esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussions
### Experimental Setup
#### 3.1.2 Dataset Pre-Processing  
The general data preprocessing steps, as shown in Figure 2, were used for all the datasets as part of the text classification pipeline. These data preprocessing steps were performed using the Python NLTK [24] library and were explained in Section 1 of this current paper. The vectorized vocabulary for each dataset was generated using Python  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Documents** & **V vocabularies** & **V vocabulary Segments** & **Categories** \\ \hline (1) Twitter US Airline dataset [21] & 14,640 & 100 & 10 & 3 \\ (2) Conference Paper dataset [22] & 2507 & 100 & 10 & 5 \\ (3) SMS Spam dataset [23] & 5574 & 50 & 5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of the datasets.  
sklearn CountVectorizer. A lower and upper bound frequency of the vocabularies was set to reduce non-semantic vocabularies and computational complexity.  
Each vectorized dataset was later split into 70% training and 30% test instances. Label encoding was used to encode the labels.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### A 100-Node Scenario
#### 6.2.4 Comparison and Discussion  
Considering the general trend, the NN-based approach shows better performance than the GA-based and GWO-based approaches.  
Figure 10: The achieved (17,794 m) path planning solution for visiting the 100 nodes with the GWO in 1000 iterations.  
Figure 9: The achieved (10,803 m) path planning solution for visiting the 100 nodes with the GA in 1000 iterations.  
Figure 11 shows the total distance travelled by the OSA with different algorithms (NN-based approach, GWO-based approach, and GA-based approach) to visit the 100 sensor nodes given in Figure 7.  
From Figure 11, we can make the following observations for the performance of the algorithms for 100-sensor scenarios. Considering the general trend, the NN-based approach performs better than the GWO-based and GA-based approaches. The NN-based approach not only gives better results than the GWO-based approach and the GA-based approach but also the NN-based approach solves in a much shorter time (0.168395 s) than the GA-based approach's time to reach the solution (6.294668 s). The NN-based approach performs 37.4 times faster than GA-based approach. Considering all these results, increasing the number of sensor nodes from 50 nodes to 100 nodes leads to a significant difference between the NN-based approach and GA-based approach.  
From Table 2, we can make the following observations. In the first iteration, the GA-based approach and GWO-based approach started with high values of path lengths (28,000-30,000 m), while the NN-based approach achieved an efficient solution very quickly, at around 9.05 km. At the 100 th iteration, the GA-based approach achieves significantly better results than GWO-based approach (8,054 m, i.e., 29% less than the GWO-based approach). At the 300 th iteration, the NN-based approach achieves better results than the GWO- and GA-based approaches. At the 600 th iteration, the GA-based approach achieves better results than NN-based approach. At 1000 iterations, the GA-based approach significantly outperforms the GWO-based approach (6991 m, i.e., 39.3% less than the GWO-based approach), while the NN-based approach is much faster in obtaining a shorter path than the GWO- and GA-based approaches, suggesting a practical and robust solution.  
Figure 11: The achieved path lengths for visiting the 100 nodes by all the algorithms (NN-based approach, GWO-based approach, and GA-based approach) in 1000 iterations.
# Predictive Quantization and Symbolic Dynamics
## 6 Applications
### Information Rate and Volatility  
The intuition behind information dynamics can be seen by examining how \(IR\) corresponds to volatility in a financial time series. In Figure 8, the volatility in stock prices is shown together with an instantaneous information rate plot. The data are taken from the daily returns of the S&P500 stock index from 1 October 1983 to 30 August 1991. The volatility of the series was estimated by taking the root of the five-day average of squared returns, as described in Ref. [21]. One should note that the application of information rate analysis is performed here on a short time basis, depending on the length of the repeated suffix at each data point in time.  
