esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Generation of the Mesh
### Mesh Element Quality  
The mesh quality parameters, considered below and calculated for each element, are as follows.  
Assuming that \(S\) denotes the total surface, and \(V-\)the total volume of the tetrahedron, the following parameters have been used [18]: The first of the defined parameters was\[SV=\frac{\sqrt{S}}{\sqrt[3]{V}}\]  
The second parameter was the ratio of the longest edge and the radius of the inscribed sphere [24]:  
\[ER=\frac{l_{max}}{r_{ins}},\]  
ranging from \(ER_{min}=2\sqrt{6}\approx 4.89898\), for a regular tetrahedron, to infinity.  
The third one is the value applied in the TetGen program [25; 26] i.e., the longest edge divided by the smallest height (EH)  
\[EH=\frac{l_{max}}{h_{min}},\]  
with the range similar as in the previous example i.e., from \(EH_{min}=\sqrt{\frac{3}{2}}\approx 1.22474\) to infinity, following the same rule as before: \"the larger the value, the worse the tetrahedron\".  
The last two parameters are \(MAXS\) and \(MINS\), and they are computed as the ratios of the largest (\(S_{max}\)) or the smallest (\(S_{min}\)) face measure, respectively, to the total surface (\(S\)) of the tetrahedron.  
The formula for the first of those two parameters is  
\[MAXS=\frac{S_{max}}{S}\]  
and its value ranges from 0.25 to 0.5 i.e.,  
\[MAXS\in[0.25;0.5)\]  
The formula for the second parameter is  
\[MINS=\frac{S_{min}}{S}\]  
and its value ranges from 0 to 0.25 i.e.,  
\[MINS\in(0;0.25]\]  
The parameters mentioned above (SV, ER, EH, MAXS and MINS) is positive and dimensionless. All of them, except MINS, have the smallest value for the regular tetrahedron. The MINS value behaves differently: the closer to zero, the worse the tetrahedron; the maximum value 0.25 is reached for a regular tetrahedron. Moreover, the value 0.25 of both the MAXS and the MINS does not necessarily mean that the tetrahedron is regular--it may be a sliver-type (see Figure 8 in [25]). Other irregular tetrahedron with the vertices at (-0.001,0,0), (0.001,0,0), (0,-0.001,100) and (0,0.001,100) is a wedge-type one (see Figure 8 in [25]).  
The numerical approximation of a gradient of a given function depends strongly on the largest angle (planar angle for a triangle and spherical or dihedral angle for a tetrahedron) [27]. Thus, the condition number of the stiffness matrix should be kept to a minimum.  
Theoretically, to solve a partial differential equation numerically, the above mentioned quality measures \(Q_{1}\)-\(Q_{5}\) should be as close to the ideal ones as possible for each of the mesh elements. However, not all deviations from the ideal have strong negative influence on the calculations with FEM. Moreover, it turns out that tetrahedrons with faces far from optimal are still of a quality enough to be used for numerical analysis of the model [23].
## 1 Introduction  
The emergence of the COVID-19 pandemic in early 2020 posed unprecedented challenges to healthcare systems worldwide. It disrupted medical infrastructure, necessitated rapid adaptations in healthcare delivery, and significantly altered patient behavior [1]. During the initial phase, many hospitals prioritized COVID-19 care, leading to the deferral of non-urgent surgical procedures, including those in urology [2]. Nevertheless, timely intervention for urological emergencies, such as urinary stone disease, remained critical to avoid serious complications [3]. Pandemic-related restrictions and public fear of infection contributed to delayed healthcare-seeking behavior and, in some reports, more severe clinical presentations at admission [4]. Additionally, extended home confinement and lifestyle changes, such as reduced physical activity and increased weight gain, were suggested to elevate the risk of stone formation [2].  
In contrast to the strict lockdowns imposed in many countries, Japan adopted a unique approach, issuing non-mandatory emergency declarations that encouraged voluntary behavioral restrictions while maintaining public access to healthcare. Most medical facilities remained open, offering a distinctive setting in which to evaluate the pandemic's effects on urolithiasis care.  
