esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This research proposes a multi-sensor feature fusion method for the identification of two-phase flow patterns in a subsea M-type jumper. The experimental tests were conducted at Dalian Maritime University's multiphase flow laboratory. In the test, flow rates were controlled to generate four different flow patterns: stratified flow, slug flow, annular flow, and bubbly flow. The obtained signals from two different sensing modalities, vibration and capacitive laminar imaging, are used to improve the accuracy issue of single-sensor testing with a single perspective and incomplete information. The proposed model merges their extracted features in a supervised machine learning approach to identify flow patterns. As compared to the model only employing ECT image data to determine flow patterns, the suggested method has a high identification accuracy of 95.3%, according to detailed experimental results.  
This study combines machine learning and two different types of sensor information to identify flow patterns, and directly inputs the flow pattern information into the neural network, which simplifies the extraction of features and improves the experimental efficiency. It overcomes the lengthy feature extraction procedure and incomplete feature information in the traditional flow pattern identification approach, which is important for the design and operational effectiveness of underwater production systems as well as the safety.  
Conceptualization, S.L. (Shanying Lin), J.X., S.L. (Shengnan Liu), M.C.O. and W.L.; methodology, S.L. (Shanying Lin), J.X., S.L. (Shengnan Liu), M.C.O. and W.L.; software, S.L. (Shanying Lin) and J.X.; validation, S.L. (Shanying Lin); formal analysis, S.L. ([PERSON]), J.X., S.L. ([PERSON]), M.C.O. and W.L.; investigation, S.L. ([PERSON]), J.X., S.L. ([PERSON]), M.C.O. and W.L.; resources, S.L. ([PERSON]); data curation, S.L. ([PERSON]) and J.X.; writing--original draft, S.L. ([PERSON]) and J.X.; writing--review and editing, S.L. ([PERSON]), J.X., S.L. ([PERSON]), M.C.O. and W.L.; visualization, S.L. ([PERSON]); project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (52271258), the Central Guidance on Local Science and Technology Development Fund of LiaoNing Province (2023H6/100100049), LiaoNing Revitalization Talents Program (XLYC2007092), 111 Project (B18009), and the Fundamental Research Funds for the Central Universities (3132023510).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
ASA regions are defined by low water availability caused by the hydrological balance of precipitation and evapotranspiration and may also be affected by irregularities in the timing of precipitation, resulting in the frequent occurrence of droughts [1; 29]. Unfortunately, many ASA regions of the world are also politically insecure particularly in the Middle East and Africa. Both factors make these regions vulnerable in terms of crop production and yield. RS could be used to provide the required comprehensive spatial coverage and timely crop monitoring data on which to base early warning systems that could significantly reduce food insecurities. Here, we demonstrated how RS can help improve the monitoring of agricultural productivity and, thereby, the assessment of food security by providing key information through the growing cycle. This information includes identifying and estimating crop area, monitoring agricultural crop status and health, and forecasting future crop yield. We demonstrated that RS data can play a vital role, especially given that the required data are freely available for these applications. Moreover, the data are available in both 'normal' situations and 'abnormal' situations where access to other data is greatly restricted due to natural and anthropogenic crises. Since data from relatively new satellite sensors such as Landsat 8 and the Sentinel series of sensors are available for free and their specification can meet the requirements of these agriculture applications, ASA countries should consider adopting these datasets into their national agricultural monitoring systems. Researchers and investigators should also explore new RS techniques in these regions such as to provide more accurate and robust results to support national policy and local decisions in support of ensuring food security. We recommend that researchers should focus on integrating already available fusion techniques into their agricultural monitoring systems such as creating fused datasets with a fine spatio-temporal resolution that can be used to classify crop type and forecast crop yield for ASA regions. Lastly, governments and funders should provide adequate resources to strengthen the capacity of countries in ASA regions. In particular, they should aim to provide the appropriate training and infrastructure that will enable them to incorporate such data and techniques into their agricultural monitoring systems to increase the capability to forecast crop production and yield and reduce food insecurity.  
