esa-sceva/satcom-chunk-collection huggingface dataset:
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix B CFO Estimation
### Fractional Estimation  
As shown by the results in Figure 8, the CP based fractional CFO estimation outperformed the Synch based fractional CFO one and therefore was the method adopted by SCATTER PHY. Next, we describe the CP based fractional CFO estimation.  
\[\psi(k)=\sum_{n=0}^{N_{\text{CP}}-1}\frac{r_{d}^{*}(n+k(N_{\text{FFT}}+N_{\text{ CP}}))r_{d}(n+k(N_{\text{FFT}}+N_{\text{CP}})+N_{\text{FFT}})}{2\pi}, \tag{10}\]  
\[\hat{\epsilon}_{\text{FFO}}=\frac{1}{N_{\text{symb}}}\sum_{k=0}^{N_{\text{symb }}-1}\arg\left(\psi(k)\right), \tag{11}\]  
where \(r_{d}(n)\) is the detected, integer CFO corrected, and aligned subframe, \(N_{\text{symb}}\) is the number of OFDM symbols used to average the CFO estimation, and \(N_{\text{CP}}\) is the length of the OFDM cyclic prefix. As can be seen, the employed CP based fractional CFO estimation was obtained by averaging the estimated fractional CFO over the selected number of OFDM symbols.
## 2 The Proposed Converter
### The Proposed Converter Architecture  
The proposed converter comprises of two different non-isolated DC-DC converter topologies. The first one is the Zeta converter (Figure 1). The second one is the Mahafzah converter (Figure 2) [32]. Figure 3 shows the proposed converter. From Figure 3, it can be seen that the proposed converter has only one common DC power supply \(\mathrm{V_{in}}\), one common controlled switch M, and one common input inductance \(\mathrm{L_{in}}\). Moreover, the proposed converter has three separate output terminals (the voltage across the third output is the sum of \(\mathrm{V_{o1}}+\mathrm{V_{o2}}\)). Two coupling capacitors, \(\mathrm{C_{p1}}\) and \(\mathrm{C_{p2}}\), and two output inductors, \(\mathrm{L_{o1}}\) and \(\mathrm{L_{o2}}\), are used to limit the energy transfer to the output terminals.
###### Abstract
We also combine the three cache allocation mechanisms (DCAM, AARM [18], Equal [19]) with the four cache strategies (LCE [9], CAPC [20], ProbCache [21], Prob [22]), and conduct experiments to compare their performance. Experiments show that, among all combinations of cache allocation mechanisms and cache strategies, the combination of DCAM and CAPC performs best in terms of loss recovery delay, cache hit ratio, transmission completion time, and overhead. The remainder of this paper is organized as follows. We review the related work about cache allocation mechanisms in Section 2. Section 3 describes the problem based on the constructed model. Section 4 introduces the proposed dynamic cache allocation mechanism in detail, including the weight definitions corresponding to the three parameters and cache quota calculation method. Then we describe the experiment environment settings, evaluate the performance, and discuss the simulation experiment results in Section 5. Finally, we conclude the work in Section 6.
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 4 Hydraulic Manipulator System Modeling and Controller Design
### Design of ANFIS Self-Tuning PID Controller  
#### 4.4.1 Fuzzy PID Controller  
The hydraulic manipulator system studied in this paper is relatively complex, with high nonlinearity and uncertainty, it is hard to establish a mathematical model accurately, so that the conventional PID control cannot achieve the ideal control effect. Therefore, the fuzzy logic algorithm with nonlinear linguistic mapping, fuzzy inference and strong robustness is combined with the PID controller to adjust PID parameters.  
As demonstrated in Figure 7, the FPID controller has two inputs: error \(e(t)\) (between the desired joint angle and the actual joint angle) and error change rate \(ec(t)\). Both the input signals are transformed into fuzzy values \(E\) and \(EC\) in the fuzzy universe. Then, the increments of the PID parameters, \(\Delta K_{p},\Delta K_{I},\Delta K_{d}\) can be obtained through fuzzy inference. The increments here are fuzzy variables, which need to be clarified and finally added to the initial values of PID parameters.  
