esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 3 Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme
### Fuzzy Logic-Based Correlated Region Selection
In this process, the membership degree of the fuzzy set is interpreted into some specific values (crisp value). In this work, the centroid (COG) method is used for defuzzification to obtain a crisp output value. **Example:** To further explain the fuzzy-based selection, we consider an example with three crisp inputs values; x = 16, y = 19 and z= 55 for density, messages and energy in the correlated regions, respectively as shown in Figure 4.  
After the fuzzification process, the fuzzified inputs are evaluated according to certain appropriate rules as shown in Figure 5. Based on the rules, each crisp value has a membership grade that is used for generating output. According to the rule, for the crisp input values (medium, medium and high), the output is medium. While for crisp input values (medium, high and medium), the output is high. Figure 3: The Mamdani rules used for fuzzy logic-based correlated region selection. After completion of the Mamdani rule evaluation process, the defuzzification process is performed. In the defuzzification process, the attained aggregated fuzzy set is converted to a number through the COG technique as demonstrated in Figure 6.  
Figure 4: Input variables for performing fuzzification. (**a**) Input variable “density” for fuzzification. (**b**) Input variable “messages” for fuzzification. (**c**) Input variable “energy” for fuzzification. Figure 5: Mamdani Rule evaluation process for crisp input values of x = 17, y = 19 and z = 55. (**a**) If x is 0.8 (medium), y is 0.14 (medium) and z is 0.75 (medium) then the output w is 0.75 (medium). (**b**) If x is 0.8 (medium), y is 0.79 (high) and z is 0.75 (medium) then the output w is 0.79 (high).
## 3 State of the Art  
### LTE and Wi-Fi in a Nutshell  
This section briefly describes the mechanisms that LTE and Wi-Fi use to transmit in the way that they were initially designed. The analysis of the core differences among these mechanisms will give us the insight to understand in depth the reasons why LTE cannot operate next to Wi-Fi without appropriate coexistence and cooperation mechanisms. Moreover, it will be used as a basis for the subsequent description of the different cooperation protocols.
## 5 Analysis and Discussion
### Analysis of Learning Effects According to Educational Methods  
Two knowledge tests were conducted to assess the educational effectiveness of the proposed system. In the preliminary questionnaire, all subjects answered that they had no knowledge of aircraft maintenance. This ensures an equivalent level of prior knowledge in all participants.  
Table 2 shows the mean values of the knowledge test results for the two groups. In the post-test, the proposed system group scored an average of 75.00 out of 100 (SD = 20.455, N = 20), 9.5 points higher than that of the video group (M = 65.50, 22.705). In the retention-test, the proposed system group scored an average of 68.75 points (SD = 19.390, N = 20), 15.25 points higher than that of the video group (M = 53.25, SD = 18.516). It can be seen that the average scores of the proposed system group are higher in both the post-test and the maintenance verification.  
Table 3 shows a comparison of the values obtained by subtracting the retention-test mean from the post-test mean for each group. The retention-test score of the proposed system group decreased from the post-test score by an average of 6.25 points (SD = 10.371. The retention-test score of the video group decreased from the post-test score by an average of 12.25 points (SD = 16.341).  
The results confirm that the proposed system group scored higher in both the post-test and the retention-test. In addition, we calculated the difference between the post-test and retention-test scores of each group and found that more knowledge was retained by the proposed system group.
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 6 Image Quality Improvement Experiment Based on Inertial Reference Laser  
An image restoration experiment was performed to further verify the performance and application effect of the image motion measurement method based on an inertial reference laser. The measured image motion information was used to restore a degraded image. After restoration, the MTF value of the degraded and restored images was calculated to quantitatively evaluate the restoration effect.
## 3 Interpolation Algorithm
### Discussion on the Physical Meaning and Scope of Applicability of the Assumptions  
The structure is modeled as a Bernoulli beam. This theory assumes that transverse shear deformations are negligible, which is appropriate for slender beams where the length is significantly greater than the cross-sectional dimensions. It is particularly suitable when bending dominates the response, and shear effects do not contribute appreciably to the overall deformation.  
