esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamic Modeling of the Proposed QVIP  
### Conceptual Design of QVIP  
In vibration control systems, a standalone negative stiffness structure may destabilize the platform. Hence, integrating positive stiffness structures is essential to achieve stable and tunable QZS. Addressing this, we propose the QVIP with QZS for vibration control of offshore floating platforms in ultra-low frequencies. Leveraging the foldable parallelogram geometry, the QVIP combines a horizontal spring with two oblique spring sets to realize stable and tunable QZS. Figure 1 illustrates the 3D design of the QVIP. During compression, the vertical component force of horizontal/tensioned springs initially increases then decreases, generating negative stiffness, while compressed springs consistently provide positive stiffness. Adjusting their balance enables QZS design. Figure 1 shows that horizontal/tensioned springs exhibit positive stiffness initially but transition to negative stiffness with larger displacement, risking instability. To extend the QZS range and enhance vibration isolation, this paper analyzes and optimizes the equivalent stiffness of the three-spring system.
## 1 Introduction
[38] investigated the reliability of ground surface settlement behind excavations employing a first-order reliability method (FORM) implemented with a variance reduction technique while considering spatial variable shear strength and stiffness of soft clay of the Harding small strain (HSS) model. Concerning the application of the MCC model in reliability analysis, [PERSON] et al. [39] investigated the effect of uncertainty of an individual parameter on one-dimensional foundation consolidation and settlement problems based on the Monte Carlo simulation (MCS) method combined employing the stochastic finite element method (SFEM). [PERSON] and [PERSON] [40] investigated the effect of the uncertainty of parameters on the probability of foundation settlement under inclined loading. There are two issues in the current study. (1) Although numerous studies have focused on the spatial variable stiffness and strength of soils, minimal attention, so far, has been paid to investigating the effect of the spatial variable MCC model parameters on the ground surface settlement of excavation. (2) Despite the deformation of the excavation being inseparable from the seepage process, the current probabilistic analysis emphasized mainly static stress analysis, with little research on the transient hydro-mechanical coupling problems during draining. Given the above issues, the novelties of this study lie in the following aspects. Firstly, it focused on the effect of spatial variability of deformation parameters when introducing the MCC model in excavation settlement problems. Secondly, this study particularly investigated the time-dependent deformation and reliability of ground surface settlement of the entire draining, bracing, and excavating process since the staged construction style. The statistics of surface settlement, region with the maximum settlement and the reliability of the excavation were examined. A further study of the effect of the controlled standard settlement threshold and coefficient of variation (COV) of the parameters on the results was also discussed. The framework and findings will contribute to an increased understanding of reliability-based foundation excavation design and control.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 6 Conclusions  
In this article, a fog computing framework for the management and orchestration of smart city applications in 5G wireless networks is presented. Fog computing has been introduced to deal with the growing amount of connected devices in the upcoming years, by placing computational resources on the edges of the network. This way, fog computing solutions can provide effective ways to overcome the strict requirements introduced by IoT scenarios, such as low latency, high energy efficiency and high mobility. The proposed framework extends the current state-of-the-art within fog computing, NFV and MANO paradigms by providing autonomous capabilities for the resource provisioning in 5G-enabled smart cities. The main contribution of our work is its fully-integrated and autonomous fog node management system with data analysis and decision making functionalities alongside a foreseen novel application layer P2P fog protocol based on the OSPF routing protocol enabling the exchange of application service provisioning information between fog nodes and the cloud layer. Moreover, an anomaly detection use case has been evaluated focusing on the proposed framework. The evaluation results show that the presented approach achieves a substantial reduction in terms of network bandwidth usage when compared with traditional centralized cloud solutions. Our approach only requires 1.46% of the available network bandwidth between a fog node and a cloud node. Moreover, the proposed framework can send timely alerts to IoT sensors in case abnormal samples are detected, since a first-level analysis is performed in fog nodes. As future work, prototypes and proof-of-concepts of our fog computing framework will be implemented. Moreover, simulation studies will be carried out to evaluate the operational aspects of the foreseen fog protocol.  
