esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ground Response Analyses and Input Motions
### Two-Dimensional Analyses  
The 2D numerical analyses of the seismic site responses were performed using the QUAD4M computer code [15]. This code is a dynamic, equivalent, linear, two-dimensional computer program. The geometry of the model was improved using a mesh of a finite number of elements with triangular and/or quadrilateral shapes interconnected at their common nodes. QUAD4M solves the elastodynamic computation of the volume wave transfer using the step-by-step integration of the accelerogram in the time domain. Seismic waves were minimised and induced by artificial reflection at the domain boundaries. The base half-space, i.e., bedrock, can be modelled as elastic, representing the response to an infinite field condition. To minimise the effects of artificial reflections from seismic waves at the lateral boundaries, the domain was laterally extended. The lateral boundaries were modelled as adsorbent, where a horizontal viscoelastic damper was applied. For numerical efficiency, the mesh was extended in two directions to further minimise the influence of reflected waves. The domains were meshed using triangular elements. In QUAD4M computation, the 2D mesh size was adapted to the velocity model (mesh adaptivity procedure) to reduce the computational cost. The maximum element size \(L_{max}\) was assumed equal to  
\[L_{max}=\frac{V_{s}}{f_{max}\ 8} \tag{2}\]  
This wavelength corresponds to the ratio between the lowest value of \(\mathrm{V_{S}}\) in the model and a frequency of 10 Hz, chosen as a \"compromise\" frequency balancing the computational cost and the engineering interest.  
The STRATA computer code was used to compare the results of the 1D and 2D analyses.  
It is important to note that comparisons between the 1D and 2D results are not entirely unassailable, as damping is implemented differently in the two codes. In the 1D code (STRATA), damping is frequency-independent, while, in the 2D code (QUAD4M), Rayleigh damping is applied, which introduces a frequency dependence at lower frequencies.  
The full Rayleigh dual-frequency control formulation allows for the adequate modelling of viscous damping that varies slightly with the frequency in the relevant frequency range (fundamental frequency and predominant frequency of the seismic input). As a result, this formulation provides a more conservative estimate of the local seismic response.
## 3 The Construction of Wavelet Packet Signature Analysis
On the other hand, due to the trade-off between time localization and frequency resolution, the resolutions in time and frequency domains cannot reach their highest levels concurrently. Therefore, the signal processing tool is required to have the frequency resolution power of the Fourier transform and the time resolution power of the wavelet transform. To obtain frequency characteristic associated with BRB and develop data analysis of wavelet transform, further FFT analysis can be enhanced the overall effectiveness of feature extraction. In general, spectral analysis and wavelet packet transform represent a signal from different perspectives.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 2 WENO-ZQ Scheme and Numerical Tests
### Governing Equations and Numerical Method
However, the dispersion of the MUSCL scheme is higher than that of the spectral method, indicating that the calculated wave amplitude exceeds the theoretical value, which may result in an overestimation of prediction efficiency in internal flow. The WENO-ZQ5 scheme exhibits improved dispersion characteristics over the fifth-order WENO-JS (WENO-JS5) scheme across all wave number ranges. In the range of \(\phi<0.95\), the WENO-ZQ5 scheme exhibits lower dissipation compared to the WENO-JS5 scheme, indicating a superior ability to resolve high-frequency waves. This implies that the WENO-ZQ5 scheme is more effective in capturing detailed flow structures and preserving waves or vortices within the flow field. As a result, the WENO-ZQ5 scheme can capture the same level of detail with fewer computational grid cells. Figure 1: Spectral properties of WENO-ZQ5 scheme with three sets of linear weights. Figure 2: Spectral properties of the 3 rd-order accuracy schemes.
## 5 Experimental Results
### Experiments in Case 2: The PMSMs Have the Same Speed with Different Load Torques  
The dc-link capacitor current is experimented for a scenario where one PMSM (PMSM-1) has a constant speed of 2000 rpm and a constant load torque of 9 Nm, and the other PMSM (PMSM-2) has a varying load torque with a constant speed of 2000 rpm. The experimental results show that the dc-link capacitor current increases when PMSM-2 is operating if there is no phase difference between the carrier waves, as shown in Figure 18. On the contrary, the dc-link capacitor current decreases when PMSM-2 operates if there is \(\pi/2\) rad phase difference between the carrier waves. The dc-link capacitor current of two motor systems is higher than that of one motor system when there is no phase shift. Conversely, the dc-link capacitor current of two motor systems is lower than that of one motor system if there is a phase shift.
