esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Isometric Strength and Countermovement Jump  
No statistical differences were seen in relative isometric hip ABD strength in the dominant (+2.42%, \(p=0.864\), ES [C.I.] = 0.01 [-0.08, 0.11]) (Figure 0(a)) and non-dominant limb (-1.46%, \(p=0.834\), ES [C.I.] = \(-0.02\) [-0.12, 0.07]) (Figure 0(b)); nor in the relative isometric hip ADD strength in the dominant(-2.10%, \(p=0.399\), ES [C.I.] = 0.10 [0.00, 0.19]) (Figure 0(c)) and non-dominant limb (+3.38%, \(p=0.349\), ES [C.I.] = 0.11 [0.02, 0.21]) (Figure 0(d)). In addition, no differences were obtained in ADD/ABD strength ratios in dominant (1.14 vs. 1.19, \(p=0.220\), ES [C.I.] = 0.28 [0.19-0.37]) and non-dominant  
\begin{table}
\begin{tabular}{c c c} \hline
**Velocity Range** & **Distance Covered (m)** & **Total Distance (\%)** \\ \hline
0.0–6.0 km\(\cdot\)h\({}^{-1}\) & 2692.04 \(\pm\) 614.70 & 49.36 \(\pm\) 9.71 \\
6.1–12.0 km\(\cdot\)h\({}^{-1}\) & 1781.15 \(\pm\) 380.97 & 32.47 \(\pm\) 4.53 \\
12.1–18.0 km\(\cdot\)h\({}^{-1}\) & 851.66 \(\pm\) 282.16 & 15.74 \(\pm\) 5.48 \\
18.1–21.0 km\(\cdot\)h\({}^{-1}\) & 107.59 \(\pm\) 75.87 & 1.98 \(\pm\) 1.40 \\
21.1–24.0 km\(\cdot\)h\({}^{-1}\) & 21.59 \(\pm\) 23.66 & 0.40 \(\pm\) 0.44 \\ \(>\)24.1 km\(\cdot\)h\({}^{-1}\) & 2.47 \(\pm\) 6.19 & 0.05 \(\pm\) 0.12 \\ \hline \end{tabular} Abbreviations: km-h\({}^{-1}\) = kilometers/hour; m = meters.  
\end{table}
Table 1: Mean distances and % of total distance covered during the match at different velocity ranges.  
limbs (0.98 vs. 0.96, \(p\) = 0.600, ES [C.I.] = 0.14 [0.05-0.24]) when comparing them pre and post-match. However, for isometric knee flexion strength, statistical differences were obtained in the non-dominant limb (7.0%, \(p\) = 0.047; ES [C.I.] = 0.29 [0.20, 0.38]) (Figure 1f) but no differences were reported in the dominant limb (0.1%, \(p\) = 0.983; ES [C.I.] = 0.11 [0.02, 0.21]) (Figure 1f). Finally, neuromuscular fatigue was measured by a countermovement jump test after match-play (23.0 \(\pm\) 4.9 vs. 20.5 \(\pm\) 6.6 cm, \(p\) = 0.008, ES [C.I.] = 0.44 [0.34, 0.53]).
