esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Machine Learning Model Development
#### 3.4.2 Hyperparameter Optimization  
The model's hyperparameters were optimized using Bayesian optimization with the Tree-structured Parzen estimator (TPE) algorithm [26]. This approach was selected over metaheuristic algorithms due to its superior efficiency in exploring high-dimensional spaces and handling conditional hyperparameters. The optimization process evaluated parameters across extensive ranges: LSTM layers (1-3), units per layer (32-256), dense layers (1-3), dropout rates (0.1-0.5), learning rates (0.0001-0.01), and batch sizes (32-128).  
Figure 5: Flowchart of the hybrid LSTM-dense model architecture.  
After 100 optimization trials using 5-fold cross-validation, the TPE algorithm achieved optimal performance with two LSTM layers (128 and 64 units), two dense layers (64 and 32 units), a dropout rate of 0.2, learning rate of 0.001, and batch size of 64. This configuration demonstrated superior performance compared to random search, grid search, and genetic algorithms, achieving convergence in fewer trials while maintaining better prediction accuracy.  
The connection weights in Figure 6 are color-coded to show the network's structure: purple connections from input layer, teal for middle layers, yellow for pre-output layers, and red for final output connections. The diagram illustrates the progressive narrowing of the network from input through hidden layers to output, with ReLU activation functions in hidden layers and sigmoid activation in the output layer. Dropout layers (rate: 0.2) are positioned between major layer transitions.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
## 3 Parameters Optimization Design Based on TSEMO Algorithm
In order to ensure the automatic execution of the optimization process, after building the TSEMO algorithm framework in MATLAB, COMSOL software is used to create the solution model of the coils coupling and then saved it as a script file in MATLAB. In COMSOL Multiphysics 5.6 with MATLAB, you can run the COMSOL solver model as a single function script from within MATLAB. This method enables MATLAB to transfer the coils information generated by input variables to COMSOL script files in the form of TXT files and then receive the simulation output of the COMSOL solution model. Data transmission between the two is completed by reading and writing Excel files. The optimization process is automated through the standard interface of COMSOL and MATLAB. Figure 8 depicts the specific optimization procedure. When \(R_{L}=50\)\(\Omega\), take the initial points as 160, the population number as 25, and the number of iterations as 90 times. In this case, the solution time on the PC is 20 h. The final Pareto front visualization is drawn in Figure 9, where X represents the sampling point in the optimization process of the algorithm, O represents the Pareto front data set and represents the optimal system performance currently found. Obviously, due to the introduction of the Gaussian process proxy model, most algorithm sampling points are near the optimal value, which means even if the optimization time is short, the optimization effect of the optimal value obtained by the optimization process will not be poor. Figure 8: The structure of automation optimization process. Figure 9: The visualization of final Pareto front. The two most important indicators in a WPT system are \(K\) and \(\eta\). Ideally, designers need coils with high \(K\) and high \(\eta\). However, the Pareto front visualization shows that the \(K\) and \(\eta\) of S-S compensated WPT system conflict; a specific set of coils can't achieve the best PTE and TP simultaneously. The main reason for this phenomenon is the frequency separation phenomenon. That is, in the over-coupling state, the system efficiency is very high, but the voltage across the load is much smaller than the under-coupling and critical coupling. When the system input voltage is limited, designers need to choose between PTE and TP according to the actual situation of the system. The co-simulation of MATLAB and COMSOL makes the optimization program not only get the visualization of efficiency and voltage modulus gain, but also give the date of system frequency and coils position in the coupling structure, ensuring that the optimized value can be easily realized in the limited coupling mechanism.
