esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results, Discussions and Comparison
### Case 2: Organic Flexible Hydrogenated Amorphous Silicon a-Si:H Solar Cell
The current-voltage and power-voltage characteristics resulting from the extracted parameters by Firefly algorithm along with experimental data have been illustrated in Figure 11. The Figures show the reconstructed model is in good agreement with the experimental data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Current**} & \multicolumn{2}{c}{**FA**} & \multicolumn{2}{c}{**Q-N**} & \multicolumn{2}{c}{**SOMA**} \\ \cline{2-7}  & **Current (A)** & **IAE** & **Current (A)** & **IAE** & **Current (A)** & **IAE** \\ \hline
0 & 7.365\(\times 10^{-4}\) & 7.3656 \(\times 10^{-4}\) & 0.0041 & 0.0041 & 8.6804 \(\times 10^{-4}\) & 8.6804 \(\times 10^{-4}\) \\
0.0158 & 0.0152 & 6.0 \(\times 10^{-4}\) & 0.0100 & 0.0058 & 0.0131 & 0.0027 \\
0.0302 & 0.0361 & 0.0059 & 0.0305 & 0.0003 & 0.0334 & 0.0032 \\
0.0619 & 0.0653 & 0.0034 & 0.0591 & 0.0028 & 0.0623 & 0.0004 \\
0.0868 & 0.0744 & 0.0124 & 0.0680 & 0.0188 & 0.0715 & 0.0153 \\
0.1142 & 0.1023 & 0.0119 & 0.0955 & 0.0187 & 0.1004 & 0.0138 \\
0.1604 & 0.1623 & 0.0019 & 0.1549 & 0.0055 & 0.1679 & 0.0075 \\
0.3044 & 0.3002 & 0.0042 & 0.2835 & 0.0209 & 0.3018 & 0.0026 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between the calculate results of flexible silicon a-Si:H solar cell. Figure 10: Individual absolute error compared to, (**a**) \(I\)-\(V\) and (**b**) \(P\)-\(V\) for each current measurement by different algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistical Errors** & **FA** & **Q-N** & **SOMA** \\ \hline Standard deviation (SD) & 4.925 \(\times 10^{-3}\) & 8.46 \(\times 10^{-3}\) & 7.86 \(\times 10^{-3}\) \\ Root mean square error (RMSE) & 6.1634 \(\times 10^{-3}\) & 12.3924 \(\times 10^{-3}\) & 7.9529 \(\times 10^{-3}\) \\ Residual sum of squares (SSE) & 3.6384 \(\times 10^{-4}\) & 1.2286 \(\times 10^{-3}\) & 5.0604 \(\times 10^{-4}\) \\ Mean bias error (MBE) & 6.62401 \(\times 10^{-3}\) & 1.2424 \(\times 10^{-2}\) & 7.4912 \(\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance indexes of flexible silicon a-Si:H solar cell. The comparative statistical error used in this paper compare the difference between estimated and experimental electric parameters, term by term. The mean bias error (MBE) provides information on the overestimation or underestimation of the solar cell performance. Therefore, the obtained results are more accurate than those found by Q-N and SOMA, can better reveal the actual behavior of solar cells and the model is efficient. Generally, the lower these parameters are, the more the model is efficient.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 4 Model Configuration and Training Parameters
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{Tree} & Number of Trees & 100 \\  & Max Number of Splits per Tree & 160 \\  & Number of Features per Sample & 160 \\  & Number of Samples & 18,000 \\  & SNR values & 36 \\  & Signals per SNR & 500 \\  & Train/Test Split & 70\% Train/30\% Test \\ \hline \multirow{2}{*}{Random Forest} & Number of Trees & 100 \\  & Predictors per Split & 13 (approx. \(\sqrt{160}\)) \\  & Minimum Leaf Size & 5 \\ \hline \multirow{3}{*}{SVM} & Kernel Type & Linear \\  & Standardization & Enabled \\  & BoxConstraint & 1 \\  & Epsilon & 0.1 \\ \hline Linear Regression & Polynomial Degree & 1 \\ \hline \multirow{8}{*}{Hardware} & Device Model & Dell Vostro 15 3510 \\  & Processor & 11 th Gen Intel(R) Core(TM) i7- \\  & Installed RAM & 16.0 GB (3200 MHz) \\ \cline{1-1}  & Graphics Card & 2 GB (Multiple GPUs installed) \\ \cline{1-1}  & Storage & 1.38 TB (840 GB used) \\ \cline{1-1}  & Operating System & Windows 64-bit (x64-based processor) \\ \cline{1-1}  & Software & MATLAB 2021a \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The numeric parameters used in the AI-based channel estimation models. ```
1:Dataset: dataset[1..18,000][1..160]
2:Labels: label[1..18,000][Integer]
3:Outputs: Trained Model: TrModel
4:Test Accuracy, Precision, Recall, F1-score
