esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Mutual Relationship between Both Distributions of Fatigue Strength and Fatigue Life  
In this study, the authors showed that both fatigue strength distribution and fatigue life distribution could be derived from the joint probability density function of \(h(\rho_{n},\xi)\), given by Equation (11). It was also confirmed that the analytical results were in good agreement with the statistical aspect of the experimental fatigue data in the very high cycle regime. In this place, mutual relationship between the distribution aspects of the fatigue strength and the fatigue life is further discussed to reconfirm the reasonability of the present probabilistic model for the _P-S-N_ characteristics. The mutual relationship between both distributions is illustrated in Figure 14, where the ordinate is the fatigue strength \(\sigma_{w}^{*}\) and the abscissa is the fatigue life \(N\). When any point \(P(N_{\text{fix}},\sigma_{w}^{*})\) is taken in this diagram, the probability that the fatigue life at the stress level \(\sigma_{w}^{*}\) is less than \(N_{\text{fix}}\) always corresponds to the probability that the fatigue strength at the definite stress cycles \(N_{\text{fix}}\) is lower than \(\sigma_{w}^{*}\). In other words, the cumulative probability of \(N_{\text{fix}}\), \(G(N_{\text{fix}},\sigma_{w}^{*})\), always corresponds to the cumulative probability of \(\sigma_{w}^{*}\), \(F(N_{\text{fix}},\sigma_{w}^{*})\)[30]. \(G(N_{\text{fix}},\sigma_{w}^{*})\) gives the distribution function of the fatigue life at \(\sigma_{w}^{*}\) and \(F(N_{\text{fix}},\sigma_{w}^{*})\) gives the distribution function of the fatigue strength at \(N_{\text{fix}}\). Thus, we have  
\[G(N_{\text{fix}},\sigma_{w}^{*})\ =\ F(N_{\text{fix}},\sigma_{w}^{*}) \tag{18}\]  
Equation (18) implies that the dashed area \(G(N_{\text{fix}},\sigma_{w}^{*})\) is always equal to the other dashed area \(F(N_{\text{fix}},\sigma_{w}^{*})\) in Figure 14.  
Based on this equality of Equation (18), one could obtain the _S-N_ curve corresponding to any level of the fracture probability. Thus, the probabilistic _S-N_ curves at the respective fracture probabilities could be analyzed as _P-S-N_ curves. As described in Sections 4.2 and 4.3, the distribution characteristics of the experimental _S-N_ data were well explained from either viewpoint of the fatigue strength distribution or the fatigue life distribution. The equality of Equation (18) is the reason why the reasonable _P-S-N_ curves could be equally obtained from different viewpoints of the fatigue strength distribution and the fatigue life distribution.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Ablation Study  
To investigate the function of the Multi-level Vessel Characteristics in VEPO-S2S and the Feature Fusion and Decoding Module, we designed several ablation experiments, which are introduced in the following part:  
1. \(w\)_/o sa_. Delete the input of the Shallow-level Attributes in the Multi-level Vessel Trajectory Representation Module, including the vessel length, width, draft, and type (see Section 4.3.1);
2. \(w\)_/o k1_. Delete the Sailing Location Preference in the Multi-level Vessel Trajectory Representation Module (see Section 4.3.1);
