esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Developed Applications  
Building a TUI is a complex process that encompasses multidisciplinary knowledge including engineering, art, and social sciences [50]. Therefore, to build prototypes focused on teaching educational topics aimed at blind children, cooperation between different areas of knowledge was necessary (see Figure 8). After defining the stages of the METUIGA methodology, educational experts who had previously worked with blind people were consulted to choose the educational topics that, in their view, with tangible user interfaces could enhance their learning and understanding using tangible objects. Likewise, due to the UCD approach followed by METUIGA methodology, tangible interfaces were designed in close collaboration with educators and blind people. One of the most important benefits of this multidisciplinary approach is that designers can move beyond the focus on computers to physicality, namely from screens and applications on smartphones to tangible interfaces [51; 52]. Subsequently, programmers oversaw reflecting what was established by the designers.  
Figure 7: METUIGA methodology evaluation stages.  
The developed prototypes demonstrate that it is possible to generate applications that combine the characteristics of tangible user interfaces and gamification techniques to teach educational topics to blind people, providing a new interaction experience. This new experience focuses on providing a computer application enriched by gamification elements with which the user interacts through physical elements. On the other side, due to the lack of vision of the target users, we resorted to auditory feedback to indicate the different states of the gamified elements (see Figure 9).
## 5 Numerical Examples
### Effect of Including Hydride Precipitates  
In the last example, we investigate the impact of including hydrides in the polycrystalline microstructure on the mesh quality and CPFEM results. Zircaloy is selected as the material system due to the presence of needle-like hydrides formed during the manufacturing process as well as in-service irradiation. To accurately represent Zircaloy, a synthetic microstructure was created in DREAM.3D using the parameters \(\mu=4.0\) and \(\sigma=0.2\) and a Primary Equiaxed shape. A 2D slice with dimensions of 950 \(\upmu\)m \(\times\) 950 \(\upmu\)m was then extracted, converted to a NURBS model, and meshed using the PolyCISAMR algorithm. The resulting conforming mesh, which consists of \(\approx 0.25\) million elements with a maximum aspect ratio of 3.2, is illustrated in Figure 30. The precipitates introduced along grain boundaries represent 5.4% of the volume fraction. A random texture was sampled for grain orientations, with a hexagonal close-packed (HCP) crystal structure. The hydride phase was modeled using an isotropic plasticity model with higher stiffness and strength compared to the Zircaloy matrix. The microstructural model was subjected to plane stress tensile loading, as in the previous examples, although the strain rate was lower at 0.000025 1/hr to match the calibrated conditions from [49] for the constitutive model (see Appendix B).  
Figure 28: Axial stress-strain curves for Slices 1–5 with different random microstructures adopted from the same 3D polycrystalline microstructure.  
Figure 29: Effect of the polycrystalline microstructure on the axial stress \(\sigma_{xx}\) at strain level 0.02: (**a**) Slice 2; (**b**) Slice 4; (**c**) Slice 5.  
The CFPE approximation of the normal stress and strain fields in this slice at two different applied strains are illustrated in Figures 31 and 32. Compared to previous examples, a notable increase in sites of stress concentrations in the alloy microstructure is evident in this problem, especially at an applied strain of 0.008. This behavior is attributed to the presence of the hydride phase, which is stiffer and attracts more stress due to deforming less than the surrounding matrix. Additionally, the HCP Zircaloy phase is more anisotropic than the BCC Grade 91 material from the previous studies in both the elastic and plastic regimes. This greater anisotropy leads to larger stress gradients between grains with different crystal orientations, which is another contributing factor to the localizations. Despite the presence of sharp gradients in the fields, a close examination of the contours indicates that the fields remain free of oscillations and are fairly well-resolved on the high-quality conforming meshes generated using PolyCISAMR.  
Figure 30: Polycrystalline microstructure with hydride precipitates (in blue) and the conforming meshes generated using PolyCISAMR.
# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
## 3 Research Method  
### Solving the Dispersion Curve of the Steel Pipe  
A dispersion curve of the steel pipe was drawn based on the semi-analytical finite element method. Material parameters of the pipeline: Q235b. Specifications: diameter, 219 mm; wall thickness, 5.5 mm; elastic modulus, 2.1 \(\times\) 10\({}^{11}\) Pa; Poisson's ratio, 0.32; and density, 7850 kg/m\({}^{3}\). The drawn dispersion curves of the group velocity and phase velocity are presented in Figure 1.  
