esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of an Ag Nanofilm on Low-Temperature Cu/Ag-Ag/Cu Chip Bonding in Air
## 4 Conclusions  
Low-temperature Cu bonding with an Ag nanofilm was investigated. Cu/Ag-Ag/Cu bonding was performed below 200 \({}^{\circ}\)C, resulting in a good bonding interface and a shear strength of 14 MPa at 180 \({}^{\circ}\)C. A 15 nm Ag nanofilm effectively prevented Cu surface oxidation, but Ag remained at the bonded interface following low-temperature bonding. Cu/Ag-Ag/Cu solid state diffusion bonding should be further studied to achieve a complete solid solution of Ag into Cu at low temperatures. In addition, it is necessary to evaluate annealing after bonding to further improve bonding quality.  
Conceptualization, S.E.K.; Methodology, Y.K., and S.P.; Validation, Y.K., S.P., and S.E.K.; Formal analysis, Y.K.; Investigation, Y.K., and S.P.; Data curation, Y.K., and S.E.K.; Writing--original draft preparation, Y.K., and S.E.K.; Writing--review and editing, S.E.K.; Visualization, Y.K.; Supervision, S.E.K.; Funding acquisition, S.E.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2B6003921) and the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korean Government (MOTIE) (P0008458, The Competency Development Program for Industry Specialist).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: TEM and EDS analysis of Cu/Ag-Ag/Cu bonding at 180 \({}^{\circ}\)C: (**a**) TEM image of bonding interface, (**b**) magnified TEM image of the red box, and (**c**) EDP profile across the interface.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
###### Abstract  
Electric and hybrid marine vessels are marking a new phase of eco-friendly maritime transport, combining electricity and traditional propulsion to boost efficiency and reduce emissions. The industry's advancements in charging infrastructure and strict regulations help these vessels lead the way toward a sustainable and economically viable future in shipping. In this review, electric and hybrid marine vessels are discussed, including past applications and trend demonstrations. This paper systematically analyzes maritime vessels' energy management and battery systems, highlighting advances in lithium-based and alternative battery technologies. Additionally, the review examines the impact of these technologies on sustainability and operational efficiency in the maritime industry. This paper contributes to the field by presenting a holistic view of the challenges and solutions associated with the electrification of maritime vessels, aiming to inform future developments and policymaking in this dynamic sector. Unlike many existing reviews that focus exclusively on battery chemistries or energy management algorithms, this manuscript integrates multiple aspects of maritime electrification--including propulsion types, charging infrastructure, grid systems (MVDC), EMS, BMS, and AI applications--into one cohesive systems-level review. This cross-sectional integration is particularly rare in the literature and enhances the practical value of the review for designers, policymakers, and shipbuilders.  
e-mobility; electrification; energy management system (EMS); energy storage; Li-ion batteries; marine vessels +
Footnote †: journal: Applied sciences  
1 MOBLIERS Team, Kayseri University, 38050 Kayseri, Tarkjye 1  
2 Department of Computer Engineering, Adana Alparslan Turkey Science and Technology University, 01250 Adana, Turkey; [EMAIL_ADDRESS]  
3 AAU Energy, Aalborg University, DK-9220 Aalborg, Denmark; [EMAIL_ADDRESS]
## 3 Experimental Steps
### Electrofacies Construction  
#### 3.2.1 Clustering the Logs  
The purpose of clustering is to classify a dataset into several groups which are externally isolated and internally homogeneous on the basis of a measure of similarity and dissimilarity among the groups. Since electrofacies are defined empirically, the number of electrofacies classes is arbitrary. Usually, the number of defined electrofacies depends on the number of log properties used in the system and the joint characteristics of the statistical distributions of the log readings [38]. It also represents the target of electrofacies classification and the way in where the final categorization will be interpreted and used. In our experiment, we selected five electrofacies classes to be constructed by a non hierarchical k-means clustering algorithm, which is one of the most popular and widespread partitioning clustering algorithms because of its superior feasibility and efficiency in dealing with a large amount of data [39]. Several studies have suggested and used k-means clustering for constructing electrofacies [18; 27].  
