esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we exploited multi-dimensional information to consider VLC user communication states. With NOMA, we introduced strong relevant features, including channel gains, the desired data rate, and outage probability. Using multiple features in FL systems, we comprehensively performed NOMA GRPA based on dynamic user priority and conducted a tradeoff between throughput and QoS fairness maximization. First, for the system throughput and fairness, the AUDR of our proposed method (NOMA FL) was shown to be greater than that of PLS in UDN conditions and OFDMA FL without limited UDN conditions. Second, although the AUDR of NOMA FL approached that of FTPA, our method showed better results in user satisfaction than those of three other studies. Finally, in a small-cell illumination scenario with UDN conditions, the AUDR of our method may achieve the best performance. In future work, the relationship between the semi-angle of the LED and NOMA-VLC FL should be investigated. A performance analysis of the interference components in FL could be also developed.  
All authors contributed to this paper. [PERSON] and [PERSON] proposed the idea. [PERSON] and [PERSON] developed the framework of this paper. [PERSON] performed the experiments and analysed the simulation data. [PERSON], [PERSON], and [PERSON] provided the laboratory and financial funding support. [PERSON] prepared the original draft of this paper. [PERSON], [PERSON], [PERSON] and [PERSON] revised this paper.  
This research was funded by National Natural Science Foundation of China (Grant No.61671477).  
Figure 10: Comparisons of AUDR with varied field of views (FOV) and QoS expectations for 30 Mbps.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (No.61671477). The authors wish to acknowledge the valuable technical support of [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review
### Background of the Research Related to the Proposed Method
In VIKOR, the multi-criteria ranking indices to evaluate the parameter values of the maximum \"group utility of the majority\" and the minimum \"individual regret of the opponent\" are determined to rank and select the optimal solution. For example, [PERSON] and [PERSON] [30] combined the fuzzy VIKOR approach and SWARA to select the most appropriate lean techniques to eliminate the waste in construction processes. [PERSON] et al. [31] proposed a multi-criteria decision-making framework for construction project management, which combined a picture fuzzy normalized projection model and the VIKOR method under fuzzy environment. [PERSON] et al. [32] developed a risk assessment approach using the Pythagorean fuzzy-numbers-based VIKOR method for underground mining projects, which significantly improves the overall safety levels. Accordingly, the safety planners should thoroughly evaluate PPE performance from many aspects, taking a variety of factors into account, such as safety, productivity, and comfort. Therefore, establishing the assessment criteria hierarchy and determining their weights can be a difficult and challenging problem. PPE planning can be considered an MCDM problem for determining the optimal PPE plan among various PPE types. In order to conduct a comprehensive evaluation of PPE performance, this research focuses on three problems: the identification of assessment criteria, their corresponding weight determination, and the development of a novel integrated model. A set of main criteria and sub-criteria was identified by summarizing the criteria based on the previous research and the safety regulations to implement the PPE planning process. As significant variables in the MCDM problems, the weights should be determined for the selection of an appropriate PPE plan. Furthermore, an integrated MCDM model based on ANP and VIKOR in a fuzzy environment is proposed to choose the most suitable PPE alternative plan for the construction project. Based on the proposed model, the method would be beneficial for construction managers, safety planners, and work crews on the jobsite.
## 4 Discussion
### Determination of MinPts in Clustering  
In this paper, we set four kinds of MinPts: 2, 5, 8, and 11. The total experiment time was 180 days. The number of clusters was recorded every five days. The curve fitted by the recording point is shown in Figure 13. When MinPts = 2, the number of clusters kept growing and did not reach a stable state. When MinPts = 5, the number of clusters fluctuated greatly, but began to show a stable trend. When MinPts = 8 and 11, the number of clusters could reach a stable state through a period of clustering, but the time for MinPts = 8 to reach a stable state, about 40 days, was shorter than that of MinPts = 11, which was about 60 days, after which the number of clusters was approximately the same. Therefore, MinPts = 8 and TWW = 40 days were the best values among the four sets. This setting not only ensured that the cluster number reached a relatively stable state after a period, but also minimized the time required to reach a stable state.  