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### Homogenous Macro/Small Cell Deployment
#### 3.1.2 Interference
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Homogenous** & **Homogenous** & **HetNet with** & **HetNet with Small** & **HetNet with Small** \\  & **Macro Cells** & **Small Cells** & **Uniformly Distributed** & **Cells Deployed near** & **Cells Deployed near** \\ \hline Number of & & & & & & \\ interfering rings & 2-macro & 2-small & 2-small, 2-macro & 1-small, 2-macro & 1-small, 2-macro \\ (\(N_{i}\))-cell type & & & & & \\ \hline Number of cells & 19 macro cells & 19 small cells & 19 macro, 19 small cells & 19 macro, 7 small cells & 19 macro, 13 small cells \\ \hline Cell radius & \(R_{\mathrm{macro}}\) = 1000 & \(R_{\mathrm{small}}\) = 200 m & \(R_{\mathrm{small}}\) = 200 m, \(R_{\mathrm{macro}}\) = 1000 m & \\ \hline UE distribution & & & & Uniform distribution \\ \hline Pathloss model & & & Free space model PL = 32.45 + 20 log\({}_{10}\)(\(r\)) + 20 log\({}_{10}\)(\(f\), shift\({}_{\mathrm{kHz}}\)) & \\ \hline Antenna type & & & & & \\ at eNodeB & & & & & \\ \hline Pathloss & \(\alpha_{macro}\) = 0.7, 0.8, & \(\alpha_{\mathrm{small}}\) = 0.7, 0.8, & \(\alpha_{\mathrm{small}}\) = 0.7, 0.8, 0.9, 1 & \\ compensation & 0.9, 1 & 0.9, 1 & \(\alpha_{macro}\) = 0.7, 0.8, 0.9, 1 & \\ factor “\(\alpha\)” range & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System and simulation parameters for each small cell deployment scenario. \(\rho_{ue}p_{\rm o}\mathrm{A}^{-\alpha}(-2R+r)^{\alpha\eta}\mathrm{A}r^{-\eta}2 \pi rdr\). Therefore, the generalized expression for the interference offered by the \(n\)th interfering ring to the central cell using Equation (7) can be given as:  
\[ \begin{array}{l}I_{n}=2\pi\int_{(2n-1)R}^{2 nR}\rho_{ue}p_{\rm o}\mathrm{A}^{- \alpha}(2 nR-r)^{\alpha\eta}\mathrm{A}r^{-\eta}rdr\\ +2\pi\int_{2 nR}^{(2n+1)R}\rho_{ue}p_{\rm o}\mathrm{A}^{-\alpha}(-2 nR+r)^{ \alpha\eta}\mathrm{A}r^{-\eta}rdr\end{array}  \tag{9}\]  
Hence, the total interference offered to the central cell from the interfering rings can be approximated as:  
\[I_{Ext}=\sum\
olimits_{n=1}^{N_{i}}I_{\rm n} \tag{10}\]  
where \(N_{i}\) represents the number of interfering rings around the central cell. \(N_{i}\) = 2 was considered for this work.
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 2 Lead Toxic Effects
Indeed, each vegetable has a specific uptake capability and a different translocation factor to the edible parts. Herbs show the highest levels, whereas fruits present the lowest, even if their overall contribution to human intake is greater than for other species [24]. As for leafy and root vegetables, there is a higher uptake of metals for the former, especially in the case of lettuce and spinach [25; 26; 27; 28]. As an example, tomatoes show a scarce accumulation capability, while turnips and carrots accumulate moderate lead concentrations [21; 22]. The uptake ability is influenced by pH, and the lead absorption in lettuce and carrots increases at low pH values (\(<\)6) [21]. Safety of vegetables destined for human consumption cannot therefore always be guaranteed, even after a careful washing before consumption. A recent study analyzed how the type of washing solution of vegetable samples can influence the residual Pb concentration and proved that even the addition of a detergent to washing water does not guarantee a significant decrease in the lead content [23]. Amendments are frequently added in soils to improve their quality, and this practice can provide different and somehow opposite effects. It can render lead more soluble and therefore more available [22], but, in contrast, it can reduce the bioavailability of lead through its immobilization into insoluble lead compounds as observed with the employment of phosphate compounds [29].
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
## 2 Materials and Methods
### Preparation, Purification and Marking of Monoclonal Antibodies  
The 6- to 8-week-old BALB/c mice were immunized with PPI as the immunogen, and blood was taken as a negative serum control before immunization. The initial immunization dose was 100 ug/mouse. The PPI was emulsified with the same volume of ferritin complete adjuvant and then immunized the mice and then immunized mice at an interval of 2 weeks. The serum antibody titer of mice was detected 14 days after the third immunization. The mice with relatively high titer were immunized by intraperitoneal injection of immunogen 100 ug/mouse three days before cell fusion. Then, the blood was sacrificed, and the immune -spleen cells were isolated. The immune spleen cells and the prepared mouse myeloma cells SP2/0 were fused. Positive monoclonal hybridoma cells were screened by ELISA. After three subclones and a completely positive ELISA test, expand the culture and construct the cell line. The screened hybridoma cells stably secreting target antibodies were inoculated in BALB/c mice aged 6 to 8 weeks at 1 to \(5\times 10^{6}\)/mouse with incomplete first adjuvant. When the abdomens of the mice were significantly enlarged, and the skin being touched by hands was tense, the ascites were collected with a 10-mL needle. The ascites was centrifuged, the supernatant was collected, and the titer was determined. The antibody was purified with a Protein G gel column, and the purified monoclonal antibody(mAb) was identified by SDS-PAGE gel electrophoresis. The obtained monoclonal antibody was diluted to 1 mg/mL with 0.1 M PBS buffer (pH 7.4), and the interfering substance was removed by ultrafiltration. Water-soluble biotin was added, and the reaction was performed at room temperature for 1 h. The free biotin was removed by ultrafiltration.
