esa-sceva/satcom-chunk-collection huggingface dataset:
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 6 Analyses of Simulation Experiments
### Roadway Width Analysis  
Figure 23 shows the variation in field strength with increasing distance from the relay node and the receiving point for widths of 0.6 m, 0.7 m, and 0.8 m of the alleyway. Let the transmitting frequency of the antenna be 0.3 GHz. The relative permittivity of the roadway wall is 6, the distance from the relay node to the plane \(m_{5}\) is \(w_{3}\) = 0.3 m, the distance from the receiving point to the plane \(m_{6}\) is \(w_{2}\) = 0.3m, the heights of both the relay node and the receiving point are 0.3 m, and the moving mode of the receiving point is in line with Figure 20.  
From Figure 23, as the distance between the relay node and the receiving point increases, a smaller width will lead to greater attenuation of the field strength at the receiving point. This is because wider roadways can reduce reflection losses and provide more propagation paths. Under the effect of multipath, the superposition effect of electromagnetic waves is enhanced, which is consistent with the conclusion of Figure 17. Therefore, to  
Figure 22: Field strength at the receiving point as a function of the distance between the relay node and the receiving point for different dielectric constants.  
minimize electromagnetic interference, the sensor should be placed in a narrower U-shaped roadway to ensure accurate measurements.
# Instantaneous Networking Service Availability for Disaster Recovery
## 2 Related Work
### Information Sharing Systems for Disaster Resilience  
In addition to communication systems, the information sharing system is also important for supporting people in emergencies, with functionalities such as warning, estimation of damage, safety confirmation, rescue guidance, etc.  
Many efforts have been devoted to the development of information sharing systems for disaster situations and emergencies. [PERSON] et al. [20] introduced an information sharing platform, named the SIP4D system, that facilitates mutual information exchange among various information systems. The SIP4D system has an objective of realizing mutual information exchange among various systems. It employs an onsite response team to collect information and identifies the needs at the site of the disaster. The collected information is transmitted to the SIP4D information system, which manages the information and can release the information to public.  
Furthermore, [PERSON] et al. [21] introduced a real-time information sharing system based on Twitter. The developed system provides location information and nearby information to users. [PERSON] et al. [22] proposed an information supporting system that allows users to actively play a role in disaster management.
## 3 The Proposed SP-IoUAV Model
### Privacy-Preserving Ensemble
#### 3.2.2 Differential Privacy  
With our proposed model, we implement a fundamental privacy-preserving technique called differential privacy that protects individual data points while allowing accurate analysis of aggregate data. Differential privacy prevents sensitive information from being extracted about particular individuals or entities by adding carefully calibrated noise to the data. It can be defined as follows:  
Given two neighboring datasets \(D_{1}\) and \(D_{2}\) that differ in only one data point, and a privacy parameter \(\varepsilon\) (epsilon), a randomized algorithm \(M\) provides \(\varepsilon\)-differential privacy if for all possible output sets \(S\):  
\[\Pr[M(D_{1})\in S]\leq e^{\varepsilon}\cdot\Pr[M(D_{2})\in S] \tag{2}\]  
As part of our proposed model, each UAV data point is perturbed with differential privacy noise before it is sent to the central server for analysis. In addition to preserving the overall utility of the data for analyzing trends and patterns, this noise ensures that the statistical properties of the data remain intact.  
Our proposed differential privacy model involves the following steps:  
\[\text{Sensitivity}=\max_{\text{neighboring datasets }D_{i}D_{i}^{\prime}}\|x-x^{\prime}\|_{1} \tag{3}\]  
For each data point \(x\in D_{i}\):  
\[\Delta x=\max_{\text{neighboring datasets }D_{i}D_{i}^{\prime}}\|x-x^{\prime}\|_{1} \tag{4}\]  
Apply Laplace noise to each data point:  
\[x^{\prime}=x+\text{Lap}(0,\Delta x/\varepsilon) \tag{5}\]  
Our proposed differential privacy model is explained in Algorithm 2.  