3. \(w\)_/o k2_. Delete the Voyage Time Preference from the Multi-level Vessel Trajectory Representation Module (see Section 4.3.1); \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline Kalman & \(1.73\times 10^{-4}\) & \(2.94\times 10^{-4}\) & \(5.13\times 10^{-4}\) & \(7.13\times 10^{-4}\) & \(8.83\times 10^{-4}\) \\ VAR & \(0.87\times 10^{-4}\) & \(3.38\times 10^{-4}\) & \(81.62\times 10^{-4}\) & - & - \\ ARIMA & \(2.69\times 10^{-4}\) & \(5.26\times 10^{-4}\) & \(7.99\times 10^{-4}\) & \(10.77\times 10^{-4}\) & \(13.62\times 10^{-4}\) \\ LSTM & \(0.43\times 10^{-4}\) & \(1.11\times 10^{-4}\) & \(2.01\times 10^{-4}\) & \(3.11\times 10^{-4}\) & \(4.40\times 10^{-4}\) \\ BiLSTM & \(1.50\times 10^{-4}\) & \(1.52\times 10^{-4}\) & \(2.60\times 10^{-4}\) & \(3.79\times 10^{-4}\) & \(4.94\times 10^{-4}\) \\ GRU & \(\mathbf{0.36\times 10^{-4}}\) & \(\mathbf{0.95\times 10^{-4}}\) & \(1.72\times 10^{-4}\) & \(2.63\times 10^{-4}\) & \(3.67\times 10^{-4}\) \\ BiGRU & \(1.53\times 10^{-4}\) & \(1.77\times 10^{-4}\) & \(2.53\times 10^{-4}\) & \(3.09\times 10^{-4}\) & \(3.26\times 10^{-4}\) \\ LSTM-LSTM & \(0.70\times 10^{-4}\) & \(1.30\times 10^{-4}\) & \(2.02\times 10^{-4}\) & \(2.85\times 10^{-4}\) & \(3.77\times 10^{-4}\) \\ BiLSTM-LSTM & \(0.88\times 10^{-4}\) & \(1.53\times 10^{-4}\) & \(2.28\times 10^{-4}\) & \(3.08\times 10^{-4}\) & \(3.98\times 10^{-4}\) \\ GRU-GRU & \(0.51\times 10^{-4}\) & \(1.04\times 10^{-4}\) & \(3.59\times 10^{-4}\) & \(5.14\times 10^{-4}\) & \(6.84\times 10^{-4}\) \\ BiGRU-GRU & \(1.34\times 10^{-4}\) & \(2.82\times 10^{-4}\) & \(4.45\times 10^{-4}\) & \(6.17\times 10^{-4}\) & \(7.96\times 10^{-4}\) \\ Transformer & \(0.81\times 10^{-4}\) & \(1.09\times 10^{-4}\) & \(1.58\times 10^{-4}\) & \(2.20\times 10^{-4}\) & \(2.93\times 10^{-4}\) \\ METO-S2S & \(2.02\times 10^{-4}\) & \(2.03\times 10^{-4}\) & \(2.21\times 10^{-4}\) & \(2.71\times 10^{-4}\) & \(2.92\times 10^{-4}\) \\ Ours & \(0.53\times 10^{-4}\) & \(0.96\times 10^{-4}\) & \(\mathbf{1.48\times 10^{-4}}\) & \(\mathbf{2.13\times 10^{-4}}\) & \(\mathbf{2.88\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The quantitative analysis results of each trajectory point under the MAE evaluation index.
###### Abstract  
In this study, we developed a carbon black (CB)-embedded carbon nanofiber (CNF) as a Pd support, which showed a high level of formic acid oxidation reaction (FAOR) activity. For the support preparation, heat treatment involving calcination at 1000 \({}^{\circ}\)C in a nitrogen atmosphere (carbonization) followed by calcination at 850 \({}^{\circ}\)C in water vapor (steam activation) was conducted to form a CB, which contained carbon nanofibers made from a polyacrylonitrile (PAN) fiber prepared by electrospinning. This catalyst showed a high level of FAOR activity. In this situation, the CB was also heat-treated, therefore, it was unclear whether the origin of the high FAOR activity of the CB-embedded CNF was caused by the CNF itself or the heat treatment of the CB. In order to establish the cause of the high FAOR activity of the CB-embedded CNF, the CBs underwent several heat treatments; i.e., stabilization, carbonization, and steam activation. Two types of carbon black with different pore structures, i.e., Ketjen black and Vulcan XC-72, were used to investigate the FAOR activity. The appropriate heat treatment of the CB promotes the improved FAOR activity; however, excessive heat treatment caused a deterioration in the FAOR activity, especially for Ketjen due to the presence of numerous micropores. However, by embedding the CB into the CNF, the FAOR activity improved, especially in the case of Ketjen, even though the embedded CB underwent several heat treatments. The optimum ratio of CB/PAN in the CB-embedded CNF was also investigated. The highest FAOR activity was observed at 0.25 CB/PAN for both the Vulcan and Ketjen. The electronic state of Pd3d in which the binding energy of the metallic Pd shifted to a lower binding energy suggested that the metal-support interaction is strong at the CB/PAN ratio of 0.25. On the basis of these results, it was found that heat treatment of the CB by embedding it in the CNF is a promising way to achieve a metal-support interaction without destroying its structure.  
