esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Panoramic Live Broadcast
[32] designed a feasible panoramic stereo video live-broadcast framework based on the current situation, which is similar to our system below. At present, research on panoramic video is in a mature stage. Now and in the future, researchers will still explore better approaches to reduce transmission bandwidth and improve image quality. However, the research on the 3D sound effect of panoramic video needs to be expanded. Meanwhile, more research on applications on panoramic video is required as a supplement.
## 1 Introduction  
With the improvement of the automobile electromization, it results in the rapid increase of modules numbers and more complicated systematic functions for realization more advanced software and electronic functions. For general examples, systems like the airbag, the self-adaptive cruise control, and the traction control are implemented on distributed electrical and electronic architecture to obtain more comfortable and safer vehicle environments.  
Whereas the commonly used CAN (Controller Area Network) system has severely restricted the function expansion and cost control of vehicles [1], in 2000, FlexRay union was established by companies of BMW, Daimler Chrysler, Motorola, and Philips and the FlexRay network was developed to have a 20 M/bps of bit rate, the high throughput, and the certainty, as well as a hybrid high-speed communication network with time-division and flexible time-division multiple access. In 2010, FlexRay 3.0 protocol was published [2]. The FlexRay bus network has been applied to high-class commercial vehicles like BMW X5, BMW 7 series, and Audi A8 series [3; 4]. In order to satisfy the increasing demand of communication network in vehicle electronic control system under the premise of ensuring the safety and reliability of vehicle bus network system, FlexRay message scheduling fusion technology is a one of the key points.  
In this study, based on Next Fit Decreasing (NFD) algorithm for static segment of FlexRay bus, a message optimization scheme will be mentioned to achieve the high efficiency of FlexRay network. This scheme will resolve problems, like synthesizing the schedule of FlexRay static segment, solving the signal and Frame ID (FID) allocation problems, reducing the bandwidth and improving network utilization at the same time, as well as reducing the number of FID.
## 3 Results
### The Results of Joint Angle Prediction
#### 3.3.3 Prediction Results of Different Times in the Future  
Table 4 is the result of model training. Table 4 delineates the predicting results of different times in the future. The findings reveal that the increase in time in the future leads to a decrease in prediction accuracy.  
Experiments on real-time prediction of joint angles were also conducted. During arm side raises, the RMSE of shoulder joint angle prediction result was 0.0418, 0.0599, and 0.0735, respectively, when the prediction time was 10 ms, 30 ms, and 60 ms; during arm front raises, the RMSE of the shoulder joint angle prediction result was 0.0441, 0.0605, and 0.0748, respectively, when the prediction time was 10 ms, 30 ms, and 60 ms; during elbow curls, the RMSE of elbow joint angle prediction result was 0.0485, 0.0634, and 0.0780, respectively, when the prediction time was 10 ms, 30 ms, and 60 ms.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline
**Time in the Future (ms)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
10 & 0.0019 \(\pm\) 0.0001 & 0.0366 \(\pm\) 0.0012 & 0.0098 \(\pm\) 0.0006 & 0.970 \(\pm\) 0.002 \\
30 & 0.0032 \(\pm\) 0.0002 & 0.0588 \(\pm\) 0.0017 & 0.0297 \(\pm\) 0.0021 & 0.941 \(\pm\) 0.003 \\
60 & 0.0056 \(\pm\) 0.0003 & 0.0691 \(\pm\) 0.0023 & 0.0491 \(\pm\) 0.0027 & 0.934 \(\pm\) 0.002 \\ \hline  & \multicolumn{4}{c}{**(b)**} \\ \hline
**Time in the Future (ms)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
10 & 0.0020 \(\pm\) 0.0001 & 0.0387 \(\pm\) 0.0009 & 0.0108 \(\pm\) 0.0007 & 0.963 \(\pm\) 0.001 \\
30 & 0.0034 \(\pm\) 0.0002 & 0.0499 \(\pm\) 0.0018 & 0.0346 \(\pm\) 0.0022 & 0.938 \(\pm\) 0.003 \\
60 & 0.0064 \(\pm\) 0.0004 & 0.0681 \(\pm\) 0.0024 & 0.0506 \(\pm\) 0.0036 & 0.931 \(\pm\) 0.003 \\ \hline  & \multicolumn{4}{c}{**(c)**} \\ \hline