## 3 Results and Discussion
It should be noted that coupling strength was similar to that discussed above (Loop-I: \(\sim\)\(-\)23.29 dB and Loop-II: \(\sim\)\(-\)28.06 dB) but fine-tuning of optical delay setting were different. This better stabilization results due to less sensitivity of optical delay phase setting [36]. 3.1.3. Loop-I: \(\sim\)\(-\)22 dB and Loop-II: \(\sim\)\(-\)22 dB  
We also assessed the influence of equal coupling strengths (Loop-I: \(\sim\)\(-\)22 dB and Loop-II: \(\sim\)\(-\)22 dB) from both external feedback cavities on the cavity induced side-bands. The feedback strength in both feedback loops was adjusted using variable optical attenuator attached in each external feedback cavity. This arrangement yields optimum suppression in cavity induced side-modes and under full resonance, a maximum 33 dB suppression in first harmonic occurred. Besides, on full delay range (0-84 ps), side-band suppression takes place from 13 to 33 dB. Side-band suppression versus delay is shown in Figure 3 (red squares). Measured experimental SMSR on integer resonance as well as on full delay range (0-84 ps) are summarized in Table 1 under three chosen combinations of power split ratios  
Figure 3: Side-mode suppression ratio (SMSR) as a function of delay tuning (0-84 ps) for dual-loops with coupling strengths: \(\sim\)\(-\)23.29 dB (c): \(\sim\)\(-\)28.06 dB (black squares), \(\sim\)\(-\)23.29 dB: \(\sim\)\(-\)28.06 dB (c) (blue squares), \(\sim\)\(-\)22 dB: \(\sim\)\(-\)22 dB (red squares). and optical delay phase settings. In column 1, coupling strength is given, SMSR at integer resonance is listed in column 2, and SMSR versus maximum available delay range tuning (0-84 ps) are presented in column 3.
# Co-Operatively Increasing Smoothing and Mapping Based on Switching Function
## 2 Related Work
### Topological Factor and Switching Function  
The scenario for this paper involves a satellite-denial environment without anchor nodes. It is impractical to deploy anchor nodes in some situations, so the UWB localization scenario without anchor nodes should be considered. [PERSON], in [20], proposed a cooperative vehicle-infrastructure localization method based on a factor graph, which utilizes the topological information between vehicles as constraints to improve the localization accuracy and efficiency of vehicles and other targets in the environment. The papers that have also proposed topological factor constraints include [21; 22; 23], which is also referred to as the symmetric measurement equation (SME) factor, a topological factor intended to make a difference between the sum of all measured distances and the sum of estimated positions. It provides the covariance of this topological information, which is brought to the back-end for optimization.  
In paper [24], a robust switchable constraint-based SLAM back-end method for positional maps is proposed which is capable of identifying and rejecting outliers during the optimization process. A later paper, [25], based on this switching function used the same design for satellite factors. For the switching function, some papers have incorporated it into their topological constraints. The paper [26] proposes a co-operative localization method based on mutual ranging and target orientation measurement between aircrafts using the iSAM algorithm for state estimation, adding topological constraints to the loss function to improve accuracy and adding a switching function before each factor of the loss function to adjust the switching in real time in response to the input information in order to improve the robustness of the system.
# Plant Disease Diagnosis in the Visible Spectrum
## 3 Materials and Methods
### Segmentation Using GMM  
In order to achieve the segmentation of the plant from the background, the intensity level of each pixel is used, that is the scale of grays; for the case of backgrounds that are solid or with low noise, the process is the following.  
* Prepare the image using Gaussian Blur.
* Classify the pixels in two segments using a GMM model.
* Identify the segment that belongs to the plant.
* Closure of the segment that belongs to the plant.
## 2 ISF Coefficient Quantization in AMR-WB
### Quantization of ISF Coefficients  
Before a quantization process, a mean-removed and a first order MA filtering are performed on the ISF coefficients to obtain a residual ISF vector, that is,  
\[\mathbf{r}(n)=\mathbf{z}(n)-\mathbf{p}(n), \tag{6}\]  
where \(\mathbf{z}(n)\) and \(\mathbf{p}(n)\) respectively denote the mean-removed ISF vector and the predicted ISF vector at frame \(n\) by a first order MA prediction, defined as  
\[\mathbf{p}(n)=\frac{1}{3}\mathbf{\hat{r}}(n-1), \tag{7}\]  
where \(\mathbf{\hat{r}}(n-1)\) is the quantized residual vector at the previous frame.  