**Keywords:** formic acid oxidation reaction; embedded CNF; heat treatment; morphology; pore distribution
## 3 Synthetic Examples
### Comparative Quantification and Reconstruction Analysis  
To quantify the performance of the SSGST and the other algorithms for generating TF representations of synthetic examples, we choose Renyi Entropy [30] as a quantitative performance index. The Renyi entropy is an objective indicator to evaluate the energy concentration of a time-frequency result; a lower Renyi entropy value denotes a more energy-concentrated time-frequency representation. The corresponding Renyi entropies are listed in Table 1. As can be seen in Table 1, in terms of the first and second synthetic examples, the SSGST result has the lowest Renyi entropy compared with the other time-frequency transforms. In the synthetic seismic signal processing, the Renyi entropy of the SSGST result ranks behind only the SSCWT. Therefore, in summary, compared with the other methods mentioned above, the SSGST method is more likely to obtain an energy-concentrated time-frequency result.  
We adopted the mean square error (MSE) to validate the reconstruction ability of the SSGST method. The corresponding MSE of the SSGST and the other algorithms are listed in Table 2. As shown in Table 2, the MSE values of SSGST are in the range of acceptable reconstruction error while taking the MSE values of other time frequency transforms as references.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rényi Entropy** & **CWT** & **GST** & **SSCWT** & **SSGST** \\ \hline Signal\({}_{1}\) & 5.1846 & 6.0156 & 2.3329 & 1.1779 \\ Signal\({}_{2}\) & 6.6262 & 4.9479 & 1.6304 & 0.8297 \\ Signal\({}_{3}\) & 7.0540 & 5.0889 & 1.2518 & 1.4250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Rényi Entropies of three synthetic examples based on the Continuous Wavelet Transform (CWT), Generalized S-transform (GST), Synchrosqueezing continuous wavelet transform (SSCWT), and Synchrosqueezing Generalized S-transform (SSGST) methods. Signal\({}_{1}\) is the double hyperbolic chirped signal. Signal\({}_{2}\) is the double hyperbolic chirped signal. Signal\({}_{3}\) is the synthesized signal.  
Figure 6: Time-frequency spectra of the synthesized signal based on (**a**) CWT; (**b**) GST; (**c**) SSCWT; (**d**) SSGST.  
Therefore, the SSGST method improves the resolution of time-frequency and also has a good reflection of strong or weak amplitude and high or low frequency. The newly proposed method can also reconstruct the original signal well with a lower reconstruction error.
