esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
We suggest policy-based support to provide rapid first aid using local personnel. As the current major medical infrastructure is concentrated in urban areas, more emergency medical facilities should be established in rural areas to improve accessibility to EMS. However, it may be less efficient to build new emergency medical centers to cover all rural areas due to the personal and social expenditure needed. Instead, a more efficient method is to provide first aid to emergency patients to improve the survival rate. Based on our analysis, when first aid is provided by a primary respondent before an EMT-P arrives, the survival rate increased by 70% of the villages in rural areas. In addition, the survival rate increased in rural areas where an EMT-P or a healthcare provider provides first aid and then transfers the patient to an emergency medical facility. In urban areas, however, access by an EMT-P or a healthcare provider increased the time required for patient transfer and ultimately decreased the survival rate. Healthcare providers can also be used as additional personnel in rural areas to provide prompt and proper first aid. To further reduce the transfer time of emergency patients in urban areas, the government should consider introducing a traffic sign control system based on artificial intelligence on the road.  
Based on some studies revealing the importance of reducing EMT-P dispatch time and implementing AED by the public [57; 58; 59], establishing an efficient response system that could increase patients' survival rate if they receive quick first aid could be through additional dissemination of AEDs to places with potential risks (e.g., with many acute cardiac arrest patients, highly populated, and largely transient populated places) as well as through continuous training for the public on how to use AEDs and CPR. Because the most effective way to increase the survival rate is immediate first aid treatment, the government should train the public to administer CPR as well as to install and educate AEDs in populated places. One effective way to offer faster first aid is to foster the human infrastructure, including police, general staff of community health centers, apartment or building managers, and village representatives (town chiefs), to increase opportunities to provide first aid. These approaches would also be reasonable ways to improve the survival rate of emergency patients considering both the access time to emergency medical centers in urban areas and more people who can help provide first aid in rural areas before an EMT-P arrives.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 2 Related Work  
One of the greatest challenges for deploying a sustainable wireless mesh network is the channel allocation problem. The overall performance of the network can be increased by allocating suitable channels to minimize the network interference. One of the greatest challenges for deploying a sustainable wireless mesh network is the channel allocation problem. The overall performance of the network can be increased by allocating suitable channels to minimize network interference. [PERSON] et al. [14] used partially overlapped channel (POC) based design to improve the network capacity. Their POC algorithm was divided into two steps, neighbor-to-interface binding, and interface-to-channel binding. The first step determined the relation between interfered nodes. The second step determined which channel should be used to minimize the network interference. Although implementing a centralized channel assignment is simple, this puts the burden on the center node. If this node cannot operate properly, the networks will be disrupted.  
In [15], the authors proposed a channel assignment scheme based on learning automata. They used a dynamic prediction method to change the channel assignment to improve the network's overall performance. Every node independently calculates a utility value in a stochastic iterative procedure to respond to the currently selected channel. The stochastic procedure is used to solve the problem without giving any information on the optimum action (initially, equal probabilities for all are attached to the proceedings). The allocation of channels varies adaptively with dynamic network conditions by changing the network traffic patterns.  
The authors in [16] proposed a method for dynamic channel allocation depending on Bayes' theorem. They aimed to improve the network's throughput and minimize the loss packets by giving the priority of change to the links with the bottleneck. Bayes' theorem is a mathematical method used to measure conditional probabilities. The authors measured the statistical deduction on dropped packets to determine the impact of interference on the crowded links. Therefore, the overloaded links were identified using the traffic capacity.  
In [17], the authors introduced communication constraints and measured the overall performance under the usage of partially overlapped channels (POCs). By using a real test-bed, the measured network throughput was improved after using more parallel communications. However, the interference was increased under certain circumstances.  
The authors in [18] implemented a channel selection process between the APs base station (BS) and the host stations (STAs). The selection of channels occurs dynamically, in which each STA operates to allocate appropriate communication bandwidth to all STAs and BS regardless of their level of packet generation.  
