esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Scenarios
#### 2.5.3 Action 3: Integration of Photovoltaic Panels (PV)  
The integration of renewable energy sources can suppose a significant profit, but in contrast to the previous two actions, an initial investment is required. However, over time, the savings can potentially be more significant and, on top of that, it can have multiple environmental benefits.  
Action 3 would imply the integration of PVs to reduce the dependence on the grid. In this case study, the energy demand of the separate units was considered as one combined demand (as in Action 2), which would be partially covered by the PV panels (the grid will provide the rest). The exportation to the grid of the energy excess produced by the PVs (surplus) is discussed considering the most common FiT (feed-in tariff) approaches, named as net metering and net billing [27].  
As the solar energy sector evolves rapidly, the efficiency, performance, and costs of PV technology regularly changes, that can be verified in a variety of reports such as those published by the InternationalRenewable Energy Agency (IRENA) [28] and the Fraunhofer-Institute for Solar Energy Systems (ISE) [29]. For that reason, we considered three scenarios based on parametric costs (\(\xi\)/Wp) issued by the National Renewable Energy Laboratory (NREL) [30], reflecting the aspect of the price of the PV panels, cPV, (which includes the module itself, the structure, and the installation costs):  
* Scenario A--low cost, represents 0.68 \(\xi\)/Wp;
* Scenario B--intermediate cost, represents 1.18 \(\xi\)/Wp; and
* Scenario C--high cost, represents 1.48 \(\xi\)/Wp.  
The maintenance costs (cM) in each of the three scenarios were 0.02 \(\xi\)/Wp/year.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments
### BS to UE Communications
#### 5.3.2 Constant Machines' Height  
A special case of interest is when the height of industrial machines is the constant and the same. In this case, for a given heights of BS, UE, and machines as well as for coordinates of UE, there are such coordinates at LoS projections at axes, \(x_{p}\) and \(y_{p}\), that the machines'height starts to be higher than the LoS height. These coordinates can be found by solving (4) and (5) with respect to \(x=H_{B}\). We have,  
\[x_{P}=l_{x}\frac{h_{A}-H_{B}}{h_{A}-h_{U}},\,y_{P}=l_{y}\frac{h_{A}-H_{B}}{h_{A}- h_{U}}. \tag{9}\]  
Now, the intensity of lines over \(x\)- and \(y\)-axes inducing machines that may potentially block the LoS path can be found as the ratio of segments, where the machines' height is always higher than the LoS path to the overall lengths of projections on axes, i.e.,  
\[\Lambda_{x}=\lambda_{x}\frac{l_{x}-x_{p}}{l_{x}},\,\Lambda_{y}=\lambda_{y} \frac{l_{y}-y_{p}}{l_{y}}, \tag{10}\]  
that need to be further substituted to (8).
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
## 2 Synthesis of Radiation Patterns of a Concentric Ring Antenna Array Using the Taguchi Method
### Design and Validation of Concentric Ring Antenna Arrays Using CST Microwave Studio  
#### 2.2.1 18-Element Antenna Array (\(N_{1}=4\), \(N_{2}=6\), and \(N_{3}=8\))  
The Table 10 presents optimal excitation values for several well-known optimization methods in the literature, such as Evolutionary Programming (EP) [26] and Firefly Algorithm (FA) [24], which are selected for comparison with the Taguchi method (see Figure 18). The best results are obtained using excitations optimized by the Taguchi method. The best results are defined as those that offer a radiation pattern with not only a reduced Side-Lobe Level (SLL) but also the best Half-Power Beamwidth (HPBW) of the main lobe, which is a crucial parameter for antenna array synthesis. For this validation, an 18-element concentric ring antenna array is considered. Figure 18a shows the structure of the concentric ring array with element counts of \(N_{1}=4\), \(N_{2}=6\), and \(N_{3}=8\) for the first, second, and third rings, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CCAA**} & \multirow{2}{*}{**Number of Elements**} & \multirow{2}{*}{**Uniform**} & \multirow{2}{*}{**EP [26]**} & \multirow{2}{*}{**FA [24]**} & \multirow{2}{*}{**Taguchi**} \\ \cline{3-6} \multirow{3}{*}{\(N_{1}\)} & & & & & \\ \cline{3-6}  & **1** & 1.0000 & 0.3416 & 0.7025 & **0.5417** \\ \cline{2-6}  & **2** & 1.0000 & 0.0496 & 0.1410 & **0.4623** \\ \cline{2-6}  & **3** & 1.0000 & 0.3242 & 0.6770 & **0.5417** \\ \cline{2-6}  & **4** & 1.0000 & 0.0283 & 0.1215 & **0.4623** \\ \hline \multirow{2}{*}{\(N_{2}\)} & **5** & 1.0000 & 0.5321 & 0.9999 & **0.4931** \\ \cline{2-6}  & **6** & 1.0000 & 0.2114 & 0.4349 & **1.0000** \\ \cline{2-6}  & \(7\) & 1.0000 & 0.1923 & 0.4084 & **0.8540** \\ \cline{2-6}  & **8** & 1.0000 & 0.4901 & 0.9999 & **0.4931** \\ \cline{2-6}  & **9** & 1.0000 & 0.1876 & 0.4076 & **1.0000** \\ \cline{2-6}  & **10** & 1.0000 & 0.1994 & 0.4305 & **0.8540** \\ \hline \multirow{2}{*}{\(N_{3}\)} & **11** & 1.0000 & 0.1204 & 0.2352 & **0.5184** \\ \cline{2-6}  & **12** & 1.0000 & 0.2555 & 0.4789 & **0.8743** \\ \cline{2-6}  & **13** & 1.0000 & 0.3527 & 0.7366 & **0.5000** \\ \cline{2-6}  & **14** & 1.0000 & 0.2450 & 0.4831 & **0.1893** \\ \cline{2-6}  & **1 Figure 18b illustrates the case of uniform excitations, where all elements are excited with equal amplitude and phase.  
Figure 18c presents the excitations optimized using Evolutionary Programming (EP).  
Figure 18d depicts the excitations optimized with the Firefly Algorithm (FA).  
Figure 18: Synthesis of 3D radiation patterns for an 18-element array (\(N_{1}=4\), \(N_{2}=6\), and \(N_{3}=8\)) at 2.45 GHz. **(a)** Structure of the concentric ring array (\(N_{1}=4\), \(N_{2}=6\), and \(N_{3}=8\)). **(b)** Uniform excitations. **(c)** Excitations with Evolutionary Programming (EP). **(d)** Excitations with Firefly Algorithm (FA). **(e)** Excitations with Taguchi method.
## 5 Experiments and Results
### Evaluation Metrics
#### 5.2.4 FID Score (Frechet Inception Distance)  
The FID score compares the distribution of generated images to real images in the feature space of a deep neural network, measuring the quality and variety of generated images. Lower scores indicate a better quality and similarity to the real images.  
\[\text{FID}=||\mu_{x}-\mu_{g}||^{2}+\text{Tr}(\Sigma_{x}+\Sigma_{g}-2(\Sigma_{ x}\Sigma_{g})^{1/2}) \tag{6}\]  
* \(\mu_{x}\) and \(\mu_{g}\) are the featurewise means of the real and generated images, respectively.
* \(\Sigma_{x}\) and \(\Sigma_{g}\) are the covariance matrices of the real and generated images, respectively.
* Tr denotes the trace of a matrix, which is the sum of the elements on its main diagonal.
## 2 Datasets
### InIRA  
The InIRA is a Ku-band spaceborne side-looking radar with incidence angles ranging from 2\({}^{\circ}\) to 8\({}^{\circ}\). It configures two antenna beams used for interferometric processing, which facilitate SSH measurement. It is also a synthetic aperture radar, affording high azimuth resolutions. Using interferometric and synthetic aperture technology, the InIRA can measure ocean SSH with a high spatial resolution in both the range and azimuth directions (30 m \(\times\) 30 m). The InIRA data used in this study were intermittently collected from December 2016 to October 2018 in different tracks. In preparation for the analysis and retrieval, each InIRA dataset was divided into images with a dimension of approximately 5 km. Then, the parameters used were extracted from each image, and collocated data were obtained through linear interpolation according to the center of the image.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 5 Parametric Analysis of Main and Sub\(-\)Frame Collisions  
### Influence of Collision on Structural Response  
The collision between the main and sub\(-\)frame was achieved by increasing the seismic wave acceleration to analyze the dynamic response under the collision of the main and sub\(-\)frame. Three different seismic waves, the El Centro wave, Taft wave, and artificial wave, were input in the X direction of the structure, and the peak acceleration was adjusted to 0.4 g. The collision stiffness in the line Elastic collision unit was set to \(5.0\times 10^{7}\) kN/m, used to simulate rigid collisions between the main and sub\(-\)frames.  
