esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment and Methods
### Evaluation Methods of NO Oxidation and Energy Efficiency  
In each test, the concentration of NO, NO\({}_{2}\) and NO\({}_{\rm X}\) (NO+NO\({}_{2}\)) were measured by gas analyzer for 30 times, as shown in Figure 5. The standard deviations of NO, NO\({}_{2}\) and NO\({}_{\rm X}\) are 2.2, 3.3 and 1.8 ppm, respectively. The average concentrations of NO, NO\({}_{2}\) and NOx were taken as the final test results.  
To explore the effect of DBD structure on NO oxidation, the oxidation degree of NO\({}_{\rm X}\) was selected as the evaluation index. The oxidation degree of NO\({}_{\rm X}\) is defined as follows:  
\[\alpha=\frac{\varphi(NO_{2})}{\varphi(NO_{x})}\times 100\% \tag{1}\]  
where \(\alpha\) is oxidation degree of NO\({}_{\rm X}\); \(\varphi(NO_{2})\) is the concentration of NO\({}_{2}\), ppm; and \(\varphi(NO_{x})\) is the concentration of NO\({}_{\rm X}\), ppm. The discharge power was obtained by Lissajous figure (see Figure 3). The calculation of discharge power is given in Equation (2):  
\[P=fC_{m}K_{x}K_{y}KA, \tag{2}\]  
Figure 4: **(a)** A cross-sectional view of the DBD reactor; and **(b)** the various inner electrodes used in the experiment.  
Figure 5: The concentration of NO, NO\({}_{2}\) and NO\({}_{\rm X}\) (NO+NO\({}_{2}\)) measured by gas analyzer.  
where \(f\) is discharge frequency, Hz; \(K_{x}\) is x-axis sensitivity of oscilloscope, V/grid; \(K_{y}\) is y-axis sensitivity of oscilloscope, V/grid; \(K\) is voltage decrease ratio of high voltage probe, 1000:1; and \(A\) is area enclosed by Lissajous figure. To evaluate the energy consumption level of DBD reactor, the specific energy density (SED) is defined as follows:  
\[SED=\frac{P}{Q}\times 60, \tag{3}\]  
where \(SED\) is specific energy density, J/L; \(P\) is discharge power, W; and \(Q\) is gas flow, L/min.
## 3 Results
In contrast to multispectral images, RGB images presented a better performance with the K-Nearest Neighbors (KNN) algorithm, which achieved the best F1 score in all classes, except for gliricidia, where Random Forest (RF) predicted a better result but still maintained a satisfactory overall average, compared to the other methods used. A similar pattern emerged in terms of recall, where KNN outperformed the other methods in all classes except for gliricidia, where RF once again demonstrated a superior result. In precision metrics, K-Nearest Neighbors (KNN) stood out by achieving superior results for all studied classes. It is noteworthy that Random Forest (RF) outperformed the Gaussian Mixture Model (GMM), despite GMM showing a better result in precision regarding the classification of exposed soil. Concerning the individual accuracy of the models, the recorded values were 81.78% for GMM, 85.40% for KNN, and 85.08% for RF. The Kappa index results were 0.78, 0.82, and 0.82, respectively. Both the K-Nearest Neighbors and Random Forest algorithms presented Kappa indices above 0.80, classified as 'almost perfect', while the Gaussian Mixture Model was classified as'substantial'. The Random Forest and K-Nearest Neighbors algorithms stand out, showing very similar values, with particular emphasis on the KNN algorithm in the context of RGB samples. An overall view of these individual results for each algorithm, including Precision, Recall, and F1 Score values in different RGB and multispectral sample scenarios, can be obtained through the analysis of the box plots presented in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Gaussian Mixture—RGB**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.8188 & 0.9359 & 0.7193 & 0.7186 & 0.7564 & 0.8007 & 0.9821 & Precision \\
0.82 & 0.9186 & 0.7899 & 0.584 & 0.7303 & 0.9255 & 0.9718 & Recall \\
