esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dielectric Constant Results  
As illustrated in Figure 5, an increase in the HSO content is accompanied by a corresponding rise in the dielectric constant. The polar nature of HSO facilitates enhanced dipole interactions within the composite, which consequently elevates the dielectric constant. Conversely, an increase in CPP content has the effect of reducing the dielectric constant due to the low polarity of this substance. The incorporation of CPP into the composite material results in a notable decline in the overall dielectric properties. These findings highlight the importance of precise adjustment of the HSO and CPP ratios to achieve optimal dielectric properties. Equation (4) presents the quadratic model proposed by the RSM design for the dielectric constant.  
\[\begin{split}\text{Dielectric constant = +11.98270 - 0.304265 \times CPP+0.868628 \times HSO -0.09787 \times}\\ \text{MDI}-0.000556\times CPP\times HSO-0.000417\times CPP\times MDI-0.000417 \times HSO\times\\ \text{MDI}+0.004309\times(\text{CPP})^{2}-0.054091\times(\text{HSO})^{2}+0.00135\times(\text{MDI})^{2}\end{split} \tag{4}\]  
As evidenced by the ANOVA results presented in Table 5, the model is highly significant overall and successfully explains a substantial proportion of the variance (\(p\) < 0.0001). The effect of the HSO (B) factor on the model is particularly strong and significant (F = 1302.97, \(p\) < 0.0001), thereby underscoring the dominant influence of HSO on dielectric properties. Additionally, the CPP (A) factor yields a statistically significant effect (F = 7.53, \(p\) = 0.0287), though its impact is markedly less pronounced in comparison to that of HSO. In contrast, the MDI (C) factor does not demonstrate a statistically significant effect on the model (\(p\) = 0.8429). The binary interactions (AB, AC, BC) were found to be insignificant (\(p\) > 0.05), indicating the absence of synergistic or antagonistic interactions among the factors. However, only the B\({}^{2}\) term (the quadratic effect of HSO) is significant, thereby underscoring the importance of nonlinear effects of HSO. The model's low error rate (residual SS = 0.0507) and the non-significant \"Lack of Fit\" test (\(p\) > 0.05) serve to reinforce the model's reliability. Therefore, it can be concluded that HSO is the most dominant factor influencing the dielectric constant, while the effects of other factors are more limited.
## 4 Results and Discussion  
### Gaps Related with Digital LCA Data  
The gaps related to the identification of the product-related data are analyzed in Table 1.  
The gaps related to the LCA information are analyzed in Table 2.  
The gaps related to the results of the LCA and the product's performance data are analyzed in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Gap** & **Name** & **Analysis** \\ \hline \multirow{4}{*}{1} & Unique identification of the EPD & \begin{tabular}{c} The options for the RSL should be predefined in the relevant PCR and provided in the digital format as enumerated values. It can be based on parameters to be \\ service life (RSL) \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Information modules, defined in \\ CEN/TC 350 or ISO/TC 59/SC 17 \\ standards, not included in IFC 4.3 \\ \end{tabular} \\ \hline \multirow{4}{*}{8} & Parametric definition of all the & \begin{tabular}{c} IFC should include parameters for general properties, but the parameters specific \\ for a product family should be defined in the relevant PCR. Therefore, this gap \\ cannot be resolved solely by revising the IFC schema. \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Unique identification of the \\ constituent materials, including \\ their percentage in mass \\ or volume \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} The unique identification of materials should be globally recognized to ensure \\ interoperability. Therefore, this gap cannot be resolved solely by revising the IFC schema. \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Unique identification of the \\ constituent materials, including \\ their percentage in mass \\ or volume \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} The identification of LCA sources should be globally recognized to ensure \\ interoperability. Therefore, this gap cannot be resolved solely by revising the IFC schema. \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Identification of any LCA \\ databases or sources for \\ background data \\ \end{tabular} \\ \hline \multirow{4}{*}{10} & \
\begin{tabular}{c} Parametric description of any \\ other LCA assumptions or \\ decisions, such as \\ processes excluded \\ \end{tabular} \\ \cline{2-3}  &
\begin{tabular}{c} The declaration of this information should be globally standardized in a revision \\ of ISO 22057. The PCR should include the relevant criteria for this information, \\ which should then be used to compare the assumptions with the actual building. \\ This will help determine if the information can be transferred with modifications. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Gaps related to the identification of product-related data.
