esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Simulation Condition for Evaluation
Regarding the boundary conditions in SimNIBS, the electrodes and the other areas have different initial conditions. The initial conditions of the electrodes are determines using the Dirichlet boundary condition, while those of other areas are determined using the Neumann boundary condition. The electrical conductivity of each tissue varies depending on the frequency of the stimulation, and arbitrary values can be set in SimNIBS. Therefore, in this study, the electrical conductivity values [27; 28] shown in Table 1 are used for the simulation. These electrical conductivity values are assumed to be linear and isotropic. Regarding the other stimulation conditions, an alternating current stimulation at 1 mA will be simulated, which is in accordance with the previous work [11; 22]. Moreover, the electric field on the eyeball surface is simulated, and the default value is used as the electric field value. The default value of the electric field is calculated on the basis of the magnitude of the electric field vector, and it is always a positive value. Using the method for designing electrode placements that are able to stimulate intended regions on the lower side of the eyeball surface described in Section 3, in this subsection, the electrode placements used in the simulation presented in this study are shown. Figure 4 shows the electrode placements used in this simulation, and Table 2 shows the coordinates. In correspondence with the stimulation regions of each of the conditions shown in Figure 3, five electrode placements are shown in Figure 4. The pink-colored electrodes shown in Figure 4 are anodes, and the blue-colored electrodes are cathodes. The size of the electrode is determined with reference to the gel pad of foc.us [29] used in the previous research [15], which has a size of 42 mm \(\times\) 42 mm. The coordinates in Table 2 represent the x, y, and z coordinates from left to right. The origin of the coordinates corresponds to the center of the human head model, as shown in Figure 5. \begin{table}
\begin{tabular}{c c c} \hline
**Tissues** & **Electrical Conductivity (Default) [S/m]** & **Electrical Conductivity (10 Hz) [S/m]** \\ \hline White matter & 0.12600 & 0.027656 \\ Gray Matter & 0.27500 & 0.027512 \\ \(CSF\)1 & 1.6540 & 2.0000 \\ Bone & \(1.0000\times 10^{-2}\) & 0.020028 \\ Head skin & 0.46500 & \(2.0000\times 10^{-4}\) \\ Eye ball & 0.50000 & 0.41113 \\ \hline \end{tabular} \({}^{1}\)_CSF_ (Cerebral Spinal Fluid).  
\end{table} 
Table 1: Comparison of electrical conductivity between the default value obtained in SimNIBS and the value obtained with 10 Hz stimulation [27; 28]. Figure 4: Electrode placements for the stimulation of the lower eyeball surface; figures were produced by the authors using SimNIBS (29 September 2021).
## 8 Conclusions
\begin{tabular}{l l} IDC & International Data Corporation \\ TM & Text Mining \\ NLP & Natural Language Processing \\ NE & Named Entity \\ NER & Named Entity Recognition \\ RE & Relationship Extraction \\ ML & Machine Learning \\ AI & Artificial Intelligence \\ EHR & Electronic Health Record \\ WEKA & Waikato Environment for Knowledge Analysis \\ CLAMP & Clinical Language Annotation, Modeling, and Processing \\ AWS & Amazon Web Services \\ EU & European Union \\ HIPAA & Health Insurance Portability and Accountability Act \\ n2c2 & National NLP Clinical Challenges \\ i2b2 & Informatics for Integrating Biology and the Bedside \\ NIH & National Institutes of Health \\ NCBC & National Centers for Biomedical Computing \\ EMR & Electronic Medical Record \\ SVM & Support Vector Machine \\ RF & Random Forest \\ NN & Neural Network \\ CRF & Conditional Random Field \\ ME & Maximum Entropy \\ BERT & Bidirectional Encoder Representations from Transformers \\ BETO & Spanish BERT \\ BIO & Beginning, Inside, Outside of Entity \\ POS & Parts of Speech \\ BFS & Breadth First Search \\ CNN & Convolutional Neural Network \\ RNN & Recurrent Neural Network \\ LSTM & Long Short Term Memory \\ GCN & Graph Convolutional Network \\ CDT & Concept Dependency Tree \\ GNN & Graph Neural Network \\ TP & True Positive \\ TN & True Negative \\ FP & False Positive \\ FN & False Negative \\ ROC & Receiver Operator Characteristic \\ AUC & Area Under the Curve \\ ODIE & Ontology Development and Information Extraction \\ ADE & Adverse Drug Events \\ PHI & Personal Health Information \\ \end{tabular} \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Publication** & **Task** & **Methods** & **Performance** \\ \hline [PERSON] et al. (2011) [84] & Drug–disease interaction extraction from clinical texts & Linguistic hybrid rule-based method using shallow parsing, syntactic simplification, and pattern matching & Did not perform well with an average precision and a very low recall \\ \hline [PERSON] et al. (2011) [85] & Clinical RE on 2010 i2b2 dataset & Combination of Rule-based and ML methods & Model performed decently with a micro-average F1-score of 0.7326 \\ \hline [PERSON] et al. (2015) [86] & Automated extraction of medication discrepancy & Matching of drug names with their attributes from a prescription list and confirming it by means of co-location information & Performed well in identifying the medical discrepancies \\ \hline [PERSON] et al. (2021) [87] & RE between different clinical words & Path similarity analysis on the terms extracted by scraping and POS tagging & Successfully converted the data into a classified form \\ \hline [PERSON] et al. (2021) [70] & Adverse drug event extraction on 2018 n2c2 dataset & BFS based on the co-location information & Left-only rule-based approach (macro-average F1-score: 0.83) between the drug and the non-drug entity types & Left-only rule-based approach (macro-average F1-score: 0.83) performed the best amongst other rule-based models \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the rule-based approaches for clinical RE.
## 1 Introduction
[18] developed a two-layer stacking ensemble model to assess landslide susceptibility in Jiacha County, demonstrating the improved predictive performance of advanced machine learning approaches over traditional single models. These recent contributions underscore the necessity of combining robust models with contextual geohazard analysis to improve landslide risk assessment. Although ML models have shown great potential for landslide susceptibility mapping, their effectiveness largely depends on the quality of the input dataset. Since AI models learn from existing data, a reliable and well-curated dataset is essential for training, evaluating, and validating model performance. A critical issue, often overlooked in the literature, is the classification of conditioning factors for earthquake-induced landslides using a broad range of datasets. Moreover, despite the valuable insights offered by [PERSON] et al. [14] and [PERSON] et al. [17], research on co-seismic landslides following the 2015 Nepal Earthquake has notable gaps. One key limitation is the inadequate consideration of rainfall influence on post-earthquake slope stability. Rainfall can significantly modify slope hydrology and trigger slope failures even after a long gap from the end of seismic shaking. However, most studies focus only on the immediate effects of an earthquake and ignore the precipitation-led delayed slope response. In this research work, we adopt a more comprehensive approach by integrating observed rainfall data from not only the month of earthquake occurrence but also the subsequent two months. This enables a better understanding of hydrological ablation dynamics and enhances the predictive power of landslide susceptibility models. Our study introduces a series of methodological innovations aimed at enhancing the accuracy, interpretability, and generalizability of ML models for earthquake-triggered landslide mapping in the Nepal Himalaya. Unlike previous studies, we also conduct a comparative evaluation of LR, RF, and XGBoost models, demonstrating that ensemble learning methods yield higher accuracy and lower false-negative rates. This enables more reliable identification of high-risk areas, which is crucial for informed hazard mitigation. A novel contribution of this work is the incorporation of observed rainfall ablation data to account for delayed hydrological triggers of the landslides. To reduce spatial autocorrelation and improve model generalization, we implement cluster-based cross-validation and a Leave-One-Cluster-Out (LOCO) approach across distinct geomorphic zones. To further improve model transparency, we apply SHAP (SHapley Additive exPlanations) analysis to determine the relative importance of predictor variables and interpret model behavior. The framework also includes a comprehensive spatial analysis of conditioning factors. Collectively, these innovations address critical methodological limitations and offer a robust, interpretable, and transferable strategy for modeling earthquake-induced landslide susceptibility in tectonically active mountain regions.
