esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology
#### Deep-Learning-Based Fault Prediction  
A pre-trained CNN model was trained by Geophysical Insight in their Paradise software on the SEAM (SEG Advanced Modelling) model which is a large synthetic dataset. Numerous sub-volumes were extracted from the impedance model and faults were inserted. These were used as the realistic synthetic training dataset. The CNN model attempts to identify locally reflected seismic patterns ignoring numerous artefacts and noise present in the seggy 3D in the process. A general CNN workflow used for automatic fault prediction is shown in Figure 4. This workflow normally consists of a simple 1-layer CNN network followed by one fully connected layer for fault classification. Seismic input images are 32 by 32 pixels. The convolution masks (kernel) are 9 \(\times\) 9 pixels in size. In the convolutional layer, 16 features are produced. The dimensions of the output features after convolution are reduced using 2 \(\times\) 2 maximum pooling, which also controls overfitting. The SoftMax cross entropy is performed to determine the probability error between the classification and the true labels, and the fully connected layer contains 1024 neurons. The model is a binary classification identifying the presence/absence of a fault.  
In this study, pre-trained CNN models, available in the Paradise software, were applied to automatically predict faults. The workflow in Paradise involves applying structurally oriented filtering (named SOF3D) to sharpen the discontinuities of the seismic amplitude volume before applying the CNN models. It is a useful strategy for eliminating incoherent noise and enhancing event continuity without smoothing over dipping planes [45; 46]. Three different architecture models operated on the data, known as aggressive, conservative, and mixed-angle engine. Both conservative and aggressive models were trained on synthetic faults with high angles, while the mixed-angle model was trained on faults with varying angles. The conservative CNN model produced fewer false positives and mostly identified continuous faults, whereas the aggressive model revealed more discontinuities in seismic amplitude data with shorter and less continuous fault identification in 3D. The mixed-angle model generally works well on data of varying angle faults. In the current study, we found that the result of the mixed-angle model is geologically meaningful.  
Figure 4: The general CNN workflow used for fault prediction (modified after [44]). Reprinted with permission from the Society of Exploration Geophysicists.
## 2 Real Bridge Test-Based Fatigue Performance Analysis
### Influence of Fatigue Performance  
#### 2.3.1 Deformed Region of Diaphrag-3 Collapse  
In order to determine the influence of diaphragm deformation on the fatigue performance of DU welded joints in the collapse region, a program was written according to the principle of the rain-flow method [14; 15] (see Appendix A), and the stress time-history curve obtained from the test was converted into a fatigue stress spectrum. The stress histories over 2 h were collected, and the developed rain-flow counting method was used to count the stress ranges and the corresponding number of cycles of each measuring point, as shown in Figure 3. According to then Eurocode3 specifications [16], the DU welded joints in steel bridge decks follow the FAT71 fatigue strength curve. Consequently, stress ranges below 5 MPa were excluded from the spectrum analysis, as they have a negligible impact on fatigue damage accumulation [11; 17; 18; 19]. For arc notches (see Figure 3a), the maximum stress ranges of the measuring points B1 and B5 are 42 MPa and 47 MPa, respectively. However, the maximum stress ranges of the measuring points A1 and A5 decrease by 50% and increase by 35.6%, respectively, which indicates that the out-of-plane deformation of the deformed side in diaphragm-3 is significantly enhanced.  
For the DU weld (see Figure 3b), compared with the normal diaphragm, the maximum stress range of the deformed diaphragm increased by nearly 50%, which illustrates that the DU weld (A3) of diaphragm-3 (i.e., the deformation diaphragm) was subjected to greater  
Figure 2: Strain gauge locations in the stress test scheme for a real bridge.  
stress ranges and more fatigue damage than diaphragm-4 (i.e., the normal diaphragm). Therefore, diaphragm deformation could lead to a higher risk of cracking on the deformed collapse region of diaphragm-3.
