esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Preliminary Performance Assessment  
The algorithm functionalities have been tested on a hypothetical airport representing Milano Malpensa (LIMC) airport, one of the two Italian hubs. A one day-long dataset has been used, which reproduces the actual traffic experienced on the LIMC surface during June 15, 2010. Traffic data has been retrieved with the help of the LIMC operator. Such data encloses scheduled and actual time of arrival/departure, actual off-block and on-block times, the runway used, the gate assigned and all other relevant flight information. LIMC airport data, available in the relevant section of the Aeronautical Information Publication (AIP) Italy document [14], has been coded into the format required by the XRP tool, following the procedure outlined in the previous section. The traffic sample has an average of 38 movements per hour (Figure 8) and peak values of more than 60 movements per hour. A comparison was made between data with and without the XRP algorithm enabled, and the results are shown in Figure 8. The details of the validation procedure can be found in [15].  
Data suggest that the algorithm is capable of speeding up the aircraft traffic on the airport surface with respect to the unregulated case. However, this effect decreases as the number of aircraft concurrently moving on the airport surface increases. This is mainly due to the conflict resolution strategy selected by the algorithm. As the traffic increases on the airport surface, Solution 1 (TSAT modification) does not allow for solving the conflicts without introducing new ones. In these conditions, the algorithm typically requires the conflicting aircraft to either take a longer route or rest at an HP until the route is cleared by other traffic. Nonetheless, the algorithm is always capable of finding a conflict-free solution, even in the presence of five simultaneous conflicts. This is done within at most 3 s, which is a value well below the O(10 s) typical solution update interval requested by ATCo decision support tools.  
Figure 7: _Cont._
Figure 8: XRP preliminary validation: traffic sample (**top**), aircraft travel-Time (**middle**), and computational time (**bottom**). (source: [15]).
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 2 Data Description  
Data utilized in this study includes three parts, i.e., the GNSS observations, GNSS satellite ephemeris, and the ERA5 reanalysis.
## 7 Conclusions  
In order to detect the PIS signal produced by the gearbox fault, DC offset cancellation was used to process the fault signal. Through a comparative study based on an implanted bearing fault case, the CK was demonstrated to be superior to the cross-correlation coefficient to determine the iteration number of DC offset cancellation. For the implanted bearing fault case, the PIS produced by the fault are very strong. Therefore, detection of the PIS using CK is more effective. However, for the naturally-developed bearing fault compound with gear fault, the PIS are very weak for the CK to detect. In this circumstance, the CK loses its power and cannot find the proper iteration number for DC offset cancellation. The order of the high-speed shaft gear mesh frequency was used as the iteration number because every DC offset cancellation can suppress one harmonic mesh frequency. This is very useful for PIS extraction. All of above results are demonstrated on a fixed-axis gearbox. For a planetary gearbox, DC offset cancellation has a limited effect on the fault diagnosis enhancement, especially for those low fault frequency parts, such as the planet gear, ring gear, etc. In this paper, because of the merits, the S transform was used to display the time-frequency characteristics of the DC offset results. This made us observe the fault information from the time-frequency contour which is more intuitive. However, for the low fault frequency parts, it is very difficult for the time-frequency contour to display them because of the long samples. At the end of this paper, we had a detailed discussion about the DCoffset cancellation and the S transform-based application. In the future, these time-frequency contours will be used for deep learning-based fault classification.  