This research was performed partially within the projects \"Service-oriented management of a virtualized future internet\" and \"Intelligent DEnse And Long range IoT networks (IDEAL-IoT)\" under Grant Agreement #S004017N, both from the fund for Scientific Research-Flanders Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO-V).  
[PERSON], [PERSON] and [PERSON] conceived of and designed the proposed framework. [PERSON] and [PERSON] conceived of and designed the use case scenario. [PERSON] performed the evaluations. [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
5G-PPP 5G Infrastructure Public Private Partnership  
AE  Application Entity  
API  Application Programming Interface  
BSS  Business Support System  
CN  Cloud Node  
CoAP  Constrained Application Protocol  
COHERENT  Coordinated control and spectrum management for 5G heterogeneous radio access networks  
CSE  Common Service Entity  
CSMA  Carrier Sense Multiple Access
# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 3 The Proposed Approach
### Loss Function  
The RCNN generates estimated rotation and translation vectors from pairs of RGB-D images [38]. In order to compare the difference between these output values and true values and drive the predicted results closer to true values, we need to design a loss function. The loss function here consists of two parts: 2D space loss and 3D space loss, which are calculated by RGB-D sequences and the predicted transformation matrices. Algorithm 1 shows the overall architecture in detail.  
Let \(I_{1},I_{2},...,I_{t},I_{t+1},...,I_{N}\) be a batch of RGB images ordered by time sequence, \(D_{1},D_{2},...,D_{t},D_{t+1}\), \(...,D_{N}\) be the corresponding depth images and \(Q_{1},Q_{2},...,Q_{t},Q_{t+1},...,Q_{N}\) be the point clouds which are back-projected from depth images. Figure 4 shows the loss function calculation process. RGB-D images at time \(t\) and \(t+1\) generate a predicted transformation matrix \(\hat{T}_{t,t+1}\) through the RCNN. All pixels in image \(I_{t}\) are processed according to \(D_{t}\), \(\hat{T}_{t,t+1}\) to obtain image \(\hat{I}_{t+1}\). The 2D space loss describes the difference between \(I_{t+1}\) and \(\hat{I}_{t+1}\). As for 3D space loss, its calculation method is similar to 2D space loss, which projects all points in the point cloud \(Q_{t}\) to \(\hat{Q}_{t+1}\), through the transformation matrix \(\hat{T}_{t,t+1}\). The difference between \(Q_{t+1}\) and \(\hat{Q}_{t+1}\) is the 3D space loss.  
Figure 4: Loss calculation for training.
## 3 Theoretical Foundations
### Complex Network Theory
Following complex network theory, we integrate node degree (\(K_{i}\)) and betweenness centrality (\(B_{i}\)) to compute node importance [30], as shown in Equation (1): \[ND_{i}=B_{i}^{\prime}+\sqrt{K_{i}^{\prime}}\] (1) where \(ND_{i}\) denotes the criticality of node \(i\), \(B_{i}^{\prime}\) represents the normalized betweenness centrality, defined as \(B_{i}^{\prime}=\frac{B_{i}}{MAX(B_{i})}\), and \(K_{i}^{\prime}\) corresponds to the normalized degree centrality, calculated as \(K_{i}^{\prime}=\frac{K_{i}}{MAX(K_{i})}\), for \(i=1,2,\ldots,n\). (5) Edge Importance (\(S_{ij}\)) Edge importance quantifies the significance of edges within the network. The importance of an edge is determined by the importance of its connected nodes--edges linking more important nodes have higher importance. We calculate edge importance based on the importance scores of its terminal nodes, as shown in Equation (2): \[S_{ij}=\frac{ND_{i}+ND_{j}}{2}\] (2) where \(S_{ij}\) denotes the criticality of the edge connecting nodes \(i\) and \(j\), with \(i,j\in\{1,2,\ldots,n\}\).
