esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vehicle Dynamics Model
### Half-Car Dynamics Model
#### 4.1.2 Lateral Acceleration Calculation  
Based on a traditional 2-DOF bicycle model [17], the dynamics equations can be expressed as follows:  
\[\begin{array}{l}M_{1}a_{y}-m_{b}h_{r}\hat{\varphi}-\left(F_{yf}+F_{yr} \right)=0\\ I_{z}\dot{\gamma}+I_{zx}\dot{\phi}-I_{xy}\dot{\phi}^{2}-\left(aF_{yf}-bF_{yr}+M \right)=0\end{array} \tag{19}\]  
where \(I_{z}\), \(I_{xy}\) and \(I_{zx}\) are moment of inertia along \(Z\) axis, \(X\)-\(Y\) axis and \(Z\)-\(X\) axis, respectively. \(M\) is yaw moment, \(F_{yf}\) and \(F_{yr}\) represent lateral force of front wheel and rear wheel. Other parameters are shown in Table 2. More details are stated in [17; 18].  
To solve for the desired lateral acceleration, according to vehicle theory [17], front tire slip angle \(\alpha_{f}\) and rear tire slip angle \(\alpha_{f}\) can be calculated as follows:  
\[\left\{\begin{array}{l}\alpha_{f}=\frac{v_{y}}{v_{x}}+\frac{a_{Y}}{v_{x}}- \delta_{f}\\ \\ \alpha_{r}=\frac{v_{y}}{v_{x}}-\frac{b_{Y}}{v_{x}}\end{array}\right. \tag{20}\]  
where \(v_{x}\) and \(v_{y}\) are longitudinal and lateral velocity, respectively. a and b are distance from C.G. to front axle and rear axle. \(\delta_{f}\) is front steering angle. \(\gamma\) represents yaw rate.  
In this paper, acceleration in \(Y\)-direction and yaw angle were obtained as follows:  
\[\dot{v}_{y}=\frac{-2\left(C_{f}+C_{r}\right)v_{y}-2\left(aC_{f}-bC_{r}\right) \gamma}{M_{1}\cdot v_{x}}+\frac{2C_{f}\cdot\delta_{f}}{M_{1}}-v_{x}\cdot\gamma \tag{21}\]  
\[\dot{\gamma}=\frac{-2\left(aC_{f}-bC_{r}\right)v_{y}-2\left(a^{2}C_{f}+b^{2}C _{r}\right)\gamma}{I_{z}\cdot v_{x}}+\frac{2a\cdot C_{f}\cdot\delta_{f}}{I_{z }} \tag{22}\]  
Desired lateral acceleration for a driver's steering input is expressed as follows:  
\[a_{y}=\dot{v}_{y}+v_{x}\gamma=\frac{-2\left(C_{f}+C_{r}\right)v_{y}-2\left(aC _{f}-bC_{r}\right)\gamma}{M_{1}\cdot v_{x}}+\frac{2C_{f}\cdot\delta_{f}}{M_{1}} \tag{23}\]  
Figure 6: Influence of vertical force on tire lateral force.  
**Remark.**_Tire cornering stiffness is calculated using MF in Section 4.1.1, and other parameters can be obtained using CARSIM(r) simulation (version 8.02; Publisher: Mechanical Simulation Corporation, Detroit, MI, USA, 1996-2010)._
###### Abstract
Traditional DEMATEL models mostly collect research data in the form of questionnaires or expert scoring. When faced with complex system evaluation factors, the evaluation subject needs to spend a certain amount of time and energy to compare and measure each factor, which will make the final research data one-sided and subjective, and then cause partial distortion of the evaluation data, thereby reducing the accuracy of the evaluation results. In order to solve this problem, the BPNN model can effectively reduce human bias and improve the reliability and accuracy of the evaluation results by adjusting and optimizing the system data. The BPNN model (Back Propagation Neural Network) [19] is a basic and typical network type of an artificial neural network. It consists of an input layer, a hidden layer, and an output layer. Its main feature is that it calculates the error loss between the actual output and the expected output of the input sample, and transmits this error signal back to the network to achieve parameter adjustment and optimization of the neural network. This process involves adjusting the weights and biases to minimize the error, so that the network can more accurately approximate the complex input-output relationship and can effectively solve the problem of direct impact on the matrix assignment bias when there are too many evaluation system elements [20]. In summary, this paper proposes an analysis of factors affecting the promotion of prefabricated housing in Hainan Province based on the BPNN and DEMATEL methods by combining the special economic and environmental background of Hainan Province. Through the grounded theory, an index system of factors affecting the promotion of prefabricated housing in Hainan Province was constructed. For the first time, the BPNN-DEMATEL model was used to systematically analyze the key factors affecting the promotion of prefabricated housing in Hainan Province and their evolution path, in order to provide a reference for the promotion of prefabricated housing in Hainan Province.