This study aimed to investigate the impact of the COVID-19 pandemic on emergency urolithiasis management at a single Japanese institution, focusing on changes in clinical presentation, treatment decisions, and ureteroscopic outcomes before and after the onset of the pandemic. Our goal was to clarify the clinical consequences of the pandemic and identify strategies to sustain essential urological services during future healthcare disruptions.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 3 Results  
### U-Net Training  
After 50 epochs, the model achieved a Jaccard Index of 0.961 and a loss of 0.01 (Figure 5). Because of the high-quality training data used, the model achieved a Jaccard Index of 0.8 after 4 epochs and 0.9 after 11 epochs.  
Figure 5: The Jaccard Index and loss function for each epoch. The number of epochs was restricted to 50. A marginal improvement may have been recorded if allowed to continue.
# Design and Optimization of Lattice Structures: A Review
## 2 Lattice Structures
### Properties and Applications of Lattice Structures
#### 2.2.2 Applications  
The excellent properties of lattice structures fill in the blank of manufacturing industry and provide unprecedented opportunities for structures with better manufacturing performance. The properties of lattice structures determine their wide application fields. Because of lightweight and high strength, lattice structures are often used in the structural design of aircraft, rocket, and other aerospace fields [63, 64, 65, 108, 109, 110], automotive fields [47, 56, 57]. In addition, lattice structures have bio-compatibility and high strength, which can be designed into the shape of human tissue and bone to replace the diseased organs. Lattice structures have been tremendously used in the medical field because of flexible mechanical properties and structural characteristics, which can meet specific requirements [48, 49, 50, 51, 52, 53, 54, 55, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]. The specific applications of lattice structures are shown in Figure 9.  
Figure 8: Apparent thermal conductivity against actual volume fraction [98].  
Figure 9: Applications of lattice structures: (**a**) winding of helical rib [63]; (**b**) aerial vehicle wing structure [56]; (**c**) fan blade [57]; (**d**) biomedical implant [7, 121].
## 3 Results and Discussion
### Computational Study
#### 3.3.2 Interaction Energies (IE)  
To obtain the most stable configurations and binding energies of structures, the DFT approach at the B3 LYP level using the 6-31G+(d,p) basis set was widely used. In this research, clonidine and tetraphenylborate were investigated as a complex. The complexes (molar ratio 1:1) in the gas phase have binding energy (\(\Delta\)E\({}_{\rm b}\)), BSSE energy, and changes of Gibbs free energy (\(\Delta\)G) of, \(-\)107.22, 0.007642, and \(-\)87.43 kcal/mol, respectively. The clonidine-tetraphenylborate complex is the largest absolute value of \(\Delta\)E\({}_{\rm b}\), and the BSSE energy indicates that it is the most stable clonidine-tetraphenylborate complex yet discovered. It also has a negative \(\Delta\)G value, indicating that it is simple to generate spontaneously and that the result is consistent with the experimental method.
# Diffusion and Consolidation of Slag-Based Geopolymer for Concrete Payment Rehabilitation
## 3 Results and Discussion  
### Single-Gradation 9.5-4.75 mm Grouting Test  
When the particle size of the grouting aggregate was 9.5-4.75 mm, as the grouting pressure increased, higher grouting pressures led to better slurry consolidation, resulting in more uniform specimens, as shown in Figure 3. When the grouting pressure was 0.2 MPa, the geopolymer slurry diffused and solidified a certain height of aggregate, and then significant slurry bulbs and veins appeared; however, the height of the solidified aggregate did not reach the height of the grouting test mold. When the grouting pressure was 0.3 MPa, the geopolymer slurry almost diffused and solidified the entire aggregate of the test mold. However, the aggregate at the bottom of the test mold was not consolidated, and there were pits in some areas. When the grouting pressure was 0.4 MPa, the geopolymer slurry filled the inside of the test mold and consolidated the aggregate, but there were certain pores on the specimen surface.  
As shown in Figure 4, under the condition of a 0.2 MPa grouting pressure and subtracting the simulated void area with a reserved thickness of 10 mm, the consolidation thickness of the geopolymer slurry in the 9.5-4.75 mm aggregate was 50 mm. A hemispherical slurry bulb was formed in the lower part of the consolidation zone, and there were many developed slurry veins. It could be seen that the first splitting surface was relatively thick, with a maximum thickness of about 50 mm. The second splitting surface developed diagonally along the first splitting surface, but was not fully developed, with a maximum thickness of about 20 mm.  
