esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 4 Results and Discussions
### Radiation Characteristics  
The normalized \(E\) and \(H\) plane radiation patterns taken from simulations and well as measurements at 4 GHz, 6 GHz and 10 GHz are shown in Figure 10. Both \(E\)- plane and \(H\)- plane patterns are omni-directional in nature and radiate in all directions as intended. Slight disagreement between measured and simulated patterns is observable because of the measurement setup constraints and fabrication tolerances. Overall the radiation envelope of measured and simulated patterns is similar which ensures the effective radiation behavior.  
Figure 8: MIMO performance parameters: (**a**) CCL; (**b**) ECC; (**c**) DG; (**d**) TARC; (**e**) radiation efficiency.  
Figure 9: Simulated and measured peak gain.
## 5 Experiment
### Results Analysis
#### 5.2.4 Time Cost  
In blockchain-based hierarchical federated learning experiments, the consumption of communication time is affected by a combination of several factors, including model download time, local training time, gradient/model upload time, global aggregation time, consensus time, and cross-layer communication time. In our experimental tests, we define  
Figure 5: Comparison of the results of the experiment.  
Figure 6: Results of experiments with malicious nodes.  
the communication time as the sum of the gradient upload time and the time the model hash is uploaded to the blockchain.  
Figure 7 reflects the communication time cost of the four algorithms with different numbers of nodes. As shown in Figure 7a, we use the MNIST dataset to test the performance of the CNN model under the four federated learning architectures. As the number of nodes increases, the communication time of BHFL does not increase significantly compared to the other three algorithms due to its semi-asynchronous aggregation. When the number of nodes increases to 9, the communication time of BHFL is about 5.4 s. The communication times for BHFL-ST, BHFL-SYNC, and BHFL-AVG are 13.1, 8.3, and 10.9, respectively. As shown in Figure 7b, we also tested the communication time of the MLP model under four architectures, and we found that the communication time of BHFL performs comparably with other algorithms. It is relatively stable and fluctuates little with different numbers of nodes. This is due to the multi-objective optimization of BHFL in terms of node selection, which makes the selection of nodes to participate in aggregation more precise and efficient. Compared to other mechanisms, this optimization reduces unnecessary communication overhead. Figure 7 illustrates that the BHFL algorithm is able to effectively reduce the communication overhead while ensuring the quality and accuracy of the training results, which is well-proven to be a reasonably efficient federated learning algorithm.  
When the amount of data is large, there are more model updates to be transmitted during the training process, which leads to a larger amount of gradient information being transmitted per communication round, thus increasing the communication time. In Figure 8, we also perform comparison experiments in environments with different amounts of MNIST data. Figure 8a,b show that the communication time of the BHFL algorithm increases gradually with the increase in data volume, but the communication time of BHFL is the lowest compared to the other three algorithms under the CNN model and the MLP model, which proves that the BHFL algorithm is more effective in reducing the communication overhead when the data volume increases.  
Figure 7: Time cost analysis. (**a**) Time cost analysis of different node numbers for CNN model. (**b**) Time cost analysis of different node numbers for MLP model.
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Indirect HbA1c Biosensors Based on the Determination of Fructosyl Valine
#### 2.3.1 Fructosyl Amino Acid Oxidase (FAO)-Modified Biosensors  
For the FAO-based biosensors, the enzyme can be either confined in the film made of poly(vinyl alcohol) cross-linked photochemically with stilbazole (PVA-SbQ) [54], or immobilized on the surface of nanoparticulate materials, such as iridium (Ir) NPs [55], Fe\({}_{3}\)O\({}_{4}\) NPs [56], ZnO NPs [57], and Au NPs [58]. The NPs, PPy, and graphene nanosheet are promising materials to be used for the fabrication of the FAO-modified biosensors.  