Figure 12: Clean result of two trajectories with the same MMSI. Left picture is the Uncleaned trajectories, the lines between the dots crossing land. Right picture is the cleaned trajectories, two trajectories are separated.  
Figure 13: Dependence of the number of clusters with time for four MinPts settings. MinPts is the minimum number of trajectories to form clusters.
## 6 Simulation Results and Analysis
The figure shows that the RMSE of the discussed methods maintains a robust performance with the change in transmit power. Moreover, like the WLS-K method, the proposed WLS-U method also shows a superior performance for \(P_{0}\) from \(-60\) to \(-10\) dBm.
## 3 The Supporting Communication Protocols  
As noted in Section 1, one of the core ideas of our proposal is to disseminate the arrival times of platoons to the intersections along their routes, and to maintain that information at each intersection.  
This is a big deal with conventional communication protocols in VANETs, not so much because they have difficulties to tackle the high mobility of the nodes (the literature of VANET routing is now rich and extensive [31]), but because they do not provide the means to maintain persistent information at any given location without the support of external infrastructure. For this reason, our proposal is grounded on a virtualization layer and specific routing algorithms presented in [12; 13], adding new mechanisms to manage platoons and VTLs. The stack of protocols we use is represented in Figure 1.  
In the following subsections, we summarize the mechanisms than underpin our proposal from the virtualization layer (Section 3.1) and the network layer (Section 3.2). Further details and experimental assessments can be found at [12; 13], showing that these underlying protocols can attain good performance in urban scenarios of V2V and V2I communications.
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
###### Abstract  
Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSUs) are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.  
VANET; hybrid RF-VLC; emergency routing protocol; message type; congestion management Article An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs [PERSON], 1,*, [PERSON], 1 and [PERSON]  
1 Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON MSB 2K3, Canada; [EMAIL_ADDRESS]  
2 Department of Computer Science, Toronto Metropolitan University, Toronto, ON MSB 2K3, Canada; [EMAIL_ADDRESS]  
20 December 2020
## 2 Critical Communications Issues In Microgrids  
The design of a communication network is a critical and crucial part of a smarter grid. There is a need for an adaptive, intelligent, robust and future-proof architecture, where the network can autonomously react and take actions without human intervention [29].  
A wireless network infrastructure is distinguished by different domains [30], mainly due to its geographical size and functionality. The primary objective of each field is to support strict requirements and provide connectivity to the entire network [31]. The microgrid environment is characterized by two main domains, mainly due to geographical size and data rate, which are the home area network (HAN), which supports the communication area between last-mile users and their appliances, and the neighborhood area network (NAN). This layer is responsible for controlling and monitoring the DERs' power production, energy storage systems (ESSs) and their interconnection with the sub stations, monitoring and controlling the whole microgrid area.  
The flow of information between these domains is modeled in a centralized manner [32]. In a centralized manner, the MGCC receives data from all connected devices and takes action. Therefore, each domain has to maintain strict requirements as latency, reliability, and availability. In islanded applications, besides power consumption, which affects the microgrid survival time, latency is also essential. Latency is the time interval between an action and reaction of some event. Table 1 exemplifies all main communication features and latency requirements for microgrid operation [33; 34].  
Besides all these requirements, a communication infrastructure must obey aspects as availability, robustness, flexibility, security and a little infrastructure cost. Several known technologies have been investigated for this purpose [35].  
However, wireless communication seems to be a more appropriated candidate for microgrid environments. They are robust, flexible, reliable and require low infrastructure investment [36]. In addition, actual wireless communications standards aim to suppress the imposed communication requirements for machine type communications (MTC) [37]. Thus, Long Term Evolution (LTE) technology [38] will provide higher coverage, support low latency applications, and support a massive number of devices and scalability for different smart communications applications scenarios. Therefore, this is the communication technology considered for this research.
