esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a triple-band low-noise amplifier (LNA) fabricated using a 0.18 \(\upmu\)m Complementary Metal-Oxide-Semiconductor (CMOS) process. The LNA uses a double-peak load network with a switched component to accomplish the triple-band operation. Moreover, noise reduction using a substrate resistor to ameliorate the noise performance is presented. Noise reduction of 1.5 dB can be achieved at 2.5 GHz without additional dc power and extra manufacturing costs. An input matching technique is realized simultaneously using a gyrator-based feedback topology. The triple-band LNA can be realized by using a dual-band input network with a switched matching mechanism. The target frequencies of the triple-band LNA are 2.3-2.7 GHz, 3.4-3.8 GHz, and 5.1-5.9 GHz, covering the operating frequency bands of time-division long-term evolution (TD-LTE), mid-band Fifth-generation (5G), LTE-unlicensed (LTE-U) band, and Wireless LAN (WLAN) technology. The measured power gains and noise figures at 2.5, 3.5, and 5.2 GHz are 12.3, 15.3, and 13.1 dB and 2.3, 2.2, and 2.6 dB, respectively.  
low-noise amplifier; 5G; triple-band; noise reduction Article
## 2 Design of the Joint Module  
### Joint Configurations  
The joint structures of typical AACMMs and robotic arms are compared below. (1) AACMMs utilize precision shaft and encoder systems that are small and lightweight. (2) Robotic arms, which are drive oriented to ensure smooth movement and a higher deceleration ratio, typically use a motor and harmonic reducer to drive joints. (3) The encoder installation in an AACMM is placed near the joint output with high accuracy based on its measurement purpose, whereas the encoder precision of a robotic arm is typically lower because the encoder is installed at the back of the motor for control purposes. (4) The output of a robotic arm is directly connected to the reducer, and the concentricity between the bearing and harmonic reducer is required to be high. (5) The joints in a robotic arm can be divided into two main types of configurations, namely rolling and pitching joints. Similarly, Joints 1, 3, and 5 in an AACMM are rolling joints, while Joints 2, 4, and 6 are pitching joints, as shown in Figure 1.  
We designed joints model for a self-driven AACMM based on the advantages of existing AACMMs and robotic arms, as shown in Figure 2.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Mean** & **SD** & **Min** & **Mdn** & **Max** & **Significance \({}^{*}\)** \\ \hline Control T0 & \(-\)4.48 & 0.40 & \(-\)5.10 & \(-\)4.40 & \(-\)4.10 & A \\ Control T1 & \(-\)5.92 & 0.22 & \(-\)6.20 & \(-\)5.80 & \(-\)5.70 & A \\ Control T2 & \(-\)5.44 & 0.40 & \(-\)5.80 & \(-\)5.60 & \(-\)4.80 & A \\ \hline Ozonized Gel T0 & \(-\)2.36 & 3.57 & \(-\)4.40 & \(-\)3.70 & 4.00 & B \\ Ozonized Gel T1 & \(-\)5.72 & 0.44 & \(-\)6.40 & \(-\)5.70 & \(-\)5.20 & A \\ Ozonized Gel T2 & \(-\)4.32 & 0.48 & \(-\)4.80 & \(-\)4.20 & \(-\)3.70 & A \\ \hline Ozonized Oil T0 & \(-\)4.68 & 0.56 & \(-\)5.20 & \(-\)4.80 & \(-\)3.80 & A \\ Ozonized Oil T1 & \(-\)6.22 & 0.43 & \(-\)6.90 & \(-\)6.00 & \(-\)5.90 & A \\ Ozonized Oil T2 & \(-\)4.78 & 0.48 & \(-\)5.50 & \(-\)4.80 & \(-\)4.30 & A \\ \hline Ozonized Oil + Regen T1 & \(-\)4.82 & 0.48 & \(-\)5.60 & \(-\)4.80 & \(-\)4.30 & A \\ Ozonized Oil + Regen T2 & \(-\)5.88 & 0.37 & \(-\)6.20 & \(-\)5.90 & \(-\)5.30 & A \\ Ozonized Oil + Regen T3 & \(-\)3.02 & 3.82 & \(-\)5.00 & \(-\)4.80 & 3.80 & A \\ \hline \end{tabular} \({}^{*}\) Different letters show a statistically significant difference between the groups (\(p<0.05\)).  