**Time in the Future (ms)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
10 & 0.0021 \(\pm\) 0.0001 & 0.0402 \(\pm\) 0.0010 & 0.0119 \(\pm\) 0.0006 & 0.959 \(\pm\) 0.002 \\
30 & 0.0036 \(\pm\) 0.0003 & 0.0592 \(\pm\) 0.0031 & 0.0374 \(\pm\) 0.0023 & 0.937 \(\pm\) 0.003 \\
60 & 0.0058 \(\pm\) 0.0003 & 0.0706 \(\pm\) 0.0033 & 0.0594 \(\pm\) 0.0041 & 0.929 \(\pm\) 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 4: (a) Prediction results of different times in the future during arm side raises. (b) Prediction results of different times in the future during arm front raises. (c) Prediction results of different times in the future during elbow curls.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{4}{c}{**(c)**} \\ \hline
**Input signals** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **_p-Value_** \\ \hline MMG & 0.0021 \(\pm\) 0.0001 & 0.0402 \(\pm\) 0.0010 & 0.0119 \(\pm\) 0.0006 & 0.959 \(\pm\) 0.002 & / \\ IMU rotation angle & 0.0059 \(\pm\) 0.0002 & 0.0831 \(\pm\) 0.0036 & 0.0683 \(\pm\) 0.0026 & 0.926 \(\pm\) 0.002 & 0.0041 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 1 Introduction
In contrast, additive technology allows designers more autonomy, addressing specific challenges, such as varying infill density [7]. Additive manufacturing builds materials layer by layer and relies heavily on input parameters. The mechanical behavior of 3D-printed materials is influenced by factors such as nozzle diameter, material composition [8], added filler [9; 10; 11], layer orientation [12; 13] and thickness [14; 15; 16], nozzle feed rate [15], grid angle [7; 16] and width [17], and, notably, filler density [7]. Impact strength refers to a material's capacity to withstand cracking, fracturing, or plastic deformation when subjected to sudden and intense impact or shock loads. This crucial property gauges the material's ability to endure abrupt forces. As the layer thickness increases, the impact strength of PLA materials declines. A reduced layer thickness will enhance the bonding between layers and boost their capacity to absorb energy [18]. As for the influence of infill orientation on the impact strength, research carried out by [PERSON] et al. [19] showed that for 0\({}^{\circ}\)- and 45\({}^{\circ}\)-oriented specimens, the impact strength was nearly identical, while for z-oriented specimens, the impact strength was lower due to the weaker layer interface, which is in agreement with the research of [PERSON] et al. [20]. Moreover, [PERSON] et al. showed that a rectilinear pattern and increased infill density of printed PLA samples exhibited greater impact strength [21]. Even though there are numerous studies regarding the impact strength of PLA-based FDM samples, none of them deals with the problem of the sample thickness as a crucial parameter of the impact fracture resistance with respect to infill density and layer surface. This comprehensive study seeks to provide insights into how the layer surface, infill density, and sample thickness influence the localized impact absorption capabilities, as evidenced by the outcomes of Charpy testing. Additionally, optical microscopy, FTIR spectroscopy, and SEM analysis will contribute to a thorough understanding of the microstructural changes, chemical composition, and surface morphology of the printed samples, further enhancing our understanding of their performance and behavior in response to impact forces. Die-cutting tools used in the printing industry and other manufacturing sectors are designed to cut and shape various materials. The cutting surface of the die is typically made of hardened steel strips, known as steel rules, which are used to cut materials such as paper, cardboard, plastics, wood, cork, felt, fabrics, and paperboard. The cutting surface of the die is the edge of hardened steel strips, and these steel rules are usually located using saw or laser-cut grooves in plywood. The mating die can be a flat piece of hardwood or steel, a male shape that matches the workpiece profile, or it can have a matching groove that allows the rule to nest into it. This research opens up possibilities for utilizing additive technology in the production of die-cutting beds in the printing industry.
