esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the SOA Formation in the Reaction of Furfural with Atmospheric Oxidants
## 3 Results and Discussion
### Gas-Phase Products
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & \multicolumn{4}{c}{**Molar Yield (\%)**} \\ \hline  & Cl & Cl + NOx & OH & NO\({}_{3}\) \\ HCl & (72.9 \(\pm\) 0.9) a  & (56.6 \(\pm\) 2.1) a  & – & – \\ CO & (22.9 \(\pm\) 0.4) a  & (11.4 \(\pm\) 0.4) a  & (23.8 \(\pm\) 0.3) a  & \\
5-chloro-2-(5\(H\))-furanone & (6.8 \(\pm\) 0.1) a  & (6.0 \(\pm\) 0.2) a  & – & – \\ Maleic anhydride & (6.1 \(\pm\) 0.5) b  & (6.7 \(\pm\) 0.2) b  & (8.8 \(\pm\) 0.6) b  & (20.0 \(\pm\) 3.0) b  \\
2-nitrofuran & & (10.8 \(\pm\) 0.5) b  & – & (58.6 \(\pm\) 8.6) b  \\
5-hydroxy-2(5\(H\))-furanone & & Xa  & \\ (P6) & & Xa  & \\ nitro-2-furylketone (P7) & & Xa  & & \\ \hline \hline Products identified by a FTIR and b GC-TOFMS; Unconfirmed X. The yield error is 2 times the standard deviation calculated from the least–square fit. & & & \\ \end{tabular}
\end{table} 
Table 1: Product yields confirmed by FTIR and GC-TOFMS analysis for the reaction of furfural with different oxidants. such nitro-2-furylketone (P4), which are common products to those obtained in the reaction with chlorine atoms in the presence of NOx. This may be due to high levels of NO2, which make furfural react by following a parallel path (marked in red in the proposed reaction mechanism). This behavior was not observed with the OH radical, where the concentrations of NO2 due to the decomposition of methylnitrite employed as the precursor is lower.
## 5 Computational Cost Reduction Methods
### AP Selection Methods
#### 5.1.1 Strongest APs  
The benefit of selecting the highest power APs is to provide a high probability of coverage over time. In the online phase, the strongest technique is achieved by selecting K subset of L APs with the highest power readings from the online RSS vectors, where K \(<\) L. The K subset APs with the highest power readings is created by sorting the online RSS vectors in descending order, then choosing the top L subset APs [39]. Accordingly, the indices of fingerprints on the RM are sorted to determine the location of MS.
## 2 Materials and Methods
### Proposed Method
#### 2.3.2 Reflected Component Adaptive Gamma Correction  
Gamma correction enhances the luminance and contrast of an image to better match the physiological properties of the human eye, which perceives brightness non-linearly [35]. The formula is as follows:  
\[i_{out}=i_{in}{}^{\gamma} \tag{14}\]  
where \(i_{in}\) is the input image luminance value, \(i_{out}\) is the output image luminance value, and \(\gamma\) is the gamma value, which usually takes a value between 0.5 and 2.5.  
The luminance and contrast of an image can be adjusted by controlling the gamma value. However, the optimal gamma value is different for different images under different lighting conditions. Therefore, it is necessary to adaptively adjust the gamma value according to the characteristics of the image itself.  
The histogram reflects the characteristics of the image well, the position of the main peaks reflects the brightness of the image, and the degree of concentration of the peaks reflects the range of variation of the brightness of the image, which is the conclusion from visual observation. Measured from the data, the histogram mean corresponds to the brightness of the image, and the standard deviation corresponds to the range of variation in the brightness of the image. Based on the above principle, adaptive adjustment of gamma value was achieved by the following equation, i.e.,  
\[\gamma=\frac{\log\bigl{(}\frac{\mu}{255}+0.01\bigr{)}}{\log\bigl{(}\frac{\sigma }{255}+0.01\bigr{)}} \tag{15}\]  
where \(\gamma\) is the gamma value, \(\mu\) is the mean value of the image histogram, \(\sigma\) is the standard deviation of the image histogram, and the 0.01 constant is added to avoid the situation of the denominator being 0 in practical applications.  