0.8172 & 0.9272 & 0.753 & 0.6444 & 0.7431 & 0.8586 & 0.9769 & F1 score \\ \hline \multicolumn{6}{c}{**K-Nearest Neighbors—RGB**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.8563 & 0.9712 & 0.7872 & 0.7368 & 0.8062 & 0.8633 & 0.973 & Precision \\
0.8562 & 0.9148 & 0.7959 & 0.7405 & 0.7733 & 0.9276 & 0.985 & Recall \\
0.8558 & 0.9422 & 0.7915 & 0.7387 & 0.7894 & 0.8943 & 0.979 & F1 score \\ \hline \multicolumn{6}{c}{**Random Forest—RGB**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.8533 & 0.9653 & 0.782 & 0.7207 & 0.8087 & 0.8615 & 0.9817 & Precision \\
0.8533 & 0.9208 & 0.7899 & 0.74 & 0.7695 & 0.9239 & 0.9754 & Recall \\
0.8529 & 0.9425 & 0.7859 & 0.7303 & 0.7886 & 0.8916 & 0.9785 & F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the metrics for the Gaussian Mixture Model, K-Nearest Neighbors algorithm, Random Forest—RGB.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Forward/Backward Secrecy  
The forward and backward secrescies are guaranteed by the use of random values (\(\mathrm{R_{H}}\), \(\mathrm{R_{CH}}\), \(\mathrm{R_{P}}\), \(\mathrm{R_{CP}}\), \(\mathrm{R_{D}}\), \(\mathrm{R_{CD}}\), \(\mathrm{R_{PC}}\), \(\mathrm{R_{CPC}}\)) newly generated in each authentication session, during the calculation of the system keys, as the one generated in the PUP phase \(\mathrm{K_{CP}}=\mathrm{h_{3}(ID_{P}\parallel R_{P}\parallel R_{C})}\). Therefore, if an intruder discovers old system keys, it cannot use them in future authentication sessions (backward secrecy). On the other hand, if an intruder discovers future system keys, it cannot use them in past authentication sessions (forward secrecy).
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 4 Deep Learning-Based Fault Prediction
### Training Process and Loss Function  
The training process for the 1D-CNN model was designed to balance computational efficiency with predictive accuracy. The model was trained to minimize the error between actual and predicted fault probabilities using the Mean Absolute Error (MAE) as the loss function. The MAE is well-suited for regression tasks, providing robustness to outliers while ensuring accurate fault probability predictions. The loss function is defined as follows:  
\[L_{\text{MAE}}(\theta)=\frac{1}{N}\sum_{i=1}^{N}\lvert y_{i}-f_{\theta}(x_{i})\rvert\]  
where \(y_{i}\) is the true fault probability and \(f_{\theta}(x_{i})\) is the predicted probability.  
The model was trained using historical machine state data where the true fault probabilities were known. The input features (temperature, vibration, operating hours, and tool wear) were normalized and the target output (fault probability) was scaled to a range of 0 to 100. Training was conducted using the Adam optimizer, which was chosen for its adaptive learning rate capabilities, and the hyperparameters in Table 4 were used.  
The hyperparameters were determined through grid search to optimize model performance. The sequence length of 10 reflects the rolling prediction mechanism, which captures temporal dependencies over a 10-min window. The batch size of 32 was selected to balance memory usage and training efficiency, which is particularly critical in edge computing environments.  
Training was performed in batches, with sequences of input features fed into the convolutional layers to extract local temporal patterns. These patterns were then passedthrough fully connected layers to predict fault probabilities. The model achieved convergence in an average of 22 epochs, with early stopping employed to prevent overfitting and reduce training time.  
By employing these hyperparameters and training strategies, the 1D-CNN model demonstrated both computational efficiency and strong predictive performance, as discussed in the subsequent evaluation section.
