esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Considerations
### Previous Measures of the Issue Publics  
Most previous literature related to issue publics has employed demographic variables and personal issue importance as individual measures to identify issue publics [1]. Older people were assumed to be members of the issue publics on health care issues, and women were treated as issue publics on abortion or breast cancer issues [15]. However, this demographic-based approach is problematic because it may overrepresent or underrepresent the issue publics on social issues. Other studies used personal issue importance as a measure of issue specialists. For example, those considering an abortion issue to be important are assumed to be abortion issue publics [9]. Previous research showed that those who perceive an issue to be personally important show stable opinions and become cognitively and behaviorally involved in the issue [9, 16, 17]. However, this measure tends to overlook the core aspect of issue publics. By definition, issue publics are different from attentive publics who are generally interested in a wide range of issues. Rather, issue publics should be (1) passionately interested in a particular issue, but (2) uninterested in other issues in general due to their limited cognitive capacity. However, the issue importance measure does not capture the second part of the definition. An individual perceiving the environmental issue to be personally important can be either an environmental specialist or a generally attentive citizen who perceives the environment to be also important like all other issues.
# Geotechnical and Structural Damages Caused by the 2023 Kahramanmaras Earthqu in Golbasi (Adyaman)
## 3 2023 Kahramanmaras Earthquakes
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**ZA**} & \multicolumn{4}{c}{**ZE**} \\ \cline{2-9}  & **DD1** & **DD2** & **DD3** & **DD4** & **DD1** & **DD2** & **DD3** & **DD4** \\ \hline S\({}_{\text{S}}\) & 2.363 & 1.218 & 0.394 & 0.253 & 2.363 & 1.218 & 0.394 & 0.253 \\ S\({}_{\text{1}}\) & 0.680 & 0.327 & 0.095 & 0.059 & 0.680 & 0.327 & 0.095 & 0.059 \\ F\({}_{\text{S}}\) & 0.800 & 0.800 & 0.800 & 0.800 & 0.800 & 0.926 & 1.997 & 2.392 \\ F\({}_{\text{1}}\) & 0.800 & 0.800 & 0.800 & 0.800 & 2.000 & 2.692 & 4.200 & 4.200 \\ S\({}_{\text{DS}}\) & 1.890 & 0.974 & 0.315 & 0.202 & 1.890 & 1.127 & 0.787 & 0.605 \\ S\({}_{\text{D1}}\) & 0.544 & 0.262 & 0.076 & 0.047 & 1.360 & 0.880 & 0.399 & 0.248 \\ T\({}_{\text{A}}\) (s) & 0.058 & 0.054 & 0.048 & 0.047 & 0.144 & 0.156 & 0.101 & 0.082 \\ T\({}_{\text{B}}\) (s) & 0.288 & 0.268 & 0.241 & 0.233 & 0.719 & 0.781 & 0.507 & 0.410 \\ T\({}_{\text{AD}}\) (s) & 0.019 & 0.018 & 0.016 & 0.016 & 0.048 & 0.052 & 0.034 & 0.027 \\ T\({}_{\text{BD}}\) (s) & 0.096 & 0.089 & 0.080 & 0.078 & 0.240 & 0.260 & 0.169 & 0.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of earthquake parameter values for the epicenter of the earthquake. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Station Code**} & \multirow{2}{*}{**TEC-2007 Seismic Zone**} & \multirow{2}{*}{**TEC-2007 PGA (g)**} & \multicolumn{2}{c}{**PGA**} \\ \cline{3-4}  & & & & **2018/2007** \\ \hline
1 & 0201 (Center) & 2 & 0.300 & 0.245 & 0.82 \\
2 & 0205 (Kahta) & 2 & 0.300 & 0.244 & 0.82 \\
3 & 0208 (Golbasi) & 1 & 0.400 & 0.510 & 1.28 \\
4 & 0210 (Center) & 2 & 0.300 & 0.246 & 0.82 \\
5 & 0213 (Tut) & 1 & 0.400 & 0.440 & 1.10 \\
6 & 0214 (Celikhan) & 1 & 0.400 & 0.593 & 1.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing the results within the parameters of the previous two regulations [48,49]. In TEC-2007, the design spectral acceleration coefficients take constant values for different soil classes in the same earthquake zone. However, local ground effect coefficients, which started to be used with the current earthquake regulations, mean that different spectral acceleration coefficients are obtained for each soil class. With the current regulation, the spectral acceleration coefficient has increased in the case of poor ground conditions in Golbas. While the PGA and S\({}_{\text{DS}}\) values suggested in the previous map decreased in the Adiyaman/Center and Kahta districts with the current map, they increased in Golbas. A large part of Adiyaman province is located on the East Anatolian Fault Zone, one of the most important tectonic elements of the country. Palu-Sincik (145 km), Celikhan-Erkenek (45 km) and Golbas-Turkoglu (90 km) segments of the East Anatolian Fault Zone pass through Adiyaman province. In the instrumental period, no damaging earthquake was recorded except the Celikhan earthquake with Mw = 5.1 in 2004 (Adyaman-IRAP).
