esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Milk Quality in Skopelos Goats Under Low- and High-Input Farming Systems
## 4 Discussion
In the extensive system, goats grazed outdoors throughout the year and were exposed to greater temperature fluctuations and higher ambient humidity, particularly during winter and early spring, which may have contributed to increased microbial contamination and under stress. In contrast, in the intensive farming system, animals were housed indoors, where the temperature and humidity were more controlled and stable. This controlled environment likely contributes to better under health and lower bacterial contamination, as it reduces environmental exposure. Although environmental data were used descriptively to characterize the study regions, they were not included as covariates in the statistical analysis. Future studies could benefit from integrating environmental variables (e.g., temperature, humidity, and rainfall) into statistical models to better assess their impact on SCC and TBC. This would allow for a clearer distinction between the effects of management practices and environmental stressors and could help identify periods of increased risk that require targeted hygiene interventions, particularly in extensive systems. In the analysis of farming systems, significant interactions were observed between year and lactation stage (LS), showing how these factors are interrelated. In the extensive system, milk yield, protein, and lactose decreased as lactation progresses, while fat content increased. These trends remain consistent across both years, suggesting that extensive farming results in a predictable milk composition over time, though individual goats may vary, as seen in the PCA. These results are consistent with the findings by [PERSON] et al. [18] in Nguni goats, [PERSON] et al. [68] in goats grazing Mediterranean woody rangelands, and [PERSON] et al. [67] in goats reared in extensive farms. In the intensive system, milk yield and composition vary across the years, suggesting that factors such as management practices, diet, and feeding regimes influence milk composition [16; 20; 55; 69; 70]. The PCA supports this by showing a tighter cluster for intensive farming, indicating greater control over milk traits. This suggests that while intensive farming supports higher production, maintaining a consistent milk composition requires careful management.
## 4 Results
### Back-Calculated Results of Elastic Modulus
#### 4.2.3 High-Polymer Materials  
High-polymer materials typically exhibit superior strength performance, making them suitable for high-load applications. The back-calculated elastic modulus results for Section 5 after high-polymer grouting treatment are shown in Figure 9. As illustrated, the grouting-treated pavement achieved an average elastic modulus of 676.15 MPa, significantly higher than that of the untreated adjacent areas, indicating exceptional stiffness and excellent mechanical properties. The relatively low standard deviation of 25.04 MPa further demonstrates consistent performance across test points, confirming uniform material behavior.
# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 2 Materials and Methods  
### Data Collection and Model Variables Description  
The experimental data for various polymer-based composites were collected to train the ANN model for predicting dielectric constants from the references [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Table 1 shows the kinds and dielectric constants of all polymer matrices and ceramic nanofillers used in this work [50; 51].  
From the data points, the four groups of input parameters were considered: (i) the properties of matrix and filler (dielectric constant, diameter of filler, and volume fraction), (ii) the morphology of filler (particle [0D], nanowire [1D], and nanosheet [2D]), (iii) the properties of shell (dielectric constant and thickness), and (iv) operating parameter (frequency), as shown in Figure 1.  
The total experimental datasets available for modeling was 744. The data used in this study show the distribution of a normalized \(\varepsilon_{r}\,\,\varepsilon_{composite}/\varepsilon_{matrix}\)) with a range from 0.25-5.75 in Figure 2. The normalized \(\varepsilon_{r}\) of most composites exceeds 1 with an average value of 1.27, indicating that the dielectric constant is enhanced by adding ceramic nanofillers due to their higher dielectric constant. In other hands, some data show values below 1, which is the case with nanofillers with a lower dielectric constant than polymers such as boron nitride (BN). Model training mainly concentrated on minimizing a mean squared error (MSE) to estimate the accuracy of the prediction model given as  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2} \tag{1}\]where \(y_{i}\) is measured output data from the result in previous research, \(\hat{y}_{i}\) is a calculated output data from the prediction model, and \(N\) is a total number of datapoints.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Matrix** & **Dielectric Constant** & **Filler** & **Dielectric Constant** \\ \hline Polypropylene, (PP) & 2.1 & BNNS & 4 \\ Polyimide, (PI) & 3.2 & Al2O3 & 9.4 \\ Poly(vinylidene fluoride-co-hexafluorpropylene), & 8.4 & TiO2 & 110 \\ P(VDF-HFP) & 8.6 & NaNbO3 & 200 \\ Poly(vinylidene fluoride-trifluoroethylene), & 12 & SrTiO3 & 200 \\ P(VDF-TrFE) & 12.3 & BaSrTiO3 & 300 \\ Poly(vinylidene fluoride-co-chlorotrifluoroethylene, & 41 & BaTiO3 & 1000 \\ Poly(VDF-TrFE-CFE) & & & \\ fluoride-trifluoroethylene-chlorotrifluoroethylene, & 42 & & \\ P(VDF-TrFE-CTFE) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dielectric constant values for different polymer matrixes and nano ceramic fillers.  