Within this modeling framework, further assumptions are made regarding the behavior of the beam's cross-sections. First, it is assumed that the cross-sections do not undergo significant deformation under applied loads and can be considered rigid. This means the shape and size of the cross-section remain essentially unchanged during loading, which is a valid assumption for beams made of stiff materials or with geometrically compact sections. Additionally, the cross-sections are assumed to remain planar and orthogonal to the deformed axis of the beam. This condition, central to Euler-Bernoulli theory, implies that no warping or shear distortion occurs and that the beam exhibits mostly pure bending deformation.  
Another simplifying assumption is the absence of bending-torsion coupling. Physically, this means that the effects of torsion and bending can be treated independently, and one does not influence the other. This assumption holds when the cross-section is  
Figure 3: Load case.  
symmetric and loading is applied through the shear center, such that torsional and flexural responses are decoupled.  
The model also assumes that all principal axes of the cross-sections remain parallel along the length of the beam. This implies a geometrically uniform or symmetrically varying beam without twist or out-of-plane curvature, ensuring that bending occurs consistently in the defined principal directions.  
Finally, the last two assumptions are introduced to suit the subsequent calculations.  
Here the used FEA software is Calculix Version 2.17 [[http://www.calculix.de/](http://www.calculix.de/)]([http://www.calculix.de/](http://www.calculix.de/)) (accessed on 9 January 2025) in combination with FreeCad CAD system version 1.0 [[https://www.freecadweb.org/](https://www.freecadweb.org/)]([https://www.freecadweb.org/](https://www.freecadweb.org/)) (accessed on 11 December 2024). All this software is free.
## 3 Results
### Kinetic Features  
Figure 5 provides an overview of the VO\({}_{2}\) kinetic feature values as calculated with the different exponential (methods A, B and C) and full BJ-TF models. Methods A and B resulted in a smaller TD and a higher TC, MRT, Amp and SSG compared to method C and the BJ-TF full model (Figure 5). On a group level, method C and BJ-TF did not show significant differences between the kinetic feature values of the included patients (Figure 5). On an individual level, the median (\(\pm\) IQR) of the absolute differences between the feature values as calculated with method C and BJ-TF were \(4\pm 13\) s, \(11\pm 17\) s, \(7\pm 8\) s, \(27\pm 43\) ml\(\cdot\)min\({}^{-1}\) and \(0.5\pm 0.7\) ml\(\cdot\)min\({}^{-1}\)\(\cdot\)W\({}^{-1}\) for TD, TC, MRT, Amp and SSG, respectively. These absolute differences were respectively 24%, 20%, 9%, 4% and 4% of the respective median feature values as extracted from BJ-TF models. MRT values varied less than TC values between the different methods (Figure 5).  
Two patients had extremely large kinetic feature values in all applied models (e.g., TC \(>\) 200 s and/or SSG \(>\) 20 ml\(\cdot\)min\({}^{-1}\)\(\cdot\)W\({}^{-1}\)). One of these patients cycled at a very low load during CWRT (22 W), the other patient showed a VO\({}_{2}\) response that increased almost linearly. When applying method A, one additional patient had extremely large kinetic feature values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Order D(z\({}^{-1}\))**} \\ \cline{3-4}  & & **1** & **2** & **3** \\ \hline \multirow{4}{*}{**VO\({}_{2}\)**} & \multirow{4}{*}{**1**} & \(-6.94\) (2.31) & \(-5.69\) (2.12) \\  & & & 2.8 (1.3) & 2.1 (1.1) \\ \cline{1-1} \cline{3-4}  & & \(-6.82\) (2.91) & \(-6.20\) (2.11) \\ \cline{1-1} \cline{3-4}  & & \(-2.5\) (1.7) & 2.0 (2.6) \\ \cline{1-1} \cline{3-4}  & & \(-6.75\) (2.63) & \(-6.21\) (2.69) \\ \cline{1-1} \cline{3-4}  & & \(-2.1\) (1.6) & 2.0 (1.6) \\ \cline{1-1} \cline{3-4}  & & \(-3\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance characteristics for identification of the noise model order. Dark squares show noise model orders that resulted in visually inappropriate full model fits. Upper values for every noise model structure are Young Identification Criterion (YIC) values, and lower values are normalised root-mean-squared error values (NRMSE) (%).  