The authors in [19] investigated the channel assignment for unmanned aerial vehicle (UAV) communications using 5G technology. They used millimeter wave (mmWave) bands for cellular services with their applications.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Simulation Analysis
#### 4.2.2 Waveband Analysis  
Targets' different infrared properties and distance from the sensor cause them to show different levels of brightness on the sensor at the same time. Because of changes in the solar vector and observation distance, some of a target's signal may be lost in the sensor system noise response, rendering it undetectable. Additionally, certain targets have a lower signal than stars in the FOV, making them harder to identify within the image. Figure 10 displays the imaging results of satellite Sat11 sensors on a target group simultaneously using different wavebands. The simulation scenario indicates that, because of the exclusion of interference from the imaging time of the sensor and the spatial relationship between the sensor and targets, the targets exhibit the same geometric features; the SNR of the medium wave sensor is more significant than that of the medium-long wave and long wave sensors. Traversing the sensor band reveals that increasing the band's width can significantly enhance the sensor's SNR within the background noise of deep space.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
###### Abstract  
The coastal urban multi-terminal DC (CU-MTDC) is a prospective solution for enhancing the power supply security of the coastal urban power (CUP) grid and integrating the large-scale offshore wind farm. However, the large DC disturbances may significantly impact the CUP power supply security. The existing DC unbalanced power distribution methods are difficult to be applied in the CU-MTDC because of the complicated optimization process and the expansion of the influence range of a DC fault. To solve the above problems, this paper proposes a coordinated control strategy of CU-MTDC under abnormal conditions. First, calculation principles for the active power reference of center coastal urban power (CCUP) grid converters are proposed. Second, a DC unbalanced power coordinated distribution strategy under abnormal conditions is used based on the dynamic priority control to ensure power supply security of the critical AC lines. Third, the controller parameters of CCUP grid converters are calculated. Through a simple control process, the number of the regulation converters is dynamically scheduled according to the DC unbalanced power. The DC fault influence range on the urban power grid can be limited in a sufficiently small area. Simulation verified the effectiveness of the proposed control strategy.  
coastal urban multi-terminal DC (CU-MTDC); power supply security; offshore wind farm; coordinated control strategy under abnormal conditions; dynamic priority control
## 3 Results
### Linear Conservation Laws
#### 3.1.1 Scalar Case  
This is the simplest case of Equation (19):  
**Problem 1**.: _In Equation (19), let \(\underline{U}\) consists only one component and each \(\underline{A}\) consists a single number:_  
\[\underline{U}(\vec{x},t)=\left[U(x,y,z,t)\right],\qquad\underline{A}^{x}=\left[ -10\right],\qquad\underline{A}^{y}=\underline{A}^{z}=\left[0\right].\]  
_The following boundary conditions_  
\[\underline{U}(x=0,y,z,t)=\left[-10\right]\eqqcolon\underline{U}_{\mathrm{L}}, \qquad\underline{U}(x=4,y,z,t)=\left[+10\right]\eqqcolon\underline{U}_{ \mathrm{R}},\]  
_and the initial condition_  
\[\underline{U}(x,y,z,t=  
Figure 4: Third-order solution of Problem 1 on big (\(h\approx 2^{-2}\)) cells.  
Figure 3: First-order solution of Problem 1 on tiny (\(h\approx 2^{-5}\)) cells.  
Figure 2: First-order solution of Problem 1 on small (\(h\approx 2^{-4}\)) cells.  
In Table 1, we show the measured error and time cost of each solver/mesh pair. The seconds consumed by first-order solutions are somewhat exaggerated, since we use the same high-order quadrature rules for both first- and third-order solvers, which is necessary to integrate non-constant errors. If we did not have to measure the errors, then low-order numerical integrators could be used, which might save some time.  
The following conclusions can be drawn from both Figures 1-6 and Table 1:  
* Both mesh refinement (decreasing \(h\)) and order increment (increasing \(p\)) can help to improve accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\mathbf{L_{1}}\)**-Error with Respect to the Analytic Solution**} \\ \hline \hline \(\mathbf{p}\)\(\mathbf{h}\) & \(\mathbf{2^{-2}}\) & \(\mathbf{2^{-3}}\) & \(\mathbf{2^{-4}}\) & \(\mathbf{2^{-5}}\) \\ \hline
1 & 2.858 & 2.095 & 1.524 & 1.108 \\
2 & 1.258 & 0.771 & 0.463 & 0.275 \\
3 & 1.021 & 0.590 & 0.341 & \\ \hline \multicolumn{3}{c}{**Time Cost (in Seconds) Measured on a Single Core Whose Main Frequency Is 2.7 GHz**} \\ \hline \hline \(\mathbf{p}\)\(\mathbf{h}\) & \(\mathbf{2^{-2}}\) & \(\mathbf{2^{-3}}\) & \(\mathbf{2^{-4}}\) & \(\mathbf{2^{-5}}\) \\ \hline
1 & 0.373 & 1.129 & 16.533 & 306.293 \\
2 & 1.580 & 14.894 & 253.821 & 4986.391 \\
3 & 4.147 & 61.425 & 906.914 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy and time cost of each solver–mesh (\(p\)–\(h\)) pair.  
Figure 5: Comparison between solutions of Problem 1 given by running the same solver on different meshes.  
Figure 6: Comparison between solutions of Problem 1 given by running different solvers on the same mesh.  
* The solver of the highest order (\(p=3\)) on the coarsest (\(h\approx 2^{-2}\)) mesh defeats the solver of the lowest order (\(p=1\)) on the finest (\(h\approx 2^{-5}\)) mesh in accuracy but saves quite a lot of time.
