esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Metaheuristic Algorithm
### Hybridization SCSO and GWO  
The SCSO algorithm uses the R parameter to control the movement of search agents within the search space. This parameter also ensures a balance between exploration and exploitation. Inspired by the unique characteristics of sand cats in nature, the R parameter emphasizes the sensitivity range and decreases from 2 kHz to 0 over time. In contrast, the GWO algorithm uses the A parameter to control the movement of search agents. When the A parameter value is greater or less than 1, it influences the balance between exploration and exploitation. However, this mechanism can lead to drawbacks, such as trapping in local optima, especially in large search spaces. To address this issue, the hybrid algorithm incorporates the R parameter to enhance control over agent movement and improve the balance between exploration and exploitation.  
In this context, the COSGO algorithm benefits from the R-value of the SCSO algorithm to switch dynamically between two well-known metaheuristic algorithm strategies. When the R-value is between \(-1\) and \(1\), the search agents follow the behavior rules of the SCSO algorithm; otherwise, they update their positions based on the GWO algorithm. COSGO also includes a mechanism to validate the fitness of newly updated positions. Updated positions are first checked against the search space boundaries. If they fall outside the allowed limits, correction parameters are applied to move them back within boundaries. Subsequently, a sorting mechanism is employed to merge the new positions with the existing ones, resulting in a combined population of old and new individuals. Likewise, the fitness values of both the existing and new individuals are merged. All individuals are ranked in ascending order based on their fitness values, with the best individual placed at the top. The population is then updated by selecting the top individuals according to the original number of search agents, ensuring that the best solutions are retained.  
Grey wolf optimization (GWO) was employed due to its effective global exploration capabilities, simple structure, and minimal parameter requirements, which make it highly adaptable across a variety of optimization problems. On the other hand, the Sand Cat Swarm Optimization (SCSO) algorithm has demonstrated efficient local search behavior and a balanced exploration-exploitation trade-off, making it a suitable candidate for hybridization. The integration of chaotic opposition-based learning further enhances diversity and helps avoid premature convergence. Comparative studies such as [13; 14] have also highlighted the complementary strengths of these algorithms, supporting the rationale for their combination in the proposed framework.
## 1 Introduction
The wavelet transform (WT) method also easily ignores the reasonable high values when there are frequent variations in the time series [35]. The harmonic analysis of time series (HANTS) method was later proposed by [PERSON] [36] and has been widely used to reconstruct time-series datasets of the NDVI, LAI, and land surface temperatures (LSTs) [37; 38; 39]. The method has also been successfully used to predict Landsat values at any given time and has achieved relatively high accuracy in Landsat data reconstruction [9]. However, the HANTS may result in a large deviation from the original time series if there are long-term data gaps [40]. In this paper, a hybrid time series reconstruction method has been proposed. Several time series outlier recognition and gap-filling methods have been applied to fill the long-term gaps. A high-precision model continuous change rate tracking model (CCTM) has been developed for the reconstruction of time series RSLP. The CCTM model divides the yearly time series into four seasons and reconstructs each seasonal time series with a least-squares solver. The CCTM model decomposes the land surface variations into four parts by the variation types: uniform variation part, accelerated variation part, decelerated variation part, and short-term periodic variation part. Every part is represented by a designed function. In addition, the SG filter is adapted for LAI, GPP, and surface reflectance time series data to effectively control the degree of smoothing and retain the original characteristics by setting limited iteration times or adding smoothing controlling parameters. Current time series reconstruction methods do have their advantages, but the long-term gaps, over-fitting, and remaining noise issues are still disturbing [41]. More importantly, none of the methods above can precisely predict the time series values on a daily scale. The objectives of this paper are: (1) to provide solutions to the time series deficiencies of long-term gaps, over-fitting, and remaining noise issues; and (2) to develop a comprehensive continuous time series reconstruction method that can precisely predict time series values.
# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 4 Experimental Results
### Coal Grinding Mill  
The reason why the coal mills are chosen for verification of this IoT device is based on several factors. Firstly, the environment in which they operate is filled with significant ambient noise [18] and demonstrating that the state detection algorithm is functional in such conditions is crucial for the applicability of the proposed device. Furthermore, depending on the size of the industrial coal grinding system and construction parameters of the mill itself, the dominant frequencies in the acoustic specter of the mill can vary, simulating the diversity of acoustic signatures of different rotary machines.  
Coal grinding mills are widely used in thermal power plants for the purpose of pulverizing chunks of coal into small powder, which can then be easily transferred to the burner system. Fan mills, on which we mostly concentrate in this paper, have several impact plates within the housing of the mill. The impact plates rotate around the center, and the friction between the plates and the coal causes pulverization. This effect is most efficient when the mill has been serviced and the impeller with the plates is new. With time, however, the impact plates get worn and their efficiency decreases. Generally, new grinding plates last around 60 working days in the mill, but that time can vary significantly depending on the load of the mill and the quality of coal. Therefore, a condition detection algorithm will be applied to this machine for the purpose of detecting the amount of wear on the impact plates of the mill.  
Figure 12 depicts the coal grinding mill itself (left), with the motor that rotates impact plates in front of it. The schematic of its cross-section is shown in Figure 12 (right). This particular mill has a rotary frequency of 12.5 Hz, it has 10 impact plates rotating around the center, and the microphone is located on the side of the machine, close to the movement of the plates. Therefore, the frequency of the plate's movement next to the microphone is 125 Hz. These are dominant frequencies in the specter and considering the appearance of their higher harmonics and the possibility of a slight variation of the base rotary frequency, the informative frequency bandwidth is between 10 Hz and 500 Hz. The recording sampling frequency is 48 kHz with a 16-bit resolution. Taking this into account, the preprocessing parameters are defined as \(L=45\) s, \(d=15\) s, \(F_{\rm s}=4800\) Hz.  
Figure 12: Coal grinding mill (**left**) and its cross section (**right**) [14].
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Security Analysis and Hardware Metrics of the Proposed PRNGs-CS
#### 3.3.1 PRNGs-CS Resilience against Statistical Attacks
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**LSP-PRNG**} & \multicolumn{2}{c}{**LST-PRNG**} & \multicolumn{2}{c}{**LSPT-PRNG**} \\ \cline{2-7}
**Test** & \multicolumn{1}{c}{_p-Value_} & \multicolumn{1}{c}{**Prop. (\%)**} & \multicolumn{1}{c}{_p-Value_} & \multicolumn{1}{c}{**Prop. (\%)**} & \multicolumn{1}{c}{_p-Value_} & \multicolumn{1}{c}{**Prop. (\%)**} \\ \hline Frequency test & 0.494 & 100 & 0.475 & 98 & 0.115 & 97 \\ Block-frequency test & 0.760 & 99 & 0.319 & 99 & 0.851 & 97 \\ Cumulative-sums test (2) & 0.757 & 100 & 0.691 & 98 & 0.394 & 96.500 \\ Runs test & 0.367 & 100 & 0.883 & 98 & 0.988 & 98 \\ Longest-run test & 0.983 & 99 & 0.936 & 100 & 0.437 & 98 \\ Rank test & 0.720 & 98 & 0.972 & 99 & 0.658 & 98 \\ FFT test & 0.575 & 98 & 0.475 & 97 & 0.475 & 99 \\ Nonperiodic-templates (148) & 0.527 & 99.115 & 0.489 & 99.074 & 0.484 & 99.054 \\ Overlapping-templates & 0.596 & 99 & 0.911 & 100 & 0.384 & 100 \\ Universal & 0.335 & 98 & 0.335 & 99 & 0.035 & 97 \\ Approximity entropic & 0.475 & 99 & 0.367 & 100 & 0.262 & 100 \\ Random-excursions (8) & 0.352 & 99.792 & 0.471 & 98.242 & 0.371 & 99.107 \\ Random-excursions-variant (18) & 0.468 & 98.611 & 0.367 & 98.438 & 0.374 & 98.710 \\ Serial test (2) & 0.460 & 99 & 0.900 & 99.500 & 0.647 & 99.500 \\ Linear-complexity & 0.401 & 99 & 0.554 & 99 & 0.071 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _p-values and Proportion results of NIST test for the proposed PRNGs-CS. property, for all proposed PRNGs-CS, we compute the Hamming Distance between two sequences produced \((S_{1},S_{2})\) that are just the LSB (Least Significant Bit) of parameter \(P_{s}\) different. Compute the mean Hamming Distance HD among the two generated sequences for five random secret keys. \(H_{D}(S_{1},S_{2})\) is illustrated by the formula below:  
\[H_{D}(S_{1},S_{2})\!=\!\frac{1}{Nb}\sum_{i=1}^{Nb}\left(S_{1}(i)\oplus S_{2}(i)\right) \tag{40}\]  
where \(Nb\) is the number of bits in a produced sequence. Table 6 illustrates the average Hamming distance values for the proposed LSP-PRNG, LST-PRNG, and LSPT-PRNG. These values are relative to the optimal value of 50%. This result demonstrates the high sensibility of the secret key of the proposed PRNGs-CS.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver  
Interleavers play an important role in the minimum distance of turbo codes and also in the performance of the error floor. Interleavers are classified into the block and convolutional interleavers [31]. Some interleavers of interest applied to turbo codes are reviewed below.
## 3 Experimental Section
### Load-Deflection Curves  
Based on the result of the four-point load test, the load-deflection curves of the midspan of each beam were obtained. The load and corresponding deflection at both the first cracking load and the ultimate load for all beams are listed in Table 4.  
The ratio of the first cracking load to the ultimate load (\(\mathrm{P_{cr}}/\mathrm{P_{u}}\)) is listed in Table 4, and this ratio serves as an indicator of beam ductility and structural behavior. The (\(\mathrm{P_{cr}}/\mathrm{P_{u}}\)) ratio is a good measure to obtain an idea of how a beam or structure reacts when it is loaded; it reflects the ability of structures to withstand loads prior to cracking and ultimate failure. Generally, all specimens exhibited a linear response before cracking. Then, with an increase in the applied load, the ratio of the load to midspan deflection decreased, which means the stiffness of the tested beams decreased. Consequently, a plastic response was noted due to the yielding of the longitudinal tension reinforcement, which lasted until the end of the test, as shown in Figure 10.  
Figure 10: _Cont._Figure 10: _Cont._  
As observed in Figure 10a, the load-deflection curve for the control specimen (GF-1 ST) with a straight tendon profile layout exhibited a linear response before cracking (elastic stage), in which the load varies linearly with deflection. As the load continues to increase, the first cracks appear in the concrete at 142.60 kN and a deflection of 0.95 mm. This marks the transition from elastic to inelastic behavior (elastic-plastic stage), and the slope of the load-deflection curve gradually decreases. This means the deflection increases faster than the load due to a decrease in stiffness resulting from cracking. As the load increased, a new inflection point was found in the load-deflection curve (the slope of the load-deflection curve gradually decreased again). Then, a plastic response (plastic stage) was observed due to the yielding of longitudinal tension reinforcement until the test was finished. In this stage, the load-deflection curve tends to be horizontal while the load no longer rises, and the deflection increases sharply. The ultimate load (maximum load) was 358.70 kN, and the deflection was 36.80 mm. The ratio (\(\rm{P_{cr}/P_{u}}\)) was 39.75%, as shown in Table 4.  