```
Data: Dataset \(D_{i}\) from UAV \(i\), privacy parameter \(\varepsilon\) Result: Privatized dataset \(D_{i}^{\prime}\)
1 Calculate sensitivity of the dataset using Equation (3);
2 for each data point \(x\in D_{i}\)do
3 Calculate \(\Delta x\) using Equation (4);
4 Apply Laplace noise using Equation (5);
5 end for
6 Return the privatized dataset \(D_{i}^{\prime}\) containing all the privatized data points;
7 End
```  
**Algorithm 2** Differential Privacy Algorithm  
With differential privacy, UAV data privacy is preserved by adding Laplace noise before transmission. Based on the privacy parameter \(\varepsilon\), sensitivity is calculated to determine the noise magnitude. As a result, the original data are protected while allowing accurate analysis at the central server.
## 2 Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**LPWAN** & **Reference** & **Objective** & **Results** \\ \hline \multirow{6}{*}{LoRaWAN} & [23] & Evaluating SCHC on LWM2M traffic over LoRaWAN & Up to 75\% compression ratio \\ \cline{2-4}  & [24] & Studying the channel usage of SCHC on LoRaWAN & Efficiency decreases when the probability of error increases \\ \cline{2-4}  & [25; 26] & Experimentally evaluating the latency of commercial deployment & Less than 1 s for small packets in the uplink; End-to-end latency in the order of 0.5 s and 2.5 s for a local and remote end user sending 16 bytes \\ \cline{2-4}  & [28] & Studying the behavior of SCHC in real implementation & IPv6/UDP/CoAP packets can be transmitted over LoRaWAN network; low footprint on a LoRaWAN device; Tx time is reduced up to 40\% \\ \hline \multirow{2}{*}{Sigfox} & [22] & Determining the energy performance of SCHC packet transfer over Sigfox & Empirical model has been derived, which can be used to determine the lifetime of battery-powered devices \\ \cline{2-4}  & [27] & Proposing multimodal approach with multiple IoT technologies & SCHC is crucial for the functioning of the approach \\ \hline \multirow{2}{*}{Multi} & [29; 30] & Discussing solutions for IPv6 over LoRaWAN, Dash-7 and NB-IoT & SCHC reduces the overhead of IPv6 and other protocols \\ \hline \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & [16] & Comparing major header compression standards & SCHC will likely be widely adopted in resource-constrained IoT \\ \cline{2-4}  & [17; 18; 19] & Comparing SCHC of IPv6 protocol to RoHC and 6 LoWPAN & SCHC leads to the lowest compression residue \\ \cline{2-4}  & [20] & Mathematical model to compute the parameters for the SCHC ACK-on-Error fragmentation mode & Determines the optimal tile, window and \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing work on applying SCHC on LPWAN. environment provided by Orange and using Acklio's implementation of SCHC at both the device and network sides. SCHC is used to compress the headers of the UDP, IP, DTLS (when used) and DLMS (the application layer) layers, as well as the DLMS payload. As a result of the compression, the size of the data to be transmitted is reduced, which will lead to the decrease of the energy demand of the device as well as the latency of the message exchange. The latency and energy consumption of the device are measured for different scenarios with different radio conditions and message sizes. The measurements show a significant reduction in the latency of message exchanges and the energy consumption of the NB-IoT device by up to 40% while maintaining the interoperability provided by the IP layer. The section below provides some technical background for readers not familiar with SCHC and cellular IoT networks.