# Microencapsulation of Tomato
## 3 Results and Discussion
### Response Variables of Experimental Design
#### 3.3.3 Antioxidant Activity  
The AA values of encapsulated tomato pomace extract with arabic gum ranged from 0.9 to 4.6 \(\upmu\)mol trolox.mg\({}^{-1}\) lycopene, whereas that with inulin particles varied between 1.0 and 3.5 \(\upmu\)mol trolox.mg\({}^{-1}\) lycopene. These results showed a loss of AA after encapsulation ranging from 0 to 77.7% and from 13.4 to 75.2% for arabic gum and inulin particles (Table 1), respectively. Similarly, [PERSON] et al. [39] observed the loss of approximately 72% of AA when encapsulating the ginger extract with maltodextrin and arabic gum by spray drying. This decrease is attributed mainly to the exposure of the feed solution to inevitable environmental factors, such as oxygen and light, during the spray drying process.  
The second order model did not fit the AA experimental data obtained for both arabic gum and inulin particles, since a coefficient of determination below 0.70 was observed. Nevertheless, looking at Table 1, the value of AA of bioactives encapsulated in arabic gum particles tended to increase with increasing drying temperature, for the same wall material concentration. This might have been due to the formation of a rapid crust on the surface protecting the bioactive compounds from degradation. Though, the results obtained for the inulin particles (Table 1) showed that AA values tended to decrease with increasing drying temperature in most cases. For example, with 15% inulin, the AA decreasedfrom around 2.6 to 1.4 umol trolox.mg\({}^{-1}\) lycopene, and from 3.2 to 1.4 umol trolox.mg\({}^{-1}\) lycopene, when the temperature was changed from 155 to 200 \({}^{\circ}\)C and from 110 to 200 \({}^{\circ}\)C, respectively.  
Regarding the effect of wall material concentration on AA, from Table 1, a decrease of AA when increasing the biopolymer content on the feed was envisaged. In fact, for a fixed drying temperature of 155 \({}^{\circ}\)C, increasing the arabic gum concentration from 5% to 35% led to an AA decrease from 4.5 to 0.9 umol trolox.mg\({}^{-1}\) lycopene. In the case of inulin, increasing its concentration from 5% to 25% led to an AA decrease from 3.5 to 2.1 umol trolox.mg\({}^{-1}\) lycopene. These results are in line with those reported above for the variation of EE and LC with wall material concentration, and might be related to lower droplets drying rates occurring when the feed viscosity is higher.
# Entropy Analysis of a Flexible Markovian Queue with Server Breakdowns
## 1 Introduction  
The concept of entropy was introduced by [PERSON] in his seminal papers, [PERSON] [1]. In information theory, entropy refers to a basic quantity associated to a random variable. Among a number of different probability distributions that express the current state of knowledge, the maximum entropy principle allows to choose the best one, that is the one with maximum entropy.  
Originally, the entropy was created by [PERSON] as part of his theory of communication. However, since then, the principle of maximum entropy has found applications in a multitude of other areas such as statistical mechanics, statistical thermodynamics, business, marketing and elections, economics, finance, insurance, spectral analysis of time series, image reconstruction, pattern recognition, operations research, reliability theory, biology, medicine, and so forth, see [PERSON] [2].  
In operations research, and particularly in queueing theory, a large number of papers has used the maximum entropy principle to determine the steady-state probability distribution of some process. The earliest document using entropy maximization in the context of queues that came to our attention is that of [PERSON] et al. [3]. Among the latest theoretical papers applying the maximum entropy principle we cite [PERSON] et al. [4], [PERSON] [5], and [PERSON] et al. [6], while [PERSON] et al. [7], [PERSON] and [PERSON] [8], and [PERSON] et al. [9] present recent applications.  
