esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study demonstrated that hull and impurity content significantly influence the cold-pressing efficiency and oil yield of high-oleic sunflower seeds. The highest oil yield (76.52%) was obtained from seeds with 20% hulls and 5% impurities, while the lowest (39.24%) was from completely cleaned seeds, highlighting the positive role of hulls in enhancing oil drainage. Multiple linear regression models accurately predicted pressing efficiency, with R\({}^{2}\) values of up to 0.93, confirming strong correlations between seed composition and oil yield. Cluster analysis grouped samples based on hull and impurity content, pressing efficiency, and cake characteristics, revealing three distinct patterns of processing performance. These findings provide valuable insights for optimizing cold-pressing conditions, improving oil recovery, and enhancing the sustainability of sunflower oil production. The practical implications of this research include recommendations for oil producers to strategically adjust seed preparation methods to maximize efficiency. By fine-tuning hull and impurity levels, manufacturers can optimize oil yield without compromising product quality. Additionally, the findings could inform the development of industry guidelines for sunflower seed processing in cold-pressing applications. Future research should explore large-scale validation of these findings in industrial settings, assessing the scalability of hull and impurity optimization under different processing conditions.  
Conceptualization, R.R.; methodology, T.L., S.K. and R.R.; software, T.L.; validation, T.L., G.N., S.J., S.K. and R.R.; formal analysis, T.L., S.K. and R.R.; investigation, R.R.; resources, T.L., G.N., S.J., S.K. and R.R.; data curation, T.L. and R.R.; writing--original draft preparation, T.L. and G.N.; writing--review and editing, S.J., S.K. and R.R.; visualization, T.L. and R.R.; supervision, R.R.; project administration, R.R.; funding acquisition, G.N. and R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** This research was supported by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia, under contract numbers 451-03-136/2025-03/200134 and 451-03-137/2025-03/200134. Also, this work was created as a collaboration supported by COST (European Cooperation in Science and Technology) under COST Action 20133: Cross Border Transfer and Development of Sustainable Resource Recovery Strategies Towards Zero Waste (FULLRECO4 US), [https://fullreco4 us.eu/](https://fullreco4 us.eu/) (accessed on 15 February 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
## 4 Experiment and Performance Analysis
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**AGNews**} & \multicolumn{2}{c}{**R52**} & \multicolumn{2}{c}{**MR**} & \multirow{2}{*}{**Mean Value**} \\ \cline{2-2} \cline{6-7}  & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Mean Value**} \\ \hline TF-IDF + LR & 87.20 & 0.32 & 88.77 & 0.76 & 74.34 & 0.90 & 83.50 \\ CNN & 85.88 & 0.28 & 90.81 & 0.17 & 73.84 & 0.30 & 83.51 \\ BiLSTM & 81.91 & 1.42 & 86.12 & 0.68 & 71.76 & 1.41 & 79.93 \\ fastText & 76.62 & 1.74 & 90.34 & 0.17 & 71.76 & 5.86 & 79.57 \\ TextGCN & 79.15 & 7.62 & 90.94 & 0.28 & 76.54 & 0.16 & 82.21 \\ TensorFlowGCN & 86.94 & 0.82 & 94.81 & 0.29 & 77.84 & 0.41 & 86.53 \\ SHGCN & 87.63 & 0.97 & 92.76 & 0.69 & 83.81 & 2.90 & 88.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of recall under different models. obviously lower than that of SHGCN, which indicates that mining the relationship between documents and words is effective for mining the features of short text.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.6 Generalization across Modalities  
DDPM's ability to generalize across different imaging modalities may be limited. Variations in data distribution and imaging characteristics between modalities can impact the model's adaptability [79; 83].
# Analysis of Research Hotspots and Trends in Confined-Space Work Safety Based on CiteSpace
## 3 Results  
### Analysis of Amounts of Literature Published  
The annual volume of the published literature is a key indicator of the prominence and activity within a research field [31]. CiteSpace was used to compile statistics on the annual and cumulative amounts of domestic and international literature published on confined-space work safety. The results are illustrated in Figures 1 and 2.  