Figure 4: Example plot of VO\({}_{2}\) data and the BJ-TF system and full (system + noise) model. The estimated TD value was 20 s.
# Evaluation of the Performance of Previous Concrete Inspired by CO\({}_{2}\)-Curing Technology
###### Abstract
Rundoff containing suspended particles clogs pores within pervious concrete pavements during their lifetime [19]. Heavy metal percolation greatly affects groundwater reserves [20]. The two most fundamental components of pervious concrete are cement paste and aggregate, and both have the individual capacity to adsorb heavy metal ions from aqueous environments [21]. Cement is well known as a soil stabilizer for use in the sequestration of heavy metals in soil mixing [22]. The hydration of cement produces highly alkaline conditions and causes heavy metal ions in urban runoff to immobilize with CSH gel and portlandite crystals, which are the main products formed due to the mixing of cement with water [23]. Despite heavy metal immobilization within concrete, some studies show the risk of heavy metal leaching from cement products containing solid waste, especially when exposed to flowing water [24]. Hardened cement paste is severely attacked by such conditions, causing a decrease in the alkalinity of the cement matrix and the degradation of the main cement hydrates [25], leading to the desorption of immobilized heavy metals from the cement matrix. Carbonation is considered beneficial for the resistance to metal ion leaching of cement-based materials [5]. Carbonation is known to be an important factor that affects the formation of cement hydrates [26], as well as the composition and alkalinity of the cement matrix [27]. Heavy metals, originally presenting as hydroxides in the matrix, are progressively converted to carbonates, thus changing their solubility [28]. To date, laboratory research has been conducted primarily to evaluate the mechanical performance of CO2-cured pervious concrete mixes [29], but studies on understanding the behavior of such mixes against urban runoff conditions and aggressive chemicals are still lacking to the best of our knowledge. This study demonstrated the removal of suspended solids and toxic heavy metals from simulated runoff conditions in pervious cement concrete samples cured in a CO2-rich environment. X-ray tomography and falling head permeameter were used to evaluate the -pore-related properties of these samples, while the characteristics of the simulated runoff solutions were examined with the help of inductively coupled plasma mass spectrometry (ICP-MS), turbidity, and pH meters. A phenolphthalein indicator was sprayed to locate carbonated regions in the pervious concrete samples, and the results of the X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies reveal an alteration in the concrete microstructure due to exposure to CO\({}_{2}\). An acid attack experiment was also performed on such samples to evaluate the effect of concrete carbonation on the degradation properties, including weight and compressive strength losses. These experimental findings allow us to understand the beneficial role of CO\({}_{2}\) treatment in the performance of pervious pavement mixes under runoff conditions and aggressive chemicals.
# The Vision-Based Data Reader in IoT System for Smart Factory
## 3 Results
### Illumination Normalization  
For the proposed VDR system, image preprocessing on the captured images should avoid over and under enhancement with color and contrast distortions, since the factory is usually not well-lit. Furthermore, the gray value in the image is very sensitive to the variation of lighting conditions. Unfortunately, images may be captured from the same scene under uneven illuminations, especially for the proposed recognition tasks of the VDR system. Even for the human visual system, it is quite difficult to recognize the same object from images that are captured under considerably different illuminations [34]. For the same reason, the proposed VDR system will also be difficult to have good detection and classification results if the training and testing datasets are taken in those situations. For example, images might be taken from different factories.  
The illumination normalization process is used to reduce the lighting effect of images that are captured under uneven lighting conditions. We generated a basic model for a  
Figure 8: The vision-based data reader.  
Figure 7: The proposed condition monitoring by the VDR system.  
standard lighting condition that will be useful to adjust the observed images that are captured under different illuminations to a uniform one. For the proposed VDR system, a histogram-based statistical model \(M\) is generated by extracting the features from training images \(S\) under standard lighting conditions. At the testing stage, each input image \(T\) will be adjusted by the trained statistical model \(M\) to be an approximation \(T^{\prime}\) under standard lighting conditions. Without loss of generality, the intensity histogram of an image is treated as the probability density function. The histogram-based statistical model \(M\) is a one-to-one mapping function which is shown in the following Equation (1) [35].  
