esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Measures for Detecting Influencers on Social Network
### Social Network  
There are many kinds of object on a social network. In this paper, two main kinds are studied: users and their posts on the network. A social network can be represented by a relationship between users and posts [26; 36].  
**Definition 1**.: ([26]). _A social network is a relational model, called the Social-Net model, which is represented by a triple:_  
\[\textbf{(U,P,A)}\]  
**U** is a set of users, **P** is a set of posts, and **A** is a set of relations and actions between users and posts on the social network.  
**U**--_Set of users_  
Figure 5: Determination of influential measures of users.  
Each user \(u\in\mathbf{U}\) has the structure as follows:  
\[u=(\text{Profile, Pos, Fr, Fol, Fold})\]  
* _Profile_: the personal information of a user.
* \(\text{Pos}=\{p_{u}\mid p_{u}\in\mathbf{P}\}\): set of post of the user \(u\).
* \(Fr=\{s\mid s\in\mathbf{U}\text{ and friend}(s,\,u)\}\): the set of other users who are friends of the user \(u\).
* \(Fol=\{s\mid s\in\mathbf{U}\text{ and follow}(s,\,u)\}\): the set of other users who are followers of the user \(u\).
* \(Fol=\{s\mid s\in\mathbf{U}\text{ and follow}(s,\,v)\}\): the set of other users who are followed by the user \(u\).  
**P--**_Set of posts_  
Each post \(p\in\mathbf{P}\) is a tube as follows:  
\[p=(\text{Seeder, }\theta,\text{Content, React, Share, Comm})\]  
* \(\text{Seeder}\in\mathbf{U}\): the user seeds the post \(p\).
* \(\theta\in\) Time: a timestamp when the post \(p\) is posted.
* _Content_: the content of the post \(p\).
* \(\text{React}=\{s\mid s\in\mathbf{U}\text{ and reaction}(s,\,p)\}\): the set of users who have reactions with the post \(p\).
* \(\text{Share}=\{s\mid s\in\mathbf{U}\text{ and share}(s,p)\}\): the set of users who share the post \(p\).
* \(\text{Comm}=\{s\mid s\in\mathbf{U}\text{ and comment}(s,p)\}\): the set of users who have comments on the post \(p\).  
**A--**_Set of actions and relations_  
\[\mathbf{A}=\mathbf{A}_{\text{R}}\cup\mathbf{A}_{\text{P}}\]  
* \(\mathbf{A}_{\text{R}}\): a set of relations between two users.
* \(+\) follower \(\subseteq\mathbf{U}\times\mathbf{U}\): the relation about following between two users.
* \(+\) friend \(\subseteq\mathbf{U}\times\mathbf{U}\): the relation about friendship between two users. This relation is symmetric.
* \(\mathbf{A}_{\text{P}}\): a set of relations as actions between a user and a post.
* \(+\) reaction \(\subseteq\mathbf{U}\times\mathbf{P}\times\mathbf{N}\): a user reacts to a post. The kind of this reaction is a natural number.
* \(+\) comment \(\subseteq\mathbf{U}\times\mathbf{P}\): a user comments on a post.
* \(+\) share \(\subseteq\mathbf{U}\times\mathbf{P}\): a user shares a post.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 2 Centralized MPPT Architecture Based on LoRa
### Power Optimization of PV Systems
#### 2.1.2 DC-DC Converter  
DC-DC converter topologies for MPPT in PV applications include Buck, Boost, Buckboost, Forward, Push-Pull, Flyback, and Cuk-Sepic. All of them are governed by the duty ratio (also known as duty cycle) of the Pulse Width Modulation (PWM) that drives the gate of the power switch device [35]. The variation of the converter's duty cycle results in a change in the operating point of the PV module. Through effective control, this operating point is systematically manipulated to attain the MPP. The duty cycle control signal is set by the MPPT algorithm.  
