esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 6 RL-ChoA Engineering Example Application Analysis
### Welded Beam Design Problem  
The structure of the welded beam [59] is shown in Figure 7. The goal of structural design optimization of welded beams is to minimize the total cost under certain constraints. The design variables are: \(x_{1}(h)\), \(x_{2}(l)\), \(x_{3}(t)\) and \(x_{4}(b)\), and the constraints are the shear stress \(\tau\), the bending stress \(\sigma\) on the beam, the buckling critical load \(Pc\) and the tail of the beam, and beam deflection \(\delta\). The objective function and constraints are as follows:  
Consider:  
\[\mathbf{x}=[x_{1}\ x_{2}\ x_{3}\ x_{4}]=[h\ l\ t\ b];\] \[\text{Minimize}f(\mathbf{x})=1.10471x_{1}^{2}x_{2}+0.04811x_{3}x _{4}(14+x_{2}). \tag{23}\]  
Subject to:  
\[\begin{split}& g_{1}(\mathbf{x})=\sqrt{(\tau^{\prime})^{2}+2\tau^{ \prime}\tau^{\prime\prime}\frac{x_{2}}{2R}+(\tau^{\prime\prime})^{2}}-\tau_{ max}\leq 0;\\ & g_{2}(\mathbf{x})=\frac{6 PL}{x_{3}^{2}x_{4}}-\sigma_{max}\leq 0 ;\\ & g_{3}(\mathbf{x})=x_{1}-x_{4}\leq 0;\\ & g_{4}(\mathbf{x})=0.10471x_{1}^{2}+0.04811x_{3}x_{4}(14+x_{2}) -5\leq 0;\\ & g_{5}(\mathbf{x})=0.125-x_{1}\leq 0;\\ & g_{6}(\mathbf{x})=\frac{4 PL^{3}}{Ex_{3}^{3}x_{4}}-\delta_{max} \leq 0;\\ & g^{\prime}(\mathbf{x})=P-\frac{4.013 Ex_{3}x_{4}^{3}}{6L^{2}} \Bigg{(}1-\frac{x_{3}}{2L}\sqrt{\frac{E}{4G}}\Bigg{)}\leq 0;\end{split} \tag{24}\]  
where  
\[\tau^{\prime}=\frac{P}{2x_{1}x_{2}},\tau^{\prime\prime}=MRJ,M=P(L+ \frac{x_{2}}{2}),\] \[J=2\Bigg{\{}\sqrt{2}x_{1}x_{2}\Bigg{[}\frac{x_{2}^{2}}{12}+ \bigg{(}\frac{x_{1}+x_{3}}{2}\bigg{)}^{2}\Bigg{]}\Bigg{\}},\] \[R=\sqrt{\frac{x_{2}^{2}}{4}+\bigg{(}\frac{x_{1}+x_{3}}{2}\bigg{)} ^{2}},P=6000 lb,\] \[L=14 in,E=30\times 10^{6}psi,G=12\times 10^{6}psi,\] \[\tau_{max}=13,600 psi,\sigma_{max}=30,000 psi,\] \[\delta_{max}=0.25 in.\]  
with  
\[0.1\leq x_{1},x_{4}\leq 2.0\ and\ 0.1\leq x_{2},x_{3}\leq 10.0\]  
Table 10 shows the average value of each optimization result of the five algorithms for solving the welded beam design problem. Each algorithm is independently run 50 times, the maximum number of iterations is 1000, and the population size is 30.  
It can be observed from Table 10 that RL-ChoA shows superior performance on the optimization problem of welded beams. Although the single variable is not optimal, it is optimal in terms of the total cost.
## 3 Results and Discussion
### Angle Variation
The results indicate that the inclusion of angles other than 90\({}^{\circ}\) [-] have a positive impact on accuracy (error) for measuring the depth, width, and volume of potholes. However, this impact is low compared to the time-to-time effort to acquire this extra number of images, and the processing time is almost double. Likewise, there is no specific oblique angle or clear trend to ensure a reduction of the error. Therefore, working with vertical angles in this method remains the most efficient option for the  
Figure 7: Average relative error behavior of volume for different severity levels. (**a**) Low severity; (**b**) mean severity; (**c**) high severity. acquisition of images with application in measurement of potholes on pavements, and in the same way remains consistent as a simple and fast method against more accurate technologies. Therefore, from now on, combinations with 90\({}^{\circ}\) angle will be discussed without any other variation or combination.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 3 Mac Layer in IEEE 802.15.7 Standard
### User Association  
The most studied part of the MAC layer are back-off mechanisms (as the literature suggests) which limit the data-rate but improving the reliability of the network. Nonetheless, in networks with high node mobility, it is fundamental to evaluate the association process and back-off algorithms in the same simulation.  