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 4 Experimental Results
### Results with Inpainting-Augmented Train Set  
We repeat the same experiment like before but with the inpainting-augmented train set. Confusion matrices and the precision, recall, and F1-score for the positive class are given in Figure 9 and Table 5, respectively. Notably, the classification performance of the binary CNN-based classifier, ResNet, ViT, and OpenCLIP drops compared to previous experiments. The amount of the performance drop is the largest for the binary CNN-based classifier. Only the F1-score of ConvNeXt and InfMAE increases. In addition, only the two models, ConvNeXt and InfMAE, achieve the perfect recall.  
Figure 10 presents Grad-CAM heatmaps for the same original images shown in Figure 8. As before, heatmaps of ResNet remain closely aligned with the target locations. Compared to the previous results, the heatmaps for ConvNeXt and ViT now exhibit better alignment with the AP landmine regions. However, for the rest models, it remains unclear whether their predictions are primarily based on core (foreground) features. The classification results on the synthetic landmine-free images are given in Table 6. A comparison between Tables 4 and 6 reveals a substantial reduction in the number of false positives across all models. This suggests that the proposed augmentation method effectively encourages the models to focus on core features during classification. Although the qualitative evidence for InfMAE is inconclusive, its zero false positive in the quantitative evaluation supports its improved focus on meaningful features.
###### Abstract  
In this work, gliding arc discharge (GAD) was used to produce air plasma and investigate the influence of the discharge parameters on the composition of the air plasma, vibrational temperatures, and the production of NO\({}_{\mathrm{x}}\). It was demonstrated that the main particles obtained in the GAD air plasma were N\({}_{2}\)\({}^{\star}\), N\({}_{2}\)\({}^{\star}\), N\({}^{\star}\), NO\({}_{\mathrm{y}}\), and O. It was observed that the reduction in the discharge frequency increased the intensity of the excited nitrogen molecules lines and reduced the nitric oxide (NO) and nitrogen dioxide (NO\({}_{2}\)) gas concentrations. The increase in the output voltage prolonged the duration of arc discharge and enhanced the intensities of the emission lines of the N\({}_{2}\)\({}^{+}\), O, and NO\({}_{\mathrm{y}}\) species and the concentrations of NO and NO\({}_{2}\) gasses. It was shown that the increase in the air flow rate from 6.7 L/min to 15.6 L/min decreased the concentration of the produced NO gas by 24%. Additionally, the line intensities of all main species in air plasma were enhanced. Studies have shown that the NO and NO\({}_{2}\) gas concentrations (selectivity of NO and NO\({}_{2}\)) can be controlled by varying the air flow, output voltage, and discharge frequency. The highest NO\({}_{\mathrm{x}}\) concentration of 2380 ppm was produced at 250 V, when the direct air flow was 11.2 L/min.  
gliding arc discharge; air plasma; emission spectra; nitrogen fixation; nitric oxide; nitrogen dioxide +
Footnote †: journal: Applied sciences
## 5 Conclusions  
This research examines the implementation of the most advanced variant--ETCS L3. The heuristic procedure was proposed within the methodology of solving this problem. It contains several important partial steps. After the basic characterization of the ETCS L3 safety system level, the specific railway section (Senec-Sladkovicovo on double-track corridor line of the ZSR network) was subsequently selected for the practical application of the variant. Practical throughput and other appropriate computations were calculated for this railway section and comparison of the current state with the ETCS L1 and L2 implementation alternatives was performed.  