## 3 System Model and Problem Formulation
### Problem Formulation
\begin{split}\text{T}_{n}(a_{n},a_{-n})=\left\{\begin{array}{ ll}-\text{T}_{n}^{loc},&\text{if}\quad\alpha_{n}=1,\beta_{n}=\theta_{n}=0\\ -\text{T}_{n}^{loc+sep},&\text{if}\quad 0<\{\alpha_{n},\beta_{n}\}<1, \theta_{n}=0,\alpha_{n}+\beta_{n}=1\\ -\text{T}_{n}^{loc+edges},&\text{if}\quad 0<\{\alpha_{n},\beta_{n}\}<1, \theta_{n}=0,\alpha_{n}+\theta_{n}=1\\ -\text{T}_{n}^{loc+sep+edg},&\text{if}\quad 0<\{\alpha_{n},\beta_{n}, \theta_{n}\}<\!\!1,\alpha_{n}+\beta_{n}+\theta_{n}=1\end{array}\right.,\end{split}  \tag{15}\]  
where \(a_{-n}\) denotes the joint action of others except for TaVn. Task processing time can reflect the system's processing capacity to a certain extent, and lower processing time means higher processing capacity. Therefore, our goal is to maximize the total benefits of the system, which can be expressed as  
\[ \begin{split}\mathcal{P}&\text{max}\sum\limits_{\{a_{n} \}}\text{T}_{n}(a_{n},a_{-n})\\ \text{s.t.}&\text{(C1)}:\alpha_{n}+\beta_{n}+\theta_{n}=1,0 \leq\{\alpha_{n},\beta_{n},\theta_{n}\}\leq 1,n\in\mathcal{N}\\ &\text{(C2)}:\!0\leq\sum\limits_{n\in\mathcal{N}}\rho_{n}\leq 1,0 <\rho_{n}\leq 1\\ &\text{(C3)}:\!-\!\text{T}_{n}(a_{n},a_{-n})\leq\tau_{n},n\in \mathcal{N}\end{split}  \tag{16}\]  
Constraint C1 ensures the integrity and separability of tasks. Constraint C2 ensures that the utilization of computing resources of ES does not exceed the maximum. Constraint C3 bounds the maximum delay of TaVn.
## 3 Results and Discussion  
### Fourier Transform Infrared (FTIR) Analysis  
Figure 5 displays the results of Fourier transform infrared spectroscopy (FTIR), which compares the chemical compositions of the cellulose nanocrystals (CNCs) sample to that of commercially supplied microcrystalline cellulose (MCC). Similar peaks may be seen in both graphs, suggesting that there is little difference between the chemical structures of CNCs and MCC. Therefore, the cellulose's fundamental chemical structure was maintained [29]. It is also worth noting that the hydroxyl group -OH peaked at 3344 cm\({}^{-1}\) in both plots. In the meantime, we detected vibrations at 2916 cm\({}^{-1}\) due to -CH stretching, 1646 cm\({}^{-1}\) due to -OH bending of the absorbed water, 1430 cm\({}^{-1}\) due to -CH and -OCH in-plane bending, 1317 cm\({}^{-1}\) due to -CH deformation, and 1059 cm\({}^{-1}\) due to -C-O stretching. COC, -CCO, and -CCH deformation modes and stretching vibrations with C-5 and C-6 atom motions of 898 cm\({}^{-1}\), and -C-OH out-of-plane bending mode with motions of 668 cm\({}^{-1}\). Both graphs also revealed the cellulose's primary chemical makeup, which matched the nano-cellulose structures previously reported in the literature via controlled hydrolysis of MCC [30].  
The CNCs' plots also reveal that the peak at 3344 cm\({}^{-1}\) was stronger and more distinct than the MCC. This may be because of the increased number of hydrogen bonds inside the CNCs, which resulted from the removal of the amorphous component and consequently improved the crystallinity of the samples. Due to cellulose's sensitivity to crystal structure at absorbance values around 850 to 1500 cm\({}^{-1}\)[31], the crystallinity index was calculated by dividing (\(I_{a}\)) the intensity of the 898 cm\({}^{-1}\) band by (\(I_{b}\)) the intensity of the peak 1430 cm\({}^{-1}\) band in Figure 5 (Equation (2)).  