The intention of this paper is to resume work on a paper started by [PERSON] et al. [10], who studied a flexible queueing system and used an analytical method to obtain the initial steady state probability vector. For other possible approaches to calculate the probabilities see the references in Reference [10]. The objective of this paper is threefold. First, the maximum entropy principle is used to derive the initial steady state probability vector and make sure it is in agreement with the one obtained by [PERSON] et al. [10]. Second, we use the maximum entropy principle to obtain the tailsteady state probability vector. Third, improve both initial and tail probability vectors by providing more information to the maximum entropy technique.  
The rest of this paper is structured as follows. In Section 2, we describe the flexible queueing system and recall the results obtained by [PERSON] et al. [10]. Our main results are presented in Section 3 where we use the maximum entropy principle to obtain the different probabilities. The theoretical results are verified with numerical illustrations. The paper is concluded in Section 4.
# Shunt Damping Vibration Control Technology: A Review
## 2 Fundamentals of the Piezoelectric and Electromagnetic Transducers  
Shunt damping vibration control technology is a multi-physics coupling problem. It contains a transducer and a shunt circuit. This section summarizes the electrical fundamentals of the piezoelectric and electromagnetic transducers.
## 4 Results and Discussion
### Security Aspects  
The security feature of authenticated encryption (AE) is an important aspect of the design. AE provides both confidentiality (encryption) and data integrity (authentication) in a single operation. It ensures not only that the data are kept secret from unauthorized parties (confidentiality), but also that the data have not been tampered with during transmission or storage (integrity). The proposed AE scheme focuses mainly on reducing the logic resources within FPGA devices to construct a compact AE suitable for resource-efficient applications. The generic composition method used to integrate encryption and authentication primitives in this design has the primary purpose of improving the footprints. The security of the proposed AE scheme is directly tied to the encryption and authentication mechanisms that were utilized to construct this AE. Therefore, the security level for encryption is based on the cryptanalysis of the original LED block cipher, whereas the security level for the authentication is based on the cryptanalysis of the PHOTON hash functions.  
The LED block cipher has proven confidentiality and was designed to be resistant to the following attacks: brute-force attacks, differential cryptanalysis, linear cryptanalysis, and slide attacks. On the other hand, the PHOTON hash function has proven authenticity and was designed to be resistant to the following attacks: preimage attacks, collision attacks, second preimage attacks, differential cryptanalysis, linear cryptanalysis, and slide attacks. No cryptanalysis of post-quantum attacks is reported in the original paper of the utilized primitives. Moreover, the cost of quantum attacks is very high [29]. There are some analyses of the attacks applied to the single architectures of the LED block cipher and the PHOTON hash function. However, the main objective of this paper is to reduce the footprints without compromising the security level of the original utilized cryptographic primitives. Table 5 shows the security provability of some related AE schemes compared to the security of the proposed scheme based on the original primitives.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 3 Controller Design
### DL-STSM Design  
Figure 2a depicts the control diagram based on the STSM algorithm. It comprises a slow voltage loop, a fast current loop, and the control target. For the synthesis of STSM control, three main steps are necessary for design:  
* Designing the current loop by determining the control law gains to ensure its stability;
* Solving the transfer function of the closed-loop current loop;
* Designing the voltage loop by synthesizing the voltage control law that ensures loop stability and meets specified requirements.  
For further information about the synthesis of this controller, please refer to the work of [18].  
The STSM control law for the current loop \((I_{STSi})\) and for the voltage loop \((V_{STSM})\), respectively, can be defined as  
\[u_{k}=-\int_{0}^{t}\alpha_{k}sgn(S_{k})dt-\lambda_{k}\sqrt{|S_{k}|}sgn(S_{k}) \tag{55}\]  
\[I_{ref}=-\int_{0}^{t}\alpha_{o}sgn(S_{o})dt-\lambda_{o}\sqrt{|S_{o}|}sgn(S_{o}) \tag{56}\]  
where \(sgn(.)\) represents the sign function, and \(S_{k}\) and \(S_{o}\) are the sliding surfaces of current and voltage, respectively. \(\lambda_{k}\), \(\lambda_{o}\), \(\alpha_{k}\), and \(\alpha_{o}\) are the designed controller parameters \((k=1,\ldots,4)\). The synthesis of these parameters based on the PSO algorithm is provided in Table 1.