## 5 Results and Discussion
### Weight Obtained by PCA  
For a fair comparison with SHAP, PCA weights were obtained using the data in the training set, and the weights derived from this dataset were validated using the test set (Table S4). When examining CDI studies, information from the first component or the first two components is commonly used [115]. The contribution ratios of the principal components following the PCA analysis for the districts are shown in Table S5. The first two components accounted for more than 60% of the variance.  
When examining the weights obtained from the PCA analysis, the input with the highest weight is VCI at 0.23, followed by TCI at 0.19. The third highest weight is GPP at 0.18, while the fourth is NDWI at 0.17. The lowest weight is attributed to PCI, with an average of 0.10. Compared to SHAP, only the TCI input shares the same ranking, while the weight rankings for all other inputs are significantly different.  
\[CDI=0.16\times NDWI+0.19\times TCI+0.10\times PCI+0.23\times VCI+0.18\times GPP+0. 10\times SMCI \tag{11}\]  
To analyze the relationship between the inputs after the PCA analysis, PCA loadings plots were created. These plots allow us to see which inputs are closely related to each other in each district and their contribution to explaining the variance. The axes represent the components that explain the variance in the data, with the direction of the vectors indicating the relationship between the variables and the components, and their length reflecting their contribution. The angle between vectors signifies the correlation between variables; small angles indicate a positive relationship, while large angles suggest a weak association.  
Vectors pointing in the same direction imply that the variables exhibit similar behavior. The districts with the highest explanation ratio in the first two principal components were selected to create Figure 3. Among the values of the four districts in the graph, VCI provides the highest contribution to both the first (PC1) and second (PC2) components and TCI significantly contribute to PC1 and PC2. PCI and SMCI values have relatively low contributions. Additionally, a strong positive relationship can be observed between VCI and GPP in all regions, while a negative correlation can be observed between VCI and TCI and between NDWI and GPP, as these inputs intersect each other perpendicularly.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## 3 Results and Discussion
### Volume Resistivity  
Figure 3 shows the change of the volume resistivity of the nano-MgO/EP composite dielectric with the MgO nanoparticle content at room temperature. As the content of MgO nanoparticles increased, the volume resistivity of the composite dielectric first increased and then decreased. When the content of MgO nanoparticles was 1%, the volume resistivity of the composite dielectric reached the maximum, which was 26.8% higher than that of the pure epoxy resin, and then the volume resistivity began to decrease. When the content of MgO nanoparticles was 7%, the volume resistivity reached the minimum, which was 6% lower compared to the pure epoxy resin. This indicates that the doping of an appropriate amount of MgO nanoparticles can increase the volume resistivity of the epoxy resin, and excessive doping will decrease the volume resistivity. It was hypothesized that the concentration and mobility of carriers can affect the dielectric resistivity. The doping of trace nano-MgO increased the volume resistivity of the epoxy matrix because the size of the nanoparticles is small and there was a Coulomb blocking effect, in which the migration process of carriers in composites increases the resistivity. At the same time, the addition of nanoparticles introduced a large number of interfaces and deep traps into the matrix. The traps affect carrier mobility by trapping carriers, making the resistivity of the composites increase. As the content of MgO nanoparticles increased, the volume resistivity decreased significantly. The large specific surface area and high surface energy of the MgO nanoparticles have a strong influence on adsorbing impurities, so doping with nanoparticles will increase the carrier concentration in the composite system. Therefore, the concentration of carriers in the medium led to a decrease in the resistivity of the composites [32].  
