esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 3 Results
### Sensitivity Analysis
For \(\mathcal{M}_{2}\) (see Figure 8), the initial access node predictor, we can observe that ExecuteDCOM, AllowedToDelegate, MemberOf, GetChanges, and GetChangesAll are correlated with negative predictions. The last two are commonly associated with the Domain Admin, and as such, it appears that the network might have learned to associate nodes supporting these edges as less likely to be initial access nodes. Conversely, AdminTo edges are strongly correlated with positive predictions. This edge type is associated with a user account, and links to potential HasSession edges from the target system. It would make sense that the network learned to identify nodes followed by an AdminTo edge as likely initial access nodes for identity snowball attacks. Regarding node features, we can observe that operating system characteristics are correlated with positive predictions, while user account attributes, e.g., hasSpin, are negatively correlated with predictions. For \(\mathcal{M}_{3}\) (see Figure 9), the impact node predictor, we can observe the following: **(1)** Open, HasSession, and GetChanges are highly correlated with the decision--when the input node does not have these types of edges, it is less likely to be predicted as an initial access node. We can make the same observation for MemberOf edge type, but the correlation is weaker. Considering that the attack paths describe identity snowball attacks--meaning that the \"optimal\" exit node is the Domain Controller node--this node must have existing Open edges with all other nodes in the graph. As such, it makes sense that the network has learned to identify nodes with high counts of Open edges as likely exit nodes. The same observation applies to GetChanges, as these edges generally originate from DA nodes. Regarding features, the correlation with decision is fuzzier. Most features have a more  
Figure 8: Sensitivity analysis of node features (_left_) and edge types (_right_) using SHAP for \(\mathcal{M}_{2}\)  
Figure 7: Sensitivity analysis of node features (_left_) and edge types (_right_) using SHAP for \(\mathcal{M}_{1}\)  
centered impact, which means that their influence on the decision actually depends on the sample and does not exhibit a strong correlation with the decision.
# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 4 Results
### SSL for Limited Labeled Data  
Figure 7 shows the performance of SSL models, where (a) SSL-imn denotes the models utilizing the pretrained simCLR on the ImageNet dataset, and (b) SSL-burn denotes the models pretrained on unlabeled skin images. While these two simCLR models, SSL-imn and SSL-burn, were pretrained on different unlabeled datasets, they were further trained to be fine-tuned on the same datasets of labeled datasets of burn images via supervised learning for various dataset sizes (i.e., dataset20, dataset40, and more, as shown on the X-axis in Figure 7). We also tested two different model updating approaches, including FT-all that updates the model parameters of all layers, and FT-last that updates only the last layer model parameters.  
As shown in Figure 7a, FT-all achieved better accuracy than FT-last for all the cases in SSL-imn. The pretrained simCLR in SSL-imn was based on a large-scale, general image dataset, ImageNet, and thus it incurred domain differences when training burn severity assessment models. We also represent the baseline in Figure 6 in the dotted red line for comparison. FT-all showed better accuracy than the baseline, while FT-last showed worse accuracy than the baseline.  
In Figure 7b, the SSL-burn models can be seen to show relatively robust accuracy even for small datasets. Both FT-all and FT-last achieved close to 62.6\(\sim\)68.1% accuracy on dataset60 and dataset80. This was different from SSL-imn (in Figure 7a) where FT-last showed relatively lower performance of about 48.2\(\sim\)49.6% for the same datasets. This highlights the benefits of SSL-burn which exploits burn image features in pretraining, leading to fast adaptation with a small amount of labeled samples through fine-tuning on entire model parameters.  