## 5 Discussion and Conclusions
### Nomenclature  
\begin{tabular}{l l} \(C\) & chord (m) \\ \(C_{PT,R}\) & relative total pressure coeff. \(C_{PT,R}=\frac{p_{T,R}-p_{tr}}{p_{T,Inf}-p_{nf}}\) \\ \(D\) & diameter (m) \\ \(G\) & flow rate (kg/s) \\ \(G_{ND}\) & Non-dimensional flow rate: \(G_{ND}=\frac{G_{N}\sqrt{RT_{m}}}{P_{n}D_{nf}}\) \\ \(h\) & blade height (m) \\ \(I\) & total pressure unresolved unsteadiness; \(I_{PT,R}=\sqrt{\frac{1}{N}\sum P_{T,R,unres}^{2}}\) (see Ref. [30] for more details) \\ \(inc\) & incidence angle (deg.) \\ \(Kp\) & velocity coeff., \(Kp=\frac{U}{V_{1}}\) \\ \(l\) & work (J/kg) \\ \(M\) & Mach number \\ \(M_{u}\) & Peripheral Mach number: \(M_{u}=\frac{U_{N,HD}}{\sqrt{\gamma RT_{m}}}\) \\ OP & operating conditions \\ \(p\) & pressure (bar) \\ \(r\) & radius (m) \\ \(Re\) & Reynolds number \\ \(rps\) & rotational speed (revolution per second) \\ \(T\) & temperature (K) \\ \(V\) & velocity (m/s) \\ \(U\) & peripheral speed (m/s) \\ \(\mathrm{Y_{stat}}\) & stator total pressure loss \(Y_{stat}=\frac{P_{1/m}-P_{t}}{P_{t,m}-P_{s}}\) \\ \(\alpha\) & absolute flow angle (from axial) (deg.) \\ \(\beta\) & expansion ratio \\ \(\delta\) & deviation angle (deg.) \\ \(\chi\) & reaction degree: \(\chi=1-\frac{1-\beta\frac{1-\gamma}{\mathrm{d}\omega_{\mathrm{tot}}^{2}}}{1- \beta\frac{1-\gamma}{\mathrm{d}\omega_{\mathrm{tot}}^{2}}}\) \\ \(\gamma\) & specific heat ratio \\ \(\mathrm{n}\) & efficiency \\ \(\mathrm{\Omega s}\) & streamwise vorticity: \(\mathrm{\Omega_{s}}=\frac{\left(\
abla_{\mathrm{\tau}}\dot{\psi}\right)\cdot \ddot{\psi}}{\|\ddot{\psi}\|}\) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\theta\) & circumferential coordinate (deg.) \\ \(\varphi\) & Flow coefficient: \(\varphi=\frac{G/\rho_{m}}{rpsD_{nf}^{2}}\) \\ \(\psi\) & Work coefficient: \(\psi=\frac{I_{u}}{U_{nf}^{2}}\) \\ \end{tabular}
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 4 Proposed NSC-OFDM-IM
### Receiver Design and Detection  
The NSC-OFDM-IM receiver system was designed as illustrated in Figure 5. The guard interval is removed from the time domain received signal, and then, it is fed into the FFT block for frequency domain representation. Since the transmit signal is affected by the wireless channel environment, it is necessary to remove or, at least, to minimize the channel disturbance from the received signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Incoming Bits** & **Bits Decimal Value** & **Null Subcarrier Index** \\ \((p_{0})\) & (Decvalue) & (Location in Complex Subgroup) \\ \hline \{00\} & 0 & \(decvalue+1=1\) \\ \{01\} & 1 & \(decvalue+1=2\) \\ \{10\} & 2 & \(decvalue+1=3\) \\ \{11\} & 3 & \(decvalue+1=4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proposed scheme index modulator.  
