esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Water uptake Measurements  
Figure 1 shows the water uptake in terms of percentage of the initial weight for the in-bulk cured samples at each condition. The graph is in good agreement with the typical behavior of water uptake for this kind of samples, previously stated in other studies [38; 41; 42]. It is observed that water uptake is more prevalent in the initial stages and then the weight gain is going less significant until the water saturation is reached at 2 weeks of aging.  
A similar water uptake behavior is found at every condition although some slight differences can be noticed. In this context, two opposite effects can play an important role. The first one is the hydrophobic behavior of carbon nanofillers, which can be introduced into the free volume of the polymer improving the barrier properties and leading to a reduction in the water uptake [43; 44]. The second one is the amphiphilic behavior of the SDS that remains attached to the CNT surface [45; 46], which can lead to an increase of the water absorption induced by the hydrophilic head groups [47]. In addition, CNT dispersion also plays a significant role. A poor dispersion can induce the presence of larger agglomerates, higher heterogeneity and higher distributed porosity. This irregular distribution leads, thus, to an irregular effect of the barrier properties of CNTs, which, in combination with the higher porosity, promotes a higher water uptake. However, a better dispersion of nanofillers improves the barrier properties leading, thus, to a lower water uptake.  
The combination of these effects, as shown in the schematics of Figure 2, thus, explains the slight differences observed for each condition. In the case of the CNT reinforced adhesive without surfactant, a poor CNT dispersion is achieved, as stated in previous studies [31], so that the hydrophobic effect of CNTs is not so prevalent. Alternatively, the samples with 0.25 wt% and 1 wt% SDS show a similar trend, with a slightly lower water uptake than the sample without surfactant. In this case, the effect of the better CNT dispersion achieved was slightly prevalent over the amphiphilic effect of SDS surfactant. In the case of the neat adhesive, the water uptake was given directly by the physical behavior of the epoxy matrix.  
Figure 1: Water uptake graph for in-bulk specimens.
## 2 Problem Formulation
### Obtaining \(\underline{\mathbf{T}}_{s}\) Using the Mode-Matching Technique  
The mode-matching technique is used to relate the coaxial line modes at the the two sides of the screen. The mode coefficients at the position of the screen are denoted \(\tilde{\mathbf{a}}^{\pm}\) and \(\tilde{\mathbf{b}}^{\pm}\) to distinguish them from the mode coefficients at the two ends of the sub-unit cell. Assuming and suppressing a time-dependence \(\mathrm{e}^{\mathrm{i}\omega t}\), the transverse electric and magnetic fields at both sides of the screen can be expressed as:  
\[\mathbf{E}_{1t}(\mathbf{r}) = \sum_{v=1}^{\infty}(\tilde{a}_{v}^{+}\mathrm{e}^{-\mathrm{i}k_{ \mathrm{i}}zz}+\tilde{a}_{v}^{-}\mathrm{e}^{\mathrm{i}k_{\mathrm{i}}zz})\, \mathbf{e}_{v}\] (7) \[\mathbf{H}_{1t}(\mathbf{r}) = \sum_{v=1}^{\infty}((\tilde{a}_{v}^{+}\mathrm{e}^{-\mathrm{i}k_{ \mathrm{i}}zzz}-\tilde{a}_{v}^{-}\mathrm{ewhere the modal impedance, \(Z_{v}\), is:  
\[Z_{v}= \begin{cases}\eta&\text{TEM}\\ \dfrac{\eta k_{zv}}{k}&\text{TM}\\ \dfrac{\eta k}{k_{zv}}&\text{TE}\.\end{cases}  \tag{11}\]  
Here, \(\eta\) and \(k\) are the free-space wave impedance and \(k_{zv}\) is the longitudinal wavenumber of the \(v\)-th mode of the guiding structure, with \(\mathbf{e}_{v}\) being the transverse field of the \(v\)-th mode of the coaxial transmission line as detailed in the Appendix A [48]. It is worth noting here that the cos-/sin-degeneracy explicitly shown in the Appendix A gives rise to two modes with index \(v\) if \(m\
eq 0\). The transverse modal fields are orthonormalized over the cross-section \(\mathcal{X}=\mathcal{A}+\mathcal{M}\) of the coaxial line with the inner product of modes \(u\) and \(v\) defined as the usual \(L^{2}\) scalar product on the relevant domain:  
