esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Hard Voting Model  
The ensemble model demonstrated significant improvements in precision, recall, and F1 score compared to individual models, as shown in Figures 12 and 13. These figures highlight the performance metrics of the ensemble model on both the original and augmented datasets, clearly indicating the advantages of data augmentation and the hard voting approach for crash severity classification.  
To enhance crash severity classification, we implemented an ensemble method using a hard voting classifier. This involved combining predictions from three top-performing models, BERT (DistilBERT), RoBERTa, and XGBoost, each trained on the augmented dataset. This hard voting mechanism capitalized on the strengths of each model, improving the overall classification performance, especially for the minority \"Property Damage\" class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Precision** & **Recall** & **F1 Score** \\ \hline \hline \multirow{2}{*}{Fatal} & 1.00 & 0.98 & 0.99 \\  & 0.99 & 0.98 & 0.99 \\  & 0.49 & 0.86 & 0.62 \\  & 0.98 & - & - \\  & 0.83 & 0.94 & 0.87 \\  & 0.99 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ensemble model performance on the augmented dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1 Score** \\ \hline \hline \multirow{2}{*}{XGB-TFIDF} & Fatal & 0.9958 & 1.0 & 0.9979 \\  & Personal Injury & 1.0 & 0.9652 & 0.9823 \\  & Property Damage & 0.3443 & 1.0 & 0.5122 \\ XGB-Word2 Vec & Fatal & 0.9167 & 0.9231 & 0.9199 \\  & Personal Injury & 0.9875 & 0.9841 & 0.9858 \\  & Property Damage & 0.0000 & 0.0000 & 0.0000 \\ XGB-BERT & Fatal & 0.9514 & 0.9580 & 0.9547 \\  & Personal Injury & 0.9899 & 0.9949 & 0.9924 \\  & Property Damage & 0.0000 & 0.0000 & 0.0000 \\ BERT-LSTM & Fatal & 0.99 & 0.98 & 0.98 \\  & Personal Injury & 0.99 & 0.93 & 0.96 \\  & Property Damage & 0.19 & 0.86 & 0.31 \\ RoBERTa-LSTM & Fatal & 1.00 & 0.97 & 0.98 \\  & Personal Injury & 0.98 & 1.00 & 0.99 \\  & Property Damage & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for models on the augmented dataset.  
Figure 12: The performance metrics of the ensemble model on the original dataset.  
Figure 13: The performance metrics of the ensemble model on the augmented dataset.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{\begin{tabular}{c} **Total Phenol** \\ **(mg\({}_{\text{GA}}\)/100 \({}_{\text{BS}}\))** \\ \end{tabular} } & **Betacyanin** & **Betaxanthin** & \
\begin{tabular}{c} **Antioxidant** \\ **Capacity** \\ **(mg\({}_{\text{VE}}\)/100 \({}_{\text{BS}}\))** \\ \end{tabular} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Feed 3.5P \\ 3.5P125 \\ 3.5P150 \\ 3.5P150 \\ 3.5P150 \\ 7P150 \\ \end{tabular} } & 935 (5) \({}^{\text{AA}}\) & 1015 (17) \({}^{\text{AA}}\) & 525 (7) \({}^{\text{AA}}\) & 314 (6) \({}^{\text{AA}}\) \\  & 771 (25) \({}^{\text{BB}}\) & 941 (42) \({}^{\text{abA}}\) & 480 (21) \({}^{\text{abA}}\) & 235 (9) \({}^{\text{BB}}\) \\  & 644 (40) \({}^{\text{CA}}\) & 845 (57) \({}^{\text{BA}}\) & 433 (26) \({}^{\text{BA}}\) & 213 (4) \({}^{\text{CA}}\) \\  & 7P1 & 939 (4) \({}^{\text{AA}}\) & 1041 (14) \({}^{\text{AA}}\) & 525 (5) \({}^{\text{AA}}\) & 304 (11) \({}^{\text{AA}}\) \\  & 7P125 & 896 (14) \({}^{\text{BA}}\) & 981 (54) \({}^{\text{abA}}\) & 506 (26) \({}^{\text{AA}}\) & 263.41 (0.08) \({}^{\text{BA}}\) \\  & 7P150 & 737 (28) \({}^{\text{CA}}\) & 905 (42) \({}^{\text{BA}}\) & 474 (26) \({}^{\text{AA}}\) & 223 (4) \({}^{\text{CA}}\) \\ \hline \hline \end{tabular}  
* For each bioactive compound, the same small letter in superscript within a column indicates homogeneous groups established by ANOVA (\(p<0.05\)) comparing process and temperature (feed, SD125, and SD150) in samples with 3.5% of pea protein. For each bioactive compound, the same capital letter in superscript within a column indicates homogeneous groups established by ANOVA (\(p<0.05\)) comparing pea protein percentage (3.5 and 7) in samples with the same process and temperature (feed, SD125, and SD150). 3.5P125 or 3.5P150, powder with 3.5% of pea protein concentration spray dried at 125 or 150 \({}^{\circ}\)C, respectively; 7P125 or 7P150 powder with 7% of pea protein concentration spray dried at 125 or 150 \({}^{\circ}\)C, respectively.  