Interestingly, as shown in Figure 7b, the accuracy of FT-all of SSL-burn started low but increased rapidly alongside more samples, showing that the accuracy achieved by FT-all and FT-last was reversed on dataset80. More importantly, FT-all on dataset80 yielded comparable performance (i.e., 68.1% in accuracy) to the baseline on dataset510, indicating the sample-efficiency of SSL-burn, such that the same level of accuracy to the baseline was able to be achieved by only 16% of the samples, compared to what the baseline uses. In other words, the SSL-burn model learned on a small dataset in data limitation conditions achieved a comparable level of accuracy to the baseline learned on a six-times larger dataset in our experimental settings. SSL-burn was pretrained on unlabeled skin images, and thus its transfer to burn severity assessment models remained in similar domains. Direct comparison between SSL-burn and the baseline on the same dataset also highlighted the sample-efficiency of SSL-burn, showing up to 16.3% and 14% accuracy improvement by FT-last of SSL-burn on dataset20 and dataset40, respectively.
## 4 Results and Analysis
### Full-Field Strain Measurement of Typical Specimens by DIC  
The digital image correlation (DIC) technique is a non-contact measurement method for deformation. Full-field strains from the DIC analysis are based on the matching of pixel subsets in images, which can reveal the deformation evolution and the progressive fracture process of specimen. In the experiments with rock, this technique combined with a high-speed camera was applied for strain measurements under impact and explosion loads [23]. In this experiment, a high-speed camera (i-speed 716, IX-Cameras Inc.) was used to capture rock dynamic fracture processes in dynamic Brazilian tests with an image resolution of 672 \(\times\) 216 pixels and a frame rate of 100,000 fps, corresponding to a time interval of 10 \(\upmu\)s. The first frame captured by the high-speed camera was chosen as the reference image for DIC analysis, and the frame without obvious stress fluctuation in the calculation results was defined as 0 \(\upmu\)s. Figures 6 and 7 show the vertical strain fields distributed on the observed surface of the Breccia and Andesite specimens, respectively. Figure 6 shows the evolution process of the vertical stress field of the Breccia specimen at 4.86 m/s striking velocity, which is the typical result mentioned in Figure 3. An obvious strain concentration area appeared after the incident wave was reached, as shown in Figure 6b,c. As the loading continued, the main cracks initiated in the center of the specimen then extended to the contact points, as shown in Figure 6d-f. Figure 7 shows the similar vertical stress field evolution process of the Andesite specimen, which displayed the typical results at 4.88 m/s striking velocity mentioned in Figure 4. The difference between the typical results of the two igneous rocks was the initiation time of the main crack. As shown in Figures 6 and 7, the times of observable main crack initiation in the Brazilian specimens were 100 \(\upmu\)s for Breccia and 230 \(\upmu\)s for Andesite. These were approximated with the times at which the specimens lost dynamic equilibrium, 92 \(\upmu\)s for Breccia and 225 \(\upmu\)s for Andesite, as shown in Figures 3d and 4d. Where the main failure processes of the Brazilian specimens were concerned [23], the results of the DIC were presented from 80 and 210 \(\upmu\)s, respectively, as shown in Figures 6 and 7. In a word, the results of the DIC technique reproduced the strain field evolution on the surface of the Brazilian specimens under SHPB loading.  
Figure 5: Tensile strengths of Breccia and Andesite versus stress rate.
## 4 Discussion
### Implications for Marine Environmental Changes  
The bulk refractive index of suspended particles (\(n_{\rm p}\)) is a key parameter that reflects the intrinsic characteristics of suspended particles, is closely related to the particulate composition and size structures [2], and significantly affects the optical properties of suspended particles. Actually, different \(n_{\rm p}\) distribution ranges are indicative of different types of suspended particles present in water bodies such as 1.01-1.09 mainly for organic algal particles, \(>\)1.15 for water regions dominated by inorganic particles and detritus, and 1.09-1.15 for complex and mixed particulates [2; 3; 48; 49]. Satellite-derived \(n_{\rm p}\) values in the study areas spanned a wide range (approximately 1.01-1.22), thus implying a large spatial heterogeneity. Three types of water regions can be generalized as (1) offshore oceanic water areas dominated by phytoplankton, (2) river plume and sediment-laden nearshore regions dominated by inorganic mineral particles, and (3) coastal regions controlled by organic and inorganic mixed materials. Although no in situ method exists for directly measuring the refractive index of suspended particles, the validation revealed the robustness of the currently proposed method to detect \(n_{\rm P}\) for the study areas and further distinguish waters dominated by different types of suspended particles. In short, the spatiotemporal detection of \(n_{\rm P}\) provides a new data record that enriches suspended particulate properties from space, and importantly improves understanding of the particulate biogeochemical properties and their role in exploring the changes of marine environments.
