esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Effectiveness of Surface Defect Removal by Slide Burnishing
## 4 Conclusions  
Pre-ground-shaped samples made of C45 steel, with surface defects on the circumference in the form of scratches (having different nominal depths) were subjected to the slide burnishing process. The following conclusions summarize the research results:  
1. After slide burnishing, regardless of the applied force F and feed f, scratches with nominal depths of 10 \(\upmu\)m and 20 \(\upmu\)m were removed.
2. For the scratches with their nominal depth ranging from 30 \(\upmu\)m to 70 \(\upmu\)m after SB, only their depth decreased (it became 2 to 27 times smaller than before slide burnishing).
3. The use of slide burnishing changed surface topography. The parameters Sa, Sz, Sp, and Sv decreased.
4. After slide burnishing, the parameters of the Abbott-Firestone curve, Ra and Rt, were lower than those after grinding regardless of the SB conditions.
5. As a result of the SB treatment, the surface layer was strengthened. The microhardness increased by 20-38% at a depth of 1 \(\upmu\)m, and the thickness of the strengthened layer was 10 \(\div\) 15 \(\upmu\)m.
6. In the surface layer, just below the slide burnished layer, there occurred compressive residual stresses, the value of which oscillated around \(\sigma=-707\)\(\pm\) 19 MPa regardless of the slide burnishing parameters.  
Figure 14: Residual stresses after slide burnishing using different technological parameters. The white bar indicates the microhardness before slide burnishing.  
Taking into account the obtained surface layer properties and the effectiveness of \"crushing\" surface defects on C45 steel samples, it can be stated that the most advantageous effects were obtained when the slide burnishing process was conducted with F = 150 N and f = 0.03 mm/rev.
* Based on the obtained results, it can be assumed that the proposed method of reducing the depth of surface defects will work well in the case of small-depth defects. For large-depth defects, slide burnishing is less effective in reducing them.  
Conceptualization, K.Z., A.S. and J.M.; methodology, A.S. and J.M.; software, J.M.; validation, K.Z., A.S. and J.M.; formal analysis, K.Z.; investigation, A.S. and J.M.; data curation, A.S. and J.M.; writing--original draft preparation, K.Z. and A.S.; writing--review and editing, A.S.; visualization, A.S. and J.M.; supervision, K.Z.; project administration, A.S.; funding acquisition, A.S. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Lublin University of Technology, FD-20/IM-5/107 and FD-20/IM-5/071.  
The data presented in this study are available on request from the corresponding author due to the data also form part of an ongoing study at this time.  
The authors declare no conflicts of interest.  
The following abbreviation is used in this manuscript:  
SBS Slide Burnishing
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.3 Feature Extraction
In this way, the effect of short-term fluctuations in the audio signal is reduced, and a more stable feature vector is obtained. In addition, by using mel-frequency filter banks and discrete cosine transform (DCT), the frequency distribution perceived by the human ear is better modeled, and the distinctive features of the sound are extracted more effectively. The selection of these parameters is made in order to minimize the information loss while characterizing the audio signals and increasing the classification performance. Before feeding the MFCCs to the network, the features are vectorized. Popular Python libraries such as Librosa, Scipy, Numpy, and Pandas played a pivotal role in implementing the feature extraction and normalization steps, enabling an efficient and streamlined process.
## 3 Experiments and Analysis  
### Experimental Settings  
#### 3.1.1 Datasets  
Following Section  
sions of \(1024\times 1024\) pixels. The dataset is split into training (65%), validation (25% of training), and test (35%) sets. It includes significant lighting, shadow, and phenology variances [9].  
S2 Looking: This dataset comprises 5000 pairs of satellite images (0.5-0.8 m/pixel) captured over three years, each with dimensions of \(1024\times 1024\) pixels. The dataset (3500 training, 500 validation, and 1000 test images) incorporates significant weather and radiation variability [10].
## 3 Results and Discussion
### Discussions
#### 3.3.1 Comparative Analysis
These results align with the findings from the experiment conducted by [PERSON] et al. [35], where the DAH structure outperformed the REH structure.