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 2 Related Work
### Deep Learning for Fault Diagnosis  
Deep learning enables end-to-end training, eliminating the need for manual feature engineering by domain experts. Consequently, numerous researchers have adopted deep-learning approaches to effectively diagnose faults. For instance, [PERSON] et al. [16] successfully combine variational mode decomposition (VMD) with a one-dimensional convolutional network to diagnose bearing faults. VMD effectively removes random noise from the original signal and contributes to feature enhancement. [PERSON] et al. [17] employs a deep belief network to accurately identify bearing defects. The network is initially pre-trained using minimal batch random gradient descent and subsequently fine-tuned using conjugate gradient descent and backpropagation. Additionally, [PERSON] et al. [18] proposes a method that leverages tiny convolution kernels in the second layer for multi-layer nonlinear mapping and larger kernels in the primary convolutional layer for feature extraction and noise reduction. [PERSON] et al. [19] suggest the utilization of a long-term and short-term memory network for diagnosing faults in time-varying working condition data. The loss function incorporates the outputs from each time step, and dropout regularization is employed to enhance training efficiency and diagnostic capability. [PERSON] et al. [20] also employs long-term and short-term memory networks for bearing fault diagnosis, integrating residual modules with long-term and short-term memory to leverage both deep local features and global time series features of bearing vibration signals.  
Although deep learning has demonstrated its effectiveness in fault diagnosis, it heavily relies on a substantial volume of labeled data. However, due to security considerations, machines may halt immediately after a fault occurs, leading to an insufficient number of fault samples available for training deep-learning models.  
Figure 1: Federated few-shot learning for machinery fault diagnosis across multiple domains.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 2 Overview of IEEE 802.11 Physical Layer  
In this section, we briefly introduce an orthogonal frequency division multiplexing (OFDM) based physical layer specification in IEEE 802.11 protocols and its channel estimation principle.
## 3 Results and Discussion  
### Physico-Chemical Characterization of the Hydrolysates  
#### 3.1.1 Chemical Composition of Protein Isolates and Hydrolysates  
The proximate compositions of white, red, and black kidney bean plants, protein isolates, and hydrolysates are shown in Table 1. Black kidney bean has higher protein contents in the different forms, total, isolate and hydrolysates (T, I, and H), amounting to 24.5, 91, and 88%, respectively. Alternatively, red kidney beans had the highest carbohydrate content in the different forms (T, I and H), recording 65.1, 35.4, and 8.7%, respectively. Ash content was, however, highest in all forms of white kidney bean (T, I, and H), reaching 5.1, 7.2, and 9.2%, respectively. [PERSON], et al. [68] found that black bean proximate composition was 23.1% protein, 4.3% ash, 1.2% fat, 71.4% of total carbohydrate, and protein isolate was 81.6% protein, and 11.4% carbohydrates. Generally, ash content increased with protein isolation or hydrolysis by Alcalase, but protein content significantly increased by 73-75% in isolate forms (BKI, RKI, and WKI), and slightly decreased in the hydrolysates forms (BKH, RKH, and WKH), probably due to the increments in ash content by 11-21%. A similar reduction in protein content of black bean protein was observed in a previous study after 2 h-Alcalase hydrolysis. These reductions were associated with an increase in the ash content of black bean protein hydrolysates, possibly as a result of supplying inorganic alkali to keep the pH constant during hydrolysis [68]. A similar reduction in protein content accompanied by an increase in ash content was observed in Alcalase-hydrolyzed chickpea protein [69].
## 4 Optimization Mathematical Model  
The combination of sizing and topology optimization (STO) is a powerful approach for reducing structural configuration weight while maintaining its overall stiffness and structural integrity [35]. Furthermore, the sizing and topology optimization tools can be integrated into a multiple-computer-aided engineering software. In this investigation, STOP was performed based on the integration of the Finite Element Method (FEM) into an optimization solver.  
This study's main objective is the determination of the most efficient solution for producing lightweight components of a variable morphing wing while maintaining their optimal mechanical properties. Therefore, the STO results were obtained for ribs, spars, and support elements for each segment, and then were tested for the validation of their mechanical properties (displacements and stresses).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Section Number** & **Lift Load (N)** & **Ultimate Load** \\ \hline y\({}_{1}\) & 257.36 & 1158.1 \\ y\({}_{2}\) & 243.77 & 1096.97 \\ y\({}_{3}\) & 232.15 & 1044.65 \\ y\({}_{4}\) & 213.325 & 959.96 \\ y\({}_{5}\) & 198.29 & 892.28 \\ y\({}_{6}\) & 175.96 & 791.8 \\ y\({}_{7}\) & 158.62 & 713.77 \\ y\({}_{8}\) & 152.7 & 687.15 \\ y\({}_{9}\) & 147.12 & 662.04 \\ y\({}_{10}\) & 134.61 & 605.75 \\ y\({}_{11}\) & 79.11 & 355.995 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loads calculated for each section of a VSMTW.  