## 4 Case Study
### Key Performance Indicators  
In the evaluation of our IoV scenario, we quantified user satisfaction across various Key Performance Indicators (KPIs)--Latency, Privacy, Mobility, and Computing Capacity. The Matlab simulation environment facilitated a comprehensive analysis of the performance of the DLAaS framework under various conditions. The definitions and formulas of the KPIs we use are as follows:  
* Latency measures the delay experienced by Vehicular Users (VUs) in obtaining responses from the DLAaS framework.
* **Simulation:** The latency satisfaction, denoted as \(S_{\text{Latency}}\), is calculated as the percentage of users for whom the latency requirements are met: \[S_{\text{Latency}}=\frac{\text{Number of users meeting latency requirements}}{\text{Total number of users}}\times 100\%\]  
* Privacy represents the level of data security and confidentiality maintained during DL processes.
* **Simulation:** Privacy satisfaction, denoted as \(S_{\text{Privacy}}\), is calculated similarly based on the percentage of users for whom privacy requirements are met.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Integrated T/NTN Layer** & **Computation Resources (FLOPS)** & **Communication Resources (Mbps)** & **Storage Resources (GB)** \\ \hline VU & 10,000 & 20 & 10 \\ \hline RSU & 30,000 & 40 & 30 \\ \hline LAP & 30,000 & 30 & 10 \\ \hline HAP & 50,000 & 50 & 50 \\ \hline Satellite & 70,000 & 90 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network Resource Allocation Across Layers.  
\[S_{\text{Privacy}}=\frac{\text{Number of users meeting privacy requirements}}{\text{Total number of users}}\times 100\%\]  
* Mobility reflects the ability of VUs to maintain seamless connectivity while traversing diverse geographical regions.
* Mobility satisfaction, denoted as \(S_{\text{Mobility}}\), is calculated based on the percentage of users meeting mobility requirements. \[S_{\text{Mobility}}=\frac{\text{Number of users meeting mobility requirements}}{\text{Total number of users}}\times 100\%\]
* **Computing Capacity:*
* Computing capacity denotes the ability of the DLaaS framework to handle computational demands efficiently.
* Computing capacity satisfaction, denoted as \(S_{\text{Computing}}\), is calculated similarly based on the percentage of users meeting computing capacity requirements. \[S_{\text{Computing}}=\frac{\text{Number of users meeting computing capacity requirements}}{\text{Total number of users}}\times 100\%\]
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 2 Modeling
### Velocity Command Generator  
The translational and angular velocity commands are shown as \(v^{cmd}\) and \(\omega^{cmd}\), and are calculated from the linear movement of the control device against the initial position of the control device.  
\[v^{cmd} = V^{max}\cdot x_{1}^{res}/L^{max} \tag{8}\] \[\omega^{cmd} = \Omega^{max}\cdot x_{2}^{res}/L^{max} \tag{9}\]  
where \(V^{max}\) and \(\Omega^{max}\) are the maximum values of translational and angular velocities, respectively. These values were determined from the mobile robot specifications. \(x_{1}\) and \(x_{2}\) represent the translational and angular positions of the linear motors, respectively. Superscripts \(\bigcirc^{cmd}\) and \(\bigcirc^{res}\) denote the command and response values. \(L^{max}\) denotes the maximum displacement of the linear motor. The UDP sends translational and angular velocity commands to the mobile robot.  
Figure 3: Mobile robot position and orientation.
## 3 Integration of Multi-Domain MEC Environments
### Host Interconnection in MEC

Table 2: Summary of the MEC reference points and their scope. has recognize the relevance of this kind of scenarios by triggering a new initiative, named Operator Platform [30], which intends to promote the availability and accessibility of integrated edge computing from distinct operators under a single unified API. Business and Technical Implications of the Integration of MEC Environments from Multiple Administrative Domains  
The integration of assets from different administrative domains drive both business and technical implications. These aspects have to be taken into account when designing a full operational solution. In some cases, these aspects could be already part of the technical specifications, while in other cases, they motivate gaps necessary to be addressed, for which distinct approaches could be considered in the future.
