esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
###### Abstract
The goal of this work is to provide a fast hole-filling method for triangular mesh based on hole size, especially towards the additive manufacturing repair. The developed method is efficient, robust, and has the ability to recover the geometric shapes of the missing regions when closing holes. Our method can be used in the fields of 3D geometry measurement, geometry modeling, intelligent manufacturing as well as additive repair, etc. The developed approach consists of two main stages: preprocessing and hole-filling. In the first stage, a group of basic concepts is defined, followed by hole detection and boundary cleaning. In the second stage, three different hole-filling algorithms are discussed in detail, which are then used to fill three types of holes classified according to hole size. The main contributions of this work are given as follows:  
1. A robust and efficient hole-filling method is proposed. In most cases, each hole occurring in one mesh can be automatically assigned a proper hole-filling algorithm based on hole size, which can not only produce satisfactory hole-filling results but also achieve a good balance between the efficiency and filling quality. 2. A fast and shape-recovery hole-filling algorithm for large-sized holes is developed. It can not only directly close large-sized holes without any further refinement and fairing steps, but also ensure that the filling patches are compatible with surrounding meshes well. The rest of this paper is organized as follows: Section 2 briefly discusses the related work. The overview of our method is presented in Section 3. Section 4 shows the preliminaries. The fast hole-filling method is discussed in Section 5. Experimental results and discussion are given in Section 6, followed by the conclusion in Section 7.
## 4 Proposed Optimal UFLS Based on WAMS
\(LB\) and \(UB\) are the lower and upper bounds constraints which can be summarized as \[ \begin{bmatrix}P_{m,\min}\\ f_{ss,\min}\\ t_{m,\min}\\ k_{\min}\\ P_{stage,\min}\\ P_{\textit{Shed,\min}}\\ f_{ts,\min}\\ \sigma_{\min}\\ t_{d,\min}\end{bmatrix} \leq \begin{bmatrix}P_{m}\\ f_{ss}\\ t_{m}\\ k\\ P_{stage}\\ P_{\textit{Shed}}\\ f_{ts}\\ \sigma\\ t_{d}\end{bmatrix} \leq \begin{bmatrix}P_{m,\max}\\ f_{ss,\max}\\ t_{m,\max}\\ k_{\max}\\ P_{stage,\max}\\ P_{\textit{Shed,\max}}\\ f_{ts,\max}\\ \sigma_{\max}\\ t_{d,\max}\end{bmatrix}  \tag{11}\]  
Equation (10) describes a general optimization problem, to implement the UFLS, we shall describe the objective function describing the system and this is given by Equations (12)-(14). \[F_{obj}\left(Z\right)=\alpha\left|\Delta w_{ts}\right|+\beta\left|\Delta w_{ ss}\right|+f\left(Y\right) \tag{12}\]  
\[F_{obj}\left(Z\right)=\alpha\left|\Delta w_{ts}\right|+\gamma\sum_{j=1}^{M} \left|\Delta P_{j}\right|+f\left(Y\right) \tag{13}\]  
\[F_{obj}\left(Z\right)=\varepsilon\int\frac{1}{2}\Delta w(t)^{2}dt+\gamma\sum _{j=1}^{M}\left|\Delta P_{j}\right|+f\left(Y\right) \tag{14}\]  
where \(\alpha,\beta,\gamma\) and \(\varepsilon\) are the constant cost coefficients of transient frequency deviations \(\left(\Delta w_{ts}\right)\), steady-state frequency deviations \(\left(\Delta w_{ss}\right)\), load-shed \(\left(\Delta P_{j}\right)\) and integral of squared error (ISE) of the frequency \(\left(\int\frac{1}{2}\Delta w(t)^{2}\right)\), respectively. The function \(f\left(Y\right)\) is a further term that can be used to describe an economic dispatch problem. In situations where the cost function of the generators is available, this term can be considered in the optimization. However, in this paper \(f\left(Y\right)\) is neglected and therefore the constant cost coefficients can be considered as weighting coefficients in the optimization. The aim of the first objective function described in (12) is to minimize the total load-shed such that the minimum system frequency with load-shedding should be equal or above the minimum allowable system frequency and at the same time giving a standard steady-state frequency. In (13) instead of incorporating the final steady-state frequency into the objective function, the load to be shed is considered in the objective function in the aim of minimizing the load to be shed. The ISE criteria is adopted in the third objective function given in (14) and it effectively zeros the frequency deviation which is one of its main advantages.