The research results show that only the implementation of ETCS L3 will bring a significant increase in throughput on double-track corridor line equipped with automatic block. The ETCS system prevents dangerous situations. In principle, it can be argued that each of the proposed options is better than the current situation in terms of safety. In terms of the impact on the practical throughput determined analytically for the prospective train traffic diagram according to the principles of the ZSR methodology contained in regulation D24, the individual ETCS variants are not equivalent. Each variant affected the practical throughput on a different scale. The practical throughput of up to 336 trains per day was  
Figure 13: Mutual comparison of the resulting practical throughput (in number of trains per day) for individual proposals.  
achieved with the ETCS L3 variant. This variant offers up to 177 trains per day more than the current state.  
In the future, it will be possible to apply the research introduced for ETCS L3 applications to other important lines on the Slovak railway network. The ETCS L3 variant will continue to be researched and developed in the field of transport science, as it is an important progressive element in railway infrastructure. It is also one of the key tools to achieve fast, safe, and reliable railway transport.  
Conceptualization, M.D. and J.G.; methodology, M.D.; formal analysis, V.S. and M.D.; investigation M.D., V.S., J.G., V.L. and M.V.; resources, M.D. and J.G.; data curation, M.D. and J.G.; writing--review and editing, M.D., V.S., J.G., V.L. and M.V.; visualization, M.D., V.S. and V.L.; supervision, J.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak Research and Development Agency under the contract no. APVV-19-0444.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
## 2 Methods
### Finite Element Method
#### 2.4.5 Solution Procedure  
In the present work, the aforementioned layerwise theory with the equivalent model of the foam-filled corrugated cores and the finite element formulation based on this theory are adopted to investigated the effects of the thermal and aerodynamic loads on the natural frequencies and structural stabilities of the HCS plates. The solution procedure of coupled fluid-structure-thermal analysis of the HCS plates is illustrated in Figure 2. Firstly, the static stabilities subjected to thermal loads are investigated by buckling analysis and evaluated by the critical temperature change. The critical temperature change limits the maximum temperature allowed in subsequent analysis for steady-state and uniform temperature distributions. Secondly, the dynamic characteristics subjected to thermal loads are investigated by pre-stressed modal analysis in thermal environments and evaluated by the natural frequencies and mode shapes. The geometric stiffnesses induced by the initial thermal stresses are obtained from static analysis. Thirdly, the dynamic stabilities subjected aerodynamic loads are investigated by coupled fluid-structure analysis based on the natural frequencies and modal shapes from modal analysis, which is also called aeroelastic analysis, and evaluated by the critical dynamic pressure. Finally, the dynamic stabilities subjected to both thermal and aerodynamic loads are investigated by coupled fluid-structure-thermal analysis, which is also called aerothermoelastic analysis, and evaluated by the critical dynamic pressure.  
The complex eigenvalue is obtained by solving Equation (42). The imaginary part of the eigenvalue is the frequencies and the real part is the damping terms. The dimensionless critical dynamic pressure \(\overline{\lambda}\), corresponding to the critical airflow velocity, is determined for the HCS plates when the damping of a \(r\)th order mode changes from negative to positive or the coalescence of frequency curves of two adjacent mode occurs.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 3 Results
### Analysis Findings for Scenario-3 Classification Models  
The analysis results for the triple classifications made for \"copied\" behavior, \"dicey\" behavior, and \"did not copy\" behavior in the study are presented in Table 7. In Table 7, XGBoost is the most successful model proposal that detects 'Copied', 'Dicey', and 'Did Not Copy' class labels in terms of accuracy in the online exam with a rate of 97.7%. However, MCC performs close to 7-layer DNN, DT, and RF in terms of sensitivity and specificity. In addition, the KNN and RF models are also very strong and have high AUC values, indicating that their ability to discriminate between classes is also very good.  