## 3 Results and Discussion  
### Yields  
The extracts yields obtained for the three plant matrices are reported in Table 1. The highest yield for thyme was obtained for 2 h Soxhlet extraction (S2h), while the lowest for maceration (M) and ultrasound assisted extraction (UAE) (\(p<0.05\)). The best yield for hemp was achieved, indeed, for 6 h Soxhlet extraction (S6h), while the lowest for M and UAE extracts (\(p<0.05\)). For this matrix, no significant differences were recorded among RSLDE extraction times (N2h and N6h) and S2h (\(p>0.05\)). A totally different behavior was observed for coriander seeds extracts: the best yield was found for UAE and, then M; the lowest was N2h.  
According to these data, Soxhlet seems to be the most suitable technique for the extraction of plants aerial parts in terms of yield. However, it should be pointed out that this extraction technique carried out at high temperature allows the co-extraction of the fibers [25; 26; 27; 28; 29]. These contribute to the dry extract weight. On the other hand, ultrasound assisted method seems to be the best extraction technique to process plant seeds.  
Lower yields were obtained for coriander seeds with respect to hemp and thyme. This is common to other plant species. In fact, the best yields of extraction are usually recovered from stems and leaves [30; 31]. In any case, our findings are in line with literature data, falling within the intervals normally reported in several works for the same species for some of these techniques [28; 32].
# Intelligent Framework Design for Quality Control in Industry 4.0
## 3 Materials and Methods
### Post-Production Model for Machine Vision  
The second experiment is performed to inspect the quality of fabricated products; hence, it is post-production quality control. The machine vision system as described in Section 1 is deployed to monitor the quality of the product and to classify between a good product and a defective one. We use the metal hex nut data set [33] for our experimentation. The convolutional neural network (CNN) is trained for the quality control of hex nut products using machine vision. The convolutional neural network (CNN) model is selected, which outperforms others for image classification problems. The CNN is preferred over other machine learning models due to a variety of reasons, including its inherent ability to capture spatial and hierarchal features in image patterns [42]. The convolutional layer enables a CNN to recognize the patterns irrespective of the location in the images; this is called translational invariance [43]. The pooling layer allows the down sampling of the spatial dimension of the feature map, thus reducing the computational complexity [44]. The data set consists of 1800 images of good hex nuts and 1800 images of defective hex nuts. The 1800 images are divided into 1400 for training and 400 for test purposes. Images of good and defective parts are provided in Figure 6 below.  
As mentioned earlier, the CNN is the most preferred deep learning technique for image classification; therefore, a CNN is used to predict the quality of hex nuts. The architecture of the CNN is provided in Table 5. The input image size is (150,150), the first layer has 64 neurons, and _relu_ is used as an activation function followed by max pooling. The second layer has 32 neurons with the _relu_ activation function. _Sigmoid_ is used in the output layer for binary classification of the good and defective products. Adam is used as an optimizer with a learning rate of 0.0001. The loss function is binary cross-entropy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No.** & **Fault Type** & **Number of Samples** \\ \hline
1 & Good hex nut training images & 1400 \\
2 & Good hex nut test images & 400 \\
3 & Defective hex nut training images & 1400 \\
4 & Defective hex nut test images & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and test samples of the good and defective products.  
Figure 6: A defective and a good hex nut.
## 4 Results and Discussion
### Analysis of Random Field Results
#### 4.4.1 Effect of Different COV  
Some literature studied that the shear strength parameters, cohesion and internal friction angle, in geotechnical materials varied from 0.1 to 0.5 [6]. The FOS is calculated, and the parametric study of the spatial variation of shear strength parameters is investigated during the slow water drawdown. All the results of the random field have similar properties with the histograms of the FOSs depicted in Figure 11, which correspond to the parameter set (\(l_{x}=50\) m, WL = 5 m) versus five different COVs. For comparison, the same deterministic  
Figure 10: The FOS vs. different WLs for definite and random field analysis.  
Figure 9: The result of FOSs from random field analysis.  
results were 1.684, as marked with an arrow in Figure 11. Figure 11 also shows that all the mean values in the five subfigures are lower than the deterministic value of 1.684. The differences between the mean value and deterministic value become larger with the increase in COV of shear strength. The statistical result from Figure 11 shows that although the FOS obtained from deterministic FOS is close to the mean value of that based on random field analysis, still nearly half of the FOSs are less than the deterministic value. The result from Figure 11e shows that there is a large difference between the mean value and the deterministic value, and the failure probability was about 23.3%. This result presents a high risk of landslides for the overall slope system, despite the mean value from the random field analysis and the deterministic value both being located at a relatively safe level. Figure 11 also shows that the range of FOSs increases with the increase in the COV, having similar properties to the previous study. The result also indicates that the random field method is a more reasonable method for assessing the stability of the geotechnical slope.  