## 1 Introduction
[PERSON] et al. [31] used the PCE method for uncertainty calculation and performed robust low boom design using atmospheric uncertainties. [PERSON] and [PERSON] [32] measured the uncertainty in the sonic boom level using the results of a small number of expensive analyses and based on three different fidelity levels with the multi-fidelity PCE method. [PERSON] et al. [20] performed low-dimensional sonic boom uncertainty analysis using MFMC and MF-PCE methods. To the best of the authors' knowledge, the LRA method has not been applied to any engineering problems in the aviation field yet and the LRA method has not been implemented within a multi-fidelity modelling approach. The authors implemented the LRA method into an uncertainty quantification process for sonic boom prediction and further developed a novel multi-fidelity approach of the LRA method which is called the MF-LRA method in this paper. This new approach is comprehensively compared with the multi-fidelity PCE method. In addition, it is observed that the literature lacks high-dimensional numerical multi-fidelity benchmark problems; so as a contribution of the authors, a new multi-fidelity extension of the Sobol'-Levitan function is implemented and presented in this study. Within this perspective, this paper first aims to present a comprehensive performance assessment by constructing a multi-fidelity extension to a Low-Rank Approximation and comparing the results with a competing approach of multi-fidelity Polynomial Chaos Expansion for multi-fidelity analytical test cases. Then, the present approach is used for the uncertainty quantification of high-dimensional supersonic aircraft design problems within an application-oriented framework. The remainder of the paper is as follows: In Section 2, some of the basic properties and concepts are introduced for the present methods and the details of the multi-fidelity extension of the surrogates are explained. In Section 3, the details of the computational experiment (e.g., the selected sampling strategy for the design of experiments (DoE)) are given; accuracy metrics and performance results for both analytical and high-dimensional engineering problem test cases are also demonstrated. In the final section, the results are discussed and current challenges along with the recommendations for further investigations are presented.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 3 Results
### Data Classification
#### 3.2.2 Normalized Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Predicted Class**} \\ \hline  & \multicolumn{2}{c}{**Walking Downstairs**} & \multicolumn{2}{c}{**Walking Upstairs**} & \multicolumn{2}{c}{**Running**} & \multicolumn{2}{c}{**Standing**} & \multicolumn{2}{c}{**Walking**} \\ \cline{2-7}  & Walking Downstairs & 1359 & 455 & 2 & 13 & 171 \\ \cline{2-7} Actual Class & Walking Upstairs & 214 & 1631 & 1 & 18 & 136 \\ \cline{2-7} Actual Class & Running & 20 & 32 & 1914 & 26 & 8 \\ \cline{2-7}  & Standing & 1 & 12 & 0 & 1984 & 3 \\ \cline{2-7}  & Walking & 60 & 125 & 1 & 53 & 1761 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Confusion matrix related to non-normalized and imputed data from the accelerometer and magnetometer sensors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Predicted Class**} \\ \hline  & \multicolumn{2}{c}{**Walking Downstairs**} & \multicolumn{2}{c}{**Walking Upstairs**} & \multicolumn{2}{c}{**Running**} & \multicolumn{2}{c}{**Standing**} & \multicolumn{2}{c}{**Walking**} \\ \cline{2-7}  & Walking Downstairs & 1545 & 325 & 1 & 3 & 126 \\ \cline{2-7}  & Walking Upstairs & 204 & 1684 & 1 & 5 & 106 \\ \cline{2-7} Actual Class & Running & 5 & 46 & 1917 & 21 & 11 \\ \cline{2-7}  & Standing & 0 & 13 & 0 & 1987 & 0 \\ \cline{2-7}  & Walking & 19 & 118 & 2 & 43 & 1818 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Confusion matrix related to normalized and non-imputed data from the accelerometer, magnetometer, and gyroscope sensors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Predicted Class**} \\ \hline  & \multicolumn{2}{c}{**Walking Downstairs**} & \multicolumn{2}{c}{**Walking Upstairs**} & \multicolumn{2}{c}{**Running**} & \multicolumn{2}{c}{**Standing**} & \multicolumn{2}{c}{**Walking**} \\ \cline{2-7}  & Walking Downstairs & 1883 & 117 & 0 & 0 & 0 \\ \cline{2-7}  & Walking Upstairs & 84 & 1787 & 0 & 14 & 115 \\ \cline{2-7} Actual Class & Running & 4 & 18 & 1946 & 0 & 32 \\ \cline{2-7}  & Standing & 0 & 6 & 0 & 1989 & 5 \\ \cline{2-7}  & Walking & 12 & 130 & 7 & 0 & 1851 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Confusion matrix related to normalized and non-imputed data from the accelerometer sensor. Considering the accelerometer, magnetometer, and gyroscope sensors' data, the results obtained with imputed and normalized data are reported in the confusion matrix presented in Table 17. The implemented method reported an accuracy of 99.82%, a precision of 99.82%, a recall value of 99.82%, and an F1 score of 99.82%.