The frequency domain representation of the signal contains two types of data: one is the complex data embedded in the phases of the active subcarriers, and the other one is the index data conveyed by the indices of the null subcarriers. Thus, we first need to obtain the indices of the inactive subcarriers along the OFDM spectrum, and then, the complex data locations over active subcarriers are extracted by excluding the null carrier locations. To do so, in our framework, after applying the FFT process to the time domain received signal, the complex signal vector is processed in two parallel ways: one is to construct the log-likelihood ratio vector to be used for obtaining the null carrier indices, and the other is to equalize the frequency domain signal by using the channel frequency response vector to achieve the complex modulated data.  
The frequency domain equalization is an easy task to remove the channel effect since it functions over each carrier separately. In case the channel is unknown at the receiver side, before the equalization, one should take into consideration that the wireless channel impulse response should be estimated to properly equalize the frequency domain received signal. Let us consider that the receiver has the perfect channel knowledge and thus does not need a channel estimation process. In this case, the signal is equalized by a one-tap equalizer, and now, the received signal in the N-point frequency domain is ready for constructing the complex domain subgroups.  
After applying the frequency domain equalization, the complex data are divided into subgroups with a size of \(\tilde{M}\). The indices of the equalized subcarriers in the complex groups are given by,  
\[\hat{X}=\{\hat{X}_{1},\hat{X}_{2},\ldots,\hat{X}_{\tilde{M}}\} \tag{16}\]
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 3 Theoretical Development
### Data Collection
#### 3.2.1 Objective Marginal Probability  
The marginal probabilities are obtained by analyzing literature review and ATC accident reports. Two nodes of \"ATC Clearance Error\" and \"Distraction\" are taken as examples to illustrate how to obtain the marginal probability from the database. Of the 142 aviation accidents/incidents recorded by the \"SKYbrary\" between 1980 and 2019, 48 accidents/incidents were related to \"ATC Clearance Error,\" and 29 were related to \"Distraction.\" According to the above 142 relevant records, the marginal probabilities of nodes were calculated, as shown in Table 2.
## 2 Materials and Methods
### Data Analysis  
The data analysis combined quantitative and qualitative approaches, depending on the availability and quality of the extracted information. Various elements were compared across the included studies, such as author, year, study type, number of participants, intervention, outcome measures, key findings, and risk of bias. This comparison allowed for an evaluation of methodological consistency and the relevance of findings in different contexts.  
For studies reporting numerical data on the range of motion (ROM) of the proximal interphalangeal joint (PIPJ), mean differences (MD) and weighted mean differences (WMD) were calculated with 95% confidence intervals (CIs) [11; 12]. The certainty of the evidence was assessed using the GRADE system [13; 14], considering methodological quality, consistency, and precision of results.  
The risk of bias was evaluated using the Cochrane Risk of Bias tool (RoB 2) for randomized controlled trials (RCTs) and the ROBINS-I tool for non-randomized studies [15; 16]. Several key domains were analyzed, including study design, blinding, attrition, and co-interventions, assigning each a risk level categorized as low (0), uncertain (1), or high (2). The assessment was conducted independently by two reviewers, resolving any discrepancies through consensus.  
To ensure the accuracy and reproducibility of calculations, JAMOVI software 2.5 (The JAMOVI Project, 2024) was used. Additionally, Python (2024) and Matplotlib (2024) were employed for graphical data representation, bias detection, and the generation of a heatmap to visualize bias patterns across the included studies (see Figure 2). In this heatmap, each row represents an evaluated criterion, and each column represents a study, providing a clear overview of the extent of bias in the analyzed evidence.  
Figure 1: PRISMA flow chart of study inclusion.  
This methodology structured the analysis rigorously, ensuring that conclusions were based on the best available evidence and emphasizing the importance of considering bias risk in result interpretation.