Figure 2: Thermal Conductivity of MgO/epoxy composites.
## 2 6-DOF Displacement Estimation Using Vision Sensor
\begin{split}{}^{F}D_{T}(x,y,z,\theta,\varphi,\psi)=T(x,y,z)R_{x}( \theta)R_{y}(\varphi)R_{z}(\psi)\\ =\left[\begin{array}{ccc}c_{\varphi}c_{\psi}&-c_{\varphi}s_{ \psi}&s_{\varphi}&x\\ s_{\theta}s_{\varphi}c_{\psi}+c_{\theta}s_{\psi}&-s_{\theta}s_{\varphi}s_{\psi}+ c_{\theta}s_{\psi}&s_{\theta}c_{\varphi}&y\\ -c_{\theta}s_{\varphi}c_{\psi}+s_{\theta}s_{\psi}&c_{\theta}s_{\psi}s_{\psi}+ s_{\theta}s_{\psi}&c_{\theta}c_{\varphi}&z\\ 0&0&0&1\end{array}\right]\end{split}  \tag{4}\]  
\[ \begin{split}{}^{F}D_{T}=^{F}D_{C}\cdot{}^{C}D_{T}\end{split}  \tag{5}\]  
In Equation (4), \({}^{F}D_{T}\) is the transformation matrix composed of the 6-DOF relative displacement between fixed coordinate relative to the target coordinate, and \(F\) and \(T\) indicate the fixed and target structures, respectively. The matrix consists of the product of translation matrix \(T(x,y,z)\) along \(X\), \(Y\), and \(Z\) axes with rotation matrices \(R_{x}(\theta)\), \(R_{y}(\varphi)\), and \(R_{z}(\psi)\) about \(X\), \(Y\), and \(Z\) axes, respectively. In the equation, \(S_{\theta}\) and \(C_{\theta}\) denote sin\(\theta\) and cos\(\theta\), respectively. The details of each matrix can be found in [19]. In Equation (5), \({}^{F}Dc\) and \({}^{T}Dc\) are the relative displacements between fixed or target coordinates relative to the camera coordinates, indicated as \(C\). \({}^{C}D_{T}\) can be estimated by inverting \({}^{T}D_{C}\). The  
Figure 3: A block diagram of the displacement estimation process. Figure 2: Homogeneous transformation between world and image planes. relative displacements estimated from images taken at different time \(t-1\) and \(t\) are used to estimate the structural behavior, as follows:  
\[{}^{T,t}D_{T,t-1}={}^{T}D_{F,t},{}^{F}D_{T,t-1} \tag{6}\]  
where \({}^{T}D_{F,t}\) equals the translational and rotational displacements between the fixed and target structures at time \(t\).