5:Define train_set[1..12,600][1..160], test_set[1..5400][1..160]
6:Define train_label[1..12,600], test_label[1..5400]
7:Define TrMod, prediction[1..5400], correct \(\gets 0\)
8:// **Step 1: Split dataset into 70% training and 30% testing**
9:for\(i\gets 1\) to 12,600 do
10:for\(j\gets 1\) to 160 do
11: train_set[\(i\)]\(\leftarrow\) dataset[\(i\)]
12:endfor
13:train_label[\(i\)]\(\leftarrow\) label[\(i\)]
14:endfor
15:for\(i\gets 12\),601 to 18,000 do
16:for\(j\gets 1\) to 160 do
17:test_set[\(i-12,600\)]\([j\)]\(\leftarrow\) dataset[\(i\)][\(j\)]
18:endfor
19:test_label[\(i-12,600\)]\(\leftarrow\) label[\(i\)]
20:endfor
21:// **Step 2: Train Tree-Based Model**
22:TrMod\(\leftarrow\) fit\(\mathrm{tree}(\mathrm{train\_set},\mathrm{train\_label})\)
23:// **Step 3: Test the Model**
24:for\(i\gets 1\) to 5400 do
25:prediction[\(i\)]\(\leftarrow\) Predict(TrMod, test_set[\(i\)])
26:if prediction[\(i\)] = test_label[\(i\)] then
27: correct \(\leftarrow\) correct + 1
28:endif
29:endfor
30:for\(i\gets 1\) to 540 do
31:prediction[\(i\)]\(\leftarrow\) Predict(TrMod, test_set[\(i\)])
32:if prediction[\(i\)] = test_label[\(i\)] then
33: correct \(\leftarrow\) correct + 1
34:endif
35:endfor
36:// **Step 4: Calculate Metrics**
37:accuracy \(\leftarrow\)\(\left(\frac{\mathrm{TP+TN}}{\mathrm{TP+TN+TN}}\right)\times 100\)
38:precision \(\leftarrow\)\(\frac{\mathrm{TP+TN}}{\mathrm{TP+TN}}\)
39:recall \(\leftarrow\)\(\frac{\mathrm{precision\_recall}}{\mathrm{precision\_recall}}\)
40:f1_score \(\leftarrow\) 2 \(\times\)\(\frac{\mathrm{precision\_recall}}{\mathrm{precision\_recall}}\)
```  
**Algorithm 1** Tree decision-based training and evaluation (70/30 split).
## 4 Solution Method
### Overview of Our Approach
This two-stage approach balances exploration and refinement, efficiently identifying solutions that not only maximize rewards in deterministic settings but also maintain robustness when facing the uncertainties inherent in real-world UAV operations with varying environmental conditions. To account for uncertainty in travel times, our simheuristic algorithm incorporates a Monte Carlo simulation module. This module evaluates candidate solutions under stochastic conditions, providing a more realistic assessment of solution quality in uncertain environments. Each promising deterministic solution undergoes multiple simulation runs to estimate its expected performance when travel times vary according to predefined probability distributions. A critical aspect of our methodology lies in the integration of the A* algorithm and neural network predictive modeling during the solution generation process. As illustrated in Figure 6, for each potential edge between nodes in our problem, we employ a two-step approach to determine both the optimal route and its expected travel time. First, the A* algorithm calculates the optimal path between nodes while ensuring obstacle avoidance. Operating on our 2D grid-based environment representation, A* efficiently explores the state space to determine the shortest feasible path between any two nodes. This process is essential for ensuring UAV routes remain physically viable in environments with obstacles. Figure 5: A schema of Stage 2. Simultaneously, our neural network model predicts the travel time for each path segment. The network takes as input multiple features including UAV speed, path distance, trajectory angles, wind conditions (speed and direction), and payload weight. These predictions account for the complex interplay of environmental factors that impact UAV performance beyond mere distance considerations. The integration of these components occurs for every potential edge in our solution space: both between intermediate nodes and between nodes and depot locations. The best-found paths and their predicted travel times are stored in an efficiency list, which serves as the foundation for our merging process during solution generation. This approach ensures that all routes generated by our simheuristic algorithm are not only feasible regarding obstacle avoidance but also accurately reflect expected travel times under varying environmental conditions.