The confusion matrix, average loss curve, average accuracy curve, average ROC curves, and ROC curve of all models for the XGBoost model are given in Figure 6.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** & **Specificity** & **MCC** & **Error Rate** & **AUC** \\ \hline KNN & 0.939 & 0.927 & 0.946 & 0.924 & 0.969 & 0.897 & 0.062 & 0.980 \\ \hline SVM & 0.923 & 0.912 & 0.899 & 0.892 & 0.945 & 0.868 & 0.077 & 0.948 \\ \hline RF & 0.962 & 0.937 & 0.930 & 0.927 & 0.966 & 0.919 & 0.039 & 0.998 \\ \hline LR & 0.885 & 0.886 & 0.872 & 0.855 & 0.925 & 0.811 & 0.115 & 0.963 \\ \hline XGBoost & **0.977** & 0.941 & 0.958 & 0.946 & 0.985 & 0.956 & 0.023 & 1.000 \\ \hline DT & 0.969 & 0.933 & 0.953 & 0.939 & 0.981 & 0.944 & 0.031 & 0.967 \\ \hline
7-layer DNN & 0.969 & 0.978 & 0.950 & 0.945 & 0.979 & 0.949 & 0.031 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Metric analyses of models used in triple classification with K-10 cross-validation.  
Figure 4: 10-layer DNN model descriptive SHAP summary analysis.  
Figure 5: LIME analysis for 10-layer DNN model.  
Figure 7 shows the attributes and their influence values that positively and negatively affect the prediction variable in the XGBoost model in the triple classification positive class (copied) shape analysis. It is understood that the 'total_e_time', 'w_number', and'supervision' attributes contribute more to the model prediction.  
Figure 8 shows the classes and the attributes that are effective on the classes in the triple classification LIME analysis for the XGBoost model. In the analysis, while 'w_number' and 'total_e_time' attributes increase the prediction rate of the 'copied' class,'supervision'  
Figure 6: K-10 cross-validation result triple classification graphs of the XGBoost model: (**a**) XGBoost triple classification complexity matrix; (**b**) XGBoost model loss; (**c**) XGBoost model accuracy; (**d**) ROC curves for all models.  
Figure 7: XGBoost model descriptive SHAP summary analysis.  
and 'exam-type' attributes decrease it. In the analysis, the negative attribute values given for the 'dicey' class increase the class prediction, while the 'exam_type' attribute with a positive value decreases it.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 5 Potential Research Directions
### Resilient Cyber-Physical System Design
In [58], the authors propose an SDN-based real-time cyber-physical attack detection method that simultaneously considers physical dynamics and network characteristics; the proposed SDN-based method also provides attacker isolation and communications path reconfiguration to recover the physical system from a PDA. At the application layer, studies have been conducted to handle computing system attacks and guarantee the resiliency of a CPS. In [86], malicious code execution defense strategies are proposed, where the methods of restarting the computing system and utilizing a trust execution environment for the computing hardware platform guarantee safety against malicious code injection attacks. A simplex computing architecture for a secure UAV control is proposed in [87], which switches from the normal control mode to a safe control mode to stabilize the UAV when the attacker damages the virtual machine of the computing system, such as through the manipulation of the control parameter. Moreover, the ML-based applications are vulnerable to cyber-physical attacks against the computing layer, which drastically deteriorates the performance of ML applications [96]. If safety-critical ML applications are attacked, performance degradation in the ML-based detectors means that cyber-physical attacks will be missed. Thus, ML-based attack detection methods should be secure and should guarantee their own resilience. To mitigate application software attacks targeting a neural network model, a pruning-based and fine-tuning-based defense strategy is proposed in [81], which reduces backdoor attack success rates to 0% with only 0.4% accuracy degradation in the original ML application. A watermarking method for an ML model is proposed in [143], enhancing the security level of the ML model against a backdoor attack.
