esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.8 Post-Quantum Cryptography  
Post-Quantum Cryptography is a security framework designed to protect against quantum computing attacks that could break traditional cryptographic algorithms [112; 121]. It is increasingly being adopted in blockchain-based systems to ensure long-term security against quantum threats [141; 142]. Since widely used cryptographic schemes like RSA and ECDSA are vulnerable to quantum attacks, post-quantum encryption offers a future-proof security solution [121; 143]. Its primary advantages include enhanced resistance to quantum-based threats and long-term cryptographic security. However, ongoing standardization efforts and computational efficiency challenges currently limit its widespread adoption [141; 142]. Post-Quantum Cryptography is particularly relevant for public and financial sectors, where blockchain security must be strengthened against future quantum computing risks [112; 141].
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### TerraSAR-X, TanDEM-X  
TanDEM-X [18], a radar satellite jointly developed by EADS (European Aeronautic Defence and Space company) Astrium and the German Aerospace Center (DLR), was launched on 15 June 2007. On the basis of its high-resolution mission, a radar satellite called TerraSAR-X was added to form a dual-satellite high-precision synthetic aperture radar interferometer, which was completed on 21 June 2010 [19]. This project adopted a dual-frequency GPS receiver ranging system to achieve the high-precision measurement of inter-satellite baselines. Similar to the GRACE system, the spiral formation shape was used to achieve complete synthetic aperture imaging. The space baseline estimation accuracy needs to reach 1 mm to complete this precision Earth observation mission. The orbital altitude of the satellite is 514 km, and the satellite utilizes an active X-band phased array SAR antenna, a laser angle reflector, a ranging TOR (a tracking, occultation and ranging device), and a dual-frequency GPS occultation receiver (GPS occultation receiver and IGOR). IGOR's carrier phase accuracy reaches the mm level. The formation can complete flexible interference modes, including dual-station chase, single-station chase, and switching chase modes.
## 3 Results and Discussion
### Estimation of the Level of Potentially Toxic Elements Pollution  
#### 3.2.1 EF  
Table 3 reports the calculated EF values.  
According to the literature [38], EF values < 2 indicate deficient to minimal enrichment. In particular, values between 0.5 and 1.5 indicate an entirely crustal material/natural origin metal, while EF > 1.5 suggests a more likely anthropogenic one [39]. Moreover, 2 \(\leq\) EF < 5 means moderate enrichment; 5 \(\leq\) EF < 20 significant enrichment; 20 \(\leq\) EF \(\leq\) 40 high enrichment and EF > 40 extremely high enrichment.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline
**Site** & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} \\
**ID** & & & **EF** & & & **Igo** & & **CF** & & **PII** & **ID** & **EF** & **Igo** & **CF** & **PII** \\ \hline \multirow{9}{*}{\(1\)} & As & 0.14 & \(-\)3.49 & 0.13 & & & As & 0.15 & \(-\)3.49 & 0.13 & & As & 0.15 & \(-\)3.50 & 0.13 & \\  & Cd & 0.34 & \(-\)2.19 & 0.33 & & & Cd & 0.54 & \(-\)1.58 & 0.50 & & & Cd & 0.37 & \(-\)2.17 & 0.33 & \\  & Cu & 1.80 & 0.19 & 1.71 & & & & Cu & 1.85 & 0.19 & 1.71 & & & Cu & 1.89 & 0.17 & 1.69 & \\  & Hg & 0.07 & \(-\)4.48 & 0.07 & & & & Hg & 0.07 & \(-\)4.48 & 0.07 & & & Hg & 0.08 & \(-\)4.48 & 0.07 & \\  & Ni & 0.26 & \(-\)2.62 & 0.24 & 0.21 & & Ni & 0.24 & \(-\)2.78 & 0.22 & 0.22 & & Ni & 0.24 & \(-\)2.83 & 0.21 & 0.20 \\  & Pb & 0.26 & \(-\)2.61 & 0.25 & & & Pb & 0.27 & \(-\)2.58 & 0.25 & & & Pb & 0.26 & \(-\)2.68 & 0.23 & \\  & Sb & 0.06 & \(-\)4.79 & 0.05 & & & Sb & 0.06 & \(-\)4.81 & 0.05 & & & Sb & 0.06 & \(-\)4.81 & 0.05 & \\  & Tl & 0.08 & \(-\)4.25 & 0.08 & & & Ti & 0.07 & \(-\)4.54 & 0.06 & & & Ti & 0.06 & \(-\)4.71 & 0.06 \\  & Zn & 0.69 & \(-\)1.20 & 0.65 & & & Zn & 0.67 & \(-\)1.29 & 0.61 & & & Zn & 0.67 & \(-\)1.33 & 0.59 & \\ \hline \end{tabular}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline
**Site** & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Metal**} & \multicolumn{3}{c|}{**Index of contamination**} \\
**ID** & & & **EF** & & & & & & & & & & & & & & & & & & \\ \hline \multirow{9}{*}{4} & As & 0.19 & \(-\)3.22 & 0.16 & & & As & 0.15 & \(-\)3.40 & 0.14 & & & As & 0.16 & \(-\)3.21 & 0.16 & \\  & Cd & 0.42 & \(-\)2.03 & 0.37 & & & Cd & 0.36 & \(-\)2.17 & 0.33 & & & Cd & 0.43 & \(-\)1.79 & 0.43 & \\  & Cu & 1.89 & 0.13 & 1.64 & & & Cu &  
In our case, the obtained EF values are lower than 2 for all investigated sites, indicating no or minimal enrichment.
# InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage
## 4 Discussion of Results
### Analysis of Feature Use  
Figure 10 shows the use of different parts of the software by the user. For damage annotation, a significant amount of the annotation time is spent by users on the mask viewer, further indicating that the IPTs are used extensively. As damage usually takes amorphous and complex shapes the IPTs are tremendously useful in delineating boundaries and speeding up damage annotation. On the other hand, for material annotation, the users prefer to use the photo viewer the most. The IPTs do not provide the same increase in efficiency for material annotations as they do for damage annotations due to the different nature of boundaries between materials. Figure 11 shows the filters used most often for different classes. The guided filter is most frequently used for both cracks and spalling and is especially effective in filtering out edge features. The morphology operations allow users to control the thickness of the crack annotated and is also used extensively for crack annotation. The color filter is clearly the most used for corrosion annotation. As the color of corrosion is usually distinctly reddish hues, color-based filtering is highly effective. The local adaptive threshold and simple threshold are most effective for annotating cables. There is no clear choice by the annotators for the annotation of rebar. The mask type used most often for material annotation is the label mask. This is because materials are mutually exclusive and material annotations fill up the entire image. Thus, the option to create annotations excluding previously created annotations avoids duplicating work describing the edges of materials. Finally, Figure 12 shows the distribution of different tools used. The brush tool is almost exclusively used for annotating damage types, and the most commonly used tool even for material annotation. The polygonal tool is also usedheavily for material annotations. The analysis of the user data indicates that the features incorporated into InstaDam are used extensively and that the mask viewer is a valuable addition to improve the efficiency of annotation.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 5 Other Magnetic-Photocatalytic Composites
### Hexerrites Composites
However, it should be noted that, during both studies, the composition of both phases was not optimized, and the mechanism of the photocatalytic reaction was not examined in detail. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Photocatalyst**} & **Pollutants** & \multicolumn{1}{c}{**Experimental**} & \multicolumn{1}{c}{**Findings**} & **Ref.** \\ \hline CdFe\({}_{12}\)O\({}_{19}\)/CdTiO\({}_{3}\) & MO; MB; MV & 400 W UV light; & 71\% deg. MO; 61\% deg. MB; & [83] \\  & & 1 g\(\cdot\)L\({}^{-1}\); 5 ppm; 70 min & 55 deg. MV & [83] \\ \hline PbFe\({}_{12}\)O\({}_{19}\)/GN and & \multirow{2}{*}{MO} & \multirow{2}{*}{UV light; pH 2-3} & 75\% deg. (PbFe\({}_{12}\)O\({}_{19}\)/GN); & \multirow{2}{*}{[121]} \\ PbFe\({}_{12}\)O\({}_{19}\)/CNT & & & 50\% deg. (PbFe\({}_{12}\)O\({}_{19}\)/CNT) & \\ \hline BaFe\({}_{12}\)O\({}_{19}\)/g-C\({}_{3}\)N\({}_{4}\) & \multirow{2}{*}{Hg\({}^{2+}\)} & Xe lamp (\(\lambda>420\) nm; & \multirow{2}{*}{100\% red. of Hg\({}^{2+}\) ions} & \multirow{2}{*}{[122]} \\  & & 425 