esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 2 System Model  
We consider a single-reader system where the tags are the only interfering entities. As is known, RFID system performance is strongly affected by the amount of channel noise in the return link process. If there is an error in identifying the tag response signal, the system needs to repeat another inventory round. This requires a long time to identify tags information. Thus, we introduce a channel coding algorithm in the Gen-2 system to guarantee high probability of tag identification, while reducing the time spent for the procedure.  
The system model consists of the proposed encoder (Section 2.1), placed before the FM0 modulator into the tag as in [8], and the proposed decoding scheme (Section 2.2) implemented inside the reader.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 4 Proposed Credit Card Fraud-Detection Approach  
This study employs a hybrid feature-selection method, combining the IG-based filter and GA-based wrapper methods. Firstly, the IG technique ranks the attributes to identify the most significant attributes, and the threshold value is obtained by computing the standard deviation [1] of the IG values. Therefore, features with IG values greater or equal to the threshold are selected, while those below the threshold are discarded. The standard deviation has been widely employed in the literature to achieve excellent feature-selection thresholds [54; 55; 56]. Secondly, the top-ranked features are fed into the GAW, which uses the ELM as the learning algorithm.  
The GA aims to identify the best feature subsets from a series of combinations known as generations [57]. Furthermore, after the ELM classifies the chromosomes, they are assigned a fitness value. Generally, the accuracy metric has been used as the standard fitness function [58]. However, it gives equal importance to samples in both majority and minority classes; hence, it is unsuitable for imbalanced classification problems [59].  
Since the majority class samples outnumber the minority class, the fitness function will be biased toward the former. Furthermore, since there are more majority class samples, the accuracy metric will return high accuracy values, which could be misleading. Therefore, this study employs the G-mean metric obtained by the chromosomes as the fitness value to handle the imbalanced class. G-mean is a vital metric for imbalanced classification problems, and it considers the classifier's performance for the majority and minority classes. The G-mean can be represented mathematically as follows:  
\[\textit{G-mean}=\sqrt{\frac{TP}{TP+FN}\times\frac{TN}{TN+FP}} \tag{8}\]  
where true positive (TP) indicates a fraudulent transaction that is correctly predicted by the model, false positive (FP) indicates a legitimate transaction that the model wrongly predicts as fraud, true negative (TN) indicates a legitimate transaction that is correctly identified, and false negative (FN) indicates a fraudulent transaction that is predicted as legitimate. The proposed approach is outlined in Algorithm 1.  
The GAW approach starts with randomly initializing the GA's population, where every candidate feature subset is encoded as a chromosome. The next step involves training different ELM neural networks based on each chromosome, and the fitness value for each feature subset is computed. Thirdly, a new population is generated using genetic operators. The procedure continues until the stopping criterion is obtained, i.e., the maximum number of generations. This stopping criterion has been used extensively in the literature to obtain excellent GA performance [60; 61; 62]. Meanwhile, the rationale behind this approach is that the GA wrapper would select the best feature subset that would lead to enhanced prediction performance.
## 4 Experimental Verification and Analysis
### Model Parameter Settings  
The network structure and parameter settings of the CAM_MCFCNN model are presented in Table 3, where R is the scaling factor, C represents the number of convolutional kernels, KS stands for the convolution kernel size, S is the sliding step of the convolution kernel. The number of channels of the input module was set according to the number of sensor channels deployed in the experiment. In the feature extraction module, since the vibration signal is one-dimensional, in order to obtain a larger sensing field, a larger size convolution kernel was used. Other parameters of the model were set according to relevant experience. Specifically, the model used a fixed learning rate, which was set to 0.0015. The minimum batch is 100, and the number of iterations is 100.  
Figure 5: Raw vibration signals of five health states of planetary gearbox collected by multi-sensors at 1500 rotation speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Label** & **Describe the Fault State** & **Number of Samples per View** & **Number of Views** \\ \hline \multirow{6}{*}{A/B/C/D} & 0 & Normal & 600/600/600/200 & 9 \\  & 1 & Single tooth wear & 600/600/600/200 & 9 \\ \cline{1-1}  & 2 & Two teeth wear & 600/600/600/200 & 9 \\ \cline{1-1}  & 3 & Three teeth wear & 600/600/600/200 & 9 \\ \cline{1-1}  & 4 & All teeth wear & 600/600/600/200 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The training set and test set at different speed.