Figure 3: Consolidated specimens with particle sizes of 9.5–4.75 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Particle Size (mm)** & \multicolumn{3}{c}{**Grouting Pressure (MPa)**} \\ \hline
9.5–4.75 & 0.2 & 0.3 & 0.4 \\
4.75–2.36 & 0.4 & 0.6 & 0.8 \\
2.36–1.18 & 0.8 & 0.9 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: The grouting test pressure.  
Due to the slightly larger pore size of the aggregate with a particle size of 9.5-4.75 mm, the slurry diffused in a hemispherical shape to form slurry bulbs. When the pores in some positions of the aggregate were filled, the slurry began to seek diffusion along the weak surface, gradually forming a splitting surface. After the first splitting surface was generated, the surrounding aggregate was compressed, and a second splitting surface usually appeared along the direction of intersection of the first splitting surface. At the end of grouting, the specimen would appear in the form of bulbs at the top and slurry veins at the bottom.
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 3 Peak Age of Information
### Average PAoI  
For any MTD, Figure 3 illustrates an example of the evolution traces of AoI, where \(t_{i}\) denotes the arrival time of the \(i^{th}\) packet and \(t_{i}t^{\prime}\) denotes the successful transmission time of the \(i^{th}\) packet. As can be seen from Figure 3, the AoI will continuously increase with time until the packet is successfully transmitted, and then the AoI decreases to the elapsed time since the packet was generated. Due to the limited buffer queue, when there is a packet in the buffer queue, a newly arriving packet will be discarded, such as \(t_{i+2}\). Let \(k\) denote the \(k^{th}\) successfully transmitted packet, and its PAoI \(A_{k}\) can be written as follows:  
\[A_{k}=D_{k-1}+W_{k}+D_{k}, \tag{6}\]  
where \(D_{k}\) represents the access delay, which is the duration between the successful transmission of the \(k-1^{th}\) packet and the successful transmission of the \(k^{th}\) packet. \(W_{k}\) represents the idle period, which is the duration from the successful transmission of the \(k-1^{th}\) packet to the arrival of the next packet. Since \(W_{k}\) and \(D_{k}\) are independent identical distribution random variables, for simplicity, the subscript \(k\) will be dropped in the following analysis.  
Denoting the access delay of MTDs in group \(\mathcal{G}_{A}\) as \(D^{A}\), and the idle period as \(W^{A}\), the average access delay of group \(\mathcal{G}_{A}\) is given by [19] the following:  
\[E[D^{A}]=\frac{1}{p^{A}q^{A}}. \tag{7}\]  
Since the departure of successfully transmitted packets and the arrival of new packets may occur concurrently, in accordance with the Bernoulli arrival process of packets and the mathematical expectation of the Bernoulli distribution, the average idle time of group \(\mathcal{G}_{A}\) is as follows:  
\[E[W^{A}]=\frac{1}{\lambda}-1. \tag{8}\]  
Finally, combining Formulas (6)-(8), the PAoI of MTDs in group \(\mathcal{G}_{A}\) is derived as follows:  
\[A_{A}=\frac{2}{p^{A}q^{A}}+\frac{1}{\lambda}-1. \tag{9}\]  
Considering the presence of \(G\) logical groups in the network, the network-wide average PAoI is as follows:  
\[A=\frac{\sum_{g=1}^{G}n^{g}A_{g}}{\sum_{g=1}^{G}n^{g}}. \tag{10}\]  
According to Formula (10), the optimization problem of PAoI can be formulated as follows:  
\[A^{*}=\underset{0<q\leq 1}{\min}A. \tag{11}\]  
However, solving this problem entails significant complexity. The successful transmission probability under multi-AP scenarios, (5), is an implicit equation that cannot be explicitly resolved. \(p^{A}\) depends not only on the channel access probability of its own group's MTDs but is also nested with the channel access probabilities of other groups through the inter-group interference term \(R_{j}\), resulting in a highly nonlinear objective function that is intractable for direct optimization. To address these challenges, the subsequent sections employ a heuristic PSO algorithm to dynamically adjust the channel access probabilities across all groups within the solution space.  