As shown in Figure 1, research on confined-space work in China has been published since 1985, and it can be categorized into three distinct stages: the initial stage (1985-2005), the slow development stage (2006-2008), and the rapid development stage (2009-2023). The proportions of the literature published in these three stages are 5.49%, 4.12%, and 90.39%, respectively. The majority of research occurred after 2009, coinciding with the increased incidence of confined-space work accidents in China, prompting the country to focus more on work safety in these environments. In 2009, the State Administration of Work Safety issued the Notice on Further Strengthening the Confined Space Work Safety of Industrial, Commercial, and Trading Enterprises. Additionally, since 2009, Beijing has issued several regulations, including integrating underground confined-space work supervisors into the management of special operators and implementing local standards, such as the Technical Code for Underground Confined Space Work Safety.  
Figure 1: Statistics on the number of Chinese publications.  
Figure 2: Statistics on the number of English-language publications.  
Since 1966, research on confined-space work has been published internationally, predating Chinese publications by 11 years. As a whole, it can be divided into three stages: the budding stage (1966-1985), the slow growth period (1986-2005), and the research explosion period (2006-2023). During 1966-1985, the published literature was extremely scarce, with a total of five articles in 19 years. At this stage, the literature briefly described the hazards in confined spaces, indicating that people were just beginning to pay attention to the hazards in confined spaces. From 1986 to 2005, the number of documents increased gradually, with a total of 41 publications in 19 years. During this period, the contents of the literature were gradually enriched, including ventilation prevention, statistical analysis of accidents, and safety management methods. From 2006 to 2023, there were 102 publications in 17 years, and the contents of the documents were more diversified, including systematic safety management methods, statistical analysis of accidents, automatic gas detection methods, automatic operation methods, etc. Especially in recent years, with the rapid development of computers, the Internet of Things, robots, and other technologies, researchers have increasingly applied Internet of Things technology and robot technology in remote gas monitoring.
## 4 Experiments
### Object Classification  
Through the clustering method mentioned in Section 3.2, these foreground objects were classified into three categories: pumping unit, person, and vehicle. In total, 1200 images were randomly selected as the image dataset to train and verify the performance of the classification network, which included 500 images of the pumping unit, 500 person images, and 200 vehicle images. In the monitoring video, there were a large number of foreground objects and a small number of typical targets, which means that each category of targets appeared repeatedly. 30 percent of images in the image dataset were randomly selected as the training set, and the remaining 70% as the testing set. The training process of the classification network is shown in Figure 8. The model tends to be convergent after 50 training iterations. The trained model can achieve high accuracy and low loss.  
The classification network obtained by retraining GoogLeNet through the fine-tuned method was used for moving-object detection in the pumping-unit monitoring scene. Figure 9 shows the classifications of moving objects in the scene identified by the classification network. After moving objects are recognized and classified, the pumping unit is not regarded as an abnormal object, while persons and vehicles were output as abnormal objects. If there is no moving pumping unit in the  
Figure 8: Training process of GoogLeNet. (**a**) Accuracy and (**b**) loss curves.  
Figure 7: Foreground detection in light condition changes cases. Screenshots and corresponding foreground detection results are illustrated from the first to the second rows, respectively. Numbers in the third row are time.  
detected foreground objects, it means that the pumping unit has stopped working, and an abnormal alarm should be given.  
To evaluate the proposed method, a histogram of oriented gradient (HOG) features and a multiclass support vector machine (SVM) classifier were used for comparative experiments. SVM is a classical classification method, while HOG features are a feature descriptor that is used for object detection in computer vision and image processing. It forms the features by calculation and statistics of the HOG in local areas of the image. HOG features combined with SVM classifiers have been widely used in image recognition [31]. The confusion matrices of the retrained net and SVM are presented in Figures 10 and 11, respectively. The experiment classification results of the three classes are listed in Table 2. To assure confidence in the experimental results, the experiment process was repeated 10 times. The average values of each metric are reported. The overall accuracy of the proposed method was 0.9988, while of the SVM was 0.9500. In the application of pumping-unit monitoring, the performance of the proposed method was obviously better than that ofa the classical SVM with HOG features.  
Figure 10: Confusion matrix of retrained GoogLeNet.  
Figure 9: Classification of moving objects by retrained GoogLeNet. (**a**) Input images; (**b**) foreground; (**c**) classification; (**d**) anomaly objects.
# Image Interpolation Based on Spiking Neural Network Model
## 6 Conclusions  
In recent years, many techniques in which the edges of an image are used to increase the success of interpolation have been developed. However, current techniques use approaches such as gradients or wavelets that calculate the gray-level difference between pixels linearly. Additionally, it is observed that previously developed techniques include very similar steps, and the greatest differences are in the edge detection processes. On the other hand, it is known that the edge detection success of SNNs simulating the HVS is quite high. In this study, a new SNN model is proposed to be used in the interpolation  
Figure 8: Examples of interpolation results. (**a**) Original images; (**b**) GEI interpolation results; (**c**) the results of the proposed method.  