## 2 Efficiency Analysis and Derivation of Wireless Power Transmission
### Inductive Power Transmission Link Characteristic Analysis
\[\eta_{\rm t}=\frac{R_{\rm ref}}{R_{\rm ref}+r_{\rm t}}=\frac{k^{2}Q_{\rm t}Q_{1 }}{1+k^{2}Q_{\rm t}Q_{1}} \tag{5}\]  
where  
\[Q_{1}=\frac{Q_{\rm r}{}^{2}r_{\rm r}R_{\rm L}}{\omega_{0}L_{\rm r}\left(Q_{ \rm r}{}^{2}r_{\rm r}+R_{\rm L}\right)} \tag{6}\]  
According to Equations (2) and (5), the overall power transmission efficiency of the inductive power transmission link, \(\eta_{\rm IPL}\), is calculated as follows. \[\eta_{\rm IPL}=\eta_{\rm r}\eta_{\rm r}=\frac{k^{2}Q_{\rm t}Q_{1}Q_{\rm r}^{2} r_{\rm r}}{(1+k^{2}Q_{\rm t}Q_{1})(Q_{\rm r}^{2}r_{\rm r}+R_{\rm L})} \tag{7}\]  
Figure 4 shows the inductive power transmission link's total efficiency \(\eta_{\rm IPT}\) changing trend with different \(k\) and \(Q_{\rm t}\) values. It is evident that the larger values of \(k\) and \(Q_{\rm t}\) are beneficial for improving the power transmission efficiency of the inductive power transmission link. Moreover, the efficiency of \(\eta_{\rm IPT}\) is also enhanced as \(Q_{\rm r}\) increases. These trends indicate that a stronger coupling and higher quality factor lead to better overall  
Figure 3: Inductive link with SP compensation equivalent circuit. **(a)** Inductive link. **(b)** Receiver equivalent circuit. **(c)** Transformer model. **(d)** Inductive link equivalent circuit. efficiency in WPT. Figure 5 illustrates the changing trends of the efficiencies \(\eta_{\tau}\), \(\eta_{\tau}\), and \(\eta_{\rm IPT}\) with different \(R_{\rm L}\) values. As the \(R_{\rm L}\) increases, the receiver efficiency \(\eta_{\tau}\) decreases, while the transmitter efficiency \(\eta_{\tau}\) exhibits an increasing trend. The inductive power transmission link system's overall efficiency reaches its maximum efficiency at an optimal \(R_{\rm L}\) value range, as highlighted in the green shaded region of Figure 5.  
When applied in cochlear implants, permanent magnets are employed to ensure a fixed position between the receiver and transmitter coils. Thus, the value of \(k\) between the two coils remains unchanged. Additionally, the \(k\) is less than 0.5 in the loosely coupled transformer [33]. Consequently, a more effective method for improving the power transmission efficiency of the inductive power transmission link is to adjust the quality factor of the two coils. However, a higher quality factor may result in a lower bandwidth of data transmission, which does not facilitate the high data rate design target. Consequently, there is a trade-off between high efficiency and high data rate.
