esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
## 3 Multi-Objective Inverse Kinematics Problem Statement
### Denavit-Hartenberg Forward Kinematics Model of the Robotic Arm  
The Denavit-Hartenberg, DH, convention provides a systematic methodology to express the forward kinematics model of a serial robot, using a generic transformation matrix modeling the position and the orientation between two sequential frames \((i)\) and \((i-1)\), \((T_{i}^{i-1})\)[29; 30]. Table 1 reports the DH parameters of the system, where i indicates the joint sequence, and \(a_{i}\) is the distance between \(O_{i-1}\) and \(O_{i}\) along \(x_{i}\). \(\alpha_{i}\) is the angle between \(z_{i-1}\) and \(z_{i}\) around \(x_{i}\). \(d_{i}\) is the distance from \(x_{i-1}\) towards \(x_{i}\) along \(z_{i-1}\). \(\theta_{i}\) is the angle of joint i which is the angle between \(x_{i-1}\) and \(x_{i}\) around \(z_{i-1}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frame (i)** & \(a_{i}\) **(cm)** & \(\alpha_{i}\) **(°)** & \(d_{i}\) **(cm)** & \(\theta_{i}\) **(°)** \\ \hline
1 & 1.374 & 90 & 0 & [0, 180] \\
2 & 12 & 0 & 0 & [0, 170] \\
3 & 0 & 90 & 0 & [0, 135] \\
5 & 0 & 90 & 11.965 & [0, 180] \\
5 & 9.887 & 0 & 0 & [0, 180] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denavit–Hartenberg parameters of the 5-DOF robotic arm.  
Figure 2: (a) A 3D rendering of the 5-DOF robot manipulator. (b) Kinematic and frames representations.  
The position and orientation of the end-effector of the 5-DOF robotic arm are expressed as a \(4\times 4\) homogeneous matrix \((T_{5}^{0})\), which is the result of the multiplication of the above matrices, as in Equation (9).  
\[T_{5}^{0}=T_{1}^{0}\cdot T_{2}^{1}\cdot T_{3}^{2}\cdot T_{4}^{3}\cdot T_{5}^{4}= \begin{bmatrix}r_{11}&r_{12}&r_{13}&p_{x}\\ r_{21}&r_{22}&r_{23}&p_{y}\\ r_{31}&r_{32}&r_{33}&p_{z}\\ 0&0&0&1\end{bmatrix} \tag{9}\]  
Note that \((T_{i}^{i-1})\) stands for the homogeneous transformation matrix describing transformations of frame \((i)\) toward frame \((i-1)\)[30,31].  
The terms \(r_{11},r_{12},r_{13},r_{21},r_{22},r_{23},r_{31},r_{32}\), and \(r_{33}\) are the elements that describe the orientation of the end-effector, while \((p_{x},p_{y},p_{z})\) are the coordinates of the end-effector with respect to the base frame, whose final expression is in Equation (10).  
\[\left\{\begin{aligned} P_{x}=a5C4C5(C1C2C3-C1S2S3)+a5C4C5S1+\\ (a55S+44(C1C2S3+C1S2C3))+(a2C1C2+a1C1)\\ \\ P_{y}=a5C4C5(51C2C3-S1S2S3)-a5C4C5\cdot C1\\ +(a55S+44(S1C2S3+S1C2C3))\\ +(a2S1C2+a1S1)\\ \\ P_{Z}=a5C4C5(S2C3+C2S3)+\\ (a55S+44(S2S3-C2C3))+a2S2\\ \end{aligned}\right. \tag{10}\]  
\(Ci\) stands for \(Cos(\theta_{i})\) and \(Si\) represents \(Sin(\theta_{i})\). The terms \((ai)\) and \((di)\) are the Denavit-Hartenberg, DH, parameters indicated in Table 1.  
This Denavit-Hartenberg kinematic modeling approach allows to retrieve the position of the end-effector as well as the orientations. Orientations needs to be converted to Euler angles which are commonly used in orientation control for robotics. This transformation is detailed in the next paragraph.
## 6 Model Building and Analysis
### Fuzzy Logic Modelling
#### 6.3.2 Assigning Membership Function  
The existing literature review shows that different forms of membership function are used depending on the type of problem. The factor's fuzzy membership is such that the real input can be converted into a fuzzy number value in the range [0,1]. 1. For all the independent variables, the values were normalized so that they could be brought to scale between 0.0 to 1.0. The normalized data were calculated using Equation (5). \[z_{i}=\left(x_{i}-x_{min}\right)/\left(x_{max}-x_{min}\right)\] (5)
2. The independent variables are assigned Gaussian membership function with a range from 0.0 to 1.0. In this study, we are dealing with expert opinions, and hence instead of giving it a crisp boundary, it is assigned a waveform membership. The membership function value and the corresponding range are shown in Table 5.
