esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Performance Evaluation  
To test the performance of the presented GA, for the 20 cases in Table 3, comparisons are made between GA and the upper bound of the average number of wafers being produced in each robot cycle. Let UB denote such an upper bound. For Step \(i\) with \(d_{i}=1\), \(i\)\(\in\)N\({}^{+}_{n}\), a PM at the step has to process one virtual wafer after no more than \(m_{i}\) real wafers being processed. This means that in every \(m_{i}+1\) robot cycles, \(m_{i}\) real wafers are processed at most since each robot cycle completes one wafer only. Thus, the upper bound of the average number of real wafers being produced at the step is \(m_{i}/(m_{i}+1)\) in each robot cycle. Similarly, at Step \(i\) with \(d_{i}=2\), \(i\)\(\in\)N\({}^{+}_{n}\), the upper bound of the average number of real wafers being produced is \(m_{i}/(m_{i}+2)\) in each robot cycle. Then, we have  
\[\text{UB}=min(\{m_{i}/(m_{i}+1)\}\mid\ d_{i}=1,i\)\(\in\)N\({}^{+}_{n}\)\(\},\)\(\{m_{i}/(m_{i}+2)\}\mid\ d_{i}=2,i\)\(\in\)N\({}^{+}_{n}\)\(\}) \tag{11}\]  
In Table 3, GAP-G-U is the gap between the results obtained by GA and UB. It follows from the comparison results in the table that for Cases 1-5, 8, 10, 12, 13, and 20, the values of GAP-G-U equal zero, implying that GA finds the optimal solutions for these cases. For Cases 6, 7, 9, 15, 16 and 18, the values of GAP-G-U are less than 5%; while for Cases 11, 14,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multirow{2}{*}{**UB**} & \multicolumn{4}{c}{**GA**} \\ \cline{3-5}  & & **Results** & **Running Time** & **GAP-G-U** \\ \hline  
\\ \hline \multicolumn{5}{c}{Average:} & 45.09 s & 2.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results.  
17, and 19, all values of GAP-G-U are between 6% and 7%. Furthermore, the average GAP-G-U of the 20 cases is 2.15%, that is acceptable from the perspective of real applications. Moreover, the average time to find a solution by the constructed GA is just 45.09 s which is quite short. Therefore, the proposed method in this work can be put into practice.
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 3 Additive-Type Flame Retardants  
Additive-type flame retardants for rigid polyurethane foam are typically nonreactive compounds that are added directly to the foam formulation to enhance its thermal stability and flame retardancy [27; 28; 70; 71; 72]. They can be either halogenated or nonhalogenated and can be divided into various subcategories based on their chemical composition, such as phosphorus-based, nitrogen-based, or metal-based flame retardants [73]. Additive flame retardants have various benefits compared to reactive flame retardants, such as their ease of integration into foam formulations and lower costs. Nevertheless, their constrained interfacial compatibility with the matrix results in a decline in the mechanical strength and thermal conductivity of the foam [74]. Furthermore, they may present certain disadvantages, such as potential migration from the matrix, which can influence the mechanical and thermal characteristics of RPUF, in addition to environmental concerns linked to their utilization.