## 4 Discussion  
### EO and FW Chemical Composition Evaluation  
Diterpenes are not usually present in EOs but are sometimes encountered as minor, insignificant constituents [26]. The marigold EO was rich in monoterpenic hydrocarbons (ocimene, limonene, terpinene, myrcene, etc.) and acyclic monoterpenic ketones (tagetone, dihydrotagetone, tagetone), which are the primary compounds, in addition to smaller amounts of sesquiterpene hydrocarbons and oxygenated compounds. Within these groups, the chemical diversity is quite high. Figure 11 shows the main chemical structures of the compounds identified in the marigold EO, which were also found in many other studies [27; 28].  
There are studies that mention the impact of geographical origin on the chemical diversity [29] of the EOs obtained from _T. patula._ In the studies [30; 31], limonene, \(\alpha\)-terpinolene, 4-vinylguaiacol, and \(\gamma\)-terpinene are mentioned as the main compounds; however, there is a lack of evaluation of some compounds, such as \(\beta\)-ocimene, \(\beta\)-caryophyllene, piperitone, and piperitone.  
Figure 10: The fumigant effect of marigold EO, “_Namuk_” variety, on mycelial growth in _B. brongniartii_ at different concentrations (after 2, 4, and 7 days).  
Two studies conducted on the EO obtained from marigoldg [31; 32] found that the main identified compounds were limonene, (Z)-\(\beta\)-ocime, \(\alpha\)-terpinolene, (E)-tagetone, (Z)-tagetone, piperitone, piperitone, and \(\beta\)-caryophyllene in variable amounts, confirming the results obtained in the present work.  
[PERSON] et al. mention that in Italian essential oils obtained from marigold inflorescences, the main compounds are piperitone (28.9%), terpinolene (5.8%), \(\beta\)-caryophyllene (3.8%), limonene (3.5%), linalool (2.7%), myrcene (1.8%), and terpinen-4-ol (1.1%) [32]. In the case of the _\"Nanuk\"_ variety, some of these compounds were identified, but in different quantities (usually higher)--for example: pipertones 1.36-9.01%, \(\beta\)-caryophyllenes 3.45-13.38%, limonenes 4.42-7.83%, and linalool 2.34-4.27%.  
As in the investigated case, other studies [33; 34] also obtained significant variations regarding the compounds obtained during the 3 years (2018-2020), depending on the year. Therefore, the primary factor influencing these variations is likely to be the fluctuation in climatic conditions. Regarding the compound classes, in marigold FW (_\"Nanuk\"_ variety), mainly monoterepne compounds were identified (55.10-95.80%), especially oxygenated ones (95.80%), followed by sesquiterpenes (37.61% in 2019) and diiterpenes in smaller amounts (1.67-5.23%).  
It should be noted that the chemical composition of EO and FW largely depends on a series of endogenous and exogenous factors, including genetic traits of the plant/variety, plant organs from which extraction is performed (roots, leaves, stems, capitula), growth conditions, drying and storage, and stress factors (weather conditions during the cultivation year, disease, and pest attacks) affecting the plant. The chemical composition of the EO is influenced by extraction methods and solvents used, extract standardization, etc. [33].
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 3 Discrete Time Fractional-Order Sine Chaotic Map (DFSCM)
### Discussion  
Based on the observations from the three different fractional chaotic maps obtained from the literature, this section depicts the chaotic response of the novel discrete fractional map proposed in (8). The proposed DFSCM exhibits highly non-linear behaviour, and the chaotic response of the map (8) is visualised in the comparison with the three other discrete fractional maps from the literature in Figure 2. The long-term behaviour of the system ensures the suitability of the system to be employed for cryptosystems, and the randomness of the state variables is exhibited in the form of the phase portrait plane in Figure 3. The phase portrait plane also visualises the trajectories distributed at a highly random level. The main advantages of the proposed DFSCM are the long-term chaotic behaviour, which ensures complexity, and the improved chances of generating a highly secure secret key based on the DFSCM (8).
