esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Longitudinal Dynamic Models  
Basic vehicle dynamics can be defined by car-following models, which describe the longitudinal dynamics of a manually operated vehicle based on observations of the vehicle itself and vehicles in front. A standard car-following model is as follows:  
\[a_{i}=f\left(h_{i},\dot{h}_{i},v_{i}\right) \tag{3}\]  
where \(a_{i}\) is the acceleration of vehicle \(i,f()\) is a nonlinear function, and \(v_{i}\), \(\dot{h}_{i}\), and \(h_{i}\) are the velocity, relative velocity, and headway of vehicle \(i\), respectively.  
In this work, we apply the IDM, which is a type of ACC system, for the longitudinal control of human-driven vehicles due to its capacity to depict realistic driver behavior [7]. The IDM is a commonly used car-following model. In the IDM's acceleration command, the speed of a vehicle in a non-signalized intersection environment and the identification (ID) and headway of the leading vehicle can be set to be obtained by the \"get\" methods. The acceleration of the vehicle is calculated as follows.  
\[a_{IDM}=a\left[1-\left(\frac{v}{v_{0}}\right)^{\delta}-\left(\frac{s^{*}(v, \Delta v)}{s}\right)^{2}\right] \tag{4}\]  
where \(a_{IDM}\) is the acceleration of the vehicle, \(v_{0}\) is the desired speed, \(\delta\) is an acceleration exponent, \(s\) is the vehicle's headway (the distance to the vehicle ahead), and \(s^{*}(v,\Delta v)\) indicates the desired headway, which is expressed by:  
\[s^{*}(v,\Delta v)=\ s_{0}+max\left(0,vT+\frac{v\Delta v}{2\sqrt{ab}}\right) \tag{5}\]  
where \(S_{0}\) denotes the minimum gap, \(T\) denotes \(a\) time gap, \(\Delta v\) denotes the velocity difference compared with the lead vehicle (current velocity-lead velocity), \(a\) denotes an acceleration term, and \(b\) denotes comfortable deceleration.  
The typical parameters of an IDM controller for city traffic are represented in Table 1 based on [41].
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Hybrid Neural Networks  
Standalone models often focus on extracting either temporal or spatial features. To model both spatial and temporal correlations in traffic data simultaneously, deep learning hybrid models integrate statistical analysis, machine learning, and deep learning techniques for spatiotemporal data processing. These hybrid models aim to overcome the limitations of single models, enhancing both the accuracy and robustness of traffic flow predictions. By combining various models and techniques, hybrid models can more comprehensively account for the spatiotemporal characteristics of traffic flows, providing stronger support for traffic management and planning. These hybrids primarily include categories such as CNNs, RNNs, LSTM, and GCNs. The computational complexity, interaction dynamics, and adaptability of the combined components collectively influence the overall network's computational overhead.  
In recent years, the decomposition-reconstruction (DR) hybrid model has gained significant attention from researchers. [PERSON] et al. classified DR-based models and thoroughly studied their applications in this field. A hybrid model based on DR for short-term traffic state prediction is shown in Figure 10[37]. [PERSON] et al. [38] proposed an attention mechanism-based spatiotemporal graph convolutional network (ASTGCN) model, which simultaneously models the three temporal attributes of traffic flows while using graph convolutional models and standard convolutions to extract spatial and temporal features. Furthermore, this model employs a spatiotemporal attention mechanism to effectively capture the dynamic spatiotemporal correlations in traffic data. Experimental results on the PEMS dataset show that the prediction performance of the ASTGCN surpasses that of other baseline models.
## 4 Results and Discussion
### Limitation of the Study  
Owing to the consideration of just English-language research, the selected search terms, and repository restrictions, it is possible that certain relevant papers were missed regardless of the thorough search throughout databases. Valuable data can be obtained in non-peer-reviewed papers, unpublished term papers, and dissertation studies.