## 4 Results
### Model Testing on DS1 (Climate Change Dataset)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{LR} & \multicolumn{3}{c}{SVM} & \multicolumn{3}{c}{NB} & \multicolumn{3}{c}{RF} \\ \cline{2-13}  & & _Accuracy_ & _Precision_ & _Recall_ & _Accuracy_ & _Precision_ & _Recall_ & _Accuracy_ & _Precision_ & _Recall_ \\ \hline \multirow{3}{*}{**1,1**} & **LIWC** & 78.02 & 76.83 & 80.99 & 77.24 & 72.58 & **90.27** & 76.47 & 73.41 & 85.71 & **78.05** & 74.89 & 78.08 \\ \cline{2-13}  & **n-grams** & 75.00 & 73.11 & 80.44 & 72.74 & 71.05 & 78.13 & 76.15 & 73.84 & 82.64 & 72.71 & 74.67 & 71.17 \\ \cline{2-13}  & **n-grams** & 78.02 & 78.19 & 78.68 & 76.14 & 78.02 & 73.46 & 76.91 & 75.80 & 81.15 & 73.43 & 72.57 & 75.71 \\ \hline \multirow{3}{*}{**1,2**} & **n-grams** & 79.16 & 76.96 & 83.52 & 78.80 & 76.31 & 83.52 & 81.47 & 78.77 & 86.48 & 74.62 & 74.05 & 73.41 \\ \cline{2-13}  & **n-grams** & 78.80 & 78.32 & 80.22 & 75.77 & 76.97 & 74.18 & **81.47** & 77.48 & 88.74 & 74.96 & 74.00 & 77.20 \\ \cline{2-13}  & **n-grams** & 78.79 & 76.72 & 81.98 & 76.52 & 75.02 & 79.67 & 79.56 & 77.67 & 82.69 & 72.38 & 70.80 & 69.73 \\ \cline{2-13}  & **n-grams** & 78.40 & 77.53 & 80.99 & 77.21 & 76.96 & 77.14 & 77.26 & 75.83 & 81.98 & 74.22 & 73.21 & 76.54 \\ \hline \multirow{3}{*}{**1,3**} & **n-grams** & 80.68 & **78.89** & 83.52 & 76.13 & 74.82 & 78.96 & 79.94 & 77.94 & 83.57 & 75.04 & 73.31 & 72.53 \\ \cline{2-13}  & **n-grams** & 79.56 & 78.33 & 82.53 & 76.52 & 76.84 & 75.66 & 78.77 & 76.16 & 85.05 & 75.34 & 74.22 & 77.97 \\ \cline{2-13}  & **n-grams** & 76.14 & 74.19 & 79.62 & 76.14 & 75.61 & 77.36 & 78.42 & 75.97 & 83.46 & 71.25 & 75.01 & 68.30 \\ \cline{2-13}  & **n-grams** & 79.16 & 78.12 & 81.76 & **79.52** & 77.11 & 83.96 & 79.57 & 76.50 & 83.46 & 74.97 & 73.86 & 77.25 \\ \hline \multirow{3}{*}{**3,3**} & **n-grams** & 68.99 & 67.07 & 74.34 & 68.59 & 67.79 & 72.75 & 70.11 & 69.69 & 72.03 & 67.08 & 64.05 & 65.99 \\ \cline{2-13}  & **n-grams** & 78.80 & 77.59 & 81.76 & 74.96 & 73.67 & 77.97 & 74.92 & 74.21 & 76.48 & 74.20 & 73.11 & 77.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of selected machine learning models achieved by applying natural language processing techniques to the climate change dataset (DS1). approach. All models provide maximum accuracy using the combined approach of data analysis (except logistic regression), but the average accuracy of the model obtained by the analysis using the combined approach (77.04%) surpasses the one obtained by the analysis with n-grams exclusively (75.27%). The stated average values are given and further elaborated upon in Section 4.5. Table 4 shows models generally achieved much higher recall compared to precision. The maximum precision of 78.89% was achieved using logistic regression on (1,3)-gram analysis while the SVM analysis with LIWC achieved a response of 90.27%. The maximum precision of 78.89% was achieved using a logistic regression with (1,2)-gram analysis while the maximum response of 90.27% was obtained using SVM on LIWC analysis.
## 3 System Model and Architecture Design
### System Communication Model  
As shown in Figure 2, the proposed system model for SBD2D communication takes into consideration the proximity of the nodes and data aggregation to further enhance the energy efficiency. The model consists of one base station (BS), three antennas, and three relays. Each antenna covers a sectored cell of the beekeeping apiary. Within the model, a set of devices (network nodes) is evenly distributed throughout the two-dimensional area, facilitating direct communication using SBD2D links when the nodes are near each other. This approach inherently supports SBD2D communication among all the devices in the scenario. SBD2D communication is defended inherently by all the devices in this scenario. We also assume that the direct links between the beekeeper's mobile devices may use several radio access technologies (RATs) such as Bluetooth, Wi-Fi, and GSM. These devices perform D2D and, once there is a connection establishment, the SBD2D pair must be linked to the BS, and they act as a relay node to receive the data from the mobile network of the beekeeper. The signal that is received by the relay is retransmitted between the BS and mobile and can be used to boost the network throughput and enlarge the cellular network system. Because the infrastructure of the relay does not require a wired connection to the network, it offers backhaul savings costs. In this scenario, there will be a pairing procedure (discovery phase), which will be managed by the SBD2D controller like a centralized entity in a SBD2D communication pairing. The interference is the critical factor that masks the QoS when transmitting signals from the beekeeper user equipment (UE) to the BS or from the BUE (beekeeper user equipment) to the network antenna. The interference problem is minimized because the communication phase in SBD2D starts when the SBD2D pairing process has been established.  
