esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Laboratory Measurements
Figure 11 shows the printed tool (\(d\) = 22 mm) with mounted PMs and the measuring procedure of the axial component of magnetic flux density (\(B_{\mathrm{z}}\)) at the middle of the distance between opposite PMs (on Path 1 in Figure 9), with PM thickness \(h_{\mathrm{PM}}=5\) mm and different rotor disk thicknesses. Results of the measurements of the axial component of magnetic flux density (\(B_{\mathrm{z}}\)) for different rotor disk thicknesses and two stator thicknesses are shown and compared to the calculated values (using FEM and the developed mathematical model) in Table 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(h_{\mathrm{TM}}\) & \(d\) (\(d_{\mathrm{z}}\) + 2 \(d_{\mathrm{sg}}\)) & \(d_{\mathrm{Fe}}\) & \(B_{\mathrm{z,FEM}}\) & \(B_{\mathrm{z,Modal}}\) & \(B_{\mathrm{z,Meas}}\) & \(B_{\mathrm{z,FEM}}\)/\(B_{\mathrm{z,Meas}}\) & \(B_{\mathrm{z,Model}}\)/\(B_{\mathrm{z,Mass}}\) \\ (mm) & (mm) & (mm) & (T) & (T) & (T) & (\%) & (\%) \\ \hline
5 & 17 & 5 & 0.4401 & 0.4367 & 0.4490 & 98.02 & 97.27 \\
5 & 17 & 6 & 0.4581 & 0.4551 & 0.4720 & 97.06 & 96.41 \\
5 & 17 & 10 & 0.4671 & 0.4700 & 0.4790 & 97.52 & 98.12 \\
5 & 17 & 11 & 0.4676 & 0.4687 & 0.4800 & 97.41 & 97.64 \\
5 & 17 & 15 & 0.4686 & 0.4718 & 0.4830 & 97.02 & 97.68 \\
5 & 22 & 5 & 0.3758 & 0.3737 & 0.3870 & 97.10 & 96.57 \\
5 & 22 & 6 & 0.3810 & 0.3842 & 0.3950 & 96.45 & 97.27 \\
5 & 22 & 10 & 0.3851 & 0.3838 & 0.4020 & 95.79 & 95.46 \\
5 & 22 & 11 & 0.3856 & 0.3820 & 0.4020 & 95.92 & 95.02 \\
5 & 22 & 15 & 0.3861 & 0.3929 & 0.4020 & 96.04 & 97.74 \\  & & & & & Average & 96.83 & 96.92 \\ \hline \end{tabular}
\end{table} 
Table 10: Measurements of the \(B_{\mathrm{z}}\) and comparison with the calculated values. Figure 10: Tool developed for the measurement of the axial component of magnetic flux density: (a) 3D model, (b) printed tool. Table 10 also shows the accuracy of the presented methodology, since the average matching between the measured values and calculated values is 96.92%.
# Effect of Nitrogen Atoms in the CNT Structure on the Gas Sensing Properties of PANI/CNT Composite
## 2 Materials and Methods  
### CNTs Synthesis  
Carbon nanotubes were synthesized by the catalytic chemical vapor deposition (CCVD) method in a gas-phase reactor under argon atmosphere [22]. Ferrocene Fe(C\({}_{5}\)H\({}_{5}\))\({}_{2}\) was used as a catalyst for the growth of carbon nanotubes (the ferrocene-precursor ratio 1:100). A comparative analysis was conducted for the following CNTs synthesized: undoped carbon nanotubes (uCNTs) synthesized from toluene and nitrogen-doped carbon nanotubes with a low (N-CNTs) and high (N\({}^{+}\)-CNTs) content of nitrogen synthesized from acetonitrile and the acetonitrile + triphenylphosphine mixture (99.9:0.1), respectively. The synthesis temperature for all variants of CNTs was 800 \({}^{\circ}\)C. During the preparation, the argon flow rate was maintained at 150 mL/min and the precursor flow rate was 10 mL/h. The total synthesis time was 30 min. Purification of CNTs from the catalyst was carried out by washing in 10% HCI for 48 h. Then the nanotubes were annealed in air at a temperature of 390 \({}^{\circ}\)C to remove amorphous carbon inclusions.