## 2 Nature and Characteristics of _A. altissima_
### Ailanthus altissima Biomass Chemical Analysis
[PERSON] et al. [42] recorded 26.60% hemicelluloses content, 46.70% cellulose, 26.20% lignin, and 0.5% ash content, which chemical composition corresponds to a heating value of 18.93 MJ/Kg. [PERSON] et al. [43] also found high levels of lignin (25.84%) and extractives (8.3%), such as phenolic compounds, flavonoids, and terpen, and recorded quite low ash content, indicating that it could be suitable for use as a biofuel. [PERSON] et al. [44], in the chemical analysis, carried out on the wood of _Ailanthus_ grown in the Trabzon area, reported 49.8% cellulose, 29.1% hemicelluloses, 21.3% lignin, and 0.21% ash, while in the respective wood from Artivin area, the results showed 46.5% cellulose, 24.4% hemicelluloses, 24.9% lignin, and 0.35% ash. [PERSON] and [PERSON] [32] chemically characterized the wood and bark of _A. altissima_ originating from three different locations in North Greece, with a focus on the horizontal variability assessment of its chemical composition (Table 1). Significant differences were found in the chemical composition of biomass obtained from these different locations. Furthermore, bark presented higher extractive content, slightly higher lignin content, much higher ash, and slightly lower holocellulose content than wood. As the tree age increased, the wood and bark presented lower lignin, though a linear increase of holocellulose and extractives contents. Additionally, the heartwood demonstrated higher extractive content, lignin and ash, and lower holocellulose content than sapwood. [PERSON] and [PERSON] [45] investigated the kinetics of the hydrolysis of _A. altissima_ chips using an alkaline-glycerol solution and highlighted that the hydrolysis rate increased with increasing temperature, biomass concentration, and alkaline concentration. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Study/Source** & **Part** & **Extratives (\%)** & **Lignin (\%)** & **Holocellulose (\%)** & **Ash (\%)** & **Applications** \\ \hline [PERSON] [37] & wood & - & 14 & 46 & 0.9 & \\ \hline \multirow{3}{*}{[PERSON] et al. [46]} & \multirow{3}{*}{wood} & 19.89 & \multirow{3}{*}{22.19} & \multirow{3}{*}{80.12} & \multirow{3}{*}{-} & \multirow{3}{*}{pulp-paper} \\  & & 18.6 hot water & & & & \\ \hline [PERSON] [47] & wood & \(-\) & 26.2 & 46.7 & 0.5 & \\ \hline [PERSON] and [PERSON] [39] & stem & 3.5 & 25.19 & 47.18 & 1.25 & \\ \hline [PERSON] et.al. [42] & biomass & - & 26.2 & 46.7 & 0.25 & - \\ \hline [PERSON] et al. [44] & wood & \(-\) & 21.3 & 49.8 & 0.21 & - \\ \hline \end{tabular}
\end{table} 
Table 1: Findings of _A. altissima_ chemical analysis.
# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 4 Discussion
### Suggestions for Future Research  
Due to the required modifications to the NSGA-II algorithm, the computational complexity became so great that it took days to construct the Pareto front using a conventional computer. Ways to automate the process could be explored in further studies, such as the use of the R-NSGA-2 algorithm. While both NSGA-II and R-NSGA-II follow the general NSGA framework, R-NSGA-II is specifically designed to reduce the computational load by incorporating more advanced techniques to manage the diversity of solutions, potentially impacting performance in multi-objective problems [44]. The study's limitations include the exclusion of dynamic factors like traffic flow changes over time, lighting, and weather conditions, which can impact cycling safety and comfort. Future implementations could enhance the model by integrating these variables to reflect the real-time influences on route preferences.  