# A Comparison of Dental Arch Width and Length on 3D Digital and Plaster Models
## 3 Results  
The difference in the measurements was found to be small and not statistically significant. The differences observed in the digital measurements were greater in contrast to manual measurements obtained from plaster models (Tables 1 and 2).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Intraoral Scan (IOS)**} & \multicolumn{3}{c}{**Digitalized Plaster Model Obtained**} & \multicolumn{3}{c}{**Digitalized Plaster Model Obtained**} & \multicolumn{3}{c}{**Plaster Model (PM)**} \\ \multicolumn{1}{c}{} & \multicolumn{3}{c}{**from Against Impensation (DPMs)**} & \multicolumn{3}{c}{**from Silence Impensation (DPMS)**} & \multicolumn{3}{c}{**Paster Model (PM)**} \\ \hline Parameter & Mean \(\pm\) SD \({}^{\star}\) & \(p^{\star\star}\) & Dahlberg & Mean \(\pm\) SD \({}^{\star}\) & \(p^{\star\star}\) & Dahlberg & Mean \(\pm\) SD \({}^{\star}\) & \(p^{\star\star}\) & Dahlberg \\ \hline McLCW & \(-0.01\pm 0.10\) & 0.828 & 0.00 & \(-0.01\pm 0.09\) & 0.89 & 0.00 & \(-0.03\pm 0.08\) & 0.449 & 0.01 & 0.00 \(\pm\) 0.07 & 1 & 0.00 \\ McLCW & \(-0.04\pm 0.07\) & 0.226 & 0.01 & \(-0.01\pm 0.07\) & 0.669 & 0.00 & \(0.00\pm 0.04\) & 1 & 0.00 & \(-0.02\pm 0.08\) & 0.621 & 0.01 \\ McMMW & \(-0.24\pm 0.52\) & 0.36 & 0.08 & \(-0.06\pm 0.01\) & 0.001 & 0.002 & \(-0.03\pm 0.05\) & 0.231 & 0.01 & 0.04 \(\pm\) 0.09 & 0.374 & 0.01 \\ McMMW & \(-0.03\pm 0.08\) & 0.421 & 0.01 & \(-0.01\pm 0.07\) & 0.816 & 0.00 & \(-0.01\pm 0.06\) & 0.719 & 0.00 & 0.06 \(\pm\) 0.05 & 0.07 & 0.02 \\ MAAP & \(-0.05\pm 0.13\) & 0.431 & 0.02 & \(-0.02\pm 0.09\) & 0.631 & 0.01 & \(0.01\pm 0.09\) & 0.887 & 0.00 & 0.00 \(\pm\) 0.00 & 1 & 0.00 \\ MdAP & \(-0.02\pm 0.08\) & 0.549 & 0.01 & \(-0.06\pm 0.06\) & 0.106 & 0.02 & \(-0.03\pm 0.06\) & 0.32 & 0.01 & \(-0.02\pm 0.11\) & 0.704 & 0.01 \\ \hline \hline \end{tabular} Mean \(\pm\) SD \({}^{\star}\)—mean and standard deviation represent differences in measurements. \(p\)—significant level. \({}^{\star\star}\) \(t\)-test for repeated measurements in assessing systematic error.  
\end{table}
Table 1: Random and systematic errors of measurements on different models by same examiner.  
Figure 3: The measurements of maxillary and mandibular arch perimeter dimensions (**a**) using soft wire which was modeled to the individual shape of the maxillary and mandibular arch on plaster models and (**b**) using Ortho Analyzer software.  
The difference in measurements between the two examiners was small and not statistically significant. Only the case of the intraoral scan (IOS) was significant, amounting 0.55 \(\pm\) 0.3 mm (\(p\) = 0.014) with a random error of 0.17, according to [PERSON] (Table 2). The intra-examiner difference was only significant in the case of the digitalized plaster model obtained from the alginate impression (DPMA), amounting 0.06 \(\pm\) 0.01 mm (\(p\) = 0.001) with a random error of 0.02 (Table 1).
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
## 3 BES Coupling with Other Technologies for Metal Recovery
### Coupled Redox Fuel Cells  
In coupled redox fuel cells, substances with low redox potential are oxidized at the anode, releasing protons and electrons. Conversely, substances with high redox potential are reduced at the cathode, transforming the chemicals into different species and generating electrical energy. Organic pollutants in wastewater, such as urea, phenol, and ethanol, typically exhibit low standard potentials and are prime candidates for anodic oxidation deposits [109]. Methanol is frequently used in fuel cell systems due to its high energy density and ease of handling; however, its oxidation (as in direct methanol fuel cells) typically requires noble metal catalysts, such as platinum, to overcome the high activation energy and facilitate efficient reaction kinetics [110]. However, within BES frameworks, microbial activity at the bioanode (electroactive biofilm) facilitates the degradation of such organic compounds without the need for expensive catalysts.  