The FAO enzyme can react with both FV and \(\varepsilon\)-fructosyl lysine (\(\varepsilon\)-FL) substrates. This latter reaction would give a false positive, so it is not an ideal element for HbA1c enzyme-based biosensors. [PERSON] and coworkers used a novel protein, fructosyl amino acid binding protein, to detect FV [66]. Its high sensitivity and specificity for FV over \(\varepsilon\)-FL makes the new sensing system ideally suited for the measurement of HbA1c. In addition, the protein engineering of FAOx was recently reviewed [67]. The FAO enzyme is still very expensive, and to obtain the FV will inevitably increase the cost and inconvenience for HbA1c assays.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Dataset Definition and Generation  
#### 2.1.1 Design Space  
In regard to the design variables, the choice rested on four key parameters in aeroelasticity: wing aspect ratio (AR), sweep angle (\(\Lambda\)), torsional stiffness (\(G\)), and angle of attack (\(\alpha\)). The AR is vital to improve aerodynamic efficiency and fuel consumption, although it poses aeroelastic and structural challenges [1]. The sweep angle (\(\Lambda\)) influences transonic flow onset and aircraft aeroelastic stability [29]. \(G\)/ relates how wings respond to aerodynamic loads and is critical to averting aeroelastic instabilities [29]. Lastly, \(\alpha\) impacts lift and drag, affecting flight stability. Besides their relevancy to and impact on the aeroelastic behavior, SHARPy's ability to modify these variables without coding was fundamental in their selection. The upper and lower boundaries (UB and LB, respectively) of these parameters are listed in Table 1 alongside the initial values that correspond to the Goland wing specifications. These boundaries were later adjusted based on the preliminary optimization results.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 4 Anomalies and Anomaly Detection in Blockchain
### Anomalies in Blockchain Networks  
Despite its advantages, blockchain technologies are not completely secure, remaining susceptible to specific attacks and issues [83; 89]. Attacks on the blockchain network are launched to impact the capital or popularity of the network, leading to a decrease in its market value. A typical case is the double-spending attack that engages malevolent actors to expend the same cryptocurrency or digital asset on multiple occasions, thereby eroding the trustworthiness and dependability of transactions. The challenge of double-spending arises from delays in disseminating pending payments across the network, thereby enabling a Bitcoin client to engage in multiple transactions involving the same Bitcoin. Another per-ilous predicament emerges in the form of the 51% attack, where an individual or collective entity seizes control over more than half of the mining power within the network, with the ultimate purpose of manipulating transactions. On the other hand, the prevalence of Sybil attacks should not be overlooked, where assailants fabricate multiple counterfeit identities or nodes to acquire dominion over a significant portion of the network, thereby impeding consensus and influencing the validation of transactions [90]. Similarly, Eclipse attacks [91] constitute another grave hazard, where malefactors encircle a victim's node with malevolent nodes, granting them the ability to manipulate or censor the victim's transactions at will. Selfish mining poses a formidable threat to the equity of the blockchain network because a miner or coalition of miners intentionally withholds valid blocks from the network, gaining an unjust advantage over honest miners. Vulnerabilities inherent in smart contracts within Ethereum can be exploited through tactics such as reentrancy or arithmetic overflows/underflows, leading to unintended repercussions and financial detriment [90; 91; 92]. Also, the blockchain ecosystem remains susceptible to distributed denial-of-service (DDoS) onslaughts [93], strategically designed to inundate the network with an overwhelming surge of requests or transactions, culminating in network congestion and the potential for disruptive consequences. Ransomware attacks have likewise emerged as a consequential menace within the blockchain sphere, wherein malicious actors encrypt users' data and demand a ransom, effectively holding their information hostage. Moreover, the exploitation of users' trust through Ponzi schemes that leverage blockchain technology poses a grave threat, luring unsuspecting participants with deceptive promises of exorbitant returns, ultimately resulting in financial losses [90; 91]. Table 1 illustrates some typical cases of blockchain anomalies along with their brief descriptions and their occurrences.
## 2 Non-Orthogonally Multiplexed Cognitive Radio (NOMCR) System
### Fast Convolution Filtered OFDM Based-NOMCR System
#### 2.2.2 FC-F-OFDM Based NOMCR System Model
The demodulated SU signal can be expressed by  
\[\mathbf{x}_{\text{m},\text{r}}^{\text{SU}}=\hat{\mathbf{G}}_{\text{m},\text{r} }^{\text{su}}\cdot\mathbf{r}_{\text{su}}(\text{n}),\text{ where }\  
(i.e., CP length + IFFT size). Therefore, the CP induced cyclic frequency of the PU-OFDM signal is given by, \(\alpha_{\mathrm{k}}^{\mathrm{Pu}}=\frac{\mathrm{k}}{\mathrm{N}_{\mathrm{sym}}^{ \mathrm{Pu}}}\), where \(\mathrm{k}=0,1,\ldots,\mathrm{N}_{\mathrm{sym}}^{\mathrm{Pu}}-1\). Moreover, as evident from the waveform structure of FC-F-OFDM, different sub-bands can have different number of sub-carriers. This means the CP sizes of the sub-bands can be different. Due to different CP lengths, the CP induced cyclostationary periods of each sub-band of the FC-FB will be different from that of the PU-OFDM. Moreover, even if the numbers of samples in each sub-band are equal, the CP length can be adjusted to be different from that of the PU-OFDM. If \(\alpha_{\mathrm{k,m}}^{\mathrm{su}}\) denotes the kth cyclic frequency of the mth sub-band of the SU-FC-FB, then \(\alpha_{\mathrm{k,m}}^{\mathrm{su}}=\frac{\mathrm{k}}{(\mathrm{N}_{\mathrm{m, sym}}^{\mathrm{su}})}\), where the cyclostationary period of SU-FC-FB's mth sub-band is denoted by \(\mathrm{N}_{\mathrm{m,sym}}^{\mathrm{su}}\). It is evident that the signal patterns of the individual components of the NOMCR signal can be utilized for implementing cyclic-FRESH filter based SIC receiver under a low SNR regime.