The research is partially supported by the China NSFC under Grant Number 71401173 and by the Natural Science Foundation of Hebei Province under Grant Number E2015506012.  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Experimental Results and Discussion
### Comparison with Different Classical Models
We compared the number of FLOPs and parameters for the six experimental models mentioned in the experiments, and the results are shown in Table 9. From Table 9, we can see that RiceDRA-Net's FLOPs were only higher than those of AlexNet and MobileNet, but these models constitute an early structurally simple network model and a lightweight network, respectively. Furthermore, the recognition accuracy of both AlexNet and MobileNet is far inferior to that of RiceDRA  
Figure 14: Confusion matrix generated with CBG-Dataset. Net. Comparing RiceDRA-Net with VGG-19 and ResNet-101, the number of FLOPs for RiceDRA-Net was much lower than for these two models. Comparing RiceDRA-Net with the benchmark model DenseNet-121, we can see that RiceDRA-Net improved the accuracy of rice leaf disease recognition without changing the number of floating-point operations. It can also be observed from Table 9 that the number of parameters in RiceDRA-Net was only higher than that of MobileNet, a lightweight model. However, the recognition accuracy of RiceDRA-Net for rice leaf diseases was much higher than that of MobileNet. It is worth mentioning that RiceDRA-Net has achieved a reduction in the number of parameters while improving the accuracy of the model compared to the benchmark model DenseNet-121. In summary, RiceDRA-Net has excellent performance compared with other models in terms of the number of model parameters and computational power.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
### Performance Enhancement and Robustness  
Improve sign language sentence recognition accuracy and processing efficiency with novel algorithms [8]. This study seeks to create a model that performs well across varied datasets, minimizes recognition errors, and handles complicated linguistic scenarios [9]. This goal involves building adaptive algorithms to simplify signing settings to create a more reliable and versatile recognition system for real-world communication [10].
# Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
## 3 Results
### MPA Performance versus other Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Opt** & **Err** & **Time (s)** & **SD Err** & **SD Time (s)** \\ \hline la01 & 666 & 0.00\% & 0.5 & 0.00\% & 1.0 \\ la02 & 655 & 0.00\% & 1.3 & 0.00\% & 1.0 \\ la03 & 597 & 0.84\% & 1.8 & 0.57\% & 1.4 \\ la04 & 590 & 0.24\% & 1.2 & 0.42\% & 1.2 \\ la05 & 593 & 0.00\% & 0.6 & 0.00\% & 0.9 \\ la06 & 926 & 0.00\% & 0.6 & 0.00\% & 1.1 \\ la07 & 980 & 0.00\% & 1.2 & 0.00\% & 1.1 \\ la08 & 863 & 0.00\% & 0.6 & 0.00\% & 1.3 \\ la09 & 951 & 0.00\% & 0.7 & 0.00\% & 1.1 \\ la10 & 958 & 0.00\% & 0.6 & 0.00\% & 1.0 \\ la11 & 1222 & 0.00\% & 0.8 & 0.00\% & 1.2 \\ la12 & 1039 & 0.00\% & 0.7 & 0.00\% & 1.2 \\ la13 & 1150 & 0.00\% & 0.6 & 0.00\% & 1.1 \\ la14 & 1292 & 0.00\% & 0.7 & 0.00\% & 1.0 \\ la15 & 1207 & 0.00\% & 30.53 & 0.00\% & 1.7 \\ la16 & 945 & 0.31\% & 41.37 & 0.49\% & 9.3 \\ la17 & 784 & 0.06\% & 39.54 & 0.14\% & 8.8 \\ la18 & 848 & 0.20\% & 41.10 & 0.32\% & 8.9 \\ la19 & 842 & 1.01\% & 42.09 & 0.59\% & 9.0 \\ la20 & 901 & 0.50\% & 32.90 & 0.17\% & 4.7 \\ la21 & 1046 & 3.03\% & 79.47 & 0.82\% & 15.0 \\ la22 & 927 & 2.08\% & 74.67 & 0.66\% & 14.3 \\ la23 & 1032 & 0.00\% & 45.00 & 0.00\% & 6.6 \\ la24 & 935 & 3.50\% & 64.67 & 1.20\% & 12.3 \\ la25 & 977 & 3.52\% & 77.63 & 1.14\% & 24.7 \\ la26 & 1218 & 1.61\% & 105.97 & 1.01\% & 23.5 \\ la27 & 1235 & 4.49\% & 120.50 & 0.93\% & 33.8 \\ la28 & 1216 & 3.15\% & 113.70 & 1.00\% & 28.2 \\ la29 & 1152 & 7.77\% & 120.67 & 1.16\% & 37.3 \\ la30 & 1355 & 0.61\% & 112.00 & 0.75\% & 29.6 \\ la31 & 1784 & 0.00\% & 64.37 & 0.00\% & 6.4 \\ la32 & 1850 & 0.00\% & 85.33 & 0.00\% & 12.0 \\ la33 & 1719 & 0.00\% & 73.57 & 0.00\% & 8.4 \\ la34 & 1721 & 0.36\% & 172.47 & 0.50\% & 33.9 \\ la35 & 1888 & 0.03\% & 95.20 & 0.15\% & 17.9 \\ la36 & 1268 & 4.53\% & 79.67 & 1.10\% & 23.1 \\ la37 & 1397 & 4.94\% & 94.00 & 1.06\% & 25.5 \\ la38 & 1196 & 7.02\% & 106.03 & 1.31\% & 30.5 \\ la39 & 1233 & 4.25\% & 99.47 & 1.49\% & 24.0 \\ la40 & 1222 & 3.69\% & 109.00 & 1.06\% & 26.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean errors, computations times (s) and their standard deviations for the analyzed JSSP datasets.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 6 Discussions  
In the previous section, we demonstrated the potential advantage of mixed Cadzow iterations. The following questions arise for use in practice. The first important issue to discuss is the stability of the demonstrated effect. One source of perturbation is that the signal rank can be inaccurately estimated. Of course, if the rank is underestimated, most methods fail. The question is the robustness of the method to rank overestimation. Let us take the time series from Section 5.2 with a signal of rank 2 and consider a projection onto a subspace of rank 4 in alternating projections (target rank 4). Figure 4 shows that the general error behaviour is similar to that in Figure 3; the only differences are the convergence rate and the number of accurate steps required.  
The second example is related to the stability of the result concerning the signal rank. We changed the signal from the previous example to \(s_{n}=\sin(2\pi n/6)+0.5\sin(2\pi n/8)-0.3\sin(2\pi n/12)\). Figure 5 demonstrates the same effect: similar behavior of the error dynamics and a lower rate of convergence. Note that the convergence for the case of rank 6 is much faster than for the overestimated rank 2.  
Both examples show that the number of first accurate iterations depends on the structure of the series. Therefore, the choice of the number of accurate steps should be investigated in the future.  
The next point of discussion is the possibility of speeding up the implementation of Cadzow iterations. Ideas from [21], including the randomized linear algebra approach [22; 23], can be applied to Cadzow iterations. Combining the mixed alternating projection approach and approaches to single iteration acceleration may be a challenge for future research.  
Figure 3: RMSE depending on \(T_{1}+T_{2}\). The signal rank 2 and the target rank 2.
## 2 Materials and Methods
### Effect of Inlet Concentration  
The influence of the inlet dye concentration was investigated at a constant flow rate of 7.7 mL min\({}^{-1}\), a bed height of 2 cm, and decreasing the initial dye concentration from 500 to 100 mg L\({}^{-1}\) for the blue and red dyes and from 100 to 25 mg L\({}^{-1}\) for the black dye. The results are shown in Figure 5 and Table 3.  