The association process begins when an un-associated node detects a beacon. After a random period, it sends an association request during CAP. The coordinator immediately sends an ACK frame to inform that the request has successfully arrived. At the same time, the coordinator sends a request to higher layers to determine source availability. When the ACK frame arrives at the node, the _macResponseWaitTime_ counter is initialized. This time is longer than the ACK time because the resolution of the association request depends on several aspects such as scheduling algorithms or even the availability of network-related task time. When the request is resolved, a response arrives at the coordinator. It adds the new node's address to its associated nodes' table and sends the corresponding association response command to the node. The node stores its address and starts to use the network. The association process is described in Figure 3.  
Figure 2: The back-off algorithm during the Contention Access Period (CAP) of standard IEEE 802.15.7.  
Besides, the coordinator expels users using a disassociation notification when it is not possible to support communication anymore or when there is a notification from an upper-layer. The standard does not define how the coordinator must physically communicate with upper layers or the specific frame formats.
## 5 SSFXG Architecture and Basic Interactions
### NW Service Provisioning
#### 5.2.1 NW Subscriber Requesting a Wireless Communication Service  
As explained in Step 1 Figure 3, during the service request and synchronization phase, the process begins with the NW subscriber sending a service request message to a nearby NW resource. This request serves as an announcement of the subscriber's presence and a  
Figure 3: Subscription identity verification stage.  
declaration of intent to access NW services. Upon receiving the service request, the NW resource coordinates with its associated NW provider to facilitate secure communication and resource allocation.  
The NW provider responds by issuing a temporary NW warrant to the NW resources within its jurisdiction (Step 2 Figure 3). This NW warrant is a critical component of the communication process, as it provides the necessary public keys for authenticating and encrypting data exchanges between the NW resource and the NW provider. The warrant ensures that all communications within the NW are secure, preventing unauthorized access or tampering.  
Upon receiving the NW warrant, the NW subscriber must validate its authenticity and validity. This verification process involves checking multiple parameters, such as the timestamp of issuance, digital signatures from the NW provider or broker, and the warrant's expiration date. These checks ensure that the warrant is legitimate and valid for secure communication (Step 3 Figure 3).  
During this phase, the NW subscriber also generates verification parameters that will be used to confirm its identity within the NW further. This is achieved by concatenating the concealed \(DID_{SUB}\) (a secure version of the subscriber's decentralized identifier) with the hash value of the subscriber's NW identity subscription numbers, referred to as \(HASHID\) (Step 3 Figure 3). These concatenated values form a unique verification signature that can be used to securely identify and authenticate the subscriber within the federated wireless communication NW.  
This process establishes a secure and reliable connection between the NW subscriber and the NW, ensuring that all subsequent communications and interactions are both authenticated and encrypted. By integrating mechanisms such as NW warrants, public key cryptography, and verification parameters, the NW maintains a high level of security and trust among all participating entities.
## 2 Water Properties
### Dielectric Constant and Ionic Product  
Water's dielectric constant as well as its ionic product determine its behavior as a solvent for the ionic dissociation of salts. Water's dielectric and dissociation constant diagrams show how its dielectric constant decreases and its dissociation constant increases as temperature and density increase [20]. This causes water above its critical point to behave more like a non-polar solvent than a polar solvent, considerably affecting hydrolysis of the metal salt, and generating an oversaturation and nucleation of particles (see in more detail in Section 3).  
The fact that, under these conditions, water turns into a non-polar solvent opens a promising field of study based on the generation of hybrid metal-organic particles which could be mainly applied to medical purposes.  
In order to sum up and as a quick guide of the changes that water properties show as conditions vary, Table 1 shows the specific values of water physicochemical properties depending on the hydrothermal operating conditions applied to the precipitation of micro- and nanoparticles.  