3. The relative weight of each factor at the first level of the hierarchy is determined--the normalized global weights calculated for each main factor and sub-factor. This step was carried out for sub-factors of financial and non-financial categories. Since only one factor falls under market diversification, the weight of only one factor was calculated at the factor level. The normalized global weights allow the sub-factors to be compared to each other. The globalized weight is calculated by multiplying the weight of the individual subfactor to the weight of the main factor [45]. The last column shows the normalized global weights. It shows that the Market condition and Customer Engagement factor is the highest weight at 1.000, closely followed by the Employee Culture Environment, which is second highest at 0.984, as shown in Table 6.
4. The next step involves the development of if-then rules--that is, the impact of sub-factor on the output--as shown in Table 7.  
Figure 10: Overall Hierarchical Fuzzy Model. The independent factors serve as input factors. The normalized input values of the linguistic variables are multiplied by the sub-factor weight to evaluate the equivalent impact, as shown in Table 8.
6. The crisp output value received from the first layer of the hierarchical fuzzy model acts as input for the next layer of the hierarchy model. 7. The second level of the hierarchical fuzzy model is developed using the Fuzzy Logic Toolbox in Matlab R2015. The input variables for the second layer, which are numeric values, are assigned membership functions. 8. The input membership function shape is assigned Gaussian wave membership with five membership functions for each main factor. The range of each membership function is from zero to one, as shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Range**} & \multicolumn{2}{c}{**Membership Function**} \\ \hline
0.0–0.2 & \multicolumn{2}{c}{Very High} \\
0.2–0.4 & \multicolumn{2}{c}{High} \\
0.4–0.6 & \multicolumn{2}{c}{Moderately low} \\
0.6–0.8 & \multicolumn{2}{c}{Low} \\
0.8–1.0 & \multicolumn{2}{c}{Very Low} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Membership function range value.
## 2 Related Works
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Reference** & **Year** & **Key Focus** & **Region** & **Main Findings** & **Service of Greatest Impact** \\ \hline
[15] & 2020 & Broadband impact on economic growth & Southeast Asia & Found that broadband increases GDP growth in ASEAN countries & Broadband Internet \\ \hline
[16] & 2019 & Telecom infrastructure and development & Sub-Saharan Africa & Explored the role of mobile networks in socio-economic development & Mobile networks \\ \hline
[17] & 2017 & Digital divide in rural areas & China & Highlighted digital exclusion in rural Chinese regions due to limited infrastructure & 5G Networks \\ \hline
[18] & 2008 & ICT adoption for inclusive development & India & ICT adoption shown to reduce inequality in urban vs. rural education & ICT infrastructure \\ \hline
[19] & 2012 & Broadband expansion policy effects & Latin America & Examined how broadband expansion policies improved regional development & Fiber optics \\ \hline
[20] & 2017 & Role of cloud computing in telecom & Middle East & Showed how cloud services support growth of telecom in the Gulf Cooperation Council & Cloud-based services \\ \hline
[21] & 2017 & Telecom infrastructure’s role in digital economy & South Korea & Telecom infrastructure found to be a cornerstone for digital economic transformation & High-speed Internet \\ \hline
[22] & 2017 & Mobile technologies and the digital divide & Nigeria & Demonstrated mobile technology’s potential to close the rural-urban & Mobile broadband \\ \hline
[23] & 2021 & AI applications in telecom sector & Global & Discussed how AI integration is transforming telecom efficiency & AI-driven telecom services \\ \hline
[24] & 2018 & Smart cities and telecom networks & Europe & Smart city development heavily reliant on robust telecom networks & IoT and 5G networks \\ \hline
[25] & 2018 & Closing the digital divide in rural communities & Kenya & Explored strategies for increasing broadband penetration in rural areas & Satellite Internet \\ \hline
[26] & 2010 & Telecom policy and economic growth & South Korea & Found telecom policies directly correlate with national economic growth & Fiber optics and 5G \\ \hline
[27] & 1998 & Impact of telecom investment on education and health & Chile & Showed telecom infrastructure improving access to digital education and healthcare & Fiber and wireless networks \\ \hline
[28] & 2024 & Broadband penetration and social equity & China & Demonstrated how increasing broadband access reduces economic inequality & Broadband Internet \\ \hline
[29] & 2022 & Digital economy and telecom infrastructure & China & Explored how digital economy growth is directly tied to improvements in telecom infrastructure & Fiber optics and AI-driven services \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works in telecommunications and the digital divide.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 3 Classification on the 10-Class MSTAR Targets  
The standard moving and stationary target acquisition and recognition (MSTAR) database [17] has 10 SAR targets, which will be discussed in the next section. Instead of putting the MSTAR images into the SRC directly, some measures should be taken to reduce the dimensions of the MSTAR images.  