## 3 Results
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Data Set** & **Our Approach** & **Other Classifiers** \\ \hline Breast Cancer  Data & 0.912 & Immune-inspired semi-supervised Algorithm, introduced in [49]: 0.973 \\ \hline \multirow{6}{*}{Breast Cancer Wisconsin} & \multirow{6}{*}{0.957} & Extreme Learning Machine Neural Networks, introduced in [50]: 0.978 \\  & & Immune-inspired semi-supervised algorithm, introduced in [49]: 0.965 \\ \cline{1-1}  & & Support vector machines combined with Feature Selection, introduced in [51]: 0.997 \\ \hline \multirow{6}{*}{Data Banknote Authentication} & \multirow{6}{*}{0.993} & Deep Neural Network with PCA and LDA, introduced in [52]: 0.99 \\ \cline{1-1}  & & Decision tree approach, introduced in [53]: 0.994 \\ \cline{1-1}  & & Random Forest approach, introduced in [54]: 0.948 \\ \cline{1-1}  & & Neural Network-Genetic Algorithm, introduced in [55]: 1 \\ \hline \multirow{6}{*}{HTRU 2} & \multirow{6}{*}{0.89} & Classical classifiers: C4.5: 0.74; MLP: 0.752; NB: 0.692; SVM: 0.789 \\ \cline{1-1}  & & GH-VFDT Algorithm, introduced in [37]: 0.862 \\ \cline{1-1}  & & A hybrid ensemble method, introduced in [56]: 0.918 (with respect to a voting threshold parameter) \\ \hline \multirow{2}{*}{Immunotherapy} & \multirow{2}{*}{0.8} & A hybrid classification approach. introduced in [57]: 0.851 \\ \cline{1-1}  & & Considering evaluation of machine learning techniques [58]: 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison regarding other classifiers with respect to the F-score measure. Classification accuracy comparison with fuzzy classification techniques for chosen data sets is shown in Table 8 as well.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis  
The analysis component performs identification of network situations from metrics provided by the aggregation module and reaches diagnoses that contribute in decision-making tasks. In general terms, the analysis studies any aspect related with the incidences reported by the 5G use cases and the risks that could compromise the system requirements. In this context, situations are divided into two main groups: events and risks. Events are defined as situations that occur within 5G mobile networks which a priori do not display harmful features but are useful in diagnosis. The events are grouped into four categories: discovery, removal, modification and notification, which are described as follows:  
* _Discovery events_--include all situations related to incorporation of new assets to the system. For example, this occurs when incorporating new nodes into the network, establishment of new connections between previously existing resources, or deployment of new virtualization layers. Each time a discovery event is communicated, the asset inventory is updated.
* _Removal events_. Unlike discovery events, removal events indicate situations related to the elimination of 5G resources. These are the cases of deletion of assets, removal of connections between nodes, or elimination of virtualization resources. As in discovery events, each time a removal event is communicated, the asset inventory is upgraded.
* _Modification events_. They include every situation related with the modification (not removal) of an existing resource. For example, this occurs when varying the location of the asset (i.e., changes to IP address, MAC address, etc.), and changes between communication protocols or software updates. As in the previous cases, modification events involve changes to the asset inventory.
* _Notification events_. They report specific situations in the network that are not related to changes in the assets inventory, such as periodic reviews of the bandwidth status, presence of unused resources or requests for special configurations.  
On the other hand, risks are inherently damaging, and they may be inferred from network mapping or directly reported by the use case agents. An example of the first case is the identification of bottlenecks, congested regions or resources depletion. In the other case, a striking example relates to defensive use cases, where security NFV-Apps (Intrusion Detection Systems, honeypots, etc.) directly reports intrusions such as malware spreading or denial of service threats. The bases of 5G situation analysis are shown in Figure 3. They include: detection, risk assessment, asset inventory management, risk map, prediction, diagnosis and countermeasure tracking. The following briefly describes the most important features of each of them.  
Figure 3: Situation analysis on 5G.