\end{table} 
Table 3: Median (min, max) for the value L for the two groups at each experimental time. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Mean** & **SD** & **Min** & **Mdn** & **Max** & **Significance \({}^{*}\)** \\ \hline Control T0 & \(-\)4.56 & 0.36 & 4.20 & 4.50 & 5.10 & A \\ Control T1 & \(6.08\) & 0.22 & 5.80 & 6.00 & 6.30 & B \\ Control T2 & \(5.48\) & 0.41 & 4.80 & 5.70 & 5.80 & A \\ \hline Ozonized Gel T0 & \(4.00\) & 0.37 & 3.70 & 4.00 & 4.60 & A \\ Ozonized Gel T1 & \(5.82\) & 0.49 & 5.20 & 5.80 & 6.40 & B \\ Ozonized Gel T2 & \(4.48\) & 0.47 & 3.80 & 4.70 & 4.90 & A \\ \hline Ozonized Oil T0 & \(4.86\) & 0.65 & 3.80 & 4.90 & 5.40 & A \\ Ozonized Oil T1 & \(6.24\) & 0.42 & 5.90 & 6.10 & 6.90 & B \\ Ozonized Oil T2 & \(4.94\) & 0.57 & 4.40 & 4.90 & 5.70 & A \\ \hline Ozonized Oil + Regen T1 & \(5.02\) & 0.51 & 4.40 & 4.90 & 5.80 & B \\ Ozonized Oil + Regen T2 & \(5.82\) & 0.37 & 5.20 & 5.90 & 6.10 & B \\ Ozonized Oil + Regen T3 & \(4.62\) & 0.41 & 4.00 & 4.90 & 4.90 & B \\ \hline \end{tabular} \({}^{*}\) Different letters show a statistically significant difference between the groups (\(p<0.05\)).  
\end{table} 
Table 2: Median (min, max) for the value E for the two groups at each experimental time. No significant changes were found at any time in any subgroup for A and B values.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 3 Methodology
### Algorithm 2  
Since user flow requests are not likely to be similar, initially separating them at the data plane produces better outcomes. Therefore, classifying flow requests before they reach the controllers can lead to better load balancing efficiency. The volume of data of the AP is kept as a triplet of ordered pairs, namely vol\({}_{d}\) = (n\({}_{d}\), us\({}_{d}\), pdp\({}_{d}\)), where n\({}_{d}\) represents the greatest number of WDs that an AP can maintain in its association table, us\({}_{d}\) denotes the numerals of WDs currently associated to the AP and pdp\({}_{d}\) represents the average packet distortion proportion of the APs linked wireless connections. The APs load is also saved as three tuples, namely bd = (msir\({}_{d}\), cpc\({}_{d}\), mu\({}_{d}\)), where msir\({}_{d}\) is represented as the mean signal inaccuracy rate, cpc\({}_{d}\) is the central processor consumption and mu\({}_{d}\) is the memory utilization of APs as per wireless connections. We utilize an assessment formula to calculate, which represents [PERSON]'s fairness index \(J_{f}\) as;  
\[J_{f}=\frac{\sum_{k=1}^{mb}\frac{usd}{nd}(\max(bd))^{2}}{nd\sum_{k=1}^{mb}\frac {usd}{nd}(\max(bd))^{2}} \tag{5}\]  
Equation (5) shows that the total load of the extended services set (ESS) is squared in the numerator, while the denominator consists of the multiple of the number of APs and the aggregate of each APs load squared. Therefore, \(J_{f}==1\) if each of the APs are uniformly loaded. The mb in Equation (6) is the number of basic service sets (BSSs) in an ESS. The controller computes the mean utilization level of the BSS since APs are likely to exhibit varying load levels.  
\[Q=\frac{\sum_{k=1}^{mb}\frac{usd}{nd}\max(bd)}{mb} \tag{6}\]  
The magnitude of \(Q\) varies from 0 to 100, and a value of 0 (\(Q=0\)) or 100 (\(Q=100\)) indicates the minimum or maximum utilization level, respectively. The controller employed the mean load level to classify the APs into overloaded and underloaded categories. Algorithm 2 define the steps involved.  