The reasons are that the dimensions of the MSTAR images are so large that it makes the classifier take too much time, and simultaneously, the speckle noise in the MSTAR images will influence the performance of the classifier; moreover, the dimensions of the raw SAR images are not consistent with each other, e.g., images of BTR70 are 128 \(\times\) 128 pixels, 2S1 are 158 \(\times\) 158 pixels, T62 are 172 \(\times\) 173 pixels. So it is necessary to reduce the dimensions, and before that, we need to unify the sizes of the images. There is a registration method in [16] to center the training and test images. Then the images are cropped into the same size according to the \"center\". However, we do not think the method should be adopted here to achieve the unification. The shadow of the target in the SAR image is useful for classification, but the method, however, calculates the \"center\" according to the intensity of the image, and then it crops the \"center\" in a 44 \(\times\) 44 pixel patch, which barely contains the shadow as seen in Figure 1. The reason is that the intensity of the shadow is even lower than the speckle noise, thus making the \"center\" located between the target and speckle noise. The center 64 \(\times\) 64 pixel patch adopted in [10] does not contain enough information of the shadow. Therefore, the center 80 \(\times\) 80 pixel patch, whose center is located according to the size of the raw image, is adopted here because it contains nearly the whole shadow and excludes most speckle noise.  
After the unification of all images, conventional and unconventional feature extraction methods are applied to extract the features and reduce the dimensions of the cropped images. Then, the feature vectors are inputted into the SRC to achieve the classification. The overall procedure of the classification is summarized in Figure 2. The two successive steps, cropping the images and extracting feature vectors, are applied to both test and training samples to generate the test and training vectors, respectively. Then, the feature vectors are inputted into the Sparse-Representation module, which constructs the dictionary and solves the sparse coefficients. The dictionary will be achieved by the training vectors, and it is combined with the test vectors to solve the sparse coefficients. The dictionary and sparse coefficients are to be utilized to reconstruct the test vectors. Then the reconstructive errors are calculated to infer the identities of the test samples.  
Figure 1: The 128 \(\times\) 128 pixel patch is one of the BTR70 raw images, and the other three patches are cropped patches from the 128 \(\times\) 128 pixel patch.  
Figure 2: The flow-process diagram of SRC.
## 2 Single-Mode Rate Equations for EAM-VCSELs  
A coupled-cavity VCSEL possesses at least two longitudinal modes that experience a generic mode anticrossing effect as the two cavities are tuned in resonance to each other [17; 18; 19; 20; 21; 22]. When the two cavities are strongly detuned, the standing wave patterns of these two modes are profoundly different--each of them being mostly concentrated in a separate cavity [18; 19; 21; 22]. In such a case, only the mode that is situated mostly in the VCSEL cavity providing the gain suffices to be considered as the second one is strongly depressed. Considering such strongly detuned cavities, the single-(composed) mode rate equations for EAM-VCSELs are  
\[\frac{dn_{ph}}{dt}=(\Gamma v_{g}g-v_{g}\alpha_{tot})n_{ph} \tag{1}\]for the photon density \(n_{ph}\) and  
\[\frac{dn}{dt}=\frac{\eta I}{qV}-\frac{n}{\tau_{e}}-v_{g}g\pi n_{ph} \tag{2}\]  
for the carrier density \(n\). Here \(v_{g}\) is light group velocity, \(\Gamma\) is the variable confinement factor in the VCSEL QW region and \(g=a(n-n_{0})(1-\epsilon n_{ph})=g_{0}(1-\epsilon n_{ph})=g_{0}-a_{p}n_{ph}\) with \(g_{0}=a(n-n_{0})\) and \(a_{p}=g_{0}\epsilon\); \(\epsilon\) being the gain compression coefficient. \(\alpha_{tot}\) is the total effective cavity loss resulting from both internal losses and radiation losses; it depends on the internal photon distribution in the device and is obtained from the mode solver.  