# Microwave Liquid Crystal Technology
## 7 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Technology**} & \multirow{2}{*}{**Implementation**} & \multirow{2}{*}{**f**} & \(\boldsymbol{\Delta\phi}\) & **IL** & **FoM** \\ \cline{3-6}  & & **(GHz)** & **(°)** & **(dB)** & **(°/dB)** & **REF** \\ \hline
**GaN** & 2-bit RTPS & 80 & 82.5 & 3.7 & 17 & [47] \\ \hline \multirow{3}{*}{**MEMS**} & 4-bit loaded line & 60 & 250 & 3 & 83 & [48] \\  & Waveguide MEMS & 106 & 47 & 3.4 & 13 & [49] \\  & 3-bit switched line & 77 & 315 & 5.7 & 55 & [45] \\ \hline
**MEMS \& CMOS** & RTPS (CMOS 0.18 \(\upmu\)m) & 65 & 144 & 3.2 & 42 & [50] \\ \hline \multirow{3}{*}{**LC**} & Ridged waveguide & 96 & 500 & 7.2 & 70 & [51] \\  & Metallic waveguide (magnetic bias) & 100 & 309 & 2.3 & 135 & [39] \\ \cline{1-1}  & Subwavelength fiber & 103 & 140 & 2.3 & 60 & [43] \\ \cline{1-1}  & Step-index fiber & 103 & 390 & 3.9 & 100 & [42] \\ \hline
**LC \& MEMS** & Loaded line & 93 & 190 & 4.5 & 42 & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different phase shifter technologies at W-band.
# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 3 Materials and Methods
### Model Generation Pipeline  
The following steps were used to generate the classification models (see Figure 2):  
1. The arrhythmia data set was obtained (in CSV format) for pre-processing before the models were trained and tested. 1. The class characteristic \"dxholterfinal\" was normalized: 0 for ARRHYTHMIA and 1 for NORMAL. 2. Subsequently, imputation of missing values was conducted using the average technique. 3. In the previous subsection, it was clarified that there were 184 NORMAL records and 68 ARRHYTHMIA records; therefore, the data were balanced using the SMOTE algorithm to obtain 184 records for each case. 4. Age and the minimum, maximum, and average heart rates were normalized on a scale from 0 to 1, as they have different scales. This can cause certain features to dominate others, negatively impacting model performance. 2. After pre-processing, the models were trained and tested. 1. The data set was divided as follows: 80% for training and 20% for testing. 2. Machine learning and deep learning algorithms and architectures were implemented for training and testing, and parameter optimization was carried out using grid search algorithms. The python GridSearchCV function was used, with the following parameters: estimator=log_reg, param_grid=param_grid, cv=StratifiedKFold(n_splits=5), verbose=2, n_jobs=1, scoring=f1'. Subsequently, accuracy, sensitivity, and specificity metrics were calculated for each model.  
Figure 1: Graphical interfaces of the “MiCardio” smartwatch application.  
3. Heart rate samples were taken from the patients' smartwatches and tested with the chosen model and algorithm to validate our proposal. Finally, the maximum, minimum, and average heart rates corresponding to 20 h of uninterrupted use were extracted. Data on the age and sex of each patient were also tabulated. Our model's input data included the patient's age and sex, as these variables are correlated with the detection of arrhythmia; in particular, older individuals have a greater probability of arrhythmia, and females are more at risk of cardiovascular diseases.
4. The samples were used as input data into the model, displayed on a terminal such as a smartphone or a computer. The model provided a classification result of AR-RHYTHMIA or NORMAL.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 6 Demonstrative Implementation
### Claims and Verifiable Credential  
The VC is formatted as JSON-LD, which contains the following information in the form of a Uniform Resource Identifier (URI), to define the means and protocol for parties to communicate: credential type, issuer, date, the credential and its subject, cryptographic proof of the issuer, optional metadata, and revocation condition.  