###### Abstract  
There has recently been an increase in interest in structural health monitoring (SHM) using wireless sensor networks. For SHM, in particular, it is important to accurately and efficiently measure the dynamic acceleration response using wireless sensor networks in real-time. For the purpose, a CAFB (cochlea-inspired artificial filter bank) has been developed in a previous study, which is a dynamic data compression technology. Since the developed CAFB can select and compress only the interested range of frequency signals from an entire response of a structure, it efficiently provides a real-time dynamic response based on wireless networking. CAFB of the previous study is optimized to selectively acquire low-frequency signals of sub-10 Hz, which is required for SHM of long and large-scale structures. According to the CAFB's optimization using an El-Centro seismic waveform, six band-pass filters, 1.0 Hz interval, and 0.6 Hz bandwidth have been adapted. This article is to evaluate dynamic acceleration response performance of civil structures using the CAFB developed in the previous study. To achieve the purpose, the optimally-designed CAFB was embedded in an intelligent data acquisition (IDAQ) system. To evaluate the performance of the IDAQ system with the embedded CAFB, the real-time dynamic response was investigated for a model cable-stayed bridge, measured by a wire-measuring system and the CAFB-based IDAQ system simultaneously. The results show excellent agreement between the compressed dynamic response acquired by the CAFB-based IDAQ system and that acquired by the wire measuring system. In addition, the measurement from the CAFB-based IDAQ system revealed the modal information of the model bridge. The developed CAFB can determine and reconstruct the entire dynamic response from compression with modal information only; its efficient operation illustrates its potential to be utilized in real-time structural health monitoring.  
cochlear-inspired artificial filter bank (CAFB); intelligent data acquisition (IDAQ); band-pass filter optimizing algorithm (BOA); peak-picking algorithm (PPA); reconstruction error (RE); compressive ratio (CR); structural health monitoring (SHM); data compressing 2018/361; doi:10.3390/app8030361
# Adhesive Single-Lap Joint Evaluation Using Ultrasound Guided Waves
## 3 Materials and Methods
### Experimental Setup  
The ultrasonic experimental setup shown in Figure 2 was used to evaluate the defects. It is composed of a Panametrics pulser/receiver model 5800, a pair of immersion broadband transducers (Imasonic model IM 0.5 25 P) with a 500 kHz central frequency and a 25 mm active diameter, and a Tektronix digital oscilloscope (model TDS1002B). The aluminum alloy (5083-O) plates are 200 mm in length, 125 mm in width, and 4 mm in thickness. The size of the bonded region is 60 mm \(\times\) 125 mm (whole width). The bonded zone has the same dimensions as the one presented in the simulation model described in the previous section.  
The plates were bonded together with an epoxy adhesive (Araldite 2014, Vantico, Duxford, Cambridge, UK), of 150 \(\upmu\)m in thickness, achieved by a uniform pressure of 0.1 Kg/m\({}^{2}\). The adhesive was then cured according to the manufacturer's instructions at room temperature (25 \({}^{\circ}\)C) for 24 h.  
The approach used to produce artificial defects in the bonded line consisted of removing a circular portion of aluminum equal to the adhesive thickness in one of the plates, and then that zone was covered with sticky tape to avoid adhesive penetration, as described in [57].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **Density** & **Longitudinal** & **Transversal** \\  & **[kg/m\({}^{3}\)]** & **Velocity [m/s]** & **Velocity [m/s]** \\ \hline Water & 1000 & 1500 & - \\ Epoxy & 1077 & 2540 & 1055 \\ Aluminum & 2660 & 6300 & 3130 \\ Air & 1.2 & 340 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acoustic properties of the materials used in simulation.  
Figure 1: Schematic of the simulation setup for an adhesively bonded single-lap joint inspection using the pitch-and-catch configuration and guided waves.