## 2 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multicolumn{6}{c}{**Area percent (\%)**} \\ \cline{3-8}  & & & **A** & **B** & **C** & **D** & **E** \\ \hline
1. & Dimethyl sulfide & \(<900\) & 1.1 & - & - & - & - \\
2. & 2-Methylfuran & \(<900\) & 0.8 & - & - & - & - \\
3. & (\(E\))-2-Methylbut-2-enal & \(<900\) & 1.1 & - & - & - & - \\
4. & 2,3-Dihydro-4-methylfuran & \(<900\) & 0.6 & - & - & - & - \\
5. & Octane & \(<900\) & 0.5 & - & - & - & - \\
6. & 2-Furancarboxaldehyde (furfurral) & \(<900\) & 5.7 & - & - & - & - \\
7. & 2-Methylbutanoic acid & \(<900\) & - & - & 0.2 & - & - \\
8. & 2-Furanmethanol & \(<900\) & - & - & 0.2 & - & - \\
9. & 4-Methyloctane\({}^{*}\) & \(<900\) & - & 0.1 & - & - & - \\
10. & 1,4-Dimethylbenzene\({}^{**}\) & \(<900\) & - & 0.2 & - & - & - \\
11. & Ethenylbenzene (styrene) & \(<900\) & 0.6 & - & - & - & - \\
12. & Heptan-2-ol\({}^{*}\) & \(<900\) & 1.4 & - & - & - & - \\
13. & Nonane & 900 & - & 9.3 & - & 0.1 & - \\
14. & 3-Methylpentanoic acid (3-methylvaleric acid) & 941 & 0.6 & - & 0.3 & - & - \\
15. & Benzaldehyde & 965 & 16.5 & 0.8 & - & - & - \\
16. & Phenylacetaldehyde & 1048 & 0.8 & - & - & - & - \\
17. & _trans_-Linalool oxide (furan type) & 1076 & 20.5 & 0.6 & - & - & - \\
18. & 4,5-Dimethyl-2-formylfuran & 1078 & - & - & 0.8 & - & - \\
19. & Methyl 2-fluoroate & 1084 & - & - & 0.6 & - & 0.3 \\
20. & _cis_-Linalool oxide (furan type) & 1091 & 4.1 & - & - & - & - \\
21. & Linalool & 1101 & 2.0 & - & - & - & - \\
22. & Nonanal & 1102 & 0.7 & - & - & - & - \\
23. & Hotrienol & 1106 & 3.1 & - & - & - & - \\
24. & 2-Phenylethanol & 1116 & 14.9 & 3.4 & 1.2 & 0.7 & 0.9 \\
25. & 2,3-Dihydro-3,5-dihydroxy-6-methyl-4H- & 1145 & - & - & 0.3 & - & - \\  & pyran-4-one & 1147 & 0.9 & - & - & - & - \\
26. & 4-Ketoisophorone & 1150 & 1.4 & - & - & - & - \\
27. & Lilac aldehyde (isomer I)\({}^{**}\) & 1158 & 2.6 & - & - & - & - \\
28. & Lilac aldehyde (isomer II)\({}^{**}\) & 1158 & 2.6 & - & - & - & - \\
29. & Benzoic acid & 1162 & - & - & 1.2 & 0.9 & 0.2 \\
30. & Pinocarvone & 1170 & 0.6 & - & - & - & - \\
31. & Lilac aldehyde (isomer III)\({}^{**}\) & 1173 & 1.1 & - & - & - & - \\
32. & (\(E\))-3,7-Dimethyl-octa-1,5-diene-3,7-diol & 1191 & - & - & 0.3 & - & - \\
33. & Octanoic acid & 1193 & 1.0 & - & - & - & - \\
34. & Dodecane & 1200 & - & 0.3 & 0.2 & - & - \\
35. & 5-Hydroymethylfurral & 1230 & - & - & 1.4 & 0.6 & 0.7 \\
36. & 3-Pyridinecarboxylic acid (nicotinic acid) & 1261 & - & - & 5.6 & 0.8 & - \\
37. & Phenylacetic acid & 1269 & - & - & - & 1.2 & 0.3 \\
38. & Nonanoic acid & 1273 & 1.5 & - & - & 0.2 & - \\
39. & 4-Vinyl-2-methoxyphenol & 1314 & 0.9 & - & 0.4 & 0.2 & 0.2 \\
40. & 2,4,6-Trimethylened\({}^{**}\) & 1332 & 0.4 & - & - & - & - \\
41. & (\(E\))-8-Hydroxylinalool & 1367 & - & - & - & 0.2 & - \\
42. & Decanoic acid & 1370 & 0.3 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Acer_ spp. honey volatile organic composition obtained by HS-SPME and USE followed by GC and GC/MS analysis.