```
1:APs operating in the same ESS.
2:Retrieve MAC address of WDs.
3:List of WDs.
4:AP updates load reports to the controller periodically
5:Controller computes Equations (5) and (6)
6:Categorize the APs as either AP load\({}_{\text{max}}\) or AP load\({}_{\text{min}}\)
7:if AP is categorized as AP load\({}_{\text{max}}\) and exceeds the average threshold for load then
8:Reassociate the WD to the APload\({}_{\text{min}}\)
9:endif
```  
**Algorithm 2** Data Plane Load Balancing
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 4 Results and Discussion
### Analysis of the Breakage Behavior of RAP  
The extraction test found that the particle clustering phenomena occurred mainly in the coarse aggregate (>4.75 mm). To further analyze the crushing law and clustering stability of RAP, in this paper, the Cantabro-Crushing test was used to crush the particle (>4.75 mm) respectively. The cluster stability of each particle size was evaluated by the index of the Crushing loss rate (_CL_) for RAP 1 and RAP 2, and results are shown in Figures 6 and 7, respectively.  
Figure 5: Percentage Loss (_PL_) Rate of Each Particle Size after Extraction Test.  
Figures 6 and 7 illustrate that the loss rates of the two kinds of RAP materials gradually increase with the increase of the revolutions. As for the same RAP material, with the increase of the particle size, the loss rate of the particles gradually increased at the same revolutions. It indicated that the clustering stability of the RAP materials decreases with the increase of the particle size or revolutions. Compared with these two kinds of RAP, it can be found that the crushing loss rate of RAP 1 was obviously greater than that of RAP 2, indicating that the clustering stability of RAP 1 was lower than that of RAP 2.  
To describe the broken law, the RAP materials can be divided into three types of structures firstly, as is shown in Figure 8. The first one is weak cluster structure. It mainly refers to some flat, needle-shaped particles, and it mainly is composed of small particles aggregates wrapped with asphalt binder. The second one is strong cluster structure. This structure mainly refers to some particles with tight clusters and better grain shape, and it is generally formed by small particles wrapping around the large particle aggregates. The last one is old aggregate, and which is composed of independent stone particles.  
Figure 6: Effect of Revolutions on the Crushing Loss Rate of RAP 1.  
Figure 7: Effect of Revolutions on the Crushing Loss Rate of RAP 2.  
Therefore, according to the results of Figures 6 and 7, the process of RAP crushing can be further divided into three stages, qualitatively. The first breaking stage is dominated by weak cluster structure, which is the crushing of the flat, needle-shaped particles. Followed by the second breaking stage, it is dominated by strong cluster structure which was the crushing of blocky-shaped particles. The last stage is the crushing of aggregates. This was consistent with a process in which the trend of RAP loss rate is from rapid to steady.
## 3 Scientometric Analysis: Results and Discussion
### Science Mapping
#### 3.2.3 Mapping of Authors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline \multicolumn{6}{c}{Publication count} \\ \hline
1 & [PERSON]. & 10 & 244 & 25.29 & 24.4 & 2.52 \\
2 & [PERSON]. & 7 & 129 & 10.35 & 18.42 & 1.47 \\
3 & [PERSON]. & 6 & 85 & 82.506 & 14.16 & 1.37 \\
4 & Walker T.R. & 5 & 745 & 40.09 & 149 & 8.01 \\
5 & Duarte A.C. & 5 & 764 & 36.95 & 152.8 & 7.39 \\
6 & Prata J.C. & 5 & 764 & 36.95 & 152.8 & 7.39 \\
7 & Rocha-Santos T. & 5 & 764 & 36.95 & 152.8 & 7.39 \\ \hline \multicolumn{6}{c}{Total citations} \\ \hline
1 & Duarte A.C. & 5 & 764 & 36.95 & 152.8 & 7.39 \\
2 & Prata J.C. & 5 & 764 & 36.95 & 152.8 & 7.39 \\
3 & Rocha-Santos T. & 5 & 764 & 36.95 & 152.8 & 7.39 \\
4 & [PERSON]. & 5 & 745 & 40.09 & 149 & 8.01 \\