In addition, worthy of study would be to investigate other methods of solving MOOP using weights for each decision variable with stated preference techniques (using questionnaires). Another issue worth investigating is the combined use of cycling and public transport, where carrying bicycles is allowed. The attitudes of cyclists towards public transport could be analyzed so that the proposed routes include the partial or total use of public transport. For example, in this study, the use of the tram from Zappeio to Syntagma stations could be explored as a solution and compared with the other solutions of the Pareto front.  
Furthermore, using artificial intelligence, it is worth investigating how one could use sophisticated multi-objective optimization algorithms (such as NSGA-II or R-NSGA-2) to find paths that balance multiple conflicting objectives. AI systems can learn and continuously improve by incorporating feedback from cyclists. This could include feedback on proposed routes, such as perceived safety, comfort, or enjoyment. Machine learning techniques can be used in studies to adapt and improve route suggestions by adapting to continuous feedback, as they can consider big data, such as traffic congestion, road closures, or sudden changes in weather conditions, to dynamically adapt route suggestions in real-time.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 2 Related Works
### COVID-19 Pandemic and Online Collaboration  
The COVID-19 pandemic has generally forced people to use online applications such as virtual conference application or social media platforms to help connect people at a distance. People have relied on these platforms to perform a variety of activities such as learning, meeting, and collaborating with their peers. Schools have forced teachers and students to shift from the usual face-to-face settings to online settings for teaching and learning. Meetings between workers and co-workers in companies also take place in a virtual setting to share, present, and discuss information at hand. Some people use virtual conference applications to perform a collaborative task and to discuss ideas and solutions to a given problem.  
Several previous studies on online communication have been conducted in order to find ways to support remote collaboration during the pandemic. One previous study has used remote collaboration to design homes without the need for the designer to be present [17]. Collaborative activity can also be used for group learning, which has been shown to be more effective than individual learning [18]. According to a previous study, students now learn better by collaborating with peers online, which is preferred by students [19]. For remote collaborative learning, previous research has found that the provision of opportunities to collaborate, discuss, and reflect on their professional development is beneficial [20]. Previous research identified opportunities for new and augmented approaches for remote collaborative research through Distributed Participatory Design by distributing geographies, backgrounds, ages, and abilities to overcome these new barriers during and after COVID-19 [21]. Previous studies have also looked at security issues such as user privacy, which is more vulnerable to be abused, especially during remote collaboration [22]. Currently, there has been a lack of study on the issues and limitations of performing a remote collaboration using the current virtual conference application, particularly during a pandemic period. Section 2.3 compares the various virtual conference applications available for real-time collaboration and its features.
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
## 4 The Estimation Model of the Number of Cycles Required to Reach Liquefaction, \(N_{\rm L}\)
### The Modification of \(N_{l}\)  
Factors such as the relative density, porosity, consolidation ratio, and initial effective consolidation stress all affect the \(N_{\rm L}\). The influence of the relative density on the dynamic strength of soil is clear, in that the dynamic strength of the material increases with an increase in the relative density [35]. The empirical formula proposed by [PERSON] [30] reflects the liquefaction of materials when the dynamic stress is near zero. At extremely small amplitudes, the value of \(a\) is associated with the relative density of the material and the number of cycles required to induce liquefaction. The larger the relative density, the larger the \(a\) value and the number of cycles required to cause liquefaction. Based on the relationship between \(a\) and \(D_{\rm r}\)[36; 37; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47], the \(a\) value is distributed within a curve band. This implies that the mean curve of the relationship between \(a\) and \(D_{\rm r}\) can be calculated, as depicted in Figure 10.  
Figure 9: Calculation results of cyclic numbers to liquefaction.  
The relative density is selected as the parameter of interest in the \(N_{\text{L}}\) estimation model so that the empirical formula of Xu [30] can be modified by the relationship between the \(a\) value and the \(D_{\text{r}}\) mean curve. The expressions are given by the following:  
\[\left\{\begin{array}{l}\text{lg}N_{\text{L}}=a-b\frac{\overline{\tau}}{\sigma _{0}^{\prime}}\\ a=2e^{D_{\text{r}}}\end{array}\right. \tag{4}\]  
where \(N_{\text{L}}\) is the number of cycles required to reach liquefaction, \(a\) is related to the relative density of the test material, \(b\) is the empirical constant, \(\overline{\tau}\) is the mean shear stress, \(\sigma_{0}^{\prime}\) is the initial normal stress on the \(45^{\circ}\) slope of the sample, and \(D_{\text{r}}\) is the relative density.  