For the DC-DC power converter, the electric model of a boost converter was used. Equation (7) presents the boost converter average state-space model in continuous conduction mode. \(L\) is the inductance, \(C\) is the capacitance, \(R\) is the \(V_{\rm o}/I_{\rm o}\) ratio, and \(d\) is the duty cycle:  
\[\dot{x} =Ax+Bu \tag{7}\] \[y =Cx,\]  
Figure 3: The irradiance and temperature influence on the PV curves. (**a**) Effect of the irradiance variations. (**b**) Effect of the temperature variations.  
Figure 2: The power–voltage and current–voltage curves of a generic PV module.  
\[\text{where}\ \mathbf{x}=\begin{bmatrix}P_{\text{pv}}&V_{\text{o}}\end{bmatrix}^{T},u=V_{ \text{pv}},\ A=\begin{bmatrix}0&\frac{d-1}{L}\\ \frac{1-d}{RC}&-\frac{1}{RC}\end{bmatrix},\ B=\begin{bmatrix}\frac{1}{L}\\ 0\end{bmatrix},\text{ and }C=\begin{bmatrix}0&1\end{bmatrix}.\]  
The voltage output of the converter \(V_{\text{o}}\) is a function of the input voltage \(V_{\text{pv}}\) and the applied duty cycle \(d\). Equation (8) shows that relationship:  
\[V_{\text{o}}=\frac{1}{1-d}\ V_{\text{pv}}. \tag{8}\]  
To avoid scenarios where the converter has to perform outside the continuous conduction form, \(d\) was limited by software to values in the range of 0.1 to 0.9.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 1 Introduction
These measures support decision-making for balancing performance through incident prevention and production investment. However, the relationship between productivity and safety performance across different industries has been less reported, despite its significance for national competitiveness and business excellence. Previous studies have primarily focused on productivity or safety performance separately, lacking an integrated approach to measuring the socio-economic impact of both performances. Balancing productivity and safety is essential in industrial settings, yet existing research does not provide a comprehensive measure that captures both capabilities. To address this gap, a new economic measure should incorporate multiple inputs and outputs, enabling a more holistic assessment of industrial performance. This study aims to identify the relationship between productivity and safety performance in industrial production economics by proposing the integrated economic measure of the Productivity-Safety Effectiveness Index (PSEI). The major tenet of this study is to define the PSE to find better units that have high productivity as output with a low accident rate (i.e., high safety performance) as input. For this, we use data envelopment analysis (DEA), which calculates the relative importance of analysis units based on the input and output of units [23; 24]. It is especially noted that DEA is useful for calculating the proposed index among industries using multiple inputs and outputs simultaneously. For this study, DEA enables researchers to identify the highly efficient industries that have higher productivity with fewer risks. During the DEA process, the PSEI is defined as a ratio of productivity to safety performance (or incident rate). In other words, the PSEI of industries answers the following question. Which industries have higher productivity, providing better safety conditions? Along with this question, the public database of both productivity and safety is collected. First, safety performance, as input, includes three indices: fatal rate (person), frequency rate (accident), and severity (loss time). Second, productivity, as output, is considered with three indices: labor productivity, production capacity, and operation ratio. This study also presents the effective manufacturing industries of South Korea that have outperformed in occupational safety performance and productivity.
