esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ultra-high-performance concrete (UHPC) has been following economic and environmental trends for the past two decades. Limited research has been conducted on the significance of superplasticizers in UHPC products, despite the high costs they entail for projects. The current study assesses UHPC based on rheological properties and mechanical characteristics considering different factors. In this study, the effects of different levels of superplasticizer derived from sulfonated naphthalene formaldehyde (SNF: 0.7%, 0.8%, and 0.9%), silica fume (SF: 15%, 20%, and 25%), and the water-to-binder ratio (w/b: 0.18, 0.20, and 0.22) were examined. Fresh tests such as slump flow, Vicat needle, and squeezing, as well as hardened tests like compressive strength, flexural strength, and electrical resistivity, were conducted. In the analysis, an artificial neural network (ANN) model and a fuzzy logic (FL) model were employed to forecast compressive strength results at 7 and 28 days. The results indicated that a higher SF dosage reduced slump flow and set time, whereas the opposite was observed for SNF and the w/b ratio. Three distinct behaviors were identified in the squeezing flow test findings: (1) specific elastic behavior and low plasticity, (2) extensive plastic behavior and significant dilatancy, and (3) heightened responsiveness to compressive flow rate and material ratio. SNF demonstrated promise in enhancing compressive, flexural, and electrical strength. The prediction models suggested that the FL (error range 3.18-4.36%) and ANN (0.74-1.03%) models performed well in predicting compressive strength at 7 and 28 days. The encouraging findings from this study set the stage for further sustainable and cost-effective construction methods.  
ultra-high-performance concrete; superplasticizers; squeeze flow; mechanical properties; artificial neural networks; fuzzy logic +
Footnote †: journal: Applied sciences
###### Abstract  
Vacuum laser beam welding enables deeper penetration depth and welding stability than atmospheric pressure laser welding. However, contaminated coupling glass caused by welding fumes in the vacuum space reduces laser transmittance, leading to inconsistent penetration depth. Therefore, a well-designed protective system is indispensable. Before designing the protective system, the contamination phenomenon was quantified and represented by a contamination index, based on the coupling glass transmittance. The contamination index and penetration depth behavior were determined to be inversely proportional. A cylindrical protective system with a shielding gas supply was proposed and tested. The shielding gas jet provides pressure-driven contaminant suppression and gas momentum-driven contaminant dispersion. The influence of the shielding gas flow rate and gas nozzle diameter on the performance of the protective system was evaluated. When the shielding gas flow was 2.0 L/min or higher, the pressure-driven contaminant suppression dominated for all nozzle diameters. When the shielding gas flow was 1.0 L/min or lower, gas momentum-driven contaminant dispersion was observed. A correlation between the gas nozzle diameter and the contamination index was determined. It was confirmed that contamination can be controlled by selecting the proper gas flow rate and supply nozzle diameter.  
laser welding; vacuum; coupling glass; contamination; shielding gas; protective system; welding consistency +
Footnote †: journal: Applied Sciences  
1 Jointing Research Group, Korea Institute of Industrial Technology, Incheon 21999, Korea; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] or [EMAIL_ADDRESS] (C.K.) Department of Mechanical Engineering, Yonsei University, Seoul 03722, Korea; [EMAIL_ADDRESS]  
2
## 2 Materials and Methods
### Operating Conditions of the Photoelectrochemical Reactor  
The photoelectrochemical reactor was operated under controlled conditions to study the degradation of dye contaminants through photocatalysis. The reactor's performance was influenced by several key parameters, including the applied current density, UV light exposure, and the reaction time. The conditions were optimized to ensure effective photocatalytic degradation while minimizing external variables. The reactor was operated at three different current densities, 0.2, 1.0, and 2.0 mA \(\times\) cm\({}^{-2}\), with the absence of current also considered as a baseline condition. These current densities were selected based on prior studies indicating their relevance in promoting efficient electron transfer during the photocatalytic process. The reactor was exposed to UV light from the LED bulbs (total power of 9 W) to enhance the photocatalytic activity. Control experiments were performed without UV exposure to evaluate the effects of current alone.  
The reaction was conducted at ambient temperature and atmospheric pressure, with all experiments carried out in duplicate to ensure reproducibility. During each experiment, samples were taken at regular intervals (every 30 min) to monitor the dye concentration and evaluate the degradation progress. The duration of the experiments was varied depending on the reaction kinetics observed, with typical experiments lasting up to 180 min. The reactor's configuration was designed to ensure uniform exposure of the electrodes to the UV light, and the geometry of the electrodes (Ti sheets with an electroactive area of 28.6 cm\({}^{2}\);) was chosen to optimize the photocatalytic reaction. The electroactive area was sufficient to facilitate efficient electron transfer while maintaining a manageable current density.  
