esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 4 Tooth Profile Deviation Prediction Model Based on ISSA-LSSVM  
### Model Construction  
Utilizing the ISSA, two core parameters \(C\) and \(\sigma^{2}\) of the LSSVM are iteratively optimized to enhance the model's prediction capability. Figure 6 illustrates the procedure for predicting tooth profile deviation in rigid gears using the ISSA-LSSVM approach.  
The concrete steps for predicting tooth profile deviation in rigid gears cover the following stages:  
Step 1: Data set division. The WEDM process parameters are used as inputs to the model, and the rigid gear profile deviation is used as the output of the model. Then divide the WEDM test data into training and test sets and normalize them.  
Step 2: Initialize the parameters of the model, including the optimization intervals for \(C\) and \(\sigma^{2}\), population size, maximum number of iterations, and safety threshold. Adjust the proportions of discoverers and followers according to Equations (4) and (5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & **Peak Current** & **Pulse Width** & **Pulse Interval** & **Tracking** & \multirow{2}{*}{\(F_{\alpha}\) **(mm)**} \\  & **(A)** & **(us)** & **(us)** & **(us)** & **(Hz/s)** & \\ \hline
7 & 1.5 & 24 & 9 & 100 & 0.161 \\
8 & 1.5 & 32 & 8 & 150 & 0.173 \\
9 & 2 & 8 & 8 & 250 & 0.156 \\
10 & 2 & 16 & 9 & 200 & 0.165 \\
11 & 2 & 24 & 6 & 150 & 0.178 \\
12 & 2 & 32 & 7 & 100 & 0.185 \\
13 & 2.5 & 8 & 9 & 150 & 0.158 \\
14 & 2.5 & 16 & 8 & 100 & 0.171 \\
15 & 2.5 & 24 & 7 & 250 & 0.212 \\
16 & 2.5 & 32 & 6 & 200 & 0.223 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(Cont.\)  
Figure 6: Flowchart of ISSA-LSSVM model prediction.  
Step 3: Select the mean squared error (MSE) as the model fitness function. The \(MSE\) is commonly used in the evaluation of regression models to help measure the predictive effectiveness of the model for continuous variables.  
Step 4: Calculate and sort the individual adaptation values for all sparrows to determine the current best and worst fitness, along with their respective positions.  
Step 5: Update the positions of the discoverer, follower, and guard, respectively. Calculate and update the fitness value of each sparrow and find the best position for the global fitness values through comparison.  
Step 6: Determine whether the current iteration number reaches the maximum value. If this condition is satisfied, optimize the model parameters and build a prediction model for tooth profile deviation using ISSA-LSSVM. If not, return to step 3 and repeat the iteration process.  
Step 7: Predict the profile deviation of rigid gears using the constructed ISSA-LSSVM model and generate all prediction results.
# Effect of Daily Intake of a Saffron Infusion on Blood Cholesterol Levels
## 3 Results and Discussion
### Effect on the Lipid Profile
Although no significant differences were found in LDL-C and HDL-C after ingestion of the saffron infusion, the results point to a trend of improvement in these values. Thus, a slight decrease in the level of LDL-C was observed, from 124.86 \(\pm\) 35.76 mg/dL to 115.86 \(\pm\) 27.04 mg/dL, while HDL-C increased from 56.57 \(\pm\) 12.42 mg/dL to 60.14 \(\pm\) 16.34 mg/dL. In contrast, [PERSON] et al. (2016) [38] found no significant differences in the lipid profile when saffron was given in a double-blind placebo-controlled clinical trial. This difference in the effectiveness of saffron ingestion may be due to dose and presentation. While in our study 50 mg/day was supplied, in their study a smaller quantity was given of 30 mg/day. The presentation used to administer the saffron was also different; [PERSON] et al. (2016) [38] supplied it in capsules, and in our study it was administered as an infusion, in which the metabolites were already extracted from the saffron before ingestion. On the other hand, [PERSON] et al. (2019) [39], in a meta-analysis on the effect of saffron intake on the lipid profile, found significant differences in TC, TG, and LDL-C, but found no significant differences in HDL-C. The effect of saffron intake on the lipid profile could represent a new nutritional tool to battle cardiovascular disease. Cardiovascular disease remains a first order issue for national health systems all over the world. Approximately 9% of health expenditure in Europe goes to cardiovascular disease [40], and it is estimated that the United States will triple this in the next three decades [41]. It is estimated that by optimizing risk factors, including cholesterol, 80% of cardiovascular disease could be prevented [42]. Diet and exercise play an important role in dyslipidemia'emia control, but saffron has no specific role in these recommendations. The effect reported in this study, that saffron may impact the cholesterol and triglyceride level, could add another tool to the fight against atherosclerosis, although it needs to be confirmed in larger clinical trials.