Figure 4: Generic planform wing with its span sections (\(y_{n}\)) and chord numbers for the VSMTW.
## 3 Results and Discussion
The lower activity observed in the aqueous extract can be attributed to its reduced content of phenolic compounds, particularly flavonoids and phenolic acids, which are more efficiently extracted with methanol due to their intermediate polarity. These compounds are known to exert their antiglycation and MGO-trapping effects through mechanisms such as carbonyl trapping, metal chelation, and radical scavenging, which are closely linked to their antioxidant capacity. Since water preferentially extracts highly polar components such as sugars, amino acids, and certain organic acids, it may result in a lower concentration of bioactive polyphenols, thereby explaining the reduced activity of the aqueous extract. This suggests that the antiglycation effect is at least partially mediated by the antioxidant mechanisms of polyphenic compounds, which are less abundant in the aqueous extract. This investigation has certain limitations as it focuses on the in vitro evaluation of the biological activity of carob flour extracts without considering the economic feasibility of their use as a functional food ingredient. Additionally, further in vivo studies, including animal experiments and clinical trials, are necessary to fully confirm their health benefits. In addition, extraction conditions were intentionally standardized to allow a direct solvent comparison. However, this approach did not account for the impact of variables such as temperature or extraction time. Future studies should address these factors to optimize the extraction process, assess large-scale feasibility, and validate the functionality of the extract under industrial conditions.
## 3 Results
### Results of Rosenthal Analytical Model  
The results achievable with [PERSON]'s analytical model [2] were calculated and compared with the experimental data obtained in the case study (Section 3.1). In this way, they can be used as a term of comparison for the model developed in this work through the ML model. In Table 9, the R\({}^{2}\) and MAE calculated for the Rosenthal solution for the melt pool depth and width, respectively, are obtained, providing as input the process parameters investigated in these case studies, including the layer thickness, in the range of 30 to 90 \(\upmu\)m, and the building platform temperature variation from 80 to 170 \({}^{\circ}\)C.  
Figure 3: Comparison of the gradient boosted trees model prediction results (orange line) and the physical results for melt pool depth (blue line). The validation of the ML regression model is carried out on 20% of randomly selected data from the dataset, in this case using 79 randomly extracted physical observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MAE** \\ \hline Gradient boosted trees & 0.751 & 10.59 \\ \hline Deep learning (Keras) & 0.749 & 11.454 \\ \hline Regression tree & 0.746 & 11.392 \\ \hline Polynomial regression & 0.706 & 13.167 \\ \hline XGBoost tree ensemble & 0.667 & 13.219 \\ \hline Random forest & 0.649 & 13.905 \\ \hline Linear regression & 0.472 & 19.103 \\ \hline XGBoost linear ensemble & 0.125 & 26.319 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results in terms of R\({}^{2}\) and MAE of the different ML models tested considering the melt pool width.
## 2 Design of the Printed Patch Antenna
### Design and Analysis of Coplanar Dipole  
The coplanar dipole (see Figure 3) has very good broadband properties and is very small, which can be used for the miniaturization of UWB communication systems [13; 14; 15; 16]. Extreme bandwidth systems are among the promising systems for modern radio communication. UWB technology is characterized by a large bandwidth of at least 500 MHz or 20% bandwidth satisfying the condition [17]  
\[\frac{B_{f}}{f_{c}}>0.2 \tag{1}\]  
where \(B_{f}\) is the bandwidth for a 10 dB drop, and \(f_{c}\) is the center frequency of the band.  
In UWB technologies, the bandwidth is used to compare antennas with each other. To make it clear exactly where the Bf value is read, this value is defined for a 3 dB or 10 dB drop. Wideband technologies do not have a harmonic carrier, and the information is encoded in a sequence of very short pulses (0.2 to 1.5 ns) [17]. Each radio channel can have a bandwidth of more than 500 MHz, depending on its center frequency.  