\[\left\langle\mathbf{e}_{u},\mathbf{e}_{v}\right\rangle_{\mathcal{X}}=\int_{ \mathcal{X}}\mathbf{e}_{u}\cdot\mathbf{e}_{v}^{*}\,\mathrm{d}S=\left\langle \mathbf{e}_{u},\mathbf{e}_{v}\right\rangle_{\mathcal{M}}+\left\langle\mathbf{e }_{u},\mathbf{e}_{v}\right\rangle_{\mathcal{A}}=\delta_{uv} \tag{12}\]  
where \(\mathcal{M}\) and \(\mathcal{A}\) correspond to the regions of the metallic screen and air aperture (indicated in Figure 3), \(\delta_{uv}\) is the Kronecker delta, and \({}^{*}\) denotes the complex conjugate. A consequence of (12) is that the inner product can be equivalently performed either over the metallic screen or in the air aperture, which saves computational resources. To relate the modes at the two sides of the screen, we apply the transverse field continuity conditions. The metallic screen is placed at \(z=0\), on which the transverse electric field must vanish because of its condition of being a perfect electric conductor.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification
### General Instruction for User-Defined Parameters
#### 2.2.1 Size of the Hankel Matrix  
The number of rows, \(i\), and columns, \(j\), is one parameter for establishing the block Hankel matrix. However, it can be observed from Equation (3) that the total number of samples is equal to \(2i+j-1\), so the column number can be uniquely defined if the row number, \(i\), is selected.  
The maximum value of the row number, \(i\), is not explicitly restricted, although a large value may frustrate the identification of high-frequency modes because of their relatively small energy. Oppositely, the minimum value has some rules of thumb; for example, the row number, \(i\), multiplied by the number of measurements, \(m\), needs to be equal to or larger than the system order according to the Cayley-Hamilton theorem [19], which implies.  
\[im\geq 2n \tag{10}\]  
Furthermore, the samples used in the rows or columns must cover the fundamental frequency (or lowest frequency) of the structural system, \(f_{0}\). Some studies recommend \(i\geq 1/f_{0}\Delta t\) for lagged vectors by considering a complete cycle [24; 42], while others recommend \(2i\geq 1/f_{0}\Delta t\) for considering the Nyquist frequency [43; 44].
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
###### Abstract  
With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitely (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring.  
computer vision; pest monitoring; Internet of Things; smart farming; deep learning Article Internet of Things Meets Computer Vision to Make an  
1 Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, Portugal 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2 Polytechnic Institute of Leiria, School of Technology and Management, 2411-901 Leiria, Portugal 2
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 3 Methodology  
The flowchart shown in Figure 1 depicts how machine learning may be used to identify attacks using DDoS. The first step is to choose two datasets, CICDDOS 2019 and KDD CUP-1999, which are then analyzed using exploratory data analysis (EDA) in order to find trends, connections, and important features. Correlation analysis, feature engineering, and data visualization are all part of the EDA phase and help to improve the quality of the data used for training models and comprehending the dataset. Following EDA, the characteristics that were processed are preserved and several machine learning techniques are used. If the model's predictions are suitable it is determined with an accuracy check. If not, the method reflects the standard cycle of continual improvement in machine learning model building, progressively suggesting changes to the features and algorithms until the required accuracy is obtained.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results
### Comparison Experiment
#### 3.1.1 Comparison Experiment of the UGS-1 Dataset
\begin{table}
\begin{tabular}{l c c c c c} \hline  & \multicolumn{3}{c}{**Overall Results**} & \multicolumn{3}{c}{**Class IOU**} \\ \hline