## 3 Results and Discussion
### Compound Protease Washing Test  
The washing effect of alkaline protease alone is limited. [PERSON] et al. [39] reported that using multiple enzymes to work together in the washing process can decompose and remove stains efficiently. In this experiment, a compound detergent formula composed of alkaline protease, keratinase, and trypsin was prepared, and the total enzyme activity of each group of experiments was set to 80 U. In the experiment, the mixing ratios of alkaline  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Washing Performance**} & \multicolumn{2}{c}{**Reflectivity (\%)**} \\ \cline{3-4}  & & **(a) Trypsin** & **(b) Keratinase** \\ \hline \multirow{6}{*}{Protein-fouling cloth} & Enzyme & 32.90 \(\pm\) 0.51 & 29.38 \(\pm\) 1.53 \\  & Before washing & 13.23 \(\pm\) 1.12 & 13.23 \(\pm\) 1.12 \\  & Basal detergent & 20.90 \(\pm\) 0.56 & 20.90 \(\pm\) 0.56 \\  & Hyaluronic acid & 32.45 \(\pm\) 1.18 & 36.53 \(\pm\) 3.55 \\  & Sodium Alginate & 37.13 \(\pm\) 1.80 & 40.35 \(\pm\) 1.84 \\  & Sodium Gluconate & 35.53 \(\pm\) 2.01 & 38.83 \(\pm\) 1.69 \\  & 4A zeolite & 32.68 \(\pm\) 1.51 & 35.38 \(\pm\) 1.47 \\ \hline \multirow{6}{*}{Blood-dirty cloth} & Enzyme & 37.60 \(\pm\) 0.88 & 25.20 \(\pm\) 2.50 \\  & Before washing & 10.78 \(\pm\) 1.04 & 10.78 \(\pm\) 1.06 \\  & Basal detergent & 14.93 \(\pm\) 1.37 & 14.93 \(\pm\) 1.34 \\  & Sodium Hydroxyethyl Cellulose & 35.85 \(\pm\) 1.14 & 29.93 \(\pm\) 1.37 \\  & Sodium Alginate & 35.68 \(\pm\) 1.60 & 26.43 \(\pm\) 2.56 \\  & Hyaluronic acid & 35.85 \(\pm\) 0.17 & 24.40 \(\pm\) 2.12 \\  & HPMC & 35.78 \(\pm\) 1.14 & 21.18 \(\pm\) 2.10 \\  & Sodium polyacrylate & 35.35 \(\pm\) 0.31 & 27.75 \(\pm\) 2.66 \\  & CMC & 36.75 \(\pm\) 1.05 & 28.68 \(\pm\) 2.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Washing effect of detergent auxiliaries mixed with trypsin or keratinase on dirty cloths.  
protease and keratinase were 2:6, 4:4, 3:5, 6:2, and 1:7. Stain removal showed a slight difference when alkaline protease and keratinase were added in different proportions to the detergent during washing protein-soiled cloths. When the detergent alkaline protease was mixed with keratinase at a ratio of 1:7, the effect was greatest in washing soiled cloths with blood (Table 5).  
The detergent added with trypsin exerted obvious washing effects on protein stains. The addition amount of trypsin ranged from 5 U to 20 U. Each group to add 80 U enzyme activities of protease, wherein the proportion of alkaline protease and keratinase added in the detergent was set to 1:7. Four experimental groups had 5, 10, 15, and 20 U of trypsin added. When the amount of trypsin added in the compound detergent was 10 U, the effect of washing the protein-soiled cloth improved. When the addition amount of the composite detergent trypsin was 5 U, the washing effect with bloodstains became more pronounced (Table 6).
# Using Game Engines for Visuo-Haptic Learning Simulations
## 6 Results and Discussion
### Performance  
Performance tests were applied to all the developed scenarios. The metrics to be evaluated were the average percentage of CPU consumption and GPU (VRAM) utilization in each simulation. Moreover, the steps considered to determine the development time necessary to create interactive visuo-haptic simulators were the following: the learning time it took to understand the simulation framework, the implementation of the simulation in terms of graphic visualizations, and the force feedback calibration. Tests were performed on a Dell Inspiron i75592512 BLK, Intel Quad Core i7-6700 HQ 2.6 GHz Processor, NVIDIA GeForce GTX 960M 4 GB GDDR5, 16 GB DDR3L SDRAM, Windows 10 64-bit operating system.  