Sample of Input Data for a VC  
[ {  \"@context\": \"[[http://schema.org](http://schema.org)]([http://schema.org](http://schema.org))\",  \"@type\": \"bankingAccount\",  \"agent\": {\"did\": \"did:ethr:0 xff0d...de1 aebd143e\" },  \"bankingCredential\": {  \"issuer\": {\"name\": \"Bank A\" },  \"description\": \"customer valid account\",  \"accountName\": \"User A\",  \"accountNo\": \"012345678\",  \"status\": \"Active\", //or \"Revoked\"  \"time\": \"2021-11-29T08:00:00.000-07:00\"  } ] The application allows authorities, such as authorized banks in our case, to issue claims and VCs and store them on the IPFS. The authorized entities, such as central banks, regularly monitor banks and apply rules policies. Based on this, we consider the bank as a trusted authority to issue VCs. Off-chain there are two GUI interfaces: the first allows the bank to create pairwise DIDs and issue VCs; the second enables verifiers and other open banking agencies to validate the customer's VCs.  
The following is a list of SSBI subset functionalities to handle DIDs and VCs:  
* Create_DID;
* Create_VC;
* Resolve_DID;
* Verify_VC;
* List_existing_DIDs;
* List_existing_VCs;
* Retrieve_VC;
* Send_signed_VC.  
The on-chain component is part of a front-end user interface (Android or iOS app hosting identity operations) and React Native application (customer and open banking agents). The functionality includes creation of DIDs, removal of DIDs, and VC manipulation for issuance and verification, in addition to underlying connectivity with the banking card and wearables to perform key management operations and Ethereum transaction signing. Sample snapshots to show SSBI sub-functionalities presented in Figure 8. The flow was validated and tested using Ethereum Blockchain. The off-chain part is based on Java script and a Node.js application that communicates with the client through web3.js and API implementation. The encrypted data are stored in an SQLite3 database [46] and are accessible via a JDBC driver. The open banking API layer includes financial operations such as Account API, Payment, Money transfer, and Card life cycle.
## 5 Simulation Study
### Results and Discussions  
In this section, the simulation results of the WMN-based NAN under consideration are presented and discussed. In order to evaluate the performance of the proposed OLSR multiple metrics (\(OLSR_{MM}\)), the average PDR, average delay and blocking probability for all traffic types are measured and compared with other OLSR link metric types: ETX, ML and MD.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 4 Functional Tailoring of Graphene-Based Membranes
### Biopolymers  
Biopolymers have been proven to be excellent dopants for antimicrobial materials due to their rich functions, diverse nanostructures, and excellent biocompatibility [77]. Chitosan is a polysaccharide extracted from the exoskeleton of crustaceans and the mycelium of some fungi. Due to its unique properties, chitosan is considered a promising reagent for molecular separation, food packaging, artificial skin, bone substitutes, water treatment, electrochemical sensors, biosensors, and several other applications. Chitosan is an interesting graphene immobilization matrix, which can be modified by chemical methods such as cross-linking, complexation and grafting, and different functional groups can also be introduced. [PERSON] et al. studied the synthesis of chitosan-GO (CS-GO) nanocomposite films and their application prospects in food packaging [78]. Nanocomposite films were prepared by crosslinking GO and chitosan at a high temperature (120 \({}^{\circ}\)C). The results showed that the CS-GO membrane had better antibacterial properties against Gram-negative _Escherichia coli_ and _Gram-positive Bacillus subtilis_. The GO/CS/ZnO composite exhibited impressive antibacterial activity against _Escherichia coli_ and _Staphylococcus aureus_, and excellent adsorption performance for methylene blue [79].  
[PERSON] et al. studied a composite material containing GO, lactoferrin (LF) and chitosan [80]. In this composite, LF and chitosan enhanced the toxicity of GO to bacteria and reduced bacterial viability. [PERSON] et al. investigated the synthesis, structure, and antibacterial properties of a novel nanocomposite film composed of bacterial cellulose (BC), GO, and copper oxide (CuO) nanosheets [81]. Nanocomposite films were synthesized by combining GO-CuO nanohybrid with BC matrix using a uniform blending method. The antibacterial activity of the nanocomposites against Gram-positive bacteria was superior to that of Gram-negative bacteria. The antibacterial activity of the BC/GO-CuO nanocomposite was higher than that of BC/CuO, and the antibacterial mechanism of the nanocomposite was elucidated. [PERSON] et al. selected hydroxypropyl cellulose (H), CS and polyoxyethylene (P) as membrane materials to improve the hydrophilicity, antibacterial properties and yield of nanofibers, respectively [82]. In addition, graphene (G) was added to enhance the antibacterial properties of the membrane. The antibacterial activity of the membrane was demonstrated by experiments against _Escherichia coli_ and _Staphylococcus aureus_. The highly hydrophilic HCP and HCPG membranes prevented bacterial adhesion. The presence of such membranes, especially graphene-embedded HCPG membranes, also greatly reduced bacterial growth. The small pore size of the HCP and HCPG nanofibrous membranes prevented bacterial penetration and infection.