In this paper, the fuzzy subsets of E, EC, \(\Delta K_{p},\Delta K_{I},\Delta K_{d}\) are \(\{NL,NM,\ NS,\ ZE,\ PS,\ PM,PL\}\), which represent negative large, negative medium, negative small, zero, positive small, positive middle and positive large, respectively. According to the design requirements, the domain scopes of \(E,\ EC,\ \Delta K_{p},\Delta K_{I},\Delta K_{d}\) are \([-6\ 6],[-3\ 3],[-3\ 3],[-0.03 0.03],\)\([-0.6\ 0.6]\), respectively. The membership functions of all parameters are set in gaussianfunction, and the fuzzy control rule table can be obtained according to the tuning principles of PID parameters and expert experience.
## 2 Methodology  
### Unit Cell and Array Design  
A few factors, such as the size of the substrate, the type of substrate, the number of resonance frequencies, and the properties of the metamaterial structure, are usually taken into account in order to produce a metamaterial structure with good performance. The unit cell and array metamaterial structures were designed and simulated using the frequency domain-based CST Microwave Studio 2018. The CST Microwave Studio software is capable of electromagnetic analysis and design at a broadband frequency. In this study, Rogers RO4003C was chosen as the substrate, and the size was 9 \(\times\) 9 mm\({}^{2}\). The thickness of the Rogers RO4003C substrate was 0.508 mm. The Rogers RO4003C substrate acquired a dielectric constant value of 3.55 and tangent loss (8) of 0.0027. The metamaterial resonator was designed using copper material and was printed on the Rogers substrate. The thickness of the copper was 0.035 mm, and the electric conductivity (\(\sigma\)) was 5.8 \(\times\) 10\({}^{7}\) S/m. The full dimensions of the metamaterial structure are illustrated in Figure 1 and Table 1.  
The metamaterial was designed with a combination of square- and oval-shaped split-ring resonators. There were two square-shaped split-ring resonators and three oval-shaped split-ring resonators. The first square ring was designed with a length and width of 8.00 mm, respectively, whereas the length and width of the second square ring were 6.40 mm, respectively. The gap between the first and second square ring was 0.30 mm. The square- and oval-shaped split-ring resonators were connected with a 0.50 mm wide horizontal strip that was divided in the middle by a 0.20 mm gap. Splits were placed in a parallel way at two square-shaped splits with a gap of 0.50 mm and one oval-shaped split with a gap of 0.20 mm. The three oval rings had a vertical diameter of 0.50 mm and a horizontal width of 0.3 mm. From the center, the radius of the outer oval ring was 2.5 mm, the middle oval ring was 1.8 mm, and the radius of the inner oval ring was 1.1 mm. Section 4.2 goes into greater detail about the evolution of the metamaterial design. The unit  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Dimensions, mm** \\ \hline Thickness of substrate, \(\mathrm{t_{s}}\) & 0.508 \\ \hline \(\mathrm{L_{s}}\) and \(\mathrm{W_{s}}\) & 9.00 \\ \hline a and b & 8.00 \\ \hline c and d & 6.40 \\ \hline w\({}_{1}\), w\({}_{2}\) and g\({}_{1}\) & 0.50 \\ \hline w\({}_{3}\) and g\({}_{3}\) & 0.30 \\ \hline g\({}_{2}\) & 0.20 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the oval-square-shaped split-ring resonator-based metamaterial.  
Figure 1: Unit cell metamaterial design, **(a)** perspective view and **(b)** top view of unit cell metamaterial design in CST Microwave Studio.  
cell by itself is incompatible with the analysis. As a result, the unit cell structure has been aligned into a 4 \(\times\) 4 array.