## 3 Results
### COX-2 Interactions
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**COX-2** & **Limonene** & **Camphor** & **Linalool** & **Menthol** & \(\alpha\)**-Pinene** & \(\beta\)**-Pinene** & **AA** \\ \hline Conformations & 3 & 13 & 19 & 26 & 36 & 46 & 7 \\ \hline RMSD from & & & & & & & \\ Reference & 59.204 A & 67.674 A & 66.971 A & 67.752 A & 67.087 A & 67.770 A & 58.016 A \\ Structure & & & & & & & \\ \hline Binding Energy & \(-5.96\) & \(-6.48\) & \(-5.79\) & \(-6.42\) & \(-6.18\) & \(-6.31\) & \(-7.07\) \\  & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol \\ \hline Inhibition & & & & & & & \\ Constant, Ki & 43.00 \(\upmu\)M & 17.84 \(\upmu\)M & 56.61 \(\upmu\)M & 19.80 \(\upmu\)M & 29.44 \(\upmu\)M & 23.61 \(\upmu\)M & 6.55 \(\upmu\)M \\ \hline Total Internal & \(-0.13\) & +0.00 & \(-0.37\) & \(-0.16\) & +0.00 & +0.00 & \(-1.18\) \\ Energy & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol \\ \hline Torsional Free & +0.30 & +0.00 & +1.49 & +0.60 & +0.00 & +0.00 & +4.47 \\ Energy & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol \\ \hline Unbound Energy & \(-0.13\) & +0.00 & \(-0.37\) & \(-0.16\) & +0.00 & +0.00 & \(-1.18\) \\ kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol & kcal/mol \\ \hline \end{tabular}
\end{table} 
Table 2: In silico docking results for interactions between selected terpenes and COX-2: binding energy, conformational parameters, inhibition constants (Ki), and energy descriptors compared to arachidonic acid (AA).
## 5 Experiments  
### Datasets  
Our evaluation is based on seven public datasets (Cora [40], Citeseer [40], Pubmed [41], A-computer [42], A-photo [42], Ogbn-Arxiv [43], and Ogbn-Products [43]). Table 1 shows the statistics of the public datasets.  
Table 1 lists the datasets utilized in this study, where #Nodes and #Edges denote the number of nodes and edges in each dataset, respectively. Additionally, #Features represents the feature dimensions within the dataset, and #Classes indicates the number of categories included in the dataset.  
Cora is a benchmark citation dataset where each node represents a paper and edges denote citation relationships between papers. Similarly, Citeseer also represents papers as nodes, but with higher feature dimensions compared to Cora. Pubmed is a benchmark dataset formed by diabetes-related papers in Pubmed data. Its node feature is the weighted  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\#Nodes** & **\#Edges** & **\#Features** & **\#Classes** \\ \hline Cora & 2485 & 5069 & 1433 & 7 \\ Citeseer & 2110 & 3668 & 3703 & 6 \\ Pubmed & 19,717 & 44,324 & 500 & 3 \\ A-computer & 13,381 & 245,778 & 767 & 10 \\ A-photo & 7487 & 119,043 & 745 & 8 \\ Ogbn-Arxiv & 169,343 & 1,166,243 & 128 & 40 \\ Ogbn-Products & 2,449,029 & 61,859,140 & 100 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
Figure 5: Node feature enhancement module schematic.  
frequency of TF-IDF, and the category is the type of diabetes. A-computer and A-photo are two benchmark datasets extracted from Amazon co-purchase graphs. In these datasets, nodes represent products, while edges denote frequent co-purchases between products. The features encode product reviews using a bag-of-words model (BoW), and the labels represent product categories. Ogbn-Arxiv is a directed dataset that contains publicly available papers in the field of computer science on Arxiv. Each paper is a node, the citation relationship between papers is used as an edge, and its research field is the category of the paper. Ogbn-Products is an undirected dataset that represents the Amazon product co-purchase network. The nodes represent products, and the edges represent two products that are usually purchased by the same customer. Each node has category information, indicating the category of the product. Among the above-mentioned datasets, Arxiv and Products are relatively large-scale datasets and are often used for benchmark testing of graph neural networks. They are already benchmark datasets in the field of graph machine learning.