One can visually observe that regions of extreme change in the stock price correspond to drops in the instantaneous information rate graph. When changes in the stock index are unpredictable, or in other words, when patterns of price change cannot be attributed to the repetition of previous sub-sequences, the information rate significantly diminishes. Such a measure can be used for change detection or segmentation.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Principles of Electricity Generation with Microbial Fuel Cell (MFC)
#### 3.1.1 Electrodes
For close water systems like waste-water processing plants ensuring of continuous oxygen supply becomes a common challenge [69]. Researchers have worked on providing additional air circulation at the cathodes, especially with innovative mechanical solutions. In many cases such additional units increase the production cost. While multiple examples of mechanical aeration procedures are reported, use of such units require additional cost [61; 70; 71; 72]. Many works have been undertaken on improving the flexibility of such system including use of comb type [73], brush type [74] and rotating type [75] cathodes. Air cathodes [70] have emerged as a popular low-cost solution providing sustainable aeration at cathodes. In this configuration the cathode surface partially remains open to air and continuously receives oxygen supply. [PERSON] et al. [76] reported a generalized air cathode configuration which consists of a catalyst layer that faces the solution side of the cathode, a gas diffusion layer that faces air, and a conductive base material layer. The conductive layer also acts as a current collector as well as a mechanical support provider. Biocathodes also became an efficient solution for continuous oxygenation requirement for MFCs. Biocathodes are defined as cathodes with attached microorganisms serving as ORR biocatalysts [77]. Biocathodes emerged as a solution where bio-fouling at cathodes was utilized as an advantage rather than a disadvantage where bacterial and micro-algae [78] were grown intentionally to aid ORR catalyst operations instead of using additional expensive catalysts. A second generation of these cathodes also include a natural oxygen generating mechanism by incorporating growth of photosynthetic bioreactors [79] on the same platform. Figure 5: Classification of MFC cathodes according to their functionalities.
## 6 Results  
In this section, we present the performance assessment of our proposed models under different directed graph configurations, focusing on accurately predicting the final consensus value while accommodating the extended connectivity provided by \(k\)-path Laplacian matrices. We generated, for every graph type (Erdos-Renyi, Watts-Strogatz, and Barabasi-Albert), node size (25, 50, 100, 200, and 300), and case (base and exponential), a fixed-size training set of 2400 samples and a test set of 600 samples. Each model--XGBoost, LSTM, Extended LSTM, Transformer, and ConvLSTM--was exposed to identical training and test data to allow fair comparisons. Unless otherwise stated, we reserve 20% of the training data for validation, leading to an 80/20 train-validation split. Al the models and training data are accessible at [[https://github.com/yusef320/AI-Driven-Consensus](https://github.com/yusef320/AI-Driven-Consensus)]([https://github.com/yusef320/AI-Driven-Consensus](https://github.com/yusef320/AI-Driven-Consensus)) (accessed on 30 March 2025).  
We systematically tuned hyperparameters through predefined search grids tailored to each approach. We also performed some preliminary experiments to fine-tune these ranges and ensure robust performance across different model types. For instance, maximum tree depth, number of estimators, and learning rate were explored for XGBoost; hidden layer sizes, projection sizes, and multiple LSTM layers were considered for the recurrent-based architectures; and varying numbers of heads, hidden dimensions, and layers were tested for the Transformer models. A mean-squared-error objective-guided training, combined with early stopping triggered after 30 consecutive epochs (or boosting rounds for XGBoost) without validation loss improvement. The 30-epoch threshold was specifically selected based on our observation that during the first epoch, with our chosen hyperparameters, the model did not demonstrate a stable learning trend.  
Final evaluations were performed on the 600-sample holdout test set. The main performance metrics include Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).  
Post training, predictive performance was evaluated primarily using the RMSE and the MAPE. RMSE provides a clear representation of the typical magnitude of prediction errors in the original units of the target variable. At the same time, MAPE offers a normalized error measure by expressing the average absolute error as a percentage of the true values.  
In the following subsections, we compare predictive accuracy and computational overhead for each candidate model, emphasizing how long-range interactions (via higher-order path-Laplacian matrices) influence the quality of the consensus estimation. As discussed previously, larger values of \(k\) allow greater leverage of indirect connectivity, potentially strengthening information flow in highly sparse or disconnected graphs. The empirical findings highlight the importance of carefully chosen hyperparameters and how each architecture scales in the presence of multi-hop influences.