For the specimen GF-2 TR with a trapezoidal tendon profile and eccentricity at the end (the anchorage point of the tendon), \(\mathbf{e_{e}}\) = +80 mm, the load-deflection curve is illustrated in Figure 10b. Although its elastic stage is lower than the control beam, the first crack appeared in the concrete at 122.02 kN with a deflection of 0.96 mm, but it has a higher ultimate load of 371.63 kN and a smaller deflection of 36.76 mm at ultimate load. That means increasing the ultimate load by 12.93 kN with an increasing rate of 3.60% and decreasing the ultimate load deflection by 0.04 mm with a decreasing rate of 0.11%, as shown in Table 5. The (P\({}_{\text{cr}}\)/P\({}_{\text{u}}\)) ratio for this specimen was 32.83%, which was lower than the control beam, as shown in Table 4.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 1 Introduction
In order to solve such problems, a power smoothing method based on the allocation of the wind turbines and the rotor inertia is proposed in this paper, which is quite different from the above-mentioned power smoothing methods. In this paper, the direct-drive PMSG based WECS is chosen in the wind farm. The WTs of the wind farm are divided into two classes: control wind turbines (CWT) and power wind turbines (PWT). The PWTs are expected to output as much power as possible and then the MPPT control strategies are adopted by combing the power smoothing strategy using rotor inertia. The increment of wind power can be stored in the generator as a form of kinetic energy and released when the wind power decreases. The CWTs are in charge of the output power smoothing for the entire wind farm by giving the appropriate power according to the output power need of the wind farm. The battery energy storage system (BESS) with small capacity is installed as the back support of the CWTs to satisfy the high power demanding of the grid. The simulation model of the wind farm based on the proposed control strategies is built in Matlab/Simulink and the efficiency of the proposed strategy is verified by the simulation results.
# Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques
## 2 Materials and Methods
### Performance Measure  
As a measure of performance, the Mean Squared Error (MSE) was used, which defines the mean squared deviation between the observed and predicted values of the output parameter [44]:  
\[MSE(y,f(x))=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-f(x_{i}))^{2} \tag{14}\]  
where \(y_{i}\) represents the observed value of the output parameter and \(f(x_{i})\) is the predicted value obtained using the trained model. MSE is always non-negative, with values closer to zero defining better models [44].  
Besides the MSE, the coefficient of determination \(R^{2}\) was also used as a measure of performance, defined as [44]:  
\[R^{2}(y,f(x))=1-\frac{\sum_{i=1}^{N}(y_{i}-f(x_{i}))^{2}}{\sum_{i=1}^{N}(y_{i} -\overline{y})^{2}} \tag{15}\]  
where \(\overline{y}\) denotes the mean of \(y\). Values of \(R^{2}\) closer to one define better models. While MSE is an absolute measure of fit, \(R^{2}\) represents a relative measure of fit [44].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Optimization Variables**} & **Range of Optimization Variables** & **Adopted Value** \\ \hline Pulping pH & 7–10 & 8 \\ \cline{2-3}  & [5; 27; 28; 29; 30; 31; 32] & \\ \hline Pulping time & 4–60 min & 35 min \\ \cline{2-3}  & [6; 31; 33; 34; 35; 36; 37] & 45 \({}^{\circ}\)C \\ \hline Pulping temperature & 8–18 wt \% & 10 wt \% \\ \cline{2-3}  & [9; 33; 34; 35; 36; 37; 39] & 10 wt \% \\ \hline Flotation temperature & 20–45 \({}^{\circ}\)C & 22 \({}^{\circ}\)C \\ \cline{2-3}  & [4; 27; 35; 40; 41; 42; 43] & 22 \({}^{\circ}\)C \\ \hline Flotation consistency & 0.5–1.5\% & 1 wt \% \\ \cline{2-3}  & [6; 11; 28; 29; 31; 35; 36; 37; 41; 42] & 1 wt \% \\ \hline \multirow{2}{*}{Agitation speed} & 1000–1400 rpm & 1000 rpm \\ \cline{2-3}  & [11; 27; 28; 31; 41; 44] & 1000 rpm \\ \hline Airflow rate & 225–775 L/h & 260 L/h \\ \cline{2-3}  & [9; 11; 35; 43] & 260 L/h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization variables for flotation deinking.  