It has been clearly established the critical point of water as the point at which water turns into a supercritical state or vice-versa, and it is of the utmost importance for hydrothermal synthesis processes. Nevertheless, it is also important to point out that these data correspond to pure water and that, generally, concentrated solutions are used for hydrothermal synthesis, which would make the critical point shift to a greater or lesser extent [17].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Environmental** \\ **Conditions (25 °C** \\ **and 1 bar)** \\ \end{tabular} & \begin{tabular}{c} **Subcritical** \\ **Conditions (325 °C** \\ **and 200 bar)** \\ \end{tabular} & \begin{tabular}{c} **Supercritical Conditions** \\ **(525 °C and** **250 bar)** \\ \end{tabular} & \
\begin{tabular}{c} **Conditions (325 °C and** **250 bar)** \\ \end{tabular} &
\begin{tabular}{c} **Supercritical Conditions** \\ **(525 °D and** **250 bar)** \\ \end{tabular} \\ \hline Density (kg/m\({}^{3}\)) & 997.1 & 679.8 & 83.5 & 64.8 \\ Viscosity (\(\mu\)Pa-s) & 890.1 & 80.5 & 31.6 & 36.4 \\ Specific heat (J/g-K) & 4.2 & 6.0 & 3.5 & 2.0 \\ Dielectric constant & 78.4 & 18 & 2.5 & 2.5 \\ Thermal conductivity & 0.6 & 0.5 & 0.01 & 0.1 \\ (W/m-K) & 0.6 & 0.5 & 0.01 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water Properties under Different Pressure (P) and Temperature (T) levels.  
Figure 7: Water thermal conductivity variation with temperature under different pressure levels. (Plotted according to the data provided by the NIST (www.nist.gov; 10 June 2020)).
## 3 Obstacle Avoidance Scenario
### Avoidance Plan and Control Architecture  
The control architecture for avoiding obstacles for a UAV describes how the avoidance actions will be organized from perceiving the environmental data. Three types of control architectures meet the obstacle avoidance requirement. These are reactive control, deliberative planning, and hybrid integration control [36]. The functional diagram is shown in Figure 8. Typically, the flying mission and obstacle avoidance operation are separate works. This control architecture is called reactive control, where the UAV will start flying according to the mission plan, and during the mission, when it senses the obstacle locally, it will avoid the obstacles and follow real-time sensor data.  
In the deliberative planning architecture, at first, the UAV starts flying according to the flight plan, then during operation, it senses the working environment and updates the map model. After the primary flying, the final map will be determined, and it will generate the optimal sequence of the collision-free path. However, this method takes a longer time to determine the definitive plan, and without accurate positioning data, this method will not be sufficient.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Obstacle**} & **In Flying** & **Removable/** & \multirow{2}{*}{**Solution**} & \multirow{2}{*}{**Comment**} \\  & **Zone** & **Replicable** & & \\ \hline Other drones & Yes & Yes & - & - \\ \hline Human & Yes & Yes & - & - \\ \hline Other moving non-living things & Yes & Yes & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Using reactive control and deliberative planning, [PERSON] et al. [98] made a hybrid architecture where the execution layer will be in three parts, namely, the deliberative layer, control execution layer, and reactive control layer, which are also shown in Figure 8c. Here, the deliberative layer generates an optimal collision-free plan, which is then transferred to the reactive layer to generate the UAV's control action. The execution layer connects the other two layers.
## 2 Sensor Design
### Number of Electrodes and Their Arrangement  
The number of electrodes proper to a given application depends on the geometry of the sample and its physical parameter. For some investigations, like proximity or displacement measurement application, in which there is a direct relationship between capacitance measurements and the distance of a specimen, a single sensing element, like an annular sensor is often enough to infer the proximity of the specimen from capacitance measurement. For more complex situations, such as imaging or NDT applications, an array of electrodes would be more adequate. In addition, the arrangement of the electrodes and the available space in the system to place the electrodes must be considered [9; 16].
