esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) \(\pm\) standard deviation (SD) inside record was \(1.85\pm 1.82\) mmHg. It was \(4.37\pm 2.49\) mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE \(\pm\) SD from \(12.46\pm 6.56\) mmHg dropped to \(6.37\pm 4.90\) mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.  
right ventricular systolic blood pressure; heart sound signal; bidirectional LSTM (Bi-LSTM) network; corrective factors; detection of pulmonary hypertension
## 3 Experiments and Result Analysis
### Results and Analysis
#### 3.3.4 Comparison of Overall Processing Procedures  
The overall processing procedures and their performance evaluation are listed in Table 9. For classifiers of the traditional SVM, CNN and the proposed MSCDN, the data compression methods such as PCA, LLE, S-SAE and NC-SAE were used to obtain the compressed 9-dimensional features. Different from the above methods, the LSTM in [PERSON] et al. (2015) can directly perform the classification with the 36 \(\times\) 7 feature maps for a single pixel. Although the LSTM method avoids the feature compression procedure, the classification accuracy was poor. Whereas the combination of data compressor and trained classifiers can achieve remarkable crop classification performance. From Table 9, we can conclude that: (1) the combination of the proposed NC-SAE and MSCDN obtained the best performance; (2) with the expansion of the input size for CNN and MSCDN, the classification accuracy for these two classifiers has remarkably increased. However, it is worth noting that the phenomenon of over-fitting appears in NC-SAE + MSCDN for 15 \(\times\) 15 sample case as shown in Figure 9, so the classification accuracy will be somewhat inferior to its competitors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Sample Size**} & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \cline{2-4}  & **VA** & **OA** & **Kappa** \\ \hline \(15\times 15\) & 91.07 & 95.21 & 94.21 \\ \(35\times 35\) & **98.56** & **99.33** & **99.19** \\ \(55\times 55\) & 99.10 & 99.47 & 99.37 \\ \hline \hline \end{tabular} Note: The numbers using bold represent the best performance.  
\end{table}
Table 7: The classification performance of MSCDN with different sample size.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Sample Size**} & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \cline{2-4}  & **VA** & **OA** & **Kappa** \\ \hline \(15\times 15\) & 90.26 & 94.23 & 93.05 \\ \(35\times 35\) & 96.53 & 97.91 & 97.48 \\ \(55\times 55\) & 97.07 & 98.29 & 97.94 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The classification performance of CNN with different sample size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Input Sample Size**} & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \cline{2-5}  & **VA** & **OA** & **Kappa** \\ \hline \(15\times 15\) & 90.26 & 94.23 & 93.05 \\ \(35\times 35\) & 96.53 & 97.91 & 97.48 \\ \(55\times 55\) & 97.07 & 98.29 & 97.94 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification accuracy with different methods.
# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
###### Abstract  
In modern manufacturing, ensuring quality control for geometric features is critical, yet detecting anomalies in circular components remains underexplored. This study proposes a real-time defect detection framework for metal parts with holes, optimized for deployment on a Raspberry Pi 5 edge device. We fine-tuned and evaluated three deep learning models ResNet50, EfficientNet-B3, and MobileNetV3-Large on a grayscale image dataset (43,482 samples) containing various hole defects and imbalances. Through extensive data augmentation and class-weighting, the models achieved near-perfect binary classification of defective vs. non-defective parts. Notably, ResNet50 attained 99.98% accuracy (precision 0.9994, recall 1.0000), correctly identifying all defects with only one false alarm. MobileNetV3-Large and EfficientNet-B3 likewise exceeded 99.9% accuracy, with slightly more false positives, but offered advantages in model size or interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that each network focuses on meaningful geometric features (misaligned or irregular holes) when predicting defects, enhancing explainability. These results demonstrate that lightweight CNNs can reliably detect geometric deviations (e.g., mispositioned or missing holes) in real time. The proposed system significantly improves inline quality assurance by enabling timely, accurate, and interpretable defect detection on low-cost hardware, paving the way for smarter manufacturing inspection.  
real-time defect detection; industrial quality assurance; deep learning architectures; visual inspection systems 2025
## 2 Background and Motivation  
### Dental Health Care System  
Dental healthcare system installation is not without its difficulties, despite its many benefits. Due to the potential need for substantial investment in software, hardware, and employee training, initial expenditures may be unaffordable. Furthermore, strong security measures are required to protect sensitive patient data from breaches in order to ensure data privacy. Adoption by practitioners can sometimes be difficult, especially for more established practices that could find the switch to digital systems intimidating [6].  
