esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Correlant Cherry Extraction Processes  
#### 2.2.1 Conventional Extraction (CE) Experiments  
The conventional extractions were carried out in an orbital shaking water bath (Grant, OLS 200, Cambridge Ltd., Cambridge, UK). In each experiment, an amount of 1 g of comelian cherry freeze-dried powder was transferred to a 15 mL falcon test tube, followed by the addition of 10 mL of solvent. The tube was placed in the water bath, where it was kept under mild shaking conditions (150 rpm) for the desired extraction time. After the extraction time was completed, the tubes were centrifuged at 5000 rpm and 9\({}^{\circ}\)C for 20 min (Thermo Scientific Sorvall ST16R, Hudson, New York, USA), and the supernatant was kept at 4 \({}^{\circ}\)C until analysis.  
A Box-Behnken experimental design was used to study the effect of three experimental variables on the bioactive compound's recovery: X\({}_{1}\), the extraction temperature (30, 40, and 50 \({}^{\circ}\)C); X\({}_{2}\), the extraction time (15, 30 and 45 min); and X\({}_{3}\), the solvent used (hydroalcoholic mixtures, 60, 80, and 100% ethanol, \(v/v\)). Three responses were studied: the TPC, the TFC, and the TAA content of the extracts. The experimental plan consisted of 15 runs, including three repetitions at the central point of the experimental design (hydroalcoholic solution with 80% ethanol (\(v/v\)), 40 \({}^{\circ}\)C, and 30 min) in order to evaluate the reproducibility of the experimental results and to estimate the curvature of the model and the nonlinear responses. All the experiments were randomized. In the optimization process, the target was to find the combination of the experimental variables that simultaneously maximized the TPC, the TFC, and the TAA of the extracts.
## 3 Mathematical Formulation and Methodology  
Several procedures are carried out to process the data collected from the measurement campaigns. In order to keep the quality of the collected data higher, the measurement campaigns that had a success rate of received packages less than 98% were discarded due to the low SNR. Such a threshold was obtained empirically in previous experiments, whose results presented low adherence when comparing experimental and theoretical distribution functions.
# Cyber-Attack Scoring Model Based on the Offensive Cybersecurity Framework
## 1 Introduction
The scoring score was calculated in two steps: (1) the first was to calculate the score for how many Offensive Cybersecurity elements were used in each stage of the CKC; (2) Second, we calculated how many cyber-attack techniques were used in 12 ATT&CK. Finally, the first and second steps were combined to calculate the final score. We utilized published analytical reports for investigating the techniques used. The main contributions of the proposed scoring model using an offensive cybersecurity framework can be summarized as follows:  
1. We defined and derived the comprehensive offensive cybersecurity framework and taxonomy;
2. We performed a content analysis of public reports of cyber-attacks and identified detailed techniques used in cyber-attacks;
3. We provided a systematic scoring model based on the offensive cybersecurity framework;
4. We calculated the score results of ten fileless and eight APT group cyber-attacks. \begin{table}
\begin{tabular}{l l} \hline \hline
**Nations** & **APT Groups** \\ \hline \multirow{2}{*}{China} & APT1, Common Crew, PLA Unit 61398, Group 3, APT2, PLA Unit 61486, Buckeye, Gothic Panda \\  & Sofacy, APT28, Sednit, Pawn Storm, Group 74, Fancy Bear, Grizzy Steppe, APT29, \\  & Dukes, Group 100, Cozy Duke, Cozy Bear, Cozer \\ North & Lazarus Group, Labyrinth Chollima, Bureau 121, Whois Hacking Team, Hidden \\ Korea & Cobra, DarkHotel, Luder, Karba, APT-C06, Dubium, Fallout Team, Tapaoaux \\ Iran & Cutting Kitten, TG-2889, Ghambar, COBALT GYP5Y, Magic Hound, Timberworm, \\  & Elfin, Refined Kitter, APT33, Holmium, Shamoon2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Names of various Advanced Persistent Threat (APT) groups (sample). The remainder of this paper is structured as follows. Section 2 provides the background and presents a literature review of research pertaining to offensive cyber security. Section 3 explains our overall methodology toward offensive cyber security and addresses each element of offensive cyber security in detail. Section 4 discusses the scoring results assigned to cyber-attacks. Finally, Section 5 provides considerations for future work and conclusions.