K-means clustering [40] is a distance-based clustering method for finding clusters and cluster centres in a set of unlabelled data. This is a fairly tried and tested method in which the goal is to group points that are'similar' (based on distance) together. This is done so by regarding the centre of data points as the centre of the corresponding clusters (centroids). The core idea is to update the cluster centroid by iterative computation, and the iterative process will be continued until some convergence criteria are met.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **GR** & **NPHI** & **DRGO** & **PE** & **DPHI** \\ \hline
**Attribute Name** & Gamma Ray & Neutron Porosity & Density Correlation & Photoelectric Effect & Density Porosity \\
**Unit** & api & _d_cep & _g_/cc & none & deep \\
**Min.** & 46.49 & 0.1053 & \(-\)0.05 & 0.5105 & 0.081 \\
**Mean** & 140.85 & 0.2942 & 0.02471 & 1.8684 & 0.254 \\
**Max.** & 385.89 & 1.2316 & 0.3244 & 3.7343 & 0.8082 \\ \hline
**CT10** & **CALI** & **BHVT** & **DTC** & **HDIA** \\ \hline
**Attribute Name** & Conductivity & Caliper & Borehole Volume & Compressional Sonic & Hole Diameter \\
**Unit** &.mmo/m & in & m3 &.uspf & in \\
**Min.** & 113.9 & 5.297 & 0.4481 & 60.81 & 5.297 \\
**Mean** & 1240.4 & 5.863 & 2.0633 & 101.69 & 5.863 \\
**Max.** & 9215.5 & 7.58 & 4.265 & 159.61 & 7.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summaries of the selected well log attributes.  
Figure 1: Feature importance.  
To get a proper distribution of the dataset, the well log values of all the independent features were standardized to z-scores. The cluster classes are illustrated in Figure 2.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### Feature Modeling-Based Approach
#### 4.3.2 Findings Analysis  
CyberSPL assists cyber professionals by automating the analysis of non-conformance with cybersecurity policies. This method combines feature models' capacity with automated verification and diagnosis. The evaluation showcased performances in operational development (DevOps). Additionally, the method based on attack scenario incorporates feature model variability to represent the vulnerability of the target system. It integrates attack scenarios to uncover insights about potential exploitation areas. The evaluation showed that 5 out of 18 attacks failed to exploit the identified vulnerabilities, with detailed reasons listed in Table 5. In addition, the AMADEUS method integrates the SPL techniques, used in CyberSPL, with feature models to automate the infrastructure inventory analysis, scraping vulnerability databases, extracting vulnerability configuration, and inferring possible attack vectors. Despite some limitations, the evaluation demonstrated high accuracy in generating and validating attack vectors. In the same field, AMADEUS-Exploit, an extension of AMADEUS, adds an exploit layer to feature models and improves reasoning capacities. This method was evaluated in a real scenario, identifying 4000 vulnerabilities and 700 exploits. Generally, AMADEUS-Exploit has proven more its scalability and efficiency in vulnerability detection and management.  
In practical case studies, the previous methods can be employed within enterprises to identify, assess, and prioritize vulnerabilities in this infrastructure. For AMADEUS-Exploit and AMADEUS, the inventory assets are evaluated using web scrapers or querying NVD. The outputs affect all potential vulnerabilities and exploits associated with the discovered system. If the system runs PostgreSQL version 16.4, the tool might find vulnerabilities like CVE-2020-0985 (REFRESH MATERIALIZED VIEW CONCURRENTLY executes arbitrary SQL). Then, AMADEUS-Exploit generates feature models (FMs) including all possible combinations of affected configurations, versions of product (CPEs) and exploits. As a result, the cyber team can verify that PostgreSQL version 16.4 is vulnerable under specific configurations, and no exploit currently exists for CVE-2020-0985. The reasoning mechanism prioritized CVEs with exploits that can affect critical assets, such as Adobe Commerce versions 2.4.3-p1 and 2.3.7-p2 affected by CVE-2022-24086, with related exploits already available, and focusing efforts to apply necessary preventive measures.  