## 3 Antennas with General Aspects Utilized for CubeSat
### Dielectric Lenses, Reflectors, and Beam Control
Reference [52] introduces a novel antenna design intended for CubeSat applications. This design features a dielectrically embedded mesh lens composed of stacked printed circuit board (PCB) material layers with embedded copper elements. The mesh lens consists of layers of dielectric material containing sub-wavelength-dimension metal elements arranged in a grid pattern, resulting in a flat and lightweight structure. This configuration is particularly advantageous for CubeSats with stringent size, weight, and power (SWaP) constraints. The design aims to provide a cost-effective solution for high-frequency applications, such as water detection in space missions, by leveraging the compact and efficient nature of the mesh lens. The study demonstrates the feasibility of this approach, offering a promising avenue for future CubeSat antenna designs. Reference [53], presented at the 31 st Symposium on Space Terahertz Technology in 2020, discusses designing and testing a metamaterial flat lens operating at 20 GHz. This lens is intended as a low-frequency prototype to validate design procedures for higher-frequency applications, such as those operating at 600 GHz or above 1 THz. The lens is designed to transform a plane wave into the phase of a converging Gaussian beam, with a focal length of 1.5 cm and a diameter of 2.5 cm. The lens is fabricated using 11 RO3003 circuit board laminate layers with etched sub-wavelength-sized copper patterning. The study includes measurements and simulations to evaluate the lens's performance, aiming to achieve less than half a dB of loss, significantly better than that of a Fresnel zone plate lens. The successful design and testing of this prototype are expected to facilitate the development of metamaterial lenses for higher-frequency applications in space-based missions.
# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 3 Saturable Reactor Core Looseness Experiment
### Experimental Process and Data Collection  
The iron core looseness experiment simulates different looseness degrees by adjusting the tension belt screw torque and includes five modes: normal state (NS), slight looseness (SL), extreme looseness (EL), total looseness (TL) and screw bolt off (SO). The specific experimental parameters are shown in Table 1. The nine current peaks in the table represent nine different operating conditions. That is, in engineering applications, saturable reactors usually operate under these nine operating conditions. In order to be as close to engineering practice as possible, this experimental platform directly provides corresponding currents  
Figure 3: Vibration Experimental Platform Structure.  
Figure 2: Saturable Reactor Vibration Experimental Platform.  
for the saturable reactor under nine operating conditions. Each looseness mode is tested under the mixed action of nine operating conditions.  
Three typical operating conditions are selected. Figure 4 shows the vibration signals with different iron core looseness degrees under these operating conditions. The horizontal axis corresponds to the sampling time for three different working conditions. The vertical axis represents the amplitude of the vibration signals. From the graph, it can be observed that there are many differences between the same looseness modes under the three working conditions. However, the differences in the different looseness modes under the same working conditions are not obvious. Their shapes are similar and only have differences in amplitude. However, this difference is not intuitive enough. It is difficult to accurately identify the iron core looseness degrees using vibration data directly.  
A method is needed to extract the features of vibration signals. It should meet the following two basic requirements: (1) the differences under different working conditions should be preserved and (2) the differences in different looseness modes under the same working condition should be extracted and made more obvious. Only in this way can the diagnostic effect for the iron core looseness degree be improved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Current peak & 270 A, 335 A, 465 A, 600 A, 730 A, 865 A, 1000 A, \\ Vibration sensor frequency response range & 3 Hz\(\sim\)30 kHz \\ Data collection sampling frequency & 1 MHz \\ a. screw bolt off (SO) & Tension belt screw removed \\ b. total looseness (TL) & Tension belt screw torque: 0 N-m \\ c. extreme looseness (EL) & Tension belt screw torque: 6 N-m \\ d. slight looseness (SL) & Tension belt screw torque: 8 N-m \\ e. normal state (NS) & Tension belt screw torque: 10 N-m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test parameters.  