When the frequency was 0-60 KHz, a variety of modes were generated during the propagation of UGWs: axial symmetry longitudinal mode L(0,m), m = 1, 2, where m reflects the vibration shape in the thickness direction of the pipeline; axisymmetric torsional mode  
Figure 1: (**a**) The dispersion curve of group velocity. (**b**) The dispersion curve of phase velocity.  
T(0,m), m = 1; non-axis symmetric bending mode F(n,m), n = 1, 2, 3; m = 1, 2, 3\(\ldots\), where n represents the circumferential order, reflecting the spiral propagation pattern of UGWs around the pipe wall.  
The group velocity of the T(0,1) mode is relatively stable, and its dispersion curve shows no dispersion. Both the phase velocity curve and the group velocity curve are straight lines parallel to the horizontal axis. Compared with other modes, it can accurately carry important defect information, and its propagation is mainly in the circumferential direction. Therefore, the T(0,1) mode was selected for the numerical simulation of axial defects in the pipeline.  
As the excitation frequency is 60 kHz, the displacement wave structures of the L(0,2) and L(0,1) modes are shown in Figure 2. The circular direction displacement of these two modes is 0, the displacement of L(0,2) guided waves is large in the axial direction of the in-outside wall surfaces of the pipeline, and the radial displacement is evenly distributed in the pipeline, sensitive to defects across the entire wall thickness direction, so the L(0,2) guided waves are suitable for detecting annular defects. The displacement of the L(0,1) mode is primarily radial, with larger radial displacements at both ends along the in-outside walls, while it is zero in the middle. The in-outside walls of the pipe have large axial displacement values, so the L(0,1) mode is suitable for detecting the defects of the in-outside surfaces of the pipe. The energy leakage of the UGW mode in the propagation process is related to its radial displacement distribution on the inner and outer surfaces, and the energy leakage of mode L(0,2) is less than that of mode L(0,1). It is easy to identify in many modes and mainly detects circumferential defects. Therefore, the L(0,2) mode ultrasonic guided wave is selected for detecting crack defects in the welds of circular pipes.
## 5 Fitting Large Data and Limitations  
There are challenges that face fitting experimental data (\(x,y\)) with smooth functions. These challenges impede many successful curve fitting methods from being universal, meaning the capability of performing smoothing of any data (\(x\), \(y\)) regardless of their complexity. One of the challenges is the behavior of the data, especially data manifesting frequent sharp oscillations, as in the examples of stock prices. The latter limitation is intrinsic as the data have many corners and the fitting function is smooth. Another limitation is the large size of the data leading to large scale linear systems with very large coefficient matrices that could be ill-conditioned or badly scaled. Furthermore, large data exacerbate convergence failure and speed issues in iterative fitting methods such as Levenberg-Marquardt. Fitting with a smooth function data with both limitations present simultaneously is a very complicated problem. Our method fails to provide adequate accuracy when both conditions meet in certain data, as any fitting method would do. The highest number our method was successful to fit is one-million-and-one (\(10^{6}+1\)) pairs (\(x,y\)) extracted from the sine function over \([0,\,5\pi]\) (Figure 14), with pointwise errors less than \(7.4\times 10^{-12}\) over all the one-million-and-one points. These small residuals show a clear advantage of our method when fitting very large data that behave smoothly enough. Matlab rationalfit failed to fit 201 data extracted from the same function as discussed in the previous example.  
As a proposed remedy, when the method fails to return a desired precision, the user should resort to partitioning the data over subintervals and fit them over each one. The overall fitting function is a piecewise continuous function with negligible jump discontinuities. In other words, let us consider the partition of the data over \(x\) into \(n\) subintervals  
\[I_{i=}[x_{i},\ x_{i+1}),\ 1\leq i\leq n-1,\ \ \ I_{n=}[x_{n},\ x_{n+1}].\]  
Let \(f_{i}\) be a good rational fit over \(I_{i}\) obtained either by our method or any other method. The overall fitting function \(f\) writes:  
\[f=\sum_{i=1}^{n}f_{i}\ \chi_{I_{i}}\]  
where \(\chi_{I_{i}}\) is the characteristic function \(\chi_{I_{i}}(x)=\left\{\begin{array}{cc}1,\ if\ x\in I_{i}\\ 0,if\ x\
ot\in I_{i}\end{array}\right..\)  
The jump discontinuities \(\varepsilon_{i}=|f_{i}(x_{i+1})-f_{i}(x_{i})|\), \(1\leq i\leq n\) are small when the functions \(f_{i}\) perform well the curve fitting over \(I_{i}\). In many practical applications, such derivation is sufficient as in numerical integration of a function given as discrete pairs (\(x,y\)) for instance.  