# PC5-Based Cellular-V2X Evolution and Deployment
## 5 PC5-Based C-V2X Deployment in Taiwan
### Commercial Project  
A commercial C-V2X project [29] is implemented in Chang Gung Health And Culture Village as part of its technology transformation strategy. This project promotes road safety under 5G use cases for the elderly and their caregivers. The PC5-based C-V2X is used for VRUCW (Venerable Road User Collision Warning) services to provide convenience and extra protection for the aged residents and their caretakers in the village. Once the 5G NR cellular network is ready, more C-V2X services such as SPAT, remote driving management can be implemented to build up a highly collaborative and integrated ecosystem of \"People-Vehicle-Road-Network-Cloud\" five dimensions.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Evaluating Remotely Sensed Indices of Burn Severity
Although the [77] forest mask used LANDFIRE data to estimate percent canopy cover [78], we instead used the 2015 NASA GFCC [59], so as to include coverage for the Kenow fire, which occurred in Canada. Though LANDFIRE predicts canopy cover with additional biophysical variables and field validation data than the GFCC, it is only available in the United States. Pre-fire NDVI was calculated as the mean composite from Landsat imagery collected between June 1 and 30 September the year before fire. The final forest mask used a combination of mean composite pre-fire NDVI of greater than or equal to 0.4 and tree canopy cover greater than or equal to 5%. Visual examination of the mask overlaid with high-resolution aerial imagery showed this combination of thresholds provided the most consistent masking of non-forest across the 26 fires analyzed, minimizing land cover with high NDVI, such as meadows and grassland, while retaining forested pixels with sparse canopies. Estimates of high-severity patch interior for each fire were then made by calculating the Euclidian distance of each forested high-severity pixel to the nearest low- or moderate-severity forested pixel using the distance function in GEE. Pixels greater than or equal to 90 m in the resulting distance map were classified as high-severity patch interior. Finally, differences in proportion of low, moderate, high, and high-severity patch interior between fires mapped with Landsat and Sentinel imagery were assessed using a Wilcoxon signed-rank test in R version 4.1.2 [71]. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Composite**} & \multicolumn{3}{c}{**Landsat 8**} & \multicolumn{2}{c}{**Sentinel-2**} \\
**Imager Method** & **Spectral Index** & \multicolumn{2}{c}{**Surface Reflectance (Tier 1)**} & \multicolumn{2}{c}{**Top of Atmosphere**} \\  & & & \(R^{2}\) & \(RMSE\) & \(R^{2}\) & \(RMSE\) \\ \hline \multirow{4}{*}{Extended imagery} & _dNBR_ & 0.552 & 0.538 & 0.561 & 0.526 \\  & _RBR_ & 0.606 & 0.480 & 0.613 & 0.474 \\  & _RdNBR_ & 0.613 & 0.463 & 0.643 & 0.447 \\  & _RdNDVI_ & 0.554 & 0.519 & 0.426 & 0.765 \\ \hline \multirow{4}{*}{Hybrid imagery} & _dNBR_ & 0.570 & 0.502 & 0.517 & 0.542 \\  & _RBR_ & 0.627 & 0.443 & 0.573 & 0.489 \\ \cline{1-1}  & _RdNBR_ & 0.641 & 0.423 & 0.579 & 0.487 \\ \cline{1-1}  & _RdNDVI_ & 0.562 & 0.441 & 0.515 & 0.396 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Composite imagery method model fit (\(R^{2}\)) and normalized root-mean-square error (\(RMSE\)) for non-linear least squares regression between composite burn index (CBI) plots and remotely sensed burn severity estimates using either Landsat or Sentinel imagery. Results are presented for the four spectral indices tested and computed using pre- and post-fire imagery composited using either the extended or hybrid imagery seasons (see Table 3). Models were fit to the combined dataset of 912 CBI plots from all 26 fires.
## 2 Related Works
### High Sectored Cells on the Fractional Frequency Reuse  
The high-sectored cells scheme, which consists of more than six sectors, is not a new concept in wireless communication. In [44], a sector offset configuration scheme is introduced. Each base station has two sector configuration, and a sector configuration consists of three hexa-cells. The pattern of second configuration is shifted by 60 degrees to implement the six-sectored cell. In [45], higher order sectorization gains are evaluated, with 6, 9, 12 and 15 sectored cell sites. For the D2D links, there are several six-sectored studies. The authors of [46] propose a D2D-enabled Hetnet system based on a six-sector system. It also adopts On/Off switching frequency to reduce the number of outage users. In [47], the authors propose a virtual sectoring concept to sectoring based on D2D communications. Because of the virtual sectoring, the sectoring can be controlled adaptively according to a density of D2D pairs. According to the above studies, FFR with a high-sectored cells scheme is still able to be considered. Simultaneously, it implies that the scheme is sufficiently implementable. Based on the related works, we propose a resource allocation with six-sectored cells for uplink and D2D link.
