esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature
## 4 Results
### Distribution of Publications by Year and Journal  
Figure 2 shows the distribution by year of publications related to hyper-heuristics between 2002 and 2022. Notice that more than 30% of the research articles analyzed were published in the last three years (312 out of 963). This demonstrates a considerable increase in the publication of hyper-heuristic-related topics in the literature.  
Figure 2 supports previous publications pointing out the recent growth in the interest in investigating hyper-heuristic-related works [1; 3; 4; 43]. The color of the bars represents the number of citations that took place each year. There is a considerable increase from 2012 onward, highlighting the years with the most citations in 2015 and 2019, with 1440 and 1196 citations, respectively.  
Figure 2: Yearly distribution of articles on hyper-heuristics between 2002 and 2022 (obtained on 5 April 2022).  
Subsequently, Figure 3 illustrates the distribution of the ten most-cited journals given their publications on hyper-heuristics. These ten most-cited journals accumulate close to 44% of the total citations in the field. Among these journals, the European Journal of Operational Research has the largest number of citations to works related to hyper-heuristics, with 8.69%, followed by IEEE Transactions on Evolutionary Computation, with 7.12%. Furthermore, we found no significant difference in the number of articles related to hyper-heuristic per journal. The journals with the highest number of articles in hyper-heuristics are the _Applied Soft Computing Journal_ and _Expert Systems with Applications_, both with 20 articles and a significantly lower number of citations than the most cited journal (3.12% and 2.98% of the total citations, respectively).
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm  
The ambiguity resolution is one of the primary problems in carrier phase measurement. In this section, we propose an EFK algorithm based on TOA and carrier phase measurements. This algorithm can estimate the position while estimating the integer ambiguity.
## 5 Conclusions
The proposed model also has the flexibility to obtain further insight by accommodating new experimental results. The users can update new experimental results by updating the open-source database and executing the model again. In addition, the proposed classification model may help other researchers plan experimental research. For example, it will be possible to set the dependent variable as the number of casualties or burmta area and predict it. Furthermore, this study demonstrated the functions of ML-based classification models that can be used in disaster management areas other than fire. This study helps advance the field by demonstrating that it is possible to predict property damage ratings caused by fires using simple building information and ML techniques. It provides insights into developing effective disaster management and prevention strategies by enabling rapid prediction of potential property damage in advance. The proposed classification model can be utilized in various applications related to fire safety inspections and resource allocation for firefighting activities. The limitation of this study is that the amount of property damage was graded and converted into a classification model to increase the prediction rate for property damage. This can act as an obstacle for putting this research into practical use. Further research and data are needed to predict more specific property damage. **Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] ([PERSON]); supervision, D.S. ; project administration, D.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was carried out under the KICT Research Program (project no. 20230135-001) funded from Ministry of Science and ICT. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used for learning in this study were obtained from the public data portal operated by the Korean government ([[http://data.go.kr](http://data.go.kr)]([http://data.go.kr](http://data.go.kr)), accessed on 3 October 2023) and the MATLAB code for the machine learning model was provided by Mathworks ([[http://mathworks.com/help/deeplearning/](http://mathworks.com/help/deeplearning/)]([http://mathworks.com/help/deeplearning/](http://mathworks.com/help/deeplearning/)) accessed on 3 October 2023). **Acknowledgments:** This study was carried out under the KICT Research Program (project no. 20230135-001, Development of Ultra-Fast Fire Prediction Control Response Technology in Industrial Complex) funded by the Ministry of Science and ICT. **Conflicts of Interest:** The authors declare no conflict of interest.