**Method** & **PA (\%)** & **MPA (\%)** & **MIOU (\%)** & **Low Vegetation (\%)** & **Tree (\%)** \\ \hline DensASPP(mobilenet) & 83.71 & 75.22 & 64.19 & 40.35 & 74.11 \\ ESPNetv2 & 84.94 & 75.52 & 65.68 & 41.75 & 75.63 \\ DensASPP & 85.29 & 76.10 & 66.39 & 42.81 & 76.67 \\ DFANet & 85.18 & 76.40 & 66.44 & 43.56 & 75.29 \\ ShuffleNetV2 & 86.22 & 77.73 & 68.22 & 45.74 & 77.68 \\ DeepLabv3+ & 86.31 & 78.12 & 68.38 & 45.66 & 78.08 \\ FCN8s & 87.01 & 79.49 & 69.71 & 47.35 & 79.11 \\ SegNet & 87.17 & 79.51 & 69.93 & 47.59 & 79.59 \\ HRNet & 87.53 & 78.96 & 69.97 & 46.40 & 79.92 \\ ERFNet & 87.47 & 79.22 & 70.06 & 46.95 & 79.92 \\ DABNet & 87.53 & 79.21 & 70.22 & 47.47 & 79.97 \\ ACFNet & 87.51 & 79.87 & 70.42 & 47.84 & 80.11 \\ MFFTNet & 87.97 & 79.29 & 70.55 & 46.73 & 80.54 \\ PSPNet & 87.84 & 79.99 & 70.93 & 48.66 & 80.16 \\ ResNet50 & 88.03 & 80.06 & 71.11 & 48.44 & 80.64 \\ UNet & 88.25 & 80.51 & 71.53 & 48.94 & 81.12 \\ MAFANet & 88.52 & 81.55 & 72.15 & 49.53 & 81.64 \\ \hline \end{tabular}
\end{table} 
Table 1: Results of comparison experiments on UGS-1. MAFANet network performs multi-scale attention aggregation and dual-channel information fusion; effectively realizing green space classification in complex urban environments and having strong robustness.
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 5 Experiments
### Experimental Settings  
eCley has \(17\times 13\) sub-images, but only the central \(13\times 13\) sub-images are used for all-in-focus reconstruction. Each sub-image has a resolution of \(101\times 101\) pixels after rectification. For the parameters, the noise tolerance \(\alpha=0.05\), the weight \(\beta=0.07\), \(\gamma=0.5\), \(\delta=0.08\) empirically. By comparing the MSE of a test image, the different values of \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) have similar MSE. The size of local window influence the computation time and depth estimation result, as illustrated in [41]. To balance the efficiency and effectiveness, \(W(p)\) and \(W^{\prime}(p)\) are both \(15\times 15\) in this paper. \(K\) is set as the same as in [13] and consists of 3 radius regions and 12 angle regions for a total of 36 regions.  
The qualitative evaluation metrics include the reconstruction error and the reconstructed image definition. The reconstruction error is measured by MSE (mean squared error) in [41]. The image definition is mainly measured by comparing the gradient of the image; therefore, four commonly used gradient functions (Brenner gradient \(D_{b}\), Tenengrad gradient \(D_{t}\), SMD method \(D_{s}\), and Product of intensity variance \(D_{p}\)) for evaluating image definition are used [53].  
\[D_{b}=\sum_{x}\sum_{y}|I(x+2,y)-I(x,y)|^{2} \tag{8}\]  
\[D_{t}=\sum_{x}\sum_{y}|G(x,y)|^{2},G(x,y)>T\]  
\[G(x,y)=\sqrt{G_{x}^{2}(x,y)+G_{y}^{2}(x,y)}\]  
\[D_{s}=\sum_{x}\sum_{y}(|I(x+1,y)-I(x,y)|^{2}+|I(x,y+1)-I(x,y)|^{2})\]  
\[D_{p}=\sum_{x}\sum_{y}(|I(x+1,y)-I(x,y)|*|I(x,y+1)-I(x,y)|)\]  
where \(G_{x}(x,y)\) and \(G_{y}(x,y)\) are the Sobel gradient. The higher the value of these four functions, the more drastic the changes in the image edge and the higher the image definition.
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 6 HW/SW Implementation
### Hardware Resources Utilization  
The hardware resources consumed by the integrated system are listed in Table 2. The utilization rates are reported to the available resources of the Zynq-7010 PL. Some observations can be highlighted:  
* LUTs are the most used resource, with a 51% occupation rate, when compared to FF (30%), DSP (20%) and BRAM (14%);
* The EEG_CALCDIST IP core takes 32% of the used LUTs and 31% of the used FFs;
* DSPs are only occupied by the PPG IP core;
* The three custom IP cores represent 60% of the consumed LUTs, 53% of the FFs, 47% of the BRAMs and 100% of the DSPs, and this shows that DMA and AXI peripherals demand significant hardware resources;
* Overall, the Zynq is not fully occupied, which means that further functionalities may be added to the biometric system.
