esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis  
### Experimental Setup  
**Datasets**: The study by [PERSON] et al. [27] involved the creation of a publicly available dataset consisting of Chinese and English speech. The dataset was constructed using speech samples, from which the authors randomly selected 3000K speech frames. These speech frames were then encoded using the AMR-NB (Adaptive Multi-Rate Narrowband) codec at 12.2 kbp/s, which is a low-bit-rate speech codec. The resulting coded speeches make up the cover speech dataset.  
**Steganography methods**: In this study, the speech frames from the cover dataset were further processed using specific steganography algorithms to generate steganographic speech frames. Three steganography algorithms, namely CNV [19], ACL [17], and HPS [21], were employed for this purpose. Each algorithm contributed to a separate steganographic dataset, resulting in three steganographic datasets in total. Each steganographic dataset contains steganographic frames with multiple embedding rates (the embedding rate is a parameter in steganography that represents the proportion of secret information embedded within the speech frames). The embedding rate refers to the ratio of embedded bits to the total embedding capacity. In this paper, the researchers used five different embedding rates: 20%, 40%, 60%, 80%, and 100%. These rates indicate the proportion of hidden information embedded within the steganographic speech frames.  
**Baselines**: To evaluate the performance of the proposed method, three steganalysis methods were used for comparison. The first method, DMSM, was proposed by [PERSON] et al. [31]. They used codeword-element embedding and the multi-head attention mechanism to detect steganography methods. The second method, FSMDP, was proposed by [PERSON] et al. [33]. They used the multi-dimensional perspective of codeword correlations to detect whether a frame contained secret information. The third method, Stegformer, was proposed by [PERSON] et al. [34]. They utilized dual representation and inter-frame correlation to extract steganalysis features. This paper proposes a frame-wise steganalysis method based on mask-gating attention and bilinear interaction mechanisms (MGA-BI).  
**Implementation details**: PyTorch v2.2.2 was used to implement our model. The training and testing steps were carried out using a GeForce GTX 3080 GPU with 10.5 GB of graphics memory. The hyperparameters of our method were as follows: the batch sizes in the training and testing stages were 4096, the maximum training epoch was set to 100, the loss function utilized in the model was binary cross-entropy, and Adam was used as the network training optimizer. The learning rate was set to 0.0045. The other parameters of our model are shown in Table 1.  
**Evaluation Metric**: This paper selected classification accuracy as the metric to evaluate the performance of the proposed method. Accuracy is commonly used as a performance metric in classification tasks. It is defined as the ratio of the number of correctly identified samples to the total number of samples.
## 4 Method Application and Resulting Discussion
### Parameter Settings  
In this study, the capacity of planned-installed wind farms was assumed to be 60 MW. The parameters of the wind turbines and the proposed COE are summarized in Table 1, in which the rated wind speed is in the range 10-16 m/s with a step of 1 m/s and the rotor radius is in the range 30-70 m with a step of 5 m. Assuming a regular grid for the offshore wind farm, the grid size was 10 \(\times\) 10, and the interval between the two wind turbines was 6\(D\) (\(D\) denotes the rotor diameter).  
For the IPSO algorithm, the algorithm convergence speed relied largely on the initial values of the particles at the first iteration. Meanwhile, the two important parameters, including the population size and the maximum iteration number, also significantly influenced the optimal results. With a certain set of initial values of the particles, the selected objective function value was more optimal, that is, more significant, when the population size and the maximum iteration number was bigger. However, more computation time is required. In this study, the population size and the maximum iteration number of IPSO were set to 20 and 200, respectively. By doing so, the IPSO reached convergence and the computation time was about ten days for each case. The acceleration factors \(c_{1}\) and \(c_{2}\) were set as  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values or Ranges** \\ \hline \(H_{0}\) & 10 m \\ \(\alpha\) & 0.1 \\ \(\rho\) & 1.225 kg/m\({}^{3}\) \\ \(R\) & \([30,70]\) with a step of 5 m \\ \(v_{r}\) & \([10,16]\) with a step of 1 m/s \\ \(v_{c}\) & 3 m/s \\ \(v_{f}\) & 25 m/s \\ \(C_{p,\alpha}\) & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the proposed COE minimization method.  
1.49445. Then, the obtained position layout was applied to analyze the developed COE. On the other hand, in order to analyze the relationship between the developed COE and the layout, the optimal wind turbine obtained by the developed COE and the actual installed wind turbines were taken into consideration for comparison.