# Finite Mixture Model-Based Analysis of Yarn Quality Parameters
## 2 Materials and Methods
### Theoretical Background of the Gamma Mixture Distribution  
The probability density function (PDF) of the gamma mixture distribution is expressed as given in Equation (35).  
\[f(x;\Theta)=\sum_{k=1}^{K}\pi_{k}g_{k}(x,\alpha_{k},\beta_{k}) \tag{35}\]  
where \(\Theta=\{(\theta_{1},\theta_{2},\ldots,\theta_{K})\ \}\) represents the parameter space of the mixture distribution, with each component \(\{\theta_{k}=(\pi_{k},\alpha_{k},\beta_{k})\}\) for each \(\mathrm{k}=1,\ \ldots,\ \mathrm{K}\). \(\alpha_{k}\) denotes the shape parameter of the \(k\)th component. \(\beta_{k}\) denotes the scale parameter of the \(k\)-th component. \(\pi_{k}\) denotes the mixture proportion of the \(\mathrm{k}\)-th component, satisfying \(\pi_{k}\geq 0\) for all \(k\) and the constraint \(\sum_{k=1}^{K}\pi_{k}=1\).  
Additionally, in this formulation, \(g_{k}(x,\alpha_{k},\beta_{k})\) characterizes each individual gamma component, weighted by its respective proportion \(\pi_{k}\), as Equation (36).  
\[g(x,\alpha_{k},\beta_{k})=\frac{\beta_{k}^{\alpha_{k}}x^{\alpha_{k}-1}e^{- \beta_{k}x}}{\Gamma(\alpha_{k})}x\geq 0\ and\ k=1,\ \ldots,\ K \tag{36}\]where \(\Gamma(\alpha_{k})\) is the gamma function and is given in Equation (37).  
\[\Gamma(\alpha_{k})=\int_{0}^{\infty}t^{\alpha_{k}-1}e^{-t}dt\ \ \ \ \ \ k=1,\ \ldots,\ K \tag{37}\]
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 8 Model Comparison  
The 1D CNN regression model performance was considered using the mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (\(R^{2}\)) evaluation metrics. The MSE and MAE metrics are described in Equations (6) and (7), respectively. The model prediction and corresponding actual distribution with the fit line are shown in Figure 15a. Figure 15b illustrates the error between the predicted and actual cutter wear with a histogram. Figure 15c presents the 1D CNN learning curve with 100 epochs. The reliable performance of the 1D CNN model was proved through a comparison of the six machine learning models (extratree regression, light gradient boosting, gradient boosting, random forest, AdaBoost, and k-NN regression) and two deep neural network models (MLP and LSTM). Table 6 presents a comparison of the proposed 1D CNN (deep learning), long short-term memory (LSTM), multilayer perceptron (MLP) neural network, and the six traditional machine learning models.  
The results illustrate that in terms of the correlation coefficient, mean squared error (MSE), and mean absolute error (MAE), the proposed model performed better than the six traditional machine learning models and the two deep neural networks models. The proposed 1D CNN model had a correlation coefficient of 89.6%, a mean squared error of 57.5, and a mean absolute error of 1.6. The k-NN regression had the lowest correlation coefficient of 19.04%. In terms of the neural network comparison, the LSTMRegressor had the lowest correlation coefficient of 39% and the highest prediction error value of 326.3 MSE. Based on computational time, k-NN regression had the best performance, by taking the shortest time of 0.062 s. Moreover, the proposed 1D CNN model had the fastest computational time of 3 min 22 s compared to the MLPRegressor (3 min 26 s) and the LSTMRegressor (12 min 38 s). The proposed 1D CNN model outperforms the traditional machine learning models and the two different neural network models in terms of the correlation coefficient, MSE, MAE, and computational efficiency, which makes it superior in estimating soft ground cutter wear with operational data in EPB TBM tunnelling.
## 3 System Architecture
### Experimental Setup and Dataset Collection  
For testing purposes, the digit image dataset was produced using the test setup shown in Figure 5. The edge node with the camera was fixed in front of the mechanical counter. As shown in Figure 5, only a part of the input image (ROI = region of interest) is used as the input for the digit recognition scheme, which is described in the following Section. In a real-life scenario, a mobile application communicates with the edge device via Bluetooth and  
Figure 4: Design and components of edge device is depicted on the left, whereas right images display fabricated devices.  
enables the user to define the boundaries of the ROI during the setup phase. In addition, after the device is deployed and operational, it will occasionally send a full image to the cloud to allow redefinition of the ROI and/or the camera parameters, should such an action be necessary. It will also be helpful in case of any misrecognition of digits. The digits on the counter change most rapidly in the least significant places or after the decimal point. If a digit is not recognized correctly, occasionally sending (e.g., once in a few days) the entire image of the ROI will allow for easy correction.  