## 3 DV-Hop and Error Analysis
### DV-Hop Localization Algorithm
\[J=\|b-\mathbf{A}X\|_{2}^{2}=b^{T}b+X^{T}\mathbf{A}^{T}\mathbf{A}X-2X^{T} \mathbf{A}^{T}b \tag{9}\]  
where \(J\) is the square of the modulus of the random error variable \(E\). After deriving it and setting the derivative equal to zero, we obtain Equation (10). It is also known as the canonical equation for the linear least-squares problem [43]. \[\mathbf{A}^{T}\mathbf{A}X-\mathbf{A}^{T}b=0 \tag{10}\]  
Figure 2: Multi-edge positioning method diagram. When rank (**A**) = \(n-1\), the unique solution of (10) can be expressed as:  
\[X=\left(\textbf{A}^{T}\textbf{A}\right)^{-1}\textbf{A}^{T}b \tag{11}\]
# Temporary Road Marking Paint for Vehicle Perception Tests
## 2 Related Work
Studies regarding road marking tapes implied that the tape is mainly applicable for geometrically simple lane shapes [6] and must be removed properly to prevent safety hazards [27]. TRIWO Automotive Testing, a proving ground in Pferdsfeld, Germany, states on its website that they use special temporary road marking paint to provide a wide variety of shapes [28]. Another proving ground (Digitrans) in St. Valentin, Austria, is collaborating with SWARCO to provide temporary road marking paint to test automated driving systems [29]. To circumvent the limitation of the high application efforts needed for geometrically complex temporary road marking shapes for perception tests on proving grounds, water-based road marking paint could be effective. Hence, this study aimed at answering the research question of whether water-based road marking paint could be an effective material for use in concepts of the agile altering of road marking patterns. To be considered an effective material, a road marking paint should maintain a constant visibility over the course of a test drive and be removable without causing phantom tracks. This study has an explorative character and does not examine correlations between ingredient quantities (independent variables) and changes in visibility or residue after removal (dependent variables) in consideration of weather influences and vehicle overruns (control variables).
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 6 Energy Cost
The results are shown in Figure 5:1 The proposed method is able to reduce the cost of the energy by a factor 20 and more, depending on how frequent the zero symbol emission is and, in particular, the transition from talk to silence. For example, with \(P_{0}=0.4\), which is typical, e.g., of voice services, and \(P_{TS}=0.003\), the proposed method compared to the classical one was able to save 34% of energy, while with \(P_{TS}=0.01\) the energy saved growed up to 84%. A low value of \(P_{TS}\) means that the transition between the talk and silence state has low probability to occur. A more variable source, i.e., frequent zeros distributed in the stream like several aggregated sources with different constant rate, leads to more frequent transition between talk and silence state and a consequent growing of the energy saved with our method. Figure 5:2 Average energy per frame \(E_{f}\) of the Z-BPSK (solid line) and BPSK (dashed line) as a function of the probability of zero symbol emission \(P_{0}\) for different values of the probability of transition from talk to silence state \(P_{TS}\).
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement  
In this section, the accuracy and integrity models in the weighted slope-based RAIM algorithm is introduced at first. Then, we analyze the impacts of subset size on the accuracy and integrity performance through a scenario assumption way to exhibit the importance of satellite selection in the RAIM process.
# A Siphon Drainage Method for Consolidation of Soft Soil Foundation
## 3 Numerical Simulation of Feasibility of Siphon Drainage in Soft Soil Consolidation
### Results and Discussion  
#### 3.2.1 Vertical Pore Pressure  
Figure 5 shows the vertical pore water pressure when the hole spacing is 2 m. As shown in Figure 5, the vertical pore water pressure at different depths decreases gradually over time. With respect to different soil permeability, however, the pore pressure drops at different rates for different parts. The results show: (a) the larger the soil permeability is, the faster the decrease rate of soil drops; (b) the pore water pressure nearest the drainage hole drops the fastest, and pore water pressure near the surface of the foundation drops faster than the deep parts; (c) the groundwater level decreases and remains at the depth of 6m after 50-100 days consolidation.  
From the details, the soil permeability has a great influence on the consolidation by siphon drainage. With respect to soft soils in which the permeability is \(10^{-7}\) cm/s, after 100 days of consolidation, the pore water pressure at the depth of around 8 m is about 10 kPa. However, with respect to soft soils in which the permeability is \(10^{-6}\) cm/s, the pore water pressure of soft soils buried at the depth of around 8 m is nearly zero after only 50 days. It shows that the pore water pressure of soils is sensitive to the soil permeability. The larger the permeability is, the faster the consolidation is. Siphon drainage can significantly accelerate the consolidation of soft soils with greater permeability.  