Each scenario was tested using one to six haptic devices, excluding the equilibrium of forces due to its nature. Results can be seen in Tables 2 and 3. Results show that CPU and GPU utilization were linear according to the type of simulation and number of haptic devices used.  
\begin{table}
\begin{tabular}{l l} \hline
**Question** & **Statement** \\ \hline Q1 & Do you like Unity Forces? \\ Q2 & Do you like its graphic visualization? \\ Q3 & In comparison with HapStatics, how was _Unity Forces_’s force feedback? (1 = worse to 5 = better) \\ Q4 & Do you feel that both simulators display similar physics behavior? \\ Q5 & Which of the simulators has better force feedback? \\ Q6 & Which of the simulators has better graphic visualization? \\ Q7 & What do you think about _Unity Forces_ and what would you improve? \\ \hline \end{tabular}
\end{table}
Table 1: Perception questionnaire applied to participants after testing Unity Forces.  
Figure 19: (**a**) User grabbing the first virtual block and experiencing its weight, and (**b**) User releases the first virtual block and it experiences freefall.  
Figure 18: Unity Forces simulator: User is moving the second virtual block to the left.  
Results state that HaDIU's performance was optimal and interactive applications created using the proposed architecture provide fluent visualizations. Additionally, CPU utilization proved that even though six haptic devices were connected in the environment, force feedback generation and graphic rendering were displayed properly, and no discrepancies or performance consumption were present during their connectivity. These results prove the fact that proper multiple haptic devices connectivity in game engines was achieved by using HaDIU.
## 4 Discussion
### Superiority of the \(\mathrm{f_{esc,GPR-SR}}\) Model
In addition, the 3-D radiative transfer model dataset also validated that the \(\mathrm{f_{esc\_GPR\_SR}}\) model could improve the estimation of \(\mathrm{f_{esc}}\) (Section 3.2.2). Since the DART model is too time-consuming to use for simulating a large amount of training data, a small dataset was used to evaluate the performance of the \(\mathrm{f_{esc}}\) estimation model trained with the SCOPE simulations. Additionally, ground-measured data from healthy and non-stressed vegetation were used for supplementary verification and showed that the linear correlation between APAR\(\mathrm{g_{green}}\) and \(\mathrm{SIF_{leaf}}\) could be improved for different species when soil reflectance is considered and FVC is less than or equal to 0.8. The \(\mathrm{f_{esc\_GPR\_SR}}\) model reduced the species dependency of the \(\mathrm{SIF\_{-}APAR_{green}}\) relationship for sparse vegetation to a greater extent (Section 3.3). The R2 value of the APAR\(\mathrm{g_{green}}\)-\(\mathrm{SIF_{leaf}}\) correlation for vegetables and crops decreased slightly after considering soil reflectance, which may be because most of the photographs of vegetables and crops at Nanbin Farm were taken on cloudy days, leading to greater uncertainty in the FAPAR\(\mathrm{g_{green}}\) and soil reflectance calculations using the photographs. It must be noted that the significant linear relationship between APAR\(\mathrm{g_{green}}\) and SIF exists only in the absence of environmental stress. When environmental stress exists, APAR\(\mathrm{g_{green}}\) may not be able to characterize the leaf-level SIF. So, APAR\(\mathrm{g_{green}}\) cannot simply be used as a proxy of the total SIF. In conclusion, the \(\mathrm{f_{esc\_GPR\_SR}}\) model, which accounts for soil reflectance, has been verified to increase the accuracy of \(\mathrm{f_{esc}}\) estimation in the near-infrared band, particularly for sparse vegetation and the leaf-level SIF in the near-infrared band calculated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model is less sensitive to observation angles and variations in canopy structure among multiple species.