The counter we used can be triggered to increment its state by generating an electrical pulse from the same MCU that takes images. That way, by knowing the initial value and the exact number of generated pulses, we created a labeled dataset on the spot. The procedure for the creation of the dataset consisted of the following:  
1. Successively incrementing meter values and taking photos
2. Cropping part of the image that contains digits
3. Dividing it into separate digit images (16 \(\times\) 24 pixels)  
The environmental conditions can significantly influence the recognition process [48]. Accordingly, the experiments were conducted, and the dataset was created in a dark setting (to resemble a real-world scenario, e.g., a manhole), with each image capture made using a flash LED. The captured images are preprocessed using the algorithms described in the next section to standardize the inputs of the ML model. The preprocessing procedures are designed to eliminate the potential effects of differences in the conditions under which the data were collected. Additionally, the entire apparatus was kept in a fixed position, and the validation of the results was carried out under the same laboratory conditions. The dataset consists of 3500 successive meter values, divided into 21,000 digit images.
## 3 Methodology  
### Verification Metrics  
To quantitatively compare GPM-era satellite precipitation products, several widely used statistical metrics were applied in this study. The formulas and perfect values of these metrics are listed in Table 2. The Pearson correlation coefficient (CC) describes the agreement between the satellite precipitation and gauge observations. Mean error (ME) simply scales the difference between the satellite estimates and the reference. The root-mean-squared error (RMSE) corresponds to the square root of the average of the squared differences between the estimates and the observed rainfall, and was used to measure the average error magnitude, while relative bias (RB) and relative root-mean-squared error (RRMSE) were used to calculate the systematic and random components of the error in the satellite precipitation products. For the contingency table metrics, following [PERSON] et al. [19], we adopted four skill scores, namely probability of detection (POD), false alarm rate (FAR), frequency bias index (FBI), and equitable threat score (ETS), to examine the detection capabilities in rain events of satellite estimates. The ETS is commonly used as an overall skill measure by the numerical weather prediction community, and the POD, FAR, and FBI present complementary information about misses, false alarms, and bias.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Product**} & **Temporal** & **Spatial** & \multirow{2}{*}{**Start Time**} & **Latency** & **Coverage** & **Corrected** \\  & **Resolution** & & & & & **by Gauges** \\ \hline IMERG-UC & 0.5 h & 0.1\({}^{\circ}\) & 12 March 2014 & 3 months & 90\({}^{\circ}\)N–90\({}^{\circ}\)S & No \\ IMERG-C & 0.5 h & 0.1\({}^{\circ}\) & 12 March 2014 & 3 months & 90\({}^{\circ}\)N–90\({}^{\circ}\)S & Yes (GPCC \\ GSMaP-MVK & 1 h & 0.1\({}^{\circ}\) & 1 March 2014 & 3 days & 60\({}^{\circ}\)N–60\({}^{\circ}\)S & No \\ GSMaP-Gauge & 1 h & 0.1\({}^{\circ}\) & 1 March 2014 & 3 days & 60\({}^{\circ}\)N–60\({}^{\circ}\)S & Yes (CPC daily) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coverage and spatiotemporal resolutions of satellite precipitation products used in this study.