Figure 9: Average run-time of interpolation techniques (second).  
process. Whereas 1D cubic interpolation is applied on the edge pixels determined using the proposed SNN model, standard bicubic interpolation is applied to the others. The success of the proposed method is compared to other edge-based interpolation methods using the PSNR and SSIM metrics. The results that are obtained show that the SNN-based method, which is used for the first time in interpolation studies, is quite successful. Additionally, the fact that the running time of the proposed method is not very long compared to other methods shows that it can be used in different image processing studies where the detection of edge directions is important. In the future, the proposed SNN model is planned to be used in other image-processing fields based on edge detection.  
This research received no external funding.
# A ResNet-Based Audio-Visual Fusion Model for Piano Skill Evaluation
###### Abstract
Although there have been some studies on multimodality, there are limitations in extracting complex spatio-temporal and time-frequency features, and the feature information from both modalities is not fully utilized. Therefore, we consider ResNet as the backbone network for the learning model, as it can extend the network to a deeper depth through the structures of residual connections to better extract complex features. ResNet-3D [4] and ResNet-2D [5] are used to extract the video features and audio features, respectively. Specifically, ResNet-3D is able to model both the spatial and temporal relationships between video frames, enabling it to better capture motion and dynamic changes in videos and extract spatio-temporal features. Furthermore, for audio data that have been converted into a time-frequency spectrogram, ResNet-2D can effectively extract time-frequency features and capture the frequency and time information in the audio. To fully utilize both the video and audio features, we employ ResNet18-3D and ResNet18-2D to address the issue of feature extraction, ensuring uniformity in the number of video and audio features. The main contributions of this paper are as follows:  
(1) We present a novel ResNet-based audio-visual fusion model for evaluating piano players' proficiencies by effectively utilizing both video and audio information. This approach addresses the problem where the unimodal approaches fail to utilize video information and the multimodal approaches fail to fully utilize both video and audio information. Firstly, we extract video and audio features using ResNet-3D and ResNet-2D, respectively. Then, the extracted features are fused to generate multimodal features that will be used for piano skills evaluation. (2) We propose an effective method that fully utilizes information from both the visual and aural modalities to improve the accuracy and comprehensiveness of piano skills assessment. By incorporating visual and aural information, we can obtain richer and more comprehensive features, enabling a more precise assessment of the skill levels of the performers. Moreover, we maintain a balance between the number of visual and aural features, thereby ensuring that the information from both modalities is fully utilized. (3) We conduct experiments on the PISA dataset, and the results show that our proposed ResNet-based audio-visual fusion model, with an accuracy of 70.80% and an average training time of 74.02 s, outperforms the state-of-the-art models in the evaluation of piano players' skill levels, as well as in computational efficiency.
# Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video
## 4 Experiment Results and Discussion
### Information of Datasets  
(1) UCSD Traffic Dataset: The dataset includes 254 segments of highway video data, with different statuses in different weather conditions, such as cloudy, sunny, rainy days and so on. Each video has a resolution of \(320\times 240\) pixels and \(42\) to \(52\) frames. We unify the resolution into a \(48\times 48\) standardized grayscale when we use the dataset. The standardized operation of the video clip includes subtractingaverage image and standardizing pixel intensity by the method of unit variance. This standardized operation has a better effect for reducing the impact of changes in illumination.  
The dataset is divided into three levels according to the degree of traffic congestion, which are heavy, medium and light. There are 44 video segments in heavy state, 45 video segments in medium state, and 165 video segments in light state. Some examples of the video segments are shown in Figure 3.  
(2) Capital Airport Expressway Dataset: The dataset is established by ourselves. We first select suitable videos from a large number of TSVs of Beijing Capital Airport Expressway. After the selection of record clear, situation mutative, all the required status contained videos, we convert videos from.ts format into.avi format. Then we choose videos from the same camera at different time and intercept each video segment into 5 s. Then we save these video segments as original data matrices by specific algorithm, forming our own dataset. After extracting the Grassmann manifold characteristics, the dataset is used in traffic status classification based on sparse representation and neural network. We select the videos of Capital Airport Expressway by filtering videos from 19 surveillance video cameras. The map of airport expressway in Beijing and the 19 cameras on the expressway are shown in Figure 4.  