# Drill Hole Orientation: Its Role and Importance on the Compression Response of Pure Magnesium
## 3 Results and Discussions
### Simulation Results
#### 3.1.3 Macroscopic Changes in Deformation Patterns  
In addition to the shear stress development, as [PERSON] et al. [36] pointed out, the deformation of the hole can provide valuable information on which drill hole orientation is ideal. They also provide valuable information on whether sensors placed in these drill holes could observe any disruption to their regular function. Deformation patterns of the vertical hole orientation, shown in Figure 8A, illustrate similar behaviour to that of [PERSON] et al. [36]. However, as the drill orientation changes to an angular orientation, though the entire sample undergoes compression, the deformation patterns are slightly different for the top half and bottom half of the hole. On the bottom left and top right corners of the component in Figure 8B, the stiffness of the component is comparatively lower due to  
Figure 7: (**a**) Development of normalized plastic zone area with load for different drill hole orientations and (**b**) a visual representation of the normalized plastic zone area around the drill hole (considering it to be a crack) at 15,000 N compressive load.  
the presence of the hole, resulting in higher deformations locally (as indicated by the arrows). This can generate an \"S\" shaped deformation pattern in the hole which may allow more energy absorption during compression. Finally, when the drill orientation is horizontal, the entire length of the drill hole sees a slightly uniform deformation pattern, as shown in Figure 8C1,2, demonstrating the deformation of the cross-section of the hole. A clearer picture of the tension and compression region mentioned in Section 3.1.1 is also visible in Figure 8C2.  
Overall, from Figure 8, it is evident that there exists a plane about which the deformation patterns are mirrored and inverted, and from closer observation, we notice that this plane is aligned along the axis of the drill hole. Further, both angular drill hole orientation and horizontal drill hole orientations give rise to localized deformations within the component and away from the compression direction, resulting in decreased stability in the component when compared to the vertical drill hole orientation.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods
### SD and DD  
Figure 1 shows how to obtain the SD and DD measurements [6; 70].  
In Figure 1a, \(\mathbf{o}\) denotes the unit Line-Of-Sight (LOS) vector from a specific receiver to a specific satellite. In Figure 1, \(\rho\) denotes the pseudo range between a receiver and a satellite. The carrier phase measurements are denoted by \(\phi\). Equations (1) and (2) show the formulation of the pseudo range and the carrier phase, respectively:  
\[\rho=r+\tau_{\text{long}}+\tau_{\text{tropo}}+c(\zeta_{\text{rec}}-\zeta_{ \text{sat}})+\omega_{\rho}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ProjectsCharacteristics** & **Field** & **Frame** & **Language** \\ \hline RTKLIB & GNSS (RTK and PPP) & EKF & C \\ \hline PPPLIB & GNSS (RTK and PPP) & EKF & C/C++ \\  & GNSS/INS (LC) & EKF & Matlab/C++ \\ \hline PSINS & GNSS/INS (LC and range-based TC) & EKF & Matlab \\ \hline GINAV & GNSS/INS (LC, range-based TC, PPK TC, PPP TC) & EKF & Matlab \\ \hline OB-GINS & GNSS/INS (LC) & Optimization & C++ \\ \hline \hline \end{tabular}
\end{table}
Table 2: The characteristics of open-source projects.  
\[\phi=\lambda^{-1}[r-\tau_{\text{iono}}+\tau_{\text{tropo}}+c(\zeta_{\text{rec}}- \zeta_{\text{sat}})]+N+\omega_{\phi}. \tag{2}\]  
In Equations (1) and (2), \(r\) represents the geometric distance between the receiver and the satellite in meters; \(\lambda\) denotes the wavelength; \(\omega\) with different subscripts are measurement noise and unmodelled error in different measurements; the ionospheric delay and the tropospheric delay are denoted by \(\tau_{\text{iono}}\) and \(\tau_{\text{tropo}}\), respectively; \(\zeta_{\text{rec}}\) and \(\zeta_{\text{sat}}\) refer to the clock bias of receiver and satellite, respectively; and \(c\) stands for the speed of light in meters per second. We use \(N\) to represent this integer ambiguity.  
