esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Future Research Directions  
A key area for future research is the optimizing the incorporation of a more diverse and pertinent set of variables or features. Such features may include the socio-economic data of the population, the availability of fundamental infrastructure, and environmental conditions surrounding residential areas. It is hypothesized that these variables have the potential to make a greater contribution in distinguishing the level of slum-ness in a more in-depth and comprehensive manner.  
Furthermore, the utilization of more sophisticated classification algorithms, such as XGBoost, Light-GBM, or ensemble stacking, could also be a viable option to explore, as they are adept at handling imbalanced data and high feature complexity with competitive performance. It is recommended that future research explore the application of data balancing methods, such as SMOTE or ADASYN, to address imbalances between classes [27]. For instance, this could help to mitigate the dominance of 'light slum' over'medium slum'.  
A further concern pertains to the paucity of data from the minority category, designated here as the'medium slum'. This deficiency may have ramifications for long-term prediction, specifically the potential alteration of its stability, should there be any modification in the future class distribution. Consequently, it is imperative that efforts are made to collect more balanced and representative data in future surveys. It is anticipated that these developments will enhance the reliability and precision of the classification model, thereby ensuring its efficacy in providing decision support for the dynamic conditions of residential areas in Bandung City.  
Despite the merging of SVI and RSI showing significant potential in slum classification, further research is needed to optimize this integration. This is particularly due to the lack of direct representation of more detailed slum criteria, such as safe access to drinking water, domestic waste management, waste management, and the availability of fire protection.
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 4 Conclusions  
This paper proposes a surface defect detection method based on the MobileNet-SSD network, and applies it to identify the types and locations of surface defects. In the pre-processing phase, a regional planning method was presented to cut out the main body of the defect, reduce redundant parameters and improve detection speed and accuracy. Meanwhile, the robustness of the algorithm was elevated by data enhancement. The philosophy of MobileNet, a lightweight network, was introduced to enhance the detection accuracy, reduce the computing load and shorten the training time of this algorithm. The MobileNet and SSD were adjusted to detect the surface defects, such that the proposed method could differentiate small defects from the background. The feasibility of the proposed method was verified by defect detection for the sealing surface of an oil chili filling production line in Guizhou, China. Specifically, an image acquisition device was established for the sealing surface and the deep learning framework was adopted to mark the defect positions. The results show that the proposed method can identify most defects in the production environment at high speed with accuracy. However, the system also has its limitations. Deep learning models have a certain dependence on the hardware platform because of computationally intensive processes, and they are not suitable for embedded systems with general performance. Future research will further improve the proposed method through integration with embedded chips and the Internet of Things, balancing the classification accuracy and number of parameters of the detection method, and expand the application scope of our method to complex defects in industrial processes.  
Project administration, Y.L.; validation, Y.L.; resources, H.H. and Q.X.; investigation, L.Y. and Q.C.  
This study was supported by the Major Project of Science and Technology in Guizhou Province (No. [2017]3004) and Natural Science Foundation in Guizhou Province (No. [2015]2043).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Discussion  
### Public Attitude toward FRT  
In general, the public supports using FRT to manage the red-light running behavior of e-bikers. To understand which public characteristics are related to the attitudes toward FRT, we analyzed the correlation between the four individual characteristics and the three variables using the method of the Mann-Whitney U test, Kruskal-Wallis test, and MCA.  
The results of the Kruskal-Wallis test and MCA indicate that members of the public with higher education levels are more resistant to the red-light running behavior of e-bikers (\(\chi^{2}(3)\) = 114.730, \(p\) < 0.001; Figure 4a). This finding is consistent with [PERSON] et al. [53]. Under-educated e-bikers lack safety knowledge [53], and people with higher education backgrounds comprehend more traffic safety knowledge [39; 45]. Members of the public with higher education levels are supportive towards the application effect of FRT (\(\chi^{2}(3)\) = 103.534, \(p\) < 0.001; Figure 4b), and they also show the trust of privacy protection (\(\chi^{2}(3)\) = 90.292, p < 0.001; Figure 4c). Because of more safety knowledge, people with higher education pay more attention to red-light running behavior and highly support FRT, perhaps due to their greater acceptance of new technologies. Moreover, their acceptance of FRT affects the trust of privacy protection.  
Regarding whether or not holding a driving license affects the public's attitudes toward red-light running behavior and FRT (U = 998.000, \(p\) < 0.001; Figure 4a), people with driving licenses appeared to be more resistant to red-light running behavior. This is because e-bikers with driving licenses have lower perceived behavioral control and higher moral norm than those without driving licenses [23]. Moreover, e-bikers with driving licenses are also more supportive of the use of FRT (U = 2865.5, \(p\) < 0.001; Figure 4b). The strong correlation between the attitudes toward red-light running behavior and the application effect of FRT may indicate that people with driving licenses are more supportive of FRT.