Figure 1: Schematic of the input parameters in the machine learning.  
Figure 2: Distribution of normalized \(\varepsilon_{\mathbf{r}}\) for all samples in the dataset.
## 3 Results and Discussion
### Combustion Indicators
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Marking**} & **ST, CAD** & **SOC 1, CAD** & **10\% MBF 2,** & **50\% MBE,** & **90\% MBF,** \\  & **BTDC** & **BTDC** & **CAD BTDC** & **CAD ATDC 3** & **CAD ATDC** \\ \hline BG0 & 26 & 22.5 & 4.1 & 7.0 & 22.9 \\ \hline \multirow{2}{*}{BG20} & 30 & 23.8 & 5.5 & 6.0 & 24.0 \\  & 26 & 20.1 & 1.8 & 10.4 & 25.5 \\ \hline \multirow{2}{*}{BG40} & 35 & 27.8 & 5.6 & 7.3 & 28.0 \\  & 26 & 19.3 & \(-\)1.2 & 15.9 & 40.3 \\ \hline \multirow{2}{*}{BG50} & 40 & 27.0 & 3.9 & 9.7 & 28.2 \\  & 26 & 17.8 & \(-\)4.7 & 21.2 & 42.2 \\ \hline \hline \end{tabular}  
* SOC—start of combustion, 2 MBF—mass burned fraction, 3 CAD ATDC—crank angle degrees (CAD) after top dead center (BTDC).  
\end{table} 
Table 5: Combustion parameters. The change of the in-cylinder pressure rise (PR) is visible in Figure 10. Adjusting ST and changing BG composition led to increased PR when engine works on BG20. The maximum value of the pressure rise increased by 2.7% compared to BG0 and moved 1 CAD closer to the TDC. The increase occurred due to the low decrease of \(LHV\) and optimal ST. A further increase in the CO\({}_{2}\) concentration decreased the PR by 16.5% and 24.5% at BG40 and BG50, respectively. The adjusted constant ST for 26 CAD BTDC decreased the pressure rise by 18.9%, 56.6% and 72.9% with BG20, BG40 and BG50, respectively, compared to BG0. The decrease in pressure rise is higher due to the extended timing of the flame formation phase and the low combustion speed of BG. The maximum value of the pressure increase moved further away from the TDC and the main biogas fraction burned in a larger cylinder volume, which reduced the PR and BTE. Figure 10: Change of the in-cylinder pressure rise depending on the volumetric fraction of CO\({}_{2}\). (**A**) Constant ST, (**B**) Optimal ST.  
Figure 9: Change of the in-cylinder temperature rise depending on the volumetric fraction of CO\({}_{2}\). (**A**) Constant ST, (**B**) Optimal ST.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 4 Conclusions and Proposals  
### Conclusions  
This study focused on strengthening the effect of a foam's liquid phase during the oil flooding process, in an attempt to prepare a foam system for EOR with high foaming properties and an ultra-low oil-liquid IFT. Several surfactants and polymers were evaluated. The foam composition was optimized  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Oil Saturation (\%)** & **E\({}_{0}\) (\%)** & **E\({}_{0}\) (\%)** \\ \hline
1 & 78.23 & 60.61 & 8.33 \\
2 & 79.18 & 57.30 & 10.67 \\
3 & 79.23 & 61.78 & 11.32 \\
4 & 69.26 & 48.44 & 8.69 \\
5 & 68.18 & 45.29 & 6.59 \\
6 & 66.71 & 43.11 & 11.95 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the core-flooding experiment.  