To facilitate the analysis of collision results, the dimensionless parametric gap ratio \(\gamma_{\rm G}\), defined in the following equation, is introduced in the studies in this section.  
\[\gamma_{\rm G}=\frac{\Delta_{\rm G}}{\Delta_{\rm max}} \tag{4}\]  
Figure 8: Vertical dynamic variation curve at the top of the mainframe: (**a**) Vertical displacement curve of main frame; (**b**) Vertical acceleration curve of the mainframe.  
Figure 9: Subframe dynamics variation curve: (**a**) Vertical acceleration curve of sub\(-\)frame; (**b**) Vertical displacement curve of isolation layer isolation layer.  
In the above equation, \(\Delta_{\rm G}\) is the spacing between the joints between the main and substructure, \(\Delta_{\rm max}\) is the minimum spacing between the joints to avoid collision between the main and substructure results under earthquake action. If \(\gamma_{\rm G}>1\), no collision will occur between the two, and if \(\gamma_{\rm G}<1\), a collision will occur. For the calculations in this section, \(\gamma_{\rm G}=0.2\) was chosen to investigate the influence of collision on the structural response, and \(\Delta_{\rm max}=300\) mm was defined.  
The main factors affecting the collision of the main and sub-frames in a mega-sub isolated structure are the seismic intensity and direction, the spacing between the main and sub-frames, and the height of the sub-frames. The influences of changes in these parameters on the collision response of the main and sub-frames are analyzed below.
## 5 Structural Properties of the Electrodeposited Nickel Parts  
The characteristics of the initial surface (i.e., the first layers of deposition) and the porosity of the electrodeposited nickel part are affected by the substrate material and its surface, whether it is a polymer or metal substrate. After the initially deposited layers, which is required to smooth the surface of the substrate, given that the electrolyte has sufficient throwing power and leveling characteristics, the structural properties remain mostly unchanged if all other process parameters are held constant [4; 15]. Examples of electrodeposited nickel pieces are shown in Figure 5. Figure 5a shows a scanning electron microscopy (SEM) image of the final surface of a nickel piece, electrodeposited on ABS from a Watts bath [50]. This figure indicates the roughness of final surface as well as formation of some microcracks, which is expected due to the absence of external pressure. Figure 5b  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrolyte** & **Chemical Name** & **Formula** &
\begin{tabular}{c} **Concentration** \\ **(g/L)** \\ \end{tabular} & **Applications** \\ \hline \multirow{3}{*}{Watts} & Nickel sulfate & \(NiSO_{4}\)-6\(H_{2}O\) & 200–300 & Corrosion and wear \\  & Nickel chloride & \(NiCl_{2}\)-6\(H_{2}O\) & 45–150 & resistant, decorative, \\  & Boric acid & \(H_{3}BO_{3}\) & 30–52 & structural \\ \hline \multirow{3}{*}{Nickel sulfamate} & Nickel sulfamate & \(Ni(SO_{3}NH_{2})_{2}\) & 300–450 & Abrasion and \\  & Nickel chloride & \(NiCl_{2}\)-6\(H_{2}O\) & 0–30 & corrosion resistant, \\  & Boric acid & \(H_{3}BO_{3}\) & 30–45 & structural \\ \hline \multirow{3}{*}{Sulfate-chloride} & Nickel sulfate & \(NiSO_{4}\)-6\(H_{2}O\) & 150–225 & High deposition rate \\  & Nickel chloride & \(NiCl_{2}\)-6\(H_{2}O\) & 150–225 & and slightly higher \\  & Boric acid & \(H_{3}BO_{3}\) & 30–45 & internal stress \\ \hline \multirow{3}{*}{All-sulfate} & Nickel sulfate & \(NiSO_{4}\)-6\(H_{2}O\) & 225–410 & \\  & Boric acid & \(H_{3}BO_{3}\) & 30–45 & \\ \hline \multirow{3}{*}{Hard nickel} & Nickel sulfate & \(NiSO_{4}\)-6\(H_{2}O\) & 180 & \\  & Ammonium chloride & \(NH_{4}Cl\) & 25 & High hardness and \\  & Boric acid & \(H_{3}BO_{3}\) & 30 & tensile strength \\ \hline \multirow{3}{*}{Black nickel} & Nickel ammonium sulfate & \(NiSO_{4}\cdot(NH_{4})2 SO_{4}\cdot\)6\(H_{2}O\) & 60 & \\  & Zinc sulfate & \(ZnSO_{4}\) & 22 & Plating on brass, \\ \cline{1-1}  & Sodium thiocyanate & \(NaCNS\) & 15 & bronze, or steel \\ \hline \hline \end{tabular}
\end{table}
Table 3: Common electrolytes used in nickel electrodeposition and their applications [47; 48; 49].  