## 4 Data-Driven Prediction Model
### Other Approaches in Palm Oil Prediction  
A hybrid solution incorporates more than one algorithm to boost efficiency. Integrating several ML algorithms will vastly increase the overall outcome of each algorithm by tuning, generalising, or adapting to new tasks. Table 9 shows other approaches to prediction derived from previous findings. Metaheuristic algorithms, such as the Genetic Algorithm (GA), can be used to solve a wide range of optimisation problems. The GA structures comprise two types based on the assumption of a finite solution space: local and global search [145]. The GA strings' populations, or chromosomes, are defined according to an optimisation problem.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author(s)** & **Objective** & **Dataset** & **Feature** & **Methods** & **Relevant Findings/Performances** &
\begin{tabular}{c} **Factor** \\ **Category** * \\ \end{tabular} \\ \hline
[27] & Chilli leaf disease & Images data & \
\begin{tabular}{c} Types of chilli leaf diseases, \\ up curl, down curl, \\ Cer-cospora leaf spot, \\ Geninivirus \\ \end{tabular} &
\begin{tabular}{c} SECNN (Squeeze-and- \\ excitation-based \\ convolutional neural \\ network) \\ \end{tabular} & Accuracy = 99.54\% & / \\ \hline
[26] & Wheat yield prediction & Historical data & Weather &
\begin{tabular}{c} CNN \\ DNN \\ \end{tabular} & RMSE = 0.80 & \\ \cline{3-6}  & & & & SVR & RMSE = 0.86 & \\ \cline{3-6}  & & & Regression tree & RMSE = 0.85 & / \\ \cline{3-6}  & & & Ridge & RMSE = 0.91 & \\ \cline{3-6}  & & & Lasso & RMSE = 1.19 & \\ \cline{3-6}  & & & XGBoost & RMSE = 0.73 & \\ \cline{3-6}  & & & RF & RMSE = 0.79 & \\ \cline{3-6}  & & & kNN & RMSE = 0.88 & \\ \hline
[21] & Com yield prediction & Historical data & \
\begin{tabular}{c} Time, corn variety, \\ SOM (Soil organic matter) \\ content, NFAR (Nitrogen \\ fertiliser application rate), \\ PERA (Phosphous fertiliser \\ application rate), KFAR \\ (Potassium fertiliser application rate), seeding \\ rate \\ \end{tabular} &
\begin{tabular}{c} GBDT (Gradient \\ boosting decision \\ tree) \\ \end{tabular} & \(R_{w}^{2}=0.799\) & \\ \cline{3-6}  & &  
A study employed GA to improve the generalisation ability of the Radial Basis Function Neural Network (RBFNN) and Fuzzy Radial Basis Function Neural Network (FRBFNN) [157]. Meanwhile, [PERSON] et al. [158] proposed a prediction method of fatty acid and an intelligent nonlinear model to improve the error. [PERSON] et al. [159] suggested an investigation to determine the palm oil kernel process by improving the error of response surface methodology (RSM). This method includes the adaptive neuro-fuzzy inference system (ANFIS) accuracy when combined with GA.
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Foundational Technologies and Advanced Methodologies  
#### 4.4.1 Advanced Algorithms in Predictive Maintenance  
Deep neural networks and hybrid approaches have dominated recent advancements in predictive maintenance. Convolutional networks have been employed to refine fault detection in industrial fans [58, 59], while RNN-LSTM hybrids have strengthened anomaly forecasting in electric motors [31, 73]. Autoencoders have demonstrated effectiveness in acoustic analysis and vibration diagnostics, and fractional PID controllers have been explored for nonlinear industrial settings [86].  
Furthermore, a growing body of literature suggests that combining multiple algorithms, such as decision trees, gradient boosting machines, and spectral feature extraction, often yields superior fault classification performance across diverse sectors, including hydropower, nuclear energy, and automotive assembly lines [66, 68, 85]. Specifically, multi-head attention mechanisms in advanced TCN-Autoencoder models have proven to enhance anomaly detection accuracy when dealing with high-dimensional and time-varying signals [59, 67]. These robust architectures are increasingly deployed in real-time predictive settings where fault identification must be both prompt and reliable.  
Layered ensembles of gradient-boosted trees [45, 46] have exhibited outstanding performance in diagnosing multi-fault conditions in grinding mills [47, 48]. Additionally, advanced evolutionary algorithms [49, 50] have been utilized for hyperparameter tuning, reducing iteration times and enhancing reliability metrics.  
In the energy sector, decision trees have been applied to detect electrical faults, while advanced sensor data have improved hydroelectric sustainability [18, 60]. Genetic algorithms have refined structural fault models for wind turbines, and reinforcement learning has been leveraged to optimize real-time maintenance in Industry 4.0, transforming traditional upkeep through adaptive intelligence [43].