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
###### Abstract  
Deep learning models have been widely used in prediction problems in various scenarios and have shown excellent prediction effects. As a deep learning model, the long short-term memory neural network (LSTM) is potent in predicting time series data. However, with the advancement of technology, data collection has become more accessible, and multivariate time series data have emerged. Multivariate time series data are often characterized by a large amount of data, tight timeline, and many related sequences. Especially in real data sets, the change rules of many sequences will be affected by the changes of other sequences. The interacting factors data, mutation information, and other issues seriously impact the prediction accuracy of deep learning models when predicting this type of data. On the other hand, we can also extract the mutual influence information between different sequences and simultaneously use the extracted information as part of the model input to make the prediction results more accurate. Therefore, we propose an ATT-LSTM model. The network applies the attention mechanism (attention) to the LSTM to filter the mutual influence information in the data when predicting the multivariate time series data, which makes up for the poor ability of the network to process data. Weaknesses have greatly improved the accuracy of the network in predicting multivariate time series data. To evaluate the model's accuracy, we compare the ATT-LSTM model with the other six models on two real multivariate time series data sets based on two evaluation indicators: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The experimental results show that the model has an excellent performance improvement compared with the other six models, proving the model's effectiveness in predicting multivariate time series data.  
multivariate time series data prediction; attention mechanism; LSTM +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 Fundamentals of RF Energy Harvesting
### Components of an RF Energy-Harvesting System
#### 3.2.4 Energy Storage Unit  
The energy storage unit plays a crucial role in RFEH systems by ensuring that there is a stable and continuous power supply to electronic devices. Since ambient RF energy is highly  
Figure 5: Rectenna [38].  
Figure 6: Rectifier topologies. (**a**) Single diode (single stage). (**b**) Voltage doubler. (**c**) Voltage multiplier (multi-stage) [35].  
intermittent and varies in intensity depending on the surrounding environment, an efficient storage mechanism is needed to accumulate and regulate the harvested energy before it can be utilized. The two primary storage solutions in RF energy-harvesting applications are supercapacitors and rechargeable batteries, each with distinct characteristics suited for different use cases. Supercapacitors are widely used for their ability to charge and discharge rapidly, making them ideal for applications requiring frequent energy bursts. They have a longer lifespan compared to conventional batteries, as they can withstand millions of charge-discharge cycles without significant degradation [41]. However, supercapacitors have lower energy density, meaning they store less energy per unit volume compared to batteries. This makes them more suitable for low-power, short-duration applications such as wireless sensor nodes, IoT devices, and energy buffering in transient-powered systems [42; 43]. Rechargeable batteries, such as lithium-ion and lithium-polymer batteries, offer significantly higher energy density, allowing them to store more energy for prolonged operation. This makes them ideal for applications where continuous power is required, such as medical implants, remote monitoring systems, and wearable electronics. However, batteries have longer charging times and a limited number of charge-discharge cycles, which can impact their long-term sustainability. Advances in battery technology, including solid-state batteries and nanomaterial-based electrodes have improved their efficiency, making them a viable option for RF energy-harvesting applications that demand extended operational periods [42; 43].  
A combination of supercapacitors and batteries is often used in hybrid energy storage systems to balance short-term energy fluctuations and long-term energy supply [44]. Supercapacitors can handle sudden power surges and provide immediate energy when needed, while batteries ensure sustained operation over extended durations [2]. Table 4 provides a clearer understanding of their roles in RF energy-harvesting systems and also helps determine the most appropriate storage solution based on specific application requirements.