Technical developments will influence dental healthcare systems in the future. Advanced diagnoses and individualized treatment plans will be made possible by AI and machine learning, while 3D printing will transform the production of braces and implants. Blockchain will guarantee safe and easy patient data sharing, while wearable technology will offer real-time oral health monitoring. In addition, to improve effectiveness, accessibility, and patient satisfaction, these systems combine technology, billing, analytics, medical services, and patient management. These developments offer a smoother and superior standard of dental care, which will benefit both patients and providers, despite obstacles including high upfront costs and data privacy issues [7].
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 4 Experiments and Analysis
### Analysis of the Results
#### 4.3.3. Using Similarity Matrix for Recall Prediction  
In Figure 2c,f,i, we show the ability to predict the performance of a query reformulation using the correlation between the similarity-based approach presented in Section 3.1. The values of the Similarity are spread and do not saturate to the maximum value of the sum of a row of \(S\) (in our experiments equal to 7). By increasing the number \(N\) of documents, we improve the capability to predict the performance of the query reformulation; in particular, there are no statistically significant negative correlation and the total number of negative correlations decreases from \(N=100\) to \(N=10{,}000\).  
Besides the qualitative aspects, Table 4 reports also the numerical performance comparison between the GAIN as proposed by [21], its version which employs the mean, and the similarity-based gain.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
###### Abstract  
Damage identification for liquid-solid coupling structures remains a challenging topic due to the influence of liquid and the limitation of experimental conditions. Therefore, the adding mass method for damage identification is employed in this study. Adding mass to structures is an effective method for damage identification, as it can increase not only the experimental data but also the sensitivity of experimental modes to local damage. First, the fundamental theory of the adding mass method for damage identification is introduced. After that, the method of equating the liquid to the attached mass is proposed by considering the liquid-solid coupling. Finally, the effectiveness and reliability of damage identification, based on adding mass for liquid-solid coupling structures, are verified through experiments of a submerged cantilever beam and liquid storage tank.  
structural health monitoring; damage identification; liquid-solid coupling; adding mass; sensitivity +
Footnote †: journal: Applied Sciences
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 3 System Introduction
### Framework of DI-SLAM  
The DI-SLAM method is an improvement on ORB-SLAM3, a visual-inertial SLAM framework. ORB-SLAM3 is well known for its robustness in feature point-based localization and supports monocular, stereo, and RGB-D sensors. We focus on refining the RGB-D system by fully utilizing depth information to achieve higher localization precision.  
ORB-SLAM3 is composed of four threads: tracking, local mapping, loop closing, and map merging. To enhance the system's adaptability to dynamic scenes, as shown in Figure 1, DI-SLAM introduces a parallel object detection thread based on Yolov5s. This thread is used to extract semantic information from RGB images and generate dynamic masks to assist the tracking thread in filtering static features. Additionally, during the tracking phase, the localization system combines multi-view geometry methods with a semantic mask to remove dynamic features from the current frame. Notably, our proposed multi-view geometry strategy and object detection module maintain architectural independence, ensuring system robustness even in cases of detection failure.
# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 5 Future Directions  
### Multi-Criteria Decision Making (MCDM)  
MCDM is a vital aspect of decision theory, offering ranking outcomes for alternatives by combining diverse criteria [42]. Uncertainty affects decision-making and appears in a number of different forms. As pointed out by [PERSON], uncertainty is an attribute of information [43]. In real-world MCDM problems, decision-making information often involves imprecision and ambiguity. Therefore, the criteria are not consistently expressed as real numbers and they are better suited for representation through Fuzzy sets [44], Intuitionistic Fuzzy sets [45], Pythagorean Fuzzy sets [46], and q-ROFS [47].  
Hospitals and healthcare buildings in general consist of an interesting case of study where energy consumption management and decision-making processes can reduce energy consumption. In [48], Smart Energy software achieved a reduction of 3.46% in energy consumption of a medium-sized University hospital in Brazil which equals to 100,000 kWh over a year.  
Hospitals' energy consumption has a significant environmental impact, since they are responsible for the 10.3% of the total consumed energy of the commercial buildings sector while they only cover 4.8% of the total area of the commercial buildings [49].  