Thus, the earlier techniques provided specific examples of how to use feature models for vulnerability identification through capturing the global picture and the dependence between security information elements. We will then go over another basket of techniques related to the AI-based approach.  
Figure 21: Example of FM construction used by AMADEUS and AMADEUS-Exploit. (Adapted from [106,107]).
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Curve Correction Model  
All the formulas and protocols for measurement of the performance of the PV module is based on the preconditions that the PV modules are flat. For example, determination of the output power and other fundamental electrical parameters such as short-circuit current and open-circuit voltage of the PV modules, are essential to determine the input solar energy to the product of the irradiance and aperture area. On the other hand, the definition of the aperture area for the curved PV is not identical to the surface of the module. The car roof is three-dimensionally curved, and its curvature may induce power loss by increased cosine loss and self-shading loss [1]. Another aspect that we need to consider is that the car roof PV and BIPV (building integrated photovoltaic) are not in the installation of the standard slope angle and orientation. The orientation of the car roof changes with time without correlation to the sun direction. Surrounded buildings and other objects often shade both. These factors have a different impact on the curve shape. Such interaction needs to be taken into account.  
We attempt to introduce a simple correction factor that includes complete irradiation loss to the curved surface. We call it the \"curve correction factor.\"
## 3 Results
### ABTS Radical scavenging Activity in SF and SSF Extracts in Different Solvents
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Solvent**} & \multicolumn{2}{c}{**DPPH (IC\({}_{50}\) = \(\upmu\)g/\(\upmu\)L)**} & \multicolumn{2}{c}{**ABTS (IC\({}_{50}\) = \(\upmu\)g/\(\upmu\)L)**} & \multicolumn{2}{c}{**FRAP (IC\({}_{50}\) = \(\upmu\)g/\(\upmu\)L)**} \\ \cline{2-7}  & **SF** & **SSF** & **SF** & **SSF** & **SF** & **SSF** \\ \hline Ethanol & 34.8 \(\pm\) 0.3 \({}^{\text{b}}\) & 19.8 \(\pm\) 1.2 \({}^{\text{b}}\) & 13.9 \(\pm\) 1.3 \({}^{\text{a}}\) & 11.3 \(\pm\) 0.3 \({}^{\text{b}}\) & 17.4 \(\pm\) 0.2 \({}^{\text{c}}\) & 3.5 \(\pm\) 0.2 \({}^{\text{a}}\) \\ Methanol & 5.8 \(\pm\) 0.1 \({}^{\text{a}}\) & 8.4 \(\pm\) 0.1 \({}^{\text{a}}\) & 14.1 \(\pm\) 0.9 \({}^{\text{a}}\) & 6.7 \(\pm\) 0.1 \({}^{\text{a}}\) & 6.1 \(\pm\) 0.3 \({}^{\text{a}}\) & 4.2 \(\pm\) 0.2 \({}^{\text{b}}\) \\ Water & 68.5 \(\pm\) 3.0 \({}^{\text{c}}\) & 25.4 \(\pm\) 1.1 \({}^{\text{c}}\) & 18.5 \(\pm\) 0.5 \({}^{\text{b}}\) & 56.5 \(\pm\) 0.6 \({}^{\text{c}}\) & 11.5 \(\pm\) 0.2 \({}^{\text{b}}\) & 10.0 \(\pm\) 0.2 \({}^{\text{c}}\) \\ \hline Ascorbic acid & 0.5 \(\pm\) 0.1 & & 0.2 \(\pm\) 0.1 & & 0.2 \(\pm\) 0.1 \\ \hline \end{tabular} SF: _S. fusiforme_ extract, SSF: Steamed _S. fusiforme_ extract. The different lowercase letters represent significant differences in the same column (_p_ \(<\) 0.05).  
