esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reactivity of Immobilized Phospholipase A\({}_{1}\)  
#### 3.3.1 Effect of Glutaraldehyde as a Cross-Linker of Phospholipase A\({}_{1}\)  
Figure 4a illustrated the schematic image of substrate-based W/O microemulsion association with immobilized PLA\({}_{1}\). The W/O microemulsion droplet readily diffused into the inner pore channeled under the lower water content conditions. The pore size at the Accurel surface was found to be 1-5 \(\upmu\)m (SEM images). Figure 4b demonstrates that substrate phospholipid molecules were located in the outer surrounding of the W/O microemulsion droplet. It is readily associated with immobilized PLA\({}_{1}\). The hydrolysis reaction was quickly initiated, and a high reaction rate was realized.  
Figure 4: (**a**) Schematic illustration of pore structure and W/O microemulsion droplets association with immobilized phospholipase A\({}_{1}\) on the outer and inner surfaces of microspores in Accurel particles. (**b**) Phospholipid-based W/O microemulsion droplets readily diffused into the microspores of the PLA\({}_{1}\) carrier.  
Figure 3: Comparison of the adsorbed amount of phospholipase A\({}_{1}\) per unit mass of Accurel MP100.  
To achieve the highest yield in phospholipids hydrolysis using immobilized PLA1, the Accurel MP100 with glutaraldehydeHeatment and the amount of water content in W/O microemulsion were the key factors. These are especially important for the industrial production of enzymes.  
Figure 5 shows the time course of concentration of palmitic acid using the immobilized PLA1 on Accurel with glutaraldehydeHeatment. The enzyme reactions were quickly initiated regardless of the concentrations of GA. When the GA concentrations were 10% and 6%, the amounts of produced palmitic acid were the highest. The next highest production was for GA concentrations of 3%. There is no difference in the amount of production between 1.5% and 0% GA concentrations.  
Despite the small difference in the immobilized amount of PLA1 between GA concentrations of 0-10%, the amount of produced palmitic acid varied dramatically. Therefore, the GA concentration not only effectively immobilized the PLA1 but also acted as a hydrophobic modifier of the carrier surface.  
Figure 6 shows the effect of the initial hydrolysis reaction rate for immobilized PLA1 on the GA treatment as a cross-linker of PLA1. The initial reaction rate increased with GA concentration and then dropped for GA concentrations exceeding 6%. According to the results, a GA concentration of 6% was optimal for effective immobilization of PLA1 and allowed to attain the required initiated reaction rate and reaction yield.  
Figure 5: Time course of palmitic acid production (C\({}_{\text{p}}\)) using immobilized phospholipase A1 in various glutaraldehydeHe concentrations. The glutaraldehyde concentrations, C\({}_{\text{GA}}\) were 0% (\(\Circle\)), 1.5% (\(\bullet\)), 3% (\(\blacktriangle\)), 6% (\(\blacktriangledown\)), and 10% (\(\blacksquare\)). The experiments were each conducted a minimum of three times.  
Figure 6: Effect of immobilized phospholipase A1 with glutaraldehydeHeatment on the initial reaction rate.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 6 Future Challenges and Research Scopes
### Limitations of ML Techniques in VANET  
* ML approaches can be utilized in the vehicular network setting; however, they encounter constraints such as significant memory demands and complications [185].
* SVMs show potential but encounter obstacles related to selecting an efficient kernel and managing the complexities of models.
* The adaptation of neural networks to VANETs is constrained by certain restrictions, like understanding feature independence and the occurrence of the \"zero frequency\" scenario. Due to the individualistic approach toward features, NB cannot derive significant insights from the interconnections across the parameters it sets [186]. Nevertheless, in scenarios when samples have correlated and comparable characteristics, it can function with precision.
* KNNs might be lengthy and intricate in a vehicular network [187].
* RNNs can tackle VANET difficulties but may lack efficiency in time-critical applications such as cybersecurity.
* AR methods exhibit prolonged process times and demonstrate worse efficiency than K-means, particularly in identifying prevalent risks.
* The primary challenge of utilizing EL is its more significant temporal overhead compared to alternative single-classifier-based techniques.
* PCA provides an approach utilized in neural networks to reduce the number of features effectively. This may be combined with additional methods of ML to improve efficiency.
* Reinforcement Learning necessitates substantial information and extensive computational resources [188], so its utilization in physical structures is constrained by the challenge of dealing with multiple aspects.