Figure 1b uses \(\Delta\rho\) and \(\Delta\phi\) to denote the SD pseudo range and the SD carrier phase, respectively. Figure 1b shows that SD measurements remove the effect caused by the satellite clock bias. When used in short-baseline RTK, the SD technique eliminates most ionospheric and tropospheric delays. DD pseudo range and the DD carrier phase are denoted by \(\
abla\Delta\rho\) and \(\
abla\Delta\phi\), respectively. DD measurements eliminate the influence of the receiver clock bias. In short-baseline RTK, the DD pseudo-range and DD carrier phase are represented in meters as Equations (3) and (4) show:  
\[\
abla\Delta\rho=\
abla\Delta r+\
abla\Delta\omega_{\rho}, \tag{3}\]  
\[\
abla\Delta\phi=\lambda^{-1}\
abla\Delta r+\
abla\Delta N+\
abla\Delta\omega _{\phi}, \tag{4}\]  
where \(\
abla\Delta(\cdot)\) refers to the DD operator.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Texture Features  
In addition to color-based features, the histogram of oriented gradient (HOG) features were integrated into the analysis. These features provided a detailed depiction of the gradient information inherent in the images, capturing essential texture and shape characteristics vital for weed detection. With 3780 parameters, the HOG features offered a comprehensive descriptor of the image content, thereby augmenting the accuracy and robustness of our weed classification algorithms.  
Furthermore, the gray-level co-occurrence matrix (GLCM) features, which included 72 parameters, were used. GLCM features utilized in [18] were employed for classifying both plants and weeds. These features provided insights into the spatial relationships of pixel intensities within the images and valuable information about texture patterns, enhancing our understanding of the images' structural properties.  
To expand the analysis, we integrated local binary pattern (LBP) features, which are renowned for capturing micropatterns and texture variations within images. LBP features are particularly valuable for distinguishing weeds. By incorporating LBPs alongside existing features like HOG and GLCM, we aimed to improve the discriminative capability of the classification models, ultimately enhancing weed detection accuracy.  
The sizes of the three types of texture feature vectors are detailed in Table 1.
## 3 Results and Discussion
### Artificial Neural Network Prediction Analysis  
#### 3.2.1 Background of ANN Model  
ANN methodology is dependent on mathematical models that are programmed to execute various functions. It works along similar lines to the information processing of a human brain. Many neurons are connected to input and output layers. These neurons collect the information from the sources, perform non-linear operations and present the final value as output. The total number of input and output variables decides the number of neurons. For engineering problems, generally a feed-forward back-propagation model or multilayer perceptron model is used. It consists of an input layer, output layer and hidden layer. The input layer collects the data from the sources, whereas the output layer yields results. The general layout of the MLP model is shown in Figure 21. The input data and their associated interconnecting weights are provided for the summing junctions. As per Equation (9), the outcome of the summing junction along with the bias of neurons is fed to the transfer function to generate the final output.  
\[u_{j}=F\Bigg{[}\left(\sum_{i=1}^{n}a_{i}w_{ij}\right)+b_{j}\Bigg{]} \tag{9}\]  
The selection of these functions is based on various parameters such as the complexity of the problem, biasing weights and node numbers. It is very much essential to have a proper relationship between input and output value, which ensures the realistic output value for a given input. The most common type of transfer function used in ANN is as given in Equation (10).  
\[F(X)=\frac{1}{1+e^{-X}} \tag{10}\]  
The input and output parameters used in the analyses generally have variations in terms of their range. To normalize the data and to convert them into a consistent range from -1 to +1, Equation (11) is used and is given by  
\[Y=(High_{value}-Low_{value})\frac{Y_{i}-Y_{\min}}{Y_{\max}-Y_{\min}}+Low_{value} \tag{11}\]  
The various types of back-propagation algorithms widely used in ANN applications are Bayesian Regularization, Scaled Conjugate Gradient and Levenberg-Marquardt. Bayesian Regularization, generally referred to as TRAINBR, converts a non-linear regression problem into a statistical problem. The training with this method is more robust and gives good prediction accuracy. However, it takes higher training time. The scaled Conjugate Gradient method, known as TRAINSCG, requires derivative functions for input, weights and transfer functions. This method yields results with lesser accuracy, but it is  
Figure 21: Basic structure of the artificial neuron.  
possible to achieve lesser training time. Levenberg-Marquardt, popularly called TRAINLM, is a tradeoff method in which the Jacobian matrix and the gradient vector are calculated. TRAINLM requires relatively lesser training time with moderate prediction accuracy.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Future Research Directions
#### 4.2.1 O-RAN for IoT Applications
NB-IoT is only ideal for supporting massive connectivity-intensive applications requiring data rates up to 254 kbps, which does not have mobility [92]. Among the feasible directions for addressing this issue will be dedicating a focus group within the O-RAN Alliance community which will engage both research and standardization communities to develop standards and specifications for enabling O-RAN as a viable network infrastructure to support IoT applications. After that, the yielded specifications could guide the research community's efforts towards developing theoretical foundation and analysis, modeling, and simulation testbeds, etc., until the interoperability and integration vision becomes a reality.