## 4 Simulation Results
### Results and Numerical Analysis
It should be noted that the amount of cut-off cost without considering the capacity of the lines due to local compensation of reactive power is USD 3.24 million. (C) The third scenario  
In the last scenario, by selecting another number for the first region, the last possible state is obtained, including two regions. Figure 10 shows the studied network and the scope of each area. Table 7 presents the optimal locations of distributed generation sources and their type and size. Table 8 lists the unsupplied energy in each area. The total cost of operation, the expected cost of disconnecting the customer, and the average total losses for operation, in this case, are USD 32.16 billion, USD 4.87 million, and 148.09 kW. The estimated cut-off cost, excluding line capacity depletion due to local reactive power compensation, is USD 5.89 million. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Microgrid** & \(\textbf{ENS}_{P}\) & \(\textbf{ENS}_{Q}\) & \(\textbf{EVS}_{P}\) & \(\textbf{EVS}_{Q}\) \\ \hline
1 & 88.32 & 11.29 & 92.32 & 17.25 \\ \hline
2 & 42.21 & 8.52 & 43.54 & 5.64 \\ \hline
3 & 15.54 & 3.32 & 20.86 & 3.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expected unmet energy supply (in terms of MWh and MVArh ) due to lack of active and reactive power. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Microgrid** & **Place \(DG\)** & **Type \(DG\)** & **Size \(DG\)** & **Source Location \(Q\)** & **Source Size \(Q\)** \\ \hline
1 & 11, 7, 5, 2 & _FC_, \(PV.WT\), \(PV\) & 332, 386, 342, 632 & 5, (_FC_)2 & 453, 322 \\ \hline
2 & 25, 23, 22 & _FC_, \(FC\), \(PV\), \(PV\) & 268, 332, 564, 621 & 21 (_FC_), 19 (_FC_) & 321, 298 \\ \hline
3 & 35, 31, 27, 25 & _FC_, \(PV\), \(WT\), \(WT\) & 267, 432, 610, 457 & 34, 28 (_FC_) & 465, 298 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Specifications of resources installed in the network in terms of kW and kVAr. Figure 9: Network zoning into 3 microgrids. Which each zone in separated with different colors. Different modes are possible for zoning. By comparing these three cases from the point of view of reliability index and total costs, zoning of the network into two microgrids will have high investment costs and cut-off costs, which result from the high unsupplied energy due to lack of active, reactive power according to Table 8. Therefore, this state of topology will not be appropriate. However, for the case where the network is divided into three and four microgrids, it is observed that the zoning of the system is divided into four microgrids in low supply, as detailed in Table 4. In addition, the design of three microgrids will have fewer losses, indicating that this design is more appropriate. For a better comparison, in Table 9, the design results are given to create the number of different microgrids.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Radio Transmitter** & **Manufacturer/Model** & **Frequencies** & **Antennas** & **Radiation Distance** & **Hazard from** & **License** & \\  & & & & & **Transmitter** & & \\ \hline VHF Fixed & ICOM IC-M505 & 156.0–157.425 MHz & GFE & 1 m & & Under Ships Marine Radio License & \\ \hline VHF Fixed & Salitor 6006 & 156.0–157.425 MHz & GFE & 1 m & & Under Ships Marine Radio License & \\ \hline \multicolumn{8}{c}{**GMDSS Suite**} \\ \hline MF/HF Transceiver & Salitor & 1605 KHz–30 MHz & ATU6381 & Coupler with & 1.8 m & Under Ships Marine Radio License & \\  & & & 8.5 MT. & (Rx/Tx) & ANT & & \\  & & & 800W SSB-HF & 7.0 MT. & & \\  & & & (Tx) & & & \\ \hline Satellite Communication & Salitor 6006 & 1626.5–1646.5 MHz & TT:3027C & 0.3 m & & Under Ships Marine Radio License & \\ (Sat-Com C) & & & & & & \\ \hline \multicolumn{8}{c}{**Navigation System**} \\ \hline Automatic Identification & SAAB/R5 AIS & 156–163 MHz & Celmar & 0.5 m & & Under Ships Marine Radio License & \\ System & (AIS) & & & & & \\ Navigation & & & & & \\ \hline Radar X-band (25 kW & Sperry Marine/VMFT & 9,410 MHz & Included, 2.5 m & 100 W/m\({}^{2}\) for 0.75 m & Under Ships Marine & \\ X-band transceiver & ECAT2 & 253/8/MK/VM2 & & 10 W/m\({}^{2}\) for 7.5 m & Radio License & \\ \hline Radar S-band (30 kW & Sperry Marine/VMFT & 3,050 MHz & Included, 3.5 m & 100 W/m\({}^{2}\) for 0.55 m & Under Ships Marine & \\ S-band transceiver & ECAT2 & 253/8/MK/VM2 & & 10 W/m\({}^{2}\) for 5.5 m & Radio License & \\ \hline \end{tabular}
\end{table} 
Table 1: Details of shipboard radio transmitter and antenna systems measured. VHF, very high frequency; MF, medium frequency; HF, high frequency.