## 2 Methods
### Fuzzy EDAS Method  
The fuzzy EDAS method is carried out through the following steps [25; 26]:  
Step 1: Create an average decision matrix (X):  
\[X=\left[\overline{\pi_{ij}}\right]_{nxm} \tag{9}\]  
\[\overline{\pi}_{ij}=\frac{1}{k}\oplus_{p=1}^{k}\overline{\pi}_{ij}^{p} \tag{10}\]  
where \(\overline{\pi}_{ij}^{p}\) represents the performance value of alternative \(A_{i}=(1\leq i\leq n)\) regarding criterion \(c_{j}=(1\leq j\leq m)\) which is assigned by the \(p\)th decisionmaker \((1\leq p\leq k)\).  
Step 2: Originally, in this step, criterion weights are created by averaging experts' preferences. In this research, the IMF SWARA-Z method has been applied.  
Step 3: Create an average solution matrix.  
\[AV=\left[\overline{a}\overline{v}_{j}\right]_{1\times m} \tag{11}\]  
\[\overline{a}\overline{v}_{j}=\frac{1}{n}\oplus_{i=1}^{n}\overline{\pi}_{ij} \tag{12}\]  
where \(\overline{a}\overline{v}_{j}\) is the average solutions regarding each criterion.  
Step 4: Obtain a positive distance from average (PDA) and negative distance from average (NDA) solution. They should be obtained in accordance with a criterion type:\[PDA=\left[\overline{pda}_{ij}\right]_{n\times m}\Rightarrow\overline{pda}_{ij}= \left\{\begin{array}{l}\frac{\frac{\psi\left(\overline{x}_{ij}-\overline{x}_{ ij}\right)}{k\left(\overline{x}\right)}}{\left(\overline{x}\right)}\ if\ j\in B\\ \frac{\psi\left(\overline{x}_{ij}-\overline{x}_{ij}\right)}{k\left(\overline{ x}_{ij}\right)}\ if\ j\in C\end{array}\right. \tag{13}\]  
\[NDA=\left[\overline{nda}_{ij}\right]_{n\times m}\Rightarrow\overline{nda}_{ ij}=\left\{\begin{array}{l}\frac{\psi\left(\overline{x}_{ij}-\overline{x}_{ ij}\right)}{k\left(\overline{x}_{ij}\right)}\ if\ j\in B\\ \frac{\psi\left(\overline{x}_{ij}-\overline{x}_{ij}\right)}{k\left(\overline{ x}_{ij}\right)}\ if\ j\in C\end{array}\right. \tag{14}\]  
Step 5: Get the weighted sum of positive and negative distances for all alternatives:  
\[\overline{sp}_{i}=\underset{i=1}{\overset{m}{\oplus}}\left(\overline{w}_{j} \otimes\overline{pda}_{ij}\right) \tag{15}\]  
\[\overline{sn}_{i}=\underset{i=1}{\overset{m}{\oplus}}\left(\overline{w}_{j} \otimes\overline{nda}_{ij}\right) \tag{16}\]  
Step 6: Normalize \(\overline{sp}_{i}\) and \(\overline{sn}_{i}\) values for all alternatives.  
\[\overline{ns}_{i}=\frac{\overline{sp}_{i}}{\max(k(\overline{sp}_{i}))} \tag{17}\]  
\[\overline{ns}_{i}=\frac{\overline{sn}_{i}}{\max(k(\overline{sn}_{i}))} \tag{18}\]  
Step 7: Define the appraisal score (\(\delta s_{i}\)) for all alternatives.  
\[\overline{as}_{i}=\frac{1}{2}(\overline{nsp}_{i}\otimes\overline{ns}n_{i}) \tag{19}\]  
Step 8: Rank the alternatives by decreasing values.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Tethered UAVs  
Tethered UAVs can support unlimited autonomy. This technology does not require physical landing on a charging station and avoids repeated recharging. In this technology, UAVs can get a continuous power supply through a connecting cable from a charging station. This technology ensures efficient and safe data transmission. In general, copper wires are used as power supply lines. However, optical fiber technology is also used in the tethered UAV area. Fiber optic cable supports kilowatts of power transfer using high intensity optical beams. Optical fiber technology can significantly reduce the UAV payload and power lines compared to copper lines [45]. Two examples of tethered UAVs are presented in Figure 6. [PERSON] [46] proposed a maritime application-based tethered UAV for detecting oil pollution from ships. Moreover, [PERSON] et al. [47] demonstrated a tethered UAV for nuclear power plants to support extremely long-endurance operations. In [48], the authors proposed a method for UAV localization in an indoor environment using a quasi-taut tether. In a recent study [49], the authors used UAV tethered technology for 3D deployment of aerial base stations.