## 3 The Synchronous Machine
### Energy Conversion in the Machine  
In this section we use the simplified model presented above to demonstrate energy conversion processes in the machine. The total energy stored in the machine is composed of two parts:  
* The kinetic energy of the rotor: \(E_{rot}=\frac{1}{2}J\omega_{m}^{2}=\frac{1}{2}J\left(\frac{2}{\text{poles}}\right)^{ 2}\omega^{2}\).
* The magnetic energy, represented by the energy stored in the synchronous inductance: \(E_{L}=\frac{3}{4}L_{s}\left(\hat{t}_{d}^{2}+\hat{t}_{q}^{2}\right)\).
* The kinetic energy derivative is \[\frac{\text{d}}{\text{d}t}E_{rot}=\frac{\text{d}}{\text{d}t}\left(\frac{1}{2 }J\omega_{m}^{2}\right)=J\omega_{m}\frac{\text{d}\omega_{m}}{\text{d}t},\] (107) and using \(\frac{\text{d}\omega_{m}}{\text{d}t}=\frac{1}{J}(T_{m}-T_{e})\), yields \[\frac{\text{d}}{\text{d}t}E_{rot}=\omega_{m}T_{m}-\omega_{m}T_{e}=p_{m}-p_{e}.\] (108)  
This is an intuitive result: the change in kinetic energy is equal to the difference between the mechanical and electric powers. In addition, the magnetic energy derivative is  
\[\frac{\text{d}}{\text{d}t}E_{L}=\frac{3}{2}L_{s}\left(i_{d}\frac{\text{d}i_{d }}{\text{d}t}+i_{q}\frac{\text{d}i_{q}}{\text{d}t}\right). \tag{109}\]  
Using (102),  
\[\begin{split}\frac{\text{d}}{\text{d}t}E_{L}&= \frac{3}{2}\left(i_{d}\left(-R_{a}i_{d}+\omega L_{s}i_{q}-v_{d}\right)+i_{q} \left(-R_{a}i_{q}-\omega L_{s}i_{d}+\frac{\omega}{\omega_{s}}V_{E}-v_{q}\right) \right)\\ &=\underbrace{\frac{3}{2}\frac{\omega}{\omega_{s}}V_{E}i_{q}}_{p_ {e}}-\underbrace{\frac{3}{2}(v_{d}i_{d}+v_{q}i_{q})}_{p_{s}}-\underbrace{\frac {3}{2}R_{a}\left(\hat{t}_{d}^{2}+\hat{t}_{q}^{2}\right)}_{\text{ohmic loss}}\\ &=p_{e}-p_{s}-\frac{3}{2}R_{a}\left(\hat{t}_{d}^{2}+\hat{t}_{q}^ {2}\right).\end{split} \tag{110}\]  
The change of energy stored in the inductor is equal to  
* \(+p_{e}\) is the electric power decelerating the rotor. This is also the power generated by the EMF source, as shown below.
* \(-p_{s}\) is the machine's output power.
* \(-\frac{3}{2}R_{a}\left(\hat{t}_{d}^{2}+\hat{t}_{q}^{2}\right)\) is the ohmic power loss on the armature resistance.  
The flow of power is summarized in Figure 14.  
The heart of the energy conversion process is achieved by the EMF source, which converts mechanical energy to electrical energy (or vice versa). From a mechanical perspective: \(p_{e}=T_{e}\omega_{m}\). From an electrical perspective:  
\[p_{e}=\frac{3}{2}\frac{\omega}{\omega_{s}}V_{E}i_{q}=\frac{3}{2}\left(0\cdot i _{d}+\frac{\omega}{\omega_{s}}V_{E}\cdot i_{q}+2\cdot 0\cdot 0\right)=\frac{3}{2} \left(e_{d}i_{d}+e_{q}i_{q}+2e_{0}i_{0}\right). \tag{111}\]  
This is, by definition, the power generated by the EMF source.