Figure 4: Vibration Signals of Different Looseness Modes under Different Working Conditions.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
###### Abstract  
Recent speech enhancement studies have mostly focused on completely separating noise from human voices. Due to the lack of specific structures for harmonic fitting in previous studies and the limitations of the traditional convolutional receptive field, there is an inevitable decline in the auditory quality of the enhanced speech, leading to a decrease in the performance of subsequent tasks such as speech recognition and speaker identification. To address these problems, this paper proposes a Harmonic Repair Large Frame enhancement model, called HRLF-Net, that uses a harmonic repair network for denoising, followed by a real-imaginary dual branch structure for restoration. This approach fully utilizes the harmonic overtones to match the original harmonic distribution of speech. In the subsequent branch process, it restores the speech to specifically optimize its auditory quality to the human ear. Experiments show that under HRLF-Net, the intelligibility and quality of speech are significantly improved, and harmonic information is effectively restored.  
speech enhancement; denoising; harmonic; fast Fourier convolution; dual branch structure +
Footnote †: journal: Medical Informatics
## 3 Parametric Analysis
### Gain Coefficients  
To evaluate the effectiveness of the protection system in reducing the seismic effects on the frame structure, two different gain coefficients were introduced. They read as follows:  
\[\alpha_{1}=\frac{\max\lvert u_{1}(t)\rvert}{\max\lvert\widetilde{u}_{1}(t)\rvert}, \ \ \alpha_{2}=\frac{\max\lvert\Delta u(t)\rvert}{\max\lvert\widetilde{\Delta u}(t) \rvert}. \tag{10}\]  
Here, the drift \(\widetilde{\Delta u}=\widetilde{u}_{1}-\widetilde{u}_{2}\) denotes the difference in displacements provided by the uncoupled 2-DOF model, representing the seismic response of the uncoupled frame structure. The coefficient \(\alpha_{1}\) informs about the effects of the coupling of the story connected to the protection system, whereas \(\alpha_{2}\) is a metric for the effect of the coupling on the drift of the superstructure. According to Equation (10), the values of \(\alpha_{1}\) and \(\alpha_{2}\) (when less than unity) indicate an effective performance of the protection system, as they signify a reduction in the displacement (and drift) of the coupled structure compared to that of the uncoupled structure.  
The parametric analysis involved evaluating the gain coefficients \(\alpha_{1}\) and \(\alpha_{2}\) for each set of variable parameters and a single earthquake base motion, respectively. These values were then plotted in a specific parameter plane, thus resulting in gain maps.
# Polyadic Cantor Fractal Ultrasonic Lenses: Design and Characterization
## 1 Introduction  
The diffraction phenomenon has been traditionally used to control beamforming and focalization of lenses in different physical fields such as optics, acoustics or microwaves. Conventional lenses' focusing capabilities are based on their curved shapes and their refractive materials. As an alternative to traditional lenses, different solutions have been investigated such as sonic crystals [1], planar structures built with concentric rings such as Fresnel lenses [2] or variations on the latter, pinhole zone plates [3]. It has been shown that these new lenses can improve focalization over traditional ones.  
In the acoustic field, the modulation control of acoustic beams is an important topic, because it can result in a significant advance in different applications such as medical therapy or non-destructive material analysis, which includes a wide range of areas such as material flaw detection [4], pest detection in agroforestry [5] and food analysis [6].  
The use of planar structures that are capable of guiding and focusing waves the same way as traditional lenses is very interesting. Among the possible geometries that can be analyzed, fractal structures have already been proposed [7; 8]. Particularly, Generalized Polyadic Cantor Sets (GPCSs) are very attractive because they can be generated with simple algorithms, and this is the main reason why the majority of studies are based on these type of fractals [9; 10]. Fractal lenses have been shown to become a possible improvement on focalization properties, reducing aberration simultaneously [11].  
In this work, the design and characterization of planar acoustic lenses based on GPCSs is developed, and their application as ultrasonic lenses is analyzed. These new kinds of lenses are known as Polyadic Cantor Fractal Lenses (PCFLs). These structures are proposed because of their ease of fabrication and their multifocus profile.