# Biosensors-Based In Vivo Quantification of 2-Oxoglutate in Cyanobacteria and Proteobacteria
## Results  
### Design of 2-Oxoglutate Mono-Protein Biosensor Systems  
The structures of PII and NtAcA in the apo form or in complex with 2-OG show that both proteins undergo structural transitions when bound to 2-OG. For PII, 2-OG binding triggers a conformational change in the flexible T-loop, which protrudes outward as an antenna [43]. When bound to 2-OG, the PII antenna moves closer to the C-terminal region of PII. We therefore chose to insert YFP within this loop, between residues Gly-48 and Ser-49, whereas the donor (CFP) was inserted at the C-terminus of PII (F-mT_PII construct, Figure 1A,C). For the NtAcA homodimer, 2-OG binds to the effector binding domain (EBD) inducing a twist within the two C-helices and an increased interface between the two monomers [9]. To select a suitable biosensor for 2-OG with a high FRET-ratio change in function of 2-OG binding, we engineered 10 different biosensors with variable length (F-NtAc_1 to 10). All variants contain the 2-OG binding pocket, lack the DNA binding domain, and are fused to a N-terminal YFP and a C-terminal donor fluorophore. The different variants correspond to N-terminal truncations of the EBD domain (starting at residues 7, 24, 33, or 62) and C-terminal truncations after residue 143 (last residue of the C-helix) or 163 (upstream the DNA recognition helices) (Figure 1B,D).
# An Evaluation of the Efficiency of Compartmented
## 3 Results
### 4 PB Test-Healing Efficiency  
Figure 11 shows the healing efficiency of the PA control mix and fibre mix flexural stiffness (\(S_{mix}\)). The results show a very close initial test performance (Figure 11a). However, Figure 11b shows a higher stiffness recovery for the PA fibre mix after 20 h healing at 20 \({}^{\circ}\)C. These results demonstrate the potential benefits of the PA mix containing compartmented alginate fibres encapsulating the rejuvenator.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Spec No.**} & \multicolumn{3}{c}{**Fracture Toughness \(K_{lc}\) (Pa-m\({}^{1/2}\))**} & \multicolumn{3}{c}{**Fracture Energy \(G_{lc}\) (J/m)**} \\ \cline{2-7}  & **1 st Test** & **2 nd Test** & **3 rd Test** & **1 st Test** & **2 nd Test** & **3 rd Test** \\ \hline
1 & 10.24 & - & - & 1030.88 & - & - \\
2 & 6.57 & 1.85 & 2.35 & 777.55 & 92.39 & 117.92 \\
3 & - & - & - & - & - & - \\
4 & 5.27 & 1.98 & 2.16 & 556.49 & 117.08 & 169.39 \\
5 & 5.09 & 1.11 & 2.29 & 546.55 & 34.71 & 223.51 \\
6 & 8.81 & - & - & 843.89 & - & - \\
7 & 6.95 & 3.11 & 3.45 & 569.72 & 345.16 & 318.46 \\
8 & 5.90 & - & - & 638.77 & - & - \\
9 & 6.13 & 3.50 & 1.16 & 582.55 & 237.07 & 70.25 \\
10 & 4.79 & 1.99 & 1.68 & 424.55 & 102.35 & 91.20 \\
11 & 8.90 & 3.93 & 3.77 & 1039.87 & 409.72 & 336.30 \\
12 & 8.34 & - & - & 835.12 & - & - \\ Mean & 6.85 & 2.50 & 2.41 & 700.20 & 191.21 & 189.58 \\ Std. Dev & 1.81 & 1.03 & 0.92 & 205.56 & 142.13 & 107.04 \\ Coef. Var. & 0.26 & 0.41 & 0.38 & 0.29 & 0.74 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fibre Mix-Fracture Toughness and Fracture Energy.  
Figure 11: _Cont._
# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
###### Abstract  
Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.  
Keywords:depth estimation; depth from focus; 3D reconstruction; uniformly focused image set +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Separate Analysis of the Results  
4 PB: The strain range used in the 4 PB test is very wide, and number of samples used in each group is small, which requires high precision. Therefore, more precise forming and cutting technology is needed. The fatigue life of the six asphalt mixtures decreases gradually with the increase of strain. The fatigue properties of SK70 and WA are obviously poor, SBS4 is in the middle, and 4S4P, WPES, and SBS6 are the best ones. The specific performance order is slightly different with the change of strain, and WPES performs best at high strain. With the increase of SBS content, the fatigue performance of the asphalt mixture increases continuously. The addition of WPE and PA610 improves the fatigue performance of the modified asphalt mixture in varying degrees.  