## 3 Methods
### GeoDetector  
GeoDetector is a statistical method that can detect spatial stratified heterogeneity and identify the underlying driving force [42; 43]. This method was originally applied in the field of health risk assessment [42] and has been widely used in various fields in recent years, including landslide susceptibility assessments because of its powerful factor analysis capabilities [42; 44; 45; 52; 54]. The basic assumption of the GeoDetector can be drawn as: if the variable X (factors) has an important impact on the variable Y (landslide or not), the distribution of them should be very similar. GeoDetector includes four detectors: risk detector, factor detector, ecological detector, and interaction detector. This study used the factor detector to screen for influencing factors. GeoDetector is freely available at [[http://www.geodetector.org/](http://www.geodetector.org/)]([http://www.geodetector.org/](http://www.geodetector.org/)) (accessed on 8 May 2021).  
The factor detector can detect the extent to which influencing factors explain the spatially stratified heterogeneity of a dependent variable and use the \(q\)-value to measure this property [43]. The specific concept of the \(q\) value is:  
\[q=1-\frac{\sum_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}}=1-\frac{SSW}{SST} \tag{5}\]  
where  
\[SSW=\sum_{h=1}^{L}N_{h}\sigma_{h}^{2} \tag{6}\]  
\[SST=N\sigma^{2} \tag{7}\]  
\(h=1,2,3,\ldots,L\) is the strata; \(N_{h}\) and \(N\) are the number of units in stratum \(h\) and the whole area, respectively; \(\sigma_{h}^{2}\) and \(\sigma^{2}\) are the variances of the \(Y\) in the stratum \(h\) and the whole area, respectively; \(SSW\) is the sum of variances within the stratum, and \(SST\) is the total variances of the whole area.  
The range of \(q\) value is 0 to 1, where the larger the \(q\) value, the stronger the explanatory power of the factor \(X\) to the variable \(Y\). In addition, the factor detector can also calculate the statistical significance of the \(q\) value and express it as a \(p\) value. A small \(p\) value represents strong statistical significance [43].
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 2 Materials and Methods
### Soil Erosion Using the G2 Model  
The G2 model belongs to a group of empirical models and enables the estimation of soil loss in tons per hectare on a monthly or annual basis. The structure of the input data that are processed using this model is very similar to the RUSLE method [29, 30] and the Erosion Potential Method (EPM) [31]. The G2 model consists of two main models: one for sediment production calculation (G2 los) and the other for sediment transport (G2 sed). The first G2 los model relies on the basic principles of sediment production and one part of the input parameters is calculated according to the RUSLE model, while the other part of the input parameters is calculated according to the Erosion Potential Method. The second model G2 sed relies on the principles of sediment transport by the Erosion Potential Method, by applying the retention coefficient (Ru). An innovation in the G2 model are the two parameters obtained using remote sensing data. The very structure of the model and the application of input parameters have undergone various modifications, and is still being developed. Monthly or average annual soil losses are determined on the basis of five erosion factors of which the size depends on space and time, according to the formula:  
\[\mathrm{E}=\frac{\mathrm{R}}{\mathrm{V}}\mathrm{S}\cdot\frac{\mathrm{T}}{ \mathrm{L}} \tag{1}\]  
E--soil loss for month (t\(\cdot\)ha\({}^{-1}\cdot\)); R--rainfall erosivity (MJ\(\cdot\)mm\(\cdot\)h\({}^{-1}\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\)); V--vegetation retention (dimensionless); S--soil erodibility (t\(\cdot\)ha\(\cdot\)h\({}^{-1}\cdot\)MJ\({}^{-1}\cdot\)mm\({}^{-1}\)); terrain influence (dimensionless); L--landscape effect (dimensionless).
## 6 Case Simulation
### Economic Benefit Analysis of Power Generation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Pumped-Storage** & **Non-Retrofitted** & **Increase in Revenue** & **Percentage Increase** \\
**& **Retrofitted System** & **System** & **(CNY 10,000)** & **in Power Generation** \\  & **(CNY 10,000)** & **(CNY 10,000)** & **(CNY 10,000)** & **Benefits** \\ \hline
\begin{tabular}{c} Cloudy and windy \\ Sunny and windy \\ Cloudy and less windy \\ Sunny and less windy \\ \end{tabular} & 54.78 & 51.57 & 3.21 & 6.22\% \\  & 58.81 & 55.63 & 3.18 & 5.71\% \\  & 59.74 & 56.59 & 3.15 & 5.57\% \\  & 66.60 & 63.35 & 3.25 & 5.13\% \\ \hline \hline \end{tabular}  
Assuming a 10-year system life cycle and a discount rate of 8%, the initial construction cost of the system is shown in Table 10.  