# Hybrid Precoding Design for Wireless Fading Channels
## 3 Proposed Hybrid Precoding
### Non-Linear OSLP Design  
Here, we explain how to design the symbol-level precoding vector, \(\mathbf{f}_{2}\). Since \(\mathbf{H}_{2}\mathbf{W}_{1}=\mathbf{0}\), the received signal vector, \(\mathbf{y}_{2}\), for group 2 can be simply written as follows:  
\[\mathbf{y}_{2}=\beta_{2}\mathbf{H}_{2}\mathbf{x}_{2}+\mathbf{z}_{2}=\beta_{2} \mathbf{H}_{2}\mathbf{V}_{12}\mathbf{f}_{2}+\mathbf{z}_{2}. \tag{19}\]  
If we define the equalizer for the received signal in group 2 as \(\mathbf{A}_{2}=\text{diag}(a_{2,1},\dots,a_{2,J})\), the estimate \(\overset{\star}{\mathbf{s}}_{2}\) for \(\mathbf{s}_{2}\) can be obtained as follows:  
\[\overset{\star}{\mathbf{s}}_{2}=\frac{1}{\beta_{2}}\mathbf{A}_{2}\mathbf{y}_{ 2}=\mathbf{A}_{2}\mathbf{H}_{2}\mathbf{V}_{12}\mathbf{f}_{2}+\frac{1}{\beta_{ 2}}\mathbf{A}_{2}\mathbf{z}_{2}. \tag{20}\]  
If we represent the vector obtained by moving \(\mathbf{s}_{2}\) to the CIR as \(\overset{\star}{\mathbf{s}}_{2}\), \(\overset{\star}{\mathbf{s}}_{2}\) can be expressed as follows:  
\[\overset{\star}{\mathbf{s}}_{2}=\mathbf{s}_{2}+ \begin{bmatrix}\theta_{\alpha, 1}r_{\alpha,1}+\theta_{\mu,1}r_{\mu,1}\\ \vdots\\ \theta_{\alpha,J}r_{\alpha,J}+\theta_{\mu,J}r_{\mu,J}\end{bmatrix} =\mathbf{s}_ {2}+\mathbf{R}\ \mathbf{0}, \tag{21}\]  
where \(r_{\alpha,j}\) and \(r_{\mu,j}\) are parameters representing the decision boundary for the CIR of \(\mathbf{s}_{2}\), and \(\theta_{\alpha,j}\) and \(\theta_{\mu,j}\) represent the weights for \(r_{\alpha,j}\) and \(r_{\mu,j}\)[10; 11; 12]. Here, decision boundary parameters can be easily obtained once the constellation of the transmission symbol is determined, and weight parameters must be optimized. The CIR matrix, \(\mathbf{R}\), and weight vector, \(\mathbf{\theta}\), are defined as follows:  
\[\mathbf{R}=\left[ \begin{array}{cccccc}r_{\alpha,1}&0&&\cdots&0&r_{\mu,1}&0 &&\cdots&0\\ &&\ddots&&&&\ddots&&\\ 0&\cdots&&0&r_{\alpha,J}&0&\cdots&&0&r_{\mu,J}\end{array} \right], \tag{22}\]  
\[\mathbf{\theta}=[\theta_{\alpha,1}\cdots\ \theta_{\alpha,J}\ \theta_{\mu,1}\cdots\ \theta_{\mu,J}]^{\mathsf{T}}. \tag{23}\]  
Now, the MSE, \(J_{2}\), for group 2 is defined as follows:  
\[J_{2}=E_{\ \mathbf{z}_{2}}\Bigg{[}\parallel\frac{1}{\beta_{2}}\mathbf{A}_{2} \mathbf{y}_{2}-\overset{\star}{\mathbf{s}}_{2}\parallel^{2}\Bigg{]}=E_{\ \mathbf{z}_{2}}\Bigg{[}\parallel\mathbf{A}_{2}\mathbf{H}_{2}\mathbf{V}_{12} \mathbf{f}_{2}+\frac{1}{\beta_{2}}\mathbf{A}_{2}\mathbf{z}_{2}-\overset{ \star}{\mathbf{s}}_{2}\parallel^{2}\Bigg{]}. \tag{24}\]  
If we calculate the expectation for \(\mathbf{z}_{2}\) in this equation, \(J_{2}\) can be expressed as follows:  