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 5 Numerical Results  
This section showcases the application of the proposed approach in a case study involving a Flexible Manufacturing System (FMS). Recall that the objective is to establish the optimal maintenance actions at each breakdown using the ANN regression model to predict the component's real age and then apply the extended deluge metaheuristic algorithm. Also, the selective maintenance decision for the FMS study case involves selecting the optimal number of components to be maintained, while minimizing the totalmaintenance cost \(MC(k_{i},m)\) and adhering to a reliability threshold \(R^{*}\) (see the optimization model in Section 5.2).  
Hence, the FMS components encompass diverse assembly configurations, with some arranged in series and others in parallel. The proposed methodology, leveraging machine learning and optimization techniques, is applicable to any system component structure (multi-series parallel). In contrast, the assessment of system reliability involves computing the reliability of these components, considering their respective structures. Equation (29) is utilized for series structures, while Equation (30) is applied for parallel structures. It is worth noting that the reliability of component \(i\), denoted as \(R_{i}(k_{i},m)\).  
\[R(k_{i},m)=R_{1}(k_{i},m)\times R_{2}(k_{i},m)\times\ldots\ldots\times R_{n}(k_ {i},m)=\prod_{i=1}^{n}R_{i}(k_{i},m) \tag{29}\]  
\[R(k_{i},m)=1-\left((1-R_{1}(k_{i},m))\times(1-R_{2}(k_{i},m))\times\ldots\ldots \times(1-R_{n}(k_{i},m))\right)=1-\prod_{i=1}^{n}(1-R_{i}(k_{i},m)) \tag{30}\]
# Iterative Matrix Techniques Based on Averages
## 1 Introduction  
Matrices have an important role in current engineering problems like artificial intelligence [1], biomedicine [2], machine learning [3], neural networks [4], etc. It could be said that they are, in fact, an interface between mathematics and the real world. Spectral matrix theory is closely related to big data and statistics, graph theory, networking, cryptography, search engines, optimization, convexity and, of course, the resolution of all kinds of linear equations.  
An open problem of spectral theory is the sought-for eigenvalues and eigenvectors of a matrix. For this purpose, iterative methods are especially appropriate when the matrices have a large size and they are sparse, because the iteration matrix does not change during the process.  
The classical power method of [PERSON] and [PERSON] [5] assumes the existence of a dominant eigenvalue. One of the main contributions of this paper is the proposal of an algorithm to compute the eigenvalues of matrices and operators that do not have a dominant eigenvalue. The algorithm is based on the iterative averaged methods for fixed-point approximation proposed initially by [PERSON] [6] and [PERSON] [7] and, subsequently, by [PERSON] [8], [PERSON] [9], [PERSON] [10] and others.  
There are recent articles on this topic (references [11; 12; 13; 14; 15; 16; 17; 18; 19], for instance). In general, these papers deal with the problem of approximation of fixed points of non-expansive, asymptotically non-expansive and nearly asymptotically non-expansive mappings that are particular cases of Lipschitz and near-Lipschitz maps [20; 21]. However, this article aims to prove that the usefulness of iterative averaged methods goes beyond fixed-point approximation.  
In reference [22], the author proposed a new algorithm of this type called N-iteration. The method has proved useful for the iterative resolution of Fredholm integral equations of the second kind and of the Hammerstein type [23] (see, for instance, references [24; 25]).  