The topology shown in Figure 2 is defined as a graph \(G\big{(}V,E,E^{\prime}\big{)}\), where \(V\) is a set of vertices for the sensors, \(E\) is the set of SBD2D communication links, and \(E^{\prime}\) is the set of interference edges. An SBD2D communication link \(\overline{xy}\) implies that node \(x\) transmits data to node \(y\), with \(y\) considered to be the parent node. Interference edge \(\overline{yz}\) implies that the transmission of \(y\) will interfere with the transmission of \(z\) and similarly, the transmission of \(z\) will interfere with that of \(y\). In the architecture above, a packet \(S_{i}\) generated by the \(i\)-th node \(v_{i}\), is defined by Equation (1).  
\[S_{i}=\big{(}\beta_{D_{R}},\varphi_{\overline{g}},TP,B\big{)} \tag{1}\]  
Figure 2: Proposed system model for smart beekeeping using 5G D2D communications.  
where \(\beta_{D_{R}}\) is the bandwidth, \(\varphi_{g}\) is the data rate for a given SBD2D link \(r\), \(TP\) is the total power, and \(B\) is the bandwidth. Our goal is to ensure that \(S_{i}\) is transmitted in an energy-efficient manner.
## 4 Discussion
Additionally, when human induced pluripotent stem cell-derived sensory neurons were treated with PACAP, the peptide enhanced axonal outgrowth in a dose dependent manner [36]. Together, these studies suggest a prominent role of the PACAP/PAC1 axis in mediating neuronal survival, axon regeneration and remyelination following peripheral nerve injury. Targeting the PAC1 receptor has proven to be challenging [15]. Most studies utilize the neuropeptide PACAP to stimulate receptor activation; nonetheless, as with most peptides, PACAP has limited bioavailability and is prone to rapid enzymatic degradation [37]. Despite being able to cross the blood brain barrier, albeit by passive diffusion or through the specific peptide transporter system 6 (PTS6), PACAP has an extremely short half-life [37]. In C57 BL/6 mice, it was determined that the half-life of PACAP38 administered intravenously was less than two minutes [38]. Dipeptidyl peptidase IV (DPP IV) is the main enzyme involved in the degradation of PACAP, with mice lacking DPP IV showing significantly slower clearance of the peptide [38; 39]. Despite these challenges, PACAP has a safe and high therapeutic index and low doses of PACAP are still able to exert neuroprotection and other beneficial activities. The recent discovery of doxycycline and minocycline as putative PAMs of the PAC1 receptor may provide an excellent opportunity to target the PAC1 receptor therapeutically. The two antibiotics have well-established neuroprotective and anti-inflammatory functions in the CNS [26; 40] and some evidence suggests that minocycline, at least in part, may aid in nerve regeneration in bio-artificial nerve grafts that are free from Wallerian degeneration [41]. However, a few studies have shown that antibiotic treatment can inhibit Wallerian regeneration [42], an essential process of nerve rejuvenation, whereas others do not [43]. Therefore, it is not clear if the balance between beneficial and detrimental outcomes of tetracycline's treatment can ultimately result in an improved nerve recovery or not. Our data suggest that the upregulation of plasminogen activators in RT4 Schwann cells implicates doxycycline and minocycline as agents that can improve extracellular matrix/debris clearance and consequently, promote axonal regrowth. However, additional work is required to confirm such beneficial activities using primary Schwann cells, neuronal/glial co-cultures and/or in vivo, as the repurposing of this class of antibiotics could indeed become a game changer in the treatment of nerve injury.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 6 Conclusions  
Based on the concept of co-distribution, this paper studied the layout and site selection problem of urban terminal co-distribution centers with UAV and vehicle co-distribution. We established a planning model to minimize the network operation cost and distribution time and designed a GWO algorithm based on K-means clustering to solve the optimization problem. The model and algorithm were tested using actual data from the Wangsheren area of Jinan City, China, and the results were compared with those given by PSO. The comparison results showed that GWO is highly effective for solving this type of siting problem, and the fitness value of the GWO is reduced by 34.1%. The collaborative layout scheme of air and ground co-distribution centers constructed in this paper produced significant improvements in distribution efficiency, thus improving the urban environment and ground transportation operation quality, optimizing the distribution enterprise revenue structure, and enhancing the comprehensive benefits of terminal distribution. In future work, we will consider the special distribution characteristics of different functional groups in the city and refine the applicable scenarios and constraints of drone and vehicle distribution. Additionally, we will attempt to improve the end distribution network.  