## 3 Construction Site Layout Planning Problem  
The CSLP problem focuses on the physical arrangement of sites, machines, or departments in the scope of a predetermined configuration. In general, the layout problem refers to the issues of (i) identification of the size and shape of the facilities; (ii) identification of constraints between facilities; and (iii) determination of the facilities' relative positions aimed at satisfying the constraints and enabling efficient functioning. CSLP problem consists of coordinating the restricted space in the city that should accommodate temporary facilities, such as the site office, equipment or materials, trailers, and fabrication shops. The purpose is to have an efficient functioning on the site.  
An effective layout will assist any company in improving its project performance. Moreover, the facility layout is often a costly long-term investment. Hence, it is not possible to easily perform modifications as it requires significant time and budget. Relayouting facilities is time-consuming; moreover, it disrupts the flow of materials and activities of workers [31]. Thus, proper layout planning reduces construction costs (plant, labour, and material), increases labourer and equipment productivity (labour time and equipment time), and improves safety (hazardous materials, falling objects, and waste) on a construction site.  
As discussed earlier, the CSLP can be formulated as a QAP. In this study, it is assumed that each facility occupies an identical area. Thus, it is feasible to assign any site to any facility. Currently, there are no general methods or techniques employed to handle these tasks. Each task is solved independently of the others based on project-specific assumptions and well-defined information. The purpose is to find suitable solutions that satisfy all the constraints outlined by the decision-maker. Thus, the objective of this QAP is to find the optimal assignment of \(n\) facilities to \(n\) sites. The objective function is minimizing the total travelling distance taken based on the product of distance and frequency of trips of the site workers. The total travelling distance of facilities layout can be defined as QAP optimization model as shown in the following equations:  
\[\text{Minimize }TD=\sum_{i=1}^{n}\sum_{j=1}^{n}\sum_{k=1}^{n}\sum_{l=1}^{n}f_{ ik}d_{jl}x_{ij}x_{kl} \tag{1}\]  
\[\text{Subject to }\sum_{j=1}^{n}x_{ij}=1,\ i=1,2,3,\ldots,n \tag{2}\]  
\[\sum_{i=1}^{n}x_{ij}=1,\ j=1,2,3,\ldots,n \tag{3}\]  
\[x_{ij}\in\{0,1\},\ i=1,2,3,\ldots,n,\ j=1,2,3,\ldots,n, \tag{4}\]  
where \(n\) is the number of facilities, \(x_{ij}\) and \(x_{kl}\) are members of facility-location assignment matrix (\(x_{ij}=1\) if facility \(i\) is assigned to location \(j\); \(x_{ij}=0\) otherwise; \(x_{kl}=1\) if facility \(k\) is assigned to location \(l\), \(x_{kl}=0\) otherwise);\(f_{ik}\) is the frequencies of trips of construction personnel between facilities \(i\) and \(k\); and \(d_{jl}\) is the distances between locations \(j\) and \(l\).
## 4 Results and Discussions
### Effect of Cycle's Components' Performance on the Cycle's Overall Performance  
Figures 6-9 reveal the impact of the components' performances on the performance of sCO\({}_{2}\)-BC. Figure 6 shows the variations in the cycle's efficiency (\(\eta_{cyc}\)) with a split mass fraction for numerous combinations of effectiveness of the high-temperature recuperator and low-temperature recuperator. A significant increase in the cycle's efficiency was observed, with an increase in value effectiveness for both recuperators. Specifically, an increase in the effectiveness from 0.9 to 0.98 increases the cycle's efficiency by 22.5%. Moreover, it can be seen that an increase in the recuperator's performance affected the split mass fraction's optimal value. The value \(x\) decreased marginally with a rise in the effectiveness values of the recuperator. Figure 7 shows a modification of the cycle's efficiency with a split mass fraction for various turbine efficiency values. It was observed that turbine performance significantly affects the cycle's performance; a 2% increase in the turbine's performance increased the cycle's performance by 1%. A slight decrease in the values of optimized split mass fraction can also be seen in Figure 7, with an increase in the turbine's efficiency. This reflects that load on the main compressor will decrease with an increase in the value of the turbine's efficiency. It should also be noted that the sensitivity of the cycle's performance to the effectiveness of the recuperator increases with an increase in the value of the split mass fraction as the \(\eta_{cyc}\) curve diverges along with the increasing value of \(x\). Additionally, the sensitivity of \(\eta_{cyc}\) to \(\eta_{t}\) decreases with the growth in x. Figure 8 shows the variation in \(\eta_{cyc}\) with the disparity in the evaluation of \(x\) for various values of \(\eta_{c}\) and \(\eta_{rc}\). Results suggest that \(\eta_{cyc}\) curves exist with various \(\eta_{c}=\eta_{rc}\) values. Similar trends are displayed by \(\eta_{cyc}\) curves in Figure 7 for different values of \(\eta_{t}\). Cycle efficiency significantly increased with an upsurge in the compressor's efficiency. The value of the optimal split mass fraction shifted toward lower values of the split mass fraction with an upsurge in the value of \(\eta_{t}\). Moreover, the sensitivity of \(\eta_{cyc}\) with \(\eta_{c}\) deceased with an increase in the value of the split mass fraction. Figure 9 shows a comparison of how performance parameters associated with the cycle components affected the overall cycle's performance. It can be seen that the cycle's performance was most vulnerable to the recuperator's performance, followed by the performance of the turbomachinery.  