Figure 14: Fitting a very large data set (\(10^{6}+1\)) sampled from sine function with accuracy that is comparable with samples in hundreds in size, as lower error plot shows. It might be difficult to see fitting function in red on top plot at first look due to extreme small errors and high number of data represented by blue dots.
## 3 Item Assignment Based on Weighted Associations
### Weighted Item Assignment Model  
Section 3.1 discussed the influences of profit and frequency on item assignment plans. In this section, we propose a weighted item assignment model that considers both profit and frequency as in Equation (3), which is a modified model of Equation (2). Interestingly, this optimization problem becomes a special case of the quadratic assignment problem (QAP). In Equation (3), the weights of profit and frequency are multiplied:  
\[\begin{array}{ll}\text{Maximize}&\sum_{i\
eq j,\ k\
eq l}\ w_{profit(L_{i},I_{j})}\times w_{frequency(L_{i},I_{j})}\times lift\big{(}I_{i}\to I_{j} \big{)}\times x_{ik}\times x_{i}\mathbb{I}\times y_{kl}\\ \text{Subject to}&\sum_{i=1}^{n}x_{ik}=1,k=1,\ \ldots,\ n,\\ &\sum_{k=1}^{n}x_{ik}=1,i=1,\ \ldots,\ n,\\ &x_{ik}=\left\{\begin{array}{ll}1,&\text{if item }I_{i}\text{ is located at site }S_{k}\\ &0,\text{ otherwise.}\end{array}\right.\end{array} \tag{3}\]  
Since the scales of profit and frequency are different depending on the problems, this study used relative profit and relative frequency as weights of the model. Relative profits were calculated by dividing the profit by the lowest increased profit among items. For instance, since the lowest profit in Table 2a is $10, the relative profits are calculated by dividing all profits by $10. Table 4 shows the relative profits obtained from Table 2a.  
Likewise, relative frequencies are calculated by dividing the frequencies by the lowest frequency among all of them. By dividing all the frequencies in Table 3a by the lowest  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline A & - & 1 & 1 & 2 & 1 & 1 \\ B & 1 & - & 1 & 1 & 1 & 3 \\ C & 1 &  
frequency, 1000, the relative frequencies can be obtained as in Table 5a. Then, we can rewrite the weights in Table 3b with those in Table 5b.  
According to the weighted item assignment, the objective values of Equation (3) for BASE_ASIGN, PROFIT_ASSIGN, and PRO_FRE_ASSIGN are 15, 18.4, and 21.4, respectively, as shown in Table 6. This result verifies the superiority of PRO_FRE_ASSIGN among the three assignments when considering both profits and frequencies. Since the weighted item assignment model considers the three factors of association, profit, and frequency among the items, the model can offer insight to decision-makers for finding the most effective assignment of items for cross-selling.
# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 4 Implemented Method
### Post-Processing Phase  
Since in dermoscopic images illumination is maximized at the center of the image and decreases drastically towards borders enough to produce a significant gradient that could heavily affect the segmentation results, a region of interest (ROI) was chosen centered within the image to address the segmentation problem.  
Output of the segmentation phase (Figure 2) was refined using mathematical morphology. By employing a small circular structuring element, with radius R = 1 pixel, spurious pixels inside color coherent areas were avoided using the erosion operation while holes in coherent areas were filled through the dilation. In order to better preserve the fine details of the border, a slightly bigger circular structuring element of radius equal to 3 pixels was used for the dilation.  
As the resulting segmented regions might contain more than one connected area, the segmentation of bit planes (each containing a connected area) into a new stack was carried out by means of a common splitting procedure. The obtained regions were candidates to be the starting point for the image segmentation into coherent areas.  