The microbial metabolism at the bioanode drives these oxidation reactions, transferring electrons to the electrode and generating a flow of current to the cathode. In some cases, the efficiency of this process can be enhanced by adding soluble redox mediators, such as methylene blue, neutral red, or anthraquinone-2,6-disulfonate (AQDS) [111; 112; 113; 114; 115]. These mediators shuttle electrons between the microorganisms and the electrode surface, effectively bridging the gap for electron transfer. Importantly, the redox potential of these mediators must be slightly higher than that of microbial cytochromes (typically \(-0.2\) to \(-0.5\) V vs. SHE) to ensure electron flow toward the electrode. Nature operates remarkably close to thermodynamic limits, allowing mediators with minimal overpotentials to efficiently drive the electron transfer process [111]. In this mechanism, bacteria utilize the mediators for cellular respiration, consuming organic substrates such as urea, phenol, and ethanol. The oxidized mediators are subsequently reduced on the electrode, completing the cycle [112].  
At the abiotic cathode, metals like Cr\({}^{6+}\), Au\({}^{3+}\), Ag\({}^{+}\), and Cu\({}^{2+}\) are reduced to their elemental states or less toxic forms. These reduction reactions are driven by the electrons generated at the bioanode, traveling through an external circuit. For instance, Cr\({}^{6+}\) can be reduced to Cr\({}^{3+}\), a less toxic species, while Ag\({}^{+}\) and Cu\({}^{2+}\) can be recovered as metallic deposits [109; 116].
## 2 Places, Instruments, and Data  
### Places  
The places studied are limited by orbital configurations. The BF-1 A/B satellites operate at altitudes of 579 km with an orbital inclination of 45\({}^{\circ}\). In fact, within the antenna footprint, the specular points are scattered from latitudes 53\({}^{\circ}\)S to 53\({}^{\circ}\)N, as shown in Figure 1. This area can cover most of the typhoon events [24] and coastal lines of China. The other study area is also shown in Figure 1 of the central equatorial Pacific Ocean, where the red diamonds are the 55 mooring buoys deployed by the Tropical Atmosphere Ocean (TAO) program of the National Data Buoy Center (NDBC).
## 4 Simulation and Analysis
### Proposed Algorithm for NR-MDEC Protocol  
The proposed algorithmic flowchart of NR-MDEC protocol is shown in Figure 8, which is based on the assumptions made for the four-tier structure. The algorithmic steps are presented in the following steps:  
Step-1: Creation of Virtual Environment for WSN Network.  
Step-2: Getting values of various parameters from the field.  
Step-3: Divide all nodes in 4-Categories--Normal, Advanced, Intermediate, Advanced-Intermediate.  
Step-4: Sorting of nodes as per their Residual Energies.  
Step-5: Selection of 10% nodes having the highest energy as Cluster Heads.  
Step-6: Apply energy minimization on nodes and also calculate their REs.  
Step-7: If CH-RE is greater, keep CH and repeat energy minimization algorithm else go to Step-4.  
Step-8: Stop after delivering all the packets or exhaustion of nodes energy.  
Figure 7: Energy results after virtual simulation.
# Long Length Document Classification by Local Convolutional Feature Aggregation
## 3 Model  
### Model_1: Random Sampling and CNN Feature Aggregation  
A naive solution of handling long length document is to reduce the length of a document by randomly sub-sampling and then employ classic approaches of document classification. However, naive randomly sub-sampling the document would lose the sentence structure. Here, we propose to randomly sample several blocks or sentences of the document that can at least maintain the local correlation of words in these blocks or sentences.  
Our first model, CNN_Random_Agg, in short, is based on this kind of random block/sentence sub-sampling, and then aggregates the extracted local CNN features for classification. This model is shown in Figure 1. Input X is a word vector matrix of \(n\times d\), where \(n\) is the word number of the full document and \(d\) is the dimension of the word vector. We first randomly sample a few groups of words in the full document range as \(\left\{X^{i}\right\}_{i=1}^{K}\) with \(w\) words in each group, where \(X^{i}\) is the \(i_{th}\) sampled block of words with the size of \(w\times d\), and \(K\) is the number of sampled groups. In our experiments, just 6-10% of the full document words could obtain good performance. Then, we use 1d-CNN, Relu (Rectified Linear Unit) activation function, and max-pooling operation to extract the features of each group as follows:  
\[y_{i}=\text{Relu}[\text{Conv1D}(X^{i},W)] \tag{1}\]  
\[\overline{y}_{i}=max\_pooling[y_{i}] \tag{2}\]  
Here, Conv1D is the 1d CNN operator and \(W\) is the tensor of the convolutional filters with the shape of \(h\times d\times f\), where h is convolutional kernel size, \(d\) is the dimension of the word vector same  
Figure 1: Model convolutional neural networks (CNN)_Random_Agg architecture.  