## 3 DAS Field Site Deployment
### Data Processing  
The data collected was analyzed to further understand the effectiveness of the different deployment strategies. The ease of picking first arrivals and separating the up- and down-going wavefields in the presence of noise is one of the most important aspects of the data processing flow. This process helped to calibrate the data in depth against the geophones at the bottom of the well, as the attempt to define depths in the well using tap tests did not provide accurate results [38]. To ensure that all datasets were comparable, the velocity model was derived from the surface seismic tests conducted earlier in the CMC program.  
The process of selecting the initial arrival times, known as picking first breaks, is a crucial step in the analysis of VSP data collected using DAS. This step can be challenging, particularly when working with data from different cable deployment methods. Inaccurate or unreliable first break picks can negatively impact the overall reliability and accuracy of the final image.  
Determining the precise depth of the fiber optic cable in the well is also crucial for obtaining accurate seismic data [39]. This is because the relationship between the depth of the cable and the distance traveled by the light in the cable can be affected by bends and twists in the cable. In this study, geophones near the base of the well were used for calibration to help establish the cable's true depth. However, if geophones are not available, other methods, such as acoustic logs, may be necessary to obtain a precise depth measurement. This is especially important when working with other cable deployment methods, such as the liner, tubing and wireline, as it is harder to calibrate the cable's depth  
Figure 5: CaMI Infrastructure and Vibroseis Source Vibrator Point (VP) map.  
in these scenarios. This accurate cable positioning is essential for obtaining high-quality seismic data and creating a reliable image of the subsurface.
## 2 Materials and Methods
### Crude Extract Preparation  
The collected plant material was shade dried at room temperature, then cut into pieces of approximately 1 to 2 cm and ground with an electric grinder into a fine powder. After sieving, 1 g of the powder was mixed with 30.0 mL of either analytical grade ethanol (90%) or total acetone solvent. The mixture was shaken on an orbital shaker (120 rpm) for 24 h at room temperature (25 \({}^{\circ}\)C \(\pm\) 2), filtered using a glass funnel plugged tightly with cotton, and evaporated in the fume hood (room temperature) until entirely dry. Our choice of extraction solvents was based on the fact that plants in the genus _Salvia_ possess a variety of potentially therapeutic phenolic compounds [21] that require the use of moderately polar solvents such as ethanol and acetone to achieve high extraction efficiencies [28]. The obtained yields were 0.0939 g and 0.125 g for acetone and ethanol extracts respectively. The dry extracts were dissolved in dimethyl sulfoxide (DMSO) to make stock solutions of 150 mg/mL. From these stock solutions, a series of working concentrations were prepared by serial dilution using complete cell culture media. To efficiently extract such phenolic compounds, it is recommended to use moderately polar solvents such as ethanol and acetone.  
Since the acetone extract showed higher biological activities than the ethanol extract, it was analyzed along with the crude _S. triloba_ leaves for their functional groups using Fourier Transform Infrared (FTIR) Spectroscopy. The measurements were conducted on a TGA/FTIR Nicolet 380 spectrometer using 1-mm KBr pellets within the range of 500-4000 cm\({}^{-1}\).