Further evidence of the success of the hydrolysis processing of MCC into CNCs, the crystallinity indices of the MCC and CNCs were found to be approximately 0.78 and 0.86, respectively, as listed in (Table 10). This clearly indicates the dissolution of the amorphous structure of MCC during hydrolysis. Regarding the impact of reaction time on the crystallinity of the materials, ref. [32] obtained similar findings.  
\[Crystallinity\ index=\left(\frac{I_{a}}{I_{b}}\right)\times 100\% \tag{2}\]
## 4 Electron Transfer-Based Assays
### ABTS\({}^{+}\) Assay  
#### 4.4.1 Mechanism of Action  
The 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothiaazoline-6-sulfonic acid (ABTS) assay is based on the generation of the green-blue ABTS\({}^{\bullet+}\) radical, which can be reduced by antioxidants. The reduction of the radical results in a decrease in the color intensity of the solution, and this change is proportional to the reductant content in the test sample (Figure 3). In spectrophotometric measurements, the wavelength used is 734 nm [72]. The ABTS\({}^{\bullet+}\) radical reacts with potassium persulfate in a 2:1 molar ratio; thus, the ABTS concentration should be at least twice that of potassium persulfate in the reaction mixture [88, 89].  
The ABTS\({}^{\bullet+}\) radical is soluble in both aqueous and organic solvents, which is a key advantage of the assay, as it enables the determination of both lipophilic and hydrophilic antioxidants. However, because the choice of solvent can affect the reaction kinetics [90], results obtained using different media should not be directly compared [73]. The influence of various solvents on reaction kinetics was investigated using butylated hydroxytoluene (BHT) as the antioxidant agent [91]. The reaction kinetics and the concentration of unreacted ABTS\({}^{\bullet+}\) after 60 min depended on the type of solvent used. The fastest ABTS\({}^{\bullet+}\)/BHT reaction was observed in 1-propanol, while the slowest occurred in methanol. Additionally, the use of propranolol-1 resulted in a lower percentage of unreacted ABTS\({}^{\bullet+}\) compared to other solvents [91].  
Different solvents may also require adjustments to the measurement wavelength and incubation time to achieve comparable results. It has been shown that an optimal incubation time for ABTS\({}^{\bullet+}\) is 6 h [88]. Nevertheless, ABTS\({}^{\bullet+}\) has been shown to remain stable in stock solution (with potassium persulfate) for up to two days when stored in the dark at room temperature [88]. Among the two assays that employ free radicals (DPPH and ABTS), the superior stability of ABTS\({}^{\bullet+}\) offers a distinct advantage.  
Figure 3: The scheme of reaction occurring between the ABTS radical and the antioxidant.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Material** & **Standard** & **Substrates** & \
\begin{tabular}{c} **Concentration of** \\ **DPPH Working** \\ **Solution** \\ \end{tabular} &
\begin{tabular}{c} **Type and Time of** \\ **Incubation** \\ \end{tabular} & **Units** & **Wavelength** & **Source** \\ \hline Tomato juice & \(\alpha\)- & \(\alpha\)- & \(1.0\) mL extract, \(0.5\) mL DPPH & \(300\)\(\mu\)M & \(15\) min & \(\begin{array}{c}\text{mol }\alpha\text{-}\\ \text{TE/mol}\end{array}\) & \(540\) & [86] \\ \hline _Chlorella_ & \(\mu\)_ulgaris_ & Nd & \(\begin{array}{c}25\text{ }\mu\text{L}\text{ of extract,}\\ \text{975 }\text{ }\mu\text{L}\text{ DPPH}\end{array}\) & \(60\)\(\mu\)M & \(30\) min, darkness & \(\%,\text{IC}_{50}\) & \(517\) & [87] \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Implementation of MS Circle Map in Digital Image Encryption
## 2 Materials and Methods
### MS Map and Circle Map  
First, we will introduce two chaos maps that will be used in MS Circle Map. MS Circle Map is a new function obtained from the combination of two chaotic functions from MS Map and Circle Map.  