The experimental setup was monitored for stability, and parameters such as pH, cell potential, and dye concentration were recorded regularly to ensure consistent operation. This controlled set of operating conditions allowed for a thorough assessment of the system's performance in dye degradation and its potential application in wastewater treatment. The effects of both imposed current density and UV light exposure were systematically analyzed to determine the optimal conditions for maximum degradation efficiency.  
The temperature was maintained constant throughout the experiments as specified in the methodology, ensuring consistent conditions for evaluating the degradation process. This approach allowed for isolating the effects of the studied variables without interference from temperature fluctuations.
# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 3 Proposed Method
### BART Modeling
Moreover, \(M\) represents the parameters of terminal nodes as follows: \(M=(\gamma_{1},\gamma_{2},\dots,\gamma_{r})\), where \(\gamma_{i}\) is \(i\)th parameter value of each node and \(b\) is the number of terminal nodes of each tree. \(\text{Tree}^{M}\) is entire tree with leaf parameters. Therefore, using \(m\) regression trees, we find the influence of each explanatory keyword \(X\) on response keyword \(Y\) (drone). In other words, using the results of relative influence of each explanatory keyword, we select important explanatory keywords that have a meaningful impact on the drone technology. Using the concept of Equation (7), the fundermental Equation (6) is represented as Equation (8). \[\text{drone}=\text{g}(X;\text{Tree},M)+\varepsilon,\ \ \varepsilon\ \sim N\Big{(}0,\sigma^{2}\Big{)} \tag{8}\]  
Under Equation (8), the conditional mean of keyword drone given explanatory keyword \(X\), \(E(\text{drone}|X)\) is the terminal node parameter assigned by \(\text{g}(X;\text{Tree},M)\). Next, we impose a prior to all the parameters of the sum of regression trees. The prior distribution consists of tree structure, leaf parameters, and error variance (\(\sigma^{2}\)) in (9) [30]. \[P\Big{(}\text{Tree}_{1}^{M},\text{Tree}_{2}^{M},\ \dots,\text{Tree}_{m}^{M},\ \sigma^{2}\ \Big{)}=P\Big{(}\sigma^{2}\Big{)}\prod_{i=1}^{m}P(M_{i}|\text{Tree}_{i})P( \text{Tree}_{i}) \tag{9}\]  
where \(P(\text{Tree}_{i})\) influences on the location of nodes in tree structure and \(P(M_{i}|\text{Tree}_{i})\) describes the control of leaf parameters. The prior component of error variance \(P\big{(}\sigma^{2}\big{)}\) is distributed to inverse-gamma distribution [30]. Moreover, the likelihood function specifies the responses of terminal nodes. We extract samples from the posterior distribution with response variable \(y\) in (10). \[P\Big{(}\text{Tree}_{1}^{M},\text{Tree}_{2}^{M},\ \dots,\text{Tree}_{m}^{M},\ \sigma^{2}\ \Big{|}y\Big{)} \tag{10}\]  
In our research, we applied Markov chain Monte Carlo (MCMC) to generate samples from the posterior distribution [33]. Therefore, we selected the important keywords representing drone technology using BART with prior, likelihood, posterior, and MCMC. The keyword selection process is performed by the number of covariates (keywords except drone) affecting keyword drone. The process uses the proportions of included variables for extracting important keywords [31]. The proportion of times in each keyword is calculated as total splitting rules over a splitting rule. Finally, we constructed technology scenarios of drone using BART modeling with correlation networks. Moreover, the scenarios were applied to MOT of drone such as R&D planning and new services development in drone technologies.