The stationary solution of the above equations is obtained as follows: from Equation (2)  
\[n-n_{0}=\frac{\frac{\eta I}{qV}-\frac{n_{0}}{\tau_{e}}}{\frac{1}{\tau_{e}}+v_{ g}a(1-\epsilon n_{ph})n_{ph}}, \tag{3}\]  
which replaced in the stationary expression of Equation (1)  
\[\Gamma a(n-n_{0})(1-\epsilon n_{ph})-\alpha_{tot}=0 \tag{4}\]  
leads to quadratic equation for the photon density  
\[\Gamma a(\frac{\eta I}{qV}-\frac{n_{0}}{\tau_{e}})(1-\epsilon n_{ph})-\alpha_{ tot}\left[\frac{1}{\tau_{e}}+v_{g}a(1-\epsilon n_{ph})n_{ph}\right]=0. \tag{5}\]  
Denoting \(B=\frac{\Gamma}{v_{g}a_{tot}}\left(\frac{\eta I}{qV}-\frac{n_{0}}{\tau_{e}}\right)\), we can rewrite this equation as  
\[B(1-\epsilon n_{ph})-\left[\frac{1}{v_{g}a\tau_{e}}+(1-\epsilon n_{ph})n_{ph} \right]=0 \tag{6}\]  
with solutions  
\[n_{ph}\sqrt{\epsilon}=b-\sqrt{b^{2}-c} \tag{7}\]  
with \(b=\frac{Bc+1}{2\sqrt{\epsilon}}\) and \(c=B-\frac{1}{v_{g}a\tau_{e}}\).
## 4 Numerical Simulation  
### Constitutive Model and Parameters  
The centrifuge tests could be inevitably restricted by realistic factors such as facility capacity, testing materials, and experiment costs, which pose difficulties in physically modelling the complex boundary value problems in engineering. With advantages in this aspect, the dynamic numerical analysis has become an important alternative to study the seismic response of saturated sites. Additionally, the FE analysis based on [PERSON]'s dynamic consolidation equation, which fully couples the pore pressure with the dynamic response of the soil skeleton, is one of the most promising and renowned numerical approaches [39], within the framework of which the constitutive model of soil plays a critical role and determines its prediction capability. The multi-yield surface elastoplastic constitutive, PDMY02, was applied in this study to simulate the liquefiable calcareous sand material [40; 41]. Additionally, the OpenSees, an open-source computation platform, was used to carry out the fully coupled FE analyses on the seismic response of saturated ground. The prediction capability of the numerical approach was also evaluated.  
PDMY can well describe the cyclic mobility and irreversible shear strain accumulation behavior of liquefiable sand under seismic excitation, which has been verified in many research applications [33; 42; 43; 44; 45]. It is developed based on the multiple yield surface theory proposed by [PERSON] [46], and a combination of associative and non-associative flow rules is adopted. A total of 17 input parameters are needed for the model. [PERSON] et al. [47] calibrated the parameters of sand with different densities, the method of which is referred to in this paper for calibrating the sand materials, combined with the in-situ measurement. The parameters for constitutive model are listed in Table 5.  
Figure 8: Time histories of excess pore pressure ratio at different depths during different input motions: (**a**) E1\(-\)3; (**b**) E2\(-\)3; (**c**) E3\(-\)3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Medium Dense Sand** \\  & **Dr = 50\%** \\ \hline Density/(t\(\cdot\)m\({}^{-3}\)) & 1.85 \\ Void ratio & 0.7 \\ Shear modulus/kPa & 1.21 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of PDMY02 material.
# Applications of Magnesium and Its Alloys: A Review
## 4 Magnesium and Its Alloys as a Biomaterial
### Anti-Microbial Strategies  
As all medical devices are subjected to microbial colonization and infection [68], effective prevention of bacterial infection is a top priority. Failure to do so can compromise the efficacy of the implant and, in some cases, result in infection persistence, implant failure, and even death of the patients [69]. When an infection occurs, additional surgery and repair of the implant are needed, which adds to the patient's medical burden. Bacterial biofilms are estimated to cause two-thirds of human bacterial infections. Essentially, it is a densely packed community of microbial cells that are challenging to eradicate when formed. Furthermore, their resistance to the immune system and antibiotics has been observed [68]. As such, antibiotic-loaded biomaterials and, more recently, stimulus-responsive biomaterials have also been developed to intensify the bacteria-killing process before it attaches to the implant surface [69].  