Figure 6 shows the vertical pore water pressure when the hole spacing is 1m. The results on the influences of different permeabilities are nearly the same with the condition that drain spacing is 2 m. From the details, the consolidation of soft soils is much faster than the same method with 1m drain spacing. With respect to soft soils in which the permeability is \(10^{-7}\) cm/s, after 50 days of consolidation, the pore water pressure of the soft soils buried at the depth of around 8 m has become nearly zero, which only requires half the time for the 2 m drain spacing condition. With respect to the soft soils in which the permeability is \(10^{-6}\) cm/s, the pore water pressure of soft soils buried at the depth of around 8 m can become zero only in 5 days.  
Figure 5: Vertical distribution of soil pore water pressure at corner of square column at siphon hole spacing \(d\) = 2 m.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 3 Results and Discussion
### Measurement of Paint Sample through Contact Patch Underwater and Application of Fitting Algorithm  
The measurement through the contact patch underwater of the paint sample can be seen in Figure 5B. The signal is visually different from the measurement in Figure 5A, where only a metal surface was measured. The time delay between the two rightmost marked peaks in Figure 5B was 5 ps, which, combined with the additional refraction of light through the TOPAS plastic, resulted in a thickness of 317 \(\upmu\)m using Equation (1). This illustrated the result of insufficient visual determination of two reflection peaks.  
An additional observation is the lack of reflection between the TOPAS plastic and the silicone contact patch, as expected due to the similar refractive indices of the two layers. This justifies the use of the simplified physical model shown in Figure 2E. Finally, the fitting algorithm was applied and a paint thickness was determined to be 232.8\({}^{+6.3}_{-3.1}\)\(\upmu\)m as listed in Table 1. The best fit, with a coefficient of determination of 0.825, can be seen in Figure 5C. The calculated paint thickness deviated by 1.2% from the measured thickness by the Alicona 3D scan. The high accuracy on the thickness determination despite the low \(R^{2}\)-value was due to the fact that the thickness estimation was robust to differences in the fitted and measured pulse shapes, since the temporal distance between reflections was proportional to \(n\times d\), which was independent of the specific pulse shape. It was therefore possible to attain accurate and precise thickness measurements even though the correct pulse shape was not perfectly reconstructed, which tended to decrease the \(R^{2}\)-value. The difference between the fit and the measurement could be due to the assumption of a constant index of refraction for the TOPAS and silicone layers. A better fit could likely be produced by  
Figure 5: (**A**) Measurements on a metal surface through the developed contact patch in dry and submerged conditions. The fading bar shows the expected location of the silicone reflection. (**B**) Measurement of the sample through the developed contact patch underwater. Notice that there is no reflection at the plastic/silicone interface. (**C**) Fit on the measurement from (**B**) when only considering the signal from 50 ps (\(R^{2}\) score of 0.825).  
using a more complex model for the index of refraction of the absorbing silicone such as the previously mentioned Debye model, as compared to using a constant index of refraction. This was not done due to convergence issues, but it is a topic of future work.
# Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
## 1 Introduction
Recently, research on software reliability has been conducted with various approaches. [PERSON] et al. [18], [PERSON] et al. [19], and [PERSON] et al. [20] developed criteria for judging goodness of fit for SRGMs. The criteria are calculated based on the combination of the entropy principle and various existing criteria. Most studies discussed the individual reliability of software and hardware. [PERSON] [21] also developed two multi-criteria decision-making methods for comparison of SRGMs. [PERSON] [22] introduced complex reliability that considers both hardware and software, and proposed maintenance policies applicable to these systems. Recently, as the use of open-source software and cloud services increases, research on their reliability is being actively conducted [23, 24, 25]. Finally, several software reliability studies have recently been conducted using machine and deep learning [26, 27, 28, 29]. In this study, we propose a new SRGM with two assumptions that software failures are dependent and the operating environments of the software are uncertain. The superiority of the proposed model is explained by the criteria for goodness of fit. The goodness of fit is used to determine which model fits the failure dataset for a given software product. A model with a good fit can predict the number of failures well and provide meaningful information for evaluating product reliability and establishing release policies. In Section 2, the concept of system reliability is provided. Section 3 describes the existing NHPP SRGM and the proposed model. Section 4 introduces the criteria and data used in the experiments, then discusses the results. Finally, Section 5 provides some concluding remarks.
## 2 Materials and Methods
### Data Preparation Investigations  
In this work the resulting model performance from a variety of data preparation steps are evaluated, which are labeled variations A/B/C/D/E. The individual influence of these data preparation steps are studied, and then the steps that contribute positively to model performance are combined as variation F.  
