esa-sceva/satcom-chunk-collection huggingface dataset:
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
### Motivations and Contributions  
SLMAS aims to address the security and privacy issues that smart wheelchairs encounter. These challenges include unlawful access, data manipulation, and privacy violations, which may jeopardize the wheelchair user's security and privacy. With sensors and communication tools, smart wheelchairs can connect with other hardware and software, including smartphones, Internet of Things (IoT) devices, and cloud services. Nevertheless, because these products are susceptible to cyberattacks such as eavesdropping, impersonation, and data theft, their connection also brings new security dangers. By lowering the computational and communication overheads, SLMAS aims to provide a safe and effective communication system for intelligent wheelchairs, which secures them against these security threats. SLMAS mutual authentication (MA) mechanism will ensure that only permitted devices can communicate with the wheelchair and that all communications are secured against tampering or unauthorized access.  
The rest of the article is designed as follows: a review of the literature examines the prior literature and research on the subject of the planned project in Section 2. The proposed scheme and the methodology are provided in Section 3. Section 4 offers a rigorous security assessment of the suggested research project and highlights potential weaknesses. A computation, communication, and feature comparison is given in Section 5. The article's main conclusions are outlined in the last section, along with the article's contributions and potential directions for further study.
# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods
### Image Classification  
Image classification was performed for each SAR image in Table 1 in order to analyze the temporal developments in the study area. However, as a huge number of SAR textures are used covering many different value ranges in various units, traditional classifiers for pixel-based approaches--such as k-means clustering [100] or Maximum Likelihood estimation [101]--are not suitable. We therefore chose a Random Forest classifier (RF [102]) for our study. It is based on the concept of classification and regression trees (CARTs [103]) and repeatedly uses random subsets of the training data for the modeling of target classes. This automatically makes the best use of the feature data (texture layers in our case) with the best prediction ability to make LULC estimations for the full scene. For each year, we calculated a number of n = 500 classification trees based on random subsets of \(\surd\)n = 22 features. Figure 3 shows the outcomes of the classification as an intermediate product of the analysis.  
An accuracy assessment has been performed by manually collected sampling points. These points were visually placed on the Landsat image of each year independently from the sampling areas described in Section 2.2 so they represent the real occurrence of each LULC during each time step. A number of 150 points was collected per class and year which were then compared with the results from the SAR classification for the generation of a confusion matrix.  
We achieved overall accuracies of 84.4% (2007), 83.3% (2008), 85.7% (2009), 85.0% (2010), 84.0% (2011), 80.3% (2015), 82.3% (2016) and 81.47% (2017) with respective Kappa values of 0.81 (2007), 0.80 (2008), 0.83 (2009), 0.81 (2010), 0.81 (2011), 0.76 (2015), 0.78 (2016) and 0.77 (2017). These may appear low for change detection approaches at first sight, but the proposed evaluation of changes by an aggregated index (see Section 2.4) is not pixel-based and compensates smaller misclassifications. As shown in Tables 3 and 4, urban areas and bare rock show the highest accuracies due to their clear signal in both the optical and the radar images. Bare soil areas also show high accuracies because of its distinct backscatter characteristics during the dry season, but were misclassified as grassland in some areas of transition. The tables also show that grassland was over-estimated throughout all images (low user's accuracies) while shrubland and forest reveal the lowest producer's accuracies due to their similar signature in the SAR data.  
Figure 2: Identified areas used for the stratified random sampling.
## 3 FRA Open Circuit Measurement
### Measured and Simulated FRA for Normal vs. Pitting  
Naturally, pitting or contact wear occurs due to the mechanical stress on the contact's surface during load transfers. This degradation affected the normal tap changer accompanied by coking. Unlike other defects, pitting on the arcing switch contact surface seemed to have no significant impact on the FRA measurements. Since pitting reduces the contact surface area, it results in increased resistance [11], although other research findings show a reduction in the resistance value using the dynamic resistance measurement [9; 12]. Pitting probably occurs due to the copper conductor's compaction of chemical properties, which causes more free electrons to flow in the conductor, thus increasing the conductivity. However, a further investigation is required to evaluate the cause of resistance reduction in pitting conditions. The measured and simulated FRA signatures due to pitting are presented in Figures 10 and 11, respectively. A simulated FRA signature during pitting condition was obtained using the circuit parameters listed in Table 4.  
Figure 8: Measured FRA for normal, 50%, and 100% coking.  
Figure 9: Simulated FRA for normal, vs. 50% and 100% coking.  