Conceptualization, formal analysis and visualization, S.H.Q.; reviewing papers to generate Table 2, N.R.K.; writing original draft preparation, S.H.Q. and P.M.A.; funding acquisition, S.H.Q.; writing-review and editing, J.D., N.R.K., V.A.A., A.J.T. and P.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UK Research and Innovation GCRF 323036/ARCP011217. V.A.A. is funded through a Wellcome Trust Training Fellowship (# 211208).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiments
### Quantitative Evaluation on the Model
#### 4.2.2 Quantitative Evaluation of Publicly Available Dataset  
In order to establish the accuracy of our model, we conducted a comparative experiment using the public dataset UNIV. The results of this experiment are shown in Table 3. Our results clearly show that our model surpasses other baseline models in accuracy. Our model achieves reductions of 6.8% and 12.6% in the assessment measures ADE and FDE, each compared to the Social-STGCNN model. This indicates that our model has the same property of capturing pedestrian-pedestrian interactions as well as pedestrian-surrounding environment interactions in sparsely populated datasets, which substantially improves the prediction accuracy.
## 5 Results and Discussion
### Evaluation of 1 dCNN-AE Model  
Based on the results of the previous phase, which included both model validation and hyperparameter tuning, the optimal structure of the 1 dCNN-AE model has been selected for use in the testing phase. The testing results, which utilized 10% of the dataset as unseen data, demonstrated the efficacy of the proposed optimal model in extracting spatial features and learning efficient encodings from input data. As demonstrated by the reconstruction error measure, the proposed model exhibits a low MSE, with values of 0.0058 and 0.0066 observed in the training and validation, respectively.  
To demonstrate the superiority of the proposed architecture, the aforementioned testing result of 1 dCNN-AE has been compared to those from other AE variants, specifically LSTM-AE and standard AE, using the same testing data. As can be observed in Table 3, the MSE_testing of the standard AE is 0.0093, indicating an adequate performance in terms of capturing complex hierarchical patterns. It can also be noticed that our proposed model employs convolutional layers for data processing and can extract multi-scale or hierarchical features automatically with low MSE (0.0063). In contrast to 1 dCNN-AE, LSTM-AE demonstrates a suboptimal performance with a low MSE, i.e., 0.0106. Due to its sequential processing nature, achieving parallel execution by LSTM-AE is a significant challenge, particularly when dealing with large datasets. Figure 9 illustrates the training and validation results during the development process of various variants of the AE-based feature extraction model.  
Regarding the computational time, it should be noted that AE has a relatively low computational cost in terms of training time due to the low complexity of the model structure, with training times of 21.3 s. On the other hand, 1 dCNN-AE exhibits a satisfactory computational cost, with training and testing times of 73.6 s. However, LSTM-AE requires the highest training time with 363.45 s. This is due to the fact that the required computational time for each step is dependent on the output of the previous step, which results in longer training and testing times.
## 2 Related Work
### Edge Enhancement  
Edge enhancement plays a critical role in image segmentation, especially for tasks such as defect detection [17] and medical image segmentation [18]. Traditional edge enhancement techniques often rely on methods like Sobel, Canny, or Laplacian operators to highlight the edges, but they struggle in complex environments with low signal-to-noise ratios. In recent years, deep learning-based techniques have gained widespread recognition, excelling over traditional methods by automatically learning hierarchical edge features from data. Models such as U-Net and its variants have demonstrated great promise in enhancing edge details through multiscale feature extraction and refinement. For example, [PERSON] et al. [19] introduced Edge U-Net, a deep convolutional neural network that improves the precision of brain tumor segmentation through integration of edge information. [PERSON] et al. [20] proposed ESGNet, a fusion network combining multiple modalities for semantic segmentation of high-resolution remote sensing images. ESGNet improves segmentation accuracy by digital surface models with an edge segmentation guidance module and a multi-modal adaptive fusion module. [PERSON] et al. [21] proposed a new approach for muscle shape modeling and inference, which improves segmentation accuracy and efficiency by combining probabilistic shape modeling with deep edge segmentation. In contrast, our proposed DEPANet achieves edge-aware segmentation through two novel modules: (1) DEFP for enhancing spatial detail via multi-scale edge feature propagation, and (2) EFAM for adaptive fusion of semantic and edge information without external edge annotations. Unlike CGTD-Net and EMC-UNet, DEPANet focuses on efficiently integrating edge and semantic features under purely internal supervision, achieving comparable or superior accuracy on challenging datasets with lower computational cost.  
Existing methods have advanced strip steel defect segmentation as mentioned above, but they often overlook the critical role of edge information in improving accuracy. DEPANet addresses this issue by introducing a novel Differentiable Edge Feature Pyramid(DEFP) network and Edge-aware Feature Aggregation Modules (EFAMs) within an encoder-decoder framework. Unlike traditional approaches, DEFP dynamically extracts and refines multiscale edge features using learnable Laplacian operators and Gaussian blurring mechanisms. EFAMs then integrate these edge-cues with global backbone features, ensuring precise defect localization and robust performance across varying defect sizes and shapes.