The adaptive adjustment method was negatively correlated with the image brightness and positively correlated with the change in image brightness. The above gamma correction facilitated the subsequent extraction of texture features.
# A Fundamental Study on an SAP Mixed Asphalt Mixture for Reducing the Urban Heat Island Effect
## 2 Materials and Methodology
### Performance Evaluation of SAP Asphalt Mixture  
Since the asphalt mixture mixed with the SAP was applied to serve the purpose of a reduction in urban heat island phenomenon in an environment exposed to moisture due to the water-absorbing characteristic of the SAP, the physical properties of the SAP asphalt mixture exposed to moisture were evaluated to verify the strength and durability of the mixture according to the type and content of SAP. The physical and mechanical properties of the SAP asphalt mixtures were analyzed through Marshall stability, indirect tensile strength, dynamic stability, and moisture resistance tests. In addition, Marshall stability and indirect tensile strength tests were conducted after exposing the specimens for 30 and 90 days, respectively, to evaluate the effectiveness of the SAP asphalt mixtures under outdoor environmental conditions.
## 3 Application
### Comparisons and Discussions
In addition, the measurement method of broken rock zone thickness for some mines are different, for example, the \(BRZT\) was determined by using ultrasonic detection in Baizigou Coal Mine, Shuanggou Coal Mine and Jianchang Coal Mine, while it was determined by using geophysical detecting radar in Huafeng Coal Mine. Although these methods were utilized in many mines for \(BRZT\) measurement, the \(BRZT\) values in the collected database may not maintain uniform precision, due to the influence of the measurement method and measuring instruments. As a data-driven method, the proposed models in this study are developed based on the collected \(BRZT\) database, so these models may provide nice performance when predicting \(BRZT\) of the mines contained in this database, but the performance cannot be guaranteed for the mines that have never been trained. Meanwhile, the limitations analyzed above may affect the analysis results, including intelligence predictive performance and sensitivity analysis results, and these limitations should be considered in the future investigation. Nevertheless, this study is a powerful supplement to the roadway broken rock zone (BRZ) prediction and is a pioneer work, due to the application of \(GP\) and PSO-GP having not been introduced for that engineering problem.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 5 Conclusions  
This study proposes the IPSO-SVM model for fault prediction in high-impact recorders, addressing the challenge of limited sample sizes. The IPSO-SVM model achieved a prediction accuracy of 90.5%, outperforming the traditional PSO-SVM model by 5.5%. This improvement is attributed to the optimizations introduced in the IPSO algorithm, including particle grouping and dynamic inertia weights, which effectively address issues like premature convergence and local optima.  
In addition to outperforming PSO-SVM, the IPSO-SVM model was also compared with other advanced models, including LSTM and Random Forest. The IPSO-SVM model showed superior performance, achieving a prediction accuracy that was 1.5% higher than LSTM and 7% higher than Random Forest in the fault prediction task. These comparisons demonstrate the IPSO-SVM model's ability to perform well with limited data, offering a more reliable prediction solution compared to traditional machine learning models.  
The IPSO-SVM model also demonstrated its ability to handle high-dimensional data with limited samples, making it a valuable tool for predicting failures in high-impact equipment. The model's high accuracy ensures more reliable operation of equipment, reducing costs associated with frequent replacements and downtime.  
Furthermore, this model has significant potential for application in other industrial fields, such as aerospace, military, and medical device development, where small datasets and high reliability are common challenges. For instance, it can be used to predict faults in devices like shock wave pressure recorders, thus enhancing reliability and reducing operational costs.  
In conclusion, the IPSO-SVM model offers a promising solution for fault prediction in high-impact recorders, demonstrating improved accuracy and practical applicability in industries with limited data and high reliability demands.  