\end{table} 
Table 2: The DPPH, ABTS, and FRAP IC\({}_{50}\) values of fresh and steamed _Sargassum fusiforme_ extracts.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## 3 Results & Discussion  
An extensive number of numerical simulations were performed to determine the HTC enhancement of laminar nanofluids. For validation purpose, the present results of the Nu for straight tube were compared with the correlation (\(Nu\ =\ 0.086 Re^{0.55}Pr^{0.5}\)) of [PERSON] et al. [21] and the computational results of [PERSON] et al. [48] for constant heat flux condition. Figure 1a shows a good agreement of the present results with the literature [21; 48] for water-based Al\({}_{2}\)O\({}_{3}\) nanofluid at \(\phi_{b}\) = 1%. The Nu results were also validated for constant wall temperature condition as shown in Figure 1b. It may be noted that there is a good agreement between the present results and results predicted by [PERSON] et al. [21] (\(Nu\ =\ 0.28 Re^{0.35}Pr^{0.36}\)), for the Al\({}_{2}\)O\({}_{3}\)-water based nanofluid with \(\phi_{b}\) = 4%.  
In this study, six different cases with different combinations of the geometry, nanoparticles concentration and base fluids were considered as shown in Table 6. It may be noted from Table 6 that three different base fluids and five different nanofluids with a wide range of particle volume fractions (\(0\leq\phi_{b}\leq 10\%\)) are considered for the detailed investigation.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
###### Abstract  
The present research investigates the behaviour of sustainable Self-Compacting Concrete (SCC) when subjected to high temperatures, focusing on workability, post-fire impact resistance, and the effects of fire protection coatings. To develop environmentally friendly SCC mixes, Supplementary C. Cementitious Materials (SCM) such as Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBFS), and Expanded Perlite Aggregate (EPA) were used. Fifty-six cubes and ninety-six impact SCC specimens were cast and cured for testing. Fire-resistant Cement Perlite Plaster (CPP) coatings were applied to the protected specimens, a passive protection coating rarely studied. SCC (unprotected and protected) specimens, i.e., protected and unprotected samples, were heated following the ISO standard fire curve. An extensive comparative study has been conducted on utilising different SCMs for developing SCC. Workability behaviour, post-fire impact resistance, and the influence of fire protection coatings on sustainable SCC subjected to high temperatures are the significant parameters examined in the present research, including physical observations and failure patterns. The test results noted that after 30 min of exposure, the unprotected specimen exhibited a significant decrease in failure impact energy, ranging from 80% to 90%. Furthermore, as the heating duration increased, there was a gradual rise in the loss of failure impact energy. However, when considering the protected CPP specimens, it was observed that they effectively mitigated the loss of strength when subjected to elevated temperature. Therefore, the findings of this research may have practical implications for the construction industry and contribute to the development of sustainable and fire-resistant SCC materials.  
self-compacting concrete; post-fire impact resistance; expanded perlite aggregate; high temperature; impact failure energy +
Footnote †: journal: Applied Physics Letters  
1L&T EduTech, Larsen & Toubro Limited, Chennai 600116, India; [EMAIL_ADDRESS]  
2
## 3 Results and Discussions
This comparison is shown in Figure 4.  