# A Study of Current Controlled Discharge in a Nitrogen Filled Tube
## 3 Results
### Modelling of Laboratory Experiments
#### 3.3.3 Estimation of Plasma Resistance According to NPINCH Code  
The resistance \(R\) of the plasma column of the length \(d\) can be evaluated as  
\[R(t)=\frac{E(t)d}{I(t)}, \tag{1}\]  
where \(E(t)\) is the electric field and \(I(t)\) is the electric current passing through the tube. The simulations (NPINCH) for various initial gas pressures allow us to evaluate time dependencies of resistance \(R(t)\). The results are shown in Figure 7.  
It is well known that the resistance is controlled primarily by the plasma temperature and only slightly by the plasma density. The simulations show that tube in these simulations are of the same order. In the experiment and in the simulations we see the same dependence on initial pressure, the resistance is higher for higher pressure. However, the differences between the resistance values for various pressures are much less for simulations than for experiment.  
Figure 6: Spatial distributions of (**a**) electron density \(N_{e}\) and (**b**) electron temperature \(T_{e}\) inside the tube at \(t=5.6\,\mathrm{\SIUnitSymbolMicro s}\). Electrodes are indicated by dark red color (\(N_{e}>3\times 10^{16}\,\mathrm{cm}^{-3}\)) in (**a**). Please note, that the scales for radial and longitudinal coordinates are different.  
Figure 7: Approximation of the measured electric current (\(I\)) and time dependencies of tube resistance for initial pressures of the filling gas: \(30\,\mathrm{Pa}\), \(100\,\mathrm{Pa}\), and \(300\,\mathrm{Pa}\).
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### Performance Based on Number of RREQs Varying the Number of Nodes  
The number of RREQs sent by nodes in the network can be used as a metric to measure the success of the scheme. The scheme should discard or at least reduce the number of fake RREQs by blocking the malicious nodes. That will lead to a reduction in the total number of RREQs living in the network.  
Figure 1 shows that the number of RREQs increases with an increase in the number of nodes in both AODV and TAODV. This is expected because having more nodes means more RREQs will be generated. The addition of the mechanism reduces the number of RREQs in TAODV by blocking the malicious nodes and stopping them from flooding the network with more fake RREQs. For example, if the number of nodes is 100, the number of RREQs is 51,000 in the case of TAODV and 57,000 in the case of AODV. Thus, the scheme was able to prevent 6000 fake RREQs in 100 s.
## 3 Numerical Simulation Methods and Validation
### Validation Results  
Measure the central deflection of the front, back, left, and right wall plates and take the average as \(\delta\). The deflection of the wall plates were dimensionless. In the equation \(\delta=\omega/h\), \(\omega\) is the deflection and \(h\) is the thickness. The experimental results are compared with the numerical results and listed in Table 5; the numerical simulation results are within 3% error, which is closer to the measured results.  
The experimental results of deflection and dynamic deformation processes were compared with numerical simulation results, and the results are in good agreement. It was proven that the numerical simulation method can effectively simulate the deformation characteristics of the box structure under internal blast loadings with high reliability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(h\) (mm) & \(\rho\) (kg\(\cdot\)m\({}^{-3}\)) & \(\
u\) & \(\sigma_{0}\) (MPa) & \(Et\) (MPa) & \(E\) (GPa) & \(F_{s}\) \\ \hline
2 & & & 370 & 485 & & 0.30 \\
3 & 7800 & 0.3 & 368 & 484 & 210 & 0.29 \\
4 & & & 360 & 480 & & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical properties of Q235B steel plate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(P\) (kg\(\cdot\)m\({}^{-3}\)) & _VCJ_ (m\(\cdot\)s\({}^{-1}\)) & _PCJ_ (GPa) & A (Gpa) & B (Gpa) & \(R_{1}\) & \(R_{2}\) & \(\omega\) \\ \hline
1601 & 6850 & 21 & 373.77 & 3.231 & 4.15 & 0.95 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material model of TNT [6].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(L\) (mm) & \(h\) (mm) & \(W\) (g) & \
\begin{tabular}{c} **Measured** \\ **Deflection Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Simulated** \\ **Deflection Ratio** \\ \end{tabular} & **Error** \\ \hline
300 & 2 & 23.9 & 11.95 & 12.25 & 2.5\% \\
450 & 3 & 84.3 & 13.13 & 13.3 & 1.3\% \\
600 & 4 & 194.69 & 13.33 & 13.6 & 2.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental and numerical simulation results [6].