Conceptualization, W.Q. and A.L.; methodology, W.Q.; software, A.L.; validation, W.Q., A.L. and H.Z.; formal analysis, W.Q.; investigation, A.L.; resources, A.L.; data curation, A.L.; writing--original draft preparation, W.Q.; writing--review and editing, A.L.; visualization, A.L.; supervision, H.Z.; project administration, W.Q.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Shandong Province Key R&D Program (Soft Science) Key Project] grant number [2023 RZB06052].  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 4 Experiment  
### Experiment Setting  
#### 4.1.1 Dataset  
In this research, we used face models from 111 people. For each person, one low resolution face model was acquired using Kinect and one high resolution face model was acquired using Artec Eva, with a neutral expression. The high resolution face model was considered ground truth. In the experiment, we performed leave-one-out cross validation; i.e., one low resolution face model \(M_{low}\) from the face database was chosen as a probe; meanwhile, we removed its corresponding high resolution face model \(M_{high}\) from the face database. The remaining 110 sets of face models formed the training database \(D\). We chose a high resolution model from \(D\) randomly as the reference model. When labeling landmarks on the reference model, it is better to choose inflection points or points that have large curvature. On the reference model, 158 landmarks are labeled.
## 3 RANSAC Algorithm
First, A is transposed and left multiplied to obtain \(\left(\mathrm{A^{\prime}}*\mathrm{A}\right)\), and then the left division operation is performed on the augmented matrix \(\left(\mathrm{A^{\prime}}*\mathrm{B}\right)\) to obtain the optimized center of the circle P\({}_{0}\), with the expression shown in Equation (13). \[\mathrm{P_{0}}=\left(\mathrm{A^{\prime}}*\mathrm{A}\right)\backslash\mathrm{A^ {\prime}}*\mathrm{B} \tag{13}\]  
The distances from the known center of the circle P\({}_{0}\) to the three points P\({}_{1}\), P\({}_{2}\), P\({}_{3}\), are calculated separately and the average of the three is taken as the final radius value; the expression is shown in Equation (14). It should be noted that the ideal calculation method should be to directly take the actual distance from any point to the center of the circle, and the average value is taken here to eliminate possible measurement errors. \[\left\{ \begin{array}{c}\mathrm{R_{1}}=\sqrt{\left(\mathrm{P_{0}}(1)-\mathrm{ P_{1}}(1)\right)^{2}+\left(\mathrm{P_{0}}(2)-\mathrm{P_{1}}(2)\right)^{2}}\\ \mathrm{R_{2}}=\sqrt{\left(\mathrm{P_{0}}(1)-\mathrm{P_{2}}(1)\right)^{2}+ \left(\mathrm{P_{0}}(2)-\mathrm{P_{2}}(2)\right)^{2}}\\ \mathrm{R_{3}}=\sqrt{\left(\mathrm{P_{0}}(1)-\mathrm{P_{3}}(1)\right)^{2}+ \left(\mathrm{P_{0}}(2)-\mathrm{P_{3}}(2)\right)^{2}}\\ R=\left(\mathrm{R_{1}}+\mathrm{R_{2}}+\mathrm{R_{3}}\right)/3\end{array} \right. \tag{14}\]  
The obtained P\({}_{0}\) also needs to be transposed to maintain the consistency of the data structure and to facilitate subsequent calls. After completing all the iterations, the final edge position was determined based on all the fitted obtained circles. Specifically, for each fitted obtained circle, its distance from all data points in the image was calculated. Then, based on the size of the distance, whether each data point belongs to the edge was determined. If the distance of a data point was less than a certain threshold, it was considered as part of that edge. Finally, all data points belonging to the same edge were connected to form the final edge profile.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Model Training
#### 3.1.1 Multilayer Perceptron (MLP)  
This paper presents a multilayer perceptron with four dense layers for solving a multi-output regression problem using TensorFlow. The first layer has 128 neurons with ReLU activation, 3 input features, and L1 regularization with a coefficient of 0.001 to prevent overfitting. The second and third layers have 64 and 32 neurons, respectively, with the same activation and regularization. The output layer has three neurons for the three output features with linear activation (Figure 7a). The image depicts a feedforward neural network architecture in deep learning. It consists of an input layer, dense layers, three thick layers, a ReLU activation function, L1 regularization, and an output layer. The input layer accepts data, dense layers connect neurons, and the output layer aggregates information from the previous layers for the final output.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Single Monte Carlo Run** & **100 Monte Carlo Runs** \\ \hline Range Error & \(\leq\)10\% of true range & \(\leq\)3.3\% of true range \\ \hline Course Error & \(\leq\)5\({}^{\circ}\) & \(\leq\)1.67\({}^{\circ}\) \\ \hline Speed Error & \(\leq\)1 m/s & \(\leq\)0.33 m/s \\ \hline Pitch Error & \(\leq\)5\({}^{\circ}\) & \(\leq\)1.67\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acceptance Criteria.  
