esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Water Quality Changes during GDM Filtration  
Table 2 represents the characteristics of dissolved organic matter (DOM) fractions in feed and permeate water during GDM filtration at operation cycles 16, 62, 73, and 81 for PES-UF, and cycles 31, 77, 88, and 96 for PVDF-UF and PTFE-MF. The DOM fractions could be quantified as biopolymer (>10 kDa), humic acids and building blocks (0.5-1 kDa), low molecular weight (LMW) acids (<0.5 kDa), and LMW neutrals (<0.5 kDa), based on the size exclusion chromatography-organic carbon detector (SEC-OCD) fractionation scheme [14; 29; 39]. As shown in Table 2, there were no significant changes of small molecules (<1 kDa), such as DOC concentration, humic acids, building blocks, and LMW neutrals, between feed water and permeates from three membranes. The concentration of biopolymer decreased after the GDM filtration; the removal rates of biopolymer were 16.3% for the PES-UF membrane, 18.8% for the PVDF-UF membrane, and 20.8% for the PTFE-MF membrane. The removal of a biopolymer indicated that this organic fraction accumulated on the membrane surface affecting the fouling layer characteristics. Additionally, higher concentrations of LMW acids were observed in the permeate, compared to the feed water. LMW acids can be affected by the release and degradation of organic matter from the biological fouling layer [40]. Moreover, its variation represented that biofouling or microbial activity plays a significant role in fouling layer characteristics.  
Figure 6: Measured membrane (**a**) mean stabilized permeability, (**b**) surface roughness of the clean membrane, and (**c**) ATP (adenosine triphosphate) content of the fouling layers. The mean permeability and ATP content of the fouling layer were obtained at operation cycles 81, 83, 85, and 93 for PES-UF, and 98, 100, 102, and 110 for PVDF-UF and PTFE-MF.
## 3 Methodology
### Calibration  
Calibration was performed after the dataset creation and data preprocessing. For this reason, first the ratio values were calculated directly from the input intensity values. The ratio value for \(SpO_{2}\) was calculated using Equation (36) from the input PPG signals. The ratio values for \(HbA1c\) were calculated using Equations (19) and (34) for the blood vessel and whole finger models, respectively. Then, the reference ratio values for \(SpO_{2}\) were inversely calculated from the reference \(SpO_{2}\) values using Equation (42). The reference ratio values for \(HbA1c\) were also inversely calculated in a similar way using Equations (23) and (35) for the blood vessel and whole finger models, respectively. This process of calibrating the ratio values was essential because different people have different finger widths and skin and fat layer qualities. This calibration method is used to reduce the effects of skin, fat layer, and finger widths on the PPG signal amplitude. When performing the calibration, the inputs are the ratio values calculated directly from the PPG signal and the corresponding finger widths and BMIs, while the target (reference) values are inversely calculated from the reference \(HbA1c\) values. The calibrated ratio values are then used to estimate the \(SpO_{2}\) and \(HbA1c\) values. Here, to obtain the \(HbA1c\) estimated value, the estimated \(SpO_{2}\) value was applied, and if the reference \(SpO_{2}\) value was available, this reference value could be used instead of the \(SpO_{2}\) estimate. After the ratio calibrations, additional value (\(HbA1c\)) calibrations could be conducted, if necessary. In the value calibrations, the \(HbA1c\) values estimated from the ratio calibration models could be further calibrated to improve the performance. Finger widths and BMIs were also considered as features in this case.  
Figure 3: \(HbA1c\) estimation using only two wavelengths.  
For the calibration, the XGBoost model was used. The leave-one-out cross validation (LOOCV) approach was implemented to evaluate the calibration results. LOOCV is a technique for evaluating a machine learning model's performance. In the LOOCV technique, the model is trained on all but one of the data points before being evaluated on the remaining data point. This procedure is repeated for each data point, with each point serving as the test set exactly once. The model's overall performance is then determined by averaging the performances of all iterations. LOOCV is a special case of the k-fold cross-validation, where k is equal to the number of data points. As we focused on estimating \(HbA1c\) from the PPG signals using the Beer-Lambert-based model, implementing LOOCV in the regression was reliable and unbiased for achieving the desired model performance.