## 3 Results
### Model Testing of Simple Pipe Simulation
In Figure 7e, the K-means clustering segmentation method performs better than the original one. The results of the proposed method are shown in Figure 7e. In Figure 7e, the K-means clustering segmentation method performs better than the original one. The results of the proposed method are  
binary extraction on GPR images by selecting appropriate cluster centers. Compared with the Otsu threshold method, the background interference noise is significantly reduced, and the shape of the target hyperbolic reflection is better displayed. However, the hyperbola extraction of some diffraction waves and multiple wave types is affected by greater interference from the medium. In Figure 7d, the proposed Res-CNN method can obtain a better ROI in a binarized hyperbolic region. The hyperbolic reflections from different targets are clearer, and the background disturbances are less in the binarized profile. Deep diffraction waves and multiple-wave hyperbolas can also be effectively extracted. This enables improved interpretation and analysis of the target anomaly radar image with the binary profile. The binarization outcomes of the GPR images using three evaluation criteria, namely PSNR, SSIM, and FSIM, are assessed to mitigate the subjectivity associated with visual inspection. The quantitative results are given in Table 2. In Table 2, across all metrics, including PSNR, SSIM, and FSIM, the Res-CNN method consistently outperforms the other methods, demonstrating superior reconstruction fidelity, similarity to ground truth, and feature preservation. Specifically, the Res-CNN method achieves the highest PSNR of 57.1894, the highest SSIM of 0.9933, and the highest FSIM of 0.9336, indicating its effectiveness and better performance in image segmentation tasks. To evaluate the efficacy of a skip connection in training the proposed networks for GPR image segmentation, experiments were conducted with and without skip connections, while keeping the hyperparameters consistent. The trained model was used to binarized hyperbolic region extraction for GPR images, as shown in Figure 8. It is observed that the network architecture with a skip connection structure (Figure 7d) is more effective in capturing the morphology of the hyperbola region compared to the architecture without a skip connection structure (Figure 8). The performance of the network architecture with a skip connection structure is better at separating and characterizing the ROI binarization hyperbola region.
## 3 Results and Discussion  
### RAW264.7 Cell Viability  
The effect of okra ethanol extracts, prepared using different cooking methods, on RAW 246.7 cell viability was examined by MTT assay. The results of treating the cells with 25, 50, 100, and 200 \(\mu\)g/mL okra extract (from all four cooking methods) for 24 h are presented in Table 1 and Figure 1. The cell viability after the treatment with BOE, SOE, and SVOE, showed no significant difference, indicating the lack of cytotoxicity of okra concentrations up to 200 \(\mu\)g/mL. A comparison of the different cooking methods, likewise, showed no significant difference in cell viability regardless of okra concentration. Furthermore, SVOE treatment at the concentrations tested had no effect on RAW264.7 cell viability, rather SVOE at 25-100 \(\mu\)g/mL enhanced the cell viability. These results demonstrated the lack of cytotoxicity of ROE, BOE, SOE, and SVOE at \(\leq\)200 \(\mu\)g/mL concentrations.
# Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
## 5 Stochastic Programming Applications
### The KANDW3 Problem
#### 5.2.1 The Mathematical Formulation of the KANDW3 Problem  
The formal description of the KANDW3 Problem can be defined as follows [77; 78]. The required values for parameters and constants are also expressed.  
\begin{tabular}{l l} \(i\) & The materials \((i=1,\ldots,I)\). \\ \(j\) & The products \((j=1,\ldots,J)\). \\ \(t\) & The time periods \((t=1,\ldots,T)\). \\ \(x_{it}\) & The quantity of material \(i\) in the period \(t\) (decision variables). \\ \(y_{it}\) & The quantity of outsourced product \(j\) in period \(t\). \\ \(c_{i}\) & The cost of raw material \(i\), \(c=[2.0,3.0]\). \\ \(a_{ij}\) & The amount of raw material \(i\) to a unit of product \(j\), \\ \(a=\left[\begin{array}{ccc}2.0&6.0\\ 3.0&3.4\end{array}\right]\). \\ \(h_{jt}\) & The cost of outsourced product \(j\) in period time \(t\), \\ \(h=\left[\begin{array}{ccc}7.0&10.0\\ 12.0&15.0\end{array}\right]\). \\ \(b\) & The capacity of the inventory, \(b=50\). \\ \(d_{jt}\) & Random demands of product \(j\) in period \(t\). \\ \end{tabular}  
Figure 3: The FSEDA and ASEDA performance for the product mix (PROD-MIX) Problem.  
The values for demands can be obtained from the Figure 4. The object function for the KANDW3 Problem [77; 78] can be expressed as  
\[f(x,v)=\min\left(\sum_{i,t}c_{i}x_{it}+\mathbb{E}[\sum_{j,t}h_{jt}y_{jt}]\right). \tag{13}\]  
\[\begin{array}{ll}\text{s.t.}&\sum_{i,t}x_{it}&\leq b,\\ &v\sum_{i}a_{ij}x_{it}+y_{jt}&\geq d_{jt},\ \ \forall\ j,t,\\ &x_{it},y_{jt}&\geq 0,\ \ \forall\ i,j,t.\end{array} \tag{14}\]
# A Clutter Loss Model for Satellite Communication Systems
## 5 Clutter Loss  
The total clutter loss results from the combination of the total reflection loss, i.e., given by possible multiple wave reflections, and the total diffraction loss, i.e., given by possible multiple wave diffractions.  
