esa-sceva/satcom-chunk-collection huggingface dataset:
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Aim** & **Experimental Evaluation** & **Assessment Metrics** \\ \hline P18 [80] & 2016 & This article suggests nature-based hybrid techniques for FS. The techniques are based on two strategies for swarm intelligence: ACO and PSO. & The experimental findings conclude that the proposed approaches have better efficiency for reducing the NSF and also in terms of CA. & CA and NSF. \\ \hline P19 [81] & 2015 & This paper proposes the use of a hybrid MA for FS combined with the KHA. & The test results reveal that the proposed MAKHA technique can easily find an optimal or almost an optimal set of combination of attributes by minimizing the objective function and achieving sufficient efficiency to increase the accuracy of feature classification. & CA and NSF. \\ \hline P20 [9] & 2010 & This research suggests a hybrid approach for FS based on GA and SA. & The FS results were improved by correcting the SA in the creation of the next generation (by considering two maximum and minimum thresholds). & CA and NSF. \\ \hline P21 [82] & 2009 & This article suggests a new FS framework which combines GA with ACO to increase and improve search capabilities in protein structure forecasting. & The testing results show the superiority of the proposed hybrid method (compared to ACO ans GA) and also present the low computational complexity of the suggested hybrid approach. & Predictive accuracy (PA). \\ \hline P22 [83] & 2009 & In this paper, TS is combined with binary PSO to select an optimal feature vector in FS. & Testing results from applying the method on 11 classification problems taken from the literature show that this approach simplifies features effectively. This method has the ability to obtain higher CA and to use fewer features compared to other FS methods. & CA. \\ \hline P23 [84] & 2019 & This study suggests the combination of BPSO and SCA. The aim of the approach is to perform FS and cluster analysis by employing a cross-breed approach of SCA to BPSO. & The experimental findings (on 10 benchmark test functions and seven datasets taken from the UCI repository) show that the suggested HBPSOSCA approach generally performs better than other FS approaches. & MF, BF, WF, Average NSF, SI, DI, and DBI. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Modelling Methodology
#### 3.1.2 Battery Energy Storage and Bi-Directional DC/DC Converter Models  
The MATLAB/Simulink built-in user-defined battery model is used as BES, as shown in Figure 5. It provides four types of battery types: lead-acid, lithium ion, nickel metal hydride and nickel cadmium. The required parameters of each battery model are battery types, nominal voltage, rated capacity and initial state of charge (SOC), and the description of this generic battery model is presented in [15]. In Figure 5, a one-arm two-level time-stamped-bridge block in MATLAB/Simulink is used to model the bi-directional DC/DC converter. A constant current control mode is considered for the bi-directional DC/DC converter, and its controller in Figure 6 mainly divides into two functional parts, the reference  
Figure 4: PV system model.  
current calculation and the current reference control. The former is used to determine the charging or discharging current of BES units depending on the proposed coordination control strategy, and the latter uses the calculation results from the former to generate the trigger signal of the rear converter's switching components.
## 4 A Unified Frequency Model for Multi-Machine Systems
### Structural Singular Values and Robust Performance Issues
#### 5.1.1 Robust and Stable Performance  
When judging the robust stability of a system, it is often necessary to construct an \(M\)-\(\Lambda\) model, that is, to reconstruct the system, incorporate uncertainty, and isolate the perturbation matrix \(\Delta\)(s) including system uncertainty. The remaining nominal matrix \(M\)(s) below is a stable linear transfer function matrix. The reconstructed system is shown below.  
In Figure 10, \(u\) is the input of the perturbation matrix and \(v\) is the output of the perturbation matrix. Define the structural singular value of matrix \(M\) as [30]:  
\[\begin{array}{l}\mu_{\Delta}[M(j\omega)]=\left\{\begin{array}{l}0,\det[I- M(j\omega)\Delta(j\omega)]\
eq 0,\forall\Delta(j\omega)\in\Delta^{*}(j \omega)\\ k_{m}\end{array}\right.\\ k_{m}=\frac{1}{\min\{\sigma_{\max}[\Delta(j\omega)][\Delta(j\omega)\in \Delta^{*}(j\omega)\Delta\det[I-M(j\omega)\Delta(j\omega)]=0\}}\end{array} \tag{21}\]  
where \(\min\) is used to solve the smallest element in a set, \(\sigma_{\max}\) is used to solve the largest singular value in the matrix, and \(\Delta^{*}(j\omega)\) is a set including all uncertain perturbations. It can be seen from the above equation that if the singular value of a perturbation matrix is smaller than the defined structural singular value, then the system must be closed-loop stable, so there is a robust stability theorem. For the uncertain system shown in Figure 10, if the system \(M\) is stable, then the necessary and sufficient conditions for the robust stability of the system are:  
\[\mu_{\Delta}[M(j\omega)]\leq 1,\forall\omega>0 \tag{22}\]  
where \(\omega\) is the working frequency point.  