# Comparison of Tutorial Methods in Virtual Reality Games for a Better User Experience
## 5 Results  
### Vreq  
The results of the VREQ are shown in Table 2 and Figure 10. In terms of presence, the average scores for the no-tutorial, instruction-screen tutorial, and context-sensitive tutorial cases were 5.2 points (SD = 2.5), 6.4 points (SD = 2.2), and 6.7 points (SD = 2.3), respectively. For control learnability, the average scores for the three cases (in the same order) were 4.6 points (SD = 2.6), 6.7 points (SD = 2.2), and 7.5 points (SD = 2.1), respectively. For intrinsic motivation, the average scores were 5.2 points (SD = 2.4), 7.3 points (SD = 2.1), and 7.5 points (SD = 2.1), respectively. For perceived learning effectiveness, the average scores were 5 points (SD = 2.6), 6.9 points (SD = 2.3), and 7 points (SD = 2.3), respectively. A one-way ANOVA revealed statistically significant differences in the presence, control learnability, intrinsic motivation, and perceived learning effectiveness (\(p<0.001\), \(\alpha=0.05\)). The homogeneity of variance assumption was accepted; thus, the ANOVA results were reliable. Moreover, SNK post-analysis revealed differences between the no-tutorial and instruction-screen methods and between the no-tutorial and context-sensitive tutorial methods. The experimental conditions did not have a statistically significant effect on involvement (\(p=0.176\), \(\alpha=0.05\)), whereas statistically significant differences were identified in the remaining subitems (\(p<0.05\), \(\alpha=0.05\)).
## 3 Spectrum Management Issues
### Small Cell
#### 3.3.4 Capacity Enhancement  
In [148], the authors focus on budgeted cell planning issues in the small cell network. Their results show high-spectral efficiency and capacity improvement for isolated mmWave MU-MIMO small cell users. Besides, a relay node can be introduced to enhance the coverage area in a small cell network. In [149], the authors have provided information for small cell networks, which focus on data offloading among users. They have furnished an innovative file cloud service mechanism to offload mobile user data when the demand for overall users increases. It can help share any kind of files and information from the users associated with the nearby small cell. To this end, mobile users conserve network capacity for high mobile data traffic. However, a delay is inevitable if the file size is extensive. Another research [150] has highlighted the issue of self-organizing small cell-based HetNet because its deployment suffers from challenges, such as backhauling, capacity provision, and dynamics in spatiotemporally fluctuating traffic load. To solve this issue, they set a mathematical model of an Artificial Immune System (AIS) that has the power to activate and deactivate the small cells as per the demand of traffic. Consequently, the recommended scheme helps deliver high throughput for cell-edge users and enhances BS activation speed. By contrast, if intercell interference is high, the cell activation and deactivation speed are also high.  
Table 3 summarizes the above-discussed works for increasing the performance of small size cells.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 8 Computational Costs  
We used a Intel(r) Xeon(r) CPU @ 2.20 GHz and a AMD EPYC 7B12 processor both with 13 GB dedicated Ram on Google Colaboratory to train all the models on our datasets and export the best parameters with the best scores obtained during training. In the gamma-hadron discrimination case, training our models on approximately \(10^{5}\) events takes approximately 120 s. In the energy reconstruction case, training on approximately \(10^{5}\) events takes approximately 750 s. The notable difference in computational time can be attributed to the fact that, in the ASTRI file system, the simulated values of energy are stored in a separate file, requiring an additional search operation to retrieve and associatethe energy values with the corresponding events. The libraries used for our models in Google Colaboratory are capable of deploying code to both CPUs and GPUs. Tests on GPUs NVIDIA(c) Tesla(c) T4 and NVIDIA(c) Tesla(c) K80, both with dedicated 12 GB GDDR5 VRAM, showed no noticeable improvement in computing time.