* Baseline: The baseline dataset has duplicate entries removed but no other adjustments. After removing 86,195 duplicates, there are 296,493 entries in 15 categories. Headlines with mixtures of Chinese text and non-Chinese text were retained, as were 44 English-only headlines. All other variations (B-F below) start with this dataset.
* Remove stock category: As shown in Figure 1, the stock category of news headlines was vastly underrepresented when compared to the other categories. This version of the dataset removed this category.
* Remove whitespace: This version removed any whitespace at the beginning or end of the headline.
* Standardize headline length: The lengths of the headlines were examined, and they varied from 2 to 150 characters in length. The median headline length was 23 char  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Step**} & **Naïve Bayes and** & \multirow{2}{*}{**NN-Sequential**} & \multirow{2}{*}{**BERT**} \\  & **XGBClassifier** & & \\ \hline \multicolumn{4}{c}{Segmentation, stemming, and} \\ \multicolumn{4}{c}{lemmatization} & not applicable to Chinese & – & – \\ \multicolumn{4}{c}{} & 25–5888 characters or & \\ \multicolumn{4}{c}{} & 25–167,506 jieba words & 25–5888 characters or & \\ \multicolumn{4}{c}{} & Unigrams, & 25–167,506 jieba words & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & uni-bigrams, or & Unigrams & \\ \multicolumn{4}{c}{} & uni-big-/trigrams & \\ \multicolumn{4}{c}{} & sklearn _CountVectorizer_ for & \\ \multicolumn{4}{c}{} & count encoding & \\ \multicolumn{4}{c}{(select from vocabulary)} & sklearn _TF-IDF_ for TF-IDF & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & encoding & \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \multicolumn{4}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\begin{tabular}{c} \multirow{2}{*}{**Step**} \\ \multicolumn{4}{c}{**XGBClassifier**} \\ \multicolumn  
* Best: Detailed modeling and analysis showed that, on average, the algorithms benefited most from data preparation approaches that removed the under-represented stock category (variation B), did not remove whitespace at the beginning/end of a headline (variation C), did not standardize the headline length to 3-50 characters (variation D), and combined 3 pairs of related categories (variation E).
# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 6 Discussion and Conclusions  
The study carried out highlighted the effectiveness of advanced pre-processing techniques as well as basic image processing, specifically for data obtained with GPR technology. Taking the two directional images of the same route ensured the best visualization of each path, and the combination of the two ensured a better interpretation and visualization of the examined subsoil.  
The subsequent isolation of specific regions of interest to extract patterns that would become key to basic processing and synthesis helped to enhance the analysis of the GPR data and the overall detection process. This practice of creating binary patterns, in combination with the pattern recognition application through thresholding, effectively enhances the detection of hyperbolic responses throughout the extent of the subsidiary images, proving the usefulness of the method in real scenarios such as the present one for the inspection of subsoil in a seismic area. The application of the Fourier transform to the analysis of the frequency components of the data proved extremely valuable, as this approach highlighted features that were not easily identified in the spatial domain, thus underscoring the importance of frequency analysis in GPR data.  
Subsequently, reconstruction and fusion techniques that combined the forward and reverse images successfully enhanced the visibility of hyperbolic occultations. The fusion process was highly effective in integrating the information from both images, resulting in a more detailed and accurate representation of underground structures. This combinatorial analysis helped highlight the strong hyperbolic responses in the entire dataset, a process that can be incorporated into other applications in the future.  
Finally, LBP analysis provided texture characterization, contributing to the identification of specific patterns in the image, indicating it is a powerful tool for knowledge about the structure and composition of the subsoil.  
In summary, future work could focus on improving pattern recognition techniques by improving the threshold criteria and incorporating machine learning algorithms. In addition, employing additional pre-processing or synthesis techniques can also contribute to more accurate data analysis and visualization. Current research is focused on identifying the material type of the detected objects. However, this constitutes a separate research area that has to do with the exact dielectric properties of underground soil.  
[PERSON] and [PERSON] have equally contributed to Conceptualization, Methodology, Validation, and Writing--Original Draft Preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data availability upon request.  
[PERSON] was financially supported by a scholarship from the Andreas Mentzelopoulos Foundation.  
The authors declare no conflicts of interest.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird Generation Partnership Project AMAcknowledged Mode (RLC) APAccess Point ARQAutomatic Repeat Request BERBit Error Rate BLERBlock Error Rate BSBase Station CCAClear Channel Assessment CDF Cumulative probability Distribution Function CQIChannel Quality Indicator CSCarrier Sense CSIChannel State Information CSI-RSChannel State Information CSMACarrier Sense Multiple Access CWContention Window DCIDownlink Control Information DRSDiscovery Reference Signal DSCCHDownlink Shared Data Channel EDEnergy Detection eLAAEnhanced Licensed Assisted Access FeLAAFurther Enhanced Licensed Assisted Access