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 4 Progressive Damage Model
### Finite Element Model  
The progressive failure model of face/core debonding composite sandwich structures is modelled in the well-known finite element (FE) software ABAQUS supported by Dassault SIMULIA (Dassault Systemes, Velizy-Villacoublay, France). An UMAT subroutine contained the damage initiate and propagation criterion of honeycomb core is coded to simulate the progressive failure of Honeycomb core in ABAQUS/Standard platform. Figure 14 illustrates the grid discretization and boundary conditions of the edge closed honeycomb panel with debonding defects. The 0\({}^{\circ}\) layering direction of composite material follows the red arrows direction in the Figure 14 (A) of the cross-sectional view A-A. The continuous shell element SC8R (green elements) was used to simulate each layer of the inner and outer skins, and the total number of elements is 7810. The three-dimensional solid element C3D8R (white elements) was used to simulate the honeycomb core, and the total number of elements is 16426. In order to accurately express the face/core debond, the cohesive element COH3D8 (red elements) was used to simulate the adhesive connecting the skin with the honeycomb core. The thickness of adhesive was set to 0.02 mm, and the total number of such elements was 7382. The failure methods in Section 4.1 were used to judge the failure of the three materials. To ensure the continuity of the displacement of the element nodes, the skin, the adhesive and the honeycomb core were connected by common nodes. No elements were established in the center of the specimen ((b) in A-A section view) which represented the debonding area between carbon panel and honeycomb, and general contact was generally established to prevent the inner and outer skins from embedding each other. The bottom of the honeycomb structure was fixed, and constraints in the z direction were set in the remaining three sides. Based on the buckle module of the ABAQUS/Standard solver, the concentrated force in the Y-direction was applied to the top of the honeycomb panel to analyze the structural buckling. At the same time, keywords in the input file were modified to output the node information of all modes. Based on the Static General module, the concentrated load was replaced by the displacement load, and the keywords of the input file were modified again. The node information obtained from the buckling analysis, as an initial defect, was introduced to the post-buckling analysis model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Damaged Status** & \(\lambda_{11}\) & \(\lambda_{22}\) & \(\lambda_{33}\) & \(\lambda_{12}\) & \(\lambda_{13}\) & \(\lambda_{23}\) \\ \hline Intact & 1 & 1 & 1 & 1 & 1 & 1 \\ Damaged & 0.4 & 0.4 & 0.45 & 0.4 & 0.4 & 0.4 \\ Fold & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 7: Equivalent stiffness reduction factor of honeycomb.
## 5 Experimental Results
### Performance Analysis for Other Datasets
#### 5.5.1 UR Fall Dataset (URFD)  
The UR fall dataset (URFD) included 30 fall event videos captured by two cameras, resulting in 60 instances of fall events. In addition, the dataset comprised 40 videos depicting other activities. We utilized the STFT to extract instances with a significant motion for both fall and non-fall events. For non-fall activities that did not exhibit significant motion, we randomly selected 50 frames for classification to ensure robustness in varied scenarios. Notably, the Gaussian blur module was not employed in this evaluation to maintain consistency with state-of-the-art algorithms for a direct comparison.  
The results demonstrate that the framework effectively distinguishes between fall and non-fall events with high accuracy as seen in Table 5, supporting its potential utility in real-world scenarios where precise and reliable fall detection is crucial.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Kernel Size** & **Precision (Fall)** & **Recall (Fall)** & **F1 Score (Fall)** & **Accuracy** \\ \hline \(5\times 5\) & 0.97 & 0.98 & 0.98 & 0.99 \\ \(11\times 11\) & 0.95 & 0.97 & 0.96 & 0.98 \\ \(21\times 21\) & 0.94 & 1.00 & 0.97 & 0.98 \\ \(31\times 31\) & 0.90 & 0.95 & 0.92 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of Gaussian blur on fall-detection performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Sensitivity** & **F1 Score** & **Specificity** \\ \hline Not Fall & 1.00 & 0.97 & 0.99 & 0.97 \\ Fall & 0.98 & 1.00 & 0.99 & 0.97 \\ \hline Accuracy & & & 0.99 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification report for UR fall dataset (URFD).