## 5 Conclusions  
The overall objective of this article was to estimate the impact of LFEMFs generated by power infrastructure. For this purpose, there were analyzed values of \(E\) and \(B\) obtained during measurements on objects localized in the neighborhood of HV power lines, transformer stations, etc. Furthermore, numerical simulations of the distribution of the EF, as well as MF distributions, around real buildings next to power delivery system elements were conducted. To date, it has not been clearly established to what extent EMF radiation affects the development of cancer in humans. These concerns have prompted calls for the IARC [41] to reassess the risks of RF radiation. The results of animal studies suggest the need to consider upgrading the classification to \"probably carcinogenic\" or higher [42]. Given the lack of scientific consensus on the impact of EMFs on living organisms, the evaluation of the obtained results was based on the criteria outlined in existing standards. Various standards and local regulations address the permissible levels of EMF exposure. The guidelines included in the ICNIRP 2010 standard were chosen as the basic reference point, defining the maximum permissible values of \(E=5\) kV/m and \(B=200\) uT. Compared to the previous version of this standard from 1998, the permissible value of \(B\) has been doubled. However, individual countries may introduce stricter limits on these amounts. In terms of permissible limits of \(E\), the most restrictive requirements among the analyzed examples were those of Polish regulations (1 kV/m in the case of residential areas). In the matter of permissible values of \(B\), Belgium has introduced the most restrictive regulations (\(B=10\) uT). Nevertheless, some countries introduce even lower limits of \(E\) and \(B\), but this only applies to selected aspects, e.g., the \(B\) limit in some Italian provinces has been reduced to 3 uT. Another example is Switzerland, where new installations and objects of \"sensitive use\" require \(B\) smaller than 1 uT. Of the cases examined, only one (warehouse hall near the 110 kV double-track overhead lines) did not meet the requirements set by standard ICNIRP 2010. Due to the lowest permissible \(B\) values, 8 of the 14 cases analyzed did not meet the
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 5 Analysis and Discussion
### Interaction Between Redundancy and Modulation  
The interaction between adaptive perceptual modulation and temporal redundancy plays a central role in the effectiveness of the proposed watermarking method. While each technique contributes independently, modulation enhancing imperceptibility and redundancy improving robustness, their combination leads to a complementary and mutually reinforcing effect.  
Adaptive modulation, guided by local JND thresholds, ensures that watermark energy is embedded in visually insensitive regions. This allows the system to preserve high perceptual quality, but may leave certain areas under-embedded or unmarked, particularly in low-texture or flat regions. Without additional protection, this could lead to isolated bit loss under distortion.  
Temporal redundancy addresses this limitation by replicating each bit across multiple keyframes. The majority voting scheme during extraction benefits from this dispersion, especially because instances with stronger modulation are more likely to survive compression or noise and dominate the voting process. In essence, redundancy increases robustness not just by repetition, but by leveraging the perceptual strength of individual embeddings.  
Experimental results confirm this synergy: as the redundancy factor increases, the BER decreases consistently--even when adaptive modulation is active--demonstrating that robustness and imperceptibility need not be traded off but can be optimized jointly. This interplay enables the flexible tuning of the system, balancing visual quality with decoding reliability based on the needs of the application.
## 2 Numerical Simulation Methodology
### Geometric Model and Grid Generation
#### 2.1.2 Grid Generation  
The geometric model was discretized by unstructured grids using Workbench software. In order to increase the calculation accuracy, local grid refinement was applied around the stoves, lamps, human bodies, exhaust hood and fume purifier. The number of grids was critical to the simulation results. A coarse grid might lead to a larger discretization error. However, the over-refined mesh would enlarge the computation expense. Thus, a proper grid resolution should be conducted to achieve the balance between the computation expense and computational accuracy. The computational grid is shown in Figure 2.  
Figure 1: The geometric model of the CCKEF system. (**a**) Stoves and personnel; (**b**) Electrostatic purifier; (**c**) CCKEF system.  
A grid independence test was carried out using three meshes with 200,000, 350,000 and 600,000 cells. The calculated average air temperature and air velocity at three suction vents shown in Figure 1c under different grid numbers is displayed in Figure 3. As shown, the calculated average air velocity and temperature of the solution with 350,000 grid cells were consistent with that of 600,000 grid cells. By considering the balance between the computation expense and computational accuracy, the 350,000 cell mesh was chosen for the simulation.