Figure 3: Dimensions of coplanar dipole.  
Figure 2: Display of a connector attached to an antenna made with LDM technology.  
A key point in the implementation of UWB systems is the design of the wideband antenna. The advantage of broadband antennas is the slight variation in the electrical parameters over a relatively wide frequency band. The design emphasizes the stability of the input impedance. Our designed patch antenna operates in the band from 3.1 GHz to 10.6 GHz.  
Table 2 shows the dimensions of the coplanar dipole for the selected substrates from which the patch antenna was manufactured. The proposed antennas with different types of substrates were simulated, and the results are shown in Figures 4-7. The simulation of the coplanar dipole design showed sufficient bandwidth for the reflection coefficient to drop to \(-10\) dB and a better parameter \(s_{1I}\). It is clear that the proposed antenna is suitable for broadband applications [18].  
Figure 5 shows that FR4 material has parameters that are not ideal for antenna applications. Arlon 25N and 3D material have almost identical properties. Figure 6 shows selected simulations for the antenna on the 3D substrate.
## 5 Experimental Results and Analyses
### Evaluation Method  
In the denoising field, the common evaluating criteria including PSNR, SSIM, and runtime. The PSNR and SSIM [46] are defined in Equations (40) and (41):  
\[PSNR(\mathbf{X},\mathbf{Y})=10\text{lg}\frac{(MAX(\mathbf{X}))^{2}}{\frac{1}{N^{2}}\sum\limits \limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left(\mathbf{X}_{ij}-\mathbf{Y}_{ij}\right)^{2}}, \tag{40}\]  
where \(\mathbf{X}\) denotes the original image, \(\mathbf{Y}\) is the reconstructed image, and \(MAX(\mathbf{X})\) represents the largest gray value in the original image.  
\[SSIM(\mathbf{X},\mathbf{Y})=\frac{(2u_{\mathbf{X}}u_{\mathbf{Y}}+\left(Lk_{1}\right)^{2})(2 \sigma_{\mathbf{X}\mathbf{Y}}+\left(Lk_{2}\right)^{2})}{(u_{\mathbf{X}}^{2}+u_{\mathbf{Y}}^{2} +\left(Lk_{1}\right)^{2})(\sigma_{\mathbf{X}}^{2}+\sigma_{\mathbf{Y}}^{2}+\left(Lk_{2} \right)^{2})}, \tag{41}\]  
where \(u_{\mathbf{X}}\) is the mean of \(\mathbf{X}\); \(u_{\mathbf{Y}}\) is the mean of \(\mathbf{Y}\); \(\sigma_{\mathbf{X}}^{2}\) is the variance of \(\mathbf{X}\); \(\sigma_{\mathbf{Y}}^{2}\) is the variance of \(\mathbf{Y}\); \(\sigma_{\mathbf{XY}}\) is the covariance between \(\mathbf{X}\) and \(\mathbf{Y}\); and \(L=512\), \(k_{1}=0.05\), \(k_{2}=0.05\). The parameter \(L=255\).
## 2 Materials and Methods
### Characteristics  
The surface morphology and microstructure of the TPU, PDA/TPU, CB/CNT/TPU, and CB/CNT/PDA/TPU nanofiber membranes were observed using a Scanning Electron Microscope (SEM). The structural properties of these membranes were analyzed using a Raman spectrometer. The material composition was characterized by Fourier Transform Infrared Spectroscopy (FTIR) in the range of 500-4000 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\). Thermogravimetric analysis (TGA) was performed under a nitrogen atmosphere, with a heating rate of 20 \({}^{\circ}\)C/min, raising the temperature from room temperature to 800 \({}^{\circ}\)C. Tensile testing was carried out using a tensile testing machine to evaluate the mechanical properties of the nanofiber membranes under uniaxial tensile force. The sample size was 40 mm \(\times\) 10 mm, and each sample was tested five times under the same conditions, with the average value taken. The resistance signal of the flexible strain sensor was recorded using an LCR tester, and the strain sensing performance of the sensor was tested using the tensile testing machine in combination with the testing equipment. To ensure the accuracy of the experimental setup, aluminum film electrodes were placed at both ends of the composite nanofiber membrane.