esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Calculations and Analysis
### Profile of an Optical Lattice Potential  
Figure 1 shows the real and imaginary parts of the Gaussian PT symmetric optical lattice. The real part represents the refractive index, which is symmetrical and is the highest at \(x=0\), which restricts the beam. The imaginary part represents the gain and loss, and exhibits odd symmetry. The maximum absolute value occurs near \(x=0\) (approximately at \(\pm 0.7\)). As the distance from the center increases, the imaginary part gradually approaches 0. In the region where \(x<0\) and near the center of the waveguide, loss is observed, while in the region where \(x>0\), gain is observed. As the distance from the center increases, both the gain and loss gradually approach 0. When the modulation frequency \(w\) is large (Figure 1a), the modulation range of the potential function is narrow, only covering the main lobe of the Airyprime beam, and the beam is less affected. When the modulation frequency \(w\) is small (Figure 1b), the modulation range of the potential function is wide, meaning that it can cover the entire Airy beam, and the imaginary part shows gain and loss in different directions. By comparing Figure 1a with Figure 1b, it can be seen that increasing the absolute value of \(w\) will narrow the modulation range of the PT symmetric optical lattice, with this effect mainly acting on the central region of the beam; reducing the absolute value of \(w\) will expand the modulation range, and the influence range of the beam will increase. In addition, when \(w\) is negative, the symmetry of the gain and loss of the imaginary part is reversed, which affects the direction of the gain distribution of the medium. That is to say, whether the modulation  
frequency \(w\) is positive or negative not only affects the range of modulation but also changes the distribution mode of the gain and loss.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Ubiquitous Networking for the Internet of Things  
As the demand for mobile services increases, so do mobile wireless networks need to increase their capacities and density of their networks especially to support IoT devices. However, this also presents new technical challenges related to the increasing density of wireless communications and networks. To meet this growing demand, we need to select appropriate methods to overcome these limitations, which human mobility surveys that track people do not call into question.  
Limitations on the availability of internet access in remote areas contrast with the clear abundance of pervasive broadband internet access in urban areas. This creates what can be called a digital divide, that disrupts the collection of data, which can be used for human mobility studies. The major problem is that the cost of operating such high availability internet services in these areas is rarely economically sustainable. Figure 1 provides a high-level approximation of wireless coverage in Europe.  
There are gaps in coverage that are shown by this high-level approximation. Nevertheless, actual coverage may differ from the above map and may be affected by terrain, weather, network changes, buildings, construction work, signal strength, high-usage periods, and other factors. To overcome these challenges, new broadband wireless access technologies to operate in both urban and rural areas are required. These technologies should address important use cases, including applications for human mobility tracking or human mobility forecast tasks. However, recent research has explored this subject,  
Figure 1: Areas covered by Cellular and WiFi networks (based on [14]).  
and there are several fundamental and technological challenges that should be considered in urban or remote and rural scenarios.
With the development of power electronics technology and motor control theory, the torsional vibration caused by the flexibility of the robot joints can be regarded as motion disturbance. Moreover, the active control of the system torsional vibration from the electrical perspective is more practical than from the mechanical perspective. On the other hand, for the rigid-flexible coupling dynamic system such as the flexible-joint robots, there is a contradiction between the dynamic quality and the stability precision in the control effect when the parameters of the feedback controller are determined, which is a common problem of the feedback controllers. Therefore, in order to effectively eliminate the influence of model parameters uncertainties on the vibration control effect and to solve the contradiction between the dynamic quality and stability accuracy of the existing torsion feedback controller for the flexible-joint robot, the flexible joint of the robot is equivalent to an elastic torsion spring for establishing the system electromechanical-coupling dynamics model in this paper. Then the unknown parameters of the flexible-joint robot system are identified based on the actual measurement results of the system inputs and outputs. Finally, according to the suppression requirements of the joint torsional vibration, an adaptive fuzzy inference machine is designed to adjust the parameters of the PID controller for effectively attenuating the elastic torsional vibration of the flexible-joint robot. The structure of this paper is organized as follows. The electromechanical-coupling dynamics modelling of the flexible-joint robot system is given in Section 2. Section 3 presents the system parameter identification. The main contribution of this paper is introduced in Section 4, including the design of the control strategy. Section 5 discusses the simulation and experiment results. Finally, conclusions are drawn in Section 6.