As can be seen (Figure 4b), the values of ionic conductivity (wet Ar) are near the same as the total conductivity values (wet air) below \(\sim\)450 \({}^{\circ}\)C. In other words, the nature of conductivity changes from mixed ionic-hole (dry air) to predominantly ionic (wet air, T \(<\) 450 \({}^{\circ}\)C). Figure 5: The dependencies of the conductivity values vs. \(p\)O\({}_{2}\) for the sample BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\) at dry conditions (**a**) and dry (closed symbols) and wet (open symbols) conditions (**b**) (black symbols) and conductivity values from \(\sigma\)–10\({}^{3}\)/\(T\) dependencies at dry Ar (blue symbols). The difference between conductivity values obtained under atmospheres with different water partial pressures is well visible for the Ar atmosphere, and these temperature dependencies are presented in Figure 4c. The protonic transport numbers \(t_{p}\) were calculated according to the equation:  
\[t_{p}=\frac{\sigma_{\text{{net}}\ Ar}-\sigma_{\text{{dry}}\ Ar}}{\sigma_{\text{{net }}}} \tag{8}\]  
which was used earlier by [PERSON] et al. [43] for the layered perovskites based on BaNdInO\({}_{4}\). It was established that for both compositions BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\) and BaLaInO\({}_{4}\), the protonic transport numbers increase with a decreasing temperature and reach \(\sim\)90-95% in the temperature range 300-350 \({}^{\circ}\)C.  
Therefore, the possibility of protonic transport in the two-layered complex oxide BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\) with the Ruddlesden-Popper structure was shown for the first time. It was proved that an increase in \(n\) in the formula BaLa\({}_{n}\)In\({}_{n}\)O\({}_{3n+1}\) leads to the increase in the ionic conductivity. The composition BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\) demonstrates nearly pure protonic conductivity under wet air below 350 \({}^{\circ}\)C. This allows for considering this complex oxide as a promising matrix composition for obtaining novel high-conductive protonic electrolytes based on it.
## 3 Results
### Spatial Heterogeneity over the Study Site  
The coniferous plantation is the target vegetation for EC flux measurements and also the dominant vegetation type (70.08%) in a 6 \(\times\) 6 km grid around the flux tower (Figure 3). In addition to forest, the study area is also composed of cropland (double-cropped rice, 27.83%), grassland (1.84%), waterbody (0.11%) and building (0.14%). The cropland was mainly distributed in the valley and located in the east. The double-cropped rice had a higher seasonal variation in the EVI than the coniferous plantations over the study area. During the growing season (May to October), double-cropped rice had higher EVIs than coniferous plantations, although the EVI drops during the harvest time. During the non-growing season, however, the EVI of double-cropped rice was lower than the coniferous plantation (Figure 4).  
To detect the changes in spatial heterogeneity along with the increasing scales, the FTVT in a circular window with an increasing size from 250 to 3000 m was calculated based on the land cover map (Figure 5). As more double-cropped rice was included with the increasing window size, the FTVT began with 0.93 and decreased to the bottom with 0.67 at 1600 m. Then, followed by more coniferous plantations being included, the FTVT began to increase slightly and reached 0.73 at 3000 m.  
The average EVI in the circular window was also calculated, and the trend of the EVI showed opposite trends in the growing and non-growing seasons (Figure 5). In the growing season (September 2010), due to the increasing fraction of the double-cropped rice, the EVI generally increased and was higher than the footprint-weighted EVI over the source area. While in the non-growing season (March 2010), the EVI decreased and was lower than the footprint-weighted EVI.  
Figure 2: Kernel density distribution of footprint fetch from the 80% footprint climatology contour (**a**), the source area within the 80% footprint climatology contour (**b**), the footprint symmetry index (**c**) and the footprint overlap index for seasonal and daytime-nighttime overlaps (**d**).  
Figure 4: Seasonal variation in EVIs from the double-cropped rice and the coniferous plantation in different months (the upper figure) and their spatial variation in EVIs over the flux site (the lower figures (**a**–**l**)). The symbols (**a**–**l**) in the upper figure are consistent with them in the lower figures. For example, the symbol (**a**) means 18 January 2003.  
Figure 3: The land cover map over the flux site. In a 6 \(\times\) 6 km grid around the flux tower, the study area was mainly composed of forest (coniferous plantation, 70.08%), cropland (double-cropped rice, 27.83%), grassland (1.84%), waterbody (0.11%) and building (0.14%).