\end{table} 
Table 5: Mean value (and standard deviation) of total phenol, betacyanins, betaxanthins content, and antioxidant capacity of beetroot feed and spray-dried (SD) samples in mg/100 g of beetroot solids (BS).
## 5 Methods, Results and Discussion
### Driving and Holding Current Influence on a Displacement Step
This error is attributed to the asynchronous movement of the MPMs due to the heterogeneity in friction, causing a torque to appear. Furthermore, manufacturing and assembly errors like the misalignment between the wires and the actuators' axes could contribute to this error. Figure 10 presents the straightness error measurement of the results presented in Figure 9.  
From Figure 10, the straightness error decreased as the driving current increased for both axes and for both conditions (with and without holding current). From Figure 10a (\(x\)-axis), the mean straightness error without holding current was \(13.3\,\mathrm{\SIUnitSymbolMicro m}\), and it decreased to \(4\,\mathrm{\SIUnitSymbolMicro m}\) with \(1\,\mathrm{A}\) holding current. From Figure 10b (\(y\)-axis), the mean straightness error without holding current was \(9.1\,\mathrm{\SIUnitSymbolMicro m}\), and it decreased to \(6.5\,\mathrm{\SIUnitSymbolMicro m}\) with \(1\,\mathrm{A}\) holding current. These results show that the holding current reduced the straightness error, thanks to the use of the EDA silicon cavity wall (fabricated with microfabrication techniques) as a straight guide for the MPM movement. From Figures 9 and 10, we can see that high currents are useful for small, repeatable and precise object displacement steps given the low standard deviation and straightness error. For example, injecting \(6\,\mathrm{A}\) driving current and \(1\,\mathrm{A}\) holding current generates a displacement step of: \(26.86\,\mathrm{\SIUnitSymbolMicro m}\) with a straightness error of \(2\,\mathrm{\SIUnitSymbolMicro m}\) in the \(x\)-axis, and \(26.29\,\mathrm{\SIUnitSymbolMicro m}\) with a straightness error of \(3\,\mathrm{\SIUnitSymbolMicro m}\) in the \(y\)-axis. The presented results confirm the interest of the proposed control strategy based on long displacement steps with low driving current values to achieve a high conveyance speed with low-energy consumption and finally a fine and adjustable step at the end of the trajectory with a higher driving current value. Figure 10: Holding current influence on straightness error. (**a**) \(x\)-axis results. (**b**) \(y\)-axis results.
## 3 Experiments and Results
### Computational Efficiency  
With the escalating complexity of deep learning architectures, computational efficiency has emerged as a pivotal consideration in model design and selection. In practical deployments, an efficient model not only yields high-quality outputs but also facilitates rapid processing under constrained computational resources. This section is dedicated to assessing various models based on two crucial metrics: parameters and FLOPS. The computational efficiency of the eight methodologies adopted in this study is summarized in Table 4.  