\end{table} 
Table 9: Power generation benefits under different weather scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenarios** & **Power Generation** & **Benefits** & **Increase in Revenue** & **Carbon Emission** \\  & **(MWh)** & **(CNY 10,000)** & **(CNY 10,000)** & **Reduction (tons)** \\ \hline
\begin{tabular}{c} Cloudy and windy \\ Sunny and windy \\ Cloudy and less windy \\ Sunny and less windy \\ Total annual \\ \end{tabular} & 1284.74 & 54.78 & 3.21 & 750 \\  & 1348.40 & 58.81 & 3.18 & 780 \\  & 1290.62 & 59.74 & 3.15 & 750 \\  & 1385.95 & 66.60 & 3.25 & 810 \\  & 5309.71 & 21,893.6 & 1167.09 & 281,500 \\ \hline \hline \end{tabular}  
\end{table} 
Table 11: Annual power and environmental benefits.
## 2 The Normal-Mode Helical Antenna Design Considerations
### Testing the Active Helical Antenna under Real Conditions  
Expanding on the simulation results detailed in Table 2, an evaluation of the helical antenna's performance under real conditions (in a reflection-free environment) was con  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **LNA Input** & **Helical Impedance** & **LNA Output** & **LNA Gain** \\
**(MHz)** & **(mV)** & **(\(\Omega\))** & **(mV)** & **(dB)** \\ \hline
20 & 600 & 457–351 & 777 & 2.24 \\
21 & 600 & 222–422 & 685 & 1.15 \\
22 & 600 & 68–329 & 566 & \(-\)0.50 \\
23 & 600 & 65–232 & 519 & \(-\)1.25 \\
24 & 600 & 65–123 & 508 & \(-\)1.40 \\
25 & 600 & 65–0.96 & 544 & \(-\)0.85 \\
26 & 600 & 62+145 & 631 & 0.43 \\
27 & 600 & 61+326 & 952 & 4.00 \\
28 & 600 & 56+559 & 4000 & 16.0 \\
29 & 600 & 69+900 & 2610 & 12.0 \\
30 & 600 & 48+1450 & 3150 & 14.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multisim software simulation results based on the actual impedance of the helical antenna.  
Figure 8: The input impedance of the common-drain amplifier after neutralizing input parasitic capacitances.  
ducted. The antenna underwent testing by receiving a transmitted signal in the 20-30 MHz range at a specific distance within the far-field zone.  
Initially, the helical antenna was assessed in its passive state. Subsequent testing was conducted with the antenna in its active mode without feedback neutralization and finally as an active antenna with feedback neutralization. The test results are illustrated in Figure 9, with the blue dots representing the signal strength of the passive helical antenna directly connected to the spectrum analyzer, the orange dots signifying the active helical antenna results without feedback, and the grey dots the results after implementing inductive feedback neutralization.  
As depicted in Figure 9, the resonant frequency of the helical antenna in this experiment was shifted to 27 MHz. Consequently, the high input impedance of the LNA is not particularly significant. However, by incorporating a high-impedance LNA, an enhancement in the antenna performance across the lower and upper frequency range was confirmed. This improvement is crucial because at these frequencies, the antenna impedance is very high.
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators
### Flash  
In 2024, Apple presented a novel scheme for the efficient deployment of Transformer networks utilizing flash memory [36]. The paper tackled the challenge of efficiently running LLMs that exceed available DRAM capacity by storing model parameters in flash memory and retrieving them on demand to DRAM. The proposed method develops an inference cost model that considers the characteristics of flash memory to optimize in two critical areas: reducing the volume of data transferred from flash and reading data in larger, more contiguous chunks. Within this hardware-aware framework, Apple introduces two principal techniques.  