## 6 Evaluation
### Experimental Results
Figure 9: Experiment results for the 40-node scenario show ACTOR outperforming the benchmarks at different data intervals showing: **(a)** Packet Delivery Ratio, **(b)** Average transmission power, and **(c)** Routing overhead. We also performed the experiments in a scenario that consists of 11 clients and 1 server providing a sparse topology, as illustrated using orange circles in Figure 6(a). The measurements are presented in Table 4. Another significant aspect is keeping the convergence time of the routing protocol short. The number of parent switches can be an indication of the convergence time of the routing protocol. The results also showed that ACTOR did not increase the hops distance of the nodes, reduced the transmission power, and significantly reduced the number of parent switches. Overall, the experimental results are in line with the simulations, as they both show the advantages of tuning the transmission power when wireless links are unreliable and network is dense.
###### Abstract  
Objective: The aim of this study was to compare the characteristics of periodontal ligament stem cells derived from supernumerary teeth (sPDLSCs), cultured using a three-dimensional (3D) method and a conventional two-dimensional (2D) method. Methods: The morphology, viability, and osteogenic differentiation of the cells were analyzed. In addition, gene expression was analyzed by RNA sequencing, to characterize the functional differences. Results: The diameter of the 3D-cultured sPDLSCs decreased over time, but the spheroid shape was maintained for 7 days. The osteogenic differentiation was similar in the 2D and 3D. The gene expression related to the extracellular matrix (7.3%), angiogenesis (5.6%), cell proliferation (4.6%), inflammatory response (3.7%), and cell migration (3.5%) differed (\(p<0.05\)). Conclusions: Within the limitations of this study, sPDLSCs varied in formation and function, depending on the culture method. In future, it is necessary to study tissue engineering using the advantages of 3D culture and the fewer ethical problems of supernumerary teeth.  
supernumerary teeth; periodontal ligament stem cell; three-dimensional culture; morphology; osteogenic differentiation; RNA sequencing +
Footnote †: journal: Applied Sciences
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 5 Results
### Line of Sight  
Due to the number of bits missed during the IFG (indicated by the red area in Figure 16) and the ones lost during the transmission itself, the camera can never receive 100% of the transmitted data. In fact, while the percentage of received bits of the demodulation methods remains relatively stable, evolving between 87 and 90%, ZEROES tends to lose more data as the distance increases from 60 cm, receiving only 82.2% of the transmitted data at 100 cm. This result difference is explained by the fact that ZEROES deliberately does not consider the bits identified as incorrect in order to minimize transmission errors.  
Indeed, while the BER of the demodulation methods tends to increase as a function of distance, except for BPBPLFT, the ZEROES method outperforms the others in the received errors under a distance of 75 cm, at a point where BPBPLFT reaches same performance. In order to better compare the methods' performance, a maximum BER of \(10^{-3}\) (indicated by the red dashed line in Figure 17) serves as reference since it ensures error-free transmission when employing traditional error-correcting codes. Thus, of all the implemented methods, ZEROES is the only one reaching a BER of \(10^{-3}\) at a distance of about 50 cm from the LED source (Figure 17).  
Figure 16: Received bits of demodulation methods in LOS.  
However, in this case, only 87.3% of the transmitted data are received. In comparison with the BPBPLFT method at the same distance, 89.7% of the data are received but with a BER of 9.9 \(\times\) 10\({}^{-3}\). Therefore, the additional loss of 7.5% in the received bits introduced by ZEROES is largely compensated by receiving approximately 10 times less errors in the data.