Figure 9: Unified frequency response model of multi-machine system considering PV generators.
## 3 The Proposed Multi-Scale Demodulation Method
### Description of the Experimental System  
The experimental test for the fixed shaft gearbox with a seeded tooth root crack fault is shown in Figure 19. The setup consisted of an industrial computer, a three-phase motor, a two-stage gearbox (transmission ratio = 3.59), a torque sensor, a magnetic particle brake and several elastic couplings. The industrial computer controlled the three-phase motor with a frequency converter to realize the simulation of different speeds, and the load controller controlled the magnetic particle brake to realize the simulation of different loads. The first-stage gear ratio was 23/39 and the second-stage gear ratio was 25/53. The tooth root crack fault was seeded by wire cutting, involving a penetrating crack with a depth of 2 mm and a cutting angle of 70 degrees. The faulty gear was installed on the intermediate shaft, as shown in Figure 20. The seeded tooth root crack fault gear was mounted on the second pair of gear engagement pairs of the gearbox intermediate shaft. The speed increased from 500 rpm to 700 rpm and then decreased to 500 rpm with a load of 0 Nm. The characteristic frequency of the gearbox at 500 rpm and 700 rpm is shown in Table 3. The signal sampling frequency was 5120 Hz and the sampling length was 32 s.  
Figure 19: The seeded tooth root crack fault.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Speed** & **1 st Frequency** & **2 nd Frequency** & **High-Speed Shaft** & **Intermediate Shaft** & **Low-Speed Shaft** \\ \hline
500 rpm & 191.70 Hz & 122.90 Hz & 8.33 Hz & 4.91 Hz & 2.32 Hz \\
700 rpm & 268.30 Hz & 172.00 Hz & 11.67 Hz & 6.88 Hz & 3.25 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic frequencies of the fixed shaft gear train.  
Figure 18: Effect of the given threshold in the weighted-EMD de-noising technique on demodulation performance under (**a**) 100 rpm, (**b**) 300 rpm and (**c**) 500 rpm working conditions.
# Reverse Electrodialysis: Potential Reduction in Energy and Emissions of Desalination
## 3 Results and Discussion
### GHG Emissions Savings to Desalination from RED
SEC of SWRO plant met by (i) Grid Mix (BaU); (ii) Grid Mix + RED’s Net Energy (upper limit, “current” scenario); (iii) Grid Mix + RED’s Retrieved Exergy (lower limit, “optimistic” scenario). reductions, moving from 0.38 (RED's net specific energy) and 1.41 kg CO\({}_{2}\)-eq m\({}^{-3}\) (RED's retrieved exergy) in scenario 8 to 0.58 and 1.95 kg CO\({}_{2}\)-eq m\({}^{-3}\) in scenario 13. Desalination plant capacity will define absolute GHG emission reduction; as such, the larger the SWRO plant size and local emission metric (scenario 18, 17 and 20), the greater the absolute annual GHG emissions abated, as high as 36.2-61.1 kt CO\({}_{2}\)-eq (up to 183-242 kt CO2-eq in the \"optimistic\" scenario) reaching a low of 0.2-0.8 kt CO\({}_{2}\)-eq (0.7-3.2 kt CO\({}_{2}\)-eq) in medium-sized SWRO plants located in scenarios 11, 10 and 8. Collectively, RED could potentially save 0.32 (\"current\") to 1.18 Mt CO\({}_{2}\)-eq (\"optimistic\") per year, accounting for around 0.4% to 1.6% of global operational desalination plant emissions in 2015 (76 Mt CO\({}_{2}\)-eq per year) [71].