## 5 Discussion  
The performance of the proposed ensemble approach was evaluated using the PSNR, SSIM, and MSE metrics. PSNR quantifies the denoised image quality relative to the original noisy image. It measures the ratio between the maximum signal power of the original image and the noise power affecting the denoised image. A higher PSNR score signifies greater image fidelity. The SSIM values ranging between 0 and 1 mean that 1 ideally matches the reconstructed image with the original image. The ensemble approach effectively denoised within a computational time of 0.01666 s, optimizing the trade-off between model performance and resource consumption. The updated learning rate of 0.00238 struck an improved balance between convergence speed and optimization stability, enhancing the overall training efficiency. The training for 47 epochs ensured comprehensive weight optimization and reduced the risks of underfitting or overfitting. Using 846 steps per epoch and a batch size of 45 \(\times\) 45 facilitated faster convergence while managing the computational demands. The PSNR metric validated the high quality of our denoised images, as evidenced by elevated PSNR scores indicating substantial image fidelity. The proposed approach performed better than previous experiments in [24; 56], whereby our proposed approach achieved PSNR values of 34.7585, 31.6760, 29.2267, 21.4910, and 27.6377, respectively.
## 6 Future Research Directions
### Multi-Cell NOMA System  
In NOMA systems, most researchers considered a single cell, and very few addressed the multi-cell concept because the multi-cell system causes inter-cell interference. The interference will affect the performance of the weak users at the cell edge. Therefore, to solve the interference issues along with the pairing and decoding issues, a small cell concept of 5G is to be included in the multi-cell NOMA system. This is the future challenge in the multi-cell NOMA systems to be addressed.
# The Combined Effect of Indoor Air Quality and Socioeconomic Factors on Health in Northeast China
## 4 Discussion
### Link Between Socioeconomic Factors, Indoor Air Quality, and Health
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Content**} & \multicolumn{4}{c}{**Mean \(\pm\) SD**} \\ \cline{3-6}  & & **PM\({}_{2.5}\)** & **CO\({}_{2}\)** & **TVOC** & **HCHO** \\  & & **(ug/m\({}^{3}\))** & **(ppm)** & **(mg/m\({}^{3}\))** & **(ug/m\({}^{3}\))** \\ \hline \multirow{3}{*}{Education} & Master, PhD, or specialization (26) & 42.67 \(\pm\) 20.16 & 954.19 \(\pm\) 398.83 & 0.23 \(\pm\) 0.13 & 21.57 \(\pm\) 13.35 \\  & University or Professional (74) & 47.04 \(\pm\) 28.57 & 986.47 \(\pm\) 464.41 & 0.23 \(\pm\) 0.10 & 21.44 \(\pm\) 11.20 \\  & Middle school or less (51) & 51.35 \(\pm\) 29.83 & 1100.48 \(\pm\) 581.42 & 0.33 \(\pm\) 0.26 & 23.10 \(\pm\) 15.34 \\ \hline \multirow{3}{*}{Income} & High (47) & 38.19 \(\pm\) 25.32 & 897.54 \(\pm\) 369.73 & 0.23 \(\pm\) 0.12 & 19.49 \(\pm\) 13.24 \\  & Middle (56) & 42.98 \(\pm\) 18.80 & 982.74 \(\pm\) 402.66 & 0.21 \(\pm\) 0.12 & 25.56 \(\pm\) 15.43 \\  & Low (48) & 62.71 \(\pm\) 32.51 & 1180.05 \(\pm\) 669.20 & 0.35 \(\pm\) 0.25 & 21.35 \(\pm\) 10.99 \\ \hline \multirow{3}{*}{Occupantion} & High (52) & 43.35 \(\pm\) 22.08 & 928.67 \(\pm\) 414.64 & 0.23 \(\pm\) 0.11 & 23.31 \(\pm\) 14.87 \\  & Middle (50) & 49.81 \(\pm\) 30.19 & 1051.38 \(\pm\) 475.13 & 0.24 \(\pm\) 0.10 & 22.42 \(\pm\) 13.92 \\ \cline{1-1}  & Low (49) & 49.88 \(\pm\) 30.08 & 1083.10 \(\pm\) 588.81 & 0.32 \(\pm\) 0.27 & 21.04 \(\pm\) 12.20 \\ \hline \hline \end{tabular} Note: SD-Standard deviation.  