Since the decision-making issue in electricity purchasing has multiple aspects, the authors' future work may focus on the decision-making process between different suppliers in competitive market. For example, let \(R_{1}\), \(R_{2}\), \(R_{3}\) be three competitor retailers and C\({}_{1}\), C\({}_{2}\), C\({}_{3}\) and C\({}_{4}\) the user defined criteria concerning costs, source of energy, CO\({}_{2}\) emissions and value-added services such as reliability of the supply respectively. Using q-ROFN of the form \(\alpha_{ij}=(\mu_{ij},\
u_{ij})\), with \(\upmu\) representing membership and \(\
u\) non-membership degrees of the \(\alpha\), the preference matrix may have the form of the Table 3:  
The preference information can then be aggregated using AO of q-Rung Orthopair Fuzzy Numbers (q-ROFN) and score values for each retailer can be calculated as presented in Table 4:  
The authors' future work will implement, among other intelligent techniques, a decision support system based on q-ROFNs for a healthcare facility. Healthcare facilities (Hospitals, district health centers, etc.) are a special category of commercial buildings due to their social importance and their environmental impact.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 2 Preliminaries
### Oversampling  
In binary classification problems, the imbalance of data is commonly seen, where there is a large gap between the number of positive samples and negative ones. Usually there are three ways to solve this problem: (1) threshold adjustment, which forces the classifier more inclined to the minority of classes; (2) undersampling, which is to reduce the number of the majority of samples to make the data more balanced; (3)oversampling, which is to increase the number of the minority of samples. Generally, the effect of oversampling and undersampling is better than that of the threshold adjustment, but sometimes there is a risk of overfitting, so we should use the appropriate regularization approaches together.  
Generally speaking, oversampling works better than undersampling. And random oversampling is a simple oversampling method in which the idea is to take some samples randomly from a few kinds of data and add them to the original one to generate more balanced training data [26].  
Synthetic Minority Oversampling Technique (SMOTE) [23] is an improved and prestigious sampling technique based on random oversampling. Because the strategy of random oversampling is to balance the data by simply copying minority samples randomly, it is very easy to lead to overfitting. The basic idea of SMOTE is to analyze the data of minority samples, synthesize new minority ones and add them to the original data, and balance the training ratio of positive and negative samples. The main algorithm flow of SMOTE is as follows (Algorithm 1).  
```
0: unbalanced data of positive and negative samples;
0:synthesized new minority samples;
1: Initializing minority samples \(x_{1},...,x_{n}\);
2: For \(i=1\) to \(N\):

The Euclidean distance between \(x_{i}\) and other samples is calculated, and the corresponding \(K\)-Nearest neighbor is obtained;
3: Determine the sampling rate R according to the proportion of positive and negative samples;
4: For \(i=1\) to \(N\):

According to the sampling ratio \(R\), several samples are randomly selected from the \(K\)-Nearest neighbors of \(x_{i}\), which are determined as \(o_{i}\);

Construct a new sample \(x_{new}=o_{i}+rand(0,1)\times(x_{i}-o_{i})\) according to the determined \(o_{i}\);
5:return\(X_{new}=\{x_{new1},x_{new2},\cdots,x_{newn}\}\);
```  
**Algorithm 1** SMOTE.
## 1 Introduction
The core novelty of this work is the synergistic integration of a first-arrival time constrained dynamic trace selection strategy with a Physics-Informed Recurrent Neural Network (PIRNN) framework, enabling the effective resolution of cycle skipping and a reduction in initial model dependency, all performed within a physically consistent deep learning architecture. Specifically, the aims of our research were as follows:  
1. Propose an initial velocity model-insensitive FWI method that effectively addresses the local minima caused by different initial velocity models. 2. Develop a PIRNN architecture with first-arrival time constraints by embedding the physical processes of seismic wave propagation into the recurrent neural network; through leveraging the spatiotemporal gradient information from forward propagation, this method enables back propagation under first-arrival constraints, thereby producing FWI solutions. 3. Introduce a knowledge-constrained neural network framework for FWI to avoid the instability issues of data-driven methods caused by the training data quality.
