esa-sceva/satcom-chunk-collection huggingface dataset:
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Statistical Analysis Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Feature/Algorithm**} & **PSO** & **GWO** & **FFA** & **CS** & **BAT** \\ \hline \multirow{8}{*}{TDF} & \(HFD\) & Accuracy & 97.9 & 90.7 & 66.7 & 54.9 & 99 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & HjAc & Accuracy & 97.1 & 88.4 & 68.8 & 55.1 & 97.7 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & HjComp & Accuracy & 91.2 & 78.9 & 58.8 & 48.7 & 93.8 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & HjMo & Accuracy & 96.5 & 89.3 & 67.9 & 53.5 & 97.9 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(Hur\) & Accuracy & 72.8 & 57.9 & 47.2 & 44.1 & 70.1 \\ \cline{3-8}  & Rank & 1 & 3 & 4 & 5 & 2 \\ \cline{3-8}  & \(Kur\) & Accuracy & 71.2 & 61 & 48.4 & 43.4 & 73.2 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(Skw\) & Accuracy & 78.7 & 63.7 & 50 & 44.4 & **79.2** \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \hline \multirow{8}{*}{FDF} & Summation of Ranks & 13 & 21 & 28 & 35 & **8** \\ \cline{3-8}  & \(meanF\) & Accuracy & 96.7 & 87.3 & 64.1 & 52.5 & 96.3 \\ \cline{3-8}  & Rank & 1 & 3 & 4 & 5 & 2 \\ \cline{3-8}  & \(MedF\) & Accuracy & 82.7 & 69.2 & 53.6 & 46.6 & **83.4** \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \hline \multirow{8}{*}{FDF} & Summation of Ranks & 3 & 6 & 8 & 10 & 3 \\ \cline{3-8}  & Conv\(FuzEn\) & Accuracy & 96.6 & 82.7 & 61.3 & 52 & 96.5 \\ \cline{3-8}  & Rank & 1 & 3 & 4 & 5 & 2 \\ \cline{3-8}  & \(FuzEn\) & Accuracy & 93.6 & 81.6 & 60.6 & 49.1 & 95.5 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(impen\) & Accuracy & 94.7 & 78.6 & 59.3 & 49.8 & 95.6 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(MFEmu\) & Accuracy & 85.2 & 73.6 & 54.3 & 46.8 & 88.1 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(RCMFEmu\) & Accuracy & 84.3 & 67.7 & 51.9 & 46.6 & **87.2** \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(SampEn\) & Accuracy & 87.9 & 71.5 & 52.9 & 51.9 & 91.4 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \cline{3-8}  & \(TsEn\) & Accuracy & 83.9 & 72.5 & 52.3 & 46.6 & 84.6 \\ \cline{3-8}  & Rank & 2 & 3 & 4 & 5 & 1 \\ \hline \multicolumn{2}{c}{Summation of Ranks} & 13 & 21 & 28 & 35 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The sum of ranks of meta_Heur_istic algorithms based on classification accuracy on motor/imaging EEG dataset.
## 2 Wireless Body Sensor Networks and QoS  
### Wireless Body Sensor Networks  
A WBSN consists of low-power, lightweight, small-sized and intelligent sensors that are placed in/on the human body. A general three-tier structural design for a WBSN is shown in Figure 1. The communication architecture between Tier 1 and the personal server (PS) can be flat or hierarchical, as illustrated in Figure 2a,b, respectively [2]. Studies [32,33] have shown that for QoS, a hierarchical architecture is preferable compared to the flat one, as the hierarchical architecture in WBSNs or BANs shows better network performance in terms of delay, packet delivery ratio (PDR), energy consumption and network lifetime.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 4 Experimental Setup  
The experimental evaluation was divided into four main stages. In the first stage, we investigated the application of classical machine learning techniques for the classification of UXO/nonUXO. These results served as a baseline for the subsequent development of a neural network model. The second stage focused on the design and development of a neural network architecture tailored for UXO/nonUXO classification, with emphasis on network structure. The third stage, called ablation studies, focused on dataset composition and the impact of other network parameters. Particular attention was given to the impact of meta-features, such as altitude above the seabed and sensor movement direction. The fourth stage comprised an assessment of the model's robustness to signal distortions. The following subsections detail each experimental phase.