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 4 The Proposed Method
### Building Phase
#### 4.1.2 Key Generation  
Step 1: The key used for encrypting shared intelligence is created by CRYSTALS-KYBER. The process is shown in Figure 5. Initially, a random uniform matrix \(A\) is generated. Subsequently, the binomial vector \(s\) is derived from \(A\), with the vector \(e\) representing error coding. Following this, \(b\) is computed, thus leading to the generation of the private key \(s\) and the public key \((A,\ b)\).  
Step 2: The blockchain key is constructed by SPHINCS\({}^{+}\), as shown in Figure 6. First, user \(i\) chooses a random number to create the private key seed \(SSKseed\), sets it to the WOTS\({}^{+}\) and FORS bottom layer of SPHINCS\({}^{+}\), which goes through the hash function to build the WOTS\({}^{+}\) and FORS trees, and then XMSS connects FORS and WOTS\({}^{+}\) to generate a \(SSK\) and \(SPK\).  
Step 3: The public key \(SPK\) is input into SHA256 and RIPEMD160 to obtain \(PK\) hash. The outcome is further input into SHA256 twice. The first 4 bytes of consequence are combined with \(PK\) hash, while the concatenation result is passed to BASE58 to generate an \(address_{i}\), as shown in Figure 7. Finally, \(address_{i}\) and \((A,b)\) are broadcasted on the blockchain work.
# A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning
## 5 Conclusions  
This work elaborated the complicated and non-linear issues of evaluating defect depths in composite materials via infrared thermography with a GRU learning model. The methodology proposed here employed a GRU model combined with pulsed thermography to analyze the depth of defects. The simulated samples provide an economical platform for GRU training and depth estimation. Quantitative analysis of defect depth (subsurface features) has been evaluated by a GRU based statistical method through developed neural network modeling and cross validation experimental verification. It has been proven that the GRU modeling can produce an advanced depth detection. For future work, the experimental data have to be evaluated for the robustness of the GRU model. Further, other types of deep learning models and modified versions of the GRU model have to be applied to increase the depth estimation ability in this topic.  
Q.F.; conceived and designed the experiments, Q.F.; proposed the methodology, Q.F.; performed the experiment, Q.F.; performed the data analyses and wrote manuscript, X.M.; reviewed and edited manuscript, X.M.; supervised the project. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research is conducted under the Tier One - Multipolar Infrared Vision Canada Research Chair (MIVIM) in the Department of Electrical and Computer Engineering at Laval University. Authors would also like to thank the Chinese Scholarship Council for their financial support and help.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### CBCT Measurements  
The parameters of the CBCT images were as follows: 120 kVp; 10.11-18.54 mAs; 4.8-8.9 s scan time; 0.4 voxels; and 160x60-130 FOV. A manual method was used to obtain cross-sectional sections through CBCT. The location of the reference points on the guide arch line was determined by the researcher who made the measurements in the anterior-posterior direction to the midpoint of the crest (bone width) in each region. Again, the midpoint of the bone width in the maxilla and mandible where the measurements were planned to be made on the edentulous and symmetrical toothed side were determined as reference points, and measurements were performed on the cross-sectional sections of the maxilla and mandible images obtained in this way.  
Bone measurements in areas with one missing tooth were made on 1.2 mm thick cross-sectional CBCT images obtained using the KaVo eXamVision (KaVo Dental GmbH, Biberach, Germany) program. Bone width was measured at the midpoint of the mesiodistal width of the edentulous region, 0.8 mm below the ridge crest, on the line connecting the vestibule/palatal and buccal/lingual walls (Figure 1). Bone width measurements at 2.8, 4.8, and 6.8 mm depths were performed following the same protocol as the bone width measurements performed at a 0.8 mm depth. The horizontal and vertical lines at 0.8 mm were used as the reference for the calculation of the depths. The protocol for edentulous crest measurements was applied in the same way for symmetrical toothed side measurements. Bone width measurements of symmetrical teeth were made with reference to the line that passes 0.8 mm below the lower crest of one of the buccal/vestibule and lingual/palatal bone walls (Figure 2). Bone heights were obtained through linear measurement of the distance between the reference point at the crest (the midpoint of the line at which bone width measurements were made) and the mandibular canal, maxillary sinus, mental foramen, and nasal floor. Intra-examiner reliability was calculated to assess the reliability of the measurements. The measurements in this study were performed by a single investigator specializing in oral and maxillofacial radiology. The second measurements were performed 4 weeks after the first measurement.  