# Preclinical Evaluation of the Anticoagulant Effect of an Aqueous Extract of Snow Mountain Garlic
## 3 Results
### The Area Under the Curve (AUC) of the Anticagulant Effect In Vivo of the LAE S. mountain Garlic  
In the AUC, a significant increase (\(p<0.05\)) was observed compared to the control, with doses of 500 and 1000 mg/kg/d and with the dose of 100 mg/kg ASA, showing an anticoagulant effect superior to that of ASA (see Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Period** & **K-W H** & **K-W \(p\)-Value 1 ** & **Treatment** & **Dunn’s Test 1 ** & **Median** & **IQR** \\ \hline Time 0 & 10.897 & 0.0535 & Control & – & 213.0 & 46.5 \\  & & & ASA & 1.0000 & 208.5 & 57.5 \\  & & & 250 & 0.0142 \(\ast\) & 306.5 & 26.5 \\  & & & 500 & 0.2110 & 259.0 & 51.8 \\  & & & 1000 & 0.8610 & 198.5 & 49.2 \\  & & & 2000 & 0.9402 & 199.0 & 102.8 \\
30 min & 8.166 & 0.1473 & Control & – & 213.0 & 46.5 \\  & & & ASA & 0.0124 \(\ast\) & 296.5 & 52.2 \\  & & & 250 & 0.0939 & 275.0 & 25.0 \\  & & & 500 & 0.0403 \(\ast\) & 289.0 & 98.8 \\  & & & 1000 & 0.1041 & 281.5 & 54.0 \\  & & & 2000 & 0.0187 \(\ast\) & 310.0 & 74.0 \\
1 Week & 9.810 & 0.0808 & Control & – & 231.5 & 22.2 \\  & & & ASA & 0.0512 & 325.0 & 176.5 \\  & & & 250 & 0.0214 \(\ast\) & 348.5 & 118.5 \\  & & & 500 & 0.0093 \(\ast\) & 394.0 & 196.0 \\  & & & 1000 & 0.0069 \(\ast\) & 371.0 & 45.5 \\  & & & 2000 & 0.0643 & 313.5 & 13.2 \\
2 Weeks & 13.223 & 0.0214 \(\ast\) & Control & – & 209.0 & 25.5 \\  & & & ASA & 0.0315 \(\ast\) & 427.5 & 91.2 \\  & & & 250 & 0.0229 \(\ast\) & 374.0 & 40.0 \\  & & & 500 & 0.0027 \(\ast\) & 439.5 & 164.0 \\  & & & 1000 & 0.3295 & 275.0 & 51.8 \\  & & & 2000 & 0.5484 & 245.0 & 20.8 \\
4 Weeks & 15.971 & 0.0069 \(\ast\) & Control & – & 228.0 & 28.2 \\  & & & ASA & 0.5822 & 266.5 & 66.2 \\  & & & 250 & 0.0008 \(\ast\) & 548.0 & 106.5 \\  & & & 500 & 0.0175 \(\ast\) & 450.0 & 103.8 \\  & & & 1000 & 0.0187 \(\ast\) & 444.0 & 32.2 \\  & & & 2000 & 0.2021 & 330.0 & 193.0 \\
8 Weeks & 17.235 & 0.0041 \(\ast\) & Control & – & 195.0 & 106.8 \\  & & & ASA & 0.0023 \(\ast\) & 386.5 & 58.0 \\  & & & 250 & 0.1210 & 290.5 & 43.8 \\  & & & 500 & 0.9601 & 201.0 & 45.2 \\  & & & 1000 & 0.0107 \(\ast\) & 375.0 & 112.5 \\  & & & 2000 & 0.2452 & 347.5 & 106.2 \\
12 Weeks & 16.749 & 0.0050 \(\ast\) & Control & – & 340.0 & 36.2 \\  & & & ASA & 0.2018 & 422.5 & 153.8 \\  & & & 250 & 0.4528 & 300.0 & 81.0 \\  & & & 500 & 0.0936 & 515.0 & 41.0 \\  & & & 1000 & 0.1398 & 552.0 & 208.8 \\  & & & 2000 & 0.1537 & 233.0 & 27.2 \\ \hline \end{tabular}
\end{table}
Table 1: Kruskal-Wallis and Dunn’s Test for different treatment periods vs. control.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 2 Materials and Methods
### Adsorption of Metal Ions  
The adsorption performance of gelatin monolithts in relation to Pb(II), Cu(II) and Cd(II) was studied in batch mode. The experiments were carried out in 100 mL glass conical flask at ambient temperature. The gelatin monolithts were added to the sorbate solutions (20 mL) and shaken at 90 rpm. The concentration of metal was changing in the range 10 ppm to 100 ppm. The samples of aqueous solution (0.5 mL) were taken after the desired time (the contact time was varied from 3 to 60 h), diluted 10 times and centrifuged. The concentrations of metals ions were determined by atomic absorption spectroscopy (Varian AAS800). The measurements were carried out at 228.8 nm, 324.7 nm and 217.0 nm for cadmium(II), copper(II) and lead(II) ions, respectively. The initial pH of the sorbate solution was adjusted using 0.1 N HNO\({}_{3}\) solution. The pH of the solution was measured at the beginning and the end of each experiment using a pH meter (Thermo Scientific Orion Versa Star Pro 10, USA). The model solutions of copper(II), lead(II) and cadmium(II) were prepared from reagent grade salts received from POCh (Poland). Distilled water was used in all cases. The adsorption experiments were performed at a constant temperature (23.0 \(\pm\) 1.0 \({}^{\circ}\)C).  