It is observed that \(t_{b}\) decreased significantly and sharper BTCs were obtained by increasing the inlet dye concentration, possibly because a reduced concentration gradient generates a slower transport. The mass-transfer driving force increases along with the influent concentration and dye loading rate, which results in a reduction in the length of the adsorption zone and enhanced performance [36]. Increasing the initial dye concentration led to an increase in the _MTZ_ because of a fast-transport phenomena [25]. Similar results were obtained by [PERSON] et al. [37] using agricultural biomass for the adsorption of textile dye, [PERSON] and [PERSON] [9] using _Intsia bijuga_ sawdust-based activated carbon for dye adsorption, and [PERSON] and [PERSON] [34] using activated carbon prepared from agro-wastes for the adsorption of textile industry effluent.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dye** & **Concentration** & \(Q\) & \(t_{b}\) & \(V_{df}\) & \(t_{aper}\) & \(m\) & \(q_{bed}\) & \%_Removal_ & _MTZ_ \\ \hline Blue & 500 & 30.8 & 17 & 4933 & 160 & 1.0 & 995.2 & 40.6 & 3.5 \\  & 7.7 & 42 & 4029 & 523 & 1.0 & 1283.1 & 65.0 & 3.7 \\ Red & 500 & 30.8 & 3 & 1789 & 232 & 1.0 & 896.9 & 24.4 & 3.9 \\  & 7.7 & 6 & 3098 & 446 & 1.0 & 705.3 & 42.2 & 3.9 \\ Black & 100 & 30.8 & 2 & 3818 & 124 & 1.0 & 130.4 & 36.5 & 3.9 \\  & 7.7 & 36 & 3395 & 441 & 1.0 & 153.4 & 34.1 & 3.5 \\ \hline  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \end{tabular} Concentration (mg L\({}^{-1}\)): \(Q\) (mL min\({}^{-1}\)): feed flow; \(t_{b}\) (min): breakthrough time; \(V_{df}\) (mL); volume of effluent; \(t_{aper}\) (min): total operating time; \(m\) (g): mass of adsorbent; \(q_{bed}\) (mg g\({}^{-1}\)): adsorption capacity of the PSAC bed; _MTZ_ (cm): mass-transfer zone.  
\end{table}
Table 2: Estimated parameters obtained from the BTCs corresponding to the adsorption of blue, red, and black dyes onto the PSAC in function of flow rate (bed height: 4 cm; temperature: 20 \({}^{\circ}\)C).  
Figure 5: Breakthrough curves for blue (**a**), red (**b**), and black (**c**) dye adsorption onto the PSAC at different influent concentrations (flow rate: 7 mL min\({}^{-1}\); bed height: 2 cm; temperature: 20 \({}^{\circ}\)C).
# Research on Low-Carbon-Emission Scheduling of Workshop under Uncertainty
## 1 Introduction
### Parameter Description  
The description of each parameter of the model is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Parameter Meaning** \\ \hline \(n\) & Total number of processed workpieces \\ \(m\) & Total number of processed machines \\ \(l\) & Total number of processing processes \\ \(i\) & Workpiece subscript \\ \(k\) & Machine subscript \\ \(j\) & Operation subscript \\ \(l_{i}\) & Workpiece \(i,i\in\{1,2,\cdots,n\}\) \\ \(M_{k}\) & Machine k, \(k\in\{1,2,\cdots,m\}\) \\ \(O_{ij}\) & \(j\)th process of the workpiece \(i\) \\ \(\widetilde{D}_{i}\) & Vague delivery time of workpiece \(J_{i}\) \\ \(\widetilde{C}_{i}\) & Fuzzy completion time of workpiece \(J_{i}\) \\ \(\widetilde{C}_{max}\) & Maximum fuzzy completion time \\ \(\widetilde{T}_{ijk}\) & Fuzzy processing time of operation \(O_{ij}\) on machine \(M_{k}\) \\ \(\widetilde{t}_{ijk}\) & Fuzzy idle time of operation \(O_{ij}\) on machine \(M_{k}\), and \(\widetilde{C}_{ijk}=\widetilde{T}_{ijk}+\widetilde{t}_{ijk}\) \\ \(\widetilde{ST}_{ijk}\) & Fuzzy start time of operation \(O_{ij}\) on machine \(M_{k}\) \\ \(\widetilde{ET}_{ijk}\) & Fuzzy end time of operation \(O_{ij}\) on machine \(M_{k}\) \\ \(F_{e}\) & Carbon emission factor of electricity \\ \(F_{c}\) & Carbon emission factor of coolant \\ \(P_{ik}\) & Machining power of machining on the machine \(M_{k}\) for workpiece \(J_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of each parameter of the model.