## 3 Results
### Dataset 2  
More scans from patients with no findings (normal) were added to analyse the performance of the proposed model when adding more data for training and testing. Moreover, the performance of the deep learning models on Dataset 1 and Dataset 2 was compared in terms of accuracy and run-time after adding the pre-processing stage for both datasets. Dataset 2 featured multiple scans for patients from the following classes:  
10. Normal: 199 patients  
11. Benign: 62 patients  
12. Malignant: 39 patients  
The comparison between the performance of the DL models on Dataset 2 in terms of accuracy and run-time (s) is documented in Table S6 and Figure 6(a,b). Based on Table S6 and Figure 6(a,b), it is proven that extracting features using AlexNet resulted in the best accuracy, and performance and less time was needed for training and testing, with an accuracy of  
Figure 7: Comparison between DL models using (**a**) Dataset 2 in terms of accuracy, (**b**) Dataset 2 in terms of run-time, (**c**) Dataset 3 in terms of accuracy, (**d**) Dataset 3 in terms of run-time.  
77.8% and runtime of 1040 s. Although MobileNetV2 and VGG-16 concluded with the approximately same accuracy, there is a noticeable difference in the computation time. VGG-16 took almost five times as long as MobileNetV2 to extract features. Following the three previously mentioned models, DenseNet-201 recorded the least accuracy of 65.40%, despite taking six times longer to extract features than ResNet-18. ResNet-18 achieved an accuracy of 67.43% with a runtime of 1535 s. Finally, in comparison to AlexNet, which performed the best, GoogleNet experienced a 14% drop in the model's accuracy and a runtime of 1986 s.  
A comparison between the performance of the DL models using Dataset 1 and Dataset 2 is provided in Table S7. Based on Table S7 using Dataset 2, AlexNet provided the best accuracy and run-time results, with a 21% gain in accuracy and the least percentage increase in runtime. On the other hand, the worst percentage increase in run-time was recorded with a 19% improvement in accuracy for ResNet-18. Moreover, VGG-16 and DenseNet-201 recorded the same percentage increase in run-time along with an accuracy increase of 18.22% and 19.29%, respectively. Finally, the least accuracy gain was observed when GoogleNet and MobileNetV2 were implemented to extract features, which increased the classification accuracy by 15.48% and 16.89%, respectively.