\[ \begin{array}{ll}J_{2}&=\|\mathbf{A}_{2}\mathbf{H}_{2}\mathbf{V}_{12} \mathbf{f}_{2}-\overset{\star}{\mathbf{s}}_{2}\|^{2}+\frac{1}{\beta_{2}^{2}} E\Big{[}\text{tr}\Big{(}\mathbf{A}_{2}\mathbf{z}_{2}\mathbf{z}_{2}^{H}\mathbf{A}_{2}^{H} \Big{)}\Big{]}\\ &=\|\mathbf{A}_{2}\mathbf{H}_{2}\mathbf{V}_{12}\mathbf{f}_{2}-\overset{ \star}{\mathbf{s}}_{2}\|^{2}+\frac{\sigma_{2}^{2}}{\beta_{2}^{2}}\text{tr} \Big{(}\mathbf{A}_{2}\mathbf{A}_{2}^{H}\Big{)}.\end{array}
# Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China
## 3 Results and Analysis
### Analysis of Volatile Flavor Compounds in the Two Types of Douchi
#### 3.2.2 Identification of Key Flavor Compounds Using ROAV
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Compound Name** & **Aroma Descriptors** & **Aroma Threshold** & **ROAVs** \\  & & **(ug/kg) [27]** & **LN Douchi** & **QY Douchi** \\ \hline
**Esters** & & & & \\ \hline  & Ethyl isobutyrate & strawberry-like odor [28] & 0.1 & 100.00 & - \\  & Ethyl butyrate & pineapple flavor and a floral aroma [29] & 1 & 11.24 & - \\  & Isoamyl acetate & banana [30] & 2 & 1.55 & - \\  & Ethyl hexanoate & apple [31] & 1 & 5.89 & - \\  & Ethyl heptanoate & fruity, wine-like [32] & 2.2 & 0.38 & - \\ \hline
**Alcohols** & & & & \\ \hline  & 1-Octen-3-ol & mushroom [33] & 1 & 3.17 & 100.00 \\  & 1-Penten-3-ol & vegetable, fruity [32] & 400 & - & 0.18 \\  & Hexanol & sweet, fruity, green, apple [32] & 250 & 0.00 & - \\ \hline
**Ketones** & & & & \\ \hline  & Acetone & slightly sweet, pungent [32] & 500,000 & - & 0.00 \\  & 2-Butanone & coffee, banana [32] & 50,000 & - & 0.10 \\  & 2-Pentanone & sweet, fruity [32] & 70,000 & - & 0.07 \\  & 2-Octanone & sweet floral, green, apple [32] & 50 & 0.00 & - \\  & 3-Octanone & mushroom, green, vegetable, fruity [32] & 28 & 0.09 & - \\ \hline
**Aldehydes** & & & & \\ \hline  & Furfural & bread, caramel, nuts [32] & 3000 & 0.00 & - \\ \hline
**Pyrazines** & & & & \\ \hline  & 2-Methylpyrazine & nuts, baking flavor [32] & 6000 & - & 0.32 \\  & 2,5-Dimethylpyrazine & a baking flavor [34] & 1800 & 0.00 & 8.26 \\  & 2,6-Dimethylpyrazine & fried and roasted flavor, nuts, cocoa [32] & 1500 & 0.00 & 0.86 \\  & 2,3-Dimethylpyrazine & roast, cocoa, nuts [32] & 2500 & 0.00 & 0.11 \\  & 2-Ethpyrazine & roast, cocoa, nuts [32] & 6000 & - & 0.02 \\  & 2-ethyl-5-methylpyrazine & roast, cocoa, nuts [32] & 100 & - & 0.83 \\  & 2,3,5-Trimethylpyrazine & roast, cocoa, nuts [32] & 9000 & 0.00 & 0.29 \\ \hline
**Furans** & & & & \\ \hline  & 2-Pentylfuran & has a legume and grassy flavor [35] & 6 & - & 56.48 \\ \hline
**Thiazoles** & & & & \\ \hline  & 2,4,5-Trimethylthiazolople & earthy [36] & 50 & 0.04 & 1.22 \\ \hline  & & & & \\ \hline \hline  & & & & \\ \hline \end{tabular} Note: “-” not perceived.  
\end{table} 
Table 5: ROAVs of volatile flavor compounds in Longnan and Qingyang douchi. the fermentation process or produced by the degradation of starch substances in the raw materials by microorganisms during the glycolysis process. Together, these flavor compounds constitute the unique flavor of douchi.