When evaluating the tooth regions, three regions were defined: the incis, premolar, and molar regions. Teeth 11, 12, 13, 13, 21, 22, 23, 31, 32, 33, 41, 42, and 43 were in the incisor region, teeth 14, 15, 24, 25, 34, 35, 44, and 45 were in the premolar region, and teeth 16, 17, 26, 27, 36, 37, 46, and 47 were in the molar region (the third molar teeth were not included). The mean age value was taken as a reference when defining age groups.  
When evaluating the tooth regions, three regions were formed: the incis, premolar, and molar regions. Teeth 11, 12, 13, 21, 22, 23, 31, 32, 33, 41, 42, and 43 were in the incisor region, teeth 14, 15, 24, 25, 34, 35, 44, and 45 were in the premolar region, and teeth 16, 17, 26, 27, 36, 37, 46, and 47 were in the molar region (the 3 rd molar teeth were not included). The mean age value was taken as a reference when forming age groups.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### EEG Signal Filtering with SGF  
The parameters of the Savitzky-Golay filter were chosen experimentally through the following steps. Initially, a test signal was deliberately exposed to varying degrees of Gaussian white noise, in ranges of \(-15\), \(-10\), \(-5\), \(0\), \(5\), \(10\), and \(15\) dB. The signals were filtered with all possible combinations of filter parameters, with order and frame length ranging from 3 to 41. The filter parameters were selected based on their capacity to elevate the signal-to-noise ratio (SNR) of a signal contaminated with \(-15\) dB white Gaussian noise (WGN) to a level of \(-3\) dB or better. Moreover, these parameters were chosen to ensure a cutoff frequency of at least 60 Hz, which is at least twice the highest measured fundamental frequency in the beta band (30 Hz). This choice adheres to the Nyquist criterion for signal sampling. Finally, the correlation between the test signal before filtering and the filtered output was computed for each parameter configuration identified in the previous step. These tests revealed that the filter with parameters order \(N=22\) and frame length \(M=35\) achieved the best performance in terms of SNR improvement. The outcomes regarding performance are shown in Table 1. The cutoff frequency of the filter was estimated by constructing spectrograms of the signal contaminated with white Gaussian noise at an SNR of 15 dB and of the signal at the filter's output. The spectrograms in Figure 5 illustrate how the filter's cutoff frequency fluctuates between 60 and 70 Hz.  
Figure 4: EDF file with its signals in the time domain.  
Finally, the filtering process applied to the original signal without added noise demonstrated its efficacy, with the SGF 22-35 filter achieving the highest correlation coefficient of 0.98181, which suggests substantial similarity between both signals.
# Design of Time-Synchronized Switch Based on Zynq
## 3 System Hardware Design
### FMC Connector Circuit  
This design will be equipped with an expansion connector to integrate boards with new functionalities. The connector should feature multiple available I/O interfaces, support for a wide range of signaling standards, high-speed signal transmission (up to 10 Gbit/s), and system-configurable input/output capabilities. Therefore, it has been decided to adopt a standard FMC HPC expansion connector as the solution for connecting ADC modules, ST interfaces, and other functional modules. This FMC expansion connector contains 36 pairs of differential I/O signals and supports LVDS data communication, making it capable of meeting diverse application requirements. A connection diagram for the FMC connector is shown in Figure 9.
## 3 Methodology and Overall Framework
### Predictive Maintenance on a Conveyor Belt
#### 3.3.2 Anomaly Detection with \(k\)-NN  
The \(k\)-NN approach is a distance-based novelty detection method, a suitable algorithm for our application due to its performance in classification under irregular decision boundaries. It is based on the assumption that all normal data points have close neighbors. Therefore, if a data point is far away from its neighbors, it is regarded as an anomaly [25]. Generally, the \(k\)-NN-based anomaly detection first finds the k-nearest neighbors for every data point in the training dataset. The distances to the nearest neighbors are used as a novelty score for each data point. Then, a novelty threshold is decided as the largest novelty score among the entire dataset. Finally, to predict whether a new coming data point is a novelty (normal behavior data) or an anomaly, the algorithm inserts the point into the training dataset, finds its k-nearest-neighbors, calculates its novelty score and compares the score with the threshold.  