## 3 LADRC Controller Design
### Linear Extended State Observer
\tag{15}\]  
In reference Formula (15), \(z_{1},z_{2}\) represent the estimated values of the state variables of the controlled object (in this example, \(z_{1}\) represents the observation value of \(\psi\), and \(z_{2}\) is the observation value of the \(\psi\) derivative), while \(z_{3}\) represents the real-time estimated value of the total disturbance (unknown external disturbances and uncertain models). \(\beta_{1},\beta_{2},\beta_{3}\) are the gains of the LESO. If the observer gains are chosen appropriately, LESO can achieve precise tracking of each state variable of the controlled object. To facilitate parameter tuning, the values of \(\beta_{1},\beta_{2},\beta_{3}\) are determined by \(\omega_{0}\). By reasonably selecting the parameter \(\omega_{0}\), the observed value of the \"total disturbance\" can be made closer to the true value. The Laplace transform of the LESO equation yields [19]:  
\[ \begin{array}{c}z_{1}=\frac{\beta_{1}z^{2}+\beta_{3}+\beta_{2}}{L^{\prime}( \beta)}Y(s)+\frac{b\ast}{L^{\prime}(\beta)}U(s)\\ z_{2}=\frac{\beta_{3}z^{2}+\beta_{3}z}{L^{\prime}(\beta)}Y(s)+\frac{b\ast(s+ \beta_{1})}{L^{\prime}(\beta)}U(s)\\ z_{3}=\frac{\beta_{3}z^{2}}{L^{\prime}(\beta)}Y(s)-\frac{b\ast}{L^{\prime}( \beta)}U(s)\end{array}  \tag{16}\]  
\(Y(S)\) is the Laplace transform of the system output y(t) in the time domain, and \(U(S)\) is the Laplace transform of the system input u(t) in the time domain. The characteristic equation corresponding to LESO is [18]:  
\[L^{\prime}\Big{(}s\Big{)}=s^{3}+\beta_{1}s^{2}+\beta_{2}s+\beta_{3} \tag{17}\]  
To stabilize the system, the roots of the characteristic equation must be located in the left half of the s-plane. Therefore, the three poles of the observer are uniformly placed on the left half of the real axis at \(-\omega_{o}\) (where \(\omega_{o}\) is the bandwidth of the observer, and \(\omega_{o}>0\)). Therefore, the observer gain can be obtained as \(\beta_{1}=3\omega_{o}\), \(\beta_{2}=3\omega_{o}^{2}\), \(\beta_{3}=\omega_{o}^{3}\). The estimated error of LESO can be expressed as [18]:  
\[\left\{ \begin{array}{c}\dot{e}_{1}=e_{2}-\beta_{1}e_{1}\\ \dot{e}_{2}=e_{3}-\beta_{2}e_{1}\\ \dot{e}_{3}=D-\beta_{3}e_{1}\end{array} \right. \tag{18}\]  
Among them, \(e_{i}=\psi_{i}-z_{i}\) (\(i=1,2,3\)) provides the conditions for the estimation error of LESO, which will be used for the stability proof of LESO in the following text.
## 3 Short-Reach Systems Classification and Enabling Technologies
### High-Bandwidth APD Receiver  
Compared with the PIN diode, an APD has a much higher responsivity. Based on this, it is an attractive device that can help achieve high receiver sensitivity and low-cost and long-reach transmission. For instance, a high-bandwidth Ge/Si APD can be employed. It is a promising device for future high-speed applications due to its ease of integration with other SiP devices [35].
## 3 Results
### Corrosion Studies
#### 3.4.2 Temperature Studies  
The loss in weight of copper specimens in an acidic environment at varying temperatures with and without inhibitor was measured from weight loss measurement, and the corresponding inhibition efficiency was calculated and is presented in Table 5. This table represents the inhibition efficiency of the plant extract at increasing temperature from 303 to 343 \(\pm\) 1 K for various concentrations from 100 to 1000 ppm. From the table, it was noted that the maximum inhibition efficiency at 1000-ppm concentration at room temperature was 96% and the efficiency was noted as 90%, 85%, 77% and 60% at every 10 \(\pm\) 1 K increment in temperature.