Magnesium's rapid corrosive behavior is a blessing in disguise. Its rapid corrosion increases the concentration of magnesium ions locally, which alters the pH value and prohibits bacterial activities in vivo [70]. Though recent studies have further confirmed the significant antibacterial effect of magnesium ions on Staphylococcus epidermidis and Escherichia coli (_E. coli_) [71], it is essential to note that with biofilms, bacteria can withstand external pH alterations and continue infecting the tissues [68].  
Magnesium, by itself, cannot attain excellent antibacterial performance. Therefore, novel magnesium alloys (with alloying elements that possess antibacterial, such as silver, copper, zinc, and gallium) and antibacterial coatings are developed [68].
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 3 Analytical Model  
In the previous section, we analyzed the various factors that affect acquisition. In this section, we develop a theoretical model for the acquisition failure probability. Additionally, we describe the mean acquisition time required for multiple scans to evaluate the final parameter selection.
## 3 The Architecture of the NMT System  
The NMT system employs an architecture known as sequence-to-sequence. This architecture decodes one pattern into another pattern. The input sequence \(\langle S_{1},S_{2},\ S_{3},\ S_{4}\rangle\) is words of the source language, which is then converted into another sequence \(\langle T_{1},T_{2},\ T_{3}\rangle\) using this architecture. A recurrent neural network (RNN) model can easily handle this sequential data [25; 26]. The sequence of vectors \(\langle S_{1},S_{2},\ S_{3},\ \ldots\ldots\ldots S_{n}\rangle\) acts as the input for the RNN and this sequence is processed one by one. Figure 1 shows the standard RNN.  
In the above figure, at time step \(t\), \(x^{<t>}\) is the input. For example, \(x^{<t>}\) is the one-hot vector corresponding to the sentence's first word. For each time phase \(t\), the activation function \(a^{<t>}\) and output \(y^{<t>}\) can be described as follows:  
\[a^{\langle t\rangle}=g_{1}\Big{(}W_{aa}a^{\langle t-1\rangle}\ +W_{ax}x^{\langle t \rangle}+b_{a}\Big{)} \tag{1}\]  
\[y^{\langle t\rangle}=g_{2}\Big{(}W_{ya}a^{\langle t\rangle}+b_{y}\Big{)} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MT System** & **Year** & **SMT** & **LSTM** & **MWEs** & **Transformer** \\ \hline
[11] & 2022 & ✗ & ✔ & ✗ & ✗ \\
[12] & 2022 & ✗ & ✔ & ✗ & ✗ \\
[13] & 2022 & ✗ & ✔ & ✗ & ✗ \\
[14] & 2021 & ✗ & ✔ & ✗ & ✗ \\
[15] & 2021 & ✗ & ✔ & ✗ & ✗ \\
[16] & 2020 & ✗ & ✔ & ✗ & ✓ \\
[17] & 2020 & ✗ & ✔ & ✗ & ✗ \\
[18] & 2020 & ✗ & ✔ & ✗ & ✓ \\
[19] & 2019 & ✗ & ✔ & ✗ & ✗ \\
[20] & 2019 & ✗ & ✔ & ✗ & ✗ \\
[21] & 2019 & ✗ & ✔ & ✗ & ✗ \\
[22] & 2018 & ✔ & ✔ & ✗ & ✗ \\
[23] & 2020 & ✗ & ✔ & ✔ & ✗ \\
[24] & 2019 & ✗ & ✔ & ✔ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of different machine translation systems.  
Figure 1: Recurrent softmax.  
In both the above equations, \(W_{aa}\), \(W_{ax}\), \(W_{ay}\), \(b_{y}\) are coefficients that are temporarily shared and \(g_{1}\), \(g_{2}\) are the activation functions. The activation function used for \(g_{1}\) is tanh or relu, and the activation function used for \(g_{2}\) is a softmax function for the task of MT.  
RNN causes the vanishing gradient problem and exploding gradient problem in the model. The computation time is slow in RNN. It cannot consider the future input for the current state. These limitations can be handled by the long short-term memory (LSTM) model. To improve the accuracy of the system's predictions at a particular timestamp, the NMT model must consider the sequence information from both the earlier and later points in time. Bidirectional long short-term memory (BiLSTM) can be used for this purpose. The BiLSTM model that takes input from the previous and latter sequences is shown in Figure 2.