Figure 5: Differential pressure varied with the injection volume.  
by adjusting the surfactant and polymer concentrations and combinations, with the goal of achieving a foam system with an ultra-low oil-liquid IFT and a high foaming capacity. The main conclusions are summarized below.  
1. The foam system should have both a high foaming capacity and a low oil-liquid IFT obtained by using different surfactant and polymer structures and adjusting the composition of chemical additives.
2. The compounding of DHPB and SDS (mass ratio at 1:1.5) exhibited the best foaming properties (FCI: 117,600 mL-min) and the lowest oil-liquid IFT (0.0076 mN/m). The compounding of surfactants with the same carbon number showed better results.
3. Increasing polymer concentration lowered the oil-liquid IFT reduction rate, and led to an increase in the oil-liquid IFT. The viscosity of the liquid phase depends on the polymer concentration, which expected to affect the diffusion of surfactants and gas.
4. Ultra-low IFT foam system could function as both surfactant-polymer system and foam system in flooding process, which means it mainly functions in oil-liquid IFT reduction in high permeability conditions and blocks the high permeability channels in low permeability conditions. This feature meant the ultra-low IFT foam system could be more applicable in the EOR process.
## 2 System and Process Analysis
### Information Exchange Requirements  
The information exchange requirements are described according to information units and attributes, which are collected from the literature--the interested reader is referred to [28]--and from the survey, i.e., from experienced practitioners and researchers in the domains of design/architecture, civil engineering, mechanical engineering, material science, and robotics. Considerations regarding the material-process interactions between concrete and the manufacturing process are included in the information exchange requirements. Furthermore, fabrication information modeling (FIM) concepts introduced in [32] and BIM concepts explored in [33; 34] are considered when defining the information exchange requirements for concrete printing. Previous experiences in integrating FIM-based frameworks into BIM, presented in [23], provide insights into the interactions within concrete printing when translating digital models into physical components.  
The attributes are analyzed according to completeness (i.e., if an attribute is required or optional) and with respect to interoperability, where completeness ensures the inclusion of all attributes necessary to manufacture components and interoperability ensures a common understanding between tasks and actors. The survey results provide insight into the completeness and interoperability of the attributes. For illustration purposes, the information exchange requirements for AM models are presented in Table 1, where the prerequisites of the information exchange requirements are highlighted in gray. AM models, representing the output of toolpath planning, encompass all information necessary to generate machine instructions, including process data and material specifications. Toolpaths are usually described by sets of points defining printing and axis paths. Material-process interactions are represented using toolpath profiles, which are evaluated via material modeling and manufacturing process simulations. Further information on the information exchange requirements for concrete printing may be found in a previous study by the authors presented in [28].  
Based on the information exchange requirements, the classes as well as the interactions between the classes necessary for AM data modeling are identified. In the following section, the printing information model for additive manufacturing of concrete structures is presented.  
Figure 2: Extract of the process map describing the concrete printing process [28].
# Hybrid Graph Models for Traffic Prediction
## 2 Related Work
### Traditional Method  
Classic statistical and machine learning-based methods are two major representative works in traffic prediction [17; 18]. Compared with statistics-based methods, machine learning-based methods such as support vector machines and autoregressive integral moving averages take into account the complex spatial-temporal information of the traffic network, and these methods achieve better predictive performance than linear models. These methods can effectively alleviate the complex spatial correlation and nonlinear temporal dependence of the traffic network. However, these methods rely on manual feature extraction and cannot effectively extract deep semantic information, and the predictive performance of these methods is limited.
## 4 Functionality Demonstration of the Mission Design Configurations in the ISL Network
### SS-BPSK Receiver  
Figure 8 shows the implemented GNU Radio-based SS-BPSK receiver software.  
The receiver recovered the signal through the UHD: USRP source block. The DC blocker then eliminated the transceivers' DC offset noise from the signal. After that, the signal went through a high-performance automatic gain control (AGC) unit which regulated the increase in signal amplitude from the original data to the amplified data. Synchronization of the transmitter and receiver time offsets was carried out at the polyphase clock sync block. The Costas loop corrected the phase and frequency offset as well as recovering the carrier. Finally, the signal was demodulated and saved as files in either binary or wave data formats. Figure 9 is the recovered transmitted data in binary form.  