## 2 Antenna Synthesis and Modeling
### Optimum Lattice Architecture
Since tolerance might have an adverse effect on SLL performances, a series of Monte Carlo statistical analyses were performed, considering the radiators' position tolerances, due to the etching process, up to 15 \(\upmu\)m and considering the assembling tolerances of the sectors up to 150 \(\upmu\)m. The average SLL obtained was \(-21.71\,\mathrm{dB}\) while the degraded case in 200 independent analyses was \(-21.56\,\mathrm{dB}\), therefore less than 0.3, dB worse than the nominal case.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 2 System Description
### Miniaturized Antenna
The dotted line indicates the erection scheme type of the inverted “V”, which is not adopted in this paper. erection height is above 12 m, the beamwidth expands gradually. Considering the difficulty of the construction, the 8 m height for erection is a suitable choice. Thus, the whole array pattern simulation results based on CST can be obtained as Figure 10. Figure 10a,b shows the simulation results of O and X waves, respectively. The gain of the whole array is above 17 dB. The synthetic beam-pointing is vertical upward with a 27.4\({}^{\circ}\) beamwidth. The simulation results prove that the antenna array design scheme of this paper has a good receiving characteristic and can effectively meet the requirements of the mesospheric sounding with strong feasibility and operability. The whole array is characterized by high gain, narrow beamwidth, low cost and small structural size. Compared with the common MF radars, whose antenna towers are usually higher than 20 m, or use of the digital beam forming (DBF) antenna array with dozens of antennas, the MAI system employs only several 8 m towers and six small size progressive-wave receiving antennas. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **H = 4 m** & **H = 8 m** & **H = 12 m** & **H = 20 m** & **H = 25 m** \\ \hline Directivity gain(dB) & 7.76 & 7.99 & 8.24 & 8.33 & 8.37 \\ Beamwidth(\({}^{\circ}\)) & 70 & 70.26 & 70.92 & 72.86 & 74.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The variation of the receiving antenna characteristics with the erection height. Figure 10: The pattern of the antenna array with the impedance of 50 \(\Omega\): (a) O-mode wave; (b) X-mode wave. No matter for O or X-mode wave, the antenna array has a high gain and vertical upward main lobe. When the MAI system works for ionospheric sounding, the mode of one transmitting and one receiving is usually adopted in order to reduce the amount of data and the processing time. For both of the transmitting and receiving, the 54.9 m progressive-wave antennas are employed to adapt to the wide frequency bandwidth. Switching from the mesospheric sounding mode to ionospheric mode only requires changing the antenna interface at the host device. The simulation results of the antenna's performance in HF band is shown in Figure 11. Figure 11a-d represent the cases of 3, 5, 10, and 20 MHz. Though the main lobe splits at the higher frequency band, but for the ionospheric sounding which requires less stringent directivity, it can still meet the need. The gain of full working band is more than 4 dB and the pointing direction is always upward. Figure 11: _Cont._
# Inhibition of Peroxidation Potential and Protein Oxidative Damage by Metal Mangifera Complexes
## 4 Discussion
### Evaluation of Antioxidant and Oxidative Damage Protection Properties of Metal-Mangiferin Complexes
Selenium fulfils several functions on different metabolic pathways, and these are mainly connected to its antioxidant properties, as an essential part of some antioxidant enzymes activities [36]. The higher effect of Se (IV)-MF complexes as compared to the isolated MF may be related to the synthesis of selenoproteins, which play essential roles in several diseases including neurodegeneration, cardiovascular disorders, and cancer. Thus, organometallic Se (IV)-MF complexes may influence functions of selenoproteins that might be considered for prevention and therapeutic treatment of those disorders [37]. However, the choice of the most effective, non-toxic, Se compound represents a very complex issue. Several Se salts, both inorganic and organic are being studied, to be used as a nutritional supplement or pharmaceutical. The results regarding the comparison of inorganic and organic compounds are not fully consistent, but in general, organic Se compounds show higher biological activities and lower toxicities [38]. Thus, Se (IV)-MF complexes would be investigated subsequently as a potential source of novel health compounds. Metal-flavonoid complexes design allows to obtain compounds with improved biological and physicochemical properties. In particular, generating an increase of the flavonoid antioxidant properties [39]. Coordination sites, polyphenol structure, metal ion type, and the stoichiometric ratio metal:polyphenol are important factors that influence the antioxidant activity of these coordination complexes. Therefore, the biological evaluation of these compounds is of paramount importance for its application [40]. In the present study, Se (IV)-MF and Zn (II)-MF complexes showed a higher antioxidant effect than the isolated MF in vitro, without having pro-oxidant effect. Our findings confirmed the hypothesis about the possible effect of other components on the observed antioxidant and protective effects of _Mangifera indica_ L. aqueous extract, rather than considering that MF was mainly responsible of these biological effects.