depicts an optical microscopy (OM) of the cross-section of a nickel wall, which was initially deposited on PLA substrate, which indicates uniform wall thickness upon deposition, using optimized deposition parameters. Varying these process parameters, even in small degrees, has substantial effects on the mechanical and material properties of the electrodeposited nickel. Below, we present an overview of these effects based on Watts-type electrolyte.
###### Abstract  
Recently, reconfigurable intelligent surfaces (RISs) have attracted increasing attentions in the design of full-duplex (FD) systems due to their novel capability of propagation environment reconfiguration. However, in conventional RIS-assisted FD systems, the beamforming for self-interference cancellation (SIC) and sum rate maximization (SRM) are highly coupled during RIS optimization, which significantly degrades the system performance. To tackle this issue, we exploit a novel bilayer intelligent omni-surface (BIOS) structure in FD systems. Compared with the conventional RIS designs, the BIOS provides independent beams on both sides, thus enabling more flexible achievement of SRM and SIC. For the BIOS-assisted FD system, we first formulate an optimization problem to achieve SRM and efficient SIC simultaneously. Then, we exploit the relationship between the SRM and mean square error (MSE), and propose a weighted MSE minimization with SIC algorithm to solve the problem. Specifically, we jointly design the beamforming at the base station and the BIOS with manifold optimization while guaranteeing an SIC constraint. Furthermore, we theoretically derive a lower band for the BIOS size required for efficient SIC in FD systems. Simulation results indicate that the BIOS outperforms the conventional RIS designs in FD systems, and verify the accuracy of the derived lower bound for the BIOS size.  
bilayer intelligent omni-surface; full duplex; beamforming; self-interference cancellation; sum-rate maximization +
Footnote †: journal:  
00 2024 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 2 Cooperative MIMO Communication  
### System Model  
The system model for C-MIMO communication is a centralized wireless sensor network where there is a data gathering node (DGN) and several clusters with several sensors in each cluster. Sensors in one cluster transmit the data to the sensors in adjacent cluster and step by step the data reach the DGN. Figure 1 shows the cluster to cluster communication where two clusters are shown. The system considers \(N_{t}\) number of sensors in the transmitting cluster, \(N_{r}\) number of sensors in the receiving cluster and one antenna is placed at one sensor. Also, each element in the channel matrix \(H\) is assumed to be a zero-mean circularly symmetric complex Gaussian random variable with unit variance and can be considered as follows.  
\[\mathbf{H}=\left(\begin{array}{cccc}h_{11}&h_{12}&\ldots&h_{1N_{r}}\\ h_{21}&h_{22}&\ldots&h_{2N_{r}}\\ \vdots&\vdots&\vdots&\vdots\\ h_{N_{t}1}&h_{N_{t}2}&\ldots&h_{N_{t}N_{r}}\end{array}\right).\]  
The problem here is stated from the receiver point of view, so a loss model is used to estimate the received energy. To calculate the total energy consumption, both the circuit and transmitter power are taken into count. The same transmitter and receiver blocks shown in [4] are used in this paper. Source coding, pulse shaping, and modulation block are as well omitted from the design. Throughout the paper, a system with narrowband, frequency-flat Rayleigh fading channels and perfectly synchronized transmission/reception between wireless sensor nodes is assumed. The fading is assumed constant during the transmission of each frame. In our model, a sensor with high residual energy is deployed as a cluster  
Figure 1: System model for cluster to cluster communication in wireless sensor network.  
head and it remains the cluster head until the network dies. The cluster head broadcasts its status to the other sensors in the network. Each sensor node determines to which cluster it wants to belong by choosing the cluster head that requires the minimum communication energy. Once all the nodes are organized into clusters, each cluster head creates a schedule for the nodes in its cluster. This allows the radio components of each non-cluster-head node to be turned off at all times except during its transmit time, thus minimizing the energy dissipated in the individual sensors.
## 3 Results and Discussions
### Effect of Some Parameters on the Performance of the Model  
To investigate the effect of input image size on the performance of the model, the SSD was trained using MobileDet as the backbone with the default input size, i.e., \(320\times 320\). The performance of the model is shown in Table 3. As can be seen from the table, the accuracy of the model decreases by 12%, and the time for inference decreases by almost half. Thus, there is a trade-off between the input size of the model, the accuracy and time for inference. A model with a smaller input size is less accurate, but runs faster.  