## 2 Overview of the Proposed Neural Networks and Theoretical Background
### Long Short-Term Memory Network (LSTM)  
The LSTM is a developed type of RNN to solve the gradient vanishing problem. LSTM deals with both short-term and long-term memory and to make the computation efficient and simple, it uses the concept of gates. Unlike an RNN, the LSTM has three types of gates, including the input gate i, the forgetting gate f, and the output gate o. Figure 3 shows a general structure of an LSTM unit [65, 66]. The input gate determines what information is stored in the long-term memory. Through this gate, it filters out information from variables that are not useful. The forgetting gate decides which information in long-term memory can be deleted or retained by multiplying the incoming long-term memory by a forgetting vector produced by the current input and the incoming short memory. The output gate uses the current input, the newly computed long-term memory, and the previous short-term memory to generate a new short-term memory to be passed to the cell at the next time step. The output of the current time step can also be taken from this hidden state. The sigmoid function is chosen as the gating mechanism for the three gates due to its ability to produce an output ranging between zero and one. This characteristic influences the flow of information passing through the gates. Additionally, Ref. [67] demonstrates that in large networks, back-propagation learning utilizing antisymmetric activation functions like the hyperbolic tangent function can achieve quicker convergence compared to a similar process employing non-symmetric activation functions such as the sigmoid function [59]. Each part of the cell can be expressed as follows:  
* Calculating the gating units: \[\mathrm{i}_{k}=\sigma(\mathrm{W}_{\mathrm{x}i}x_{k}+\mathrm{W}_{ \mathrm{H}i}\cdot\mathrm{H}_{k-1}+b_{\mathrm{i}})\] (8) \[\mathrm{f}_{k}=(\mathrm{W}_{\mathrm{x}d}x_{k}+\mathrm{W}_{\mathrm{ hf}}\cdot\mathrm{H}_{k-1}+b_{\mathrm{f}})\] (9) \[\mathrm{o}_{k}=(\mathrm{W}_{\mathrm{x}o}x_{k}+\mathrm{W}_{\mathrm{ ho}}\cdot\mathrm{H}_{k-1}+b_{\mathrm{o}})\] (10)
* Updating memory unit: \[\mathrm{C}_{k}=\mathrm{f}_{k}\mathrm{C}_{k-1}+\mathrm{i}_{k}\tanh\left(\mathrm{ W}_{\mathrm{x}c}x_{k}+\mathrm{W}_{\mathrm{hc}}\cdot\mathrm{H}_{k-1}+b_{\mathrm{c}}\right)\] (11)
* Calculating the output of LSTM unit: \[\mathrm{H}_{k}=\mathrm{o}_{k}\tanh\left(\mathrm{C}_{k}\right)\] (12)  
Figure 3: Structure of the long short-term memory (LSTM).
# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 3 Results
### Generalization Ability Test of the Proposed Model  
To verify the generalization performance of the model, we evaluated our model on three public datasets: Plant-Village, CGIAR, and the Wheat Leaf Dataset; and we compared it with several state-of-the-art models. Plant-Village: a dataset of 54,306 plant leaf images was used to identify 20 diseases of 14 crops. CGIAR: a dataset of 1486 wheat leaf images was used to identify three wheat diseases. The Wheat Leaf Dataset is a dataset released on the Kaggle website, and it contains a dataset of 412 wheat leaf images. This dataset has three categories: healthy wheat leaves, stripe rust, and septoria. Table 8 shows that our model has a test accuracy of 99.74%, 96.70%, and 96.70% on the three public datasets of Plant-Village, CGIAR, and Wheat Leaf Dataset, respectively. This is enough to prove that our model has a strong generalization performance and achieves the best or comparable results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Param** & **FLOPs** & **Training** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Time (h)** \\ \hline MobileNetV1 & 94.41 & 94.42 & 94.47 & 94.49 & 2.59 & 0.33 & 1.52 \\ MobileNetV2 & 95.23 & 95.27 & 95.24 & 95.26 & 3.25 & 0.31 & 1.44 \\ MobileNetV3-Small & 95.34 & 95.44 & 95.42 & 95.43 & 2.54 & 0.06 & 1.36 \\ MobileNetV3-Large & 96.75 & 96.89 & 96.76 & 96.82 & 5.48 & 0.23 & 1.45 \\ EfficientNetb0 & 96.81 & 96.92 & 96.87 & 96.87 & 5.29 & 0.40 & 1.65 \\ IRCE & 98.76 & 98.77 & 98.81 & 98.79 & 4.24 & 0.84 & 1.34 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Lightweight model performance comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{Plant-Village [35]} & LF-CNN [36] & 98.93 & 95.61 & 97.16 & 