The first step to calculate the clutter loss statistics is to generate a random urban scenario for each ray leaving the base station. This is achieved by randomly drawing, from the corresponding statistics (see Figure 2), the distances from the base station and the first and second buildings, and between the buildings from the second to the last (the building causing no further reflection or diffraction loss). The height of the buildings is also drawn randomly according to the corresponding statistic (see Figure 3). More specifically:  
* The height of all buildings is obtained from the unique CDF of the building height \(H_{b1}\);
* The distance between the base station and the first reflecting building is extracted from the \(D_{b1}\) statistic;
* The distance between the first reflecting building and the second one behind it is extracted from the \(D_{b12}\) statistic;
* For all possible reflections, from the first \"reflective\" building on, the distance between a facade and the next one is extracted again from the \(D_{b1}\) statistic;
* For all possible diffractions, the distance between a facade and the next one is extracted again from the \(D_{b12}\) statistic.
## 5 Damage Model and Numerical Test  
### Damage Model  
Through analyzing experimental data, it was found that the dissipation rate under different confining pressures exhibits similar evolutionary trends. The dissipation rate under different confining pressures can be fitted by the same function, which can be expressed as:  
\[\mathrm{y}=0.95-\frac{0.57}{1+\left(\frac{\mathrm{x}}{124}\right)^{14.43}} \tag{10}\]  
The fitting results of the dissipation rate under different confining pressures are shown in Figure 16.  
According to a previous work [31], the energy balance differential equation can be expressed as:  
\[\delta W=\mathrm{d}U+\delta A \tag{11}\]  
The input energy can be expressed as:  
\[\delta W=\sigma\mathrm{d}\epsilon \tag{12}\]  
where \(\sigma\) represents the stress tensor.  
The elastic energy can be expressed as:  
\[\mathrm{d}U=\sigma\mathrm{d}\epsilon_{e}-0.5\epsilon_{e}\mathrm{{}^{T}M_{0} \epsilon_{e}d}D \tag{13}\]  
where \(\epsilon_{e}\) represents the plastic strain tensor.  
The dissipated energy can be expressed as:  
\[\delta A=Y\mathrm{d}D \tag{14}\]  
where \(Y\) represents the damage energy dissipation rate, MPa.  
Substitute Equations (12)-(14) into Equation (11), the rock damage can be calculated as follows:  
\[\mathrm{d}D=\frac{\sigma\mathrm{d}\epsilon_{p}}{Y-0.5\left(\epsilon-\epsilon_ {p}\right)^{\mathrm{T}}\mathbf{M_{0}}\left(\epsilon-\epsilon_{p}\right)} \tag{15}\]  
Figure 16: Fitting results of the dissipation ratio \(\gamma\).  
The plastic strain can be calculated by Equation (9), and the damage variable can be obtained by incremental calculation.  
According to the stress-strain curves during the loading and unloading process, the damage can be solved as follows:  
\[D_{\mathrm{i}}=1-\frac{E_{\mathrm{i}}}{E_{0}} \tag{16}\]  
where \(D_{\mathrm{i}}\) represents rock damage of the \(i\)th cycle; \(E_{\mathrm{i}}\) represents the secant modulus of the unloading curve during the \(i\)th cycle, MPa; and \(E_{0}\) represents the initial elastic modulus, MPa.  
The comparison results of damage evolution curves under different confining pressures with experimental data are shown in Figure 17. It can be observed that the proposed model can reflect the rock damage evolution process, and the model error mainly exists in the instability process of rock near the peak stress.