Trapezoidal Beam: The preparation of test samples is complex and the amount of test data is huge. It has high precision and is suitable for scientific research instead of a field laboratory. The strain range of the test is moderate in the three methods. The fatigue life of the six asphalt mixtures decreases gradually with the increase of strain, and the fatigue performance of SK70 and WA are the worst, and 4S4P, SBS4, and SBS6 are the best. The specific performance ranking varies slightly with strain, which is similar to the conclusion for 4 PB. The values obtained from the Trapezoidal Beam tests are more concentrated in regions with a smaller strain than 4 PB.  
Overlay Tester: The strain range of the OT test is the narrowest. The test specimen is relatively simple to prepare and the test process takes less time. It is easier to popularize than the above two test methods. Similarly, the OT test again verified that the fatigue performance of SK70 and WA are poor. The fatigue performance of SBS4, 4S4P, and WPES are in the middle, and SBS6 showed the best fatigue performance. With the increase of SBS content, the fatigue life of the asphalt mixture increased. The fatigue life of 4S4P was better than that of SBS4. This result confirmed the results of the Trapezoidal Beam tests but differed from that of 4 PB.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Theoretical Validation of HSALA
#### 5.3.5 Risk Mitigation  
Finally, Table 13 highlights how HSALA helps reduce technical debt and supports long-term adaptability in a fast-changing digital landscape.  
HSALA addresses the risks related to technical debt and obsolescence through a proactive and adaptable architectural strategy, thereby enhancing the long-term viability and sustainability of the application.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **HSALA Benefit** \\ \hline \multirow{2}{*}{Reduction in Technical Debt} & Proactive integration of accessibility and security reduces future costs associated with retrofitting and patching, leading to long-term savings and efficiency. \\ \
###### Abstract  
Assessing and monitoring forest degradation under national Monitoring, Verification and Reporting (MRV) systems in developing countries have been difficult to implement due to the lack of adequate technical and operational capacities. This study aims at providing methodological options for monitoring forest degradation in developing countries by using freely available remote sensing, forest inventory and ancillary data. We propose using Canopy Cover to separate, through a time series analysis approach using Landsat Imagery, forest areas with changes over time from sectors that report a \"stable condition\". Above ground Biomass and Net Primary Productivity derived from remote sensing data were used to define thresholds for areas considered degraded. The approach was tested in a semi-deciduous tropical forest in the Southeast of Mexico. The results showed that higher rates of forest degradation, 1596 to 2865 ha year\({}^{-1}\), occur in areas with high population densities. The results also showed that 43% of the forests of the study area remain with no evident signs of degradation, as determined by the indicators used. The approach and procedures followed allowed for the identification and mapping of the temporal and spatial distribution of forest degradation, based on the indicators selected, and they are expected to serve as the basis for operations of the Reduction of Emissions from Deforestation and Forest Degradation (REDD+) initiative in Mexico and other developing countries, provided appropriate adaptations of the methodology are made to the conditions of the area in turn.  
REDD+; Landsat; forest modelling; forest degradation; Canopy Cover; Above-ground Biomass; Net Primary Productivity +
Footnote †: journal: Physics Letters B
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 6 Mathematical Model  
A mathematical model based on decomposed TPN properties is proposed. The model uses the global and local clocks of transition firing (respectively \(H\big{(}t_{i,j}\big{)}\) and \(h\big{(}t_{i,j}\big{)}\)) and the variables' decision \(A\big{(}t_{i,j},t_{i^{*},j-1}\big{)}\) and \(Z_{i,j+1}\).
# Variable Guide Vane Scheduling Method Based on the Kinematic Model and Dual Schedule Curves
## 1 Introduction
In Section 3, an analytical model that reveals relations between the actuator strokes and vane angles is derived from analysis of the link mechanism, and the methodology for adjusting the link mechanisms is proposed to obtain the  
Figure 1: Variable guide vane (VGV) link mechanism of the compressor. desired VGV schedule. The control algorithm based on dual schedule curves is presented to reduce the hysteresis of a variable inlet guide vane (VIGV) in Section 4. Finally, the application test results are presented in Section 5. A summary and conclusions follow in Section 6.