# A Practical and Effective Layout for a Safe Human-Robot Collaborative Assembly Task
## 1 Introduction
The results in [26] show that the robot slightly deviates from the nominal path if the predicted occupancy of the operator gets close to the robot planned trajectory. A more precise robot trajectory generation which can deal with the human intrusion within the workspace is shown in [27]. However, in [27] the robot can only follow waypoints across a trapezoidal path. Other researchers focus on assembly workspace layout or task optimization. The case study of the battery cell assembly proposed in [28], which is conducted comparing a laser scanner with radio wave sensors for motion tracking, shows that the relative position of the human and robot in the working area may affect the runaway motion of the robot. Concerning task optimization, in [29] a method which allows predicting the human activity pattern is implemented for the assembly of an integrated circuit. The results in [29], which are obtained by using a single depth camera for human tracking, prove that pattern prediction leads to better cycle times in sequential collaboration. This work distinguishes from the state of the art since it describes a practical solution to effectively deal with human-robot responsive collaboration by using multiple vision sensors and a local control strategy to ensure the safety of the human worker. None of the works previously discussed present a hardware/software setup as the one described in this paper, which has been adopted to carry out a responsive collaborative task. Furthermore, the choice of using a UR3 robot, Kinect v2 cameras, and implementing a collision avoidance based on artificial potential fields was made in order to use devices and algorithms well known and robust, giving the readers the possibility to replicate the setup and develop collaborative robotics applications. Moreover, this work focuses on assembly tasks where the operator is designated for the added value activities. The paper is organized as follows: The description of the task and the experimental layout are presented in Section 2. Section 3 describes the implemented control strategy. The experimental tests and the discussion of the results are reported in Section 4. Finally, Section 5 includes the conclusions and future works.
## 3 Results and Discussion
### Raman Scattering  
Figures 9 and 10 show the Raman spectra for the Mg-doped GaN films, and Zn-doped GaN films, respectively, which were deposited by radio-frequency magnetron sputtering. These figures show a peak with a predominant frequency for the silicon substrates at 515.1 cm\({}^{-1}\) (TO) (graphic located in the upper right part of Figures 9 and 10). Magnifying the frequencies belonging to GaN, the classical vibration modes A\({}_{1}\)(TO), E\({}_{1}\)(TO), and E\({}_{2}\)(High) for the hexagonal crystal structure of GaN were identified. Figure 9 shows the E\({}_{1}\)(TO), and E\({}_{2}\)(High) modes, which are overlapping, forming a shoulder with values of 550.92 cm\({}^{-1}\), and 568.02 cm\({}^{-1}\), respectively. On the other hand, A\({}_{1}\)(TO) vibration mode had a value of 527.54 cm\({}^{-1}\), with a slight shift of 2.14 cm\({}^{-1}\) to the right of the silicon peak. This slight shift in the phononic vibration A1(TO) might be related to the difference between the atomic radius of Mg and Ga, confirming the transport of Mg atoms from target to the films, and demonstrating the obtaining of Mg-doped GaN [17; 30]. Figure 10 shows the Raman spectrum for the Zn-doped GaN films obtained by radio-frequency magnetron sputtering, in which E\({}_{1}\)(TO), and E\({}_{2}\)(High) vibration modes had values of 550.93 cm\({}^{-1}\), and 566.93 cm\({}^{-1}\), respectively, while A1(TO) vibration mode had a value of 526.04 cm\({}^{-1}\). A\({}_{1}\)(TO) frequency showed a slight shift of 1.03 cm\({}^{-1}\) to the right of the silicon peak, which could be due to the incorporation of Zn atoms as dopant into GaN films [19; 30].  
Figure 8: Photoluminescence spectrum for the Zn-doped GaN films deposited by radio-frequency magnetron sputtering.