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 4 Results and Discussion
### Experiment Results
#### 4.1.2 Model Accuracy Results  
After conducting the training, testing, and validating for each dataset and model, the results can be seen in Table 3. The models' accuracy improved by reducing the number of features used in the training and detection. The improvement can be seen while reducing features from the original 21 features to 15. The validation accuracy improves significantly for Dataset 1, from 85% to 96%; Dataset 2, from 96.30% to 97%; Dataset 3, from 72.30% to 84.70%. Another improvement also happens when reducing the number of features further from 15 features to 10 features. While the improvement is not that significant, it can still be seen that there is a small increase in validation accuracy for Dataset 1, from 96% to 96.30%, and for Dataset 3, from 84.70% to 88.30%. At the same time, there is no improvement for Dataset 2.  
Figure 6 shows the confusion matrix for the models trained using ten features. For the model trained by Dataset 1, the most error in detection occurred on digit 7, which detects the hand sign as an 8. Furthermore, the validation result is very good for the model trained using Dataset 2. There is only a slight mistake when detecting some digit hand signs. The model trained using Dataset 3 has worse accuracy than the other two models. False detections mostly occurred on digits 0, 2, and 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Selected Features** & **Selected Features (Landmark Points)** \\ \hline
15 Features & 1, 2, 3, 4, 7, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20 \\ \hline
10 Features & 2, 3, 4, 8, 11, 12, 15, 16, 19, 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature selection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Scenario** & \multicolumn{3}{c}{**Features**} \\ \cline{3-5}  & & **Original 21 Features** & **15 Features** & **10 Features** \\ \hline \multirow{3}{*}{Dataset 1} & Training & 95.05\% & **98.41\%** & 98.30\% \\ \cline{2-5}  & Testing & 91.70\% & **93.40\%** & **93.40\%**  
Figures 7-9 show some sample digit hand-sign detection results from the models trained using 10 features. From the examples, the angle where the hand is tilted affects the detection accuracy. This could result from the distortions in the training data when Mediapipe gave a distorted hand detection result for training images that are rotated to a certain degree. These insufficient data may mislead the model to learn the wrong patterns, which results in a decrease in detection accuracy.  
Figure 6: Confusion matrix of the models introduced with ten features: (**a**) trained used Dataset 1; (**b**) trained using Dataset 2; (**c**) trained using Dataset 3.  
Figure 7: Prediction result using Dataset 1 and 10 features: (**a**) correct prediction; (**b**) incorrect prediction.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 5 Deflection Temperature Modelling
### Temperature Correction Model for the Deflection Basin
#### 5.2.1 Temperature Correction Based on Load Center Deflection Difference  
This method looks for the relationship between the difference before and after the temperature correction of deflections at other radial distances and the load center deflection difference based on the difference between the measured load center deflection and the calculated deflection at the load center after modulus correction.  
The plots of the deflection difference at the load center and each point at the radial distance from 20 to 90 cm were analyzed, and it was found that there was an obvious linear relationship, so the proportional function was used for fitting. The fitting results at different radial distances are shown in Figure 8. The ratio \(k\) and correlation coefficient (\(R^{2}\)) of the deflection difference at each point and the load center are obtained, and the results are shown in Table 2.  
Then, the deflection temperature correction model at different radial distances in the temperature-affected range is obtained.  