* High-order schemes are better than low-order ones in the sense of getting the same level of accuracy with less time cost.
## 4 Experiments and Results
### Contribution Analysis of the Side Signals
The three-dimensional WVD was reconstructed using the traditional method and the proposed approach, respectively. To assess the retrieval capabilities of the optimized tomographic model, the SWV differences between the observed SWV and reconstructed SWV from the three-dimensional tomographic water vapor field were calculated, and the SWV differences between the traditional method and the proposed approach were obtained, respectively. Figure 12 illustrates a comparison of the SWV differences between the two methods in the four schemes. There is a noticeably similar tendency, in that the SWV differences of GNSS signals with an elevation angle larger than 45\({}^{\circ}\) are in the range of \(-\)5 to 5 mm, while those of rays with an elevation angle lower than 45\({}^{\circ}\) sharply increase with a decreasing elevation angle in all four schemes. As far as the comparison of SWV differences between the general and optimal tomography model is concerned, a remarkable improvement can be identified in the East scheme, West scheme, and South scheme, whereas the proposed method is slightly better than the traditional one in the North scheme. The same conclusion can also be deduced from Table 3, where the statistics, including the RMSE, standard deviation (STD), and bias, for the SWV differences from the four schemes are compared. The improvements of the combined observations with respect to only the integral one are 3.11/3.34, 2.55/2.19, 2.60/2.62, and 1.27/1.35 mm for the RMSE/STD in the four schemes, respectively. In the bias comparison, there is a visible advancement of the tomography solutions when inside signals are included. In addition, the tomographic results derived from the general model and improved model were compared with radiosonde data at 00:00 UTC and 12:00 UTC, when the radiosonde data provided accurate water vapor profiles at different altitudes. Table 4 lists the mean RMSE and STD of the tomography results retrieved from the integral signals and combined signals for August 2017. It can be observed that the tomography results are closer to the radiosonde data when the inside rays are absorbed. The improved tomography model displays a smaller RMSE than the conventional one in the four test schemes, with decreases of 33.12%, 30.86%, 19.27%, and 30.67% in the mean RMSE, and  
Figure 12: Comparison of the SWV differences between the observed SWV and retrieved SWV from the three-dimensional tomographic water vapor field with the two methods in the four schemes. (**a**) Scheme East; (**b**) Scheme West; (**c**) Scheme South; and (**d**) Scheme North. the approximate consistent decrease rate of the STD for the four schemes can also be computed from Table 4.
# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
## 2 System Model and Problem Formulation
Similarly, we transform Equation (21) to a linear function to reduce the computational complexity. After the conversion, the constraint in Equation (21) is given by:  
\[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(S_{n,i,c}|h_{n,c}|^{2}-P_ {n,i,c}|h_{n,c}|^{2})\geq\psi,\forall c,n\in\mathcal{A},i\in\mathcal{B}, \tag{2}\]6  
For the given value of \(x_{n,i,c}\), the problem is now convex. Hence, optimal solution can be obtained by employing duality theory [31]. The dual problem is given below:  
\[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(S_{n,i,c}|h_{n,c}|^{2}-P_ {n,i,c}|h_{n,c}|^{2})\geq\psi,\forall c,n\in\mathcal{A},i\in\mathcal{B}, \tag{2}\]7 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(S_{n,i,c}|h_{n,c}|^{2}-P_ {n,i,c}|h_{n,c}|^{2})\geq\psi,\forall c,n\in\mathcal{A},i\in\mathcal{B}, \tag{2}\]8  
The \(D(\lambda_{i},\mu_{c},\eta,\tau_{n},\theta_{i})\) in Equation (28) is called the dual function and \(\lambda_{i},\mu_{c},\eta,\tau_{n},\theta_{i}\) are the dual variables. The dual function is given as:  
\[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(S_{n,i,c}|h_{n,c}|^{2}-P_ {n,i,c}|h_{n,c}|^{2})\geq\psi,\forall c,n\in\mathcal{A},i\in\mathcal{B}, \tag{2}\]9  
where \(L(\lambda_{i},\mu_{c},\eta,\tau_{n},\theta_{i},Q_{n,i,c},\beta_{n,i,c})\) is the Lagrangian of the problem and is written as:  
\[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]0 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]1 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]2 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]3  
Simplifying Equation (29), we get:  
\[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]4 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]5 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]6 \[\sum_{n=1}^{W/2}\sum_{i=1}^{W/2}x_{n,i,c}(P_{n,i,c}+S_{n,i,c})|f_{ c}|^{2}\leq I_{th},\forall c,n\in\mathcal{A},i\in\mathcal{B},\]7Note that \(\sum_{i=1}^{W/2}\lambda_{i}\psi-\sum_{c=1}^{W/2}\mu_{c}I_{th}-\eta P_{T}+\sum_{n=1 }^{W/2}\tau_{n}(2^{R_{T}}-1)\sigma^{2}+\sum_{i=1}^{W/2}\theta_{i}(2^{R_{T}}-1) \sigma^{2}\) are constants, thus can be ignored.