## 2 Materials and Methods
### Deep and Wide CNN Learning from Image-Based Representations
The feature map at the last \(L\) convolutional layer \(\hat{\mathbf{Y}}_{L}^{z}\) is the input of the MLP classifier and is generated by a stepwise 2D-convolutional operation performed over the input topogram set, as follows:  
\[\hat{\mathbf{Y}}_{L}^{z}=\varphi_{L}^{z}\{\cdot\}\circ\cdots\circ\varphi_{1}^{z} \{\gamma_{I}(\mathbf{K}_{i,l}^{z}\otimes\hat{\mathbf{Y}}_{i-1}^{z}+\mathbf{B}_{i,l}^{z})\} \circ\varphi_{1}^{z}\{\cdot\},\quad\{l=1,\ldots,L\} \tag{2}\]  
where \(\varphi_{l}^{z}\{\cdot\}\) is the \(l\)-th convolutional layer that holds the corresponding non-linear activation function \(\gamma_{l}\colon\mathbb{R}^{W^{\prime}_{l}\times H^{\prime}_{l}}\to\mathbb{R} ^{W^{\prime}_{l}\times H^{z}_{l}}\), \(\hat{\mathbf{Y}}_{L}^{z}\in\mathbb{R}^{W^{\prime}_{l}\times H^{z}_{l}}\) is the resulting 2D feature map of \(l\)-th layer, \(\{\mathbf{K}_{i,l}^{z}\in\mathbb{R}^{P\times P}:i\in I_{l},z\in Z\}\) is the square-shaped layer kernel arrangement, \(P\) is the kernel size and \(i\in I_{l}\) is the number of kernels at \(l\)-th layer, and \(\mathbf{B}_{i,l}^{z}\in\mathbb{R}^{W^{\prime}_{l}\times H^{z}_{l}}\) is the bias matrix. The notation \(\otimes\) stands for convolution operator. Consequently, the predicted label probability vector \(\tilde{\mathbf{\lambda}}\) is computed within the framework that optimizes the CNN-parameter set \(\Theta=\{\mathbf{K}_{i,l}^{z},\mathbf{A}_{d},\mathbf{B}_{i,l}^{z},\mathbf{\alpha}_{d}\}\), as below:  
\[\text{s.t.:}\quad\Theta^{*}=\arg\min_{\mathbf{K}_{i,l}^{z},\mathbf{A}_{d},\mathbf{B}_{i,l} ^{z},\mathbf{\alpha}_{d}}\{\mathcal{L}(\tilde{\mathbf{\lambda}}_{n},\mathbf{\lambda}_{n}| \Theta);\,\forall n\in N\} \tag{3}\]  
where \(\mathcal{L}\colon\mathbb{R}^{\Lambda}\times\mathbb{R}^{\Lambda}\to\mathbb{R}\) is the gradient descent loss function \(\mathcal{L}\colon\mathbb{R}^{\Lambda}\times\mathbb{R}^{\Lambda}\to\mathbb{R}\) employed to estimate the optimal values of weights \(\{\mathbf{K}_{i,l}^{z*}\}\), \(\{\mathbf{A}_{d}^{*}\}\) and bias \(\{\mathbf{B}_{i,l}^{z*}\}\), \(\{\mathbf{a}_{d}^{*}\}\). As widely used in deep learning methods, a mini-batch-based gradient is implemented by automatic differentiation and back-propagation procedures [46]. Figure 1: Performed 2D feature mapping procedure as EEG data preprocessing. Aiming at preserving the spatial interpretation, we transform the extracted multi-channel feature patterns onto a 2D surface through a topographic interpolation \(\mathbb{R}^{C}\to\mathbb{R}^{W\times H}\) that maps each trial feature set as a two-dimensional circular view (looking down at the head top) using spherical splines.
## 2 Materials and Methodology
### Effect of CO\({}_{2}\) Concentration (10%, 18%, and 25%)
#### 3.2.3 Concrete Aggregate (CA-L)  
In this section, the results of the carbonation of CA-L at three different CO2 concentrations (10%, 18%, and 25%) are presented and discussed. Figure 9 presents the variation in weight loss from calcium carbonate decarbonation in the period of maximum CO2 capture, as well as before and after forced carbonation. The results indicate that there is no significant difference in CO2 capture when comparing the 10% and 25% CO2 concentrations. At 10% CO2 concentration, the aggregates were able to capture approximately 18.6% of the CO2 after a 12 h period. In contrast, at 18% CO2 concentration, an anomalous result was obtained, as only 5.1% of the CO2 was captured. This result appears inconsistent with the remaining data and should be considered as an outlier. Lastly, at 25% CO2 concentration, CA-L captured 16.9% of the CO2 after 24 h. These results suggest that there may not be a significant advantage in using higher CO2 concentrations (e.g., 25%) over lower ones (e.g., 10%) for carbonation in the case of CA-L. Some reports in the literature also present similar results: the work of [PERSON] et al. [6] reported that carbonation depth is higher when concrete samples are exposed to low concentrations of CO2 and have a lower carbonation depth when the concentration is higher. This effect can be explained due to the fact that in higher-CO2-concentration environments, the formation of a fast and highly dense network  
Figure 8: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO2 uptake was achieved for each RCA at 25, 18, and 10%.  
microstructure with low pore connectivity occurs due to the formation of calcium carbonate, hindering further CO2 diffusion [52]. However, it is important to bear in mind the unique nature of CA-L that has not previously been subjected to natural exposure, which might lead to different conclusions compared to other recycled materials like MRA and RCA.