Figure 5: Variations in the values of \(T_{3}\), \(T_{5}\), and cycle efficiency with the split mass fraction.  
Figure 8: Impact of the compressor’s performance on the cycle’s efficiency.  
Figure 6: Impact of recuperator’s effectiveness on the cycle’s efficiency.  
Figure 7: Impact of the turbine’s performance on the cycle’s efficiency.
# Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
## 2 Methods
### Development of System Software
#### 2.3.2 Element/Process Virtualization
And, when the virtual model rotates around the \(X\), \(Y\), and \(Z\) axes, respectively \(\theta\), the rotated coordinates can be expressed as Equations (3)-(5). \[\left[x^{\prime}\ y^{\prime}\ z^{\prime}\ 1\right]=\left[x\ y\ z\ 1\right] \begin{bmatrix}1&0&0&0\\ 0&cos\theta&sin\theta&0\\ 0&-sin\theta&cos\theta&0\\ 0&0&0&1\end{bmatrix}  \tag{3}\]  
\[\left[x^{\prime}\ y^{\prime}\ z^{\prime}\ 1\right]=\left[x\ y\ z\ 1\right] \begin{bmatrix}cos\theta&0&-sin\theta&0\\ 0&1&0&0\\ sin\theta&0&cos\theta&0\\ 0&0&0&1\end{bmatrix}  \tag{4}\]  
\[\left[x^{\prime}\ y^{\prime}\ z^{\prime}\ 1\right]=\left[x\ y\ z\ 1\right] \begin{bmatrix}cos\theta&sin\theta&0&0\\ -sin\theta&cos\theta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{5}\]  
When the virtual model is scaled proportionally along the coordinate axis, the transformation expression can be shown using Equation (6). \[\left[x^{\prime}\ y^{\prime}\ z^{\prime}\ 1\right]=\left[x\ y\ z\ 1\right] \begin{bmatrix}s_{x}&0&0&0\\ 0&s_{y}&0&0\\ 0&0&s_{z}&0\\ 0&0&0&1\end{bmatrix}  \tag{6}\]  
In the formula, \(Sx\), \(Sy\), and \(Sz\) are the scaling ratios of the virtual model on the \(X\), \(Y\), and \(Z\) axes, respectively. The description of all device actions can be achieved through transformation and combination processes. After importing the model, the model actions can be decomposed into translation, rotation, and scaling. Through defining the action controllers encapsulated by WebGL technology, and by setting the action type and the motion distance, respectively, the action controllers are bound to the model to control the model actions using driving variables. Additionally, the scene-roaming mode can be used to enhance the user experience. By simulating the perspective of the user walking in the scene, the user can obtain a feeling of on-site observation. In this mode, users can use the directional keys to freely move in the scene and interact with each other by moving to the designated location according to their needs. If the user wants to obtain the processing status of the relevant equipment, they can click on the corresponding virtual model with the mouse, and detailed information about the equipment's operation will appear next to the model.