96.65 \\  & ResNet50 & 92.51 & 90.34 & 92.16 & 91.78 \\  & VGG16 & 94.13 & 92.10 & 93.18 & 92.35 \\  & MobileNetV2 & 97.81 & 97.42 & 97.93 & 97.65 \\  & IRCE & 99.74 & 99.71 & 99.66 & 99.68 \\  & ResNet34 & 92.11 & 92.52 & 92.74 & 92.65 \\  & VGG19 & 94.40 & 94.52 & 95.31 & 94.82 \\ CGIAR [37] & EfficientNetb0 & 93.90 & 93.12 & 93.55 & 93.38 \\  & InceptionV3 & 95.72 & 95.32 & 95.71 & 95.42 \\  & IRCE & 96.70 & 96.70 & 96.70 & 96.70 \\  & MobileNetV3\_Large & 92.17 & 91.72 & 91.98 & 91.77 \\  & ResNet34 & 89.92 & 89.94 & 91.01 & 90.91 \\ Wheat Leaf Dataset & EfficientNetb0 & 94.61 & 94.32 & 95.21 & 94.62 \\  & InceptionresnetV2 & 96.32 & 96.21 & 96.45 & 96.32 \\  & IRCE & **96.70** & **96.80** & **97.10** & **96.95** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental comparison of three datasets.
## 3 Results and Discussion
### Simulated GPR Data
In order to further evaluate the accuracy of the reconstruction, the concept of Intersection Over Union (_IoU_) in object detection is introduced in this paper, namely, the ratio of the intersection and union between the actual volume and the reconstructed volume of the defect, as shown in Equation (14):  
Figure 8:1  
where \(V_{T}\) is the actual volume of the defect in the model, while \(V_{R}\) is the volume of the reconstructed defect. If they overlap perfectly, then _IoU_ is 1. Generally speaking, if the _IoU_ is greater than or equal to 0.5, the results can be considered to be relatively accurate [62]. Table 5 shows the _IoU_s of the above six simulated defects. According to the above data, it can be seen that the method proposed in this paper could realize the 3D reconstruction of road structural defects, such as cavities, cracks, and interfacial failures, accurately, and the _IoU_ could reach about 0.6. The larger the size of the diseases and the closer they were to the road surface, the higher the accuracy of the reconstruction results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model No. ** & **Calculated Center Coordinate (m)** & **Actual Center Coordinate (m)** \\ \hline
1 & (2.693, 1.006, 1.776) & (2.700, 1.000, 1.750) \\
2 & (2.693, 1.006, 1.743) & (2.700, 1.000, 1.725) \\
3 & (2.698, 1.005, 1.716) &
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 3 Antenna Prototype Fabrication and Measurement
### Measurement of Radiation Pattern  
The far-field radiation patterns of the proposed antenna were measured in an accredited antenna chamber [28]. Figure 11 shows the seven-layer phantom (with the antenna inside) mounted on a positioner. The latter was capable of 3D rotation (180\({}^{\circ}\) in elevation and 360\({}^{\circ}\) in azimuth). The z-direction shown in the figure corresponds to the end-fire direction where the aperture of the antenna points is. Figure 12a shows the measured 3D radiation pattern at the center frequency, 4 GHz. It shows that most radiation is pointed toward the zenith (z-direction) with a high front-to-back ratio (FBR) of 16 dB. Furthermore, Figure 12b,c present the measured 2D E-plane and H-plane patterns. They were normalized by the peak gain value and then compared with the simulated radiation patterns. Good agreements can be observed, implying the prototype fabrication and measurement procedures were valid. Figure 13 shows the measured realized gain in the z-direction at the frequency range of 2 to 6 GHz. We compared the measured realized gain of the conventional Vivaldi (see Figure 10 b) with that of the proposed Vivaldi (see Figure 10). The measured realized gain data was not steady in the bandwidth of interest, and it was approximately 2 dB lower than the simulation data (see Figure 5b); however, the gain improvement of the proposed design compared to the conventional Vivaldi was clearly observed. A gain improvement is observed because of the added vertical slots, which redirect more currents along the tapered slot as described in the simulation study, Figure 4. The average improvement was 2.68 dB throughout 3-5 GHz, which is similar to the improvement demonstrated in Figure 5b. Table 3 compares previously reported brain-implanted antennas and the proposed Vivaldi in terms of their operation frequency, size, and gain. It also includes the conventional Vivaldi's gain to highlight the gain improvement of the proposed Vivaldi.  