## 5 Experimental Study and Result Analysis
### Time Complexity Analysis  
We have conducted a detailed time complexity analysis of the proposed RDH algorithm. The critical activity during the RDH process is the bit plane compression, for which we need to access all the bits from all \(K\) numbers of the most significant bit planes. Let us assume that the original image size is \(R\times C\); in that case, we need to access \(K\) times the bit planes \(R\times C\) bits. Thus, the overall operations will be \(KRC\). Since \(K\) is a constant value from \(\{0,1,2,\ldots,7\}\), the final time complexity of the proposed RDH process will be \(O(RC)\).  
Similarly, during the data extraction and image restoration phase, we need to access the bits from the bit planes and want to do the run-length decoding procedure. Again, in this phase, we need to access a constant number of bit planes for image restoration and data extraction and note that each plane has a size of \(R\times C\) bits. Hence, the time complexity of the data extraction and image recovery process is \(O(RC)\).  
Figure 10: Entropy measure observed from original image (\(E1\)) and from the final encrypted image with hidden data (\(E2\)).  
Note that the time complexity of the proposed method is linear to the input size (total number of pixels in the given image).  
We used Matlab 2020 to implement the proposed algorithms, and the simulations were carried out on a machine with an Intel I5 8 th generation processor. The average execution time observed during the experimental study of the proposed algorithms is demonstrated in Figure 12. The results show that the proposed scheme can be used in real-time applications.
# Stability Analysis of Mixed Convection Flow towards a Moving Thin Needle in Nanofluid
## 1 Introduction
According to [PERSON] [15], a needle is assumed \"thin\" when its thickness is smaller than or comparable to that of the boundary layer over it. Inspired by the work of [PERSON] [15], [PERSON] and [PERSON] [16] and [PERSON] and [PERSON] [17] further investigate the problem by considering the mixed convection flow over a vertical thin needle in viscous fluid. Since then, numerous works regarding the boundary layer flow over a thin needle were carried out [18; 19; 20; 21]. However, the previously-mentioned works deals only with viscous fluid. Following from there, [PERSON] and [PERSON] [22] grabbed the opportunity to study the boundary layer flow of a horizontal thin needle in nanofluid. In their work, they solved the classical problem of forced convection flow and heat transfer with a variable wall temperature. Starting from that, many investigations into nanofluid were carried out by [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25] and [PERSON] et al. [26] for various aspects of the problem. In contrast to the problem of the horizontal thin needle, the consideration of studying the vertical thin needle is less popular and was carried out by [PERSON] et al. [27]. In their study, they consider the mixed convection boundary layer flow in nanofluid past a stationary vertical thin needle with the presence of gravitational force. The study of the stationary needle has substantially different types of solutions from the moving needle. This is due to the entrapment of the ambient temperature for the moving surface, which causes the flow situation to represent an essentially different class of boundary layer flows. There are some practical applications that prove the boundary layer behavior on the moving surface is an imperative aspect of flow appearing in industrial and engineering processes. Some applications include the boundary layer past a liquid film in condensation processes, aerodynamic extrusion of a plastic sheet and a long thread traveling between a feed roll and a wind-up roll [28; 29]. Since numerous applications have been found in industries involving the moving surface, the purpose of this paper is to investigate the characteristics of the fluid flow and heat transfer analysis past a moving vertical thin needle in nanofluid under the influence of gravity (body) forces using the [PERSON] and Das model. This problem considers both assisting and opposing flow. The partial differential equations for momentum and energy are reduced to ordinary differential equations, which are then solved numerically using an implemented package bvp4c in MATLAB software (Matlab R2013a, Mathwork, Natick, MA, USA,1984). The side effect of the mixed convection parameter including the velocity ratio parameter, nanoparticle volume fraction parameter and the needle size are graphically presented and elaborated in detail in the next section for \(Pr=6.2\) (water).