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 4 Online Resource Allocation Scheme
### Problem-Solving  
In this subsection, we introduce how to solve the Bellman Equations (23) and (24). First, we define the sum power of satellite \(j\) consumed in the time-slot \(k\) as \(T^{j}_{k}=\sum\limits_{m,l}p^{j}_{m,nk}\). Then, we let the available power \(B^{j}_{k}\) and the consumed power \(T^{j}_{k}\) be discretized to finite sets:  
\[\begin{split} B^{j}_{k}\in\mathcal{B}^{p}&=(\mathcal{ B}_{1},\ldots,\mathcal{B}_{p}),\forall k\\ T^{j}_{k}\in\mathcal{T}^{Q}&=(\mathcal{T}_{1}, \ldots,\mathcal{T}_{Q}),\forall j,\forall k\end{split} \tag{25}\]  
where \(\mathcal{B}^{p}\) is a finite set containing \(P\) elements, and \(\mathcal{T}^{Q}\) is a finite set containing \(Q\) elements. Finally, the optimization for time-slot \(K\) in (23) is given by  
\[\begin{split}\mathcal{F}_{K}(B_{K})&=\max_{\mathbf{A }_{K},\mathbf{P}_{K}}\mathbb{E}\left[\sum_{l,m,n}B_{sc}\log_{2}\Bigl{(}1+ \gamma^{j}_{m,nK}\Bigr{)}\right]\\ s.t.&\quad T^{j}_{K}\leq B^{j}_{K},\forall j,\quad B ^{j}_{K}\in\mathcal{B}^{p},T^{j}_{k}\in\mathcal{T}^{Q},\forall j\end{split} \tag{26}\]  
For the Bellman equation in time-slot \(k\), its form is transformed into  
\[\begin{split}\mathcal{F}_{k}(B_{k})&=\max_{\mathbf{A }_{k},\mathbf{P}_{k}}\mathbb{E}\left[\sum_{l,m,n}B_{sc}\log_{2}\Bigl{(}1+ \gamma^{j}_{m,nk}\Bigr{)}+\mathcal{F}_{k+1}(B_{k+1})\right]\\ k&=K-1,\ldots 1\\ s.t.&\quad T^{j}_{k}\leq B^{j}_{k},\forall j,\quad B ^{j}_{k}\in\mathcal{B}^{p},T^{j}_{k}\in\mathcal{T}^{Q},\forall j\end{split} \tag{27}\]  
Because the available power \(B^{j}_{k}\) and the consumed power \(T^{j}_{k}\) are all in the finite set, we can solve for the optimal value through traversal.  
Algorithm 3 shows the complete process of the online resource-allocation scheme. We divide it into two phases: planning and transmission. In the planning phase, we calculate the optimal value \(\mathcal{F}_{k}(B_{k})\) for all time slots. Note that \(\mathcal{F}_{k}(B_{k})\) represents the maximized expected throughput after the \(k\)th time-slot. Hence, in the transmission phase, when we need to balance the current consumed resource and the future consumed resource in problem (22), we can solve it by replacing the mean term, which represents the future expected throughput, with \(\mathcal{F}_{k}(B_{k})\).  
For the online algorithm, in the planning phase, the computational complexity is mainly focused on the calculation of Bellman equations (19) and (20). We let \(f_{B}\) denote the complexity of calculating one Bellman equation for a state \(B_{k}\), and we calculate the Bellman equations for all possible \(P\) states at each time slot and for all the \(K\) time slots. Therefore, the complexity in the planning phase is \(\mathcal{O}(KPf_{B})\). Then, in the transmission phase, we let \(f_{c}\) denote the complexity of the convex optimization in (16), and the complexity in the transmission phase is \(\mathcal{O}(Kf_{c})\). Finally, the complexity of the online algorithm is \(\mathcal{O}(KPf_{B}+Kf_{c})\).