After considering the clarity, the congestion level and other conditions of the video, we select TSVs in Wuyuanqiao North, which is number 6 on the map. Then we intercept and select the large number of TSVs of this camera, in order to form a relative and representative video database. Some examples are shown in Figure 5.  
For the selected TSVs, first, we screened a large number of surveillance videos provided by the Beijing Traffic Information Center. After selecting the surveillance videos that meet the requirements of clear recording, diversified conditions, and including all states, we chose videos from the same camera at different time and intercepted each video segment into 5 s. Then we saved these video segments into the original data matrix. Then we extract features in original data matrix and save these features as a new data matrix. There are five datasets in this part of the experiment, which are shown in Table 1.  
Figure 3: Examples of UCSD dataset.  
Figure 4: Map of Capital Airport Expressway in Beijing.  
Figure 5: Surveillance video of Capital Airport Expressway.
# Prediction of Vibration-Mode-Induced Noise of Structure-Acoustic Coupled Systems
## 4 Results and Discussion
### Limitations and Scope for Future Work  
The simulation results demonstrated that the acoustic mode can be predicted from the vibration mode of a structure. Notably, this research was conducted using a shell structure to verify the proposed predictive formula. Therefore, the proposed prediction method must be validated by applying it to actual structures. Moreover, to precisely predict the acoustic mode, the acoustic characteristics of the acoustic domain (natural frequency and damping ratio of the acoustic mode) must be considered. In this study, the acoustic characteristics were considered through an eigenvalue analysis of the acoustic domain (reverberation chamber). However, the determination of these values in practice may  
Figure 6: Comparison between acoustic mode from measured data and acoustic mode calculated by measured vibration data using theory. (**a**) Time history of modal pressure; comparison of time history decomposed data from measurement to analyzed data from FEM. (**b**) Modal pressure; a comparison of modal enlarged time history. (**c**) Spectrum; comparison of spectrum decomposed data from measurement to analyzed data from FEM.  
be challenging. Accordingly, future work must be focused on estimating the acoustic characteristics in actual environments. The impedance and reverberation time pertaining to the acoustic boundary are key factors, and the damping ratio for the acoustic mode considerably influences the prediction of the acoustic mode from the vibration mode. The impedance considerably influences the damping [43, 44]. Therefore, future work must identify a method to calculate the damping ratio from the impedance of the acoustic boundary and reverberation time, among other factors.
## 3 Outage Probability Performance Analysis
### Outage Probability of D2D User 1  
Considering outage probability of \(D_{1}\): According to PDMA protocol, the complementary events of outage at \(D_{1}\) can be explained as: \(D_{1}\) can detect \(x_{2}\) as well as its own message \(x_{1}\). From the above description, the outage probability of \(D_{1}\) is expressed as  
\[\begin{split}& OP_{D1-bi}=\underbrace{\Pr\left(\gamma_{SD1,k*}^{ PDMA}<\varepsilon_{1},\gamma_{SD1\gets 2,k*}^{PDMA}<\varepsilon_{1}\right)}_{B_{1}}\\ &+\underbrace{\Pr\left(\max\left\{\gamma_{SD1,k*}^{PDMA},\chi_{ D1}^{PDMA}\right\}<\varepsilon_{1},\gamma_{SD1\gets 2,k*}^{PDMA}>\varepsilon_{1} \right)}_{B_{2}},\end{split} \tag{11}\]  
**Proposition 1**.: _The closed-form expression of outage probability at \(D1\) is given by_  
\[\begin{split}& OP_{D1-bi}=\left(1-\sum\limits_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\theta_{1}\right)\left(1-\sum\limits_{k=1}^{K} \left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\theta_{2}\right)\\ &+\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\theta_{1}\right)\\ &\times\left(1-\frac{\rho\lambda_{k_{1}}}{\rho\lambda_{k_{1}}+\varepsilon_{1} \omega\rho\lambda_{f_{1}}}\exp\left(-\frac{\varepsilon_{1}}{\rho\lambda_{k_{1 }}}\right)\right)\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\theta_{2}\end{split} \tag{12}\]  
_where \(\varepsilon_{1}=2^{2R_{1}}\), \(R_{1}\) is target rate for signal \(x_{1}\)._  
Proof.: See Appendix A.