# Covert Channel Based on Quasi-Orthogonal Coding
## 1 Introduction
The proposed approach and the considerations presented here are a continuation of the work presented in [16]. The work is organized as follows. In Chapter 2, the basics of creating a covert channel in a radio channel are presented. Chapter three describes how the proposed transceiver with sorting circuit works. The results of the computer simulations are included in Chapter 4. The rationale for using FSK modulation to create a covert channel is placed in Chapter 5. Chapter 6 contains the results of the tests conducted based on the universal software radio peripheral (USRP). A summary is included at the end.
## 4 Conclusions  
In the present work, AFCB was developed with the addition of RP waste as a substitute aggregate of natural sand. Based on the results and discussion, the following conclusions are made:  
* Rolled powder can be used as an aggregate in the manufacture of AFCB, reducing the liabilities of agate manufacturing companies and reducing environmental impacts due to the irregular disposal of this waste. Also, with the use of this residue, it is possible to reduce the consumption of natural sand, which is a finite aggregate and its extraction causes environmental damages, mainly in riverbeds and lakes.
* The results of the RP characterization show that the granulometry and the chemical composition were suitable for the development of AFCB. It is relevant to note that PR was used without any process of beneficiation.
* The concrete blocks showed a microstructure with a mean pore size of 264.0 \(\upmu\)m, which is characteristic of this type of material. Also, calcite formation in macropores was found due rate of hydration reactions.
* The w/c ratio and the amount of foam had a significant influence on density and compressive strength. The results for the compressive strength and density of AFCB were slightly lower than those required by ABNT 13438. However, it is important to emphasize that this standard complies with the ACCB, which are autoclaved.
* Overall, the tests enabled the best ratio between PC, RP, water, and foam to be established, which was identified for AFCB with 1.28 of w/c, thus providing the greatest restriction with the lowest density. The w/c ratio set at 1.28 with an addition of 18% foam generated a sample with a density of 430 \(\pm\) 18 kg/m\({}^{3}\), and a compressive strength about of 1.07 \(\pm\) 0.02 MPa. This result is close to meeting the requirements of the standard for density classes <450 kg/m\({}^{3}\) NBR 13438 [32] for ACCB.
* Economic and financial aspects must be emphasized, since the use of agate waste can significantly reduce the production cost of the companies that generate these wastes. This is because the transportation and environmentally correct costs, today approximately of 55 USD per m\({}^{3}\), would reduce to zero. Moreover, the AFCB manufacturing cost of using the RP can be 18% less than conventional AFCB cost.  
The two authors ([PERSON], [PERSON] and [PERSON], R.d.A.) are grateful to the Fundacao Meridional for financial support by productivity aid.  
[PERSON] and [PERSON] performed the tests, analyzed results and wrote the paper; [PERSON] assisted in the analysis of the results; [PERSON], [PERSON] and [PERSON] planned the study, assisted in the analysis of the results and writing the paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **Commercial** & **AFCB Cost (USD)** & **AFCB with RP Cost (USD)** \\ \hline Cement & 45.20 & 45.20 \\ Natural sand & 6.45 & 0.00 \\ Water & 0.40 & 0.40 \\ Agate waste & 0.00 & 0.00 \\ Commercial foam & 5.81 & 0.00 \\ Foam of study & 0.00 & 1.94 \\ Total cost & 57.86 & 47.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative production cost of 1 m\({}^{3}\) of AFCB.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in Chaos Aspect
#### 3.1.1 Chaos Domains  
Chaos is studied in three domains; space domain (spatial chaos), time domain (temporal chaos), and space-time domain (spatio-temporal chaos). All of these domains play roles in chaotic image encryption. These roles are reviewed in the following.  