## 1 Introduction
Regarding the use of plastic waste in gypsum composites, several researchers have addressed many possibilities in this field. Figure 1 shows different results found in the literature. The results present flexural and compressive mechanical strengths, as well as the final bulk density of the final composites. Figure 1: Mechanical strength and bulk density of gypsum composites with plastic waste: Reference is a plaster with a water/gypsum ratio of 0.7 by weight [24]; PP is polypropylene waste from coffee capsules [25]; EPS and XPS are expanded and extruded polystyrene waste, respectively [18]; Pipe foam insulation shredded in average diameter of 2.5 mm [26]; Plastic cable is shredded waste from low voltage cable sheaths [27]; Polycarbonate [28]; CD and DVD are shredded and screened waste [15]; LDPE is recycled low density polyethylene [29]; ELT-rubber is granulated rubber from disused types of average diameter 2.5 mm [30]; SAP (superabsorbent polymer) is 15 g additions of sodium polyacrylate [24]. As can be seen in Figure 1, plastic waste additions decrease mechanical flexural and compressive strengths, while achieving lighter composites. In all cases found, minimum strengths of 1 MPa and 2 MPa, as stated in the UNE-EN 13279-2 standard for flexural and compressive stresses, were exceeded [31]. On the other hand, this density reduction sometimes leads to an increase in the porosity of the material [24]. As these wastes have no water absorption capacity, some researchers have shown that the incorporation of these plastic wastes can have a positive impact on the durability of gypsum composites [25; 32]. However, because of this lower final density of the hardened composites, thermal behaviour satisfactory results have also been obtained, for instance, EPS waste added to plaster materials, which reduces thermal conductivity by 61.9% compared to reference material [33]. This work aims to analyse the feasibility of HDPE waste for use in the manufacture of plaster composites. More specifically, the aim is to conduct a physical and mechanical characterisation of different composites made with the incorporation of HDPE as a partial replacement of the original plaster material. In this way, this research allows us to show the possibilities for the application of this waste to produce composite materials and to explore their possibilities for the development of prefabricated products in the construction sector.
## 4 Results and Discussions
In Figure 4b, the values of pressure load and blank temperature are set to their constant values, while the relative fiber orientation and ply pullout speed vary. The figure shows that inter-ply shear stress reduces as the relative fiber orientation angle of adjacent plies increases. Since the fiber orientation angles of the two outer plies are \(0^{\circ}\) each, the maximum relative angle of the middle ply is \(90^{\circ}\). This affirms that the middle ply is easily deformable as ply pullout velocity is perpendicular to the fiber direction, and hence, the stress is low. In Figure 4c, the pressure load and the ply pullout speed were set to their constant values, and the other parameters were set to vary. As relative fiber orientation angle and temperature increase, inter-ply shear stress decreases. The figure also depicts that the shear stress is more sensitive to changes in temperature than the relative fiber orientation of adjacent plies. The increase in pressure load and ply pullout speed, as shown in Figure 4d, increases inter-ply shear stress. High pressure load hinders inter-ply slippage and therefore increases inter-ply shear stress as shown in this figure. Figure 3: Effect of individual parameters on inter-ply shear stress. (**a**) Effect of relative fiber orientation angle. (**b**) Effect of forming temperature. (**c**) Effect of pressure load. (**d**) Effect of ply pullout speed.
## 3 Digital Twin, from Concept to Architecture Model
### The Concept of Digital Twins  
A Digital Twin is a virtual model of a product, service, or process, representing one of the technologies that has a high potential for use. The beginning of DT development was marked by [PERSON] [2], who wanted to achieve \"a digital, virtual model of a physical product\". In fact, the beginning of DTs was due to the National Aeronautics and Space Administration (NASA) [3], but also due to the Air Force Research Laboratory (AFRL).  
Figure 6: The systematic review using the Prisma framework.  
Initially, regarding the implementation and use of DTs, there were limitations related to data acquisition, use of prediction algorithms, and performance. After their remediation in 2016, DTs began to be used in Industry 4.0 by Siemens, Munich, Germany. Then, [PERSON] et al. [4; 5] advanced a three-dimensional model that was originally developed by integrating two more dimensions for DT services and data.  