###### Abstract  
Effectiveness of commercial natural antioxidants from rosemary and green tea were investigated in deli-style meat products via headspace hexanal by solid-phase microextraction gas chromatography and sensory oxidation flavor by a trained panel at weeks 1, 7, and 13 of refrigerated storage. A water/oil-soluble rosemary extract at 400 mg/kg proved the most effective antioxidant in cured deli turkey (CDT). In chicken fillet (CF), a water-soluble rosemary extract at 400 mg/kg was most efficient, especially in combination with phosphate. In pulled pork (PP), none of the antioxidants were as efficient as phosphate, though all three tested antioxidants were moderately effective in PP without phosphate. Nitrite was such an efficient antioxidant on its own in CDT that hexanal levels were so low that it was not possible to build correlation models between headspace hexanal and sensory oxidation flavor throughout the storage period. Phosphate also proved very efficient on its own in both CF and PP. It was possible to build good correlation models throughout storage for both CF and PP. Hence, hexanal was found to satisfactorily predict development of oxidation flavor in different types of uncured deli meat products both with and without added phosphate.  
lipid oxidation; deli meat; hexanal; solid-phase microextraction gas chromatography; oxidation flavor; natural antioxidants; rosemary
# Explore Long-Range Context Features for Speaker Verification
###### Abstract  
Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on ResNet and analyze their complementarity. Firstly, the Hierarchical-split block (HS-block) is introduced to enlarge the receptive fields (RFs) and extract long-range context information over the feature maps of a single layer, where the multi-channel feature maps are split into multiple groups and then stacked together. Then, by analyzing the contribution of each location of the convolution kernel to SV, we find the traditional convolution with a square kernel is not effective for long-range feature extraction. Therefore, we propose cross convolution kernel (cross-conv), which replaces the original \(3\times 3\) convolution kernel with a \(1\times 5\) and \(5\times 1\) convolution kernel. Cross-conv further enlarges the RFs with the same FLOPs and parameters. Finally, the Depthwise Separable Self-Attention (DSSA) module uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel. Experiments are conducted on the VoxCeleb and CnCeleb to verify the effectiveness and robustness of the proposed system. Experimental results show that the combination of HS-block, cross-conv, and DSSA module achieves better performance than any single method, which demonstrates the complementarity of these three methods.  
speaker verification; long-range dependencies; hierarchical-split block; cross convolution; depthwise separable self-attention module +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Explore Long-Range Context Features for Speaker Verification. _Appl. Sci._**2023, 13, 1340. [[https://doi.org/10.3390/app13031340](https://doi.org/10.3390/app13031340)]([https://doi.org/10.3390/app13031340](https://doi.org/10.3390/app13031340))
## 4 Results and Discussion
### Estimation of Thinning Depth  
To evaluate defect depth evaluation effectiveness, empirical tests of lock-in thermography were conducted, and the depth was estimated by calculating the phase contrast.  
Figure 10: Results of binarization by Otsu algorithm: (**a**) 0.01 Hz, (**b**) 0.02 Hz, and (**c**) 0.03 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Defect ID**} & \multicolumn{6}{c}{**Modulation Frequency (Hz)**} \\ \cline{2-7}  & **0.1** & **0.05** & **0.04** & **0.03** & **0.02** & **0.01** \\ \hline \multirow{8}{*}{Phase SNR (dB)} & B1 & 1.57 & 6.07 & 6.32 & 16.25 & 13.08 & 18.47 \\  & B2 & 1.34 & 6.12 & 7.98 & 24.23 & 22.57 & 29.22 \\ \cline{1-1}  & B3 & 3.68 & 6.74 & 11.78 & 30.35 & 28.01 & 38.34 \\ \cline{1-1}  & C1 & 3.67 & 30.02 & 35.48 & 38.98 & 41.67 & 50.12 \\ \cline{1-1}  & C2 & 7.57 & 36.44 & 36.24 & 40.36 & 41.75 & 50.57 \\ \cline{1-1}  & C3 & 8.07 & 37.15 & 32.86 & 45.02 & 47.86 & 53.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SNR calculation values of phase data according to modulation frequency.  