\[M=C_{S}^{-1}\cdot C_{T} \tag{1}\]  
where \(C_{S}\) is the empirical Cumulative Distribution Function (CDF) of \(S\) and \(C_{T}\) is the empirical CDF of input image \(T\). Each pixel in \(T\) can be adjusted by using \(M\) to normalize the illumination from the original lighting effect. For an input testing image \(T\), the illumination normalization is focused on the ROI \(R_{T}\) and the statistical histogram features of the extracted region \(R_{T}\) are extracted by the method of [35]. According to model \(M\), the transformed statistical histogram of \(R_{T^{\prime}}\) will be similar to that for standard lighting conditions. Let \(R_{T}\) be the ROI after illumination normalization, then Equation (2) shows how to generate it by Equation (1).  
\[R_{T^{\prime}}(x,y)=M(R_{T}(x,y))=C_{S}^{-1}\cdot C_{T}(R_{T}(x,y)) \tag{2}\]  
We finally realized the illumination normalization process for each ROI. It was applied to the VDR system before digit detection. It resulted in not only high accuracy for digit region detection but also in low computation time for the training process and recognition task by a deep learning network.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 3 Direct Wave Signal Parameter Estimation Method Based on Template Matching
### Proposed Method
Comparing \(\hat{f}_{c}\) with the CF template library \(\left\{f^{(k)}|\mathbf{k}=1,2,\ldots,N_{f}\right\}\), choose \(f_{0}\) which is closest to \(\hat{f}_{c}\) in the CF template library; then, \(f_{0}\) is the precise estimation of CF. **Step 4**. Combining with the estimated parameters of the signal, including \(B_{0}\), \(\tau_{0}\) and \(f_{0}\), reconstruct a reference signal noted as  
\[s_{mf}=e^{j\left[2\pi f_{0}\frac{j}{F_{s}}+\pi\frac{R_{0}}{\tau_{0}}\left( \frac{j}{F_{s}}\right)^{2}\right]},i=1,2,...,M \tag{8}\]  
Using the reference signal \(s_{mf}\) to perform matched filtering on the whole signal; then, the peak position of the result is the accurate TOA of the direct wave pulse.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Hierarchical Classification Trees Construction by Decision Tree Algorithms  
The hierarchical classification improved the quality of the land cover product in mountainous areas by increasing the classification times and decreasing the number of land cover classes within a single classification process. The whole classification process was divided into multiple stages according to the conceptual hierarchical structure in this study. For each classification stage, an optimal classification tree was constructed from the given training sample sets and classification feature sets by using the decision tree classifier (See5 software, [[https://www.rulequest.com/see5-info.html](https://www.rulequest.com/see5-info.html)]([https://www.rulequest.com/see5-info.html](https://www.rulequest.com/see5-info.html))). Then, it was conducted to assign a unique land cover class for each object. Inevitably, this process was executed iteratively by adjusting the input training sample sets and feature sets until a satisfactory classification result was achieved in each classification stage. The hierarchical classification trees of each mapping unit were constructed independently. Training sample sets, classification feature sets and the conceptual hierarchical structure are the critical parts for classification tree construction.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 2 Related Works
### Formal Concept Analysis  
Formal Concept Analysis (FCA) [33] is a powerful and widely used method in information science that enables the creation of a concept hierarchy or formal ontology from a given set of objects and their properties. This approach is based on the mathematical theory of lattices and ordered sets, which allows for the identification of shared properties and relationships between objects in a structured and systematic way.  
The resulting hierarchy of concepts represents a logical and intuitive organization of the objects and their properties, with each concept capturing a group of objects that share a set of common attributes. Moreover, the sub-concepts in the hierarchy represent a more specific grouping of the objects and a superset of the attributes from the abovementioned concepts.  
Introduced by [PERSON] in 1981, Formal Concept Analysis (FCA) [33] has become a fundamental tool in various fields, including data mining, text mining, machine learning, knowledge management, semantic web, software development, chemistry, and biology. Its practical applications are diverse and numerous, ranging from discovering hidden patterns and relationships in data sets to developing more effective search algorithms and enhancing the quality of knowledge representation in various domains.