The wide interest in this field can also be found in various definitions found in the specialized literature, which denotes a great potential in terms of development, especially the breadth of the concept. From a multitude of definitions, we highlight the following in Table 1.  
Depending on the scope, Digital Twins can exhibit different characteristics [11]. Based on the specialized literature consulted thus far, the common characteristics are as follows:  
* _High degree of accuracy:_ From the appearance, functionalities, and content point of view, the DT must be an accurate copy of its physical counterpart. Thus, the higher the precision, the actions and simulation scenarios should achieve the same behavior both in the physical and virtual environment.
* _Dynamic:_ Communication between the physical product and its virtual twin must be continuous and bidirectional, and any change made to one must be reflected in the behavior of the other.
* _Self-evolving:_ Throughout the life cycle, the DT follows the changes along with its physical counterpart. The DT adapts and optimizes with the aid of the data received from the physical counterpart in real time, evolving with it.
* _Identifiable:_ Each real product must have its own DT. During the product life cycle, information and functional models evolve, and based on this, at any point in time, the DT can be identified in a unique mode from its physical twin [11].
## Materials and Methods
### Experimental Setup and Sample Analysis  
The coal sample was burned in a well-controlled electrically heated HDTF, as shown in Figure 2. The details of this HDTF can be found in earlier publications [36; 37]. The experimental system is composed of four parts: the feeding system; the furnace and temperature controlling system; the gas delivery system and the sampling system. The sampling system consists of a sampling tube, a cyclone, a Dekati low pressure impactor (LPI, Dekati Ltd., Kangasala, Finland) and a vacuum pump. The temperature of the DTF can be adjusted from 1400 \({}^{\circ}\)C to 1800 \({}^{\circ}\)C with the heating of the molybdenum silicide bars. The length of the reactor tube is 1440 mm, and the inner diameter is 40 mm.  
In the experiment, the furnace temperature was controlled at a high temperature of 1500 \({}^{\circ}\)C. The combustion experiments of Zhundong coal were carried out at air condition (AIR, O\({}_{2}\)/N\({}_{2}\) = 21/79) and at oxy-fuel conditions with three characterized oxygen fractions. The volume ratios of O\({}_{2}\)/CO\({}_{2}\) were 21/79 (OXY21), 27/73 (OXY27) and 32/68 (OXY32), respectively. The feeding rate of the coal sample was \(\sim\)0.15 g/min, the total feeding gas flow rate was 5 L/min and the particle residence time in the furnace was less than 1.5 s. Thermogravimetric analysis (TGA,TGA4000, PerkinElmer Inc, Waltham, MA, USA) confirmed the complete combustion achieved [38], ensuring that all the collections were inorganic PM. After complete combustion, the flue gas containing fine ash was first quenched by N\({}_{2}\), then separated by cyclone with a cutoff of \(\sim\)10.0 \(\upmu\)m and collected by the LPI, which has 13 stages for different size fractions. Stages 1-13 had a D50 size of 0.039, 0.072, 0.122, 0.205, 0.32, 0.488, 0.765, 1.231, 1.951, 3.08, 5.144, 8.099 and 10.747 \(\upmu\)m, respectively. In order to avoid acid gas condensation and particle coagulation during sampling, the entire sampling system was externally heated to 120 \({}^{\circ}\)C [39].  
Figure 1: The occurrence form of alkali and alkaline earth metals (AAEM) in Zhundong coal.  
The mass of aluminum foil in each LPI stage was accurately weighed using a Mettler Toledo Microbalance (readability, 0.001 mg, XS3 DU, Mettler Toledo Inc, Zurich, ZH, Switzerland) before and after collection, and was used to determine the mass-based PSDs and mass yields of PM\({}_{10}\). The polycarbonate membranes were used for the particle elemental analysis. The elemental compositions of the particles in each stage were analyzed using a scanning electron microscopy (SEM, JSM-6510, JEOL Ltd., Tokyo, Japan) equipped with an energy-dispersive spectrometer (EDS, EDAX GENESIS, EDAX Inc., Mahwah, NJ, USA). At least 3 replicates were performed for each condition, and the reported data and error bar are the mean values.