Figure 3: AoI evolution traces.
## 2 Materials and Methods
### Cell Growth and Dry Weight Determination  
The growth of _Haematococcus pluvialis_ was evaluated using a gravimetric technique. A 10 mL (V) sample was collected every two days from the PBR. The sample was filtered using a pre-weighed (\(W_{1}\)) acetate ultrafiltration membrane with a 0.45 \(\upmu\)m pore size. The membrane, with the retained biomass, was then dried at 105 \({}^{\circ}\)C overnight until it reached a stable weight. The following day, the membrane disk was re-weighed (\(W_{2}\)) using an analytical balance (Mettler Toledo XS105 DR, Mettler-Toledo International Inc., Columbus, OH, USA).  
The biomass collected by the filter was calculated as the difference in weight of the membrane before and after filtration. Then, the concentration was obtained by dividing  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **BG11** & **BWW** & **Exp. 1** & **Exp. 2** & **Exp. 3** \\ \hline
**Culture Medium** & **(\% vol)** & **(\% vol)** & & **Light Intensity (\(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\))** \\ \hline CTRL & 100 & 0 & 35 (6) & 140 (6) & 35 (5) + 140 (5) \\ BG11-BWW 1:1 & 50 & 50 & 35 (6) & 140 (6) & 35 (5) + 140 (5) \\ BG11-BWW 1:3 & 25 & 75 & 35 (6) & 140 (6) & 35 (5) + 140 (5) \\ BWW & 0 & 100 & 35 (6) & 140 (6) & 35 (5) + 140 (5) \\ \hline \end{tabular} Note: CTRL = control medium as BG11, BWW = brewery wastewater. The number between the parentheses represents the days that the experiment lasted under those conditions.  
\end{table}
Table 2: Experimental setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**COD Range** & **TN** & **TP** & **N-NH4+** & **pH** \\
**(mg O\({}_{2}\) L\({}^{-1}\))** & **(mg L\({}^{-1}\))** & **(mg L\({}^{-1}\))** & **(mg L\({}^{-1}\))** & **(\(\prime\))** \\ \hline
2080–5805 & 4.75 \(\pm\) 0.14 & 0.44 \(\pm\) 0.003 & 0.004 \(\pm\) 0.001 & 6.89 \(\pm\) 0.51 \\ \hline \end{tabular}
\end{table}
Table 3: BWW physical–chemical composition.  
the biomass by the filtered volume. The dry weight cell concentration (g L\({}^{-1}\)) was, thus, calculated according to the following equation:  
\[X_{dw}=\frac{W_{2}-W_{1}}{V}\, \tag{1}\]  
The specific growth rate \(\upmu\) was evaluated by linearly fitting Equation (2) during the exponential growth phase:  
\[\ln\biggl{(}\frac{X}{X_{e}}\biggr{)}=\upmu\ (t-t_{e}) \tag{2}\]  
where \(X\) and \(X_{e}\) (g L\({}^{-1}\)) represent the biomass concentrations at the generic time \(t\) and the time \(t_{e}\) when the exponential phase started.  
The batch volumetric productivity biomass \(\Pi_{\text{x}}\) (mg L\({}^{-1}\) day\({}^{-1}\)) was evaluated as:  
\[\Pi_{\text{x}}=\frac{\biggl{(}X_{f}-X_{0}\biggr{)}}{\biggl{(}t_{f}-t_{0}\biggr{)}} \tag{3}\]  
where the symbols \(X_{f}\) and \(X_{0}\) (mg L\({}^{-1}\)) refer to the final biomass concentration (at time \(t_{f}\)) and the initial one (at \(t_{0}\)), respectively.