## 4 Proposed Hybrid Model of NLMPC and Data-Driven Approach
### Optimization Problem with Data-Driven Model  
The NMPC optimization problem, incorporating the data-driven correction, is formulated in Equation (28), and this equation in the paper represents the optimization problem central to the proposed hybrid Nonlinear Model Predictive Control (NLMPC) and data-driven approach for Automatic Generation Control (AGC). This equation serves as the mathematical foundation for achieving the paper's main goal: optimizing the performance of multimachine power systems while balancing technical and economic objectives under dynamic and uncertain conditions.  
\[\underset{\boldsymbol{u}(k),\ldots,\boldsymbol{u}(k+N-1)}{min}J=\sum_{t=0}^{N} \Bigl{[}\alpha_{t}\Delta f^{2}+\alpha_{V}\Delta V^{2}+\alpha_{C}C_{g}(u)\Bigr{]} \tag{28}\]  
subject to:  
* System dynamics with data-driven correction: \[\boldsymbol{x}_{i}(k+1)=f_{i}(\boldsymbol{x}_{i}(k),\boldsymbol{u}_{i}(k))+ \hat{\epsilon}_{i}(\boldsymbol{x}_{i}(k),\boldsymbol{u}_{i}(k))\] (29)
* Output equations: \[\boldsymbol{y}_{i}(k)=g_{i}(\boldsymbol{x}_{i}(k),\boldsymbol{u}_{i}(k))\] (30)
* State constraints: \[\boldsymbol{x}_{min}\leq\boldsymbol{x}_{i}(k)\leq\boldsymbol{x}_{max}\] (31)
* Input constraints: \[\boldsymbol{u}_{min}\leq\boldsymbol{u}_{i}(k)\leq\boldsymbol{u}_{max}\] (32)
* Output constraints: \[\boldsymbol{y}_{min}\leq\boldsymbol{y}_{i}(k)\leq\boldsymbol{y}_{max}\] (33)
## 2 Proposed Method
### Adversarial Task Augmented Sequential Meta-Learning (ATASML) Framework
#### 2.3.1 Data Augmentation in the ATASML Framework  
Data augmentation is critical in the ATASML framework, ensuring that models are well-equipped for diverse operational scenarios. Techniques such as Gaussian noise addition and temporal warping are employed to enhance data variability and complexity. These techniques are depicted in Figure 1, where the transformation from the original dataset \(\mathcal{X}_{orig}\) to the augmented datasets \(\mathcal{X}_{gauss}\) and \(\mathcal{X}_{warp}\) is illustrated.  
The original dataset \(\mathcal{X}_{orig}\) consists of samples \(x_{1},x_{2},\ldots,x_{n}\) that capture the true operational dynamics. To enhance the dataset, a window of size \(W\) is utilized to segment the continuous time-series data into discrete, overlapping segments. This sliding window technique allows for comprehensive capture of temporal patterns within each segment. Subsequently, the augmentation methods applied to each segment are as follows:  
1. Noise Addition (NoiseAdd): Gaussian noise \(\epsilon\) is added to the data within each window, creating a set of noise-augmented samples \(\mathcal{X}_{gauss}\): \[\mathcal{X}_{gauss}=\mathcal{X}_{orig}+\epsilon,\quad\epsilon\sim\mathcal{N }(0,\sigma^{2})\] (3) where \(\sigma^{2}\) denotes the variance of the noise, modeling the sensor noise and other environmental variations.
2. Temporal Warping (TimeWarp): The temporal spacing of data points within each segment is modified, resulting in a set of time-warped samples \(\mathcal{X}_{warp}\): \[\mathcal{X}_{warp}(t)=\mathcal{X}_{orig}(a\cdot t),\quad a\sim U(a_{min},a_{ max})\] (4) where \(a\) is a scaling factor that simulates the acceleration or deceleration of process dynamics.  
Following augmentation, a selection process ensures that the resulting samples contribute positively to model training. This selection process, including quality assessment and anomaly detection, is critical to maintaining a high-quality augmented dataset \(\mathcal{X}_{aug}\)  
Figure 1: Illustration of the data-augmentation process within the ATASML framework. The original dataset \(\mathcal{X}_{orig}\) consists of samples \(x_{1},x_{2},\ldots,x_{n}\), which are segmented using a window of size \(W\). This segmentation process allows for the application of augmentation techniques: noise addition, resulting in \(\mathcal{X}_{gauss}\), and temporal warping, resulting in \(\mathcal{X}_{warp}\). The process culminates in a quality-assessed and filtered augmented dataset \(\mathcal{X}_{aug}\) for model training.  
The entire augmentation process, including the application of windowing and augmentation techniques, is schematically depicted in Figure 1.