\[\left\{\begin{array}{l}d_{0}=10^{0.004644(20-T)}\times d_{0T}\\ d_{20}=0.4573\times(d_{0}-d_{0T})+d_{20T},R^{2}=0.8374\\ d_{30}=0.3430\times(d_{0}-d_{0T})+d_{30T},R^{2}=0.789\\ d_{45}=0.2651\times(d_{0}-d_{0T})+d_{45T},R^{2}=0.7858\\ d_{60}=0.2148\times(d_{0}-d_{0T})+d_{60T},R^{2}=0.7622\\ d_{90}=0.1399\times(d_{0}-d_{0T})+d_{90T},R^{2}=0.66\end{array}\right. \tag{8}\]  
where \(d_{0}\), \(d_{20}\), \(d_{30}\), \(d_{45}\), \(d_{60}\) and \(d_{90}\) are the temperature-corrected deflections at radial distances of 0, 20, 30, 45, 60 and 90 cm, respectively. \(d_{0T}\), \(d_{20T}\), \(d_{30T}\), \(d_{45T}\), \(d_{60T}\) and \(d_{90T}\) are the measured deflections at radial distances of 0, 20, 30, 45, 60 and 90 cm, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Radial** & **20** & **30** & **45** & **60** & **90** \\
**Distance/cm** & & & & & \\ \hline \(k\) & 0.4573 & 0.3430 & 0.2651 & 0.2148 & 0.1399 \\ \(R^{2}\) & 0.8374 & 0.789 & 0.7858 & 0.7622 & 0.66 \\ \hline \end{tabular}
\end{table}
Table 2: Adjustment factors for deflection temperature at different radial distances.  
Figure 7: Load center deflection fitting results.
## 3 Results and Discussion
### Influence of Other Parameters
#### 3.5.3 Temperature of Cooling Surface  
The temperature distribution inside the frost crystals is mainly affected by the temperature of the cooling surface, which determines the potential barrier in the initial period of frost nucleation. The frost crystal morphologies under different temperatures of cooling surfaces (\(T_{\rm w}=-20\)\({}^{\circ}\)C, \(-28\)\({}^{\circ}\)C, \(-38\)\({}^{\circ}\)C) at 10 min are shown in Figure 12. When the temperature of the cooling surface increases, the frost crystals are hindered in a vertical direction due to the insufficient driving force of frosting, whereas they become stronger in the lateral direction. The top surface of the frost crystal becomes rounded and blunt. The change is very obvious from needle to columnar shape in lower working pressure (0.50 \(\times\) 10\({}^{5}\) Pa) around the cooling surface. In addition, the larger diffusion coefficient of water vapor leads to obvious lateral growth with the decrease in working pressure around the cooling surface.  
Figure 11: (**a**) Frost thickness; (**b**) frost accumulation; (**c**) frost density with working pressure around cooling surface under different temperatures of humid air at 60 min.  
Figure 12: Frost crystal morphologies with working pressure around cooling surface under different temperatures of cooling surfaces at 10 min.  
The frost thickness and frost accumulation under different temperatures of cooling surfaces at 60 min are shown in Figure 13a,b. The frost thickness and frost accumulation decrease with the higher temperature of the cooling surface. When the temperature of the cooling surface increases from \(-\)38 \({}^{\circ}\)C to \(-\)20 \({}^{\circ}\)C, the surface temperature of frost crystal rises. The heat transfer is deteriorated with the higher temperature of the cooling surface due to a smaller temperature difference. It means that fewer water vapor molecules diffuse into the surface of the frost crystal, making it more difficult to form the frost branches which create more gaps between the frost crystals. Therefore, the frost density increases with a higher temperature of the cooling surface, as shown in Figure 13c. Comparing the absolute differences of frost thickness, frost accumulation, and density from low to high pressure (0.25 \(\times\) 10\({}^{5}\) Pa to 2.00 \(\times\) 10\({}^{5}\) Pa), they lessen from 1.00 mm to 0.55 mm, 0.043 g to 0.011 g and 105.75 kg m\({}^{-3}\) to 48.29 kg m\({}^{-3}\), respectively, when the temperature of the cooling surface increases from \(-\)38 \({}^{\circ}\)C to \(-\)20 \({}^{\circ}\)C.
## 2 Methodology  
### Direct Grey Forecasting Model  
The DGM(2,1) model is a single sequence, second-order linear dynamic model and filled by differential equations. The DGM(2,1) model is constructed by using the relationship between the original sequence and its inverse accumulating sequence. The formula of DGM(2,1) is presented as follows [1; 4; 15].  