# Hybrid Mode Reconfigurable Antenna with V-Shaped Extrudes for Cognitive Radio Applications
## 3 Discussion  
A multi-band microstrip patch antenna design used to achieve frequency-band reconfigurability can be employed for CR applications. With two p-i-n diodes, the designed antenna illustrates multi-band switching at 1.5, 2.6, 3.2, 5.1, and 8.1 GHz with a constant radiation pattern, which is illustrated in Table 2. It also illustrates pattern reconfiguration at 3.5 GHz, with the main lobe at 7\({}^{\circ}\), 158\({}^{\circ}\), 173\({}^{\circ}\), and 175\({}^{\circ}\) and at 4 GHz, it shows 11\({}^{\circ}\), 15\({}^{\circ}\), 165\({}^{\circ}\), and 344\({}^{\circ}\), describing the hybrid reconfiguration for the frequency and radiation pattern explained in Table 3.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 5 Challenges with Solutions
### Spectrum Use/Resource Allocation  
Fog networking can provide a solution for resource management issue in future 5G networks. With a growing number of smart devices, the major issue is of spectrum use and resource allocation. Spectrum pricing and allocation scheme (SPAS) was proposed in [81] for F-RAN framework. Three areas were defined for the whole network, and double game theory was applied to give the solution for efficient spectrum use. Proposed two algorithms are named as game-model (GM-SPAS) and multiple-spectrum-reuse-technologies (MSRT-SPAS). Their presented results are effective in terms of revenue and efficiency. The problem of resource allocation with utility maximization objective under QoS constraints was formulated in [82]. Analytic Hierarchy Process (AHP) was proposed by the authors for finding the QoS requirements of IoT devices. Afterward, the matching theory was applied for user association.  
In [83], the authors have used the calculus of variation to find out the optimal spatial density of nodes such that the distribution of nodes can support the whole network. A Parallel Imperialistic Competitive Algorithm (PICA) is used to determine the initial positions of the access points. WSNs with immense scalability, immobility factor and low-deployment cost properties have limited processing and are resource-constrained. Smart Mobile Access Point (SMAP), which has been proposed by [PERSON] et al. [84] aims at achieving higher resource use and energy efficiency with the adoption of hierarchical placement of SMAP in WSNs using fog computing. [PERSON] et al. [85] have presented a framework for interconnection of fog and cloud computing, Hybrid Fog and Cloud (HFC), that provides effective, simple and productive resource provisioning. It also automates the process of configuring various virtual networks that are part of the network for interconnecting important components. The proposed architecture covers the salient features, such as security and scalability along with fog to fog, and fog to cloud communication mechanism.  
[PERSON] et al. [86] propose a resource scheduler for Networked Fog Centers (NetFCs) that not only provides services to the vehicular client but is also energy efficient. NetFCs operate from vehicular network's edge via Infrastructure to Vehicle (I2V) mobile links to the vehicular clients that are being served. Not only does the overall computation and communication energy efficiency get maximized but also the network performs better in terms of QoS requirements. Moreover, hard QoS requirements induced by the application are met, such as reducing transmission rates, delay, and jitter. Resource scheduler is responsible for admission control, dispatching the allowed traffic using minimum energy and adaptively controlling the traffic that is being injected into the mobile connections. Few of the important characteristics of the scheduler include providing QoS guarantees induced by the application, implantation of the scheduler is both scalable and distributive.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 6 Experiment Results and Analysis
### Dataset Preprocessing  
The data used in this study were collected from a bearing manufacturer in China, comprising a total of 1602 bearing images with a resolution of 4096 \(\times\) 2160. According to the morphology and origin of bearing surface defects, defects were categorized into three classes, denoted as a-c in Figure 16: inclusions, burn cracks, and scratches.  
Figure 15: StyleGAN2 network architecture.  
To avoid losing small defect features during multiple convolutions and pooling operations, a block-wise training and sliding window detection approach was employed [27]. The images were cropped to a size of 2048 \(\times\) 2160, resulting in 3283 images remaining after removing defect-free images. The dataset was divided into training, validation, and test sets with a ratio of 8:1:1. Labelimg was used to annotate the images, providing defect labels and completing the construction of the new dataset.