Figure 12 shows the contour of the shear dissipation energy at the last stage of the failure slope for this test embankment, in which the subfigures correspond to the subfigures in Figure 11. The shear strength of the soil set in this test embankment is strong because the FOS is relatively high, resulting in the failure of circular slip surface through the toe shown in Figure 12. The failure circular slip surface moved toward the bottom of the foundation with the COV of the shear strength becoming larger. In other words, the magnitude of the failure soil increased with the COV of the shear strength becoming larger.  
Figure 13 shows that the mean FOS decreases as the COV increases. The reason is that when the COV becomes greater, the fluctuation of the uncertainty of the soil strength becomes more serious. It is more possible to take much weaker strength parameters, cohesion and internal friction angle, so that the FOS becomes lower. This is consistent with the real situation that there are strong and weak areas in an actual slope system. The failure of the embankment slope occurs easily in weak soil areas.
## 4 Model for Calculating Water Jet Slotting Depth
The special fitting parameters were given in Equation (15). The fitting degree reached 86%, which indicates that the model established for calculating the slotting depth can be used to describe the relationship between the parameters and the slotting depth. Based on the fitting parameters, the effects of the jet pressure, rotation speed, and slotting time on the slotting depth can be obtained. \[h=0.7873(P-P_{c})n^{1.0005}t^{-0.4891\ln(n)+2.0301}. \tag{15}\]  
Figure 11 shows the fitting curves between the slotting depths and slotting time under the different rotation speeds at the jet pressure of 10 MPa. Thus, to improve the jet slotting depth, the rotation speed should be adopted according to the slotting time. Specifically, when the slotting times are 0-5, 5-10, and 10-15 min, the optimized rotation speeds should be 60, 50, and 40 r/min, respectively. Moreover, as shown in Equation (15), the jet pressure and the threshold rock-breaking pressure of the coal are proportional to the slotting depth. In other words, the jet pressure and threshold pressure only affect the slotting depth magnitude, and have no effect on its changing trend. Therefore, when the jet  
Figure 10: Slotting depth at different rotation speeds. pressure changes, the relationships between the slotting depth and the rotation speed and slotting time do not change. According to the abovementioned analysis, the slotting depths under different jet pressures and threshold rock-breaking pressures can be obtained. The tensile strength of coal is typically 0.28-2.25 MPa [32]. Figure 12 shows the predicted slotting depths under the jet pressure of 25 MPa with a tensile strength of 0.5 MPa. The other parameters were as follows: nozzle diameter of 3 mm and initial standoff distance of 12.5 mm. Therefore, the optimized slotting parameters were obtained as follows: when the slotting time was 0-5 min, the optimized rotation speed was 60 r/min; when the slotting time was 5-10 min, the optimized rotation speed was 50 r/min; when the slotting time was 10-15 min, the optimized rotation speed was 40 r/min; when the slotting time was 15-20 min, the optimized rotation speed was 30 r/min and the slotting depth reached 1.9 m.  
Figure 11: Fitting curves between slotting depth and slotting time at different rotation speeds under jet pressure of 10 MPa. Figure 12: Prediction curves of slotting depth versus slotting time at different rotation speeds under jet pressure of 25 MPa.
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 3 Paint Industry Wastewater Treatment Methods
### Physicochemical treatment
#### 3.1.1. Coagulation and Flocculation
The COD removal increased after 1.5 h of electrolysis for MOAE/CaCl\({}_{2}\) to 70% and for MOAE/Ca(NO\({}_{3}\))\({}_{2}\) to 75% [34], making this coagulant more efficient [11; 34]. Furthermore, in the coagulation-floculation treatment of paint wastewater, the adsorption capacity of bio-coagulant _Brachystegia etrycoma_ seed was noted [53] to achieve an even higher COD treatment efficiency of 78.6%. However, the coagulation process is not useful for the complete removal of pollutants from paint industry effluents. One of the drawbacks of this method is the dependence of the flocs dimension on temperature, small flocks are created at very low temperatures [19]. In addition, the method is not useful for the removal of microorganisms; therefore, microorganisms are still present in the effluent and can continue to grow. Using activated carbon to remove inorganic pollutants from water is a widely extended technique because of its high surface area, microporous character, and chemical nature of its surface [43]. After coagulation, the filtration of the suspended particles is needed to separate them from the liquid through a membrane or filter. Various filtration techniques (ultrafiltration, membrane filtration, microfiltration, and biofilration) are used [10; 54]. Coagulation is a simple and useful method for the elimination of the suspended particles but the chemicals present in paint industry effluents can precipitate in their toxic form; therefore, high amounts of sludge can be produced which cannot be eliminated by filtration. Furthermore, the efficiency of the treatment process depends on the pH of the wastewater. It has been shown that COD removal is higher when the pH of the industrial effluent is adjusted to be alkaline. On the other hand, at the original pH of the effluent (pH 6), a higher dosage of the coagulants is needed.