## 3 Multi-Hop State-Aware Routing Strategy Based on Traffic Flow Forecasting
### Traffic Flow Prediction Strategy Based on RNN Combined with the DDPG Method
```
Randomly initialize \(\varphi^{Q}\) and \(\varphi^{\text{\tiny H}}\)  Create target network \(Q^{\prime}\) and \(\mu^{\prime}\) with \(\varphi^{Q^{\prime}}\leftarrow\varphi^{\text{\tiny Q}}\) and \(\varphi^{\mu^{\prime}}\leftarrow\varphi^{\text{\tiny H}}\)  Initialize replay buffer \(R\) for episode = 1 to \(M\):  Initialize a random process \(N\) for action exploration  Receive the initial state \(s_{1}\) for iteration \(t=1\)do  Execute action \(a_{t}\) and observe \(r_{t}\) and \(s_{t+1}\)  Store \((s_{t},a_{t},r_{t},s_{t+1})\) in \(R\)  sample a minibatch of \((s_{t},a_{t},r_{t},s_{t+1})\) from \(R\)  set \(\psi_{t}=r_{t}+\lambda Q^{\prime}(s_{t+1},\mu^{\prime}(s_{t+1}|\varphi^{Q^{ \prime}})\big{|}\varphi^{Q^{\prime}})\)  update \(\varphi^{\text{\tiny Q}}\) with \[L=\frac{1}{N}\Sigma_{t}\left(y_{t}-Q(s_{t},a_{t}\Big{|}\varphi^{\text{\tiny Q} })\right)^{2}\]  update \(\varphi^{\text{\tiny H}}\) with \[\
abla_{\varphi^{Q}}\int\approx\frac{1}{N}\sum_{t}[\
abla_{\alpha}Q(s_{t},a_{t} \Big{|}\varphi^{\text{\tiny Q}})\
abla_{\varphi^{\text{\tiny H}}}\mu(s_{t} \Big{|}\varphi^{\text{\tiny H}})]\]  update target network with \[\varphi^{Q^{\prime}}\leftarrow\lambda\varphi^{\text{\tiny Q}}+(1-\lambda) \varphi^{Q^{\prime}}\ \varphi^{\mu^{\prime}}\leftarrow\lambda\varphi^{\mu^{\prime}}+(1-\lambda) \varphi^{\mu^{\prime}}\] endfor endfor
```  
**Algorithm 1** DDPG algorithm  
As the traffic between nodes shows the periodic feature, the RNN is used as the input neural network. Figure 3 shows the structure of RNN. The input data is the traffic snapshot of the traffic distribution. Therefore, the output data can be treated as one of the input data in the next time step. The traffic condition in the next time step is predicted according to the information of past traffic patterns. The hyperparameters of the RNN are similar to [23]. So far, the traffic prediction between nodes is finished, and the enhanced node will use this information to make further routing decisions.
## 2 Proposed System
### Joint Reason and Control Module
Figure 17 show an illustration of how this cross validation could correct the wrong coding. Assuming that the gesture recognition result \(g_{id_{g}}\) is correct and the action recognition result \(a_{id_{a}}\) is incorrect, where the correct one should be \(a_{id_{a}-1}\). Through Equation (6), our validation will compare its four coded neighbors as \((P(g_{id_{g}})P(a_{id_{a}-1}),P(g_{id_{g}})P(a_{id_{a}+1}),P(g_{id_{g}-1})P(a_{ id_{a}}),P(g_{id_{g}+1})P(a_{id_{a}}))\). Since the input gesture is correctly classified which means \(P(g_{id_{g}}|g^{(in)})\gg P(g_{id_{g}\pm 1}|g^{(in)})\), it will be easy for our system to reject \(P(g_{id_{g}-1})P(a_{id_{a}}),P(g_{id_{g}+1})P(a_{id_{a}})\). Because the true category of action is \(a_{id_{a}-1}\), the probability \(P(g_{id_{g}})P(a_{id_{a}+1})\) will be quite low and rejected. In this way, the maximum probability should be \((P(g_{id_{g}})P(a_{id_{a}-1})\) and the true action ID is \(a_{id_{a}-1}\). The corresponding relationship between the action-gesture combination and UAV flight action is displayed in Table 2 (the four undefined combinations are for pilot customization). The final flight command will be the combination of the flight strategy obtained from the scene understanding and flight action resulting from the cross-validation over the action-gesture recognition results. After receiving the flight command sent from the PC workstation, the H-infinite [39; 40] algorithm is applied to control the flying UAV. The H-infinite technique is superior to classical control techniques in its ability to solve many problems involving multivariate systems with cross-coupling among channels. As the scene-understanding algorithm is applied in our system and the flight strategy will change from scene to scene, it should be noted that, even if the UAV pilot sends the same commands to the UAV, the UAV may perform its final flight action in different manners according to its flying environment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hybrid Coding** & **UAV Corresponding Action** & **Hybrid Coding** & **UAV Corresponding Action** \\ \hline \(g_{1}a_{1}\) & Fly towards left & \(g_{2}a_{4}\) & Fly towards right \\ \hline \(g_{3}a_{1}\) & Rotate left & \(g_{4}a_{4}\) & Rotate right \\ \hline \(g_{5}a_{1}\) & Fly upward & \(g_{6}a_{4}\) & Fly down \\ \hline \(g_{2}a_{2}\) & Fly foreword & \(g_{1}a_{5}\) & Fly backword \\ \hline \(g_{4}a_{2}\) & Hover & \(g_{3}a_{5}\) & Circling \\ \hline \(g_{6}a_{2}\) & Draw square & \(g_{5}a_{5}\) & Draw S \\ \hline \(g_{1}a_{3}\) & Speed up & \(g_{2}a_{6}\) & Speed down \\ \hline \(g_{3}a_{3}\) & (Undefined) & \(g_{4}a_{6}\) & (Undefined) \\ \hline \(g_{5}a_{1}\) & (Undefined) & \(g_{6}a_{6}\) & (Undefined) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UAV flight action obtained through the cross validation process. Figure 16: Sparse coding of Gestures-Action combinations. (The orange blocks are selected into our codebook).