Chaos map is currently very developed and widely used for image encryption. In image encryption algorithms, chaos map plays an important role in producing chaotic sequences that will be used for keystream. Therefore, we chose the recently introduced chaos map function, namely, MS Circle Map, chosen because of its simplicity, ease of implementation, and efficiency in producing chaotic sequences. This is shown in the extraordinary chaotic behavior, which is characterized by a dense bifurcation diagram, a consistently positive Lyapunov Exponent value, and the NIST test produced by MS Circle Map being random.  
Among the two chaos maps mentioned above, we introduce MS Map first. MS Map is one of the new chaotic maps modified from logistic map. The modification is performed by changing the general form of the logistic map [8; 9]. MS Map can be seen in Equation (1) below:  
\[x_{n+1}=\frac{r\lambda x_{n}}{1+\lambda(1-x_{n})^{2}}(\mathit{mod}\,1) \tag{1}\]  
with the definition of x mod 1 as shown in Equation (2) [28]:  
\[x\ \mathit{mod}\ 1=x-\lfloor x\rfloor \tag{2}\]mod 1 is imposed on the modified form so that its domain and codomain lie in the interval (0, 1), or, in other words, the generated keystream taken is only its decimal value. For any x number, x mod 1 is all positive real numbers smaller than 1.  
The MS Map function consists of three key parameters: x0, r, and \(\lambda\). The domains of these parameters are defined as x0 \(\in\) (0, 1) and r, \(\lambda\in\) R. The parameter x0 serves as the initial value, while r and \(\lambda\) function as control parameters. To ensure optimal randomness in the generated keystream, the condition r \(>\lambda\) must be satisfied, as the selection of r and \(\lambda\) directly influences the chaotic behavior of the MS Map function.  
Another chaotic map employed in our study is Circle Map. Circle Map is a one-dimensional function that maps a circle to itself. The Circle Map Equation with Equation (3) is as follows [28]:  
\[x_{n+1}=\left(x_{n}+\Omega+\frac{K}{2\pi}\text{sin}\left(2\pi x_{n}\right) \right)mod\ 1 \tag{3}\]  
mod 1 is the decimal part of a number, so the value of the map is always lower than 1 but not less than 0, and the K \(\in\) R parameter. Meanwhile, \(\Omega\) is in the interval \(0\leq\Omega\leq 1\), and, since this is the single sum term in modulo 1, all other values of \(\Omega\) are already represented by this interval.  
A very interesting characteristic of the Circle Map is that it has infinite chaos possibilities. If a person wants to obtain a high-enough Lyapunov exponent, she/he can choose high or low (negative), because the value of K moves away from 0, and the exponent keeps increasing. The \(\Omega\) value of the Circle Map also decreases as K moves away from 0 [29].
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 2 Methods
### Image Pre-Processing  
The use of immersion fluids can provide better visual effects and improve the diagnostic accuracy of dermoscopic examinations. However, during the image acquisition, the artifacts like air bubbles and the surplus of the immersion oil become visible. Therefore, the very first steps in our image  
Figure 2: Illustration of the proposed system: (**Image processing**) involves air bubble smoothing and black frame removal; (**Simple Linear Interactive Clustering**) small homogeneous region grouping by oversegmenting the medical image; (**Region Adjacency Graph**) construction of RAG for the oversegmented image with assigning appropriate weights; (**Segmentation**) hierarchical region merging based on a given criteria.  