# Calculation Method of Earth Pressure Considering Wall Displacement and Axial Stress Variations
## 3 Example Verification  
In this study, the proposed method was validated through discrete element experiments and existing model tests. Discrete element experiments were used to simulate the variation of earth pressure at a point behind the retaining wall, and the earth pressure distribution curve behind the wall was obtained by connecting the earth pressure points at different depths. The proposed method was further validated using existing model tests.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 3 Multiple Relay UAVs Selection Scheme Based on Fuzzy Optimum Selection
### Three UAV Roles in Cooperative Surveillance with Ground Station  
Three roles, Relay UAV (R UAV), Articulation UAV (AUAV), and Surveillance UAV (SUAV), are designed to perform different tasks to gather more real-time surveillance information and maintain network connectivity, and the details are described in Table 1. The primary task of RUAVs is providing peer-to-peer communication links, namely maintaining network connectivity among SUAVs, AUAV and ground station. SUAVs cooperatively perform surveillance tasks and search unknown regions to gather real-time information with limited communication. Serving as a joint, AUAV is a critical role to connect surveillance UAVs swarm and RUAVs, and the goal of AUAV is achieving tradeoff between surveillance payoff and network connectivity. Due to the minimum cruise velocity constraint, fixed-wing UAVs serving as relay have to circle around specified points while providing UAV-to-UAV communication links, and it is emphasized that RUAVs perform auxiliary surveillance task around circling points to improve the UAVs cooperative surveillance utility.
## 4 Results and Discussion
### PEMD Analysis
#### 4.2.2 Peak Period DR Policy Impact on PEMD Estimation  
The proposed DR policy in this study is implemented by changing the peak-period prices only to reduce the peak-load according to operational and security goals of the GO, which has a direct impact on the estimation of the DR behavior of the residential sector. The period of DR is assumed to start one hour before (4-5) PM and after (8-9) PM the announced peak periods that were discussed in Table 3 for the winter season are constant for the whole period. The PEMD under this price policy in the winter peak-time is depicted in Figure 16, where it incentivizes consumers to shift their energy consumption outside of the peak demand period only due to the constant peak period price. Since the prices only change in the peak period, the areas on the left and right of the peak period are ignored and were considered zero based on the policy proposed.  
Consumers are inclined to shift their demand to the closest hours outside of the peak period [67], as depicted in Figure 16. The time-horizon (\(T_{b}\): backward time horizon and \(T_{f}\): forward time horizon) of the demand re-allocation depends on the type of appliances shifted. In the case that most of the appliances shifted are deferrable loads or water heating, it is assumed that the time-horizon of shifting is 4 h closest to the hour under peak pricing, where the cross-elasticates have the same value as reported in Table 10. It is also assumed that, in the last hour of the peak period, a part of their demand is shifted to the next day after 24:00, which is not considered in the current PEMD models. If more AC usage is shifted, the weight of the cross-elasticity of the peak hour period is doubled for the closest 2 h, as represented in Table 11.  
Figure 16: Winter peak time—PEMD under the proposed DR policy.  
Table 12 shows the structure of the finalized PEMD in this study. Cases 1 to 4 represent the base self-elasticity which equals the short-term self-elasticity for the residential sector of Jordan, while Cases 5 to 8 assume that the peak period will have double the elasticity. Cases [1,2,5,6] represent a lossless PEMD, where the summation of the cross-elasticities is equal to the cross elasticity in magnitude, while their counterparts take the 75% case scenario mentioned previously. Finally, cases [3,4,7,8] consider more AC usage participated in DR, where the second level of cross elasticity (L2) with double the weights of (L1) is used, as was shown in Tables 10 and 11.  