W \(\cdot\)m\({}^{-2}\)); & & \\  & & pH 4; 0.4–2 g \(\cdot\)L\({}^{-1}\); 100 ppm; & & \\ \hline BaFe\({}_{12}\)O\({}_{19}\)/g-C\({}_{3}\)N\({}_{4}\) & \multirow{2}{*}{RhB} & Vis light; T = 30\({}^{\circ}\)C; 1 g\(\cdot\)L\({}^{-1}\); & 17\% deg. (10.1\% & \\  (10.1-35.5\% wt. of & \multirow{2}{*}{RhB} & 10 ppm; & BaFe\({}_{12}\)O\({}_{19}\)/g-C\({}_{3}\)N\({}_{4}\)); & \\   Ba-ferrite) & & 40 min; 0.5 ml of 30\% H\({}_{2}\)O\({}_{2}\) & (16.8\% BaFe\({}_{12}\)O\({}_{19}\)/g-C\({}_{3}\)N\({}_{4}\)) & \\ \hline SrFe\({}_{12}\)O\({}_{19}\)/SiO\({}_{2}\)/TiO\({}_{2}\)/GO & \multirow{2}{*}{2,4-DCP} & \multirow{2}{*}{Kuala Lumpur sunlight; 482–776 W\(\cdot\)m\({}^{-2}\); 2 g\(\cdot\)L\({}^{-1}\); 100 min—776 W\(\cdot\)m\({}^{-2}\); 200 min—482 W\(\cdot\)m\({}^{-2}\))} & [124] \\  & & 50 ppm; 170–300 min & & 300 min—482 W\(\cdot\)m\({}^{-2}\)) \\ \hline SrFe\({}_{12}\)O\({}_{19}\)/ZnFe\({}_{2}\)O\({}_{4}\) & \multirow{2}{*}{MB} & 500 W Xe lamp; pH 7; & \multirow{2}{*}{97\% deg. [125]} \\  & & 2 g\(\cdot\)L\({}^{-1}\); 10 ppm; 300 min & & \\ \hline BaFe\({}_{12}\)O\({}_{19}\)/Sm\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)/Ag & \multirow{2}{*}{MB} & 400 W lamp (400–770 nm); & 45\% deg. (BaFe\({}_{12}\)O\({}_{19}\)/Sm\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)); & \\  (0–20\% Ag salt) & & 10 ppm; 150 min & & \\  & & & (BaFe\({}_{12}\)O\({}_{19}\)/Sm\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)/20\% & \\  & & & Ag) & \\ \hline SrFe\({}_{12}\)O\({}_{19}\)/BiOBr (3–15\% & \multirow{2}{*}{RhB} & 300 W Xe lamp, filter & \multirow{2}{*}{55\% deg. (15\% SrFe\({}_{12}\)O\({}_{19}\)/BiOBr); (15\% SrFe\({}_{12}\)O\({}_{19}\)/BiOBr); (131)} & \\   wt. of Sr-ferrite) & & 0.5 g\(\cdot\)L\({}^{-1}\); 10 ppm; 30 min & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of hexaferrite-photocatalyst composites.
# Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending
## 2 Prediction of Thermal Deformation
### Modified Thermal Strain  
During the cooling phase, volume expansion occurs due to phase transformation while austenite is transformed into martensite. The volumetric expansion ratio after heating and cooling in carbon steel can be calculated by Equation (13) [21].  
\[\frac{\Delta V}{V}(\%)=X_{M}(T)\frac{(2.86+0.117\mathrm{C})\cdot\left(2.86-0.014 3\mathrm{C}\right)^{2}}{2.86^{3}}+X_{F-P}-1 \tag{13}\]  
where \(X_{F-P}\) represents the portion of ferrite and pearlite.  
In this paper, it is assumed that only austenite or martensite exist in HAZ, so \(X_{F-P}\) is zero. In the cooling cycle, the thermal strain can be calculated by the integral of the thermal expansion coefficient expressed in Equation (14), and the phase transformation strain is caused by the volumetric expansion of the phase transformation. Therefore, the sum of thermal strain and phase transformation strain, called modified thermal strain, is represented by Equation (15). Taking the derivative of Equation (15) gives the equivalent thermal expansion coefficient of the cooling cycle \(a_{\mathrm{C}}^{m}(T)\), which can reflect both the linear thermal expansion and the volumetric expansion due to the phase transformation defined by Equation (16). Therefore, the modified thermal expansion coefficient is obtained from the derivative of modified thermal strain with respect to temperature, as shown in Figure 4.  