## 2 Soil Contamination with PAHs
### PAHs: Properties and Sources  
PAHs are a group of ubiquitous organic pollutants with at least two aromatic rings (Figure 1), and are poorly soluble in water (Table 1). Due to their chemical structure, PAHs have hydrophobic properties, which refers to their ability to accumulate on the surface of solid materials like soil, sediment, sewage sludge, and solid wastes. The dangers emerging from the presence of PAHs in soil are related to the toxic nature of those pollutants [42; 43]. It is noteworthy that some substances in this group have been recognized as mutagenic, carcinogenic, and teratogenic [18; 19].  
Sources of PAHs are categorized into natural as well as anthropogenic sources: hydrothermal process volcanoes, forest fires, and waste burning are natural sources of PAHs. Anthropogenic sources include waste incinerators, burning of fossil fuels during heating processes, incomplete combustion of organic matter, petrochemical spills on land, wood burning, petrol and diesel oil combustion, gasification, and plastic waste incineration [13; 14; 15; 17]. Globally, 16 to 32 PAH compounds are subjected to mandatory control, due to their harmful properties [44]. PAH persistence and hydrophobicity in environmental components are the main factors that exacerbate the pollution problem, taking into account that soils receives a considerable share of this pollution (sink), due to their complex matrix structure that facilitates the sorption of these pollutants. Soil organic matter is a decisive factor in determining the degree of PAH sorption into the soil, along with the physicochemical properties of the PAHs themselves [18; 19; 23; 45; 46]. Therefore, the remediation of soils polluted by aged PAHs has become a major issue for environmental scientists in recent years [12; 13; 14; 47; 48].
# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 5 Conclusions  
The research utilized mobility data from a revealed preferences survey to explore personal mobility habits adopted by the residents of Athens, Greece. In essence, it applied Spectral Clustering algorithms to analyze mode choices for the first time. Six distinct groups of trips were exported from this process. This gives a new classification of trips in AMA. Overall, it can be concluded that trips clustering has led to a more comprehensive understanding of the possible range of alternatives, something that was not feasible in the beginning considering the limited sample size. It can be said that the approach that was introduced in this study simplified the problem by reducing the multiple dimensions of mobility. The findings from this analysis can provide valuable and interpretable insights for the transport planning and policymaking process. It is clear from the results that the elimination of short-distance car trips, which constitute a significant proportion of all trips, should be prioritized. Finally, addressing the last-mile problem seems to be the key to mitigating car dependency and ensuring sustainability. Attractive shared mobility services and active modes can reverse this negative trend.  
Conceptualization, P.G.T.; Data curation, E.A.; Formal analysis, E.A.; Investigation, E.A.; Methodology, P.G.T.; Supervision, P.G.T.; Validation; Visualization, E.A.; Writing--original draft, E.A. and P.G.T.; Writing--review and editing, P.G.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hellenic Broadcasting Corporation (ERT). Project code: 91008900, Project name: \"Scientific Support for the Analysis of Current Transport Trends in Greece\".  
The study was conducted in accordance with the Declaration of Helsinki. The overall research project methods have been approved by the Ethics Committee of Research of National Technical University of Athens (project code: 91008900, Date of approval: 15 September 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We sincerely acknowledge the Hellenic Broadcasting Corporation (ERT) for distributing the revealed preferences survey. Their contribution has been invaluable to our study. Additionally, we would like to express our gratitude to [PERSON] for his invaluable contributions as an external collaborator, particularly for his work on the AthensPop package.  
The authors declare no conflicts of interest.  
Figure A4: Spatial distribution of origin locations of cluster 4 trips.  
Figure A5: Spatial distribution of origin locations of cluster 5 trips.  
Figure A6: Spatial distribution of origin locations of cluster 6 trips.