# Engine Fault Detection by Sound Analysis and Machine Learning
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Methodology** & **Key Findings** \\ \hline \multirow{3}{*}{[PERSON] et al. [23]} & Few signals recorded from two cars, 2 types of faults & RPM-based dominant frequency and comparison-based classification & 100\% accuracy for the first car and 90\% for the second \\  & (shell bearing and exhaust) & & classification & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [24]} & 300 signals recorded from one engine, & Wavelet transform-based feature extraction and artificial neural networks & Overall accuracy of 99\% \\  & 5 engine-related faults & & Wavelet transform-based feature extraction and comparison-based classification & Efficiency of the algorithm presented \\ \hline \multirow{3}{*}{[PERSON] et al. [25]} & Signals recorded from one engine, & Frequency domain feature extraction and artificial neural networks & Overall accuracy of 73.3\% \\  & 2 spark-ignition faults & & Fourier transform and power spectrum density for feature extraction and fuzzy logic inference for classification & Overall accuracy of 56\% \\ \hline \multirow{3}{*}{[PERSON] et al. [5]} & 36 signals recorded from 1996–2000 model Honda Civic, 3 engine-related faults & & Hilbert–Huang transform for feature extraction and support vector machines for classification & Overall accuracy of 90\% \\  & 140 signal recordings from Santana 2000 model vehicle & & Fourier, Wavelet, and MFCC-based feature extraction and support vector machines & Overall accuracy of 99\% \\  & 140 signal recordings from Santana 2000 model vehicle & & Wavelet transform-based feature extraction and vector machines for classification & Overall accuracy of 90\% \\  & & & Fourier, Wavelet, and MFCC-based feature extraction and support vector machines & Overall accuracy of 99\% \\ \hline \multirow{3}{*}{[PERSON] et al. [29]} & 100 signals recorded from various cars, 2 engine-related faults & Wavelet transform-based feature extraction and feature extraction and kernel accuracy of 91.8\% \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies on vehicle fault detection using sound signals. analysis of each identified fault type. Furthermore, the implications of our findings and insights gained from the experimental outcomes are discussed. Finally, Section 5 serves as the conclusion of this study and summarizes key contributions made in this research, and proposes directions for future research and development in the field of vehicle fault diagnosis by sound signal analysis.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 3 Proposed Method
### Pix2 pix Dehazing Network with Guided Filter Layer
#### 3.1.2 Generator  
Generators are used to generate clear images by retaining the structure and details of the image and eliminating the haze. We adopted the symmetrical encoding and decoding structure of \"U-net\" [20] and \"ResNet\" [19]. The encoder was composed of convolutional layers and performed downsampling operations, and the features were mapped to the corresponding layer in the decoding process. The decoder consisted of a convolutional layer and a nonlinear spatial transmission and performed an upsampling operation. In addition, we connected the contour information obtained by the decomposition guidance module with the highest layer of the encoder after implementing the mapping channel, which effectively prevented the global contour information from being affected by the local feature information in the downsampling process, yielding more complete and clear contour features.  
Figure 5: Frequency components after different filtering parameters.
# Salt-Fog Corrosion Behavior of GCr15 Steels Treated by Ultrasonic Strengthening Grinding Process
## 4 Conclusions  
This paper investigated the corrosion behavior of four GCr15 steel samples in a salt-frog environment. These samples were treated by ultrasonic shot peening (USP), a dry ultrasonic strengthening grinding process (DUSGP), a wet ultrasonic strengthening grinding process (WUSGP), and polishing. These process samples were named Sample polished, USP, DUSGP, and WUSGP, respectively. The capacitance arc, polarization curves, corrosion morphology, and corrosion products were analyzed.  
The sample that experienced a surface treatment showed significantly better performance in terms of corrosion resistance, especially the Sample WUSGP, which has the largest capacitive loops. The corrosion potential and corrosion current values are \(-0.3999\) V and \(9.435\) mA/cm\({}^{2}\), respectively. Corrosion morphology revealed that the Sample WUSGP has the smallest corrosion holes and cracks, exhibiting a diameter of approximately \(1.4\)\(\upmu\)m and a width of approximately \(2.1\)\(\upmu\)m, respectively, degrading 78% and 75% compared with the polished sample. Corrosion products analysis showed that the corrosion mainly occurs in the weak regions of the substrate and in the active anode area will produce Fe(OH)\({}_{2}\) film through a redox reaction. The continuous erosion of Cl\({}^{-}\) destroyed the corrosion layer. Then, the electrolyte slowly flows into the matrix to corrode at the damaged regions, but the existence of a dense strengthening layer and passivation film greatly reduces the corrosion rate.  