\end{table} 
Table 5: The PM\({}_{2.5}\), CO\({}_{2}\), TVOC, and HCHO concentration in the different education, income and occupation level.
## 3 Methodology
### Training of the SEVnet2d Segmentation Network
cause variance in the model and calculate the gradients that guide the optimization of the model parameters. Using the Adam optimizer, we iteratively adjusted the weights and biases, reducing the cost function. To measure the performance of each of the proposed SEVnets and other methods, we experimented with confusion matrix and Dice loss as segmentation evaluation metrics. Both are more classic and general indicators. The specific calculation formula is as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[Pre=\frac{TP}{TP+FP} \tag{6}\]  
\[Re=\frac{TP}{TP+FN} \tag{7}\]  
\[Spe=\frac{TN}{TN+FP} \tag{8}\]  
\[Dice=\frac{2\times TP}{2\times TP+FN+FP} \tag{9}\]  
In these equations, TP is a true sample, that is, the model predicts a positive sample, and the actual one is also a positive sample. FP is a false positive sample, that is, the model predicts a positive sample, but it is actually a negative sample. FN is a false negative sample, that is, the model predicts a negative sample, but it is actually a positive sample. TN is a true negative sample, that is, the model predicts a negative sample, and it is actually a negative sample. For urban aerial image segmentation with the purpose of garbage dump recognition, positive and negative samples represent the pixels of the garbage dump area and non-garbage dump area. According to the definition, Dice can be approximated as the consistency coefficient between the garbage dump area segmented by the model and the actual garbage dump area. Accuracy (ACC) can represent the proportion of correctly classified pixels in the aerial image, Precision (Pre) represents the probability of the garbage dump being correctly segmented, Recall (Re) represents the probability of the actual garbage dump area being correctly segmented, and Specificity (Spe) represents the probability of the actual non-garbage dump area being accurately segmented. Due to the importance placed on the accuracy of garbage site segmentation in this article, Dice and ACC are the most important indicators. When the difference between the two is not significant, other indicators will be considered. Table 1 shows the performance comparison of the performance indicators of the segmentation methods based on the ETVnet, Unet, Unet++, and Attention Unet models on the same dataset described above. In the case of a certain amount of data, the indicators of ETVnet are significantly optimized compared to other models. In fact, in the segmentation results of Unet, UNet++, and Attention UNet, the segmentation edges are not precise enough, and there are many over-segmentation phenomena. Some areas farther from the contour marked by experts will be classified into the predicted results. At the same time, U-Net++ shows some undersegmentation in the small area garbage dump. The edge contour of the segmentation results of this method is retained in the vicinity of the expert mark, and there is no significant error in aspect ratio and shape. The segmentation effect is more refined, which is closer to the ground reality.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fitting Denoising Method** & **MSE(m\({}^{2}\))** & **RMSE(m)** & **MAE(m)** \\ \hline Huber loss-based method & 0.00033 & 0.01383 & 0.01061 \\ Least-squares method & 0.00184 & 0.04157 & 0.03857 \\ RANSAC method & 0.00269 & 0.05098 & 0.04850 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical Table of denoising evaluation metrics for tunnel section top point cloud (60\({}^{\circ}\)-120\({}^{\circ}\)). Based on the actual fitting effect images and the calculated evaluation metrics, it can be observed that both the least-squares method and RANSAC can fit ellipses that approximately match the tunnel section. However, these two methods are significantly affected by noise, resulting in fitting outcomes that deviate considerably from the actual geometric characteristics of the tunnel lining structure. Additionally, due to the iterative nature of the RANSAC method in searching for the optimal model, its computational efficiency is relatively lower. Furthermore, both the least-squares method and RANSAC exhibit poor fitting results for the lower part of the tunnel lining structure. In contrast, the ellipse-fitting method based on the Huber loss function can effectively perform fitting and denoising for tunnel point clouds containing noise. The fitting results align closely with the tunnel lining structure, and the method maintains good computational efficiency.