## 3 Results and Discussion
### Application of ANN Model for the Estimation of Critical Crack Size of a Pipe

Table 3: Comparison between the predictions of normalized \(K_{\rm I}\) from the linear interpolation of API 579-1/ASME FFS-1 and the ANN model. The BiLerp prediction from API 579-1/ASME FFS-1 fails to accurately predict the \(a_{\text{C}}\), being approximately 4.13% lower than the \(a_{\text{C}}\) obtained from FEA. However, the ANN model continues to successfully predict the \(a_{\text{C}}\), with a difference of approximately 0.59% higher than the \(a_{\text{C}}\) obtained from FEA. The lower BiLerp prediction of \(a_{\text{C}}\) compared to the accurate prediction from FEA may lead to premature replacement of cracked pipes, resulting in increased costs. Moreover, for improving the fracture resistance of pipes and selecting optimal operating conditions, accurate \(a_{\text{C}}\) values are essential. This reaffirms the applicability of the present ANN model for predicting both \(K_{\text{I}}\) and \(a_{\text{C}}\) in pressurized pipes with infinitely long internal surface cracks in the longitudinal direction.
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 3 RF-Access
### Tag-Array Based Intrusion Detection
The probability \(p_{i}\) of an RSSI falling into the \(i\)th bin is:  
\[p_{i}=\frac{m_{i}}{\sum_{i=1}^{N}m_{i}}. \tag{10}\]  
The RSSI entropy in unit time can be calculated as follows:  
\[H_{R}=E[log_{b}p(M)]=-\sum_{i=1}^{N}p_{i}\cdot log_{b}(p_{i}), \tag{11}\]  
where \(b\) is the base of logarithm, which is set to two, making the unit of entropy to be \(bit\). Thus, the RSSI entropy \(f_{R}\) of the sample is:  
\[f_{R}=H_{R}/\Gamma, \tag{12}\]  
where \(\Gamma\) is the time interval between the first sampling and the last one. For Doppler frequency offsets, we can use the similar method to calculate its entropy. In addition to the above signal-level features, we observe that the application layer parameters, such as the reading rates, can also be used to conduct counting. With these features, we use the random forest algorithm [18] as the machine learning classifier to conduct counting due to its good classification results on small data sets. We adjust the parameters of the classifier to optimize the estimation accuracy and use the \"cross validation method\" to evaluate the model. Specifically, the data set is randomly divided  
Figure 10: Impact of the number of users on Doppler frequency offsets. Figure 9: Impact of the number of users on RSSI. into \(k\) mutually exclusive subsets with the similar size. We then utilize one subset as the test set and the remaining \(k-1\) subsets as the training sets to obtain \(k\) groups of training/test sets. We finally obtain \(k\) mean values of classification accuracy through carrying out \(k\) training and classification tests. Here, we adopt \"10-fold cross-validation\" with \(k\) being 10. It is worth noting that there are some good solutions to the problem of people counting, such as computer vision [8] and radar [19]. We could choose one of them to conduct people counting and use RFID to identity authentication and estimate moving direction. However, in a barrier-free access control system, this increases the deployment overhead and hardware cost for practical use. Instead, our solution is to use an existing RFID device together with a tag matrix (less than nearly 10) to achieve the same task, which might not be the best choice for people counting alone but is a good solution to the existing access control system.
## 4 Performance Evaluation
### Simulation Results
#### 4.2.1 Speed Variation  
Figure 3 presents the performance metrics of PDR, End-to-End Delay, and Average Energy Consumption against varying node speeds for QLR, DQR, and our proposed protocol under a fixed traffic bit rate of 3000 bps. Our protocol demonstrates superior performance across all node speeds.  
Figure 3: Performance Metrics vs. Node Speed.  