In the following experiment, 20% of the training dataset was used for training to investigate the effect of dataset size on model performance. The SSD with MobileDet as the backbone was trained on this smaller dataset with the same hyperparameters as the model with the best performance. As can be seen in Table 3, the performance of the model drops by 8%.  
The thermal images in the training dataset were removed from the training to examine the effect of having thermal images in the training set. The performance of the model dropped by 2%.  
The images of trunks without leaves were selected as the training set (304 images) for the next experiment, and the images of trunks with leaves were selected as the validation set (89 images). Figure 12 shows the performance of the model during the training of the model on the validation set. The model was trained in 17,500 steps with an mAP of less  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**mAP\%**} & \multicolumn{3}{c}{**Inference Time (ms)**} \\ \cline{2-6}  & **PC** & **RaspberryPi** & \multicolumn{2}{c}{**RaspberryPi**} \\ \cline{2-6}  & **GPU** & **CPU** & **TPU** & **CPU** & **TPU** \\ \hline MobileDet Edge TPU & 89 & 84.4 & 84.6 & 1048.277 & 47.75 \\ \hline MobileNet Edge TPU & 86.7 & 84.8 & 86.6 & 1235.73 & 47.79 \\ \hline MobileNet-V1 & 84 & 79.9 & 81.3 & 861.4 & 45.73 \\ \hline MobileNet-V2 & 88 & 82.8 & 83.2 & 773.717 & 47.969 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the models in terms of accuracy and inference time (ms).  
Figure 11: Comparison of mean average precision vs. latency for mobile models.  
than 0.05, while the model trained with the mixed images (with and without leaves) almost reached an mAP of 0.8 in the same step (see Figure 9). Therefore, the training set must fully reflect the real-world conditions, otherwise, the model will not be able to detect the desired object in the unseen images, which contain features not seen during training. It is important to note that the model could not detect trunks with leaves when trained on images without leaves, however adding some images with leaves (89 images) enhanced the performance of the model. Consequently, updating and re-training the model with new data obtained in the field is an important step in training and using the DL algorithms.
# Study on the Load-Bearing Behavior of Underground Steel Structures with Pitting Corrosion Damage
## 5 Results of Simulated Pitting Damage Three-Point Bending Test  
### Test Phenomena and Failure Modes of Three-Point Bending Test  
The pitting damage in the three-point bending test of I-beam was located at the center of the lower flange in the tension zone. During the loading process, when the load was relatively small, there was no obvious deformation in the flange plates and web plates of both the components with pitting damage and those without. As the load continuously increased, the deformation patterns of the components with and without pitting damage showed significant differences. For the components without pitting damage, slight local buckling first occurred in the compressed flange near the loading point, the shear deformation of the web plate continuously increased, and the web plate slightly bulged outward. The tension flange had no obvious deformation in the early stage of the test. As the load increased, local buckling occurred in the compressed flange near the mid-span point, the web plate bulged outward, and the final failure mode was the local buckling of the compressed flange near the loading point and the local buckling of the web plate (Figure 9). For the components with pitting damage, as the load continuously increased, slight local buckling first occurred in the compressed flange near the loading point, the shear deformation and buckling of the web plate were not obvious, and the tension flange plate of the pure bending section of the component had obvious deformation. Until the component reached the ultimate bearing capacity, the final failure mode was the local buckling of the compressed flange and the bending failure of the tension flange. Figures 10-12, respectively, show the failure modes of components with a triangular distribution, inclined distribution, and vertical row distribution of pitting damage.  
Figure 8: Comparison of five groups of load–displacement curves in compression test.  
Figure 9: Failure mode of three-point bending specimen without pitting damage.  
This phenomenon indicates that pitting damage can affect the failure mode of steel components. In this three-point bending test, the pitting damage was prefabricated on the tensile flange plate, and due to the limitations of the test conditions, the span-to-depth ratio of the three-point bending sample was relatively small, which led to the failure mode of the steel component changing from the local buckling of the compressed flange and the local buckling of the web to the local buckling of the compressed flange and the bending failure of the tensile flange. Based on the test results, it can be inferred that if the pitting damage is prefabricated on the web, more severe pitting damage may also cause the original bending failure to experience shear brittle failure.