Key parameters for \(k\)-NN include the number of the nearest neighbor, \(k\), and the leaf size, \(leaf\_size\), of the calculated BDtree or BallTree. The bigger the \(k\) is, the smoother the decision boundary gets; therefore, this can also be used to suppress noise. The selection of \(k\) value largely depends on the nature of the problem, whereas the \(leaf\_size\) defines the minimum cell size in KDTree or BallTree. A smaller leaf size indicates a more complicated tree, whereas a larger \(leaf\_size\) leads to a faster tree construction time and less memory space allocation. Our application of \(k\)-NN has shown that the \(leaf\_size\) does not affect  
Figure 3: Pseudo outliers and pseudo target data.  
the classification accuracy significantly under the current settings. On the other hand, \(k\) value directly affects the performance. When a very small value is chosen, for example, \(k=2\), we notice that there are areas towards lower speed values on the fingerprint that are not encompassed even though the decision boundary surrounds most of the training data. This can be regarded as over-fitting. On the contrary, when \(k\) becomes larger, the decision boundary is too far away from the training set; hence, it usually causes prediction of false positives. After running a grid search using the pseudo outliers and targets as shown in Figure 3, a trained model with the best combination is obtained for \(k=3\) and \(leaf\_size=5\). The final model is shown in Figure 4b.
## 3 Comparative Analysis of Existing Evaluation Methods
### Existing Evaluation Methods
#### 3.1.2 [PERSON]'s Liquefaction Probability Assessment Method  
[PERSON] et al. (2018) conducted field investigations on 301 liquefaction cases worldwide from 2001 to 2011, combined with the liquefaction history database, and constructed a global database containing 415 sets of sand and silt \(V_{s}\) liquefaction data. Based on this database, [PERSON] et al. adopted the Bayesian updating model, using \(V_{s1}\), \(CSR\), \(FC\), \(M_{W}\), and \(\sigma_{\mathrm{v}}{{}^{\prime}}\) as the parameters to establish the liquefaction probability and cyclic resistance ratio formulae for sand and silt:  
\[P_{L}=\varnothing\left\{-\frac{\left[\left(0.0073V_{S1}\right)^{2.8011}-1.946 ln (CSR)-2.6168 ln(M_{W})-0.0099 ln(\sigma_{\mathrm{v}}{{}^{\prime}})+0.0028 FC \right]}{0.4809}\right\} \tag{8}\]  
\[CRR=\exp\left\{\frac{\left[\left(0.0073V_{S1}\right)^{2.8011}-2.6168 ln(M_{W})- 0.0099 ln(\sigma_{\mathrm{v}}{{}^{\prime}})+0.0028 FC+0.4809\mathcal{D}^{-1}(P_{ L})\right]}{1.946}\right\} \tag{9}\]  
\(\Phi\) is the cumulative normal distribution function. [PERSON] et al. used the \(P_{L}\) = 15% contour as the deterministic liquefaction assessment curve and compared it with the liquefaction assessment curves of [PERSON] et al. [12] and [PERSON] et al. [38], pointing out that the liquefaction limit shear wave velocity of \(V_{s1}\) = 215 m/s proposed by [PERSON] et al. is overly conservative.
###### Abstract  
The achievement of United Nations Sustainable Development Goals, related to energy and resource use, is a critical issue for small and insulated communities. In many minor islands, solar energy is not correctly exploited, and electrical heaters are connected to weak grids with very a high share of generation by fossil fuels. As a consequence, there is the necessity to assess the potential and the suitability of diffusion of alternative systems to avoid dependency on the electrical grid and reduce carbon emissions. This paper aims to evaluate the technical and economic performances of some alternative systems exploiting renewable energy for domestic hot water production. Four different systems were simulated and studied: a heat pump connected to the grid, a heat pump coupled with a photovoltaic plant, a heat pump combined with a solar thermal collector, and a solar thermal plant. Moreover, heat and electricity storages were studied for reducing impacts on the distribution network. The work presents data gathered for a study on energy-retrofit strategies on Lampedusa Island (Italy, 35\({}^{\circ}\)30\({}^{\prime}\)56\({}^{\prime\prime}\) north (N), 568 degree-days). Finally, to select the most cost-effective plant, an economic analysis of the chosen systems was carried out. This analysis shows that the best net present values are associated with the heat pump (HP) coupled with a stand-alone PV system and a small battery and solar thermal-assisted HP. The shortest payback time was calculated for the solar thermal system.  