Figure 6: System block diagram.
## 3 Results
### Analysis Results of Denoising Performance of Different Methods  
Figure 9a shows the constellation diagram of receiving traffic flow signals by destination node G. Figure 9b shows the constellation diagram of receiving traffic flow signals by malicious node F. Due to the role of channel pre-equalization, the constellation diagram of node G is clear and distinguishable, while the constellations of node F are mixed and disorderly, and the useful traffic flow signals cannot be distinguished. The bit error rate was high. Therefore, it is known that the channel capacity of destination node is larger than that of the malicious node, so that a certain of secure channel capacity can be obtained. Based on this secure channel capacity, the data at the physical layer can be transmitted safely through secure channel coding. For this reason, the proposed method chooses node G to realize the data transmission of traffic flow.  
Figure 9: The constellation of received signals of two nodes in the method.  
The methods of Reference [5,6] and the method of this paper were used to detect the traffic flow parameters in Figure 9a, and a comparison was made to test the denoising coefficients of the three methods. The results are shown in Table 1.  
According to the data in Table 1, compared with the three methods, the denoising coefficient of this method was the largest, with an average value of 0.9874, while that of the Reference [5] method was 0.9075 and that of the Reference [6] method was 0.8604. According to the above data, the denoising performance of this method was the best.  
In different conditions, we tested the accuracy of this method and the traditional method and got the conclusion that this method was better than the traditional method. In order to further scientific verification, the accuracy of this method was significantly tested. The traffic flow parameters not using the method in this paper were used as the control group, and the test results obtained by the method in this paper were used as the experimental group for a significance test; the \(t\) value and \(x^{2}\) value were used for inspection, respectively, and if \(p<0.05\), the difference was statistically significant. The results are shown in Table 2.  
According to the data in Table 2, the \(p\) value between the experimental group and the control group was less than 0.05, statistically significant, which shows that the method in this paper can obtain more accurate detection results and the results are reliable.
## 3 Predict and Forward Scheme Based on Node Profile
### Forward Phase: Making Delivery Decisions for Prelearn Messages  
After the Predict phase, the Prelearn message has received enough and accurate statistics about the state of the network and nodes in the network have determined the probabilities of the future meeting with other nodes. Moreover, the optimal candidate carrying Prelearn messages starts to make delivery decisions based on the information stored in the Prelearn message. During the Forward phase, if the destination profile has appeared in the Predict phase, the Prelearn message will directly or indirectly be forwarded to the nodes belonging to the destination profile based on the probability prediction matrix \(W_{n\times n}^{*}\). Specifically, Prelearn message carriers transmit data packages to the relay nodes which have a bigger probability of future meeting with the destination profile than the Prelearn message carriers. Finally, the Prelearn messages will be forwarded to the destination profile. Otherwise, when the destination profile has not appeared in the Predict phase, the message carrier does not forward. Prelearn message until it finds a node that satisfies the message delivery function \(pf\), which is the same as the Wait phase in Spray and Wait algorithm.  
As shown in Figure 4, during the Forward phase, the \(node_{I}\), which is the last node to be routed in the Predict phase, starts to make delivery decisions for Prelean messages. The \(node_{F}\) and \(node_{H}\) belong to the destination profile that satisfies the message delivery function \(pf\) defined by the minimum weight. Moreover, according to the probability prediction matrix \(W_{n\times n}^{*}\), the \(node_{D}\), \(node_{E}\) and \(node_{G}\) are the users that possess a bigger probability of future meeting with the destination profile than the \(node_{I}\). Therefore, the \(node_{I}\) directly or indirectly sends Prelearn messages to the suitable relay nodes \(node_{D}\), \(node_{E}\) and \(node_{G}\), and the Prelearn messages will be forwarded to the destination profile \(node_{F}\) and \(node_{H}\) eventually.  
Figure 3: The optimal transition phase between Predict and Forward.