Results in Figures 10 and 11 show a slight variation in the FRA signature due to contact pitting. At the low-frequency region and due to pitting, the FRA signature tended to shift upward when compared with the normal condition due to the reduction in resistance and inductance values. At the mid and high frequency ranges, the FRA response tended to shift downward due to a decrease in the capacitance value. According to the degree of severity, it can be shown that at 100% pitting, the signature exhibited a higher shift than the 50% pitting, which was attributed to their effects on the resistive and capacitive components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **L (\(\mu\)H)** & **Ls (\(\mu\)H)** & **Rs \(\Omega\)** & **Cp (pF)** \\ \hline Normal & 500 & 0.001 & 1 & 0.512 \\ Pitting 50\% & 500 & 0.002 & 909.1 & 0.464 \\ Pitting 100\% & 500 & 0.003 & 833.3 & 0.464 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of a simulated circuit model for pitting.  
Figure 11: Simulated FRA for normal, vs. 50% pitting and 100% pitting.  
Figure 10: Measured FRA for normal, vs. 50% and 100% pitting.
# Structure and Electrical Properties of Carbon-Rich Polymer Derived Silicon Carbonitride (SiCN)
## 3 Results and Discussion
### Investigation of the Carbon Phase
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Temperature** & **1100 \({}^{\circ}\)C** & **1400 \({}^{\circ}\)C** & **1600 \({}^{\circ}\)C** \\ \hline Lateral crystallite size, L\({}_{\mathrm{a}}\) (nm) & 7.66 & 8.54 & 10.08 \\ Average continuous graphene length including tortuosity, L\({}_{\mathrm{eq}}\) (nm) & 11.06 & 15.91 & 17.76 \\ Inter-defect distance, L\({}_{\mathrm{D}}\) (nm) & 7.58 & 8.00 & 8.69 \\ Defect density, n\({}_{\mathrm{D}}\), \(\times\) 1011(cm\({}^{-2}\)) & 7.52 & 6.75 & 5.71 \\ \(\mathrm{I}\mathrm{p}\mathrm{O}/\mathrm{I}\mathrm{c}\) ratio, i.e., stacking number of graphene layer, L\({}_{\mathrm{c}}\) (nm) & 1.7 (\(\leq\)3 layers) & 1.7 (\(\leq\)3 layers) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The carbon phase structure of the carbon-rich SiCN materials. Figure 3: An XRD pattern of the C/SiCN monoliths fabricated at different temperatures. be better understood by evaluating the contribution of individual components available for electrical conduction and the impurities available as dopant. Therefore, electron-dominated carrier transport resulting in the n-type semiconductors may be possible in SiCN materials with very low sp\({}^{2}\) carbon content or high amorphous carbon (sp\({}^{3}\) carbon). However, the synthesized C/SiCN nanocomposites demonstrated hole-dominated carrier transport of p-type semiconductors, occasioned by the ordering of the sp\({}^{2}\) carbon induced via the thermal treatments but showed electron-dominated n-type semiconductor at 1100 \({}^{\circ}\)C (see Section 3.3 for further details). The elemental composition was determined using Energy Dispersive X-ray Spectroscopy, EDX, attached to a (High Resolution) Scanning Electron Microscopy (SEM). SEM images showing the morphologies as well as the EDS spectra revealing the energy peaks corresponding to the various elements identified in the carbon-rich SiCN samples are shown in Figure 4a-c. The formation of microcracks was noticed on the surface of the samples, while the wide distribution of dark spots across the morphologies attest to the high amount of graphitic carbon in the samples (see Figure 4 ai,bi,ci). The amorphous C/SiCN material prepared at 1100 \({}^{\circ}\)C contains ca. 64 at. % carbon with traces of oxygen (1.86 at. %). However, the traces of oxygen at T = 1600 \({}^{\circ}\)C have reduced to 0.59 at. % with ca. 72 at. % carbon as shown in Table 2. Typically, oxygen is present as small silica domains in these materials--at high temperatures, these react with sp\({}^{2}\) carbon to form beta-SiC and lead to CO gas release [68,69]. Moreover, a recent study by [PERSON] et. al., reported that at annealing temperature of 1400 \({}^{\circ}\)C-1600 \({}^{\circ}\)C, outgassing of N\({}_{2}\) leading to a decrease in nitrogen content and increase in both silicon and carbon content may occur (although only nitrogen atmosphere was considered in the study as against the argon atmosphere adopted in this study) [33]. Figure 4: _Cont._
## 5 Conclusions  
Taking the commonly used U29 steel support (specific to underground coal mine applications) as the research object, the expression of the slip resistance of the cable-clip joint after secondary pre-tightening was established and verified through indoor axial compression tests. On this basis, numerical tests were carried out to determine the influence mechanisms of different magnitudes of initial pre-tightening torque and secondary pre-tightening torque as well as the timing of secondary pre-tightening. The findings specific to U29 steel supports and their general implications are summarized as follows:  
1. Initial pre-tightening torque effects (U29-specific findings)  
When the initial torque exceeded 250 N-m, the influence of secondary pre-tightening on both bearing capacity and joint yield-pressure performance demonstrated a gradually decreasing trend. This threshold appears to characterize U29's material properties and cross-sectional geometry;
2. Secondary pre-tightening torque (generalizable principles)The enhanced effect on bearing capacity showed a positive correlation with torque magnitude. Notably, when secondary torque remained below initial torque levels (a relationship potentially applicable to other U-shaped supports), no significant improvement in joint performance was observed;  
(3) Secondary pre-tightening timing (system-dependent phenomenon)  
The timing of the secondary pre-tightening critically affected the U29's performance. Limited enhancement occurred when tightening was applied before the joint slip reached the critical threshold; significant improvements emerged when tightening was implemented after exceeding 50% of the ultimate slip (this slip-dependent behavior may vary with support stiffness).  