as above, and \(f\) is the number of convolutional filters. Thus, after the operation of Equation (1), \(y_{i}\), the convoluted feature for each group, is further pooled to \(\overline{y}_{i}\) by the max over time pooling operation as Equation (2). Then, for the \(K\) sampled groups of words, the extracted local CNN features \(\{\overline{y}_{i}\}_{i=1}^{K}\) are concatenated as follows:  
\[y=[\overline{y}_{1},\overline{y}_{2},\ \dots,\overline{y}_{K}] \tag{3}\]  
This aggregated CNN feature \(y\) is then input to the last full-connected layer for classification. Finally, we perform the Softmax operation and output the probability \(p\).
## 2 Materials and Methods  
### Experimental Procedure  
Twelve healthy male subjects (age: 32.9 \(\pm\) 6.9 years, height: 79.2 \(\pm\) 3.7 cm, mass: 78.8 \(\pm\) 6.6 kg) volunteered in this study. Participants had no neurological or orthopedic impairments. All participants signed informed consent forms. The experiments were performed at the University Rehabilitation Institute Republic of Slovenia. The Slovenian National Ethics Committee approved the study.  
The experimental setup consisted of the Balance Assessment Robot for Treadmill walking (BART) (Figure 1) [8; 9]. Here, only a brief description of the experimental setup is given, as a more detailed description is provided elsewhere [10; 11]. The BART was composed of a pelvic manipulator and an instrumented treadmill. The pelvic manipulator consisted of an actuated pelvic link with pelvic hrace. The actuated pelvic link interacted with the subject's pelvis. This haptic interaction was admittance controlled in transparent mode during unperturbed walking (i.e., minimal interaction forces were provided, and the pelvis could be moved freely while walking) and controlled to deliver perturbing forces to the subject's pelvis. These pelvic perturbations could be applied in anteroposterior and mediolateral directions at various moments in the gait cycle. Three walking trials on the treadmill were performed at three different walking speeds (0.4 m/s, 0.6 m/s, and 0.8 m/s). Each separate trial started with a period of unperturbed walking for three minutes, followed by a period of perturbed walking. Right-aligned perturbations were delivered to the pelvis with the right leg entering the stance phase. In case of left-aligned perturbations, the left leg entered the stance phase. Both left- and right-aligned perturbations were either directed inward or outward (Figure 1). The perturbations were applied at three different perturbation onsets (0%, 30%, and 60% of the stance phase of the gait cycle) and with three different perturbation amplitudes (5%, 10%, and 15% of the subject's body weight). Only the data for the perturbation amplitude of 10% of the subject's body weight were included in the current study. Each condition (four directions, three onsets, three amplitudes) was repeated seven times. This resulted in 252 perturbations per trial that were block randomized. The pause between two consecutive perturbations varied randomly from six to eight seconds. A perturbation was delivered to the subject's pelvis at a force impulse of 150 ms. Preceding the actual trials, all subjects were familiarized with walking on the treadmill and receiving perturbations.
# Elevator Fault Diagnosis Method Based on IAO-XGBoost under Unbalanced Samples
## 3 Sample-Balancing Method Based on RFE-SMOTE-Tomek
### SMOTE-Tomek  
#### 3.2.1 SMOTE  
The SMOTE oversampling method is proposed by [PERSON] to increase the minority class samples in the dataset by interpolating the minority class samples to generate new samples to achieve sample balance. The basic principle is shown in Figure 4.  
SMOTE algorithm steps:  
1. First, traverse the minority class samples and select each of them in turn (sample xi); compute the distance from xi to the other samples in terms of the Euclidean distance to obtain its k-nearest neighbor;
2. Randomly select some nearest neighbors to generate a new sample by linear interpolation with sample xi. The expression is as in Equation (1): \[x_{new}=x_{i}+(x_{ij}-x_{i})\theta,\] (1) where \(\theta\) is a random number between (0, 1).