analysis system is the preprocessing stage which aims at reducing the amount of artifacts and noise to obtain the best quality of the input image. In order to mitigate the detrimental effects of air bubbles, we use the square-shaped median smoothing filter with the mask size proportional to the image size as proposed in [15]. The mask size \(n\) for an image of size \(M\times N\) is defined as:  
\[n=\left\lfloor 5\cdot\sqrt{\frac{M/768}{N/512}}\right\rfloor. \tag{1}\]  
Before the dermoscopic images can be used for the segmentation step, the black frame introduced during the digitization process must be removed. For a fast and efficient black frame removal step, an RGB image is first converted to the \(HSL\) (hue, saturation and luminance) color space. A pixel is considered to be black when the lightness component of the HSL color space is less than 15 [7]. The preprocessing step increases not only the accuracy of the border detection but also reduces the computation time of the whole method.
## 4 Solution Problem
### Load Prediction Algorithm (LPA)
#### 4.1.3 Dataset  
For this work, it was necessary to use two databases to simulate users in the mobile network and perform network utilization prediction. The first database contains the user's geolocation and date (time and day), which were used to simulate the behavior of users in the high-density region, based on the real data shown in Figure 5. We edited and cleaned the data and added the variable week to identify patterns in user behavior. The simulation followed the features of the database to generate a new dataset of network utilization for each hour of the day. With the new database, we can forecast and identify which macrocells suffer from congestion and define the times and regions that are most affected by this high user demand.  
The data used by the models for prediction were generated by simulations. However, in a real network, there may be different types of problems, such as the main problem of congestion of macrocells caused by the density of daily users at different times. The creation of new macrocells is not feasible because this overload is temporary.  
The new database generated by the simulation, which is used as input to the predictive model, contains the following attributes: time, day, week, macrocell ID, and the generated load. The forecasting models used in this paper follow the supervised approach. According to the architecture, the data are divided into input (time, day, week, macrocell ID) and output (load). Then, the data are randomly divided into 70% for training and 30% for testing and validation, using the same methodology for the two models presented previously. Then, the data are normalized to improve the performance of the predictive models. After training, testing and validation were performed with specific metrics to evaluate the performance and accuracy of the models.
###### Abstract  
Recently, pre-mixed biocercamics in fast set formulations have been increasingly utilized in clinical practice as an alternative to mineral trioxide aggregate (MTA) for their shorter setting time and better handling properties. However, the impact on their osteogenic potential, due to modifications in chemical composition to promote a fast setting, is still unclear. This molecular and in vitro study compared the osteogenic potential of root repairing material putty fast set (FSP) with root-repairing material putty (RRMPU), root-repairing material paste (RRMPA), Biodentine(tm) and MTA. The null hypothesis tested was that there are no differences among the tricalcium silicate materials in terms of osteogenic potential. Standardized discs were cultured with MG-63 human osteoblastic-like cells to assess biocompatibility, the activity of alkaline phosphatase (ALP) and osteogenic potential. Biocompatibility was evaluated at baseline and after 24 and 48 h. Osteogenic differentiation was assessed after 15 days. Data were analyzed with one-way ANOVAs and [PERSON]'s post-hoc test (\(p<0.05\)). All materials showed biocompatibility and bioactivity. ALP activity, which induces mineral nodule deposition, increased in all the cements tested, with a significant increase in RRMPU (\(p<0.001\)) and FSP (\(p<0.001\)) samples versus MTA. In vitro mineralization was significantly increased for RRMPU (\(p<0.0001\)), FSP (\(p=0.00012\)) and Biodentine(tm) (\(p<0.0001\)) versus MTA. The biocercamics tested showed higher levels of biocompatibility and bioactivity than MTA; a higher capacity for mineralization was observed with RRMPU and FSP versus MTA.  