\begin{table}
\begin{tabular}{r r r r r r r r r r r r r r r r r r r r r r} \hline \hline \multicolumn{1}{c}{**Instances**} & \multicolumn{5}{c}{**C\&W Savings Heuristic**} & \multicolumn{5}{c}{**VNS\({}_{\text{red}}\)**} & \multicolumn{5}{c}{**VNS\({}_{\text{full}}\)**} \\ \multicolumn{1}{c}{**Name**} & \(n_{k}\) & \(n_{v}\) & **m** & **n** & **Dist** & \(\overline{t(s)}\) & **m** & **n** & **Dist** & **Avg** & **Imp(\%)** & \(\overline{t(s)}\) & **m** & **n** & **Dist** & **Avg** & **Imp(\%)** & \(\overline{t(s)}\) \\ \hline C205\_C21 & 4 & 20 & 2 & 21 & 1470.73 & 0.005 & 2 & 12 & 1226.48 & 1249.59 & 15.04 & 460.15 & 2 & 12 & **1205.23** & **1223.94** & 16.78 & 664.39 \\ C206\_C21 & 3 & 19 & 2 & 19 & 1399.11 & 0.005 & 2 & 12 & 1202.52 & 1222.94 & 12.59 & 519.78 & 2 & 11 & **1182.63** & **1198.54** & 14.34 & 556.03 \\ C207\_C21 & 3 & 19 & 2 & 20 & 1406.55 & 0.005 & 2 & 12 & 1195.1 & 1211.55 & 13.86 & 262.84 & 2 & 11 & **1173.7** & **1188.92** & 15.47 & 562.88 \\ C208\_C21 & 3 & 17 & 2 & 19 & 1393.28 & 0.005 & 2 & 12 & 1193.01 & 1221.40 & 12.34 & 429.15 & 2 & 11 & **1169.69** & **1188.85** & 14.67 & 652.98 \\ \hline \multicolumn{1}{c}{**average**} & \multicolumn{5}{c}{1644.94} & 0.005 & \multicolumn{5}{c}{1351.63} & 1371.56 & 16.38 & 431.21 & \multicolumn{5}{c}{**1323.66**} & **1353.69** & 17.48 & 597.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{r r r r r r r r r r r r r r r r r r r r r r r r} \hline \hline \multicolumn{1}{c}{**Instances**} & \multicolumn{5}{c}{**C\&W Savings Heuristic**} & \multicolumn{5}{c}{**VNS\({}_{\text{red}}\)**} & \multicolumn{5}{c}{**VNS\({}_{\text{full}}\)**} \\ \multicolumn{1}{c}{**Name**} & \(n_{k}\) & \(n_{v}\) & **m** & **n** & **Dist** & \(\overline{t(s)}\) & **m** & **n** & **Dist** & **Avg** & **Imp(\%)** & \(\overline{t(s)}\) & **m** & **n** & **Dist** & **Avg** & **Imp(\%)** & \(\overline{t(s)}\) \\ \hline C205\_C21 & 4 & 20 & 2 & 21 & 1470.73 & 0.005 & 2 & 12 & 1226.48 & 1249.59 & 15.04 & 460.15 & 2 & 12 & **1205.23** & **1223.94** & 16.78 & 664.39 \\ C206\_C21 & 3 & 19 & 2 & 19 & 1399.11 & 0.005 & 2 & 12 & 1202.52 & 1222.94 & 12.59 & 519.78 & 2 & 11 & **1182.63** & **1198.54** & 14.34 & 556.03 \\ C207\_C21 & 3 & 19 & 2 & 20 & 1406.55 & 0.005 & 2 & 12 & 1195.1 & 1211.55 & 13.86 & 262.84 & 2 & 11 & **1173.7** & **1188.92** & 15.47 & 562.88 \\ C208\_C21 & 3 & 17 & 2 & 19 & 1393.28 & 0.005 & 2 & 12 & 1193.01 & 1221.40 & 12.34 & 429.15 & 2 & 11 & **1169.69** & **1188.85** & 14.67 & 652.98 \\ \hline \multicolumn{1}{c}{**average**} & \multicolumn{5}{c}{1644.94} & 0.005 & \multicolumn{5}{c}{1351.63} & 1371.56 & 16.38 & 431.21 & \multicolumn{5}{c}{} & **1323.66** & **1353.69** & 17.48 & 597.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Computational results for large-sized random instances. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 3 Results and Discussion  
### Morphology and Host Range of S-SZBM1  
A cyanophage named S-SZBM1 was isolated from surface seawater of Shenzhen Bay (22.52\({}^{\circ}\) N, 113.96\({}^{\circ}\) E), Guangdong, China by infecting _Synechococcus_ CB0101. Cyanphage S-SZBM1 formed distinguishable plaques with size ranging from 1.5 to 4.0 mm after a week of growth (Figure 1A). The TEM analysis revealed that cyanophage S-SZBM1 displays myovirus morphology with an isometric and icosahedral head (67.5 \(\pm\) 1.0 nm in diameter) and a rigid contractile tail (71.7 \(\pm\) 2.6 - 113.6 \(\pm\) 4.0 nm long) (Figure 1B). Generally, myoviruses dominated the viral signal in microbial-fraction metagenomic datasets and isolated phages [41; 42; 43; 44]. Among all of the 17 tested freshwater and marine cyanobacteria species/strains, the lytic effect induced by cyanophage S-SZBM1 was only observed in the original host strain. This finding seems to be contrary to previous studies which have suggested a broad host range of myoviruses [45]. For example, myovirus S-CBM2, which was isolated by infecting _Synechococcus_ CB0101, can cross-infect several _Synechococcus_ strains, including _Synechococcus_ WH8101, WH7805, WH7803, and CB0102 [46]. It should be noted that the number and species of tested hosts were limited here, and other yet-to-be-discovered hosts cannot be ruled out.