# Enhancing Friction Pendulum Isolation Systems Using Passive and Semi-Active Dampers
## 2 Isolation System
### Semi-Active FPS  
A single-degree-of-freedom model of a structure with an isolator system formed by an FPS and a viscous damper is adopted as shown Figure 3, in which \(m_{p}\), \(c_{p}\) and \(k_{p}\) are the mass, damping coefficient and stiffness of the structure, \(x_{p}\) and \(x_{a}\) are the structure and bearing plate displacement relative to the base movement and \(\ddot{x}_{\ddot{g}}\) is the ground acceleration. Thus, the study carried out in this paper will consider three models: (i) the structure with an FPS modelled by \(k_{a}\), \(F_{f}\) (Equations (2) and (3)) and a bearing plate mass, \(m_{a}\), (ii) the structure with an FPS and a viscous damper with a damping coefficient \(c_{a}\), denoted as FPS+VD, and (iii) the structure with an FPS and a time-varying damping coefficient which is updated following a particular semi-active control law, denoted as FPS+SD. Figure 4 illustrates these aforementioned cases.  
The equation of motion of a structure modeled as a single-degree-of-freedom system subjected to a base movement is:  
\[m_{p}\ddot{x}_{p}+c_{p}(\dot{x}_{p}-\dot{x}_{a})+k_{p}(x_{p}-x_{a})=-m_{p}\ddot {x}_{\ddot{g}}, \tag{4}\]  
where \" \(\cdot\) \" and \" \(\cdot\) \" indicates velocity and acceleration, respectively. Note that Equation (4) is the same for the three cases presented in Figure 4.  
The equation of motion of the bearing plate considering a mass of \(m_{a}\) is now derived for the three cases. For the case of a single FPS isolator, this equation is as follows:  
\[m_{a}\ddot{x}_{a}+F_{f}(sign(\dot{x}_{a}))+k_{a}x_{a}-c_{p}(\dot{x}_{p}-\dot{x} _{a})-k_{p}(x_{p}-x_{a})=-m_{a}\ddot{x}_{\ddot{g}}, \tag{5}\]  
for the FPS+VD is:  
\[m_{a}\ddot{x}_{a}+c_{a}\dot{x}_{a}+F_{f}(sign(\dot{x}_{a}))+k_{a}x_{a}-c_{p}( \dot{x}_{p}-\dot{x}_{a})-k_{p}(x_{p}-x_{a})=-m_{a}\ddot{x}_{\ddot{g}}, \tag{6}\]  
in which \(c_{a}\) is a fixed damping coefficient of the viscous damper, and finally, for the FPS+SD, the equation takes the following form:  
Figure 4: Considered Models.  
Figure 3: Schematic model of an isolated-structure with a friction pendulum system (FPS) and a damper.  
\[m_{a}\ddot{x}_{a}+c_{semi}\dot{x}_{a}+F_{f}(sign(\dot{x}_{a}))+k_{a}x_{a}-c_{p}( \dot{x}_{p}-\dot{x}_{a})-k_{p}(x_{p}-x_{a})=-m_{a}\ddot{x}_{g}, \tag{7}\]  
in which \(c_{semi}(t)\) is updated continuously following a control law. Two semi-active control laws are proposed to be studied in this paper: phase control and energy-predictive-based control law.