Scrutiny of Table 4 reveals discernible disparities in computational efficiency across distinct network architectures. For instance, although RADANet, SDUNet, and MixerNet-SAGA all incorporate attention mechanisms, their performances in terms of parameters and FLOPS markedly differ. Notably, MixerNet-SAGA records a mere 1.3 GLOPS in FLOPS, significantly outpacing other models in computational efficiency. Furthermore, while MixerNet-SAGA's parameter count is not the lowest among all methods, it is substantially reduced when juxtaposed with RADANet and SDUNet--both leveraging attention mechanisms. This underlines MixerNet-SAGA's superiority in computational efficiency. Analyzing parameter volume, SDUNet tops the list with an impressive 80.24 M, whereas ResNet, with a mere 5.87 M, boasts the fewest parameters. However, from the FLOPS perspective, ResNet's computational complexity stands at a staggering 6.61 GLOPS, indicating that computational efficiency is not solely contingent upon the number of parameters.  
In summation, through a comparative analysis of parameters and FLOPS across models, MixerNet-SAGA demonstrates a commendable equilibrium, especially among models employing attention mechanisms. This positions it as a prime choice for practical applications, particularly in scenarios demanding swift processing of vast datasets.
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
## 4 Results and Discussion
The daily mean precipitation, defined as the amount of precipitation each day, ranges from 0.6 mm/day to 1.5 mm/day. The daily mean unit flow rate ranges from 10.5 m\({}^{3}\)/s to 17.1 m\({}^{3}\)/s. When examining the mean daily precipitation values during the spring season (March to May), the data show that it was 1.5 mm in 2016, 2.0 mm in 2017, 2.5 mm in 2018, and it was also 2.5 mm in 2019. Additionally, the mean daily flow rates for the same time periods were recorded as 31.4 m\({}^{3}\)/s, 26.1 m\({}^{3}\)/s, 32.5 m\({}^{3}\)/s, and 41 m\({}^{3}\)/s, respectively. Upon analysis of the data, it is evident that the unit flow rate has a positive correlation with mean daily precipitation, with the exception of the year 2016. When the unit daily energy generation values for the same monthly periods are compared, it can be seen that 28.9 MWh units of daily energy generation were obtained in 2016, 23.3 MWh in 2017, 30 MWh in 2018, and 38 MWh in 2019. The effect of the change in the actual unit daily flow and precipitation values is seen in the unit daily energy generation, except for 2016. Figure 8a-d show the characteristics of monthly total energy generation, flow, and precipitation values for the years analyzed. Monthly total flow and energy generation values have formed a distinct profile with little variation over the years. In this profile, flow rate and energy generation values increase in spring, while they show a decreasing trend in the rest of the year. On the other hand, although the profile formed by the monthly total precipitation amounts varies according to the years, it generally forms peaks in the spring and autumn months. It can be seen that the peaks in the spring months contribute to  
Figure 8: Variation in monthly energy generation, flow rate, and precipitation for the given years, (**a**) 2016, (**b**) 2017, (**c**) 2018, and (**d**) 2019.  
energy generation, while the peaks in the autumn months do not contribute significantly to energy generation. For example, in Figure 8a-c, while precipitation increases in the summer and autumn, the flow rate stays constant. This may result from the high evaporation rate in the summer and autumn seasons, which are the driest times of the year. Also, energy generation from the hydropower plant for the given time period shows an unchanging trend. Additionally, Figure 8a acts differently from other related figures. The peak of precipitation occurred before the flow rate peak, but it had a lower-level peak before the flow rate peak. The lower peak of precipitation may have supplied enough water for the river flow rate. In essence, the correlation between precipitation and river flow rate has significant importance in the functioning of hydropower facilities. Comprehending and effectively overseeing this association is important in order to maximize energy generation and guarantee the long-term viability of hydropower energy.