## 3 Materials and Methods
### Control System Design
#### 3.3.3 PID Control System  
The AMT control system for the tractor was developed using TCU hardware and a PID controller. Position control of the actuators is most important for AMT system performance [42]. A PID controller for the shuttle shifting and clutch actuators was designed. The actuator control model was developed using the transfer functions of the electric actuator, PID controller, and the potentiometer. DC motor models, as shown in Equations (10)-(12), were used for the actuators, and the PID controller used the general model shown in Equation (13). The transfer function of the potentiometer H(s) is shown in Equation (14). The potentiometer used in this study has a reference voltage (\(E\)) of 5 V and a rotation degree of 270\({}^{\circ}\)/360\({}^{\circ}\). As the transfer function was close to one, the transfer function was assumed to be a closed loop; therefore, the transfer function of the actuator control system is shown in Equation (15) and demonstrated in Figure 7.  
\[\mathrm{G(s)}=\frac{K_{m}}{s(\tau s+1)}, \tag{10}\]  
\[\tau=\frac{R_{a}J}{(R_{a}B+KK_{e})}, \tag{11}\]  
\[K_{m}=\frac{K}{(R_{a}B+KK_{e})}, \tag{12}\]  
where \(\mathrm{G(s)}\) is the transfer function of the DC motor, \(\tau\) is the time constant (s), \(R_{a}\) is the armature resistance (\(\Omega\)), \(J\) is the moment of inertia of the motor (kg\(\cdot\)m\({}^{2}\)), \(B\) is the frictional constant of the motor (kg\(\cdot\)m\({}^{2}\)\(\cdot\)s\({}^{-1}\)), \(K\) is the motor torque constant (Nm\(\cdot\)A\({}^{-1}\)), and \(K_{e}\) is the electromotive force (EMF) constant.  
\[G_{PID}=K_{P}+K_{D}\mathrm{s}+\frac{K_{I}}{s}, \tag{13}\]  
where \(G_{PID}\) is the transfer function of the PID controller and \(K_{P}\), \(K_{I}\) and \(K_{D}\) are the coefficients of the proportional, integral, and derivative, respectively.  
\[H(s)=\frac{E}{2\pi N}, \tag{14}\]  
where \(H(s)\) is the transfer function of the potentiometer, \(E\) is the voltage (V), and \(N\) is the rotation degree (\({}^{\circ}\)).  
\[T(s)=\frac{K_{m}\Big{(}K_{d}s^{2}+K_{P}\mathrm{s}+K_{I}\Big{)}}{\tau s^{3}+(1+ K_{m}K_{D})s^{2}+K_{m}K_{p}\mathrm{s}+K_{m}K_{I}}, \tag{15}\]  
where \(T(s)\) is the transfer function of the clutch and shuttle shifting electric actuators.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 4 Knowledge-Driven Applications in Telecommunications
They are majorly derived from empirical observations and theoretical foundations of many sciences including physics, mathematics, information, and communication. Equations about physical entities are the lowest level of abstraction in the knowledge representation line and have been used to develop algorithms that control the communication infrastructure to optimally use the resources of various media, such as the radio environment. Moving from physics towards the digitalization line, more abstract levels of information are to be identified that are closer to human cognition capabilities. The capability of the human mind to think at higher levels of abstraction also requires the movement from equation representations to a more human language-like form of representing knowledge. The representation of knowledge in telecommunication systems is related to the application it is aimed at facilitating. Between the infrastructure that benefits from the environment, e.g., in the RAN domain, and the operation center benefiting from the users and operators, e.g., in the network operation center (NOC) domain, from customer relationship managers (CRMs) to operations support systems (OSSs), there is the service operation domain where the telecommunication system should also generally benefit from to improve its operations. As illustrated in Figure 2, the knowledge in the different domains is associated with different reasoning processes pertinent to operations in the corresponding domains. The NOC domain implies user interaction and human reasoning based on natural language understanding and understanding capabilities, while the operation of the system requires knowledge representations that allow for the automation of the reasoning processes within the system. Getting closer to the RAN domain, the reasoning process is based on representations that can express numerical expressions. Furthermore, RAN is naturally distributed: the antennas, as in distributed antenna systems (DAS) and/or the base stations, are always geographically spread out. As a consequence of that, it is natural to study how intelligence, i.e., AI algorithms, should be distributed. Do we bring data to the algorithm, or do we distribute the algorithms? If we distribute, then how is this efficiently performed, and which technologies are suitable including transfer learning, federated learning, and split learning? The different categories of KRR, including learning and inferring, are described in the next section, and their application is further elaborated in the use-case description section.