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture
#### 3.1.2. Hata-Davidson Model  
The PL of this model can be calculated as:  
\[{\rm P_{L}HD=P_{L}Hata+A\ (h_{t},d)-S_{1}(d)-S_{2}(h_{t},d)-S_{3}(f)-S_{4}(f,d)} \tag{7}\]  
\[{\rm P_{L}Hata=69.55+26.16 log\ (f)-13.82\ log\ (h_{t})-a(h_{r})+[44.9-6.55\log (h_{t})]log\ (d)} \tag{8}\]  
The PL of this model can be calculated in the case of an urban area as:  
\[{\rm a(h_{r})=(3.2[log\ (11.75\times\ h_{r})]^{2})-4.9} \tag{9}\]  
The PL of this model can be calculated in the case of suburban or rural areas as:  
\[{\rm a(h_{r})\ =(1.1\log(f)-0.7)(h_{r})-(1.56\ log\ (f)-0.8)} \tag{10}\]  
where \({\rm P_{L}HD}\) is Path Loss of Hata-Davidson (dB), \({\rm P_{L}Hata}\) is Path Loss of Hata Model (dB), \({\rm a(h_{r})}\) is Correction Factor for mobile antenna height, \(f\) is Carrier Frequency (GHz), \({\rm h_{t}}\) is Transmitter Antenna Height (km), \({\rm h_{r}}\) is Receiver Antenna Height (km), \({\rm d}\) is Distance of Transmission (km), \({\rm A}\), \({\rm S_{1}}\) are factors that extend distance to 300 km, \({\rm S_{2}}\) is correction factor for height \({\rm h_{t}}\) of base station antenna extending the value of \({\rm h_{t}}\) to 2.5 km, and \({\rm S_{3}}\), \({\rm S_{4}}\) are correction factors that extend frequency bands.
## 3 Results  
The data obtained from the test after the exposure of the lymphocytes to different concentrations of MMS, MH, NDEA, and 2,4-D were arbitrarily organized in MG with differences of 1 \(\upmu\)c (Figure 1A-D).  
In each case, the negative control (Figure 2, Table 1) always showed a significant lower number of MG (\(p<0.05\)). On the contrary, MMS, MH, 2,4-D, and NDEA induced the largest number of groups in proportion to the concentration (Figure 2). The comparison between the number of MG and the genotoxic agents and their different concentrations did not show significant differences (\(p>0.05\)). However, modes were different among the genotoxic agents but not within the concentrations of a same genotoxic (Table 1).  
Figure 1: Migration groups and number of comets observed per group after exposure to three different concentrations of MMS (**A**), MH (**B**), NDEA (**C**), and 2,4-D (**D**). Each peak corresponds to a migration group with a different number of comets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & \\ \hline \multicolumn{12}{c}{**Experimental Groups**} \\ \hline
**Migration** & \multirow{2}{*}{**NC**} & **MMS** & **MMS** & **MMS** & **MH** & **MH** & **MH** & **MH** & **2.4-D** & **2.4-D** & **2.4-D** & **NDEA** & **NDEA** & **NDEA** \\
**in** \(\mathbf{\upmu c}\) & & & **2.5 mM** & **5 mM** & **10 mM** & **5 mM** & **20 mM** & **45 mM** & **0.5 mM** & **1 mM** & **2 mM** & **5 mM** & **20 mM** & **40 mM** \\ \hline
0 & 1 & & 1 & & & & & & & & & & & 1 & \\ \hline  
1 & & & 1 & & 3 & 1 & 6 & & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Migration groups and number of comets generated in each one caused by the exposure to MMS, MH, 2,4-D, and NDEA. The groups with the three largest numbers of comets (modes) are indicated in grey \(>\) blue \(>\) orange.  