## 2 Materials and Methods
### Research Methods
#### 2.1.3 Empirical Model of Sensitive Factors  
Corn production represents a complex system, directly affected by not only production factors, such as land and labor, but also closely related to the technical level and socioeconomic conditions [15; 16; 17; 18; 19; 20; 21; 22; 23]. In order to explore the impact mechanism of the temporal and spatial differences in China's corn production costs, this article conducted an empirical analysis of the relevant factors. Taking into consideration the many factors that affect the CPC, this paper relied on existing research results [24; 25; 26; 27; 28; 29; 30] and selected four key sensitive factors. Firstly, it took into consideration the scale of land management, measured by the sown area per labor [24; 25]. Secondly, it examined the labor force structure, which was recognized as the proportion of the non-agricultural population [26; 27]. Thirdly, this paper looked at the degree of mechanization, measured by the comprehensive mechanization level of corn cultivation and harvesting [28]. The final sensitive factor was socio-economic condition, measured by the average annual net income of farmers in each region and the proportion of non-agricultural output value [29; 30]. All the above variables are provided in Table 1.  
If the CPC has spatial autocorrelation characteristics within the study area, there will be setting deviations to use ordinary panel regression model. Therefore, the present paper uses a spatial measurement model for empirical analysis. According to the research requirements, two basic spatial panel models are constructed [31; 32]: the spatial lag panel model (SLPDM) and spatial error panel model (SEPDM). Their formulas are as follows:  
\[ \begin{split} CPC_{it}=\alpha_{0}+\alpha_{1}Land_{it}+\alpha_{2} Citizen_{it}+\alpha_{3}Machine_{it}+\alpha_{4}Income_{it}+\alpha_{5}Nonagrigd p_{it}+\lambda\sum\limits_{j=1}^{N}w_{ij}cpc_{jt}+\varepsilon_{it}\\  CPC_{it}=\beta_{0}+\beta_{1}Land_{it}+\beta_{2}Citizen_{it}+\beta_{3 }Machine_{it}+\beta_{4}Income_{it}+\beta_{5}Nonagrigd p_{it}+\rho\sum\limits_{j=1}^{N}w_{ij}cpc_{jt}+ \varepsilon_{it}\\ \end{split}  \tag{5}\]  
In Formulas (5) and (6), \(i\) represents each area of the cross section (\(i\) = 1, 2,..., \(N\)), while \(t\) indicates the time series of the study (\(t\) = 1, 2,..., \(T\)). Furthermore, the symbol \(w\) is the spatial weight matrix, and \(\alpha_{0}\), \(\alpha_{1}\),..., \(\alpha_{5}\) and \(\beta_{0}\), \(\beta_{1}\),..., \(\beta_{5}\) are the waiting estimated parameters. The symbol \(\varepsilon\) represents a random disturbance item that obeys the normal distribution and is independent of each other, while \(\lambda\) is the spatial regression coefficient and \(\rho\) stands for the spatial error coefficient. The _CPC_ represents the production cost of corn, _Land_ represents the average sown area of labor, while _Citizen_ indicates the proportion of the non-agricultural population and _Machine_ represents the comprehensive mechanization