The saved 250 bits of information received represent header information (1 byte), Sat ID (2 bytes), time (2 bytes), latitude data (11 bytes), longitude (12 bytes), altitude (2 bytes) and footer (1.25 byte). The footer includes 2 bits of information added by the software for efficient transmission. At the receiver the two bits (00) added to the footer by  
Figure 8: SS-BPSK receiver at Sat \(B\).  
Figure 9: Recovered SS-transmitted GPS data at Sat \(B\).  
software for efficient transmission were eliminated. By using the rapid tables binary text translator software [52] to convert the binary data to readable text, the received binary data conformed to the transmitted SS signal data (F, 01, 04, 33.53513942, 130.50418427, 46, _A_). The implemented system could recover the transmitted signal from Sat \(A\) to Sat \(B\) and vice versa at both 400.15 MHz and 460.00 MHz in both cases, with and without signal delay. This demonstrated the feasibility of ISL communication where each satellite can transmit or receive location and time data to or from other satellites in the network. In reception mode the satellites could decode and understand the location data of other satellites. As a result, the ISL network requirement was proven.
## 3 Results  
### Surface Roughness  
Mean and standard deviation for Ra scores of CoCr specimens after using different fabrication techniques are presented in Table 1. SLM fabricated copings displayed the highest mean values (2.251 \(\pm\) 0.310 \(\upmu\)m). Cast/LWT specimens presented the lowest mean Ra score (1.055 \(\pm\) 0.184 \(\upmu\)m). Ra values among all investigated groups were statistically significant (\(p\) < 0.05). SLM specimens showed significantly higher Ra than DLP cast copings (1.590 \(\pm\) 0.167 \(\upmu\)m) (\(p\) = 0.001). Mean Ra of Cast/LWT copings was lower than CAD-CAM (1.840 \(\pm\) 0.236 \(\upmu\)m) (\(p\) = 0.001), SLM (\(p\) = 0.001), and DLP cast (\(p\) < 0.05) specimens respectively. The micrographs obtained for surface roughness assessment are presented in Figure 2. The uniform high roughness was observed in SLM samples (2 D) and the DLP-Cast specimen displayed a smoother surface with minimal localized craters (2 C).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Group**} & \multirow{2}{*}{\
\begin{tabular}{c} **Mean** \\ **(\(\upmu\)m)** \\ \end{tabular} } & \multirow{2}{*}{**SD**} & \multirow{2}{*}{
\begin{tabular}{c} **ANOVA** \\ **_p_ Value** \\ \end{tabular} } & \multirow{2}{*}{**Cast/LWT**} & \multirow{2}{*}{**CAD-**} & \multirow{2}{*}{**SLM**} & \multirow{2}{*}{**DLP-Cast**} \\ \hline Cast/LWT & 1.055 & 0.184 & & & 1.000 & & \\ \hline CAD-CAM & 1.840 & 0.236 & & 0.001 \({}^{\ast}\) & 1.000 & & & \\ \hline SLM & 2.251 & 0.310 & & & 0.001 \({}^{\ast}
## 2 Methodologies
### Baselines and Evaluation  
The baselines for bearing fault diagnosis were set as per References [47; 48; 49]. The effect of vibration signal fault diagnosis is shown in Figure 18. Obviously, the GAN-SDAE system proposed in this paper has a higher average accuracy rate than the baselines in the references, so it improved the accuracy of the fault diagnosis.  
Figure 16: Feature visualization through t-SNE.  
Figure 17: Accuracy of different fault diagnosis methods.  
The MAE, MSE, MAD, RMSE for the GAN-SDAE system are shown in Table 3. The proposed method evaluation is satisfactory. By using improved data enhancement methods, the SDAE model can complete learning and training on demand, while significantly improving the performance.  
As mentioned previously, all current deep learning algorithms inherently face the problem of system uncertainty; furthermore, non-convergence may happen during network training. Therefore, besides the accuracy requirement for fault diagnosis, the stability of the system must be checked. The uncertainty analysis was performed to ensure the stability of the GAN-SDAE. The GAN-SDAE system was used to conduct repetitive experiments in various load domains. The synthetic standard uncertainty \(u_{\text{C}}\) for the four datasets (four loads) is illustrated in Figure 19. Overall, the value of \(u_{\text{C}}\) decreased as the number of repetitive experiments increased. When the number of repetitive experiments was above 40, \(u_{\text{C}}\) reached a stable condition at a 0.2 float.