## 3 Results
### XPS Analysis
#### 3.5.3 Analysis of Pt4f Spectra  
The curve fitted Pt4f spectra are shown in Figure 8. Each doublet corresponds to the individual state of platinum on the surface of the catalysts (Table 3). For Pt/Ti-PLA-400, two states with E\({}_{\text{b}}\) (Pt4f7/2) = 71.7 eV and 73.7 eV can be distinguished. For Pt/Ti-P25-400, two peaks with E\({}_{\text{b}}\) (Pt4f7/2) = 72.7 and 74.9 eV can be seen. Note that the Pt4f7/2 binding energies for both platinum states in the Pt/Ti-P25-400 catalyst are 1.0-1.2 eV higher than in the Pt/Ti-PLA-400 catalyst pointing to higher oxidation state of platinum. The state with E\({}_{\text{b}}\) = 74.9 eV can be assigned to Pt\({}^{4+}\)[13; 36], while the Pt state with E\({}_{\text{b}}\) = 73.7 eV has a lower value than the typical values for the Pt\({}^{4+}\) ion [22; 36; 39].  
Figure 8: Curve-fitted Pt4f XPS spectra for (**a**) Pt/Ti-PLA and (**b**) Pt/Ti-P25 catalysts, calcined at 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C.  
The state with E\({}_{\text{b}}\) = 72.7 eV, observed for the Pt/Ti-P25-400 catalyst, can be attributed to Pt\({}^{2+}\) platinum ions [36; 39]. As for the peak with E\({}_{\text{b}}\) = 71.7 eV obtained for the Pt/Ti-PLA-400 catalyst, it can be related both to metal particles with a slight positive charge (Pt\({}^{5+}\)) due to contact with the oxide support [40; 41], or to oxide particles PtO\({}_{\text{x}}\) with x \(<\) 1. In any case, this state of platinum differs from the metallic Pt\({}^{0}\) or oxide PtO states due to interaction with the support. Thus, samples calcined at 400 \({}^{\circ}\)C contain ionic forms of platinum on the surface. Calication of the catalysts at T = 600 and 800 \({}^{\circ}\)C leads to a formation of platinum species with E\({}_{\text{b}}\) (Pt4f\({}_{72}\)) = 70.6-70.9 eV (Figure 8).
# Protective Effect of Oyaksoongi-San against Asthma Induced by Ovalbumin in a Mouse Model
## 3 Results  
### UPLC-DAD-MS/MS Analysis of OYSGS  
Previous phytochemical studies showed that OYSGS contains various types of secondary metabolites, such as flavonoids, alkaloids, terpenoids, coumarins, and other phenolic compounds [22; 34; 35; 36; 37; 38; 39; 40; 41; 42]. By comparing with reference standards' retention times andmass spectra, thirty phytochemical constituents were identified. They were as follows: 18 flavonoids (catechin, epicatechin, rutin, and isoquerctrin/hyperoside from _Zizyphi Fructus_[34; 35], liquiritin apioside, liquiritin, ononin, liquiritingenin, naringenin, formonontein, and glabridin from _Glycyrrhizae Radix_[36], narirutin, hesperidin, didymin, hesperetin, nobilettin, and tangeretin from _Citri Unshius Pericarpium_[37; 38], and isosininesetin from _Aurantii Fructus Immaturus_[39]), three chalcones (isoliquiritin, licochalcone B, and isoliquiritingenin from _Glycyrrhizae Radix_[36]), two alkaloids (ephedrine from Ephedrae Herba [40] and norisoboldine from _Linderae Radix_[41]), three terpenoids (glycyrrhizae from _Glycyrrhizae Radix_[36], nomilin and obacunone from _Aurantii Fructus Immaturus_[22]), three coumarins (byakangelicin, begapten, and pabulenol from _Angelicae Dahurica Radix_[42]), and one phenylpropanol (chlorogenic acid from _Citri Unshius Pericarpium_[37]). Figure 1A presents the UV spectra at 280 nm and the base peak ion chromatograms of OYSGS in positive and negative ion modes, while extracted ion chromatograms for individual compounds are depicted in Figure 1B. The characteristics of the compounds identified in OYSGS, including the retention time (\(t_{R}\)), precursor ion (\(m/z\)), error (ppm), molecular formula, MS/MS fragments, and contents of each, are summarized in Table S1. Flavonoids, chalcones, and phenylpropanol were better detected in the negative ion mode, while alkaloids, terpenoids, and coumarins were better detected in the positive ion mode. Twenty-three of the phytochemicals were present in amounts ranging from 0.001 to 1.421 mg/g; the outliers were isoquercitrin (or hyperoside), formononetin, isoliquiritingenin, glycyrrhizin, bergapten, pabulenol, and chlorogenic acid.