Step 1: Suppose an original series with n entries is as follows:  
\[x^{(0)}=\left\{x^{(0)}(1),x^{(0)}(2),\ldots,x^{(0)}(k),\ldots x^{(0)}(n)\right\} \tag{1}\]From the original series \(x^{(0)}\), a new series \(x^{(1)}\) can be generated using a one-time accumulated generating operation (1-AGO), which is  
\[x^{(1)}=\left\{x^{(1)}(1),x^{(1)}(2),\ldots,x^{(1)}(n)\right\} \tag{2}\]  
where \(x^{(1)}(k)=\sum\limits_{i=1}^{k}x^{(0)}(i)\;k=1,2,\ldots,n\).  
The new sequence \(x^{(1)}\) is used to construct the whitenzation equation of the DGM(2,1) model by setting up a second-order differential equation directly.  
\[\frac{d^{2}x^{(1)}}{dt^{2}}+a\frac{dx^{(1)}}{dt}=b \tag{3}\]  
where \(a\) is the developing coefficient and \(b\) is the grey input coefficient. The coefficients \(a\), \(b\) can be calculated using the least-squares method as shown below:  
\[\hat{H}=\left[a,b\right]^{T}=\left(B^{T}B\right)^{-1}\!\!B^{T}Y \tag{4}\]  
where  
\[B=\left[\begin{array}{ccc}-x^{(0)}(2)&&1\\ -x^{(0)}(3)&&1\\ \ldots&&\ldots\\ -x^{(0)}(n)&&1\end{array}\right] \tag{5}\]  
\[Y=\left[\begin{array}{ccc}x^{(0)}(2)-x^{(0)}(1)&&1\\ x^{(0)}(3)-x^{(0)}(2)&&1\\ \ldots&&\ldots\\ x^{(0)}(n)-x^{(0)}(n-1)&&1\end{array}\right] \tag{6}\]  
According to Equation (3), the time response function can be given using  
\[\hat{x}^{(1)}(k+1)=\left(\frac{\hat{b}}{\hat{a}^{2}}-\frac{x^{(0)}(1)}{\hat{a }}\right)e^{-ak}+\frac{\hat{b}}{\hat{a}}(k+1)+(x^{(0)}(1)-\frac{\hat{b}}{\hat{ a}})\frac{1+\hat{a}}{\hat{a}} \tag{7}\]  
where  
\[\left\{\begin{array}{ccc}\hat{x}^{(0)}(1)=\hat{x}^{(1)}(1)\\ \hat{x}^{(0)}\left(k+1\right)=\hat{x}^{(1)}\left(k+1\right)-\hat{x}^{(1)} \left(k\right)=(\frac{b}{a^{2}}-\frac{\hat{x}^{(0)}(1)}{a})(1-e^{a})e^{-ak}+ \frac{b}{a}\end{array}\right. \tag{8}\]  
From Equation (4), the predicted sequences of the DGM(2,1) can be obtained through the following:  
\[\hat{x}^{(0)}=\left\{\hat{x}^{(0)}(1),\hat{x}^{(0)}(2),\hat{x}^{(0)}(3), \ldots,\hat{x}^{(0)}(n)\right\} \tag{9}\]
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 2 Related Work
The fuzzy logic system is utilized for CHs selection in EAUC and it comprehensively considers the features of sensors such as residual energy, position and the number of neighbors. The output of the fuzzy logic system is the possibility for a sensor node to be select as a CH. The energy-efficient unequal clustering (EEUC) [24] Mechanism is an unequal clustering algorithm. In EEUC, the size of each cluster is calculated by the distance between its corresponding CH and the sink. CHs are elected via competition and the competition range enlarges with distance increasing. Using this method, clusters close to the sink generally own less members and the energy used for intracluster communication is reduced. Unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H) [25] is a multiple CH-based clustering algorithm. The size of the cluster in UCR-H is contrary to EEUC. Clusters close to the sink own more sensor nodes. Meanwhile, multiple CHs are selected in one cluster to ease the burden of forwarding. The optimal number of clusters is calculated by linear programming. The energy degree distance unequal clustering algorithm (EDDUCA) [26] partitions the network using Sierpinski Triangle. The triangle of the outer of the sensor field generally contains more sensor nodes.