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 4 Proposed Multi-User Secure Monitoring Scheme
\begin{array}{c}x_{n+1}=1-ax_{n}^{2}+y_{n},\\ y_{n+1}=bx_{n},\end{array} \] (1) where \(a\) and \(b\) are two control parameters, and \(x_{0}\) and \(y_{0}\) are two initial conditions, which are considered as the secret key of each user. Digital words of 64-bit floating-point are used for the Henon map to generate 15 decimals. Chaotic data are amplified by 1,000 and the module 1 operation is used to increase chaotic uniformity. The chaotic data (\(CD\)) with values of (0,1) are coded to NRZ based on the following criteria: \[CDNRZ= \begin{cases}1&if\ CD\geq 0.5,\\ -1&Otherwise,\end{cases} \] (2) where \(CDNRZ\) is the chaotic data coded in NRZ. 5. The plain text modulated with BPSK and the chaotic data coded in NZR are multiplied to produce the spread spectrum signal of each user. Finally, all spread spectrum signals of \(N\) users are summed to produce the cryptogram. The algorithm to retrieve the plain text from the cryptogram is transcribed as follows:
1. The authorized specialist receives the cryptogram. 2. With the corresponding secret key of the user \(N\), the authorized specialist uses the same chaotic Henon map and generates the \(CDNRZ\) as in encryption process. 3. The same carried signal defined in the encryption process is generated. 4. The cryptogram is multiplied with \(CDNRZ\) to generate the inverse spread spectrum signal. The sign function with a length of 50 samples is applied to the inverse spread spectrum to retrieve the plain text in binary format according to the following criteria:  
\[PTB= \begin{cases}1&if\;{{\it{s}}{\it{ng}}(\;)>0},\\ 0&Otherwise,\end{cases}  \tag{3}\]  
where \(PTB\) is the recover plain text in binary format. 6. Finally, groups of 16-bit data are converted to decimal format to retrieve the original plain text of the \(N\) user.
## 2 Materials and Methods
### Characterization
#### 2.3.2 Thermal Properties  
Differential scanning calorimetry (DSC) tests were performed on the NR samples by means of a Mettler DSC30 calorimeter (Mettler Toledo, Switzerland). One specimen was tested for each composition, with a mass of approximately 10 mg. The specimens were subjected to the following thermal sequence: (I) first heating scan from \(-\)100 \({}^{\circ}\)C to 150 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, (II) cooling scan from 150 \({}^{\circ}\)C to \(-\)100 \({}^{\circ}\)C at \(-\)10 \({}^{\circ}\)C/min, (III) final heating scan from \(-\)100 \({}^{\circ}\)C to 150 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. Nitrogen was used as the purge gas at a flow rate of 100 mL/min. DSC analysis allowed the determination of the glass transition temperature of the samples and allowed us to study the effect of sulfur content on the thermal properties of the NR formulations.  
The material's ability to release/absorb heat to the environment and the kinetics of such heat exchanges depend on the thermal properties of the material. For this reason, the specific heat capacity (\(\mathrm{c_{p}}\)), thermal conductivity (\(\mathrm{\lambda}\)) and thermal diffusivity (\(\mathrm{\alpha}\)) of the NR samples were measured through Laser Flash Analysis (LFA) with a Netzsch LFA 447 instrument (NETZSCH-Geratebau GmbH, Selb, Germany). Cylindrical samples (diameter 12.7 mm, thickness 1 mm) were die-cut from the produced NR sheets. The tests were carried out at 20 \({}^{\circ}\)C, and for each specimen five pulses were performed. To compute the thermal diffusivity (\(\alpha\)), the Cowan method with numerical pulse correction was employed (Proteus(r) Software Version 8.0.3, NETZSCH Analyzing & Testing, NETZSCH-Geratebau GmbH, Selb, Germany). For the determination of the heat capacity, the reference material Pyroceram 9606 was employed, according to the standard ASTM-E 1461 [26]. The thermal conductivity was calculated according to Equation (4),  
\[\lambda=\alpha pc_{p} \tag{4}\]  
where \(\rho\) is the density of the sample (g/cm\({}^{3}\)) determined by Archimedes' method, while \(\alpha\) and c\({}_{\rm p}\) are the data provided by the LFA measurements. For each NR formulation, three specimens were tested.