The above procedure is used in this article to compute eigenvalues and eigenvectors of matrices and linear operators (Sections 2 and 3) and for the resolution of systems of linear equations (Section 4). All these techniques are applied to solving Fredholm integral equations [26; 27; 28; 29; 30] of the first kind by means of orthogonal polynomials (Section 5). In Section 6, we review some properties of compact and Fredholm integral operators, while Section 7 presents the solution of a Fredholm integral equation of the first kind in a particular case where the kernel of the operator is separable. Section 8 deals with a particular case of a Fredholm integral equation of the second kind. Several examples with tables and figures illustrate the performance and convergence of the algorithms proposed.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation
#### 2.3.1 ISO/IEC RTLS
[72] gave detailed comments on different scenarios for performing tests, the overall framework, common evaluation criteria, standardized methodologies, test and evaluation procedures, and performance metrics. The authors also analyzed compliance with the standard of IPIN and Microsoft indoor localization competitions.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Modeling of the DDboats
#### 2.3.3 A Posteriori Reconstruction of Input Disturbances
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Identified** & **Flatness-Based** & **Identified** & **Flatness-Based** \\  & **Model \(\langle\mathbf{A}\rangle\)** & **Rep. \(\langle\mathbf{A}\rangle\)** & **Model \(\langle\mathbf{B}\rangle\)** & **Rep. \(\langle\mathbf{B}\rangle\)** \\ \hline \(\sigma_{x_{1}}\) & 0.3052 & 0.3049 & 0.3050 & 0.3057 \\ \(\sigma_{x_{2}}\) & 0.3357 & 0.3363 & 0.4434 & 0.4430 \\ \(\sigma_{x_{3}}\) & 0.3190 & 0.3882 & 0.4945 & 0.5608 \\ \(\sigma_{x_{4}}\) & 0.1556 & 0.1497 & 0.1572 & 0.1488 \\ \(\sigma_{x_{5}}\) & 0.0485 & 0.0466 & 0.0490 & 0.0463 \\ \(\sigma_{x_{6}}\) & 0.0999 & 0.1215 & 0.1549 & 0.1756 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the standard deviations of the disturbance estimate \(\mathbf{H}(t)\cdot\left(\mathbf{y}_{\mathbf{m}}^{(i)}(t)-\mathbf{C}\hat{ \mathbf{x}}^{(i)}(t)\right)\) for both types of input parameterization (input representation according to the _identified model_ according to Section 2.3.1; flatness-based representation according to Section 2.3.2).
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 2 Related Work  
To ensure the quality and reliability of AIS data, scholars have conducted in-depth research on key issues such as erroneous data identification, abnormal data detection, and missing trajectory restoration of AIS data. Vehicle trajectory restoration is affected by road network constraints and other factors such as the relationships between other vehicles [12; 13; 14; 15]. In general, vessel trajectory restoration only considers its trajectory data for trajectory restoration. When the starting position of the vessel and the fixed course are known, the trajectory matching technique is indeed a reliable choice to restore the vessel trajectory [16; 17]. However, in the real navigation environment, the trajectory of a vessel often deviates from the intended course due to a variety of factors, which requires more general and flexible methods for trajectory restoration.  
By investigating related studies, this paper summarizes two approaches: traditional mathematical modeling methods and data-driven methods.
# Content-Adaptive and Attention-Based Network for Hand Gesture Recognition
## 4 Experiment and Results
### Results and Discussion
The number of parameters and floating-point operations (FLOPs) are two commonly used indicators to evaluate the complexity of a deep model. The number of parameters describes the storage space required to store the model, and FLOPs describe the computational force required to use the model. We evaluate the complexity of our model by measuring the number of parameters and FLOPs, and the results are shown in Table 5. We report the differences in model parameters, FLOPs and processing time when 8, 16, 24 and 32 frames are used as sampling lengths. As can be seen from Table 5, as the sampling length increases, the computation and processing time of the model increases accordingly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Sets** & **Precision** & **Recall** & **F1-Score** \\ \hline NVGesture & 83.56\% & 83.13\% & 82.98\% \\ EgoGesture & 93.92\% & 93.82\% & 93.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, recall, and F1-score of our proposed model on two datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Models** & **NVGesture** & **EgoGesture** \\ \hline TSN [14] & Two-stream & 65.6\% & N/A \\ \hline R3D [42] & 3D CNNs & 74.1\% & 78.4\% \\ C3D [16] & 3D CNNs + SVM & 69.3\% & 86.4\% \\ ResNeXt [2] & 3D CNNs & 66.4\% & 90.9\% \\ \hline I3D [25] & Inflated 3D CNNs & 78.3\% & 90.3\% \\ TSM [17] & 2D + 1D CNNs & N/A & 92.1\% \\ \hline Ours & Attention & 80.7\% & 92.6\% \\ Ours & Attention + Sampler & 83.2\% & 93.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison with other methods on the test sets of NVGesture and EgoGesture datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Shape** & **Parameters (M)** & **FLOPs (G)** &
\begin{tabular}{c} **Processing Time** \\ **(ms)** \\ \end{tabular} \\ \hline (1, 3, 8, 224, 224) & 121.277 M & 189.877 G & 23.5 ms \\ (1, 3, 16, 224, 224) & 121.284 M & 379.698 G & 46.3 ms \\ (1, 3, 24, 224, 224) & 121.290 M & 569.519 G & 67.2 ms \\ (1, 3, 32, 224, 224) & 121.296 M & 759.340 G & 73.4 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters, floating-point operations and inference time of our proposed model. Confusion Matrix. To evaluate the recognition accuracy of the proposed network for each type of gesture, we computed the confusion matrix table. The confusion matrix was obtained by comparing the predicted labels and the ground truth labels. We visualized the confusion matrix table obtained on two datasets, as shown in Figure 6. The values on the diagonal line represent the number of gestures correctly recognized by the model, while the values on the off-diagonal line represent the number incorrectly recognized. The value can also be distinguished based on the shade of the color. The higher diagonal value indicates the better performance of the model. In Figure 6, we use different colors to indicate the magnitude of the values separately. The confusion matrix shows that the proposed network is robust to each gesture class.