## 5 Conclusions  
In this paper, we first gave a loitering definition from a new perspective using pedestrian activity areas. We propose a loitering detection method based on pedestrian activity areas classification. The pedestrian loitering behaviors are divided into three categories, i.e., the rectangle loitering, the ellipse loitering and the sector loitering. Unlike other loitering detection algorithms judging the loitering only by trajectory angle or time, this paper converts complex trajectory processing into activity area fitting. The loitering is recognized if the pedestrian activity is detected and to be constrained in an area within a certain period of time. From the experimental results, the proposed three algorithms are very effective for detecting various loitering. No matter whether it is indoors or outdoors, it has good accuracy and robustness in loitering detection for both the PETS2007 dataset and our self-collected dataset. The proposed method can detect loitering in videos captured from different angles, and can be applied to crowded scenes also. Compared with the existing methods, the proposed method not only can detect some loitering that the existing methods could not detect, but also is practical to handle complicated situations, efficient, robust, and simplified in implementation. Self-adaptive time threshold setting for loitering detection in different scenarios is worth of further research. We will further research on this in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PETS2007 Dataset**} & \multirow{2}{*}{**Dalley’s Method**} & \multicolumn{4}{c}{**Our Method**} \\ \cline{3-5}  & & **Rectangle Loitering** & **Sector Loitering** & **Ellipse Loitering** \\ \hline S01 & 94.2 & 97.64 & 97.94 & 97.74 \\ S03 & 67.5 & 90.77 & 89.12 & 90.94 \\ S05 & / & 91.83 & 91.51 & 90.77 \\ S08 & / & 98.07 & 98.17 & 97.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the detection accuracy (%) using the PETS2007 dataset.  
Figure 17: The detected results of the PETS2007 dataset. (**a**,**b**) The rectangle loiterer detection results in the same scene S01 but from different shooting angles. (**c**) The detected result for the S05 dataset using the sector loitering detection algorithm. (**d**) The results for the S03 dataset using the ellipse loitering detection algorithm.  
**Author Contributions:** All authors of the paper have made significant contributions to this work. [PERSON] and [PERSON] contributed equally to the paper, conceived the idea of work, wrote the manuscript, and analyzed the experiment data. [PERSON] led the project, directed, and revised the paper writing. [PERSON], [PERSON], and [PERSON] collected the original data of the experiment and participated in programming.  
**Funding:** This work was supported by the National Natural Science Foundation of China, under Grant 61762061, and the Natural Science Foundation of Jiangxi Province, China under Grant 20161 ACB20004.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Methods Based on Generative Adversarial Networks  
#### 2.2.1 Generative Adversarial Network Method Based on Unsupervised Learning  
[PERSON] et al. [5] first proposed the GAN in 2014, which achieves a balance state through confrontation training between a generator and discriminator. The generator deceives the discriminator by manufacturing virtual samples, and the discriminator is responsible for distinguishing the authenticity between the real sample and the generated sample. However, the original GAN model had some problems such as an unstable training process and model collapse. Later, [PERSON] et al. [15] proposed a deep convolutional generative adversarial network (DCGAN), which replaced the multi-layer perceptron of the original GAN by introducing a convolutional neural network (CNN), thereby enhancing the feature extraction ability of the model and improving the quality of the synthesized image to a certain extent. However, the above methods do not fundamentally solve the problem of model training instability. [PERSON] et al. [16] proposed the earth mover distance to measure the difference between the true and false distributions; this enhances the stability of model training and can better deal with training instability, model collapse, and other problems in original GAN model. [PERSON] et al. [17] presented weight clipping by adding a penalty term based on the gradient of the discriminator, which can train various GAN structures steadily.  
GANs have a strong image generation and conversion ability. In recent years, GANs have been gradually applied to the field of medical image denoising, simulation [6; 7; 8], and radar image conversion [18]. In the field of sonar, some researchers have applied them to sonar image generation. [PERSON] et al. [11] proposed an unsupervised feature learning method for seafloor classification using synthetic-aperture sonar images; this could generate a small number of different types of seafloor synthetic-aperture sonar images. [PERSON] et al. [12] proposed a generative adversarial network method based onthe Markov condition to generate sonar images with a high sense of reality, enhancing the quality and speed of model synthesis data to a certain extent. [PERSON] et al. proposed the MFA-CycleGAN model, introducing the multi-granularity feature alignment mechanism into the CycleGAN framework, significantly improving the quality of cross-domain sonar image generation and providing a new idea for similar unpaired image transformation tasks [19]. However, the images generated by the above methods have strong randomness, which cannot meet the sonar data simulation task with controllable conditions in complex environments.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 2 Background
### Kbqa  
A crucial question related to KBQA is how to translate natural language problems into formal language that can be understood by the computer and obtain the answer to the problem through query and reasoning within the constructed KB. Therefore, KBQA mainly includes Semantic Parsing-based (SP-based) methods and Information Retrieval-based (IR-based) Methods.  