\[\varepsilon_{C}^{th}(T)=\alpha_{\mathrm{C}}(T)\cdot\Delta T=\int_{0}^{T}\alpha _{\mathrm{C}}(T)dT \tag{14}\]  
\[\varepsilon_{\mathrm{C}}^{th+ph}(T)=\int_{0}^{T}\alpha_{\mathrm{C}}(T)dT+\frac {\Delta V}{3V}X_{M}(T)=\int_{0}^{T}\alpha_{\mathrm{C}}^{mod}(T)dT \tag{15}\]  
\[\alpha_{\mathrm{C}}^{mod}(T)=\frac{d\left(\varepsilon_{\mathrm{C}}^{th}(T)+ \varepsilon_{\mathrm{C}}^{ph}(T)\right)}{dT} \tag{16}\]  
Figure 3: Austenitic grain size and ASTM number [17].  
temperature has been developed by regression analysis of large amounts of experimental data [22]. The temperature \(Ac1\) region would be suitable to determine the heat affected zone in which austenite begins to form during heating [6]. The critical temperature \(T_{C}\) is equal to \(Ac1\), which is the function of the chemical composition in steel following experimental results as shown in Equation (3) [22]. In this paper, HAZ is defined as a trapezoidal shape, as shown in Figure 5, which describes the shape variables of the trapezoidal HAZ. The shape is defined with variables: height (\(h\)), depth (\(d\)), breadth (\(b1\), \(b2\)), as expressed as Equation (17).  
\[b_{Zt}=\begin{cases}\frac{b_{2}-b_{1}}{d}\Big{(}Z+\frac{h}{2}\Big{)}+b_{1}&z \leq d-\frac{h}{2}\\ 0&d-\frac{h}{2}<z\end{cases} \tag{17}\]
## 1 Introduction
In general, most popular models often assumed that all vehicles were detected to collect information. This was not true because only vehicles equipped with a wireless communication system could be connected. Nowadays, many new technologies are used to better connect the information between the vehicle and the intersection management system (IMS). Traditional devices such as loop detectors can only detect the presence of vehicles when passing them. However, new devices (such as DSRC, GPS localization, and Bluetooth) are cost-effective and can collect more data (speed, distance from CVs) continuously. The increased number of connected vehicles increases the ability to transmit information between vehicles with the IMS [46; 47]. However, these papers did not study platoon formation between CVs. By merging CVs into platoons, it will greatly improve road capacity and other parameters. It can be seen that the combination of platooning vehicles and DRL is an excellent solution to improve the existing traffic problem. To achieve this purpose, we proposed the DRL method with platooned vehicles at a signalized intersection with partial detection. To compare the effectiveness of the proposed model, we implemented two benchmark options, ATSC and MP. These scenarios were all simulated with the change in platoon size (from 1 car to 3 cars in a platoon). The main contributions of this study are as follows. * We combined systems that work together, including platoon and DRL, with partial detection at a signalized intersection. DRL was the main solution to improve signal system optimization and traffic efficiency. We proposed a new state description for the mixed traffic (IMS only collected information from platoon vehicles (CVs)). Compared with two benchmark options, ATSC and MP, the proposed solution reduced the waiting time of all vehicles significantly. * A set of hyperparameters was tested to identify the main influencing parameters in the learning process. * We also considered the effect of platoon size (number of vehicles in the platoon) at the intersection to measure the average delay time, waiting time, speed of traffic, and travel time. The contribution of this paper increased the understanding of the influence of platoons at signal intersections. * Finally, we evaluated the influence of CV penetration rate on the model results and recommended reasonable rates. The rest of the paper follows: Section 2 presents the research methodology. Section 3 presents the experimental setup. Section 4 contains the results of traffic simulations and evaluations. Sections 5 and 6 give the discussion and conclusions.
# Parameter Study for the Ice Adhesion Centrifuge Test
## 3 Results
### Ice Types  
The influence of the ice formation process on the ice adhesion strength is one important aspect of this paper. Figure 6 summarizes the centrifuge test results for the materials  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Static Ice (300 rpm/s)** & **Standox** & **PUR C25** & **PTFE-Tape** \\ \hline box plots & 250 & 250 & 250 \\ box plots & 2 Standox, PUR C25, and PTFE-tape. Tests with these materials delivered quantifiable results with complete adhesive failures between the ice and test surface. For the primer coating material, only cohesive ice failures or even no ice shear-off were observed, giving the qualitative result that the ice adhesion strength was significantly higher than the appearing shear forces in the conducted tests. Therefore, the primer is not included in the figure.  