## 6 Experimental Results
For \(Q=2\), \(R=3\) and \(J=16\), we have achieved significant improvement in classification accuracies for the majority of classification tasks, namely  
Figure 8: **(a)** Confidence interval plot (with 99% confidence) of conventional K-NN entropy features obtained from EEG signals (Butterworth high-pass filtered with cut-off frequency 0.1 Hz); **(b)** confidence interval plot (with 99% confidence) of TQWT-based multi-scale entropy (\(Q=2\); \(R=3\); \(J=8\)) for three classes (normal, seizure-free and seizure); **(c)** confidence interval plot (with 99% confidence) of TQWT-based multi-scale entropy (\(Q=2\); \(R=3\); \(J=8\)) for three classes (normal, seizure-free and seizure). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Filtering Type**} & \multirow{2}{*}{\
\begin{tabular}{c} **Parameters** \\ **(Q and R)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Experiment** \\ **Type** \\ \end{tabular} } & \multicolumn{4}{c}{**Number of Levels \(J=8\)**} & \multicolumn{4}{c}{**Number of Levels \(J=16\)**} \\ \cline{3-8}  & & & **Acc (\%)** & **Sens (\%)** & **Spec (\%)** & **Acc (\%)** & **Sens (\%)** & **Spec (\%)** \\ \hline \multirow{8}{*}{\(Q=1;\,R=3\)} & S-Z & **100** & 100 & 100 & **100** & 100 & 100 \\  & S-O & 99 & 99 & 99 & 99 & 99 & 99 \\  & S-N & 98.5 & 99 & 98 & 99 & 100 & 98 \\  & S-F & 95 & 96 & 94 & 97.5 & 97 & 98 \\  & S-FNZO & 98 & 96 & 98.5 & **99** & 96 & 99.8 \\ \cline{2-8}  & & & 95 & 98.25 & & 95 & 99.25 \\  & S-FN-ZO & 93.4 & 94 & 93.33 & 95.6 & 96.5 & 95 \\  & & & 92 & 98 & & 95 & 98.67 \\ \hline \multirow{8}{*}{\(Q=2;\,R=3\)} & S-Z & **100** & 100 & 100 & **100** & 100 & 100 \\  & S-O & 99 & 99 & 99 & 99.5 & 100 & 99 \\  & S-N & 98.5 & 100 & 97 & 99 & 99 & 99 \\  & S-F & 92.5 & 96 & 89 & 96 & 96 & 96 \\  & S-FNZO & 97 & 92 & 98.3 & 98.2 & 96 & 98.8 \\ \cline{2-8}  & & & 95 & 97.25 & & 97 & 98.5 \\  & S-FN-ZO & 93.4 & 91 & 95 & 95.8 & 95.5 & 96 \\  & & & 95 & 97.67 & & 95.5 & 99 \\ \hline \multirow{8}{*}{\(Q=3;\,R=3\)} & S-Z & **100** & 100 & 100 & **100** & 100 & 100 \\  & S-O & 96.5 & 95 & 98 & 99.5 & 99 & 100 \\  & S-N & 98 & 99 & 97 & 98 & 99 & 97 \\  & S-F & 92.5 & 96 & 89 & 96 & 98 & 94 \\  & S-FNZO & 97 & 93 & 98 & 97.8 & 97 & 98 \\ \cline{2-8}  & & & 93 & 97.25 & & 95 & 98.25 \\  & S-FN-ZO & 74.6 & 77.5 & 76.33 & 94.2 & 93.5 & 94.67 \\  & & & 62.5 & 85 & & 94.5 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the classification results for various tuning parameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scale No. **} & \multicolumn{3}{c}{**QEn\({}_{\text{LH}}\)**} & \multicolumn{3}{c}{**QEn\({}_{\text{HL}}\)**} \\ \cline{2-7}  & **Seizure** & **Seizure-Free** & **Normal** & **Seizure** & **Seizure-Free** & **Normal** \\ \hline
**1** & \(0.52\pm 1.89\) & \(-5.39\pm 1.90\) & \(-6.83\pm 0.95\) & \(-15.48\pm 2.63\) & \(-18.68\pm 1.89\) & \(-17.61\pm 1.71\) \\
**2** & \(0.77\pm 1.90\) & \(-5.25\pm 1.89\) & \(-6.29\pm 1.03\) & \(-13.74\pm 2.62\) & \(-17.87\pm 1.8
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 2 Measurements
### Accelerations  
Figure 2 shows the accelerations measured on the first five passes against train location. The train forward speed is assumed constant for each GPS sample and the position of the train has been calculated while using linear interpolation of that data. The amplitude of the accelerations is at different levels as the train moves along the rail. It can be also noticed that, with the exception of the second pass, the patterns are similar, but at different levels. For example, the four signals contain a peak with high amplitude at a distance of around 4000 m. However, Pass 1 has maximum amplitude at this location of 38.15 m/s\({}^{2}\), while Pass 3 has amplitude of 59.43 m/s\({}^{2}\), which is approximately 1.5 times that of Pass 1. Pass 2, on the other hand, has low levels of amplitude at most of the locations.  