## 2 Methods
This approach models the interaction between stationary and rotating components by maintaining a fixed relative position, thereby capturing the steady-state interaction without the computational expense of a transient simulation. The k-\(\omega\) Shear Stress Transport (SST) turbulence model was employed with automatic wall functions. The k-\(\omega\) SST model combines the advantages of the k-\(\varepsilon\) and k-\(\omega\) models, providing accurate predictions of flow separation and adverse pressure gradients, which are common in turbomachinery flows. In order to control the time scale, an automatic time scale was employed, with a conservative length scale option and a time scale factor of 1.6. This configuration facilitates a balance between convergence speed and solution accuracy. The residual target was set to 1 \(\times\) 10\({}^{-5}\) to guarantee sufficient convergence of the simulations. The automatic wall function adapts to the local mesh refinement, ensuring appropriate near-wall treatment without the need for excessively fine meshes. The impact of tip clearance on leakage flows and overall turbine efficiency was explicitly modeled in the simulations, with a tip clearance of 0.2 mm being used. It is well-established that tip clearance losses significantly affect the performance of turbomachinery, with this being particularly relevant in the context of small-scale turbines. Consequently, this was an important aspect of the computational model. Figure 4: Grid sensitivity based on the turbine total-to-static efficiency. A series of simulations were conducted across the range of inlet and outlet pressures to evaluate the turbine's performance under various operating conditions. This approach allowed for the assessment of the turbine's adaptability to fluctuating waste heat sources and the identification of any performance limitations when operating beyond the design point. The operating conditions were established to reflect the turbine's operation beyond the design point, with the objective of accommodating variable waste heat sources. The inlet pressure to the turbine was varied from 330 kPa to 780 kPa in order to simulate different operating scenarios, while the outlet pressure was set between 150 kPa and 200 kPa, in alignment with the analyzed condenser pressures in ORC systems. The temperature of the working fluid at the turbine inlet was determined using the saturation temperature corresponding to the specified inlet pressure (Figure 6). An additional superheat of 5 K was added to prevent condensation within the turbine, ensuring that the fluid remained in the superheated vapor region. The entropy of the working fluid was then calculated along the specified isobar for accurate thermodynamic property definition.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 2 Background
### Gaussian Naive Bayes  
The GNB model is considered a conditional probability model (i.e., the probability that an event will occur if another event occurs), and the information about the latter event is provided to the model. Using the conditional probability and the information provided by previous events, the probability of a future event can be obtained using Equation (2):  
\[P(A|B)=\frac{P(B|A)P(A)}{P(B)} \tag{2}\]  
where \(P(A|B)\) is a posterior probability and represents the likelihood of \(A\) by the value of \(B\); \(P(B|A)\) represents the probability of \(B\) given a value of \(A\); \(P(A)\) is the prior probability and represents the probability of event \(A\); and \(P(B)\) is a marginal probability and represents the probability of event \(B\). Using [PERSON] theorem as a basis, the GNB can be formulated as follows:  
\[P(y|x_{1},...,x_{j})=\frac{P(x_{1},...,x_{j}|y)P(y)}{P(x_{1},...,x_{j})} \tag{3}\]  
where \(P(y|x_{1},...,x_{j})\) is a posterior probability and represents the data probability of the model including class \(y\) given the feature value from \(x1\) to \(xj\). \(P(x_{1},...,x_{j}|y)\) is the likelihood of feature values given their \(y\) class. \(P(y)\) is a prior probability, and \(P(x_{1},...,x_{j})\) is a marginal probability.  
The GNB is a generative model and is considered a simple and a powerful model that can be used in solving prediction problems. In addition, this model can utilize less data for training. However, the GNB has limited use in the real world as it assumes all features are independent.
## 2 Materials and Methods
### Treatment and Sampling  
The treatment method described by [PERSON] et al. [22] was adopted with minor changes. In this experiment, the CK and BTH treatments were each administered in the form of aqueous solutions to 15 blocks of vines organized in a randomized design. Each block had three biological replications. To prevent contamination, two untreated rows were created as a protective barrier between the treatments. As described in the methodology of [PERSON] et al. [21] and [PERSON] et al. [22], the BTH treatment contained 0.37 mM BTH with Tween 80 as a wetting agent, whereas the control treatment had only Tween 80. The solutions were sprayed with a high-pressure sprayer onto the berry surfaces at the pre-swelling stage. At this stage, the berries had a transverse diameter of 4.0 mm and a vertical diameter of 4.3 mm, approximately 3 weeks post-flowering. No chemicals other than BTH were applied to the experimental grapevines. Berry samples were collected at biweekly intervals from the pre-swelling stage to harvest, which covered the period from 17 June to 14 September. All experiments were conducted using grapes that were phytosanitary and 100% healthy. Several bunches of berries were randomly harvested for each treated sample, bagged, and conveyed to the laboratory. The berries were destemmed and wrapped in aluminum foil in small amounts before being frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C until use. Table S1 contains additional sampling information as well as the determination of the grape ripening stages.