## 4 Result Analysis
### Performance Evaluation  
In the present ASR, the system's performance was evaluated using WER. The WER of each speaker was calculated with a formula, as written in Equation (2).  
\[\text{WER}=\frac{S+D+I}{N} \tag{2}\]  
where \(S\) represents the number of substitutions; \(D\) is the number of deletions; \(I\) means the number of insertions. The overall WER was observed as 19.45%.  
Comparative Analysis of Existing Systems with Present System Based on Accuracy Level and Word Error Rate (WER)  
As per the results generated, the overall accuracy of the proposed system remained at 89.15% using 2D convolutional and 64-3 \(\times\) 3 filters. When comparing the performance of the proposed system with similar parameters, the system yielded optimal results. Table 7  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speakers** & **Sex** & **Recognition Accuracy** \\ \hline Speaker 1 (sp1) & F & 90.87\% \\ Speaker 2 (sp2) & F & 89.871\% \\ Speaker 3 (sp3) & F & 89.783\% \\ Speaker 4 (sp4) & M & 88.559\% \\ Speaker 5 (sp5) & F & 88.370\% \\ Speaker 6 (sp6) & F & 89.077\% \\ Speaker 7 (sp7) & M & 90.911\% \\ Speaker 8 (sp8) & F & 88.942\% \\ Speaker 9 (sp9) & M & 86.765\% \\ Speaker 10 (sp10) & F & 86.677\% \\ Speaker 11 (sp11) & F & 90.87\% \\
**Overall accuracy of system** & - & **89.153\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recognition rates of different speakers in current ASR system.  
Figure 6: Graphical overview for accuracy level of 11 speakers in current ASR system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speaker** & **Category** & \begin{tabular}{c} **Total No. of** \\ **Sentences** \\ **Recognized** \\ \end{tabular} & \begin{tabular}{c} **Total No. of** \\ **Sentences in** \\ **Audio File** \\ \end{tabular} & \
\begin{tabular}{c} **Total No. of** \\ **Original Words** \\ \end{tabular} &
\begin{tabular}{c} **Recognized** \\ **Words** \\ \end{tabular} & **Accuracy (\%)** \\ \hline Spl & F & 34.5 & 38 & 2205 & 2003 & 90.866\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speaker \(1\) details.  
compares different techniques with the proposed design using a CNN. Figure 7 depicts the proposed approach's graphical analysis of various existing methods.
## 5 Experiment and Analysis
### Evaluation of Continuous Casting Quality Knowledge Alignment Method  
#### 5.3.1 Performance of Continuous Casting Quality Knowledge Alignment Method  
The knowledge alignment results are shown in Table 8. For different pre-trained models, BERT is replaced with ALBERT [36] and ERNIE [37] for comparison. The ERNIE model is a large-scale pre-trained language model proposed by Baidu. When using the ERNIE model, the accuracy of the model decreases by 4.48%; when using ALBERT, the accuracy of the model decreases by 1.42%.  
This result indicates that using the BERT model as the backbone of the model, we propose results in the most significant performance improvement.  
To investigate the influence of different output structures of BERT on the results, experiments are conducted by changing the output structure, and the results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**ADE \(\Diamond\)+**} & \multicolumn{2}{c}{**WebNLG \(\blacklozenge\)+**} & \multicolumn{2}{c}{**SciERC \(\blacklozenge\)\(\dagger\)**} \\ \cline{2-7}  & **NER** & **RE** & **NER** & **RE** & **NER** & **RE** \\ \hline Table-Sequence [31] & 89.7 & 80.1 & - & - & - & - \\ SpERT [32] & 89.3 & 79.2 & - & - & - & - \\ PFN [28] & 89.6 & 80.0 & 98.0 & 93.6 & 66.8 & 38.4 \\ Casrel [33] & - & - & 95.5 & 91.8 & - & - \\ TPLinker [27] & - & - & - & 91.9 & - & - \\ SPE [34] & - & - & - & - & 68.0 & 34.6 \\ PURE [35] & - & - & - & - & 66.6 & 35.6 \\ SAPRM (Our Model) & 90.3 & 82.7 & 98.1 & 92.7 & 70.2 & 39.3 \\ \hline \hline \end{tabular} The symbol \(\Diamond\) represents dataset using macro-F1 while \(\blacklozenge\) means usage of micro-F1. The symbol \(\dagger\) represents usage BERT-BASE-CASED as BERT encoder, while \(\ddagger\) means SciBERT as BERT encoder.  