To achieve coherent areas characterized by a common image color, the weighted sum of the images present in the final BPS was evaluated by employing a simple algorithm. Denoting with _BP(k)_ the \(k\)th bit plane of the reduced dimension BPS, which was obtained at the output of the bit-plane orthogonalization phase, the segmented image (\(I\)) was:  
\[I=\sum_{k=1}^{N}k\cdot BP(k)\]  
Figure 2: The segmented regions obtained by binary orthogonalization of BPS. The last image is the original image of the skin lesion.  
where \(N\) is the number of bit planes composing the reduced BPS.  
In Figure 3, the output obtained by adopting the image processing detailed in the aforementioned procedure is shown: The original image and the binary orthogonal segmentation are shown on the left and on the right, respectively.  
Once the image segmented into color coherent areas is available, the detection of the skin lesion area is obtained following a two-step procedure. Firstly, segmented areas are sorted in descending order according to their extension and the smallest areas are neglected. In such a way, the segmented area which mostly covers the entire image is identified. In the second step the mean color of each area is computed and the lesion is selected as the darkest area among the available ones.
## 7 Current Challenges and Problems
### Size of the Implanted Devices  
Implantable devices need to be as small as possible, so as to be less invasive for the human body. The batteries in biomedical implants have several problems such as limited life time, chemical side effects and being large in size. Researchers are trying to eliminate the use of batteries from implanted devices in the human body and have proposed various solutions to power implantable devices such asusing inductive coupling link, power harvesting with piezoelectric material and body motion. The silicon area should be as small as possible to minimize external components that can reduce the overall size of implantable devices. Reducing the complexity of the electronic block circuits and the number of the passive elements such as capacitors and resistors would help to reduce the size of the implanted devices. This can be achieved by developing techniques and methods such as auto-zero technique [93], FPGA methods [86] and artificial intelligence methods [94].
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Multi-Objective Optimization  
The scheduling optimization problem is formulated as a MOO, which is solved using a combination of lexicographic and weighted sum approaches. This hybrid method enables operators to balance multiple optimization criteria by assigning hierarchical priorities (lexicographic approach) and adjustable weights (weighted sum approach). Incorporating diverse objective functions is particularly crucial in the heterogeneous environment of NTNs. This approach directly addresses the challenge of mission-specific solutions by providing the flexibility to model various scenarios with different objective function combinations. Additionally, it enhances resource management by allowing operators to prioritize constrained resources based on specific optimization goals. Furthermore, the MOO paradigm supports multi-customer service provisioning by accommodating diverse quality-of-service requirements and operational constraints. This adaptability makes it a robust and scalable solution for addressing the diversity of NTN scenarios.
## 3 Proposed Method
### Noise-Reduction Processing  
The objective of this part is to reduce noise and enhance contrast in the input image. The process begins by dividing the image into two regions: an upper region used to determine the time period of the image, and a lower region where the actual lane detection takes place. Specific parameters corresponding to the time period are applied to process the images. This differentiation between daytime and nighttime enables noise suppression and contrast enhancement tailored to each specific time period.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 4 Proposed Resistorless Dual-Mode Quadrature Oscillator  
The proposed resistorless dual-mode QO circuit is slightly modified from Figure 2 by removing both input signals \(v_{in}\) and \(i_{in}\), as illustrated in Figure 3. Circuit analysis of the configuration shown in Figure 3 leads to the following characteristic equation:  
\[s^{2}+\bigg{[}\frac{(g_{mA1}\eta_{2}-g_{mB2})\eta_{1}}{C_{2}}\bigg{]}s+\bigg{(} \frac{g_{mA1}g_{mB1}}{C_{1}C_{2}}\bigg{)}=0, \tag{19}\]  
where \(\eta_{2}=g_{mB2}/g_{mC2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation Mode** & **LP** & **BP** & **HP** \\ \hline VM & \(1\) & \(-1\) & \(-1\) \\ TAM & \(-\) & \(\frac{g_{mB1}}{g_{mB1}}\) & \(-\frac{g_{mB2}}{g_{mB1}}\) \\ CM & \(-1\) & \(-\frac{g_{mB1}}{g_{mB1}}\) & \(\frac{g_{mB2}}{g_{mB1}}\) \\ TIM & \(-\frac{1}{g_{mB1}}\) & \(\frac{g_{mB1}}{g_{mB1}g_{mB2}}\) & \(-\frac{g_{mB2}}{g_{mB2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Passband gains of the proposed mixed-mode universal biquad filter in Figure 2.  