# A Simple Foot Plantar Pressure Measurement Platform System Using Force-Sensing Resistors
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Subject \#**} & \multicolumn{10}{c}{**Total Load Sensors in Area (Gram)**} & \multicolumn{10}{c}{**Total Load All**} \\  & \multicolumn{3}{c}{**Rear Foot**} & \multicolumn{3}{c}{**Mid Foot**} & \multicolumn{3}{c}{**Front Foot**} & \multicolumn{3}{c}{**Thumb Finger**} & \\ \cline{2-13}  & **Left** & **Right** & **ASI** & **Left** & **Right** & **ASI** & **Left** & **Right** & **ASI** & **Left** & **Right** & **ASI** & **Left** & **Right** \\  & **Leg** & **Leg** & **(\%)** & **Leg** & **Leg** & **(\%)** & **Leg** & **Leg** & **(\%)** & **Leg** & **Leg** & **(\%)** & **Leg** & **Leg** \\ \hline
1 & 1202 & 1926 & 37.6 & 0 & 0 & 0 & 1535 & 3472 & 55.79 & 787 & 784 & \(-\)0.4 & 3.5 & 6.2 \\
2 & 1598 & 1458 & \(-\)9.6 & 890 & 1156 & 23.0 & 2464 & 4220 & 41.61 & 516 & 631 & 18.2 & 5.5 & 7.5 \\
3 & 1967 & 2102 & 6.4 & 1473 & 2188 & 32.7 & 3481 & 4260 & 18.29 & 766 & 824 & 7.0 & 7.7 & 9.4 \\
4 & 2058 & 2175 & 5.4 & 1376 & 1310 & \(-\)5.0 & 3024 & 3920 & 22.86 & 257 & 571 & 55.0 & 6.7 & 8 \\
5 & 1797 & 2233 & 19.5 & 1104 & 2217 & 50.2 & 2856 & 4158 & 31.31 & 424 & 641 & 33.9 & 6.2 & 9.2 \\
6 & 2077 & 2164 & 4.0 & 849 & 1244 & 31.8 & 2282 & 4079 & 44.05 & 354 & 411 & 13.9 & 5.6 & 7.9 \\
7 & 1883 & 2292 & 17.8 & 978 & 1255 & 22.1 & 2647 & 3415 & 22.49 & 705 & 722 & 2.4 & 6.2 & 7.7 \\
8 & 1804 & 1979 & 8.8 & 689 & 0 & 0 & 2961 & 2943 & \(-\)0.61 & 756 & 820 & 7.8 & 6.2 & 5.7 \\
9 & 1831 & 1539 & \(-\)19.0 & 827 & 920 & 10.1 & 2476 & 3977 & 37.74 & 686 & 554 & \(-\)23.8 & 5.8 & 7 \\
10 & 1651 & 1810 & 8.8 & 1116 & 968 & \(-\)15.3 & 1668 & 3005 & 44.49 & 681 & 464 & \(-\)46.8 & 5.1 & 6.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The calculation of ASI (%) of each area of every subject. When a person stands, the biggest burden occurs in soles of back and front foot for all types of foot, either left or right foot soles [1]. This corresponds to the measurement results using the MAFP tool, which is displayed in red. For a foot with a high arch, the area of the sole of the middle of the foot is small. This corresponds to the measurement result of MAFP. The middle area is displayed in light blue and dark blue. Instead, for a flat foot, the load on the soles of the middle foot is large. The sensors in the medial mid foot, i.e., in sensors 5 and 7, are seen to be exposed to the load. This corresponds to the measurement result of MAFP. The middle area is shown in yellow and green. For a normal type of foot arch it is somewhat difficult to see the comparison with the measurement results using the MAFP  
tool, because it is similar to the type of high arch foot. However, from the measurement results, using the prototype of this designed measuring instrument looks load in the central area is large enough on subjects' numbers 2 and 6 compared to subjects' number 1 and 8 for the high arch type of foot.