Figure 2: Average migration of DNA and number of migration groups observed in lymphocytes exposed to different concentrations of MMS, MH, 2,4-D, and NDEA. On the left, the average DNA migration is shown, and on the right side, the number of cells in each (comets) are indicated.  
A clear relation between the average genetic damage, the total number of migration groups, and the concentration was observed for MMS and MH (Figure 1A,B). However, no relation was observed for 2,4-D and NDEA (Figure 1C,D). All MG and number of comets generated after exposure to MMS, MH, 2,4-D, and NDEA are shown in Table 1. Each migration group represents a CGD. The groups with the highest number of comets (modes) are highlighted in color. The negative control always showed the lowest number of MG with 16, and modes of 6, 4, and 5. Regardless of their concentration, MMS and MH showed modes of 6, 7, and 8.. 2,4-D showed modes of 4, 5, 6, and even. NDEA generated modes of 2, 8, 11, 6, 8, 9, and 6, 7, 8 for the concentrations of 5, 20, and 40 mM, respectively.
## 3 Proposed Algorithm
### Access Collision Probability  
The probability of an access collision of vehicle \(i\) occurring at time \(t\) is defined as \(P_{AC}(i,t)\) and is calculated as  
\[P_{AC}(i,t)=A_{ac}\cdot\frac{2R}{lw}\cdot P_{ac}(i,j,t). \tag{13}\]  
The probability that vehicle \(i\) will have an access collision due to vehicle \(j\) at time \(t\) is given by  
\[P_{ac}(i,j,t)=(1-Pr\{N_{e}>A_{ac}\})\cdot Pr\{n_{i}(t)=n_{j}(t)\}. \tag{14}\]  
The probability that the number of empty slots is larger than the number of vehicles attempting to occupy the new resource is given by  
\[Pr\{N_{e}>A_{ac}\}=\begin{cases}\Delta t(\frac{1}{N_{e}})^{A_{ac}-1}&\text{ if }A_{ac}>1\\ 0&\text{otherwise}\end{cases}. \tag{15}\]  
The probability that vehicle \(i\) and \(j\) occupy the same time-slot becomes  
\[Pr\{n_{i}(t)=n_{j}(t)\}=\frac{1}{N}. \tag{16}\]  
Substituting (15) and (16) into (14), the probability of access collision is calculated by  
\[P_{ac}(i,j,t)=\frac{1}{N}(1-\Delta t(\frac{1}{N_{e}})^{A_{ac}-1}). \tag{17}\]  
Figure 10: Illustration of the probability that the distance between vehicles \(i\) and \(j\) are shorter than \(2R\) at time \(t+\Delta t\).
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 3 Adoption of Digital Twin Technology for the Marine Sector
### Ship Digitalization  
Along with the electrification process, digitalization has also become a key innovative trend in every operation in the maritime sector due to its efficiency, effectiveness, superlative performance, etc. New tools in designing, performance evaluation, simulation, and information safety are being introduced and the same is being improved day by day, which further assists in producing more robust models. As per the digitalization service development framework proposed by [PERSON], the process of digitalization of the maritime sector can be achieved through two different approaches [32].  
* Service-driven Perspective: Considering the ship owners' specific requisites in operation and decision making, build is performed in implementation of hardware and software that suits the ship. It is operating in the need-space, where tactical-level improvements are more considered.
* Sensor-driven Perspective: It will consider the available inputs from the already installed sensors in a particular ship to design a digitalized framework to support its operation. This method operates through the solution-space, which is the key doorway for future DT-based systems.  