## 3 Results and Discussion
### Influence of the Rotating Directions of Cutting Head on the Working Performance  
#### 3.3.1 Cutting Force of Shearer Drum  
In order to find the rotation direction of the shearer drum which would be helpful to improve the cutting performance, the cutting force of the shearer drum under clockwise and counterclockwise rotation is obtained. Figure 13 shows the differences in cutting forces under different rotary directions of the cutting head without confining pressure. According to the figure, if the cutting head rotates anti-clockwise, the cutting forces in the \(x\), \(y\), and \(z\)-directions are significantly higher than that works by clockwise rotation. In addition, for the forces in the \(x\)-direction, little differences in cutting forces could be found between clockwise or anti-clockwise rotations. For the cutting force in the \(y\)-direction, a change in the rotation direction of the cutting head means that the direction of force on the cutting head changes to the opposite direction. As for the cutting forces in the \(z\)-direction, they  
Figure 12: Displacements of nodes on the rock mass.  
are positive, which means that the direction of they are along the \(z\)-direction. This force causes the end plate of the cutting head to press against the rock wall, while during the clockwise cutting of the cutting head, the load in this direction shows a positive and negative alternating pattern, with the end plate sometimes pressing against the rock wall and sometimes far away from the rock wall.  
Figure 13 shows the comparisons of cutting forces under clockwise and anticlockwise rotary cutting conditions without confining pressure. When there is confining pressure applied on the rock mass like that shown in Figure 3c, the comparisons of cutting forces under clockwise and anti-clockwise rotary cutting conditions are shown in Figure 14. Figure 14 shows the mean cutting forces and the comparisons of them under different confining pressures. According to the figure, it is clear that under different confining pressures, the mean cutting forces in the \(x\)-, \(y\)-, and \(z\)-directions in the anti-clockwise cutting conditions are generally smaller than those in the clockwise cutting conditions. In addition, for the cutting force in the \(x\)-direction, with the increasing confining pressure on the rock mass, the mean cutting forces increased as well; For the cutting force in the \(y\)-direction, there are some differences in the cutting forces between the clockwise and anti-clockwise cutting conditions. More specifically, if the cutting head rotates clockwise, the variations of cutting forces could be divided into two stages: first increase and then decrease with the increasing confining pressure; if the cutting head rotates anti-clockwise, the cutting forces were always decreasing with increasing of confining pressure.
## 6 Summary and Future Challenges
This was confirmed in a study quantifying measurement uncertainty, which reported a relative uncertainty of \(\pm\)3.05% at a 95% confidence interval based on apple juice analysis [145]. In contrast, the DPPH method exhibits the greatest variability among all the approaches, encompassing both methodological differences and units that do not accurately reflect the true antioxidant activity. Research indicates that any alteration in these elements can significantly raise uncertainty levels, with relative standard deviations reaching up to 11%, depending on the tested substance [146; 147; 148]. Nonetheless, the results of antioxidant potential assays are generally positively correlated [69; 149], supporting the rationale for using multiple assays to obtain a more reliable assessment. Among radical-based techniques, ABTS\({}^{\bullet+}\) demonstrates greater repeatability and chemical stability compared to DPPH, which is highly susceptible to environmental factors. Moreover, none of the current spectrophotometric assays fully replicate physiological conditions, as the reactive species and chemical reagents employed are not naturally occurring in vivo. Still, certain protocols--such as ORAC and FRAP--incorporate biologically meaningful elements like peroxyl radicals and iron ions. These are, however, constrained by non-physiological pH levels: 3.6 in FRAP and 10 in the Folin-Ciocalteu assay. In contrast, the CUPRAC method operates at pH 7.0, aligning more closely with human biological conditions and offering an advantage among SET-based techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Feature** & **Advantage** & **Limitation** & **Explanation** \\ \hline \multirow{5}{*}{FRAP} & pH & X & pH = 3.6 \\  & Wavelength & X & & \\ \cline{2-4}  & Biological molecules & X & & The method includes Fe ions, which naturally occur in the body \\ \cline{2-4}  & Substrates used in the method & & X & Various molar ratios between FRAP reagent and the sample \\ \hline \multirow{5}{*}{CUPRAC} & pH & X & & pH = 7 \\  & Wavelength & X & & similar to physiological pH \\ \cline{2-4}  & Biological molecules & & X & 450 nm \\ \cline{2-4}  & the method & & & Repeatability \\ \cline{2-4}  & Substrates used in the method & & X & There is a lack of biologically relevant molecules \\ \cline{2-4}  & Substrates used in the method & & & Repeatability of substrates and the molar ratio among reagents \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The advantages and disadvantages of antioxidant spectrophotometric assays. A further obstacle is the inconsistent reporting of antioxidant capacity, often using multiple units within a single assay framework. This inconsistency hampers data interpretation and cross-study comparisons. Currently, no standard method exists for evaluating the antioxidant potential of plant-based foods. There is an urgent need to establish a reference method to guide and unify future analytical approaches.