## 2 Materials and Methods
### Casting of Mg Alloys
The extracted samples had a square section and a thickness of approximately 1.3 mm, spanning the length of the ingots (see Figure 2). This allowed us to access all the different cooling rates achieved in the directionally solidified ingot. The cuboidal sample was shaped by sanding with 600 and 1200 grit SiC. Samples produced in this manner were scanned in the XCT system to derive a 3D microstructure, and scanned using electron microscopy (EM) as well as Energy-Dispersive Spectroscopy (EDS) systems to acquire their 2D microstructural and compositional data. The composition of the alloys was verified using the energy-dispersive X-ray fluorescence spectrometry (XRF) technique (Shimadzu EDX-720, Shimadzu, Kyoto, Japan). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample ID** & **Si** & **Zn** & **Cooling Rate** \\  & **(wt.\%)** & **(wt.\%)** & **(\({}^{\circ}\)C/s)** \\ \hline Mg-0.6 Si-2 Zn-0.48 & 0.6 & 2 & 0.48 \\ Mg-0.6 Si-3 Zn-0.44 & 0.6 & 3 & 0.44 \\ Mg-0.6 Si-3 Zn-0.85 & 0.6 & 3 & 0.85 \\ Mg-0.6 Si-3 Zn-1.44 & 0.6 & 3 & 1.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Samples characterized to understand the impact of compositional cooling rate variance in the Mg–Si–Zn alloys include two different compositions (with different Zn wt.%) and three different cooling rates (\({}^{\circ}\)C/s). Figure 1: The experimental approach employed in the fabrication of directionally solidified alloys allows the production of Mg–Zn–Si alloys with compositional control and differential solidification rates within ingots.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 6 Conclusions  
This paper presents FL-SDUAN, a routing scheme for SDN-based underwater acoustic networks. First, we established the architecture of FL-SDUAN and presented the fuzzy  
Figure 14: The comparison of throughput under a different number of nodes.  
Figure 15: The comparison of complexity.  
problem of path weights in the system. Then, we developed two MST-based fuzzy routing algorithms, FPO-MST and FCO-MST, in detail. Finally, the packet delivery rate, end-to-end latency, and throughput of FPO-MST and FCO-MST were compared with EEHC, [PERSON]'s algorithm, and LEACH under different underwater network scenarios. The experimental results showed that the algorithm improved the routing performance and achieved a trade-off of performance and complexity.  
Recently, we are prototyping a fuzzy logic-based SDN controller. Next, the field deployment and experiments of FL-SDUAN will be the main work.  
Conceptualization, [PERSON] and Q.F.; methodology, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and J.M.; writing--original draft preparation, [PERSON] and Q.F.; writing--review and editing, [PERSON]; visualization, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the National Natural Science Foundation of China under Grant 61701356 and partly by the Key Scientific and Technological Project of Henan Province under Grant Nos. 222102320181 and 212102210422, in part by the Young Scholar Training Program of Higher Education in Henan Province under Grant 2019 GGJSI72, and in part by the Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant Nos. 20A520002 and 21A520001.  
Not applicable.  
The data used to support this study are available from the corresponding author upon request.  
The authors declare that there is no conflict of interest with respect to the publication of this paper. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 1 Introduction
Therefore, deformation predictions tend to underestimate the deformations of large rockfill dams. This paper presents a Visco-Elasto-Perfectly Plastic model (VEPP) for deformation prediction that, different from what is common engineering practice for rockfill dams, employs the following: (1) uses target porosity after compaction as a parameter of the non-linear elastic part; (2) considers a plastic yield surface that incorporates the actual grain size of the rockfill and target porosity after compaction as model parameters; and (3) uses a simplified creep approach. The model parameters are obtained from standard laboratory tests. The proposed model is shaped for deformation prediction of high CFRDs in practical engineering applications at early stages of design process, as it uses rockfill parameters with physical meaning estimated by standard laboratory tests programs. The performance of the VEPP model proposed was evaluated through the application to the case study of Shuibuya dam, which is the tallest CFRD dam in the world, as there are plenty of data available relative to the observed behavior of this dam. The VEPP model proposed was implemented in 2D numerical model using an explicit finite difference scheme with FLAC [25]. Simulation of deformations during construction, impounding, and during the first two years was performed, and predicted and observed settlements were compared. The paper is organized as follows. In Section 2, the proposed VEPP constitutive law is presented. Section 3 describes how the model was implemented for Shuibuya Dam. Section 4 shows the results obtained, and Section 5 includes the discussion. In Section 6, the main conclusions are summarized.