5 & [PERSON]. & 4 & 503 & 27.78 & 1257.5 & 6.94 \\
6 & [PERSON]. & 4 & 503 & 27.78 & 1257.5 & 6.94 \\
7 & [PERSON]. & 2 & 284 & 10.23 & 142 & 5.11 \\
8 & [PERSON]. & 1 & 282 & 19.02 & 282 & 19.02 \\
9 & [PERSON]. & 1 & 276 & 9.69 & 276 & 9.69 \\
10 & [PERSON]. & 1 & 261 & 9.17 & 261 & 9.17 \\
11 & [PERSON]. & 10 & 244 & 25.29 & 244 & 2.52 \\ \hline \multicolumn{6}{c}{Average normalized citations} \\ \hline
1 & [PERSON]. & 1 & 282 & 19.02 & 282 & 19.02 \\
2 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
3 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
4 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
5 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
6 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
7 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
8 & [PERSON]. & 1 & 177 & 11.93 & 177 & 11.93 \\
9 & [PERSON]. & 1 & 276 & 9.69 & 276 & 9.69 \\
10 & [PERSON]. & 1 & 261 & 9.17 & 261 & 9.17 \\ \hline \end{tabular} (A) Position in rankings; (B) name of author; (C) total number of documents published in the field; (D) total citations of documents published in the field; (E) normal citations: citations of author with documents published in the field; (F) average citations: total citations per author of documents published in the field; (G) average normal citations: normalized number of citations of author, which is equal to the total number of citations divided by the average number of citations published in the same year.  
\end{table} 
Table 3: Ranking of authors with more importance in the field in terms of total publication count, total citations, and average normalized citations. Figure 9 shows the high number of small collaboration groups in which the 119 identified authors are working. With the objective of identifying this collaboration in terms of countries, a mapping of co-authorship by country with a minimum of 10 documents (Figure 10) was developed, resulting in 22 countries that work together in the research field. Considerable collaborations between countries that include five scientific communities have been identified with red (Cluster 1), green (Cluster 2), blue (Cluster 3), yellow (Cluster 4), and purple (Cluster 5) colors; note that the one in red includes eight countries (Australia, Bangladesh, Brazil, Canada, Iran, Peru, Singapore, and The United States).
## 3 Results and Discussion
### X-ray Diffraction Analysis

Table 1: The XRD data of pure AlSb-deposited thin films recorded at 1.5 h. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Peak No.** & **20 (Degree)** & **D (A\({}^{\circ}\))** & **Hkl** & **Intensity (I)** & **FWHM** & **G.S (nm)** &
\begin{tabular}{c} **Dislocation Line Density** \\ **(m\({}^{-1}\times\)10\({}^{18}\))** \\ \end{tabular} \\ \hline
1 & 23.7 & 3.7515 & (003) & 23.7 & 0.625 & 0.29 & 0.1156 \\  & & & **2 h Deposition Time** & & & & \\
1 & 24.8 & 3.5876 & (111) & 24.8 & 0.62 & 0.22 & 0.1946 \\
2 & 23.7 & 3.7515 & (003) & 23.7 & 0.825 & 0.23 & 0.2015 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The XRD data of 10% Mg-doped AlSb deposited thin films at 1.5 and 2 h.  
Figure 2: XRD patterns of 10% Mg-Doped AlSb thin films for 1–3 h.  
the dislocation line density values. In the case of Sb, it was 0.1156 \(\times\) 10\({}^{18}\) m\({}^{-1}\) for 1.5 h of deposition and 0.2015 \(\times\) 10\({}^{18}\) m\({}^{-1}\) for 2.5 h of deposition time, with the d-spacing being the same in both conditions as 3.7515 A\({}^{\circ}\). These findings supported the crystallinity of Sb [19]; whereas the results obtained for AlSb revealed a strong peak at 24.8\({}^{\circ}\) with the reflection plan of (111), confirming its cubic structure. The grain size of the synthesized AlSb was 0.22 nm for a deposition period of 2.5 h, with a d-spacing value of 3.5876 A\({}^{\circ}\), which was smaller than Sb but with 100% relative intensity, and these results are consistent with earlier studies [20; 21; 22; 23].
