z<0.6\)), a large amount of confined volume deformed against the descending punch generating more stresses during forming.
## 4 Simulation Results and Discussion
### Result of Optimal Covariate Prediction  
The prediction results of the optimal covariate under the WLTP and the CLTC are shown in Figures 9 and 10, respectively. The vertical coordinates in these figures represent the optimal covariate value numbers for each operating condition, as shown in Table 4. The prediction accuracy of the predictor model was 91.44% under the WLTP and 88.17% under the CLTC. The predictor maintained a high prediction accuracy under both test conditions, which indicated that the optimal covariate predictor established had good adaptability to the operating conditions and a good prediction effect under the different operating conditions.  
The WLTP was selected to validate the A-ECMS based on covariate prediction using the ECMS as a reference. The results of the hybrid energy source power distribution under the A-ECMS based on covariate prediction and the ECMS are shown in Figures 11 and 12, respectively. Compared with the power distribution under the ECMS, the fuel cell stack output power in the A-ECMS was more continuous, with fewer fluctuations and peaks, which was conducive to the improvement of the fuel cell durability.  
The trajectory of the SOC is shown in Figure 13. The SOC under the ECMS was more volatile than under the A-ECMS, and the A-ECMS could effectively control the SOC fluctuation range due to the real-time adjustment of its equivalence factor. In addition, the A-ECMS could stably adjust the SOC back to around 0.6 at the end of the simulation to maintain stable battery power.  
Figure 10: Prediction of optimal covariate under China Light-Duty Vehicle Test Cycle (CLTC).  
Figure 9: Prediction of optimal covariate under Worldwide Harmonized Light Vehicle Test Procedure (WLTP).  
The trajectory of the equivalent hydrogen consumption is shown in Figure 14. The final value of equivalent hydrogen consumption was 355.9 g under the ECMS and 350.6 g under the A-ECMS, and the value for the A-ECMS was thus 1.5% lower. The value was smaller because certain prediction errors could easily occur in the process of speed prediction and determination of the equivalence factor, and the A-ECMS needed to consider the maintenance of the battery power while optimizing the overall vehicle economy to reduce the fluctuations of the battery SOC. However, the equivalence factor selected under the  
Figure 11: Hybrid energy source power allocation result under adaptive equivalent consumption minimization strategy (A-ECMS).  
Figure 12: Hybrid energy source power allocation result under equivalent consumption minimization strategy (ECMS).  
Figure 13: State of charge (SOC) trajectories under ECMS and A-ECMS.  
ECMS was the best equivalence factor under the test conditions, so the optimization effect of the A-ECMS was not significant enough under this condition.
## 4 Closed Solutions
If the effect of thickness stretching is neglected and the same procedure as IRPT is used, the closed solutions of RPT can be obtained from the following equation. \[\left(\left[ \begin{array}{cccc}j_{11}&j_{12}&j_{13}&j_{14}\\ j_{12}&j_{22}&j_{23}&j_{24}\\ j_{13}&j_{23}&j_{33}&j_{34}\\ j_{14}&j_{24}&j_{34}&j_{44}\end{array} \right]-\omega^{2}\left[ \begin{array}[] {cccc}m_{11}&0&m_{13}&m_{14}\\ 0&m_{22}&m_{23}&m_{24}\\ m_{13}&m_{23}&m_{33}&m_{34}\\ m_{14}&m_{24}&m_{34}&m_{44}\end{array} \right]\right)\left\{ \begin{array}{c}U_{ rs}\\ V_{rs}\\ W_{brs}\end{array} \right\}=\left\{ \begin{array}{c}0\\ 0\\ Q_{rs}\\ Q_{rs}\end{array} \right\} \tag{43}\]  
where  
\[ \begin{array}{l}j_{11}=A\bigg{(}\alpha^{2}+\frac{1-v_{c}^{(h)}}{2}\beta^{2} \bigg{)},j_{12}=\frac{1+v_{c}^{(h)}}{2}A\alpha\beta,j_{22}=A\left(\frac{1-v_{c }^{(h)}}{2}\alpha^{2}+\beta^{2}\right)\\ j_{13}=-B\alpha\big{(}\alpha^{2}+\beta^{2}\big{)},j_{14}=-B^{s}\alpha\big{(} \alpha^{2}+\beta^{2}\big{)},j_{23}=-B\beta\big{(}\alpha^{2}+\beta^{2}\big{)} \\ j_{24}=-B^{s}\beta\big{(}\alpha^{2}+\beta^{2}\big{)},j_{33}=D\big{(}\alpha^{2 }+\beta^{2}\big{)}^{2},j_{34}=D^{s}\big{(}\alpha^{2}+\beta^{2}\big{)}^{2}\\ j_{44}=H\big{(}\alpha^{2}+\beta^{2}\big{)}^{2}+A^{s}\big{(}\alpha^{2}+\beta^{2} \big{)},m_{11}=m_{22}=I_{0},m_{13}=-\alpha I_{1},m_{14}=-\alpha J_{1},m_{23}=- \beta I_{1}\\ m_{24}=-\beta I_{1},m_{33}=I_{0}+I_{2}\big{(}\alpha^{2}+\beta^{2}\big{)},m_{34}= I_{0}+J_{2}\big{(}\alpha^{2}+\beta^{2}\big{)}\\ m_{44}=I_{0}+K_{2}\big{(}\alpha^{2}+\beta^{2}\big{)}\\ (A,B,B^{s},D,D^{s},H)=\sum\limits_{k=1}^{NL}\int_{z_{k}}^{z_{k+1}}\big{(}1,z, f,z^{2},zf,f^{2}\big{)}\frac{E_{t}^{(k)}}{1-v_{c}^{(k)}}dz,A^{s}=\sum\limits_{k=1}^{NL} \int_{z_{k}}^{z_{k+1}}\frac{g^{2}E_{c}^{(k)}}{2(1+v_{c}^{(k)})}dz\end{array} \]  
The process of solving RPT is similar to solving IRPT.