Figure 11 shows the variation in depth of defects (40% to 90% thinning) detected in the binarized image as a function of phase difference at a modulation frequency of 0.01 Hz. As can be seen, using the third order polynomial curve results in a good fitting of the defect depth with a coefficient of determination equal to \(R^{2}\) = 0.9999. The third-order polynomial curve for expressing the defect depth as a phase angle is expressed as Equation (9). Table 4 shows the defect depth prediction results at 0.01 Hz calculated by Equation (9). It can be seen that the defect depth prediction shows good results of less than 4.14% for all detected defects. The maximum error was calculated to be \(-\)4.14% for a defect depth of 4.2 mm, and the minimum error was calculated to be 0.85% for a defect depth of 2.4 mm.  
\[Depth=0.97277+3.10884\Phi-0.78949\Phi^{2}+0.11905\Phi^{3} \tag{9}\]
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 5 Conclusions  
An improved A* algorithm based on adaptive grids was proposed. It constructs learning models using marine weather data and AIS data to evaluate the ETA and economic factor of ship operation, and it enables economical routing of ships on the basis of that factor. The adaptive grid system improves the computational complexity itself through the improvement of map retention to solve the high time complexity of the existing A* algorithm. It was combined with the 16-way search method to create a more visually natural and economical optimal solution by smoothing the optimal path. The low-cost heuristic of the A* algorithm was used to reduce the computational time for optimal route finding, and it was confirmed that the admissibility and optimality conditions of the obtained optimal route were satisfied. To validate the proposed method, the routing simulations from Gwang-yang, Korea, to Westshore Terminal, Canada, confirmed that the proposed method provides an economical route compared with the actual AIS trajectory. Future research is planned to determine who chooses the appropriate heuristic and to enable more efficient routing. In addition, the obtained route is still the near-optimal route; therefore, methods to improve the performance of the learning models will be explored.  
Conceptualization, M.A., Y.N. and Y.W.S.; data acquisition, Y.W.S. and Y.N.; methodology, M.A.; data curation: S.L.; coding, M.A. and Y.W.S.; validation, M.A.; formal analysis, M.A.; investigation, M.A.; resources, Y.N. and I.L.; writing-original draft preparation, M.A.; writing-review and editing, Y.N. project administration: K.C.K., J.B., D.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIP) through GCRC-SOP (No. 2011-0030013), Korea government(MSIT) (No. 2018R1D1A1A202086093), National Innovation Cluster Program (P0006887, Build on Cloud Intelligence Platform based Marine Data) and Human Resources Development program (No. 20164030201230) funded by the Ministry of Trade, Industry & Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT).  
The authors declare no conflict interest.
## 8 Results and Discussions
### Uncertainty Analysis from Initial Stage to Stable VANET  
In this scenario, we presented general behavior of vehicles at start of the network, as can be seen in Figure 7a. The uncertainty at the start is very high because the entities do not know much about each other, as \(w_{B}^{A}=(0,0,0,1)\). After some interaction and gathering first and second hand observation, the opinion of entities are changes and uncertainty decreases. Keeping uncertainty high at start also help us to detect and tackle many malicious attacks, such as Modification, Forgery and Black Hole attacks.  
In Figure 7b, a general behavior of nodes after some interaction is depicted. We can see that trusted nodes having values \(t_{1}=0.95\) and \(t_{2}=0.8\) are secure for communication. We can also see that once the user is attacked by an adversary they gradually decrease their trust score as they are unable to make good interaction with other entities. New incoming vehicle have less recommendation from neighbors at start, so its trust value increases with time after having some good actions.  
Performance Comparison between Standard, AASR and Our Proposed 3 VSR under Different Mobility and Attack Pattern  
Here we have considered two different scenarios and observed the behavior of our proposed scheme with other protocol also.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
###### Abstract  
Fracture properties are crucial for the applications of structural materials. The fracture behaviors of crystalline alloys have been systematically investigated and well understood. The fracture behaviors of metallic glasses (MGs) are quite different from that of conventional crystalline alloys and have drawn wide interests. Although a few reviews on the fracture and mechanical properties of metallic glasses have been published, an overview on how and why metallic glasses fall out of the scope of the conventional fracture mechanics is still needed. This article attempts to clarify the up-to-date understanding of the question. We review the fracture behaviors of metallic glasses with the related scientific issues including the mode I fracture, brittle fracture, super ductile fracture, impact toughness, and fatigue fracture behaviors. The complex fracture mechanism of MGs is further discussed from the perspectives of discontinuous stress/strain field, plastic zone, and fracture resistance, which deviate from the classic fracture mechanics in polycrystalline alloys. Due to the special deformation mechanism, metallic glasses show a high variability in fracture toughness and other mechanical properties. The outlook presented by this review could help the further studies of metallic glasses. The review also identifies some key questions to be answered.  
Keywords:metallic glasses; fracture; shear bands; mechanical properties; fracture mechanism +
Footnote †: journal: Applied