## 7 Experiments and Results
### Training  
The framework developed by [PERSON] and [PERSON] was used to train the proposed model. The study employed 22,459-word vocabularies for Maghrebi Arabic (MA) and 19,327-word vocabularies for Levantine Arabic on AD-MSA translation task, 10,185-word vocabularies for Modern Slandered Arabic is used on MSA-ENG translation task. Moreover, 2606-Token and 23-tags for Levantine Arabic (LA) is used for on the POS tagging task. 2423 Token and 23 Tags for Maghrebi Arabic (MA) is used on POS tagging task. For translation tasks, the Nadam optimization algorithm is used with \(\beta\)1 = 0.9 and \(\beta\)2 = 0.999 and a learning rate value set to 0.002. We set a recurrent dropout value to 0.20 and 0.23 to AD and MSA Bi-LSTM encoders. A dropout layer with dropout value 0.40 is added after the embedding layer for each translation task. For POS tagging task on Arabic Dialect, the Nadam optimizer is also used with \(\beta\)1 = 0.9 and \(\beta\)2 = 0.999 and learning rate value 0.002. We passed the embedding of the POS tagging task to SpatialDropout1D layer with dropout value 0.1. Although the model is initially incorporating (6 M trainable parameters for MSA-ENG task, 11.9 M trainable parameters for LA-MSA task, 1.3 M trainable parameters for POS_LEV task, 13 M trainable parameters for MA-MSA and 1.2 M trainable parameters for POS_MA: word embedding and hidden size are shown in Table 1, this model size proved to require 1190 s per epoch for LA-MSA task, 2153 s per epoch for MA-MSA task, 36 s per epoch for MSA-ENG Task and 18 s for POS_LEV task and 15 s POS_MA task. We had trained the model alternatively on the MSA-ENG and the AD-MSA task. The training data will be randomly shuffled at each epoch for all tasks. The experiment made use of a batch size of 140 for each translation task and a batch size of 50 for the POS tagging task. The model is trained by minimizing the cross-entropy loss for each translation task and by maximizing the sparse categorical accuracy for POS tagging task.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Machine Learning/ Artificial Intelligence  
The ML and AI paradigms have also permeated into the JPSAC context. This is a very broad and emerging area and it has many open challenges, both ML specific (e.g., explainability, reproducibility, fairness, data scarcity, and accessibility) and JPSAC-specific (scalability, real-time processing, access to ground truth for supervised-data labeling, security and privacy, computational constraints especially on energy-constrained devices, etc.).  
Examples of recent works discussing or applying ML/AI in the context of JPSAC are: Ref. [9] (proposing a deep reinforcement learning method for RIS-based ISAC), Ref. [61] (a magazine survey of AI uses in the context of ISAC), Ref. [62] (ML for interference management in the context of ISAC context), and [63] (a neural-network-based estimation of angles in bistatic ISAC).
## 4 Conclusions  
The \(2\times 2\) DM-based MIMO-FBMC UAMCA was described herein and the MIMO underwater channel model with a transmission distance of 1 km was used to simulate its performance. The performances of BERs and PS ratios for the proposed UAMCA with BPSK and 4-OQAM modulation technologies were demonstrated. The UAMCA evaluation parameters, MSEs, PSNRs, and DSE rates, respectively, for underwater voice, image, and data signal transmission were explored. The quality of the received voice signals with BERs of \(10^{-2}\), and \(10^{-3}\), were presented, and the quality of the received image signals with BERs of \(10^{-3}\), and \(10^{-4}\), were demonstrated. Simulation results demonstrated that the BER performances of the proposed UAMCA with BPSK modulation technology were better than those of the proposed UAMCA with 4-OQAM modulation technology. Furthermore, the BER  
Figure 11: The data symbol error (DSE) rates of the \(2\times 2\) DM-based MIMO-FBMC UAMCA with a PCE, and CEEs of 5%, 10%, and 20%, with BPSK and 4-OQAM modulation technologies.  
Figure 10: The received image signal using the \(2\times 2\) DM-based MIMO-FBMC UAMCA using BPSK modulation technology with a BER of \(10^{-4}\) and a CEE of 10%.  
performances of the proposed UAMCA degraded as the CEEs increased. As the CEEs decreased, the PS ratios increased.  