## Materials and Methods
### State-of-Charge Estimation  
One way of expressing the energy storage capacity of a battery is via the SOC. An alternative way to represent the capacity is the DoD, which is most frequently applied when discussing the lifetime of a battery after repeated use. The SOC indicates the amount of capacity available in the battery as a fraction of the total nominal capacity, while the DoD indicates the usage of the battery capacity as a fraction of the initial total nominal capacity. Here, the initial SOC was varied in order to simulate having a battery bank with a range of energy storage capacities. The initial SOC of the Li-ion batteries was calculated based on the V\({}_{OC}\) method that was used by [PERSON] et al. [66] at the beginning of each experiment. The SOC-V\({}_{OC}\) characteristics of a Li-ion cell were divided into eight segments by approximating the piecewise linear curve, with each segment expressed as a linear relationship, as shown in Equation (8):  
\[SOC=f(V_{oc})=a\cdot V_{oc}-b, \tag{8}\]  
where the varying coefficients \(a\) and \(b\) (%/V) are dependent on the \(V_{OC}\) intervals [66; 67]. Assuming a single lithium-ion cell has a V\({}_{OC}\) of 3.6 V [68], the equation is adapted with a factor of 7 to have an output voltage of 24 V. The V\({}_{OC}\) was firstly measured, and then, the initial SOC was calculated based on this method, which was implemented in a computer running LabVIEW. The calculations of the SOC during the experiments were estimated by the Coulomb counting method [66]:  
\[SOC=SOC_{0}+\frac{1}{Q_{rated}}\int_{t_{0}}^{t_{0}+\tau}I_{b}d\tau\cdot 100 \tag{9}\]  
where SOC\({}_{0}\) is the initial SOC, Q\({}_{rated}\) is the rated capacity of the battery (Ah; here, higher than \(C_{x}\)), I\({}_{b}\) is the current of the battery (A), t\({}_{0}\) is the initial time (h) and \(\tau\) is the time interval of charging/discharging (h).
# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
## 3 Pulse-Shape Discrimination
### Convolutional Neural Network
The folding of the one-dimensional input signal is shown in Figure 7. The convolution and pooling rules show that the conversion of the input signal into a two-dimensional matrix can reduce the number of operations in the convolution and pooling layers, thereby reducing the training time. The smooth signal variation in the one-dimensional vector format during convolution and pooling makes it difficult to extract signal features from networks, whereas the two  
Figure 6: CNN structure. -dimensional matrix format integrates the feature extraction of the discontinuous position of the waveform to strengthen the _PSD_ performance of the network. To verify the above inference, two CNNs were created separately, with no differences in network structure except the input signal format, and both were trained using the same input signal. The training effect was evaluated by the accuracy, which is the percentage of correct particles in the total number of particles judged by CNN. Table 3 shows the training time and training effect after the same amount of time. The training time of the input signal in the two-dimensional matrix format is shorter, and the training effect is better. Therefore, this study uses a CNN whose input signal format is a two-dimensional matrix..  
After inputting the neutron and gamma signals shown in Figure 4 into the CNN, the convolution-pooling layer extracts and transfers the features of the signal. Figure 8 shows the changes in neutron and gamma signals after passing through the first and second convolutional layers. It can be seen from the figure that CNN can extract the characteristics of neutron and gamma signals and amplify the differences between the features of different types of signals as a way to increase the accuracy of the subsequent signal classification process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Signal Format** & **Training Time (min)** & **Accuracy (\%)** \\ \hline One-dimensional vector & 142.50 & 97.73 \\ Two-dimensional matrix & 96.21 & 99.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of one-dimensional vector input and two-dimensional matrix input results. Figure 7: Format conversion diagram of input signal. In the following formula  
\[D=\frac{\sum_{i=1}^{N}(g_{i}-n_{i})^{2}}{N} \tag{12}\]  
\(D\) represents the difference between the two signals, and the larger the value of \(D\), the more obvious the difference. \(g\) is the gamma signal, \(n\) is the neutron signal, and \(N\) is the length of the signal. After calculation, the values of \(D\) of the input signal, after the first convolution layer and after the second convolution layer are \(5.437\times 10^{-4}\), \(1.8\times 10^{-3}\), and \(3.9\times 10^{-3}\), respectively, which indicates that the difference is significantly improved by each process.