bioceramic; osteogenic potential; biocompatibility; MTA; bioactivity; endodontic material; fast set biocercamics
## 2 Exosome Features
### Mechanisms of Exosome-Target Tissue Interaction and Exosome Transport
#### 2.3.1 Receptor-Ligand Interactions  
Exosomes carry surface proteins and receptors that bind specifically to ligands on target cell membranes (Figure 1). This binding initiates signal transduction pathways in the recipient cell without internalizing the exosome. For instance, exosomes carrying integrins have been shown to guide organotropic metastasis in cancer by binding to specific extracellular matrix components of target tissues [30]. Whitham et al. report that exercise induces the release of exosomes, such as those derived from muscle tissue, which exhibit a specific secretion pattern during physical activity. These exosomes are proposed to have reparative effects on tissues like the pancreas, liver, and heart [31]. Notably, the presence of the integrin \(\alpha\
u\beta\) is mainly associated with liver tropism [32].  
Receptor-receptor interactions usually occur through different mechanisms. The MHC-like receptor and Toll-like receptor mechanism is a canonical pathway, activating a signaling cascade that produces an immune response or activates some cellular response [11]. An example of this can be found in dendritic cells and T lymphocytes that interact with exosomes through their MHC-like receptor to activate specific pathways for producing more MHCII and antifutomoral receptors [33].
## 3 Thermal Buckling Analysis and Optimization
### Thermal Buckling Optimization Model Setup  
Two different ITPS configurations were constructed based on 2D optimizations as shown in Figure 6 where OPT1 and OPT2 stand for two configurations, respectively.  
Thermal buckling analysis at three critical times was conducted on these models, and results are given in Table 3, in which \(t_{w1}\), \(t_{w2}\) and \(t_{w3}\) are upper-, mid- and lower-web thickness, respectively. \(L_{\sigma 1}\), \(L_{\sigma 2}\) and \(L_{\sigma 3}\) are the critical load factors calculated at Times 1, 2 and 3, respectively. At Time 3, when BFS temperature reaches maximum, buckling occurs at the bottom surface of the ITPS and has little influence on the occurrence of unexpected deformation on the TTS. Buckling mode at Time 3 is mainly driven by \(t_{b}\), which is not one of the design variables in this study. Furthermore, by analyzing all the 40 buckling modes, it can be concluded that when the webs buckled under the conditions at Time 3, the corresponding load factors were always greater than 1 and can be assumed to be safe for the ITPS. For this reason, Time 3 was removed from the following optimization process.  
Figure 5: Temperature along the thickness direction at (**a**) Time 1, (**b**) Time 2, (**c**) Time 3 and (**d**) Time 4. Temperature contours in (**e**) 2D original design, (**f**) 2D optimized design, (**g**) 3D original design and (**h**) 3D OPT1. The paths are marked by arrows.  
Figure 6: Three-dimensional ITPS configuration: (**a**) OPT1, (**b**) OPT2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ITPS Designs** & \(t_{w1}\)**/mm** & \(t_{w2}\)**/mm** & \(t_{w3}\)**/mm** & \(L_{\sigma 1}\) & \(L_{\sigma 2}\) & \(L_{\sigma 3}\) & **m/kg** \\ \hline OPT1 & 2.5 & 2.5 & 2.5 & 0.261 & 0.304 & 0.505 & 11.137 \\ OPT2 & 2.5 & 2.5 & 2.5 & 0.349 & 0.395 & 0.482 & 9.071 \\ OPT3 & 5.0 & 5.0 & 5.0 & 1.351 & 1.537 & 0.590 & 11.131 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of critical load factor between three ITPS designs.  
It is obvious that both OPT1 and OPT2 should be optimized to avoid buckling. Comparison between OPT1 and OPT2 indicates that the buckling stability can be improved by redistributing the material of webs in \(y\)-direction. On the other hand, when webs' thickness of OPT2 was increased, the buckling stability increased as well. This design was denoted by OPT3. It can be concluded that ITPS buckling stability would be improved in two ways: modifying the webs' thickness or reshaping the webs in \(y\)-direction. Taking mass restriction into account, OPT3 was selected as basis for further optimization.