Keywords:small islands; renewable sources; energy consumption; heat pump water heater; domestic hot water +
Footnote †: journal: Applied Sciences
## 4 System Development and Experimental Verification
### Experimental Verification  
The purpose of the experiment was to carry out dynamic data collection in a real tunnel environment, to verify the performance of each software and algorithmic submodule, to fully test the system to meet the requirements of real-time detection, to evaluate the processing performance of the algorithm on obstacles of different scales, and to confirm the warning function of the system. The experiment mainly used a high-speed rail tunnel detection vehicle, which was equipped with a set of geological radar equipment integrated with a new generation of intelligent obstacle avoidance systems (see Figure 13a). Through the danger warning information fed back by the sensors, the mechanical arm system was guided to extend and retract the radar support and bending arm to avoid obstacles. The experiment mainly conducted on-site intelligent obstacle avoidance tests on protrusions on the tunnel wall and contact network frames (see Figure 13b,c).  
Figure 12: Human–Machine Interaction Design Interface.  
Figure 13: Test detection equipment and set obstacles. (**a**) High-speed rail tunnel detection train and obstacle avoidance detection diagram. (**b**) Tunnel wall protruding cable frame. (**c**) Contact network frame.
## 5 Experimental Work and Results
### Accuracy Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Algorithm** & **Cycle 1** & **Cycle 2** & **Cycle 3** \\ \hline \multirow{3}{*}{**TampereU dataset**} & ITM-OSELM & 28.73\% & 81.17\% & 81.61\% \\  & FA-OSELM & 28.55\% & 28.25\% & 21.11\% \\  & OSELM & 35.12\% & 26.28\% & 13.33\% \\ \hline \multirow{3}{*}{**UJIndoorLoc**} & ITM-OSELM & 16.48\% & 88.36\% & 91.69\% \\  & FA-OSELM & 13.12\% & 17.94\% & 24.39\% \\ \cline{1-1}  & OSELM & 30.06\% & 17.14\% & 19.56\% \\ \hline \multirow{3}{*}{**KDD99 dataset**} & ITM-OSELM & 33.91\% & 65.83\% & 75.86\% \\  & FA-OSELM & 45.91\% & 41.58\% & 50.81\% \\ \cline{1-1}  & OSELM & 18.27\% & 17.39\% & 3.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The overall accuracy in each cycle for ITM-OSELM, FA- OSELM and OSELM in the three datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Algorithms** & **Cycle 2 vs. Cycle 1** & **Cycle 3 vs. Cycle 2** \\ \hline \multirow{3}{*}{**TampereU dataset**} & ITM-OSELM & 182.52\% & 0.54\% \\  & FA-OSELM & \(-\)1.05\% & \(-\)25.27\% \\  & OSELM & \(-\)25.17\% & \(-\)49.27\% \\ \hline \multirow{3}{*}{**UJIndoorLoc dataset**} & ITM-OSELM & 436.16\% & 3.76\% \\  & FA-OSELM & 36.73\% & 35.95\% \\  & OSELM & \(-\)42.98\% & 14.11\% \\ \hline \multirow{3}{*}{**KDD99 dataset**} & ITM-OSELM & 94.13\% & 15.23\% \\  & FA-OSELM & \(-\)9.431\% & 22.19\% \\ \cline{1-1}  & OSELM & \(-\)4.81\% & \(-\)78.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Learning improvement percentage of the three models with respect to cycles, percentage of the three models with respect to cycles. and external memory that enabled ITM-OSELM to restore old knowledge in both cycle 2 and cycle 3, while both OSELM and FA-OSELM could not do it. In addition to accuracy, the models were evaluated according to standard machine learning evaluation measures. This evaluation was performed by selecting a class and assuming it as positive, and then the classifier was checked for any sample. The results of the classifier were either positive or negative. Figures 10-12 display TP, TN, FP, or FN results. For ITM-OSELM, true measures have an increasing trend from one cycle to another, whereas false measures have a decreasing trend from one cycle to another. This discrepancy does not apply to FA-OSELM and OSELM, and the result was normal considering the accuracy results. An interesting observation is that the first cycle provides nearly similar values of TP, TN, FP and FN for all three of the models while the deviation between ITM-OSELM and the other two models occur in both the second and third cycle, which support its capability of building knowledge and achieving a higher rate of correct