Figure 11: The seven-layer phantom containing the antenna prototype is mounted on the positioner inside the anechoic chamber.  
Figure 10: Comparisons of measured and simulated \(S_{11}\) of conventional and proposed Vivaldi antennas.
## 3 Path-Planning Approach
### Setting for Reinforcement Learning  
In this section, the parameters and variables for the TD3 algorithm to deal with the automatic berthing task were set as follows:  
* Observation space and state: The observation space and state were defined as the set of physical velocity, position, and orientation. The state vector \(s(x,y,\psi,u,v,\text{ and }r)\) includes the position \(x\),\(y\) as the element. The orientation \(\psi\) is the heading angle. The linear velocity is \(u\),\(v\) and the angular velocity is \(r\);
* Action: The control action includes the control input of the thruster (revolution of propeller) and steering (rudder angle) system. The action signal is continuous in the range \([-1,1]\), where \([-1,1]\) = \([-300,100]\) rpm represents the thrust system and \([-1,1]\) where \([-1,1]\) = \([-35,35]\) is the degrees for the steering system.
* Reward function: This plays a crucial role in the design of a reinforcement learning application. It serves as a guide for the network training process and helps optimize the model's performance throughout each episode. If the reward function does not accurately capture the objectives of the target task, the model may struggle to achieve desirable performance. \[r_{S}=r_{i}-r_{i-1}\] \[r=r_{Dist}+r_{LinVcl}+r_{AngVcl}+r_{Heading}\] \[r_{Dist}=-100\sqrt{x^{2}+y^{2}}\] \[r_{Heading}=-1000\left|\psi_{Target}-\psi\right|\] \[r_{LinVcl}=-2000\sqrt{u^{2}+v^{2}}\] \[r_{AngVcl}=-1000|r|\]  
In this paper, based on the boundary state, the weights 100, 1000, 2000, and 1000 were assigned to the weight of distance, heading, linear, and angular velocity, respectively. The reward value was described as the sum of the reward in each time step. It received a positive  
Figure 5: Satellite image of the Busan port.  
Figure 6: Concept of the path-planning algorithm.  
value if the state variable changed to the required value and vice versa. Furthermore, for the faster convergence of rewards in the first stage of the berthing task, the distance is more prior to the target state than the speed and heading. Thus, the reward function of distance is multiplied by the distance coefficients. It is (1.1-Distance coefficients) in the case of the reward function of the heading, resultant velocity, and yaw rate. The reward coefficients are described in Figure 7.
* Environment: The environment receives the input as the control input and the state then returns the ship's new state and the reward for this action. The environment function was built based on a maneuvering simulation that uses the MMG model as a mathematical simulation.
* Agent: The hyperparameters for the TD3 model were selected as follows: the number of hidden layers was set as two layers with 512 units for each. The learning rate for the actor and critic networks \(\alpha\) and \(\beta\) was set to 0.0001. The discount factor \(\gamma\) was 0.99. The soft update coefficient \(\tau\) was 0.005. The batch size was 128. This training process was set to 20,000/50,000 steps for the warmup of the model with the exploration noise set in Table 5.