## 4 Experiments and Analysis of Results
### Reliability Verification of the DRL-FVRestore
This is because trained agent perform restoration processing is based on several steps, namely: 1. the state of the image, 2. selecting a damaged restoration task to restore the image if there is a damaged image, 3. performing a deblurred restoration task to restore the image if there is a blur, and, finally, 4. selecting an enhanced method for image texture enhancement in appropriate amounts based on the quality and texture detail of the image. Figure 13: Output images and their PSNR values for each stage of the 4-stage DRL-FVRestore restoration process. In contrast, the EER of just a single de-motion blur restoration method with images processed by de-Gaussed blur restoration for matching recognition was not as effective as the DRL-FVRestore method, although its EER was also reduced. The reason for this is that the randomly added image problems in the test set contain missing images, which significantly adversely affect the detail of the image features, thereby causing a reduction in performance. In addition, we added experiments based on feature point matching and plotted the intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of different algorithms to analyze the reliability of the algorithms in this paper, as shown in Figures 15 and 16. It can be seen that the images restored by DRL-FVRestore are more rightward in class probability density distribution plots and feature point histogram statistical plots than single restoration method. The restored images are richer in detail than the single restoration method.The DRL-FVRestore method proposed in this paper selects the appropriate restored method according to the state of the image and can be restored continuously, which is beneficial for solving finger vein images with multiple problems. Figure 14: The ROC curves of various image restored methods on the different public datasets. Figure 15: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of DRL-FVResotore on the on the MMCBNU and SDUMLA-FV datasets. Figure 16: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of DRL-FVResotore on the UTFVP and FV-USM datasets.
# Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction
## 3 Methodology
### Overall System  
Figure 1 shows the system configuration of a modified NAS. It consists of a GA generator and a group of DNN generators and evaluators. The GA generator has a role in the control population. It creates new generations of individuals that present ANN architectures. The genetic controller is divided into a selector, genetic operator, and gene pool. The selector selects appropriate individuals to survive by applying the fitness function. The architecture designer should configure the suitable fitness function based on the design goal.  
The genetic operator has three operations: (i) a cross-over operation that blends information of parents to make various but parent-like offspring; (ii) a mutation operation that changes a random gene to differentiate it from the parents; and (iii) a non-disjunction operation [12] that makes two offspring where one has less information and the other one has more information.  
The DNN generator designs neural architectures from chromosomes in individuals and evaluates them. The deep neural architecture generator interprets chromosomes with a node checker and link checker. Every individual has several chromosomes, which mean layers and links. However, not all individuals are runnable and learnable. Some have broken connections, and some have unmatched inputs/outputs. The model checker and link checker classify these inappropriate neural architectures. The DNN architecture evaluator implements training and testing with deep learning. Users must prepare preprocessed training data and testing data. Next, fitness values, which are calculated by the DNN generators and evaluators, are sent to the GA generator.
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
###### Abstract  
This paper describes a novel feed system for compact antipodal Vivaldi antenna arrays on a single layer of substrate integrated waveguide (SIW) by using SIW H-plane right-angled power dividers. The proposed antenna systems are composed of a Vivaldi array and an H-plane right-angled corner power divider which includes an over-moded waveguide section. Based on the number of antennas in the Vivaldi array, mode converter sections at K-band and Ka-band frequencies are designed, fabricated, and measured when feeding Vivaldi antenna arrays with two, three, and four antennas. Right-angled SIW power dividers are employed to obtain controllable phase distribution over the output ports which consequently controls the beam shapes of the systems. The phase relationships in the output ports are varied to obtain different pattern directions for different applications. The two-way divider system with 180-degree phase difference and three-way divider system are fabricated and measured; simulation results are presented for other designs. The measured results are in good agreement with simulations which confirms the design approach. All systems achieve good performance and meet all design goals including a return loss better than 10 dB in the operating bandwidth, gain higher than 8 dB for all systems, and radiation and polarization efficiencies higher than 80% and 98%, respectively.  
Keywords:Vivaldi antenna array; substrate integrated waveguide; right-angled power dividers +
Footnote †: journal: Applied
## 3 Measurement Results and Discussion
### Summer Season Experiments
#### 3.1.2 Measured Images with Visible, Near-Infrared, and Far-Infrared Cameras  
Figure 8 shows the images of the first measurement at each location. For each camera, image areas with low intensity of radiation are shown in black, and areas with high intensity of radiation are shown in white. It is shown that the heat load of radiation from the visible wavelengths was largely due to direct sunlight, while the heat load from the ground was larger in the far-infrared wavelengths. A detailed analysis of which structures had a  
Figure 6: Overview of how to project onto a virtual surface.  
significant impact on the heat load on the human body in each of the measured images is presented in the next section.