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
## 5 Case Study
### Distribution Characteristics of Displacement
#### 5.4.1 Distribution Characteristics of Surface Displacement  
The surface displacement distribution resulted in outward deformation in horizontal displacement and downward subsidence in vertical displacement. The horizontal displacement was greater than the vertical displacement. The displacement of C\({}_{3\text{-}1}\) upper rock mass was larger than that of LS\({}_{337}\). The displacement of monitoring points located between J\({}_{136}\) and f\({}_{101}\) (such as #35, #39, #8, and #31) was relatively large, but the displacement near the concrete plugs (such as #12 and #18) was relatively smaller than that of other locations.  
At the strength reduction stage, the block with C\({}_{3\text{-}1}\) and LS\({}_{337}\) as the bottom slip plane gradually slipped outward with the increase in the amount of strength reduction. When the strength of structural planes decreased by 1.2, the horizontal displacement of #8, located at the exit of C\({}_{3\text{-}1}\), was 36 mm (outward, prototype value); the vertical displacement value of #7 was 42 mm (sink); the horizontal displacement of #12, located at the exit of LS\({}_{337}\), was 36 mm (outward); and the vertical displacement value of #11 was 17 mm (sink).  
During the lifting stage, the displacement curve fluctuated slightly during the second to sixth steps. The C\({}_{3\text{-}1}\) and LS\({}_{337}\) began to creep, expansive deformation occurred near the exit of the LS\({}_{337}\), the vertical displacement exhibited upper convex displacement, and displacement fluctuation was fairly apparent (Figures 8b and 9b). Most curves began to turn back during the 8 th to 12 th steps, and displacement gradually increased. During the 20 th to 22 nd steps, the lifting test began, the inflection point of the displacement curve occurred, and the displacement increased further. Most displacement curves exhibited turning and inflection points during the 35 th to37 th steps, the displacement curves decreased, and the increase became apparently greater. Finally, the displacement quickly increased, and the slope exhibited a trend of global instability.
## 3 Phage-Mediated Bacterial Detection Approaches
### Conductivity (Impedance)  
The conductivity of the microbial growth medium can be changed by the perpetuation of microbes in the medium via the transformation of small to large charged and uncharged metabolites. Bacteriophages are appropriate tools for the detection of bacterial impedance (the resistance to the current flow via the conducting medium) since the presence of phage in a sample causes the retardation of impedance in the presence of the host organism. [PERSON] and colleagues have detected _E. coli_ O157:H7 without changing the conductivity of the MacConkey-sorbitol medium in the presence of an anti-_E. coli_ O157:H7 phage (AR1) [44]. The obvious challenge of direct conductivity-based detection techniques is the necessity of an appropriate culture medium optimized for measuring the impedance, in which development is usually labor-intensive and vulnerable to bacterial contamination with the background flora. Besides, not all target bacterium release charged metabolites, which may adversely affect the impedance and conductivity measurements. Some of these problems can be overcome by employing indirect impedimetric techniques, in which metabolites such as carbon dioxide released into the medium during the cultivation of the target bacterium can be removed by the addition of potassium hydroxide to facilitate impedance measurements [45]. This method is highly specific and sensitive, and has been utilized for the detection of many foodborne pathogens, such as _L. monocytogenes_, _S. aureus_, _Salmonella enterica_, _Campylobacter_ species, _E. coli_, and _Enterococcus faecalis_[46].
## 3 Results and Discussion
### Cybersecurity and Privacy in Energy Management Systems
#### 3.5.1 Emerging Cyber Threats and Vulnerabilities in Microgrids  
The increasing complexity and connectivity of modern microgrids have expanded the range of security vulnerabilities that can be exploited through various attack methods [15; 31]. These include unauthorized access, data breaches, denial-of-service attacks, and malware targeting energy management platforms, all of which disrupt operations and compromise information integrity.  
The use of third-party platforms and cloud services for energy management increases exposure to data leaks and operational failures [23; 32]. These risks affect both the protection of information and the stable operation of the energy supply to end users.