Conceptualization, L.L. and W.Y.; methodology, L.L. and Y.D.; software, L.L. and Y.D.; validation, L.L. and W.Y.; formal analysis, L.L.; investigation, L.L.; resources, W.Y.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and W.Y.; visualization, L.L. and W.Y.; supervision, L.L.; project administration, W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61701445 and the \"Fundamental-Research-Program\" of Shanxi Province, grant number 20210302124200.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm
## 4 The Proposed Multi-Objective Five-Elements Cycle Optimization Algorithm
### General Framework of MOFECO  
The general framework of MOFECO is similar to most MOEAs, and will be illustrated in the following. First, randomly generate the initial population, which is divided into \(q\) cycles according to FECM, and each cycle contains \(L\) elements wherein the value of \(L\times q\) is the population number \(N\). Then, calculate the objective function values for each individual in the population. Second, determine whether to perform the update operation based on the force of each element. If the current individual satisfies the update condition, the update mode (local-update or global-update) is determined according to the current local-global probability \(P_{s}\), which varies nonlinearly with the number of iterations. Third, the mutation operation is performed according to the mutation probability \(P_{m}\). After the update and mutation operations, we perform the fast non-dominated sorting borrowed from NSGA-II [5] on the current population (including retained and updated individuals), and then select local and global optimal individuals which will be used as the search range for the next-generation individuals when updating. Fourth, we use the methods for the fast non-dominated sorting and crowding distance calculation on the parent and updated population, so as to create the current Pareto optimal solution set. Then, repeats the above process until the maximum number of iterations is reached. The flowchart of MOFECO algorithm is shown in Figure 3. Several parameters are configured in the initialization phase?number of individuals in each cycle \(L\), the number of cycles \(q\), the number of population \(N\), max iteration \(T\), current iteration \(k\), local-global probability \(P_{s}\), mutation probability \(P_{m}\) and weight coefficients \(w_{gp}\), \(w_{rp}\), \(w_{ga}\), \(w_{ra}\). In our proposed algorithm, the weight coefficients are set the same as in Reference [20] where \(w_{gp}=w_{rp}=w_{ga}=w_{ra}=1\) and we also set the same \(q\) and \(L\) value as in Reference [20], which proved that these values work the best for the algorithm. Relevant proof will be given in the following parameter comparison experiments in Section 5. Besides, when \(k=0\), set the value of \(X_{ij}(0)\) and calculate the value of \(M_{r,ij}(0)\), \(F_{ij\_r}(0)\).  
Figure 3: The flowchart of MOFECO algorithm.  
Next, we will describe the proposed algorithm MOFECO in detail in the following parts of this section.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 3 Experiment Task  
This study intends to explore a reliable and efficient method for eye fatigue detection; therefore, it is indispensable to present an objective eye fatigue experiment. After comprehensive research, there is a lack of authoritative and objective evaluation systems for quantitative indicators of eye fatigue worldwide. Until 18 July 2019, the China Electronic Video Industry Association released an industry standard in Beijing: \"Display Terminal Visual Fatigue Test and Evaluation Method Part 2 Scale Evaluation Method\" [13] (Figure 5). This standard combines the experience of domestic and international research and designs a highly relevant visual fatigue test scale. The test content mainly includes dry eyes, blurred eyes, double vision, eye tears, eye burning, eye pain, eye tearing, eye strain, eye irritation,  
Figure 4: System block diagram of EOG measurement.  
etc. The scale is shown as below (the original version is in Chinese, the version shown below is translated and edited into English by ourselves):  
Upon its release, the scale became the only visual industry official standard scale for eye fatigue and provided an authoritative method of measuring eye fatigue. In this article, 12 subjects aged between 24 and 32 years old, with no eye disease other than myopia, were selected and tested over a period. To make the test scenario more relevant to characters' daily eye habits, the test was divided into three periods daily. The first period is from 8:00 a.m.-9:00 a.m., when most folks are just starting to work and study, the level of eye fatigue is usually low. The second period is from 11:30 a.m.-12:00 a.m. when individuals are finishing their intensive work in the morning and the fatigue level is usually high. The third period is from 5:30 p.m.-6:00 p.m., during which the subjects showed a more pronounced variation in eye fatigue. Figure 6 presents the eye fatigue experiment process.  
Figure 5: Cont.  