## 2 Computational Methodology
### Numerical Setup and Computational Domain  
The NASA wall-mounted hump model was designed to assess the ability of turbulence models to predict separation and reattachment in an adverse pressure gradient induced separation. There are several excellent publications describing the numerical validation of this model using different numerical schemes [43; 44; 45; 46; 39; 42]. The work presented in this paper was the first time that a DBD plasma actuator model was incorporated into CALC-LES to investigate and control separated flow. Moreover, the available experimental values were mostly based on a nominally two-dimensional flow field analysis. Nevertheless, the computational domain and the geometry used in this study were three-dimensional with a spanwise length of 0.2. The computational domain and grid are shown in Figures 4 and 5 respectively.  
Figure 4 shows the entire domain geometry; however, the modified version of the grid was used to reduce the computational domain, shown in Figure 5. The grid used for this work was the mandatory grid used for hump model computations and was provided by the Advanced Turbulence Simulation for Aerodynamic Application Challenges (ATAAC) project. The grid was fine in area close to the point of incipient separations. The mesh used for our cases had \(312\times 120\times 64\) cells with \(Z_{max}=0.2\). The details of the full geometry and initial flow conditions can be found on the NASA CFDVAL2004 and ATAAC project homepage [47]. The maximum heights of the hump and the channel were \(h/c=0.128\) and \(H/c=0.91\), respectively.  
Figure 3: Linearized electric field distributions.  
Due to the three-dimensional wall boundary effects, the top wall was modeled as a slip wall. Inlet flow conditions were adapted from the results of a two-dimensional RANS SST \(k-\omega\) simulation of [PERSON] [42]. The Neumann boundary condition was used for the outflow (right) wall, and a symmetry boundary condition was used in the spanwise direction. A Reynolds number of 936,000, based on chord length, and the inlet velocity of 34.6 m/s were used. As both the velocity and length were normalized by the reference values, the dynamic viscosity was therefore \(1/Re\).
## 3 Deep-Learning-Based Channel Estimation Methods
### Data-Driven Deep-Learning-Based Channel Estimation Methods
#### 3.3.2 Application of CNN in Data-Driven Channel Estimation
Through experimental comparison, the estimation accuracy of the LCET surpasses the LS algorithm and some neural networks, closely approximating the performance of LMMSE algorithms in multi-pilot scenarios. In general, the transceiver of millimeter wave (mmWave) massive MIMO systems employs a hybrid precoding structure, and thus, the acquisition of CSI in the low SNR regime poses difficulties. To overcome this challenge, [PERSON] et al. [61] proposed ResNet-UNet, a network that combines the residual network (ResNet) and the U-shaped network (U-Net) for channel estimation. In particular, the proposed ResU-net consists of a denoiser and an estimator. Firstly, the received noisy pilot signal is converted into an image and processed by the denoiser. Then, the obtained clean pilot signal image is fed to the estimator to obtain the estimation result. Through experimental verification, the Res-UNet surpasses the conventional algorithms and the deep CNN in estimation accuracy and is robust to noisy environments. In 2023, [PERSON] et al. [62] proposed a deep residual convolutional blind denoising network (ResCBDNet) for massive MIMO visible light communication systems to estimate more realistic and accurate CSI. The proposed ResCBDNet comprises two subnetworks: the noise estimation network and the non-blind denoising network, and it transforms the sparse channel matrix into a 2D image. Initially, the scheme employs a noise estimation network to enhance the generalization ability of the true noise. It then interactively reducesthe effect of noise in the channel matrix through the adjustment of the noise level mapping. Subsequently, the noiseless estimated channel is recovered using the non-blind denoising network. Experiments demonstrate that the ResCBDnet surpasses some existing deep-learning-based methods in terms of normalized MSE (NMSE) and peak SNR (PSNR). Moreover, with its exceptional noise generalization capability, ResCBDNet demonstrates the superior MSE performance to other methods when SNR = 20 dB.