## 4 Results and Discussion
### Fluctuating Wind Characteristics
#### 4.2.5. Kurtosis, Skewness, and Probability Density Function
The probability density function distribution of fluctuating wind speed is of critical importance for the safety of structures [37]. Based on the analysis results of the kurtosis and skewness coefficients, the probability density function of the midpoint of the two towers and main beam is further analyzed. The probability density functions at three locations under uniform inflow are illustrated in Figure 52. It can be observed that the probability density functions at each location exhibit a high degree of deviation from the Gaussian distribution, demonstrating a strong non-Gaussian nature, which corresponds well to the higher kurtosis and skewness values mentioned in the previous analysis. When the oncoming wind is ABL inflow, the probability density functions at each location basically conform to the Gaussian distribution,  
Figure 49: Kurtosis and skewness coefficients of the northeast tower under ABL inflow with different wind speeds. as shown in Figure 53. Only a slight non-Gaussian nature appears at the 180\({}^{\circ}\) wind direction, which is consistent with the patterns indicated by the kurtosis and skewness coefficients. \[f(x)=\frac{1}{x\sigma\sqrt{2\pi}}e^{\frac{-(\ln x-p)^{2}}{2\sigma^{2}}},\ x\geq 0 \tag{9}\]  
where \(\mu\) and \(\sigma\) denote the mean and the standard deviation of the logarithm of the variable \(x\), respectively. Figure 50: Kurtosis and skewness coefficients of the main beam under uniform inflow with different wind speeds. Figure 51: Kurtosis and skewness coefficients of the main beam under ABL inflow with different wind speeds.
## 3 Numerical Validation of the Damping Estimation Methodology
### Sensitivity Analysis in Relation to the Free Vibration Period
#### 3.2.1 Numerical Models  
The analyses were performed with 2D numerical models of four different bridges with spans of, approximately, 7.00 m, 11.00 m, 11.50 m, and 23.50 m. The models were developed with ANSYS(r) [50] using beam elements to model the deck and rails (BEAM 188), spring-dashpot elements (COMBIN14) to simulate the ballast and railpads' horizontal and vertical stiffness and damping, as well as the flexibility of the bearing supports, and mass point elements (MASS21) to consider the track mass (ballast and sleepers). Track extensions have been modelled on both extremities of the bridge to take into consideration the continuity provided by the track-deck coupling. Table 1 show the main parameters used in the numerical models. Figure 2 illustrates the numerical model of the 11.50 m span bridge for exemplification purposes, as well as the first three global modes of vibration and respective frequencies, while Table 2 presents the first three modal frequencies of all the analysed bridges.  
Figure 2: Numerical model of the 11.50 m span bridge used for the sensibility analysis: (**a**) numerical model, (**b**) first, (**c**) second, and (**d**) third modes of vibration.
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## 6 Discussion
Therefore, a scenario with a larger number of visible satellites with varying signal energies is provided, and the simulation results demonstrate that the cross-correlation errors cause an additional RMSE, which is even more pronounced in the high \(C/N_{0}\) region. The simulation experiments validate that the joint processing of satellites can bring mitigation capabilities to the DPE. Although many researchers have explored how to eliminate and predict the cross-correlation errors of signals [48], receivers need access to a number of parameters in order to fully predict the cross-correlation [40]. The DPE provides a perfect opposite direction of thinking that fundamentally solves the cross-correlation errors. Although real GNSS scenarios may not have such a large difference in the \(C/N_{0}\) values between the signals, the cross-correlation is inherently constrained by the relative Doppler shift. This study is relevant for future GNSS positioning accuracy studies, such as those of ground-based augmentation systems [49], pseudolite positioning systems [50], or passive localization based on low-Earth-orbit satellites [51]. Nonetheless, this study still holds potential for further development. One potential avenue for further development involves incorporating the spatial geometry of the satellites to improve the modeling of cross-correlation errors. Additionally, considering that cross-correlation errors are often associated with multipath errors, extending the bounds to multipath channels presents a valuable direction. Future research will focus on these issues.
## 4 Methodology
### Sentiment Analysis
#### 4.3.1 Twitter Data Collection  
We used the Twitter API [53] to collect around 1.2 million original tweets using the Python library Tweepy [50]. We filtered the tweets by keywords associated with different COVID-19 vaccines and keywords mentioning a safe, healthy lifestyle after vaccination. Details about the keywords are mentioned in Tables 2 and 3. The tweets were collected over a five-week period beginning on 10 April 2021 and ending on 17 May 2021. We discarded the retweets during this time period with the Twitter API filter. We also only collected tweets in English we used NLTK for further data analysis. Our Twitter data collection pipeline is shown in Figure 2. Twitter's API provides access to 1% of the public tweets by random sampling in near real time. Although questions might arise regarding biased or imbalanced data for collecting just 1% sample from all tweets, it has been shown that sentiments found from the samples of tweets obtained via the API and the full tweet dataset reflect the same sentiment percentage with very little deviation (\(<\)1.8%) [54]. In compliance with the Twitter content redistribution policy [55], we only made the tweet IDs publicly available corresponding to the collected tweet text used in this work.  
number of tweets based on different pre-processing steps of the Twitter data done before the sentiment analysis.