Subsequently, S-MSVQ is performed on \(\mathbf{r}(n)\). As presented in Tables 1 and 2, S-MSVQ is categorized into two types in terms of the bit rate of the coding modes. In stage 1, \(\mathbf{r}(n)\) is split into two subvectors, namely, a 9-dimensional subvector \(\mathbf{r_{1}}(n)\) associated with codebook CB1 and a 7-dimensional subvector \(\mathbf{r_{2}}(n)\) associated with codebook CB2, for VQ encoding. As a preliminary step of stage 2, the quantization error vectors are split into three subvectors for the 6.60 kbps mode or five for the modes with bitrates between 8.85 and 23.85 kbps, symbolized as \(\mathbf{r}_{i}^{(2)}=\mathbf{r}_{i}-\mathbf{\hat{r}}_{i},i=1,2\), respectively. For instance, \(\mathbf{r}^{(2)}{}_{1,1,3}\) in Table 1 represents the subvector split from the 1 st to the 3 rd components of \(\mathbf{r_{1}}\), and then VQ encoding is performed thereon over codebook CB11 in stage 2. Likewise, \(\mathbf{r}^{(2)}{}_{2,4,7}\) stands for the subvector split from the 4 th to the 7 th components of \(\mathbf{r_{2}}\), after which VQ encoding is performed over codebook CB22 in stage 2. Finally, a squared error ISF distortion measure, that is, Euclidean distance, is used in all the quantization processes.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**Structure of S-MSVQ**} \\ \hline \multirow{3}{*}{Stage 1} & \multicolumn{2}{c}{CB1:} & \multicolumn{2}{c}{CB2:} \\  & \(\mathbf{r_{1}}\) (1–9 order of \(\mathbf{\hat{r}}\)) & \multicolumn{2}{c}{\(\mathbf{r_{2}}\) (10–16 order of \(\mathbf{\hat{r}}\))} \\  & (8 bits) & & (8 bits) \\ \hline \multirow{3}{*}{Stage 2} & \multirow{3}{*}{
\begin{tabular}{l} CB11: \\ \(\mathbf{r}^{(2)}{}_{1,1,3}\) \\ (6 bits) \\ \end{tabular} } & CB12: & CB13: & CB21: & CB22: \\  & \(\mathbf{r}^{(2)}{}_{1,1,4}\) & \(\mathbf{r}^{(2)}{}_{1,1,7,9}\) & \(\mathbf{r}^{(2)}{}_{2,1,3}\) & \(\mathbf{r}^{(2)}{}_{2,4,7}\) \\  & (6 bits) & (7 bits) & (7 bits) & (5 bits) & (5 bits) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of split-multistage vector quantization (S-MSVQ) in adaptive multi-rate wideband (AMR-WB) in the 8.85–23.85 kbit/s coding modes.
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 5 Optimal Design of Freezing Scheme and Analysis of Numerical Simulation Results
### Path Analysis
#### 5.4.2 Analysis of Observation Points
Under Optimized Scheme 1 and Scheme 2, the cooling curves for paths DJ-2, DJ-3, and DJ-4 are essentially the same, suggesting that there is no significant difference in the average temperatures of the same paths under the different schemes. This is because the freezing areas where these paths are located were not optimized, nor was the number of freezing pipes. Figure 12c reveals a notable difference in the cooling curves for path DJ\(-\)1 under Optimized Scheme 1 and Scheme 2, primarily due to the optimization of the number of freezing pipes in the downstream strong freezing zone. Ultimately, the average temperature of the original scheme stabilizes at \(-\)19.63 \({}^{\circ}\)C, while the average temperature of Optimized Scheme 2 stabilizes at \(-\)12.65 \({}^{\circ}\)C, despite a difference of approximately 7 \({}^{\circ}\)C. However, the temperature of Optimized Scheme 2 stabilizes at about \(-\)12.65 \({}^{\circ}\)C, which meets the design standard: the average temperature of the strong freezing zone is \(\leq-\)10 \({}^{\circ}\)C.  
(3) The temperature field maps, isotherm maps, and path cooling curves provide a visual representation of the freezing temperature field's evolution. Yet, they do not quantitatively depict the development of the frozen curtain thickness, an essential indicator of the frozen curtain's integrity. To quantitatively assess the frozen curtain's development, the thickness of the frozen wall must be measured. To clearly compare the freezing wall's development under the three schemes, the thickness of the freezing wall at the downstream strong freezing zone (path DJ\(-\)1) was selected as the basis for this study. As shown in Figure 13, the curve diagram of the freezing wall thickness with freezing time in the strong freezing zone of path DJ\(-\)1 indicates that the freezing wall begins to form thickness from the 3 rd day of freezing. Initially, the thickness of the freezing wall for the three schemes develops rapidly within the first 10 days, a result of the larger cooling amplitude of the brine cooling scheme during this period. In the original program, after the 10 th day, the thickness of the frozen wall was impeded by the steel pipe, leading to a gradual slowdown in its development. By the end of 40 days of active freezing, the thickness of the frozen wall reached 2.52 m. After the 10 th day of freezing, Optimization 1 and Optimization 2 developed more slowly due to the influence of groundwater flow, which slowed the development of the frozen wall. When the isotherms intersected at 23 days, the flow of groundwater was impeded, and the development of the frozen wall thickness accelerated compared to before. The freezing wall thickness of Optimized Plan 1 reached 2.2 m, and that of Optimized Plan 2 reached 2.08 m, both of which met the design criteria. Figure 13: Variation of frozen wall thickness.