# Process Optimization for Antarctic Krill (_Euphausia superba_)
## 3 Results
### Optimization of AkS by Response Surface Method  
3.3.1. Analysis of Response Surface Model  
The response surface was conducted using sensory scores as response values on the basis of the experimental results of the single-factor analysis. The experimental results are shown in Table 4.  
The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Salt/\%** & **Rock Sugar/\%** & **Oil-to-Material Ratio** & **Sensory Scores** \\ \hline
1 & 1 & 0 & 1 & 74.2 \\
2 & 0 & \(-1\) & \(-1\) & 84.1 \\
3 & 0 & 0 & 0 & 89.0 \\
4 & 0 & \(-1\) & 1 & 80.8 \\
5 & \(-1\) & 0 & \(-1\) & 83.1 \\
6 & 0 & 0 & 0 & 89.3 \\
7 & 0 & 1 & 1 & 77.4 \\
8 & 0 & 1 & \(-1\) & 82.8 \\
9 & \(-1\) & 0 & 1 & 77.4 \\
10 & 0 & 0 & 0 & 88.5 \\
11 & 0 & 0 & 0 & 91.0 \\
12 & \(-1\) & 1 & 0 & 79.6 \\
13 & 0 & 0 & 0 & 90.6 \\
14 & 1 & \(-1\) & 0 & 78.2 \\
15 & \(-1\) & \(-1\) & 0 & 83.3 \\
16 & 1 & 1 & 0 & 72.6 \\
17 & 1 & 0 & \(-1\) & 73.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Central composite results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sources** & **Square Sum** & **DF** & **Mean Square** & **F Value** & **\(p\) Value** & **Significance** \\ \hline Model & 583.74 & 9 & 64.86 & 50.42 & \(<\)0.0001 & ** \\ A & 77.50 & 1 & 77.50 & 60.24 & 0.0001 & ** \\ B & 24.50 & 1 & 24.50 & 19.04 & 0.0033 & ** \\ C & 23.46 & 1 & 23.46 & 18.24 & 0.0037 & ** \\ AB & 0.9025 & 1 & 0.9025 & 0.7015 & 0.4299 & \\ AC & 10.24 & 1 & 10.24 & 7.96 & 0.0257 & * \\ BC & 1.10 & 1 & 1.10 & 0.8570 & 0.3854 & \\ A\({}^{2}\) & 252.24 & 1 & 252.24 & 196.07 & \(<\)0.0001 & ** \\ B\({}^{2}\) & 52.02 & 1 & 52.02 & 40.44 & 0.0004 & ** \\ C\({}^{2}\) & 100.68 & 1 & 100.68 & 78.26 & \(<\)0.0001 & ** \\ Residual & 9.01 & 7 & 1.29 & & & \\ Lack of fit & 4.42 & 3 & 1.47 & 1.28 & 0.3939 & \\ Pure error & 4.59 & 4 & 1.15 & & \\ Cor total & 592.74 & 16 & & & \\  & & & & & \\ \multicolumn{4}{c}{R\({}^{2}\) = 0.98; R\({}^{2}\) adjusted = 0.97} \\ \hline \end{tabular} Note: Different asterisks indicate significant differences, \({}^{\star}\) for \(p<0.05\), \({}^{\star\star}\) for \(p<0.01\).  
\end{table}
Table 5: ANOVA of the central composite design.  
The results in Table 5 demonstrated that the \(p\)-value of the overall model for the sensory evaluation of the AkS was less than 0.01, R\({}^{2}\) = 0.98, (R\({}^{2}\)adj = 0.97) and that the value of the misfit term in the model was greater than 0.05, indicating that the error between the predicted and experimental values was relatively small, and that the model can be used to analyze and optimize the product. The initial terms, A, B, and C, as well as the interaction terms, A\({}^{2}\), B\({}^{2}\) and C\({}^{2}\), showed extremely significant performances, whereas the interaction term, AC, illustrated significant performance.