The linear relationship between \(\text{lg}N_{\text{L}}\) and \(\overline{\tau}/\sigma_{0}^{\prime}\) is established by collecting the test data [27; 34; 37; 38; 39; 43; 48]. Figure 11 shows the dynamic triaxial test results of the Xinjiang sand-graved material. The test value is slightly higher than the simulation result of the modified empirical formula because the initial consolidation ratio of this test is set to 1.5 or 2.0 and isobaric consolidation is not considered. This difference is, however, still within an acceptable range. The relationship between \(\text{lg}N_{\text{L}}\) and \(\overline{\tau}/\sigma^{\prime}{}_{0}\) for different relative densities that were obtained by the modified empirical formula shows that the larger the relative density is, the larger the intercept of the function line. This means that small dynamic loading requires more cycles to cause liquefaction of the sample.  
Figure 10: Relationship between \(a\) and \(D_{\text{r}}\).
# In Vitro Genotoxicity Assessment from the _Glycyrrhiza_ New Variety Extract
## 4 Discussion
Trisodium glycyrrhizinate (up to 10,000 ug/plate) didn't increase the number of revertant colonies in the AMES test (TA92, TA1535, TA100, TA1537, TA94, and TA98), but induced structural chromosome aberrations when administered at 4000 ug/mL in the chromosome aberration test on the CHL cells [51]. Monoammonium glycyrrhizinate (MGL) (up to 5000 ug/plate) didn't induce genotoxicity in the AMES test (TA98, TA100, TA102, TA1535, and TA1537), up to 1500 ug/mL in the chromosome aberration test using human lymphocytes, and up to 250 mg/kg (i.v.) in the micronuclelets test using mouse bone marrow. Cytotoxicity was observed when 1500 ug/mL was administered in the chromosome aberration test [51]. In sum, we suppose that WG water extract shows lower genotoxicity than the other extracts from other licorice species. WG extract didn't induce the genotoxic or the mutagenic activities in any of the genotoxic assessments, including the in vitro AMES test, the in vitro chromosome aberration test, and the in vivo mouse bone marrow micronucleleus test. This result suggests that WG extract is a low potential risk of carcinogenesis, which is better than other licorice species. In addition, our results could support the safety information of WG to patients or healthcare providers for clinical purposes and contribute to registering 'Wongam' as new variety of _Glycyrrhiza_ in the KP. The following is available online at [[https://www.mdpi.com/article/10.3390/app112110257/s1](https://www.mdpi.com/article/10.3390/app112110257/s1)]([https://www.mdpi.com/article/10.3390/app112110257/s1](https://www.mdpi.com/article/10.3390/app112110257/s1)), Figure S1: MALDI TOF MS spectrum from the Wongam water extract. Conceptualization, J.-S.J. ; resources, J.L. and J.C.; data curation, Y.-J.S., D.-G.K. and J.L. ; writing--original draft preparation, Y.-J.S. and D.-G.K.; writing--review and editing, J.L., W.K., H.-J.A., J.-H.L., J.C., S.-H.K., J.-R.S., Y.-D.J. and J.-S.J. ; visualization, Y.-J.S. and D.-G.K. All authors have read and agreed to the published version of the manuscript. This work was carried out with the support of the \"Cooperative Research Program for Agriculture Science and Technology Development Project (No. PJ01424602)\" administered by the Rural Development Administration of the Republic of Korea. The study was conducted in accordance with test guidelines from the Korean Ministry of Food and Drug Safety (MFDS, 2018) and guidelines for the testing of chemicals from the Organization for Economic Cooperation and Development (OECD, 1997) under GLP Regulations.