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Nickel Manganese Cobalt Oxide (LiNiMnyCozO2, NMC)  
Lithium nickel manganese cobalt oxide (NMC), representing a significant advancement in cathode materials, has gained prominence over lithium cobalt oxide (LCO) in large-scale consumer electronics applications due to its superior performance characteristics [112; 113]. NMC batteries exhibit a specific energy range of 150-220 Wh/kg, making them highly effective for demanding applications. These batteries are increasingly favored in electric vehicles and the maritime sector, attributed to their versatile power density, robust safety features, extended lifecycle, and ample energy density [27; 114]. The unique composition of NMC allows for fine-tuning of its properties to meet specific application requirements, further enhancing its applicability across various high-energy demand settings. LiNixCoyMn1-x-yO2 is synthesized by modifying the proportions of transition metals--increasing Ni and Mn while reducing Co in the conventional LCO cathode formulation. This adjustment in the NMC cathode material optimizes its characteristics: cobalt enhances current density, nickel boosts capacity, and manganese contributes to structural stability. Such a balanced attribute profile renders the NMC cathode material highly versatile and extensively utilized in Li-ion battery applications. Additionally, the energy density of NMC is supported by its stability at high cut-off voltages exceeding 4.5 V [115]. The stoichiometric variations of these elements, including common ratios like (1:1:1), (4:3:3), (5:3:2), (6:2:2), and (8:1:1), lead to divergences in energy and power properties, which are well-documented in the literature [111; 114]. Furthermore, the configuration of these elemental ratios directly influences the battery's cost, capacity, and thermal stability, making specific NMC composition a critical factor in battery design [111]. Thus, Li-ion batteries remain the dominant choice for marine applications due to their high energy density and design flexibility. Recent developments include high-nickel NMC formulations (e.g., NMC811), which offer enhanced capacity and improved performance at higher voltages--important for high-power maritime operations. Additionally, LTO-based chemistries are gaining attention in fast-charging, high-cycle ferry systems where safety and durability under frequent docking are critical.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
###### Abstract  
To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected by the image owner has one reference pixel and eight target pixels. The \(n\) least significant bits (LSBs) of the reference pixel and all bits of target pixel are self-embedded into other parts of the image by a method of predictive error expansion (PEE). To avoid overflowing when embedding data, the \(n\) LSBs of the reference pixel are reset to zero before encryption. Then, the pixel values of the image are encrypted after being encoded onto the points of the elliptic curve. The encrypted reference pixel replaces the encrypted target pixels surrounding it, thereby constructing the mirroring central ciphertext (MCC). In a set of MCC, the data hider embeds the encrypted additional data into the \(n\) LSBs of the target pixels by homomorphic addition in ciphertexts, while the reference pixel remains unchanged. The receiver can directly extract additional data by homomorphic subtraction in ciphertexts between the target pixels and the corresponding reference pixel; extract the additional data by subtraction in plaintexts with the directly decrypted image; and restore the cover image without loss. The experimental results show that the proposed scheme has higher security than the similar algorithms, and the average embedding rate of the scheme is 0.25 bpp under the premise of ensuring the quality of the directly decrypted image.  
information security; reversible data hiding; homomorphic encrypted image; elliptic curve ElGamal (EC-EG); predictive error expansion (PEE) Article
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 3 Experiments
### Case 1  
Using the Beijing PM2.5 average concentration dataset mentioned in Section 3.1, we compared the proposed model with the following two models by predicting 24 h ahead for the PM2.5 average concentration:  
* The GRU method [37]: The original time-series data are not decomposed, and the GRU is directly trained to establish a prediction model.
* Decomposition with the GRU-GRU-GRU method: The original time-series data are first decomposed according the method mentioned in Section 2.2, then the three components are trained to establish three sub-prediction GRU models. We can observe that this method is different from the model proposed here because it modeled the trend component by GRU, while in the proposed model, named in Figure 5 as decomposition, it was with ARIMA-GRU-GRU, which modeled the trend component by ARIMA.  
The 24 steps in predicting the results of the PM2.5 concentration with three different models are shown in Figure 5. The blue line shows the ground truth (i.e., PM2.5 measurements), while the green, yellow, and red lines are the predicted results from GRU, decomposition-GRU-GRU-GRU, and decomposition with ARIMA-GRU-GRU, respectively. As can be seen from the figure, the prediction result of the proposed model was closer to the ground truth line. Figure 6 shows the absolute error (given by Equation (16)) between the predicted results and the PM2.5 time series. The green, yellow, and red lines are the absolute error curves of the GRU, decomposition-GRU-GRU-GRU, and the proposed model (decomposition with ARIMA-GRU-GRU), respectively. Figure 6 illustrates that the absolute error (AE) of the proposed model could obtain more values around 0, which indicates that a more accurate prediction had been achieved.  