## 4 Results and Discussion
### Experiment Description
#### 4.1.2 Experimental Settings  
In this experiment, the PyTorch version is 1.9.1, the CUDA version is 11.0, and the python version is 3.9. The training and testing are performed on the GPU NVIDIA-A100-SXM4-40 GB. During each experiment, the epoch is set to 300 on the self-made pothole dataset and 200 on the pothole600 dataset because of the small number of pictures. The picture size is 460 \(\times\) 460. Based on the parameter settings commonly used in engineering, the parameters are set as follows: The batch size is set to 32. The number works is set to 8. The learning rate is set to 0.01. The optimizer is the SGD.
## 6 Therapeutic Actions of Madecassoside and Other Major Compounds in _C. asiatica_
### Kidney Diseases  
Doxorubicin (DOX) is a chemotherapeutic agent used to treat malignant neoplasms; its use is limited by serious adverse effects, such as nephrotoxicity [197], cardiotoxicity [198] and hepatotoxicity [199], which are in part mediated through free radical formation, oxidative damage and membrane lipid peroxidation [200]. In view of the demonstrated ability of mac accesssoide to reduce oxidative damage while improving the anti-oxidative enzymes status in various disease models, the effect of mac accesssoide on DOX-induced renal toxicity was examined in vitro and in vivo. Treatment with mac accesssoide significantly and concentration-dependently attenuated DOX-induced apoptosis in human proximal tubule cells (HK-2 cells). This effect was associated with the inhibition of DOX-induced ERK phosphorylation, reductions in apoptotic factors (cleaved caspases, apoptotic protease activating factor1, BAX) and an inhibition of iNOS [201]. Moreover, concomitant treatment with mac accesssoide markedly reduced DOX-induced renal injury in mice, as evidenced by the reduction in serum creatinine and BUN and the preserved structural integrity of the kidneys. These observations suggest that mac accesssoide could be valuable in preventing/ameliorating DOX-induced toxicity in chemotherapy patients [201].  
In a mouse model of obstructive nephropathy, asiatic acid intake significantly ameliorated tubulointerstitial fibrosis by suppressing tubular injury, fibroblast activation and ECM accumulation mediated via Smad7-dependent TGF-\(\beta\)1 signaling [202]. Asiatic acid was identified to function as a Smad7 agonist that inhibits Smad3 signaling [203]. Moreover, pre-treatment with asiatic acid inhibited NF-\(\kappa\)B activation and inflammatory responses, possibly from Smad7 upregulation, in cisplatin-induced acute kidney injury [204].
# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 2 Related Work
### Parameter-Efficient Fine-Tuning  
As large-scale pre-trained models become increasingly prevalent, Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical solution to reduce training cost and memory footprint. This subsection introduces core PEFT techniques, with a focus on LoRA and its evolving ecosystem, and sets the stage for their use in multimodal domains.  
Parameter-efficient fine-tuning methods aim to reduce the number of trainable parameters required for adapting large pre-trained models to downstream tasks, offering significant savings in compute and memory while maintaining competitive performance [11]. Among the earliest approaches, prefix tuning [12] prepends trainable prefix vectors to the key and value inputs of each Transformer layer, enabling task adaptation without modifying base parameters. Prompt tuning [13] simplifies this by learning a fixed input embedding prepended to the model's token sequence, though its performance often lags behind under low-resource settings. BitFit [14] updates only the bias terms in Transformer layers, demonstrating surprising effectiveness in classification tasks with minimal parameter cost. LoRA [10] introduces a low-rank decomposition into weight update paths, achieving state-of-the-art performance with significantly fewer trainable parameters and preserving the frozen base model. Recent variants such as AdaLoRA [15] dynamically allocate ranks during training based on parameter importance, while QLoRA [16] combines quantization with LoRA to scale fine-tuning to 65B models on a single GPU. These advancements collectively position LoRA and its extensions as a dominant family in PEFT, widely adopted across LLMs and increasingly explored in multimodal and generative domains.