The problem with the evaluation of any machine learning-based model is that it may result in adequate prediction on the data used for training the model but might not generalize well and fail to predict future unseen data. Cross-validation might be used to overcome this problem by dividing the data into two subsets: one for training a model and the other for model validation. The machine learning methods used in this paper were evaluated using 10-fold cross-validation, where the dataset is randomly partitioned into 10 subsets, 9 of them being used for training the model and the remaining one for model validation (testing). The cross-validation procedure is repeated 10 times, with each of the subsets used exactly once for validation, and the 10 obtained results are then averaged to produce a single estimation [44]. The optimal hyperparameters of the models were determined using the grid search, which exhaustively tries every combination of the provided hyperparameter values to select the best model [45].
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### Internal Battery Faults
#### 2.1.3 Internal Short Circuit  
A short circuit in Li-ion batteries can occur both externally and internally. An internal short circuit occurs when the insulating separator layer between the electrodes fails. This failure of the separator can be attributed to melting due to high temperature, cell deformation, the formation of dendrite, or compressive shock [7; 19; 20]. All of these can cause penetration through the separator layer, or cause the lithium ions and electrons to be released at the anode and travel across the electrolyte toward the cathode, which triggers contact between the anode and the cathode, leading to internal short-circuiting. When this phenomenon happens, the electrolyte tends to decompose by an exothermic reaction, causing thermal runaway [21]. Thermal runaway is mainly caused by heat build-up from the  
Figure 1: Internal and external Lithium-ion (Li-ion) battery faults and their causes.  
short circuit. In general, high capacity cells are at a higher risk of thermal runaway from internal short circuits than normal capacity cells [11].
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 2 Related Works and Contributions
#### Contributions and Innovations of This Work  
This work introduces a structured approach for synthesizing artificial CR sources using a shared-key KSG and local pure randomness injection. The novelty of this system lies in its ability to deterministically emulate the statistical behavior of naturally correlated sources while enabling precise control over security-relevant parameters. The core innovations of the proposed architecture include:A formal mathematical framework for generating cryptographically secure common randomness from a deterministic keystream generator, enabling predictable system behavior.
2. A tunable and transformable CR source that statistically mimics [PERSON]'s satellite model, but with enhanced control over secrecy capacity, KDR, and LR.
3. Closed-form analytical expressions for bounding the equivocation of the secret key \(K_{G}\) under conditions of full public discussion information (Theorem 1).
4. A fully autonomous SKD system architecture, capable of internal key refreshing and separation of reusable versus system-maintenance keys (Theorem 2-4).
5. Integration and optimization of the Winnow protocol for highly efficient reconciliation over short block lengths (e.g., \(N=8\)), resulting in low KDR and LR along with minimal communication overhead (Theorem 5 and 6).
6. Identification of two system operating modes--ideal secrecy and perfect secrecy--in relation to system parameters, and determination of the maximum secret key generation rate in both modes (Theorem 7).
7. Elimination of the AD phase without degrading secrecy performance, thereby simplifying protocol design and reducing complexity.
8. Practical guidelines for system parameterization to maximize the achievable secrecy rate, while ensuring sustained key confidentiality through equivocation management.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 6 Conclusions  
In this paper, we presented a speaker separation and identification system using deep learning that is integrated on the VoIP call process system to enhance call experiences and reduce the multiple speaker noise. The main aim is to send only the targeted speaker speech over the VoIP calls, unlike previous work, in which enhancing VoIP calls concentrates on reducing, cancelling, or reducing noise. This system provides a practical solution to a new-reality problem using the proposed SSI block which separates and then identifies the main speaker speech. The system integrated multiple technologies such as deep neural network, STFT, and MFCC-GMM. It was capable of separating up to four speakers with satisfactory signal-to-noise ratio. The paper also presented the main challenges such as processing time and how it can be adjusted based on the used VOIP system. Further studies are recommended to enhance and support this suggested study.  
This research receives no external funding.  
Not applicable.  
The authors declare that all relevant evaluation data are available in the figures and tables within the article. The generated and analyzed datasets are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.