Figure 5: Comparison between the predicted results and ground truth (24 steps).  
The comparisons of the RMSE, NRMSE, MAE, SMAPE, and R are shown in Table 1. It can be seen that the proposed model obtained the least RMSE, NRMSE, MAE, and SMAPE, and the highest R, where the proposed model (RMSE 80.1620, NRMSE 0.1612, MAE 55.1060, SMAPE 0.6682, and R 0.6508) showed an improvement in prediction performance when compared to the GRU (RMSE 83.0925, NRMSE 0.1848, MAE 56.3784, SMAPE 0.7133, and R 0.6389) and decomposition-GRU-GRU-GRU (RMSE 81.2412, NRMSE 0.1625, MAE 56.1870, SMAPE 0.7398, and R 0.6417).  
The RMSE, NRMSE, MAE, and SMAPE of the proposed model were 3.53%, 12.77%, 2.26%, and 6.32% lower than the GRU model, respectively, and the R value was increased by 1.86%. Compared with the decomposition-GRU-GRU-GRU model, the corresponding error indicators were reduced by 1.33%, 0.80%, 1.92%, and 9.68%, respectively, and the R value was increased by 0.57%. From the above data, the proposed model had better predictive performance. Although there was a slight outperformance, the training parameters of the ARIMA model were much fewer than the GRU model. Therefore, using the ARIMA model can simplify the model size and shorten the training time.
## 2 Methods and Materials
### Quality Control  
Prior to analysis, the calibration curves for each metal were constructed with standard samples with different concentrations. The standards used were purchased from the National Institute of Standards and Technology (NIST) and included Tomato Leaves (SRM-1573), Spinach Leaves (SRM-1570) and Apple Leaves (SRM-1515). The statistical correlation in the calibration curve for Cu, As, Ni and Pb was 0.996, 0.999, 0.999, and 0.997, respectively. The properties of calibrated graphs are shown in Table 1.  
Figure 3: Studied plants: (**a**,**a**) _Croton dioxins;_ (**b**,**b**) _Phorodendron villosum._Considering the sensitivity of assessment, the accuracy and precision of the EDXRF analysis were checked by repeatedly testing the certified reference material, Orchard leaves (SRM 1571). Five replicates were made for reference material (Cu, Pb, As, Ni), and the mean value is shown in Table 2. The results for the recoveries were between 90% and 95%, which showed a good agreement between the measured and certified values.  
The analytical method for EDXRF quantifies the relative concentration of each element present in the sample by fluorescence [32; 33].The validation studies were performed by using fundamental parameters including testing of linearity (Table 1), the limit of detection and the limit of quantification (Table 4).  
The value LOD and LOQ are calculated considering the Equations (1) and (2):  
\[\text{LOD}=3.3\frac{\text{SD}_{\text{intercept}}}{\text{slope}} \tag{1}\]  
\[\text{LOQ}=10\frac{\text{SD}_{\text{intercept}}}{\text{slope}} \tag{2}\]  
where \(\text{SD}_{\text{intercept}}=\text{S}_{\text{intercept}}\sqrt{n}\) and n number of test.
## 3 Extreme Gradient Boosting (XGBoost) Classifier Basics
### Overview of XGBoost Objective Function  
Typically, for an ML algorithm, the general objective function is the sum of loss function (\(L\)) and regularization term (\(\Omega\)) over the parameters (\(\theta\)) as follows:  
\[Obj(\theta)=L(\theta)+\Omega(\theta). \tag{2}\]  
The XGBoost's objective function (derived from Equation (2)) combines the sum of a specific loss function (\(l\)) evaluated over all \(n\) predictions (or samples) and the sum of a regularization term (\(\omega\)) for all predictors (\(K\) decision trees) as follows:  
\[Obj(\theta)=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\omega(f_{k}), \tag{3}\]  
where \(f_{k}\) is the \(k\)th decision tree function, \(y_{i}\) the actual label of the \(i\)th sample, and \(\hat{y}_{i}\) the predicted label of the \(i\)th sample. The official XGBoost hyperparameter tuning guide [23] explains the decision tree structure and the objective function in more details.  