In Figure 3a, our proposed protocol consistently achieves higher PDR compared to QLR and DQR across all node speeds. For instance, at a speed of 25 km/h, our protocol exhibits a PDR improvement of approximately 35.3% over QLR and 8.8% over DQR. This improvement is attributed to our protocol's robust clustering and AHP-based weighting, which enhance route reliability and efficiency through DQN learning. Even at higher speeds, our protocol effectively identifies and selects reliable next-hop nodes, ensuring that packets are delivered successfully despite increased mobility. End-to-End Delay for each protocol under varying node speeds is depicted in Figure 3b. Our proposed protocol consistently records the lowest delay across all node speeds. At 25 km/h, the delay is reduced by approximately 21.2% compared to QLR and 16.6% compared to DQR. This reduction is achieved through our protocol's ability to swiftly identify optimal routing paths via clustering and DQN, thereby minimizing the time packets spend traversing the network. The efficient selection of next-hop nodes, even at higher speeds, contributes significantly to the reduced delay observed.  
Consequently, as illustrated in Figure 3c, our proposed protocol maintains relatively stable energy consumption as node speed increases, while QLR and DQR show increasing energy consumption with higher mobility. At 25 km/h, our protocol consumes approximately 28.7% less energy than QLR and 13.0% less than DQR. This energy efficiency is achieved through AHP-based clustering, which ensures balanced energy usage among nodes, and DQN optimization that reduces unnecessary transmissions. Additionally, our protocol effectively manages the queue length, preventing congestion and minimizing retransmissions, which further contributes to energy savings.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 3 MF Algorithms  
MF is a method to improve image contrast of subsurface defects by increasing the contrast of defective areas and reducing the signals from sound areas [31; 33; 34]. Different types of MF algorithms have been developed, all of which are based on the assumption that [31; 34]:  
\[T_{\text{obs}}=T_{\text{ref}}=T_{\text{ideal}} \tag{1}\]  
at any time. \(T_{\text{obs}}\) is the temperature recorded by the IR camera, \(T_{\text{ref}}\) is the temperature response reflected from the defective area, and \(T_{\text{ideal}}\) is the ideal temperature response of the sound area. Equation (1) can be represented in vector form:  
\[X=S+W \tag{2}\]  
where these vectors collect all recordings over time and \(X=T_{\text{obs}}\), \(S=T_{\text{ref}}\), and \(W=T_{\text{ideal}}\). Equation (2) is multiplied by a vector \(q\) that maximizes the visibility of the reflected temperature from the defective area and minimizes the response of non-defective areas. The vector \(q\) can, therefore, be calculated by [31; 34]:  
\[\underset{q}{\text{max}}\|q^{T}S\|_{2}\text{ subject to }\underset{q}{\text{min}} \|qW\|_{2} \tag{3}\]  
where \(q^{T}\) is the transpose of \(q\). Different methods for finding \(q\) result in the different types of MF algorithms. Each MF algorithm considers a certain \(q\) vector to increase the contrast of defective areas and decrease the signals from sound areas. The \(q\) vector for the spectral angle map (SAM) is  
\[SAM=\frac{S^{T}X_{ij}}{\sqrt{S^{T}S}\sqrt{X_{ij}^{T}X_{ij}}} \tag{4}\]  
Figure 4: Step heating thermography experiment.  
where \(i\) and \(j\) imply that the calculation is repeated for all pixels of all thermal images to provide a single correlation image and \(S^{T}\). The adaptive coherence estimator (ACE) uses as \(q\):  
\[ACE=\frac{S^{T}C^{-1}X_{ij}}{\sqrt{S^{T}C^{-1}S}\sqrt{X_{ij}^{T}C^{-1}X_{ij}}} \tag{5}\]  
where \(C\) is covariance matrix of the ideal temperature vector defined as:  
\[C=\frac{1}{m}\sum_{i=1}^{m}WW^{T}. \tag{6}\]  
The \(t\)- and \(F\)-statistics use the different \(q\) vectors to improve the contrast associated with defective areas:  
\[F_{stat}=\frac{\left(S^{T}R^{-1}x_{ij}\right)^{2}}{X_{ij}^{T}R^{-1}X_{ij}-\rho ^{2}\left(S^{T}R^{-1}X_{ij}\right)^{2}}\rho^{2}(d-1) \tag{7}\]  
and  
\[t_{stat}=\frac{S^{T}R^{-1}x_{ij}}{\sqrt{X_{ij}^{T}R^{-1}X_{ij}-\rho^{2}\left(S ^{T}R^{-1}X_{ij}\right)^{2}}}\rho\sqrt{d-1} \tag{8}\]  
where \(\rho=\left(S^{T}R^{-1}S\right)^{-1/2}\).