Figure 3: Proposed resistorless dual-mode quadrature oscillator.  
From Equation (19), the oscillation condition (OC) and the oscillation frequency (\(\omega_{osc}\)) of the proposed dual-mode QO can be derived, respectively, as:  
\[g_{mA1}=g_{mC2}, \tag{20}\]  
and  
\[\omega_{osc}=2\pi f_{osc}=\sqrt{\frac{g_{mA1}g_{mB1}}{C_{1}C_{2}}}. \tag{21}\]  
Equation (20) shows that the OC may be electrically modulated via \(g_{mC2}\) without influencing the \(\omega_{osc}\). It should also be noted that the \(\omega_{osc}\) in Equation (21) can be independently adjusted by \(g_{mA1}\) and/or \(g_{mB1}\). In other words, both OC and \(\omega_{osc}\) parameters can be electronically and separately regulated by the selection of separate transconductances.  
In a sinusoidal steady state, two output voltages \(v_{osc1}\) and \(v_{osc2}\) of the oscillator have the following relationship:  
\[\frac{v_{osc1}}{v_{osc2}}=\left|\frac{g_{mB1}}{\omega C_{1}}\right|e^{-j90^{ \circ}}. \tag{22}\]  
This implies that the two voltage outputs are phase-shifted by \(90^{\circ}\). Consequently, we expect that the output voltages \(v_{osc1}\) and \(v_{osc2}\) will be in quadrature form.  
In the same way, the output currents \(i_{osc1}\) and \(i_{osc2}\) in the sinusoidal steady state exhibit a similar relationship as:  
\[\frac{i_{osc1}}{i_{osc2}}=\left|\frac{g_{mB1}}{\omega C_{2}}\right|e^{-j90^{ \circ}}. \tag{23}\]  
Consequently, the output currents are quadrature signals as well.
# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems  
Recently, numerous robot navigation systems have been proposed in response to the need to automate various robot tasks by enabling robots to comprehend their surrounding environments. In general, robot navigation systems can be classified into two main categories: geometric-based and semantic-based navigation systems, as illustrated in Figure 2. Geometric-based navigation systems typically utilize LiDAR (light detection and ranging), RADAR (Radio Detection and Ranging), or range-finder modules to construct a map of the area based on distance measurements.  
Geometric-based navigation systems utilize distance measurement technologies. For example, a LiDAR-based system employs a directed beam to estimate the distance between the LiDAR device and objects in its path, while a laser range-finder illuminates a larger area. In contrast, RADAR technology differs from LiDAR, as it uses wavelength signals instead of pulsed laser beams. However, this paper focuses on robot navigation systems that utilize semantic information specifically for indoor environments [20].  
On the other hand, robot semantic navigation systems aim to construct maps with semantic information to assist mobile robots in navigation, recognition, and decision-making. Semantic navigation enables robots to understand the environment in a manner similar to humans. Scene understanding is one of the key tasks in robot semantic navigation,encompassing object detection and recognition, depth estimation, object tracking, and event classification. To achieve efficient robot semantic navigation, a mobile robot must comprehend both geometric and visual information [21].  
As shown in Figure 2, robot semantic navigation can be further categorized into LiDAR-based, vision-based, and hybrid approaches. LiDAR-based systems gather information from LiDAR modules to categorize object types and structures, while vision-based systems employ sophisticated vision systems to recognize objects within the area of interest. Hybrid approaches combine both range-finder and vision systems. Therefore, this paper surveys, discusses, and analyzes recently developed robot semantic navigation systems for indoor environments.  
The research method employed to acquire the newly developed robot semantic navigation systems adhered to a specific criterion, which is illustrated in Figure 3. The search process involved identifying pertinent research papers using the keywords \"semantic navigation\", \"robot\", and \"indoor navigation\". Only papers published after 2010 were taken into account, with a specific emphasis on indoor robot semantic navigation systems. Research works that lacked simulation or real experiments were excluded.  
Figure 3: Research methodology for surveying recently developed robot semantic navigation systems.  
Figure 2: Classification of robot navigation systems.