## 2 Design of Antenna
### Antenna Parameterisation Process
When W\({}_{5}\) = 14 \(\upmu\)m, S11 is below \(-\)10 dB at 86.65 GHz, with the deepest point being \(-\)48.61 dB, and the bandwidth is from 86.65 to 103.13 GHz. From the results in Figure 11,  
Figure 11: (a) The diagram of W\({}_{3}\)’s location; (b) S-parameter simulation of W\({}_{3}\). Figure 10: (a) The diagram of W\({}_{5}\)’s location; (b) S-parameter simulation of W\({}_{5}\). it can be observed that when the width of the feeding transmission line is 14 \(\upmu\)m, the bandwidth is the widest, the deepest point is reached, and the frequency range matches the desired range for this design. Therefore, the results of the S-parameter analysis for W\({}_{5}\) = 14 \(\upmu\)m are adopted as the width of W\({}_{5}\). Similarly, a parameter analysis is conducted on the radiating elements of the power divider, Figure 13a. In Figure 13b, when W\({}_{7}\) = 15 \(\upmu\)m, S11 is below \(-\)10 dB at 89.92 GHz, with the deepest point being \(-\)33.24 dB, and the bandwidth is 89.92-109.72 GHz. When W\({}_{7}\) = 10 \(\upmu\)m, S11 is below \(-\)10 dB at 84.73 GHz, with the deepest point being \(-\)30.50 dB, and the bandwidth is from 84.73 to 102.15 GHz. When W\({}_{7}\) = 5 \(\upmu\)m, S11 is below \(-\)10 dB at 93.21 GHz, the deepest point being \(-\)36.93 dB, and the bandwidth is 93.21-113.67 GHz. From the simulation results in Figure 12, it can be observed that when the power divider width of the feeding transmission line is 5 \(\upmu\)m, the bandwidth is the widest, the deepest point is reached, and the frequency range matches the desired range for this design. Therefore, the results of the S-parameter analysis for W\({}_{7}\) = 5 \(\upmu\)m are adopted as the width of W\({}_{7}\). Figure 12: (a) The diagram of W\({}_{5}\)’s location; (b) S-parameter simulation of W\({}_{5}\). Figure 4:0 (a) The diagram of W\({}_{7}\)’s location; (b) S-parameter simulation of W\({}_{7}\). Finally, because this design uses the CMOS 0.18 \(\upmu\)m process, it is necessary to create slots in the antenna section to comply with the process specifications, Figure 14a. It can be observed from the results that there is no significant difference with or without slots, Figure 14b.
## 2 Sentiment Analysis Algorithms  
This section provides an overview of the current landscape of sentiment analysis algorithms. Sentiment analysis is a process that seeks to identify and categorize the emotions or sentiments expressed in written texts. To perform this task, the raw text data must undergo several steps, including data preprocessing, feature extraction, and classification.  
Data preprocessing is an important step in the sentiment analysis process, as it helps standardize the text data and remove any irrelevant or noisy elements. This step can include techniques such as stemming, lemmatization, and the removal of stop words and special characters. The cleaned text data are then transformed into features or embeddings, which are fed into the classifier for sentiment prediction.  
The classifiers used in sentiment analysis can be broadly classified into three categories: machine learning, deep learning, and ensemble learning (as illustrated in Figure 1). Machine learning classifiers, such as logistic regression, naive Bayes, and support vector machine, use mathematical models to predict sentiments. Deep learning classifiers, such as recurrent neural networks and long short-term memory (LSTM) models, leverage artificial neural networks to make sentiment predictions. Ensemble learning methods combine multiple classifiers to achieve better sentiment analysis performance. Figure 2 provides a visual representation of the sentiment analysis process.  
The choice of classifier depends on the specific requirements and use case of the sentiment analysis task. This section reviews the existing works in sentiment analysis and highlights the preprocessing techniques, feature extraction methods, and classification algorithms used in each work.  
Figure 1: Sentiment analysis approaches can be categorized as machine learning, deep learning, or ensemble learning.