## 3 Results
### X-ray Photoelectron Spectroscopy (XPS) Analysis  
The chemical structures of RB contain heteroatom N compounds, making them excellent precursors for preparing doped heteroatom carbon aerogels. To demonstrate that the heteroatom N was bound to the structure and not combusted during high-temperature carbon, we confirmed the compositions of the carbon and N-doped carbon aerogels by XPS analysis. The C 1s peak (284.3 eV) and O 1s peak (532.2 eV) were clearly visible in the spectra (Figure 3a). The atomic percentage of C was 86.82 at.% and that of O was 13.18 at.%. The N-doped carbon aerogel showed three peaks assignable to C 1s (284.4 eV), N 1s (400.7 eV), and O 1s (532.2 eV). The atomic percentages of C, N, and O in the N-doped carbon aerogel were 86.92 at.%, 2.15 at.%, and 10.93 at.%, respectively. Thus, N was successfully doped into the carbon aerogel skeleton. Furthermore, as shown in the fitted N 1s spectra of the N-doped carbon aerogel (Figure 3b), the N 1s spectrum was fitted by three peaks, namely pyridinic N (398 \(\pm\) 0.2 eV), graphitic N (401.2 \(\pm\) 0.2 eV), and pyrrolic N (400.5 \(\pm\) 0.2 eV), while pyridinic N and pyrrolic N were considered as the main contributors to the pseudocapacitance, based on the Faraday reaction and according to previous reports [52]. For example, pyridinic N and pyrrolic N could produce pseudocapacitive effects in KOH aqueous electrolyte, associated with the proton-involved redox processes, as shown in Figure 3c. Thus, the porous N-doped carbon aerogels are expected to have good electrochemical properties. Furthermore, doping heteroatom nitrogen is a promising strategy to further tailor the chemical properties of sp\({}^{2}\) carbon materials, which can efficiently improve the wettability of the interface between the electrodes and electrolytes, resulting in a considerable increase in the accessible surface area of the electrolyte. In addition, the presence of N can also promote electron transfer reactions and consequently enhance the whole capacitive performance of the supercapacitors [53].  
Figure 2: SEM images of (**a**) APMP aerogel; (**b**) carbon aerogel; and (**c**) N-doped carbon aerogel; (**d**) N\({}_{2}\) adsorption-desorption isotherms; and (**e**) pore size distributions of carbon aerogel and N-doped carbon aerogel.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Outcomes Measure Results  
Table 3 presents a summary of the performance characteristics of ML models for predicting mortality among HD patients in the included studies. Performance characteristics were reported in five studies [30; 32; 34; 35; 37] at various follow-up times. For instance, at one-year follow-up [PERSON] et al., 2020 [30], [PERSON] et al., 2023 [32], and [PERSON] et al., 2012 [34] reported the performance of logistic regression, where they found a predicted true positive mortality rate of 8.23% as compared to an actual mortality rate of 10.53%. In addition, [PERSON] et al., 2020 [30] and [PERSON] et al., 2023 [32] reported the performance of XGBoost and Random Forest, where they found a predicted true positive mortality rate of 9.93% and 8.94%, respectively, as compared to an actual rate of 13.73%. This might reflect a better accuracy for the logistic regression models; however, further examination is required, particularly by comparing the models after training with the same datasets. Since Logistic Regression, XGBoost, and Forest Plot were the most frequently assessed models within the included studies, we generated a Forest Plot for the sensitivity and specificity of the models, as shown in Figure 4. In addition, an SROC plot was generated for the three models, along with their 95% CI, as shown in Figure 5.  
Figure 4: Summary Receiver Operating Characteristic (SROC) curve for logistic regression [30; 32; 34; 37], XGBoost [30; 32] and Random Forest [30; 32; 34] in predicting mortality at 1-year follow-up. Each data point corresponds to an individual study’s sensitivity and specificity. The curve represents the trade-off between sensitivity and specificity across various decision thresholds. A curve closer to the top-left corner indicates better overall diagnostic performance.