* **Twitter Dataset 2:** We selected five topics related to maintaining safety/precautions in response to COVID-19 pandemic. We selected the precautionary actions for individuals that help reduce virus transmission during pandemics including social distancing, wearing face masks, hand hygiene, and restricting interpersonal contact to outdoor settings [11]. Additionally, we added another topic related to \"travel\" that was very restricted during the pandemic. For Twitter Dataset 2, we aimed to collect those tweets that have been shared by vaccinated people about their lifestyle after getting vaccines. Thus, we used the keywords [_vaccine, vaccination, vaccinated, shot, fully vaccinated, first dose, second dose, 1 st dose, 2 nd dose_] to scrap those tweets along with the keywords for particular topics, as listed in Table 3.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 6 Objectives and the Effects of Data Fusion
### Calculating the Effect of Data Fusion
The camera of the face recognizer (0.65). 4. Base station (0). 5. Registration database (0). 6. Face recognizer (0). The data sources 4, 5, and 6 do not contribute to the considered effectiveness objective. Figure 4: Effectiveness comparison between weakly, averagely, strongly related data sources. The data sources 1, 2, and 3 contribute strongly to each other. The estimated effectiveness value of the residence \(R\) with respect to objective 1 can be computed using Equation (3):  
\[EF(E_{R},O_{1})=0.7+(1-0.7)\times(0.7)+(1-(1-0.7)\times 0.7)\times(0.65)=0.9685\]  
**Objective 2 (effectiveness):** To calculate the estimated effectiveness value of this objective, using the Tables 1 and 2 and based on the mean values of the distribution functions, the data sources under consideration are ranked:  
1. Face recognizer (0.75). 2. Registration database (0.55). 3. Collapse detector (0). 4. UAV-camera (0). 5. The camera of the face recognizer (0). 6. Base station (0). The data sources 3, 4, 5, and 6 do not contribute to the considered effectiveness objective. The data sources 1 and 2 strongly contribute to each other. The estimated effectiveness value of the residence \(R\) with respect to objective 2 can be computed using Equation (3):  
\[EF(E_{R},O_{2})=0.75+(1-0.75)\times(0.55)=0.8875\]  
**Objective 3 (effectiveness):** This estimated effectiveness value of objective 3 is the same as the one of objective 2. **Objective 4 (total cost value):** The total cost value is estimated by adding up the cost values of the attached data sources for each element. Assuming the following five data sources are utilized:  
1. Face recognizer: 12.5 K.
2. Collapse detector: 5.5 K.
3. UAV-camera: This cost value is not included. 4. Base station: This cost value is not included. 5. Registration database: This cost value is not included. Total cost of (\(R\)) = 18 K  
**Objective 5 (total timing value):** The total timing value is estimated as follows: Assuming the following five data sources are utilized, the data sources are ranked according to their timing values:  
1. Face recognizer: 0.55. 2. Collapse detector: 0.55. 3. Registration database: 0.55. 4. Base station: 10.5. 5. UAV-camera: 8.5 K.  
Compared to the UAV, the timing values of the other data sources are negligible. As depicted in the following Figure 5, the estimated timing value, for example, with respect to objective 1, is computed in two steps:
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 1 Introduction
First, the isothermal line contact Reynolds equations are solved using the multigrid method to obtain pressure and film thickness distributions. Second, a quasi-static model for cylindrical roller bearings is established based on elastohydrodynamic lubrication theory, enabling the calculation of fatigue life via the modified L-P theory. The effects of load, speed, and viscosity are systematically investigated. Finally, the bearing fatigue life is experimentally validated. Existing research has achieved significant progress in both bearing fatigue life analyses and the solution to elastohydrodynamic lubrication problems. However, most studies have yet to integrate elastohydrodynamic lubrication theory with bearing fatigue life analysis. The contribution of this work lies in optimizing bearing fatigue life analysis methods through the introduction of elastohydrodynamic lubrication theory. It provides a novel computational model for rolling bearing fatigue life, thereby addressing a gap in the existing body of research. Furthermore, the findings can provide a theoretical basis for the design of generator bearings.