The best corrosion resistance obtained in sample WUGP can be interpreted as the high dislocation density, small grain size, needle-like, and lath-like shape of martensite formed in the treatment, which hinders Cl\({}^{-}\) from eroding the matrix. This research demonstrated that traditional ultrasonic shot peening fused the strengthened liquid and corundum, and ceramic balls for treating materials can significantly improve the corrosion resistance of steels.  
Formal analysis and writing original draft, X.X.; resources and writing, Z.Z.; conceptualization, review, and editing, X.L. and J.X.; data curation and validation, Z.G. and Z.L.; investigation, visualization, and project administration, J.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52075109, 51975136), the Science and Technology Plan of Guangzhou (202102080225), the Science and technology Special Fund program of Guangdong Province (2019B020404), the Industry-University-Research Cooperation Key Project of Guangzhou Higher Educational Universities (202235139), and Guangzhou University Research Project (YJ2021002).  
Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Acknowledgments:** We thank the Guangzhou Key Laboratory of High-Performance Metal Grinding Processing of Guangzhou University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation and Experimental Verification
### Case 2: A High-Speed Locomotive Wheelset Bearing Fault Signal under Variable Load Conditions
Figure 14a displays the IFB determined by Autogram, where the center frequency \(f_{\text{c}}=9200\) Hz and the bandwidth \(B_{w}=800\) Hz are found. From the Figure 14c, the SES obtained by Autogram, it can be seen that the outer race \(\text{FCF}\,f_{\text{o}}\) is almost surrounded by the strong background noise. By the comparison, the analysis results validate that the IFB determined by Mkurtogram contains more fault characteristics information than the IFBs identified from the FK and the Autogram. Due to the influence of the variable load conditions, the amplitude of vibration signal fluctuates dramatically, which brings some difficulty to the determination of the optimal IFB. Since FK and Autogram only represent the impulse-like characteristics by kurtosis, they both fail to identify the optimal IFB in this example. Benefiting from a unified representation of impulsivity and periodicity performed by Mkurt, the Mkurtogram characterizes the strength of concerned fault-related peaks in the sub-bands and has successfully determined an IFB contained more fault features. Therefore, the Mkurtogram is more effective to extract the wheelset bearings fault feature under complex interferences.
## 5 Case Study
### Protection Strategy Analysis
#### 5.5.1 Protection Strategy Analysis: Improving Robustness
As the strength of attack increases, the ROI of the protection method increases from 23 to 40 due to greater damage reduction. Thus, it is reasonable to consider that Level V protection, as presented in Table 6, is the optimized robustness-improvement strategy, with highest cost efficiency, in response to the terrorist attacks on the substation that are most likely to occur.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Comparison of Loss Functions  
In this experiment, the convergences of Wise-IoUv1, Wise-IoUv2, and Wise-IoUv3, CIoU, EIoU, SIoU, and DIoU are verified under the experimental premise of the same network model with equal hyperparameters. The curves of each loss function with the number of training epochs are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Name** \\ \hline Operating system & Windows11 \\ CPU & Intel(R) Core(TM) i9-9820X \\ GPU & NVIDIA GeForce RTX 3090 \\ RAM & 32 G \\ Deep learning framework & PyTorch (1.13.1) \\ Interpreter & Python (3.10) \\ CUDA version & CUDA (11.7) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.  
From the experimental results shown in Figure 9, it can be seen that the value of each loss function decreases and converges with an increase in the number of epochs, but Wise-IoUv3 converges faster and has smaller loss values compared to the other functions. Therefore, Wise-IoUv3 is chosen as the bounding-box loss function for the improved network proposed in this paper, which is effective in improving detection performance for remote sensing image targets.