The amount of adsorbed metal ions (_q_), expressed in milligrams per gram (mg/g), was calculated according to the formula:  
\[q=\frac{C_{0}-C_{t}}{m}\cdot V\]  
where:  
\(C_{0}\)-initial metal concentration, mg/L;  
\(C_{t}\)-metal concentration after time t, mg/L;  
\(m\)-mass of adsorbent, g;  
\(V\)-volume, dm\({}^{3}\).
## 7 Topology Control  
Wireless sensor networks are the group of distributed sensor nodes which communicate various information for monitoring and control purpose. Sometimes placing WSN nodes in unsecured and hostile environments is required. Due to limited battery life and restricted processing and storage capabilities of WSN nodes, security against above mentioned attacks is the biggest challenge. WSN nodes are mandatory part of smart grid communication infrastructure. Apart from cryptographic approach, WSN can be designed in such a way that their topologies have specific connectivity properties [89]. Topology control can be a practical solution for WSN nodes with limited computational and communication capabilities [90; 91; 92; 93]. An overview of various topology control schemes is described below.
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 4 Experimental Results and Discussion
### Datasets Preparation  
**CamCAN:**[36] The Cambridge Centre for Ageing and Neuroscience dataset uses epidemiological, cognitive, and neuroimaging data to understand how individuals can best maintain cognitive abilities as they age. It includes 642 healthy subjects, ranging in age from 18 to 88 years old, including 314 men and 328 women.  
**NKI:**[37] The dataset comes from the Nathan Kline Institute database and includes 335 healthy subjects aged 6 to 83 years old, including 124 males and 211 females.  
To ensure the validity of the experimental data, the original resting-state functional magnetic resonance imaging (rs-fMRI) data of the Cam-CAN and NKI datasets were preprocessed using the Data Processing and Analysis of Brain Imaging (DPABI) toolbox, including conversion from the DICOM to the NIFTI format, removal of the first ten unstable times, slice time correction, registration, standardization to MNI standard space, and denoising. Then, based on the Automatic Anatomical Labeling (AAL) atlas, which divides the human brain into 116 regions, the time series data (TS) of these 116 brain regions were extracted, and the Pearson correlation coefficient between the two brain regions was calculated to obtain the FC. The processing procedure for obtaining the FC and TS information from brain fMRI data is shown in Figure 2.  
The subsequent data preprocessing steps are as follows:  
1. **Spatial alignment**: The image is spatially aligned based on the middle layer to eliminate the influence of factors such as head shaking on the data.  
2. **Layer time correction**: During the data acquisition process, multiple layers of images are acquired at one-time point, and time correction is required to eliminate the acquisition time differences of each layer.
3. **Spatial normalization**: The fMRI data of all subjects are aligned to a standard template to extract the BOLD signal at the same position on the brain of different head shapes and volumes.
4. **Spatial smoothing**: The fMRI data are smoothed after spatial normalization to reduce noise interference.
5. **Removal of covariates**: Linear drift signals are removed through a multivariate linear regression model. These signals are interference factors caused by non-neural activities.
6. **Bandpass filtering**: A low-pass filter is used to more clearly filter and reflect the functional activity of neurons.
## 4 Numerical Simulations and Results
### Results and Discussion
#### 4.2.3 Tower Base Shear Forces and Bending Moments  
In order to assess the effect of wave-current interaction on the mooring tension of the CSS FOWT, the mean values and standard deviation values of the tower base shear forces and bending moments in the X and Y directions are displayed in Figures 16 and 17, respectively. As shown in Figure 16, when the waves and currents travel in the X direction, there is only a small change in the results, and the outcome will diminish as the amplitude of the wave increases. In Figure 17, when the waves and currents travel in the Y direction, the coupling effect of waves and currents exerts a limited influence on the tower base forces.  
Figure 18 shows the PSD of the shear force and bending moment for case 8 in the X direction. It can be seen that the wave-current interaction amplifies the value of the shear force in the frequency range of 0.05 to 0.08 Hz and 0.08 to 0.11 Hz, and intensifies the value of the bending moment in the frequency range of 0.04 to 0.07 Hz and 0.08 to 0.12 Hz.  
Figure 15: Dynamic response of the mooring tension of the CSS FOWT for case 8 in the Y direction.  
Figure 16: The mean and standard deviation values of the tower base shear force and bending moment in the X direction.  