Considering the generalizability of the current study, while the fundamental torque-slip relationship provides transferable insights, the specific thresholds (250 N-m torque limit, 50% slip criterion) are intrinsically tied to U29's dimensional parameters. For alternative U-shaped supports (e.g., U36 or U43 profiles), these parameters require recalibration through adjustment of material properties, modification of gross-sectional geometry, site-specific analysis of load conditions, and so on.  
Future research should include comparative studies extending to U36/U43 steel supports with varied rib configurations, in addition to parametric investigations on flange thickness (6-10 mm range) and yield strength (235-355 MPa grades).  
Conceptualization, Y.H. and Y.W.; software, H.T.; investigation, H.Y.; data curation, Y.W.; writing--original draft preparation, X.W. and H.T.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 4230721.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Evaluation Metrics  
Several performance metrics, as below, were used to evaluate the models:  
* AUC (Area Under the Curve): The AUC measures the model's ability to distinguish between correct and incorrect responses. It is particularly useful for handling imbalanced datasets, where the proportion of correct and incorrect responses may vary significantly across items. For each item, the AUC was calculated by comparing the predicted probabilities of correct responses (derived from the model's logit function) against the observed binary responses. The final AUC score was computed as the average across all items.
* Item-Test Correlation: The correlation between student ability estimates (\(\theta\)) and their overall test performance (mean response accuracy) was calculated to assess the consistency of the model's ability estimates. This metric ensures that students with higher ability estimates tend to perform better across all items.
* AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion): These criteria were used to compare models based on their goodness of fit while penalizing model complexity. Lower AIC and BIC values indicate a better balance between model fit and parsimony. The AIC and BIC were calculated using the negative log-likelihood value and the number of parameters in the model. The models with lower AIC and BIC values are preferred.
## 4 Results  
In this section, we validate the effectiveness of PI_DeepONet in displacement reconstruction. The strain and displacement of the structure under various loading conditions were simulated by FEM. Using the simulated strains in the selected strain measurement points and the coordinates of displacement measurement points as the inputs, the PI_DeepONet output the reconstructed displacements. The performance of PI_DeepONet was evaluated by comparing the reconstructed displacement with the FEM calculation.  
The finite element method calculates displacements by solving the global stiffness equation under known loading conditions and boundary conditions. The global stiffness equation of a structure is expressed as follows:  
\[\mathbf{Kd}=\mathbf{R} \tag{33}\]in which K denotes the global stiffness matrix; \(\mathbf{d}\) and \(\mathbf{R}\) denote the global node displacement and global node force. After the \(\mathbf{d}\) is solved, the strain \(\mathbf{\epsilon^{e}}\) in each element can be calculated as follows:  
\[\mathbf{\epsilon^{e}}=\mathbf{B}\mathbf{d^{e}} \tag{34}\]  
in which \(\mathbf{B}\) denotes the element strain matrix and \(\mathbf{d^{e}}\) denotes the element node displacement.
## 3 Results and Discussion
### Two-Stage Anaerobic Digestion of Wheat Straw and Algal Biomass  
Several anaerobic digestion configurations have been suggested so far in order to enhance energy production, including single-stage, leach-bed reactors and hybrid anaerobic digesters [19]. Among them, two-stage digestion was reported to be a promising technology with flexible operation and an enhanced performance [20].  