# Performance of Strengthened Accelerated Oscillator Damper for Vibration Control of Bridges
## 1 Introduction
Based on the fundamental structure of the Accelerated Oscillator Damper, this study employs two primary strategies: firstly, providing sufficient space to install the secondary mass, spring, and damper by connecting the secondary system to the primary structure (main girder), and simultaneously strengthening the property of the original AOD system; and secondly, introducing nonlinear devices to mitigate substantial vibration responses within the near-resonance range. The work presented here provides one of the new investigations into how to reduce the response of bridges to earthquake action. Furthermore, this study improves our understanding of the methodology involved in ice- and wind-induced vibration, such as Yellow River. The organization of this paper is as follows: In Section 2, a strengthened AOD design is introduced by connecting the secondary spring and damper to the primary structure. Additionally, the motion equations for the strengthened AOD with linear springs (SAOD-LS) are derived. The conceptual design and mechanical characteristics analysis of the nonlinear spring device (NSD) is proposed in Section 3. Section 4 describes the performance of both SAOD-LS and the strengthened AOD equipped with the NSD (SAOD-NSD), where comparisons with the traditional AOD are also given. In Section 5, we discuss the potential application of the suggested SAOD-LS and SAOD-NSD to a practical bridge project. Conclusions are drawn in Section 6. The research methods employed, and the data analysis procedures utilized in each section, are outlined below. In Section 2, the Lagrange Dynamic method is used for mathematical derivation, and the finite element (FE) analysis is employed to validate the motion equations of the SAOD-LS system. We utilize the response comparison approach to analyze the shock absorption data of SAOD-LS. In Section 3, the mechanical properties of the NSD are quantitatively derived and validated using the FE method in Section 3. In Section 4, we gain the response of various cases using the FE method. The comparison of the response amplitude shows that the SAOD-NSD has significant damping performance in the near resonance region. Section 5 of the study involves modeling an actual bridge project using the FE approach. The response data obtained are then processed using programming. The results demonstrate the outstanding practicality and robustness of the the SAOD-LS and SAOD-NSD methods.
## 2 Materials and Methods  
### Simulation Data  
In a previous study, the temporal resolution was set to 48 ms for an image reconstructed using three k-space lines, and PWV was calculated based on signals from 500 timepoints in the ICA and CCA vessels. However, there was still a limitation: the peaks of the corresponding vascular pulse waveforms could not be sensitively detected, owing to the low sampling rate [27]. To overcome this limitation, in this study, the sampling rate was increased by 10\(\times\) to improve the temporal resolution in the simulation data. We assumed undersampled acquisition from brain tissue images acquired using the SMS technique with the following parameters: imaging sequence = 2-dimensional gradient echo (2D GRE), temporal resolution = 4.8 ms, matrix size = 256 \(\times\) 256, resolution = 1.0 mm \(\times\) 1.0 mm \(\times\) 3.0 mm, time series = 1000 points, flip angle = 30, bandwidth = 320 Hz/Px, distance between two slices (\(\Delta d\)) = 60 mm, and total acquisition time = 4.8 s.  
Brain tissue images from five individuals, previously acquired using the SMS technique, were used as the background. Four regions of interest (ROIs), presumed to be the left and right ICA and CCA, were manually extracted from each image. Two simulated pulse waveforms were applied to each ROI to generate 10 sample waveforms (Figure 1). The ICA and CCA pulse waveforms were generated using the simulated pulse waveform database comprising healthy adult samples provided by [PERSON] et al. [31], considering an average heart rate of 60-100 beats per minute, with the interval between peaks within the same vessel averaging 640 ms. Subsequently, the ground truth images to be used for the simulation were temporally generated with Gaussian noise. Gaussian noise was set with a mean of 0 and standard deviation equal to the background of the brain tissue image, ranging from approximately 2.99 to 3.91. The accuracy of peak detection can decrease with the level of temporal noise, potentially affecting PWV values. Therefore, to assess the accuracy of the PWV measurements according to temporal noise levels, 10 ground truth images were generated per sample, with Gaussian noise levels varying from 10% to 100%.