Participants were asked to respond using the above eye fatigue scale in one minute, the self-test results were stored in the backend server. After the self-test, subjects wearing the self-study eye fatigue detection device begin a 3 min EOG acquisition. During this process, the subjects were asked to close their eyes the entire time and to be in a state of complete natural relaxation. The EOG signal is displayed in real-time and synchronously  
Figure 5: Display terminal visual fatigue test and evaluation method part 2 scale evaluation method [13].  
Figure 6: Eye fatigue experiment process.  
on the host computer designed in this article, and the data is saved in PC local as TXT format for follow-up analysis. After a sustained period of testing, over 300 sets of EOG data were obtained for this article.
## 3 Asymptotic Performance Analysis in Unicast System
For notational simplicity, we define \(\hat{m}_{k}\) and \(\hat{s}_{k}^{2}\) as the asymptotic mean and variance, respectively, of \(\hat{R}_{k}\) (i.e., \(\hat{m}_{k}\triangleq\mathrm{E}\big{[}\hat{R}_{k}\big{]}\) and \(\hat{s}_{k}^{2}\triangleq\mathrm{Var}\big{[}\hat{R}_{k}\big{]}\)). On the basis of the scaling law for \(N\), we can observe from (29) that the SNR increases with \(\mathcal{O}\big{(}N^{2}\big{)}\) and the ergodic achievable rate increases with \(\mathcal{O}(\log N)\). Moreover, (30) shows that the variance of \(\hat{R}_{k}\) remains constant regardless of \(N\), indicating the reduced channel hardening effect [29]. Using (29) and (30), we can characterize the outage probability, which is defined as the probability that the instantaneous data rate falls below a target rate, as follows. Lemma 2: _As \(N\) increases, the probability of the outage event \(\{\hat{R}_{k}<R_{0}\}\) tends to zero, where \(R_{0}\) represents the target desired rate given by \(R_{0}=\delta\cdot\hat{m}_{k}\) for \(0<\delta\leq 1\)._  
Proof: From (28), the outage probability can be obtained as follows:  
\[\underset{(a)}{\leq}\log\left(1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_ {k}|^{2}\Bigg{(}\tilde{\beta}_{k}+\tilde{\beta}_{k}\frac{\sum_{n}\left|\tilde{ f}_{k,n}\right|}{N}\Bigg{)}^{2}\right)\]6 \[\underset{(a)}{\leq}\log\left(1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_ {k}|^{2}\Bigg{(}\tilde{\beta}_{k}+\tilde{\beta}_{k}\frac{\sum_{n}\left|\tilde{ f}_{k,n}\right|}{N}\Bigg{)}^{2}\right)\]7 \[\underset{(a)}{\leq}\log\left(1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_ {k}|^{2}\Bigg{(}\tilde{\beta}_{k}+\tilde{\beta}_{k}\frac{\sum_{n}\left|\tilde{ f}_{k,n}\right|}{N}\Bigg{)}^{2}\right)\]8  
where \(\hat{m}_{k}=\log\big{(}N^{2}\rho_{k}\xi_{k}^{2}a_{k}\bar{\beta}_{k}^{2}\big{)}\). Consider that \(R_{0}=\delta\hat{m}_{k}\) for \(0<\delta\leq 1\), we then have  
\[\underset{(a)}{\leq}\log\left(1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_ {k}|^{2}\Bigg{(}\tilde{\beta}_{k}+\tilde{\beta}_{k}\frac{\sum_{n}\left|\tilde{ f}_{k,n}\right|}{N}\Bigg{)}^{2}\right)\]9  
It is observed in (33) that \(\hat{m}_{k}-R_{0}\) increases as \(N\) increases. Therefore, the outage probability \(\mathrm{Pr}\big{\{}\hat{R}_{k}<R_{0}\big{\}}\) eventually approaches zero according to the increase in \(N\), which completes the proof. Lemma 2 demonstrates that, as \(N\) increases, the outage probability decreases, verifying that an RIS can offer reliable SatCom for a large \(N\). Moreover, we can observe from (32) that the outage probability of an RIS-assisted SatCom system can be readily estimated using the deterministic values. Thus, we can assess the outage probability of a SatCom system without requiring extensive simulations.
# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 4 Data Experiment
### Data Test for the Model and Results
#### 4.5.4 MOS Subjective Analysis  
Since the subject of the MOS evaluation is people, different people have different feelings about a sentence. Therefore, in order to more accurately evaluate the sound quality of synthesized speech, this study randomly selected 50 Mongolian audios from the 2100 test set, composed of 15 Mongolian listeners, conducted a listening test, and then subjectively rated the quality and naturalness of the synthesized speech they heard. Finally, the average value was calculated to obtain the MOS score. Table 9 shows the average subjective opinion scores of different models.  
The experimental results show that by using real Mel spectrum as the inputs of WaveNet and ILPcnet, the MOS scores are 4.01 and 4.03, respectively. Through the comparison, it can be seen that the speech synthesis quality of the two vocoders is almost the same, but the WaveNet network model is complex and the training model takes a very long time. When the synthesized speech quality is the same, the ILPcnet network is more lightweight and faster.  
By comparing the difference between the autoregressive model and the non-autoregressive model in generating Mongolian speech, the MOS value of the autoregressive model Trans  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MOS Score** \\ \hline Real Voice & 4.62 \\ Mel + WaveNet & 4.01 \\ ParaNet & 4.32 \\ Improved Para-WaveNet & 4.21 \\ Transformer & 4.34 \\ Tactron2 & 4.45 \\ Mel + ILPcnet & 4.03 \\ Ghost-ILPCnet (without Bang pre-training) & 4.22 \\ Ghost-ILPCnet (Bang pre-training) & **4.48** \\ \hline \hline \end{tabular}
\end{table}
Table 9: MOS scores of different models.  
former and Tactron2 in generating Mongolian speech can reach up to 4.45. The non-autoregressive ParaNet series model and Ghost-ILPCnet generated Mongolian speech with the highest MOS value of 4.48. By using the Mongolian speech synthesis model based on Ghost-ILPCnet proposed in this paper, the MOS score of the Mongolian speech synthesis model reached 4.48 while ensuring synthesis efficiency. This was significantly higher than other baseline models, and only differed from the MOS value of actual speech by 0.14. The model was further improved in terms of the synthesis sound quality, speed, and performance, verifying the effectiveness of the improved model proposed in this paper.
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 4 Conclusions  
This study proposes a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. The achievements of this study can be summarized as follows. (i) A hybrid spatio-temporal clustering method is developed to segment the dataset into a series of homogenous spatio-temporal clusters, thereby effectively considering the spatio-temporal heterogeneity of the land subsidence data. (ii) We construct a heterogeneous ensemble prediction model by combining four different individual prediction models, thus leveraging the advantages of multiple models through ensemble learning methods to adapt to the complex land subsidence data distribution and improve prediction accuracy. (iii) We conducted an experiment on a land subsidence dataset collected in Cangzhou, China. The results show that the proposed method performs better than four state-of-the-art models, providing a reference for effectively managing and controlling land subsidence. This study introduces a new approach to spatio-temporal prediction, particularly as a feasible solution for addressing the spatio-temporal heterogeneity of large-scale datasets.  
Despite its strong performance in subsidence prediction, the ensemble model has some limitations. Ensemble learning performance is highly dependent on the chosen base models. Additionally, Blending is just one of many ensemble learning strategies, and other methods are worth considering. Their effectiveness in improving model performance and applicability requires further evaluation. Furthermore, the complexity of the ensemble model is typically higher than that of individual models, which may lead to higher computational costs and reduced interpretability. Future work will focus on comparing multiple ensemble learning strategies and developing methods with lower computational requirements.  
Conceptualization, B.Z. and G.W.; data curation, G.W. and Q.W.; methodology, B.Z., G.W., and J.L.; visualization, B.Z. and G.W.; writing--original draft, B.Z. and G.W.; writing--review and editing, Q.W. and M.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded through support from the National Natural Science Foundation of China (NSFC) (No. 42430110); the Water conservancy science and technology project of Guizhou, China (No. KT202410); and the Research Foundation of the Department of Natural Resources of Hunan Province (Grant No. HBZ20240132).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors gratefully acknowledge the comments from the reviewers.  
The authors declare no conflicts of interest.