The DSE rates performance of the proposed UAMCA with BPSK modulation technology were better than those of the proposed UAMCA with 4-OQAM modulation technology. As the CEEs increased, the DSE rates performance decreased. Thus, the proposed \(2\times 2\) DM-based MIMO-FBMC UAMCA is a feasible underwater low power multimedia communication solution, suitable for underwater low power and high speed underwater multimedia sensor networks. In the future, a space time block code (STBC)-based MIMO-FBMC UAMCA will be explored to achieve low transmission BERs.  
Conceptualization, C.-F.L. and S.-H.C.; methodology, C.-F.L., and S.-H.C.; software, C.-F.L., T.-J.S., H.-K.C., C.-K.L.; formal analysis, C.-F.L., and S.-H.C.; investigation, C.-F.L., and S.-H.C.; writing--original draft preparation, C.-F.L., and S.-H.C.; writing--review and editing, C.-F.L., S.-H.C., L.A.P., and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant from The Ministry of Science and Technology of Taiwan, under contract No. MOST 107-2221-E-992-027, and MOST 105-2923-E-022-001-MY3.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Collection and Variables  
The aim of this study was to describe and compare the congestion profiles of patients with preserved versus reduced ejection fraction at the time of admission. Therefore, a multimodal assessment--including physical examination, laboratory tests, and point-of-care ultrasound (POCUS)--was performed within the first 24 h of hospitalization.  
We recorded demographic and clinical variables, including age, sex, comorbidities, New York Heart Association (NYHA) functional class, and chronic home medications. Clinical signs of congestion--such as dyspnea, orthopnea, bendopnea, peripheral edema, jugular vein distension, and pulmonary crackles--were systematically assessed. Laboratory tests included assessing the levels of NT-proBNP, CA125, creatinine, sodium, and potassium, and congestion indices such as the combined congestion algorithm (CAC) were calculated.  
Echocardiographic evaluation included assessing inferior vena cava (IVC) diameter, the presence of B-lines, pleural or abdominal effusion, and the Venous Excess Ultrasound (VExUS) score, comprising Doppler patterns of hepatic, portal, and intrarenal veins. Congestion was also assessed using a clinical congestion score (range: 0-8) based on dyspnea, edema, and jugular venous distension [7].  
Although the primary analysis focused on this baseline evaluation, all patients were monitored throughout hospitalization and prospectively followed for 90 days to assess therapeutic interventions (such as intravenous loop diuretics or hypertonic saline), in-hospital mortality, and post-discharge outcomes including mortality, readmission, and diuretic requirements.
## 3 Results and Discussion
### Support Vector Method  
The Support Vector Method (SVM) is a method of machine learning applied to tasks involving predicting continuous values, which makes this especially suitable for predicting the real-time evaluation of CO\({}_{2}\)/NO\({}_{\mathrm{x}}\) emissions in fossil fuel power plants. The authors of Ref. [44] developed an SVM-based machine learning model to predict gas emissions from power plants. The authors compared the results of CEM. The results show that the SVM-based model has the capability for online prediction, with an average prediction accuracy of 95%.  
By utilizing week-long CEM data, a novel ensemble model based on the SVM is introduced for the prediction of NO\({}_{\mathrm{x}}\) emissions [45]. It needs to be pointed out that the collected data for building up the model are limited. This can arise from the limitations of time, operating conditions, and financial resources [46]. The authors of Ref. [47] proposed an SVM to study the correlation between NO\({}_{\mathrm{x}}\) emissions and the operation parameters of a 300 MW fossil fuel power boiler with an error of 1.58%. The experiment validated the model's accuracy and further showed that NO\({}_{\mathrm{x}}\) emissions could be reduced by approximately 18% through process optimization in the fossil fuel plant operations used in the machine learning model.  
As discussed above, the SVM is memory-efficient, which means that it has great potential in the development of real-time CO\({}_{2}\)/NO\({}_{\mathrm{x}}\) emissions evaluation. However, one needs to note that the SVM is not suitable for large datasets and datasets that contain high levels of noise; this requires a power plant to provide high-quality data for developing the SVM model.