# Pigment Dyeing of Atmospheric Pressure Plasma-Treated Cotton Fabric
## 3 Results and Discussion
### Dry Cleaning Fastness  
The dry-cleaning fastnesses of the pigment-dyed (1 g/L and 5 g/L) cotton fabrics was excellent. The colour change rating of the pigment-dyed (1 g/L and 5 g/L) control cotton fabrics was 4, while the colour change rating of the pigment-dyed (1 g/L and 5 g/L) atmospheric pressure plasma-treated cotton fabrics was 4-5. On the other hand, the colour-staining ratings of the pigment-dyed (1 g/L and 5 g/L) control cotton fabrics in multi-fibre stripes (acetate/cotton/nylon polyester/acrylic/wool) are all 4, but after atmospheric pressure plasma treatment, the ratings of pigment-dyed (1 g/L and 5 g/L) cotton fabrics increased to 4-5. Thus, the atmospheric pressure plasma treatment process can enhance the dry-cleaning fastness of pigment-dyed cotton fabrics.  
Generally speaking, pigment dyeing always gives poor dry-cleaning fastness, because pigments are deposited on the fibre surface, and can be detached easily during dry cleaning. In this study, the pigment particles in the pigment-dyeing solution have an effective diameter of 206.0 nm. This pigment particle size can enhance the dry-cleaning fastness (change of shade and staining on multiple fibre stripes) with a rating of 4-5. According to Figures 6 and 7, nanosized pigment particles are deposited in the cracks on the atmospheric pressure plasma-treated cotton fibre surface, and are encased in the layer of binder. This enhances the affinity between the binders and the cotton fibre surface, preventing the detachment of particles in the dry-cleaning process.
## 4 Conclusions  
A process for extraction of Cs(I) and Sr(II) from aqueous solutions using SC4A was studied. The results are promising in the sense that SC4A can sequester both Cs(I) and Sr(II) from radioactive contaminated aqueous waste. The stoichiometry of metal with respect to SC4A was determined to be 1:1 (Cs:SC4A) and 1:2 (Sr:SC4A). From the thermodynamic data, the extraction process was found to be exothermic. A possible extraction mechanism involves formation of an adjustable cavity from two SC4A molecules for the complexation of one Sr(II) ion, and the size of the adjustable cavity changes depending on the ion within an appropriate range. Furthermore, SC4A exhibited superior thermal and irradiation stability. The main structure of SC4A remained unchanged when the irradiation dose was lower than 1.1 \(\times\) 10\({}^{6}\) Gy. The distribution ratios of Cs(I) and Sr(II) remained above 27 and 20, respectively, under an irradiation dose of 1.0 \(\times\) 10\({}^{4}\) to 1.1 \(\times\) 10\({}^{6}\) Gy. In addition, SC4A/CHCl\({}_{3}\) showed efficient extraction ability when the irradiation dose was less than 5.0 \(\times\) 10\({}^{5}\) Gy.  
The following are available online at www.mdpi.com/2076-3417/6/8/212/s1, Figure S1: Synthesis of _p-tert_-butylthiacalix[4]arene, Figure S2: Synthesis of _p-tert_-butylsulfonylClax[4]arene, Figure S3: Synthesis of _p-tert_-butylsulfonylClax[4]arene tetraacetate, Figure S4: 1H-NMR of SC4A and extracted species, Figure S5: Dependence of the distribution ratio (\(D\)) of Mg(II), Ca(II), Ba(II) onto SC4A with a change in [SC4A], Figure S6: The IR of SC4A for different irradiated dose, irradiated dose, Figure S7: The MS spectra of SC4A before and after irradiation, Figure S8: TG curves of SC4A before and after irradiation, Figure S9: The UV-visible spectra of SC4A before and after irradiation, Figure S10: The 1H-NMR spectrogram of SC4A before and after irradiation, Table S1: The distribution ratio (\(D\)) of Mg(II), Ca(II) and Ba(II) onto SC4A with the change in contact time.  
This work was financially supported by the International Science & Technology Cooperation Program of China (No. 2014 DFR61080).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] contributed to the synthesis and characterization of extractants; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data, and contributed reagents/materials/analysis tools; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.