## 3 Experiment Results and Analysis
### Analysis of the Geotextile of the Tensile Force  
Figure 7 shows the strain variation curve of the strain guages of the locations of Y1, Y3, Y4, and Y6 of the geotextile in each group. In the H3 and H4 groups, the data of the test geotextile are listed before any failure, and the strain gauge sensor are not working when \(d\) is greater than 6.7% and 5.1%, respectively. The data of the location of Y1 at the center within the subsidence area and the location of Y3 at the boundary within the subsidence area are larger than the location of Y4 and the location of Y6 within the stable area. The slight change of the location of Y6 of the tensile force of the geotextile indicates that the tensile properties of the geotextile of the location of Y6 are not fully developed. The maximum tensile force of the geotextile of H2-H4 groups is the location of Y3, and the larger the subsidence width is, the larger the tensile force is. For \(d\) = 0-5%, the values of the strain gauges at each position of the H2-H4 groups (except for the location of Y6) increase rapidly, which shows that the membrane pulling effect is fully observed. When \(d\) = 5-10%, the values of the strain gauges at each position of the H2 tends to be stable, the strain gauges at each position of the H3 group still shows an increasing trend and then rapid failure, the strain gauges at each position of the H4 group failed when \(d\) = 5.1%. It indicates that the geotextile of the H3 and H4 groups are pulled out or stretched during the test and the geotextile is prone to damage when used to control the settlement with a large subsidence width.
## 4 Discussion and Conclusions  
Photobiomodulation is a non-invasive approach that showed promising outcomes in various ocular conditions from the ocular surface to the back of the eye, thus rising to the attention of the ophthalmology scientific community. The results obtained in ocular surface diseases are supported by an extensive literature, opening the novel scenario of using this technology not only for DED/MGD treatment but also for its prophylaxis after cataract surgery. The role of PBM applied in other ophthalmic fields is more contentious. In AMD, as highlighted by [PERSON] et al. in their recent review, there are many discrepancies in the robustness of evidence across studies analyzed. Some studies lacked essential elements such as randomization, placebo controls, and adequate sample sizes. Despite examining similar parameters, variations in findings were observed due to differences in baseline conditions. Particularly, evidence regarding reduced drusen volume (DV) was deemed weak, possibly influenced by the natural cycle of drusen material deposition and resorption. Larger sample sizes and precise measurements are warranted to confirm the efficacy of photobiomodulation in reducing DV. Additionally, while drusen resorption is often perceived positively, its long-term benefits remain uncertain and may even contribute to adverse outcomes like geographic atrophy (GA) and advanced macular degeneration. Further research is crucial to understand the mechanisms underlying drusen resorption and its impact on retinal health over extended periods [89].  
The application of PBM in treating diabetic macular edema remains a distant prospect, particularly considering the discouraging results from the DCRC.  
Regarding the effectiveness of PBM for treating amblyopia, there is controversy over its scientific merit. Despite yielding promising results, the scientific community has not given them much weight, as they have not led to further similar or follow-up studies. We decided to include them in our review for two reasons: to ensure completeness and to cite the studies that later inspired additional research on treating myopia.  
It is important to emphasize that currently the literature does not provide sufficient data to assert with certainty the potential role of PBM in the treatment of various ocular pathologies. We cannot exclude long-term side effects or assert that the reassuring results obtained in various studies will persist over time.  
In the near future, standardized protocols in terms of number and interval of sessions, parameters used, and light type, along with strict criteria for patient selection, will allow us to provide more robust evidence of the role of PBM in the fields of ocular diseases.  
This research received no external funding.  
The authors declare no conflicts of interest.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 4 Proposed Scheme
### Clustering Setup  
IoT-based sensor nodes are widely placed in a diverse range of smart city applications. Fog nodes are placed in different locations of smart cities for fast processing. These diversely placed wireless networks are divided into \(n\) number of small clusters and a fog node must be a part of one of these clustering network. Each of the sensor node must join one of the clustering networks.  
A node \(N_{i}\) joins a cluster in such a way that the nodes which are in close vicinity of fog nodes are placed in one cluster. If \(N_{i}\) finds fog nodes \(F_{a}\) and \(F_{b}\) in its close vicinity, then it will prefer to become a member of fog node \(F_{a}\), provided its hop count is less than the hop count of \(F_{b}\) node. In case, both the fog nodes are accessible with the same hop count, then priority will be given to the one that has less number of member nodes.