## 2 Materials and Methods
### hAMSC Growth and Phenotype Analysis  
Freshly isolated (p0) hAMSCs were subcultured until passage 5 (p5) by plating at a density of \(1\times 10^{5}\)/cm\({}^{2}\) in Advanced DMEM (Gibco, Life Technologies Limited, Paisley, UK) supplemented with 10% (v/v) fetal bovine serum (FBS), 55 uM \(\beta\)-mercaptoethanol, 1% di L-glutamine, 1% penicillin/streptomycin (Invitrogen, Milan, Italy), and 10 ng/mL epidermal growth factor (EGF; Sigma). Every time cells reached 80% of confluence, cells were detached with trypsin-ethylendiaminetetracetic acid (EDTA) (Invitrogen, Thermo Fisher Scientific, Monza, Italy), washed, counted with a Burker chamber, and replated in a new T25 plastic flask at a density of \(5\times 10^{5}\) in order to calculate their growth curve. Doubling time was calculated, inserting times and cell counts on the website [[http://www.doubling-time.com/compute.php](http://www.doubling-time.com/compute.php)]([http://www.doubling-time.com/compute.php](http://www.doubling-time.com/compute.php)).  
For phenotypic analysis, cells were then stained with fluorochrome-conjugated monoclonal antibodies against hematopoietic (CD14, CD34, CD45) or mesenchymal (CD29, CD73, CD105) markers, as previously published [25]. All antibodies were purchased from Invitrogen, ThermoFisher Scientific (Waltham, MA, USA), and were conjugated with fluorescein isothiamate (FITC), except the antibody against CD73 that required an additive incubation with secondary antibody (FITC goat anti-mouse; Sigma-Aldrich, Milan, Italy) for 30 min at 4 \({}^{\circ}\)C. At least, \(1\times 10^{6}\) cells were finally analyzed using the Amnis Flowsight IS100 (Merck KGaA, Darmstadt, Germany). Negative controls included FITC-conjugated isotype antibodies (IgG1 kappa, Invitrogen), FITC-conjugated goat anti-mouse in the absence of the primary antibody for CD73 analysis, no antibody, or unconjugated goat anti-mouse antibody.  
Analysis of positive cells was obtained with the IDEAS 6.0 Software (Amnis, EMD Millipore, Seattle, WA, USA). Cells in best focus, i.e., those with higher Gradient root mean square (RMS) values, were chosen. Brightfield scatter plots obtained by plotting Area (a parameter relative to cellular dimension) on x-axis vs. Aspect Ratio (a parameter reflecting the ratio of the cell Minor Axis divided by the Major Axis) on y-axis were generated to identify single cells events, i.e., those with an intermediate Area value and a high Aspect Ratio. The intensity of Ch02 (FITC fluorescence channel) vs. normalized frequency was used to calculate the statistics Count and Percent Gated on 10,000 single-cell events for sample.  
At p0 and p2, before phenotypic analysis, cells images were taken using the inverted microscope (Leica DMRB, Leica Microsystems, Wetzlar and Mannheim, Germany) and captured by the digital camera Leica DFC450 C.
## 4 Experimental Results and Discussion
### Experimental Settings
#### 4.1.2 Fine-Tuning Settings  
Using the previously selected architectures, we performed fine-tuning by dropping the last layer of each model, adding a new set of layers that consisted of an average 2D pooling of 7 \(\times\) 7, a Flatten layer, a Dense layer with ReLu activation and 256 outputs, a 0.5 dropout layer, and a final Dense layer with softmax activation and two outputs.  
For fine-tuning, we used 70% of our images for training, 20% for testing, and 10% for validation. For a fair comparison, we added the same layers with the same parameters on all the architectures. We trained these added layers with a batch size of 26 images and a learning rate of \(1\times 10^{-4}\) over 20 epochs, we evaluated them with the same set of images. To increase the number of images for training our architectures, we used data augmentation in order to learn more general and robust features, improving the performance of our models.  
We used the ImageDataGenerator function from Keras to create these new images. We set the rotation range parameter to 25, zoom range to 0.1, width shift range to 0.1, height and shift range to 0.1, shear range to 0.2, horizontal flip to True, and fill mode to \"nearest\". These new slightly altered images were used for training and consisted of five times the number of images of the original training batch.