The frequency spectra of the measured signals may provide insights into the data. Figure 3a shows the frequency content of Pass 3 while using a Fast Fourier Transform (FFT). It can be seen that there are two dominant frequency ranges in the signal; under 20 Hz and 60-90 Hz. As the signal is long, measured for about 6 km over a period of about 5 min., FFT has the significant disadvantage of not providing any spatial information. Therefore, a Short Time FFT (STFFT) is employed to show the frequency components of the signal in both the frequency and time (space) domains. The STFFT spectrum in Figure 3b shows that the ranges mentioned above are dominant for most locations as the train moves forward. It can be concluded that the signal components under 100 Hz contribute most of the energy of the signal.  
Figure 1: The measurement set up: (**a**) the instrumented train, and (**b**) the triaxial accelerometer.  
It has been stated in the literature [28; 29; 30; 31] that typical train eigenfrequencies are in the range of 0.5-15 Hz. This suggests that the first frequency range mentioned above is predominately related to vehicle dynamics. It has been shown in [32] that the dominant peaks in the second range are dependent on the vehicle forward velocity and rail surface irregularities. This means that the second frequency range corresponds to properties, such as rail roughness profile and sleeper spacing frequency, which is of most interest in this paper.
## 5 Simulation Method and Results
### The Performance Evaluation Results of the NN-Based Two-Step Classifier  
In the case of a one-step classifier, there were many identification errors between _Near Urban_ and _Hilly Suburban_, and to resolve the problem, we propose the two-step classifier. In the first step, one of the _Near Urban & Hilly Suburban_, _Mountainous_, _Sea with the Ground_ and _Sea_ environments is identified. Then, suppose the first classification result is _Near Urban & Hilly Suburban_, using the second classifier trained on _Near Urban_ and _Hilly Suburban_. In that case, it is finally classified into one of them. DNN and CNN can be used for steps 1 and 2, respectively, and the average classification accuracy according to them is shown in Table 6. In the case of the first step, the DNN is superior to CNN by 1.7%p, and in the case of the second step, the CNN showed better identification performance than the DNN by 4.3%p. So, the best combination is DNN + CNN, and the final identification accuracy after two steps is 95.5%, as shown in Figure 9. The reason why the accuracy of the two-step classification increases compared to the one-step classification in 5.2 is as follows. In general, classifier accuracy increases as the number of classification class candidates decreases, and the case of this paper shows the same tendency. According to Figure 7(d), when classifying into one of the five classes, 6% of _Hilly Suburban_ is incorrectly classified as _Mountainous_, and 3% of _Mountainous_ is incorrectly classified as near urban or _Hilly Suburban_. In the case of classifying the four candidates by integrating _Near Urban_ and _Hilly Suburban_ into one class, the errors are significantly reduced to 0% and 0.5%, respectively. Accuracy can be improved by classifying only the _Near Urban_ and _Hilly Suburban_ in step 2, so in the case of _Hilly Suburban_, which has the lowest success rate in one-step classification, an 8.0%p improvement in accuracy is obtained from 77.5% to 85.5%.  
Figure 8: Confusion matrixes of CNN-based one-step classification according to the input features.