## 3 Materials and Methods
### Security Framework for Threat Modeling and Assessment  
This subsection details a four-step security framework tailored to address the unique characteristics of satellite networks. The framework defines a network's architecture, identifies vulnerabilities, models potential attack scenarios, and evaluates security to design effective countermeasures, as shown in Figure 1. The first step involves defining the key components and data flow of the satellite network. The architecture is categorized into the ground, space, and user segments, with an analysis of the data flow that includes the roles of each node and their interactions with protocols. The second step focuses on identifying security vulnerabilities and threat vectors [38]. Utilizing Common Vulnerabilities and Exposures (CVEs) and the Common Vulnerability Scoring System (CVSS) [39], potential vulnerabilities in key nodes and protocols within the satellite network are identified. Based on these vulnerabilities, potential threat vectors are developed, and specific attack scenarios are defined. The third step involves performing threat modeling to systematically represent and analyze threat vectors [40]. The final step encompasses the evaluation of security and the design of countermeasures. This evaluation begins by assessing the satellite network's security posture prior to the application of any security patches.  
Figure 1: Framework stages for the security assessment of satellite networks.
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 3 Methods
### Generating a Suitability Map  
In this study, the rated value for each criterion was multiplied by its weight from the pairwise comparison, and the results were accumulated, as shown in Equation (4):  
\[\text{S}=\sum_{i}w_{i}\mu_{i}, \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Class** & **Rated Value** & **Remark** \\ \hline \multirow{4}{*}{Global Horizontal Irradiation (kWh/m\({}^{2}\)/day)} & 4.62–4.94 & 100 & Good \\ \cline{2-4}  & 4.31–4.62 & 75 & Fair \\ \cline{2-4}  & 4.00–4.31 & 50 & Low \\ \cline{2-4}  & \textless{4.00} & 25 & Poor \\ \hline \multirow{4}{*}{Temperature (\({}^{\circ}\)C)} & \(-\)20–0 & 100 & Good \\ \cline{2-4}  & 0–3 & 75 & Fair \\ \cline{2-4}  & 3–6 & 50 & Low \\ \cline{2-4}  & 6–10 & 25 & Poor \\ \hline \multirow{4}{*}{Elevation (m asl)} & 3318–4318 & 100 & Good \\ \cline{2-4}  & 2023–3023 & 75 & Fair \\ \cline{2-4}  & 1023–2023 & 50 & Low \\ \cline{2-4}  & 523–1023 & 25 & Poor \\ \hline \multirow{4}{*}{Slope (\%)} & \textless{1} & 100 & Good \\ \cline{2-4}  & 1–3 & 75 & Fair \\ \cline{2-4}  & 3–11 & 50 & Low \\ \cline{2-4}  & \textgreater{}11 & 25 & Poor \\ \hline \multirow{4}{*}{Aspect} & S & 100 & Good \\ \cline{2-4}  & SE or SW & 75 & Fair \\ \cline{2-4}  & E or W & 50 & Low \\ \cline{2-4}  & Others & 25 & Poor \\ \hline \multirow{4}{*}{Distance from a main road (km)} & \textless{25} & 100 & Good \\ \cline{2-4}  & 25–50 & 75 & Fair \\ \cline{2-4}  & 50–75 & 50 & Low \\ \cline{2-4}  & \textgreater{}75 & 25 & Poor \\ \hline \multirow{4}{*}{Distance from a major power line (km)} & \textless{25} & 100 & Good \\ \cline{2-4}  & 25–50 & 75 & Fair \\ \cline{1-1} \cline{2-4}  & 50–75 & 50 & Low \\ \cline{1-1} \cline{2-4}  & \textgreater{}75 & 25 & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rating criteria used in this study.  
where S represents the final score, \(w_{i}\) represents the weight of criterion \(i\), and \(\mu_{i}\) represents the criterion's standardized score. As the criterion weights were all summed to one, the final scores of the combined solution can be expressed using the same scale. In addition, the weightings given to each criterion determined the trade-off level relative to other criteria, which implied that high scores and weightings from standardized criteria could compensate for low scores from other criteria.