In this study, an improvement in wheat straw anaerobic digestion was shown by adding microalgal biomass. Algal cells are composed of proteins, carbohydrates, pigments, minerals and vitamins [21]. They grow fast and adapt easily, even in waste digestate [22]. The cultivation of microalgae in wastewater gives a solution to save arable land for biomass production, simultaneously utilizing the nutrients available in wastewater [23]. They are a broadly available substrate and appropriate for different applications. Algae are abundantly found and grow uncontrollably when nitrogen and phosphorus are present in excess.  
Co-digestion of wheat straw and microalgae biomass was realized for hydrogen and methane production with the advantage of producing two energy carriers. Among the extensively studied methods for obtaining hydrogen, biological hydrogen production is very promising for sustainable clean energy generation. Dark fermentation provides a way of converting organic waste into hydrogen, thus attaining the dual benefits of clean energy production and waste management [24]. The efficiency of biomethane production increases by separating the acidification phase and the methanogenic phase. High methane contents could be reached using two-stage anaerobic digestion. The rate of enzyme degradation of  
Figure 3: Biohydrogen potential of the mixture of wheat straw and algal biomass pretreated with different types of alkali reagents.  
the biomass and its conversion to VFAs is significantly dependent on the type of substrates supplied to the system.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements  
A material that can store energy when an external electric field is applied is known as a dielectric. Since the advent of modern communications, dielectric materials have been included in antennas and RF components; thus, the accurate measurement of their properties is crucial. The main parameter of dielectric properties is permittivity \(\epsilon\) and is expressed as in Equation (1) where \(\epsilon_{0}\) is called the permittivity of vacuum and \(\epsilon_{r}\) is the relative permittivity of the material.  
\[\epsilon=\epsilon_{0}\epsilon_{r}=\epsilon_{0}(\epsilon_{r}^{\prime}-j\epsilon _{r}^{\prime\prime}) \tag{1}\]  
where the permittivity of vacuum is \(\epsilon_{0}=8.854\times 10^{-12}\) F/m. The relative permittivity \(\epsilon_{r}\) is a complex number including the real part \(\epsilon_{r}^{\prime}\), which shows how much energy from an external electric field is stored. Usually, the term dielectric constant relates only to this real part, while the imaginary part \(\epsilon_{r}^{\prime\prime}\) represents how dissipative or lossy this material is to an external electric field. The ratio of the energy lost to the energy stored is called the loss tangent or \(\tan\delta\), see Equation (2).  
\[\tan\delta=\frac{\epsilon_{r}^{\prime\prime}}{\epsilon_{r}^{\prime}} \tag{2}\]  
The characterization of these two parameters in new materials is essential for the proper design of devices that make use of them.
## 2 Method  
### Framework  
This research focuses on the data object associated with maritime ships, encompassing both static and dynamic data. The research process involves three key aspects: stage 1 pertains to processing ship data, while stage 2 involves the construction of a knowledge graph model related to ships. Finally, stage 3 centers on the application of the ship knowledge graph, as shown in Figure 1. The research process can be distilled into three main stages:  
* _Stage 1: Data and processing._ Ship data processing is necessary due to the heterogeneity of data from multiple sources and issues such as redundancy, anomalies, irregularities, and other paradigmatic problems. Data pre-processing techniques including data de-duplication, data noise reduction [24], data supplementation, data fusion, and other methods are utilized to address these challenges;
* _Stage 2: Ship knowledge graph modeling (takes center stage)._ This involves using knowledge graph technology and the maritime supervision business model to establish ship graph semantic rules and achieve the semanticization of the ship data graph [25; 26]. Entity extraction and relationship extraction methods are employed to transform triadic data structures [27]. At the same time, knowledge fusion and denotation disambiguation techniques are utilized to complete the construction of the ship static graph [28]. The construction method of the ship knowledge graph in this study has significant innovative advantages in understanding actual business rules, integrating professional knowledge in the field, automating data updates, and providing powerful query interfaces. It better meets actual business needs, improves reliability and operability. Subsequently, based on AIS data and the definition of ship violations, calculations are made for ship violation features such as monitoring key ships, expired ship certificates, inconsistent ship tracks with reported ports, multiple ship tracks, and ships that do not report ports for extended periods [29]. These calculated violation features, in combination with the ship name graph identified through on-site inspections, are used to infer ship licensing violations, ultimately leading to the creation of a complete ship knowledge graph that integrates both dynamic and static data [30; 31];
* _Stage 3: The application and validation of the ship knowledge graph._ Using fused regional ship static and dynamic graphs, E-chart technology is used to conduct graph analysis and display ship violations [32]. The feasibility of the method is then verified by combining it with actual cases. _Stage 1: Data processing_  
Figure 1: Research framework of the identification of ship’s illegal behaviors using knowledge graph.