The SP-based methods analyzes components of natural language questions, converts the query into logical expressions, and then converts the query into a knowledge graph query to get the answers. [PERSON] et al. [27] parsed natural utterances into subgraphs of knowledge graph to achieve complex question answering, and the model was found practical; [PERSON] et al. [28] designed a semantic query expansion method to solve the problem of difficulty in obtaining ideal answers from data sources, which expanded query terms in question triples from three semantic perspectives and achieved multi-semantic expansion of the question triples. This method can more clearly convert natural language problem statements into logical expressions. However, the method requires many manually defined logical expression rules, which perform well in specific domains but are not generalizable when dealing with large-scale knowledge graphs. In other words, this method is suitable in specific domains but cannot transform undefined rules when dealing with large-scale knowledge graphs.  
The IR-based methods extract critical information from the question and use that to qualify the knowledge of the knowledge base and then retrieve the answer. [PERSON] et al. [29] suggested a Stepwise Reasoning Network (SRN) model on the basis of intensive learning. The SRN model formalizes the problems as sequential strategy ones and an attention mechanism is adopted to obtain exclusive information within the problem, which significantly enhances the effectiveness of question and answering based on information retrieval methods; [PERSON] et al. [30] argued that KG lacks context to provide a more precise conceptual understanding, although it contains rich structural information. For this reason, they designed a model that uses external entity descriptions for knowledge understanding to assist in completing knowledge question and answering. This approach achieves optimal results on the Common-sense QA dataset and obtains the best results in the non-generative model of OpenBookQA.
## 4 Conclusions  
In our study, O/W Pickering emulsions with CMF maintained the stability of the emulsion phase for 24 h. One of the main reasons for the efficiently stabilized O/W emulsions was the amphiphilic surface nature of the microfibers. The unmodified CMF formed an expanded and strong network that enclosed oil in small droplets, avoiding the coalescence in optimal conditions. Pickering emulsions with more than 0.50 wt% CMF had a maximum \(\mu_{a}\) with an OCEP around 65%, increasing up to 90 times the \(\mu_{a}\) of the emulsion phase with respect to the sunflower oil, for a shear rate of 1 s\({}^{-1}\). Lower O/W ratios showed that the oil and water phases were completely separated, and CMF enclosed the entire oil phase in drops without an oil phase on the top of the emulsion. Higher O/W ratios did not allow the same amount of CMF to enclose the oil, therefore a part of the drops was broken and mixed with water, and an oil phase was formed on the top of the emulsion. An increase in CMF dose helped to increase the strength of the network. Microscope images showed the stability of emulsions before and after centrifugation. High apparent viscosity emulsions had regular shape, small size, and a large number of drops with a homogenous CMF distribution. Finally, we found Pickering emulsions can be used in the food industry, replacing less unhealthy fats, such as butter. An emulsion with 50/50 O/W and 0.75 wt% CMF can obtain a low-calorie emulsion with a reduction in SFA and total fats of 85% and 23%, respectively, compared with butter. Therefore, O/W Pickering emulsions can be considered as an alternative to trans and saturated fats in the food industry.  
Conceptualization, A.B. ([PERSON]), M.C.M., A.B. ([PERSON]) and C.N.; Formal analysis, J.L.S.-S.; Funding acquisition, C.N.; Investigation, J.L.S.-S. and A.B. ([PERSON]); Project administration, C.N.; Supervision, M.C.M. and A.B. ([PERSON]); Writing--original draft, J.L.S.-S.; Writing--review & editing, A.B. ([PERSON]), M.C.M., and A.B. ([PERSON]).  
This research was funded by the Economy and Competitiveness Ministry of Spain, project CTQ2017-85654-C2-2-R, and by a grant from Universidad Complutense de Madrid and Banco de Santander to [PERSON], grant number CT17/17.  
The authors wish to thank the Economy and Competitiveness Ministry of Spain for their support of the project CTQ2017-85654-C2-2-R as well as the support of Universidad Complutense de Madrid and Banco de Santander for the grant to [PERSON] (CT17/17).  
The authors declare no conflict of interest.