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 6 Conclusions  
As a critical onboard system, the health of the aircraft fuel system (AFS) directly impacts engine thrust and the safety of the surrounding equipment. To enable timely responses to functional failures within the AFS, this study developed a multi-fault diagnostic method for a simplified AFS using machine learning. Data for this research were sourced from a simulation model and a simplified rig representing the Boeing 777 fuel system. The simulation model enhanced the rig data by introducing random uncertainty, generating sufficient samples across all fault classes. The machine learning candidates were selected from classic algorithms to ensure comparable complexity, predictive capability, and varying levels of transparency. By diagnosing the full combinations of faults in the AFS, this study uniquely transformed a typical multi-label problem--traditionally requiring multiple algorithms--into a multiclass problem solvable by a single algorithm. The evaluation of diagnostic accuracy innovatively incorporated the definition and exploration of algorithmic structural complexity, enabling a fairer comparison among the methods. After evaluating predictive performance, both local (XAI for opaque algorithms) and global (interpretation for transparent algorithms) explainable techniques were applied to address the gap in explainability for fuel system diagnostics. This evaluation framework uniquely quantified and compared the interpretability of four ML algorithms to identify the most suitable diagnostic method and tackle the three primary challenges outlined in Section 1.  
Given its predictive performance, the ANN emerged as the most suitable algorithm for this multi-fault diagnostic task. However, the interpretability provided by XAI tools did not always align well with engineering insights. While the DT demonstrated the highest interpretability consistency, this came at a cost to the overall performance. DTs also offer the advantage of easy rule extraction in simpler structures. From an engineering perspective, a method that excels at interpretability but falls short in fault detection is limited in its effectiveness, particularly when maintenance decisions depend on its output.  
This study emphasises the need for further research into machine learning interpretability to serve domain users better. Engineering knowledge should remain a central consideration in designing and implementing data-driven algorithms, and it plays a vital role in validating XAI outputs.  
Conceptualization, J.L., S.K. and I.J.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; resources, J.L.; data curation, J.L.; writing-original draft preparation, J.L.; writing--review and editing, S.K. and I.J.; visualization, J.L.; supervision, S.K. and I.J.; project administration, I.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data availability Statement: Data are available on request.  
The authors declare no conflicts of interest.
## 3 System Model
### Network Architecture  
We implemented a UANET, which consisted of \(N\) homogeneous nodes and communication links. We assumed the packet transmission power of each node was the same, so the communication radius \(R\) was the same. Each node could communicate with its neighboring nodes within the communication radius. Due to the limitation of the wireless communication range of the UAVs, some UAVs were unable to communicate with others directly and required assistance from the relay nodes to forward packets. If node \(i\) could directly communicate with the neighboring node \(j\), we assumed the link \(L_{ij}(i\
eq j)\) existed. Each node had a full-duplex transceiver, a unique identity document (ID), and a global positioning system (GPS) to provide the location, speed, and direction information. In addition, each node could acquire neighbor information by receiving hello packets periodically.
## 3 Results
### Correlation
With respect to the Eigen-decomposition features, ENT and polarimetric Alpha scattering angles (ALPT/ALPC) were highly correlated between the ENT and the ALPT of HH/VV-polarized data, and this was true for both C- and L-Band (\(R^{2}\) values of about 0.7). Additionally, ENT and ALPT were highly correlated with each other, with \(R^{2}\) values of about 0.7-0.8 (quad- and HH/VV-polarizeddata) and ENT and ALPC were moderately correlated with \(R^{2}\) values of about 0.4-0.5 (HH/HV- and VV/VH-polarized data). This is most likely because most reflection is recorded in HH and VV intensities, making their contributions higher than the intensities of HV or VH. This leads to a high correlation between the HH/VV- and quad-polarized decomposition features. In summary, this assessment indicated that Kennaugh Matrix elements K0, K1, K3, K4 and K7 of quad- and HH/VV-polarized data of C- and L-Band were highly correlated and thus can be used interchangeably in some cases, e.g., for image classification. The Kennaugh Matrix elements K5 and K6 of quad- and cross-polarized data showed lower correlation coefficient values. The correlation was generally higher in the L-band, compared to the C-Band, which is likely a result of less interaction between the incident wave and the vegetation body of the long L-Band microwaves; less volume scattering occurs.