The goal of the classifier algorithm is to decrease the objective function in (3) as much as possible. Loss function in (3) could be either log-loss function, squared loss function, or others. It controls the prediction error of the ML model, while the regularization term controls its complexity by adjusting the size of the tree structure, depth of the trees, and so on.  
In short, XGBoost [20] involves creating and adding trees to the model sequentially. New trees are created to correct the residual errors in the predictions from the existing sequence of trees. The effect is that the model can quickly fit, then start to overfit the training dataset. In general, the phenomenon of overfitting needs to be avoided for biased performance in any ML model. This is usually achieved by tuning the hyperparameters of the ML model during the training and testing phases to achieve a balanced fitting performance. Additionally, the XGBoost classifier is equipped with a \"feature_importance\" module, which can help us to understand the classifier model deeply by providing a feature score (f-score) of every feature.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 3 Applications and Use Cases of FANETs
### Cooperative Actions and UAV Air Traffic Management  
Not only to provide network connectivity services to people or user devices but a fleet of UAVs might also be used for \"physically connecting a region\" in a way that is optimized and cooperative. The FANET in this case might be used by the UAVs themselves to cooperate while performing their tasks, e.g., maintaining a physical delivery network (e.g., delivering supplies and goods) among disparate sites over a region, or to cooperate in optimizing their flight routes, or cooperate in coping with unforeseen events and accidents, or even to avoid UAV-to-UAV collisions. For example, UAVs can cooperate in path planning and with mesh-like connectivity to allow UAV-to-UAV connectivity over ranges beyond the limits of direct UAV-to-UAV transmission to provide advanced anticipative collision warning/avoidance and dynamic evasive maneuvers and re-routing well ahead of time. In future air spaces, we can envision UAVs of different sizes, uses, and capacities (e.g., small UAVs with cameras for surveillance and hobbyists UAVs, small to medium-sized UAVs for goods delivery or logistics, and larger UAVs for people transport) occupying the same or similar air space at higher densities, e.g., over urban areas [69] (where we have the notion of invisible urban _\"lightways in the sky\"_ (For example, see the upcoming project on creating highways for drones in the UK: [[https://www.youtube.com/watch?v=570?JmargTs](https://www.youtube.com/watch?v=570?JmargTs)]([https://www.youtube.com/watch?v=570?JmargTs](https://www.youtube.com/watch?v=570?JmargTs)) [last accessed on 25 July 2022])), where UAV-to-UAV connectivity can help avoid UAV-to-UAV collisions (in a way similar to, but more complex, than the \"2D\" ground vehicle-to-vehicle communications being used to help avoid vehicle-to-vehicle collisions and facilitate vehicle-to-vehicle cooperation on a large scale).
## 2 Results and Discussions
### Time Resolved Magnetic Circular Dichroism and Helicity-Dependent Absorption
This is schematically indicated in the left panel with minority electrons transferred from Pt to Co.  
We show the normalized result in Figure 7 as a function of time delay between \(-50\) fs and \(100\) fs: in the left panels, we plot the experimental data for Co and CoPt; in the right panels are the corresponding theoretically calculated values. Importantly, careful theoretical analysis shows that we can associate the _transient_ absorption changes \(\mu/\mu_{0}\) measured with opposite magnetization directions \(M_{+}\) and \(M_{-}\) with transitions predominantly into the majority and minority states, respectively. The most apparent differences of \(\mu/\mu_{0}\) between the elemental Co and CoPt alloy is the response of the absorption for a sample magnetized in \(M_{-}\) direction (open symbols in Figure 7): in Co, the absorption slightly increases or stays constant while in CoPt the absorption drops immediately after optical excitation. The interpretation is now straightforward and can be linked with the transient occupation changes, \(\Delta n_{\rm min}\), shown in Figure 6: efficient filling of Co minority states above the Fermi energy by OISTR from Pt reduces the available final states for transitions, leading to an ultrafast drop of absorption. In elemental Co, this is not observed, again consistent with the transient occupations shown in Figure 6. The theoretical calculation of the exact same observable, \(\mu/\mu_{0}\), reproduces our experimental finding very well [18]. Finally, we remark that the distinct response between the absorption measured with different magnetization directions of the sample (or opposite helicities), as observed in the experiment, further strongly supports the theoretical finding that helicity-dependent absorption measures the spin-resolved transient changes of the occupations around the Fermi level of a specific element for early times after optical excitation (\(\approx\)100 fs).