The first technique, called \"windowing\", reduces data transfer by reusing previously activated neurons, and the second, \"row-column bundling\", tailored to the sequential data access strengths of flash memory, increases the data chunk sizes read from flash memory. These methods collectively enable running models of up to twice the size of the available DRAM, with increases of 4-5\(\times\) and 20-25\(\times\) in inference speed compared to naive loading approaches in CPU and GPU, respectively.
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
###### Abstract  
Falling film evaporators are gaining popularity as substitutes to typical flooded evaporators because of their low refrigerant charge. It is important to form and keep a thin liquid film on the heat transfer surface to ensure their high heat transfer performance. In this study, as a heat transfer enhancement surface, a fine porous surface processed using thermal spray coating was applied to a smooth copper tube with an outer diameter of 19.05 mm. Heat transfer coefficients of falling film evaporation on a single horizontal tube were experimentally evaluated using the HFC-134a refrigerant. The experiments were performed at a saturation temperature of 20 \({}^{\circ}\)C with the heat flux ranging from 10 to 85 kW\(\cdot\)m\({}^{-2}\) and for film Reynolds numbers up to 673. The study aimed to clarify the effect of the coating on the heat transfer characteristics of falling film evaporation. The results revealed that the coating could suppress partial dry out and enhance nucleate boiling in the falling film. The maximum heat transfer enhancement factor was 5.2 in the experimental range. It was further noted that the effect of the coating was especially strong under a low heat flux condition.  
falling film evaporation; thermal spray coating; heat transfer enhancement; horizontal tube; nucleate boiling +
Footnote †: journal: Applied Physics Letters
# Circularly Polarized High-Gain Fabry-Perot Cavity Antenna with High Sidelobe Suppression
## 2 Proposed FPCA Design and Operation
### Mechanism of the CP-PRS as a Polarizer
The AR of 0.08 dB is determined by using Equations (1) and (2) in the analysis of the transmission characteristics. It will be compared with simulated and measured values in the simulation and experimental results section.
## 4 Metal-Oxide Nanoparticles (MONPs) in Water Treatment
### Iron Oxide Nanoparticle  
Iron oxide nanoparticles (Fe\({}_{2}\)O\({}_{3}\) NPs) have received significant attention recently due to their unique properties, which include super-paramagnetism, a large surface area, a high surface-to-volume ratio, high stability, strong adsorption capacity, low production cost, and fast separation methods [115]. Each crystalline form exhibits unique magnetic behaviors, with magnetite showcasing strong magnetic properties (ferromagnetism), hematite displaying nonmagnetic properties despite having magnetic elements within them (antiferromagnetism), and maghemite falling in between [161]. These Fe\({}_{2}\)O\({}_{3}\) NPs stand out from other NPs in environmental remediation due to their distinct magnetic properties [162; 163]. Figure 9 shows three types of Fe\({}_{2}\)O\({}_{3}\) that are commonly recognized as hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)), maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)), and magnetite (Fe\({}_{3}\)O\({}_{4}\)) [115].  
Additionally, Fe\({}_{2}\)O\({}_{3}\) NPs are useful in the photocatalytic degradation of dyes. [PERSON] et al. [157] synthesized \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) NPs using the co-precipitation method, with slight modifications, to enhance their efficacy in the photocatalytic degradation of BTEX in wastewater. Their study revealed impressive results, achieving a maximum degradation efficiency of 95% within 5 days. Moreover, under UV light and visible light irradiation, the degradation efficiency reached 97% within 90 min, highlighting the potential of these magnetite NPs for rapid and effective remediation of BTEX [165]. Recently, [PERSON] et al. [159] reported the strong adsorptive removal of Pb\({}^{2+}\) ions from wastewater using sulfur-doped  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Figure 9: Crystal structures of hematite, magnetite, and maghemite. Source: [164].  
magnetic Fe\({}_{3}\)O\({}_{4}\) NPs, achieving approximately 80% removal. Table 8 summarizes recent findings on organic dye and toxic metal ion removal using iron oxide NPs.