## 3. Results and Discussion
### Monosaccharide Composition of FPs
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monosaccharides Composition** & **FPF** & **FPV** & **FPFI** & **FPH** \\ \hline Mannose & 2.84 \(\pm\) 0.17 \({}^{\text{c}}\) & 2.87 \(\pm\) 0.21 \({}^{\text{c}}\) & 4.03 \(\pm\) 0.12 \({}^{\text{a}}\) & 3.29 \(\pm\) 0.18 \({}^{\text{b}}\) \\ Rhamose & 8.35 \(\pm\) 0.11 \({}^{\text{a}}\) & 6.86 \(\pm\) 0.03 \({}^{\text{c}}\) & 8.38 \(\pm\) 0.09 \({}^{\text{a}}\) & 7.64 \(\pm\) 0.14 \({}^{\text{b}}\) \\ Glucuronic acid & nd & nd & nd & nd \\ Galacturonic acid & 24.35 \(\pm\) 0.14 \({}^{\text{a}}\) & 11.32 \(\pm\) 0.12 \({}^{\text{d}}\) & 18.69 \(\pm\) 0.15 \({}^{\text{b}}\) & 16.26 \(\pm\) 0.21 \({}^{\text{c}}\) \\ Glucose & 9.55 \(\pm\) 0.13 \({}^{\text{c}}\) & 8.55 \(\pm\) 0.15 \({}^{\text{d}}\) & 13.46 \(\pm\) 0.18 \({}^{\text{a}}\) & 12.44 \(\pm\) 0.09 \({}^{\text{b}}\) \\ Galactose & 17.64 \(\pm\) 0.05 \({}^{\text{d}}\) & 21.30 \(\pm\) 0.12 \({}^{\text{a}}\) & 18.37 \(\pm\) 0.06 \({}^{\text{c}}\) & 20.47 \(\pm\) 0.11 \({}^{\text{b}}\) \\ Xylose & nd & nd & nd & nd \\ Arabinose & 37.26 \(\pm\) 0.15 \({}^{\text{c}}\) & 49.10 \(\pm\) 0.07 \({}^{\text{a}}\) & 37.07 \(\pm\) 0.18 \({}^{\text{c}}\) & 39.89 \(\pm\) 0.17 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Note: Data are expressed as means \(\pm\) SD (\(n=3\)). Different superscript lowercase letters (a–d) are significantly different (\(p<0.05\)). nd: No detection.  
\end{table} 
Table 2: Effects of different drying methods on monosaccharide composition. Figure 1: HPLC chromatograms of FPs obtained by different dry methods.
## 3 Obstacle Avoidance Scenario
### Optical Flow Based
#### 3.3.5 Fusion  
From the review of different obstacle detection methods, it can be observed that there are limitations for each detection method. Optical sensing cannot generate accurate ranging data, while active sensor-based methods can measure distances more accurately. In view of this, researchers use fusion methods depending on the working motive, and several fusion combinations can be found in the literature [126; 176; 267; 281; 282; 283; 284; 285; 286; 287].
# Exploring the Oncogenic Potential of Bisphenol F in Ovarian Cancer Development
## 4 Discussion
The analysis revealed a non-significant difference in the SKOV3 cell invasiveness post-BPF treatment indicating the presence of alternative mechanisms of BPF-induced carcinogenesis. The observation of enhanced migration and non-invasiveness could be attributed to the differential modulation of the signaling cascades governing ECM degradation/MMP activation and cytoskeletal destabilization/motility. Oxidative stress is the disruption in the balance between the antioxidant system and ROS [51]. Elevated levels of ROS, primary oxidants promoting oxidative stress, are key contributors to cancer and are known to enhance cancer cell proliferation and metastasis [52; 53]. Due to the structural similarity between BPA and BPF, their biological effects could be analogous, including oxidative stress generation. [PERSON] et al. (2021) demonstrated a positive association between BPA and oxidative stress markers as well as colorectal cancer risk [54]. In ovarian cells, BPA-enhanced ROS levels resulting in reduced antioxidant responses [55]. Further, in breast cancer, BPF treatment enhanced ROS and intracellular calcium levels [36]. To further understand the potential mechanisms of BPF-induced effects, we monitored T-AOC as a measure of oxidative stress response. While previous studies suggest that BPF generates ROS, our findings revealed no significant changes at 1 uM concentration, indicating that oxidative stress might not be the primary mechanism of BPF-induced cellular effects. Moreover, these results underscore the need for further investigation into the molecular mechanisms underlying BPF's actions. Future research should focus on elucidating the molecular mechanisms and signaling cascades through which BPF regulates proliferation and migration, such as the PI3K/AKT, MAPK/ERK, and estrogen-receptor-mediated pathways underlying BPF's effects in OC. For identifying novel targets post-BPF exposure, transcriptomic and proteomic are crucial. Moreover, future studies to assess its long-term effects in vivo are required to have physiologically relevant insights. Such efforts will be crucial in determining whether BPF exposure poses a significant risk in cancer development. These findings would contribute to the development of safer, non-toxic BPF alternatives. Also, understanding environmental risk factors such as BPF enhances prevention strategies, and allows early-stage detection. The limitation of the study is the use of only the SKOV3 cell line, which may not represent the OC heterogeneity. In addition, in vivo validation of the oncogenic potential of BPF in OC would further increase the impact of the study.