Clear indications show that regardless of the material, the highest impact velocities (95 m/s) of the impinging water droplets led to the lowest ice adhesion strength for the respective materials. A reduction in the impact velocity to 60 and 40 m/s led to an increase in the ice adhesion strength. A further increase was observed for the unmodified polyurethane coating, Standox, for static ice formations. Due to the comparable high standard deviations, no clear difference between static ice and impact ice (40 m/s) was observed for the low ice adhesion material, PUR C25, and PTFE-tape.  
By comparing the actual ice formations on the test samples (Table 4), one can argue that increased adhesive strength for ice, accreted at 60 and 40 m/s, may be related to the increased ice area, including the coverage of the (coated) sides of the test specimen to a certain extent. This is caused by water droplets moving over the test sample before freezing. However, this effect seems to have less significance, and for static ice with well-defined ice areas, the trend was continued.  
The potential to distinguish between the tested materials is of importance for a lab-based test design. Here, impact ice might be more realistic for technical applications (e.g., aircraft, wind rotor blade) but delivers a narrow range of test results (for 95 m/s, an impact ice minimum of 15 kPa and maximum of 63 kPa). In contrast, static ice delivers a wider result range (minimum of 41 kPa and maximum of 185 kPa), potentially allowing an improved ranking during material development processes. However, surface characteristics (especially roughness) must be considered in the test parameter definition (Section 3.5).
## 3 Methods for Analyzing the Disassembly Process
### Application of the Second Method  
After identifying and dividing components and fasteners (components identified with the code C and a number that follows in ascending order, the same order as appears in the BOM, and fasteners identified with the code F and a number that follows the same logic for the component), we can proceed with the building the DPM, as shown in Table 2 (in the following page). As with the previous method, the oil seal is considered assembled to the front flange, the shims are assembled to their respective bushings, and, for this method, the O-rings (no. 2 and no. 3 in the BOM) are also counted as a single part since they are actually wedged between of them. The target component remains the drive gear (C4 in the component table).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline  & **F1 SX** & **F1 DX** & **F2 SX** & **F2 DX** & **F3** & **F4** & **F5** & **F6** & **F7** & **C1** & **C2** & **C3** & **C4** & **C5 SX** & **C5 DX** \\ \hline F1 SX & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100,000 & 0 & 0 & 0 & 0 \\ F1 DX & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 10,000 & 10,000 & 10,000 & 0 & 0 & 0 & 0 & 0 \\ F2 SX & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100,000 & 0 & 0 & 0 & 0 \\ F2 DX & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100,000 & 0 & 0 & 0 & 0 \\ F3 & 0 & 0 & 0 & 0 & 0 & 10,000 & 10,000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F4 & 0 & 0 & 0 & 0 & 0 & 0 & 10,000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F6 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F7 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ C1 & 0 & 0 & 0 & 10,000 & 0 & 0 & 100,000 & 100,000 & 0 & 0 & 0 & 0 & 0 \\ C2 & 0 & 0 & 0 & 0 & 100,000 & 100,000 & 100,000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ C3 & 100,000 & 10,000 & 0 & 0 & 0 & 0 & 0 & 100,000 & 100,000 & 0 & 100,000 & 100,000 & 100,000 \\ C4 & 100,000 & 10,000 & 0 & 0 & 0 & 0 & 100,000 & 10,000 & 100,000 & 0 & 0 & 100,000 & 100,000 \\ C5 SX & 100,000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100,000 & 0 & 0 & 0 \\ C5 DX & 0 & 10,000 & 0 & 0 & 0 & 0 & 0 & 10,000 & 10,000 & 10,000 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Disassembly precedence matrix.  
Therefore, for example, analyzing the relationship between C1 = 1-14 (front flange assembled with the oil seal) and F7 = 16 (tongue), we write down in the cell the number 10,000 because C1 cannot be removed in the +x direction due to the presence of F7. F7 must be removed first, then C1.  
The final disassembly sequence is F7, F5, F4, F3, C2, F6, C1, F1 SX, F2 DX, F2 SX, F1 DX, CSSX, C5 DX, C4, which in BOM codes are 16, 13, 12, 11, 4, 15, 1-14, 2-3 SX, 5 DX, 5 SX, 2-3 DX, 10-7 SX, 10-7 DX,9.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 2 Monitoring Technology
### Sensor Monitoring Technology in BHM
#### 2.1.2 Wireless Sensor Technology
On the one hand, the limited battery capacity of wireless sensor nodes restricts their transmission power, and on the other hand, the transmission coverage area is related to the transmission power. Therefore, in order to avoid wasting resources and time on repeated battery replacement for wireless sensors in the future, research on reducing WSN energy consumption and maximizing its lifespan needs to be further developed.