## 2 Heliostat Orientation Control
### Control Algorithms  
#### 2.2.1 PID Controller  
The PID controller is the most commonly used in industrial applications due to its simple structure. However, its linear nature makes it not very suitable for non-linear systems. It is a control technique which reduces the error (\(e(t)\)) of a system using three control gains (Proportional, Integral and Derivative) in a mathematical operation to produce a control output (\(u(t)\)). The equation for the PID controller in the time domain is described by (6) [32].  
\[u(t)=K_{p}e(t)+K_{i}\!\int e(t)dt+K_{d}\frac{de(t)}{dt} \tag{6}\]  
When the controller is digital, it can be approximated with a backward difference and a sum for the derivative and integral terms [4], respectively. The digital PID controller is given by (7).  
\[u(n)=K_{p}e(n)+K_{i}\!\sum_{j=1}^{n}e(j)T_{s}+K_{d}\frac{e(n)-e(n-1)}{T_{s}} \tag{7}\]  
where \(n\) and \(T_{s}\) represent the number of the sample and the sample time of the digital system.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 3 System Model
### Routing Decisions
```
1:Node \(i\) receives a Hello packet from neighbor \(j\)
2:Node \(i\) update the Neighbor Table, dynamically adjust Hello interval
3:Get Attributes \(x_{t}\), \(y_{t}\), \(z_{t}\), \(v_{x,t}\), \(v_{y,t}\), \(v_{z,t}\) from Hello packet
4:for time_point \(t_{a}:(Hello_{t-1}\times 2)\)do
5: Predict the movement state \(X_{t_{a}}\) of Node \(i\) and \(j\) according to EKF
6: Calculate the distance \(d_{ij}(t_{a})\) based on Equation (9)
7:if\(d_{ij}(t_{a})>\) R then
8: Node \(j_{t_{2}}=t_{a}\)
9: Calculate the Link maintenance time \(L_{t_{2}(i,j)}\) based on Equation (10)
10:break for
11:endif for
12:Update the Neighbor table
13:Update the Adjacent list
14:Decision the \(Hello_{t}\) based on Equations (12) and (13)
```  
**Algorithm 1** Neighbor discovery  
```
1:Node \(i\) receives a packet from neighbor \(k\)
2:Forwarding packets, adjust algorithm parameters
3:Execute the \(Action\) based on Q algorithm
4:choose the max Q value of Node \(j\) as next hop
5:Schedule.async(AsynchronousWaitForReward, \(S_{t}\)).get(0)
6:functionAsynchronousWaitForReward(\(S_{t}\))
7: Asynchronous Wait for \(j\) reply \(reward\)
8:Adaptive learning parameters based on (20) and (21)
9: Update the Q table based on Equation (22)
10:endfunction
```  
**Algorithm 2** Routing decision
## 4 Upper Model Based on the Travel Success Ratio  
### Model Formulation  
The upper planning model aims at maximizing the travel success ratio _tsr_. Considering that the planning problem does not have a high demand on the computing time, each potential trip chain will be analyzed by applying the method in Section 3.2. The binary decision variable \(y_{qj}\) is defined as the success or failure of user \(j\); 1 for success, and 0 for failure. We consider that charging too many times will affect the travel experience. With \(n_{c}\) as the number of fast charging times and \(n_{c\max}\) as the maximum charging times, the mathematical expression for the upper model can be formulated as follows:  
\[\max tsr=\frac{1}{n_{ev}}\sum_{j=1}^{n_{ev}}y_{qj}, \tag{7}\]  
\[y_{q}=\left\{\begin{array}{l}1n_{c}\leq n_{\max}\\ 0n_{c}>n_{\max}\end{array}\right., \tag{8}\]  
\[s.t.d_{ii^{\prime}}>d_{\min}, \tag{9}\]  
\[n_{c}\leq n_{c\max}, \tag{10}\]  
\[x_{i}\leq 1, \tag{11}\]  
where \(n_{ev}\) denotes the number of users whose travel range exceeds the maximum travel range of EVs. \(d_{ii^{\prime}}\) denotes the shortest distance between the adjacent charging stations. \(d_{\min}\) refers to the shortest distance between charging stations. \(x_{i}\) represents the number of charging stations at node \(i\). For each traffic node, up to one charging station is acceptable.