## 3 Methodology  
### Dataset  
The dataset in [16] is used in this work. It is composed of four datasets combined to form a single dataset, with measurements from different frequencies, environments, and antenna heights. Such a type of dataset combination was performed in [11; 23]. Three of the datasets are available at [34; 35; 36] and have been used in [37; 38; 39], respectively. The fourth dataset is measurements taken by us. It should be noted that not all samples in [34] were used to maintain a balanced dataset in terms of environment type. The final dataset is made up of 12369 samples, consisting of 8 features and path loss values as the target. The features include elevation at receiver position (elevation), frequency, height of transmitting antenna (ht), height of receiving antenna (hr), latitude difference between transmitting and receiving antenna (distance_x), longitude difference between transmitting and receiving antenna (distance_y), elevation at transmitter position (tantennaelev), and distance between transmitter and receiver (distance). Table 1 presents a summary of the dataset, showing the different types of environments from which the measurements were taken and the parameters under which they were taken that include frequency, transmitting antenna height, and receiving antenna height.
## 4 RNN Structures Used for Traffic Prediction
### Hybrid Models Including RNN Techniques
#### 4.4.1 RNNs + Traditional ML Techniques  
As mentioned earlier, classical traffic flow prediction methods based on traditional ML techniques include K-NN and SVR. Some scholars have endeavored to combine these methods with the RNN family for traffic prediction. For example, [PERSON] et al. [69] proposed a spatiotemporal traffic flow prediction method combining K-NN and LSTM. K-NN is used to select neighboring stations most relevant to the test site to capture the spatial characteristics of traffic flow. At the same time, LSTM is employed to explore the temporal variability of traffic flow. The experimental results show that this model outperforms some traditional models in predictive performance, with an average accuracy improvement of 12.59%. In comparison with combining with the LSTM model, [PERSON] et al. [70] proposed a traffic flow prediction method based on the K-NN and GRU. This method calculates spatial correlations between traffic networks using Euclidean distance and captures the time dependency of traffic volume through the GRU. The experimental results demonstrate a significant improvement in the prediction accuracy of this model compared to traditional methods. The K-NN method exhibits good predictive performance in simple scenarios. However, due to its non-sparse nature, it may struggle to handle traffic scenarios with significant variations. [PERSON] et al. [71] utilized an optimized version of SVR as a traffic flow prediction method. They apply Particle Swarm Optimization (PSO) to optimize the parameters of SVR, thereby enhancing the prediction system's performance. Given the complex non-linear patterns of traffic flow data, [PERSON] et al. [72] proposed a hybrid traffic flow prediction model combining the Gravitational Search Algorithm (GSA) and SVR model. The GSA is employed to search for the optimal SVR parameters, and this model demonstrates good performance in practice.