## 3 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **LP** & **NST** & **LP-SR** & **FusionGAN** & **Proposed** \\ \hline
1 & 0.6500 & 0.7718 & 0.6699 & 0.5156 & **0.9963** \\ \hline
2 & 0.6620 & 0.9232 & 0.6685 & 0.6199 & **1.1875** \\ \hline
3 & 0.6651 & 0.7690 & 0.6993 & 0.6375 & **1.1042** \\ \hline
4 & 0.6088 & 0.7561 & 0.6281 & 0.5929 & **1.0091** \\ \hline
5 & 0.8627 & 0.9899 & 0.9435 & 0.6675 & **1.1277** \\ \hline
6 & 0.7850 & 0.9653 & 0.8214 & 0.6810 & **1.1944** \\ \hline
7 & 0.6903 & 0.8971 & 0.6923 & 0.5714 & **1.2731** \\ \hline
8 & 0.8110 & 1.0376 & 0.9095 & 0.6186 & **1.2405** \\ \hline
9 & 0.6954 & 0.6459 & 0.6968 & 0.5432 & **0.8411** \\ \hline
10 & 0.7083 & 1.1616 & 0.6935 & **1.2347** & 1.2102 \\ \hline
11 & 0.6955 & 0.7798 & 0.7026 & 0.4981 & **0.9608** \\ \hline
12 & 0.7036 & 0.8571 & 0.7122 & 0.7240 & **1.0765** \\ \hline
13 & 0.7189 & 0.7677 & 0.7398 & 0.6632 & **0.9949** \\ \hline
14 & 0.675 & 0.7842 & 0.6979 & 0.5911 & **0.8309** \\ \hline
15 & 0.7891 & 0.8699 & 0.7959 & 0.6262 & **1.0216** \\ \hline
16 & 0.7700 & 0.9220 & 0.7949 & 0.7075 & **1.1852** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The visual information fidelity fusion (VIFF) between proposed and compared methods. The bold represents the best value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **LP** & **NST** & **LP-SR** & **FusionGAN** & **Proposed** \\ \hline
1 & 0.3058 & 0.3343 & 0.2972 & 0.2452 & **0.5037** \\ \hline
2 & 0.2888 & 0.3258 & 0.2855 & 0.2525 & **0.5203** \\ \hline
3 & 0.2962 & 0.3229 & 0.2851 & 0.2548 & **0.5122** \\ \hline
4 & 0.3555 & 0.3821 & 0.3381 & 0.3038 & **0.5323** \\ \hline
5 & 0.3501 & 0.3752 & 0.3451 & 0.2977 & **0.5412** \\ \hline
6 & 0.3114 & 0.3447 & 0.3030 & 0.2646 & **0.5212** \\ \hline
7 & 0.3227 & 0.3558 & 0.3145 & 0.2537 & **0.5402** \\ \hline
8 & 0.2877 & 0.3254 & 0.2847 & 0.1620 & **0.5330** \\ \hline
9 & 0.3176 & 0. To compare the computationally intensive, we list the operation time of processing a pair of images in Table 7. Since FusionGAN runs in TensorFlow, it is not suitable to compare with other methods running in MATLAB. Our method is slower than LP-SR and LP, but it can still fuse the images in less than one second.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Pharmacology and Optogenetics

Table 2: Summary of different types of in-body antennas used in ingestible IBBD applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Antenna** & **Ref. Application** & **Frequency** & **Size (mm \(\times\) mm \(\times\) mm)** & **Substrate/Material** & **Gain (dB)** & **Bandwidth** & **SAR\({}_{\text{log}}\) mW/kg** \\ \hline \multirow{3}{*}{Spiral} & [100], Glucose sensing & 900 MHz & 3 \(\times\) 0.6 & Silicon substrate & \multirow{3}{*}{\(-\)30 dB} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & [111], Pharmacology and & & & & & & \\ \cline{1-1}  & optogenetics & 13.56 MHz & Dia = 5 mm & Polyimide & - & - & - \\ \hline Wire & [101], Cell over & 4.5 MHz & 2 \(\times\) 1 & AWG 47 (0.0355 mm) & - & 0.63\% & 0.0226 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of different types of in-body antennas used in injectable IBBD applications.
## 3 Proposed Network Model and the Problem Formulation
The mathematical model for the formulation of the modified ring network is discussed in the subsequent sections that includes four phases of operations, as the deployment and the cluster formation, followed by the cluster head selection with the intra and inter cluster communication. The symbols and notations are mentioned in Table 1.