\end{table}
Table 7: Experiment results on generalization capability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F** \\ \hline BERT [25] & 99.29 & 99.21 & 99.25 \\ ALBERT [36] & 97.87 & 97.59 & 97.73 \\ ERNIE [37] & 94.81 & 94.85 & 94.83 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experiment results of alignment.  
It can be observed that the differences between different output structures are small, and using the last layer directly as the output can achieve the best performance. Among them, using a combination of the second-to-last layer, the last two layers, and the first two layers all yield decent results.  
BERT may suffer from catastrophic forgetting due to its large number of layers. By adjusting the weight decay hyperparameter, the negative impact of catastrophic forgetting can be mitigated. Table 10 shows the influence of different weight decay settings on the results. When the weight decay is set to 1 \(\times\) 10\({}^{-4}\), our continuous casting quality engineering knowledge alignment model achieves the best performance.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 5 Applications of Microencapsulated Polypphenols in Food
### Dairy Products  
Polyphenols show beneficial effects on dairy products, both in terms of their quality and nutritional value [228; 229]. Due to their antioxidant properties, they can prevent the oxidation of fats in dairy products, which is particularly important for milk, yogurt or cheese. Protection against oxidation helps maintain the freshness and flavour of products, and increases their shelf life [230; 231; 232]. In dairy products such as creams and sauces, polyphenols can enhance emulsion stability, which affects texture uniformity and prevents precipitation of ingredients. Polyphenols can affect the texture and consistency of dairy products such as yogurts and cheees, improving their density and stability, resulting in a better sensory experience for consumers [233; 234]. Polyphenols can also interact with the fermentation microflora in yogurts and cheees, which can improve the fermentation process and affect the sensory and health properties of these products. Some polyphenols may have prebiotic effects, promoting the growth of beneficial bacteria in the gut, furthercontributing to digestive health [235; 236; 237]. Existing research has analysed the use of microencapsulated polyphenols, such as those derived from green tea or berries, in yogurt production. The study conducted by [PERSON] et al. (2019) showed that the addition of microencapsulated polyphenols can increase the antioxidant activity of yogurt, without impairing its physical properties, and thus serve as a functional ingredient to enrich healthy yogurts [186]. Some studies have investigated the effect of microencapsulated polyphenols on the quality of cheese. It has been found that the addition of polyphenols can improve the texture of cheese, prolong its shelf life and increase its antioxidant properties, which affect the taste and flavour of the final product [238; 239; 240]. The studies have shown that microencapsulated polyphenols can promote emulsion stability, improve flavour and extend the shelf life of milk and dairy drinks [241; 242; 243]. Studies on ice cream and milk-based desserts have analysed the effects of microencapsulated polyphenols on their sensory properties and stability. It was found that these additives can improve taste, texture and nutritional properties [244; 245; 246]. The research also focused on evaluating the effect of microencapsulated polyphenols in dairy products for their antimicrobial activity. It has been shown that polyphenols can reduce the growth of pathogens in yogurts and cheeses, which increases the microbiological safety of these products [247; 248; 249; 250; 251; 252]. This research highlights the potential of microencapsulated polyphenols as promising additives in the dairy industry, offering improvements in the quality, nutritional value and shelf life of dairy products.