The marine industry will be able to gain the full merits of digitalization through the service-driven approach starting from the design phase of the vessel. However, existing platforms can be improved through the second approach to par with the present smart functions. Digitalization is further considered an effective, innovative, and optimized method enabling products and services as per Industry 4.0 [33]. Asset-intensive industries such as shipping, oil, gas, energy, etc., are looking for innovations that increase efficiency and reduce the cost while effectively managing operational risks and security. Main configurations such as the navigation system, power system and automation/control system are becoming fully digitalized, leaving the old analogue systems obsolete. Integrated Platform Management Systems (IPMS) available in present ships can link all the above system architectures into a centralized topology, thus providing a more ergonomic operation and increased domain awareness for all stakeholders.
## 3 Results and Discussions
### RSM Hardness Modeling
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Sum of Squares** & **Mean Squares** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 8606.06 & 956.23 & 4.40 & 0.0318 & Yes \\ A-P & 61.33 & 61.33 & 0.2822 & 0.6117 & No \\ B-v & 347.95 & 347.95 & 1.6 & 0.2463 & No \\ C-IP & 68.16 & 68.16 & 0.3136 & 0.5930 & No \\ A\({}^{2}\) & 1546.94 & 1546.94 & 7.12 & 0.0321 & Yes \\ B\({}^{2}\) & 1149.83 & 1149.83 & 5.29 & 0.0550 & No \\ AB\({}^{2}\) & 1288.44 & 1288.44 & 5.93 & 0.0451 & Yes \\ AB & 1.96 & 1.96 & 0.0090 & 0.0090 & Yes \\ AC & 1226.40 & 1226.40 & 5.64 & 0.0492 & Yes \\ BC & 1693.48 & 1693.48 & 7.79 & 0.0269 & Yes \\ Residual & 1521.57 & 217.37 & & & \\ Lack of fit & 1521.57 & 507.19 & 5.64 & 0.2819 & No \\ Pure error & \textless 0.0001 & \textless 0.0001 & & & \\ \hline \end{tabular}
\end{table} 
Table 6: ANOVA of the hardness of the hardened layer predicted by RSM model (induction frequency = 10 kHz). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Sum of Squares** & **Mean Squares** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 26,996.43 & 2999.6 & 7.73 & 0.0067 & Yes \\ A-P & 1879.80 & 1879.80 & 4.84 & 0.0637 & No \\ B-v & 5531.42 & 5531.42 & 14.25 & 0.0069 & Yes \\ C-IP & 936.25 & 936.25 & 2.41 & 0.1644 & No \\ A\({}^{2}\) & 1717.72 & 1717.72 & 4.42 & 0.0735 & No \\ B\({}^{2}\) & 69.09 & 69.09 & 0.1780 & 0.6858 & No \\ AB\({}^{2}\) & 6612.44 & 6612.44 & 17.03 & 0.0044 & Yes \\ AB & 674.44 & 674.44 & 1.74 & 0.2290 & No \\ AC & 18.32 & 18.32 & 0.0472 & 0.8342 & No \\ BC & 11,285.69 & 11,285.69 & 29.07 & 0.0010 & Yes \\ Residual & 2717.43 & 388.2 & & & \\ Lack of fit & 2717.43 & 905.81 & 8.74 & 0.2617 & No \\ Pure error & 29,713.87 & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: ANOVA of the hardness of the hardened layer predicted by RSM model (induction frequency = 15 kHz). the MAE is 10.635 HV\({}_{0.3}\), and the MAPE is 1.36%. The final response regression prediction of the quadratic polynomial model of the actual parameters is established as follows:  
\(HV_{0.3}=-39389.1191+12.64266\times P+25591.18202\times v+26.11855\times IP-7.634 19\times P\times v-4.76\times 10^{-4}\times P\times IP-8.71768\times v\times IP-2.24 \times 10^{-4}\times P^{2}-4309.433\times v^{2}+1.29758\times P\times v^{2}\)