Spatial ChaosSpatial chaotic systems and maps are functions that only depend on an input value to determine the state. They have many uses in image encryption; for example, [PERSON] et al. [35] utilized a 2D spatial map in a novel image encryption algorithm that exhibits strong security after applying key sensitivity tests, adjacent pixel correlation analysis, keyspace analysis, and testing against various attacks. Other works in the spatial domain include that performed by [PERSON] et al. [36], where they compiled a report investigating the effectiveness of several chaotic maps in the spatial domain, those being the Arnold cat map, baker map, and logistic map. The report describes the analysis of the maps' effectiveness in a novel encryption scheme using visual, entropy, histogram, encryption quality, differential, Known Plain Text (KPA), and Chosen Plain Text (CPT) analysis.  
Temporal ChaosA temporal system only depends on a time index and the state of the system at the previous index to determine the current state. Once such pure temporal chaos system, referred to as a \"super-chaotic\" map, was utilized by [PERSON] et al. [37] in a proposed image encryption algorithm that exhibited strong security properties such as a large keyspace, high key sensitivity, and statistical analysis resistance.  
Spatiotemporal ChaosA spatiotemporal chaotic system depends on both the spatial domain (input) and the time index. Encryption schemes using spatiotemporal chaos have been proposed by [PERSON] et al. [38] and [PERSON] et al. [39] where the chaos systems were paired with the Discrete Cosine Transform (DCT). The former additionally employs the Propagating Cipher-Block Chaining (PCBC) mode to achieve the image encryption, which contrasts with the work performed by [PERSON] et al. [40], where the basic Cipher Block Chaining (CBC) mode was opted for instead. All three algorithms exhibit strong security when analyzed using encryption analysis methods.  
Another work in the spatiotemporal domain was performed by [PERSON] et al. [41]. In their paper, they proposed a novel spatiotemporal chaos model called the Logistic-Dynamic Coupled Logistic Map Lattice (LDCML). Analysis of the proposed map demonstrated strong chaotic properties, and when applied to image encryption, the further experimental analysis showed high levels of effectiveness.
## 2 Materials and Methods
### Design Parameters for Flow Uniformity  
We evaluated the flow uniformity of the RFB based on the following parameters: the symmetry coefficient (\(C_{sym}\)), variability range coefficient (\(R_{i}\)), and maximum flow rate deviation (\(D_{m}\)). These parameters are shown in Equations (8)-(10), respectively [9].  
\[C_{sym}[\%]=\frac{|J_{x>0}|-|J_{x<0}|}{|J_{x>0}|+|J_{x<0}|}\times 100 \tag{8}\]  
\[R_{i}[m/s]=v_{\max}-v_{\min} \tag{9}\]  
\[D_{m}[\%]=\frac{\dot{m}_{\max}-\dot{m}_{\min}}{\dot{m}_{avg}}\times 100 \tag{10}\]  
where J is the mass flux, \(v\) is the velocity of the electrolyte solution entering the electrode through the inlet channel, and \(\dot{m}\) is the mass flow rate. The symmetry coefficient (\(C_{sym}\)) represents the difference between the left and right mass fluxes at each bifurcation, and the variability range coefficient (\(R_{i}\)) is the difference between the maximum and minimum velocities of the solution when it is discharged to the electrode from the inlet channel. As this difference increases, the nonuniformity of the electrolyte solution introduced to the electrode increases. The maximum flow rate deviation (\(D_{m}\)) represents the maximum flow rate difference across the 32 electrode inlets of the inlet channel.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 3 Covert Channels in Hash Chains
### Hiding Pattern-Based Categorization  
All variants perform a manipulation of (pseudo-)random packet fields as described by the _random value_ pattern [9]. The random value pattern covers all hiding techniques that replace a legitimate (pseudo-)random bit string with a covert bit string that also follows a random distribution. Random value techniques have been implemented by several authors, rendering our proposed covert channel variants applicable in realistic environments. Moreover, such random values can be found in many network protocols, such as the IPID field in IPv4 or the ISN field in TCP.  
On the sender side, the variants have very low effort and are thus hardly detectable. During transmission, an isolated packet will look innocent, as the one-time passwords look like random bitstrings, so that a possible bit flip is not likely to leave any trace or signature. Hence, detection during transmission seems non-trivial.