Figure 17: _Cont._  
Figure 17: The mean and standard deviation values of the tower base shear force and bending moment in the Y direction.  
Figure 18: PSD of the tower base forces for case 8 in the X direction.  
Figure 19 depicts the PSD of the shear force and bending moment for case 8 in the Y direction. It is evident that the wave-current interaction augments the value of the shear force in the frequency range of 0.05 to 0.07 Hz and 0.08 to 0.11 Hz and magnifies the value of the bending moment in the frequency range of 0.04 to 0.07 Hz and 0.08 to 0.11 Hz.
# A Benchmark for the Evaluation of Corner Detectors
## 3 Benchmark Evaluation Analysis
### Objective Evaluation
A comparison of all the evaluated corner detectors with respect to the running time is presented in Figure 5. Except for Superpoint, which was coded and tested with an NVIDIA Titan Xp GPU, and ASJ, which was coded and tested in C, our experiments were implemented in MATLAB on a machine equipped with an Intel Core i7 16 GB RAM @ 2.3 GHz. In Figure 5, the CPU time of each method is plotted against its F score, with both averaged over the Urban-Corner dataset. Note that a desirable corner detector should achieve a high F score with a short running time. In comparison, Superpoint [6] consumed the least running time, while DMC achieved the best F score. As a result, it is suggested that Superpoint [6] can be used in real-time applications, such as target tracking and positioning [12,17]. On the other hand, DMC [33] and New-Curvature [27] can be applied in those tasks requiring high detection accuracy, such as image matching and recognition [12,16,31]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Detector**} & \multicolumn{4}{c}{**RGT**} & \multicolumn{4}{c}{**Le (pixels)**} \\ \cline{2-7}  & **Dataset 1** & **Dataset 2** & **Dataset 3** & **Dataset 1** & **Dataset 2** & **Dataset 3** \\  & **[4,38]** & **[38–40]** & **(Urban-Corner)** & **[4,38]** & **[38–40]** & **(Urban-Corner)** \\ \hline SUSAN [2] & 0.0358 & 0.1073 & 0.0363 & 1.0143 & 1.0068 & 1.5159 \\ Harris-Laplace [3] & 0.1199 & 0.2514 & 0.1895 & 0.9407 & **0.9557** & 0.8489 \\ MSCP [19] & 0.7594 & 0.7320 & 0.5175 & 1.0988 & 1.2074 & 1.0168 \\ CPDA [22] & 0.8099 & 0.7739 & 0.6101 & 1.0162 & 1.2186 & 1.0368 \\ He \& Yung [21] & 0.7842 & 0.6831 & 0.3469 & 1.0674 & 1.3225 & 1.2750 \\ Fast-CPDA [37] & **0.8201** & **0.7881** & 0.6056 & 1.0097 & 1.1953 & 1.0506 \\ DOG [35] & 0.7770 & 0.7258 & 0.5239 & 1.0558 & 1.1897 & 1.0274 \\ GCM [23] & 0.7825 & 0.6762 & 0.4923 & 1.0754 & 1.2373 & 1.0942 \\ ANDD [25] & 0.6726 & 0.4990 & 0.2478 & **0.9105** & 1.2834 & 1.2466 \\ WEAE [26] & 0.7882 & 0.7333 & 0.5305 & 1.0227 & 1.2235 & 1.0500 \\ ASJ [5] & 0.2553 & 0.4874 & 0.4439 & 0.9509 & 1.2500 & 1.5081 \\ Superpoint [6] & 0.3215 & 0.2085 & 0.4439 & 1.4401 & 1.7831 & 1.5081 \\ New-Curvature [27] & 0.7494 & 0.7558 & 0.6062 & 1.1526 & 1.3047 & 1.1493 \\ SOGGDD [7] & 0.2859 & 0.6526 & 0.3866 & 1.3778 & 1.0890 & 1.1202 \\ MSRJ [28] & 0.8539 & 0.7686 & 0.5315 & 1.0314 & 1.1853 & 1.0148 \\ Zhang \& Sun [8] & 0.2783 & 0.2860 & 0.4877 & 1.2642 & 1.3675 & 1.0825 \\ RPD [30] & 0.6606 & 0.6141 & 0.5244 & 1.2496 & 1.0800 & **0.8257** \\ DMC [33] & 0.7726 & 0.7853 & **0.6309** & **0.9105** & 1.4291 & 1.2468 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different corner detectors on three benchmark datasets. Figure 4: Evaluation results with degraded images. Figure 5: Comprehensive performance in terms of average running times and F scores of different algorithms on Urban_corner dataset.