Table 12 shows the structure of the finalized PEM
## 3 Results
### Trunk Joint Angles: RoMs and CVs  
A significant decrease in trunk flexion-extension and rotation \(ROM\) values was found in subjects with cerebellar ataxia wearing the LFE compared with the condition WOE (\(ROM_{\text{flex-ext}}\), \(\chi^{2}\) = 7.75, WOE vs. WLFE: \(p\) = 0.02; \(ROM_{\text{rot}}\), \(\chi^{2}\) = 7.14, WOE vs. WLFE: \(p\) = 0.02) (Figure 6).  
\begin{table}
\begin{tabular}{c|c c c c c c c} \hline
**Patients** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **Mean \(\pm\) Std** \\ \hline MFE & 3.12 & 4 & 4.37 & 3.5 & 3.62 & 4.75 & 4.5 & 3.98 \(\pm\) 0.59 \\ LFE & 3.25 & 4.12 & 4.62 & 3.75 & 4.25 & 4.25 & 4.62 & 4.12 \(\pm\) 0.48 \\ \hline \end{tabular}
\end{table}
Table 2: Total QUEST ratings for each patient and both exoskeleton versions utilized.  
Furthermore, the values of trunk ROMs calculated without the use of the exoskeleton were significantly higher than those of the control subjects in all three planes of space (\(ROM_{\text{flex-ext}}\), WOE vs. C: \(p\) = 0.028; \(ROM_{\text{rot}}\), WOE vs. C: \(p\) = 0.002; \(ROM_{\text{lat-ben}}\), WOE vs. C: \(p\) = 0.01).  
There was no significant difference between the values of trunk ROMs in the three planes of space in the presence of the exoskeleton and those of the control subjects, except for the trunk rotation \(ROM\) with the less flexible exoskeleton, which remained significantly higher than the values of healthy subjects (WLFE vs. C: \(p\) = 0.017) (Figure 6).  
Figure 5: For the patients without the exoskeleton (WOE), with the more (WMFE) and less flexible (WLFE) exoskeleton, and for the control (C) group, the means (\(\pm\)SD) of the displacement of the Center of Mass in the medio-lateral direction (\(\text{CoM}_{\text{dispML}}\)) were reported. Also, in each group, the values of each subject were reported (grey dots). * Statistically significant differences.  
Figure 6: For the patients without the exoskeleton (WOE), with the more (WMFE) and less flexible (WLFE) exoskeleton, and for the control (C) group, the means (\(\pm\)SD) of the range of motion (\(RoM\)) of trunk flexion–extension, rotation, and lateral bending and coefficients of variation (CVs) of the three RoMs (\(CV_{\text{flex-ext}}\), \(CV_{\text{rot}}\), and \(CV_{\text{lat-ben}}\)) were reported in the first and second row, respectively. * Statistically significant differences.  
The presence of the exoskeleton had no significant effect on the CVs of trunk ROMs; however, a decreasing trend was observed (Figure 6).  
Statistical significant differences were found between the values of the CVs of trunk. \(ROM_{\text{flex-ext}}\) in all three conditions and those of the control subjects (WOE vs. C: \(p=0.006\); WMFE vs. C: \(p=0.01\); WLFE vs. C: \(p=0.003\)), and between the values of the CVs of trunk. \(ROM_{\text{rot}}\) WOE and WMFE and those of the control subjects (WOE vs. C: \(p<0.001\); WMFE vs. C: \(p=0.02\)). No statistically significant differences were found between the values of the CVs of trunk \(ROM_{\text{lat-ben}}\) either between the three conditions or with the control subjects.
## 5 Experiments and Analysis of Results
### Experimental Setup  
All experiments were conducted on a workstation featuring an 11 th-generation Intel Core i7 processor (2.30 GHz), 32 GB of RAM, and an NVIDIA GeForce RTX 3060 GPU, running on the Windows 10 operating system. The software environment was built using Python 3.10 within the Jupyter Notebook interface. The implementation relies on a combination of widely-used libraries including NumPy, PyTorch, Pandas, and Scikit-learn for data handling, model training, and evaluation. For fairness in comparison, the experimental parameters for all models, including the DOA-BP model and the other metaheuristic optimization algorithms, were set to be identical. These settings are summarized in Table 5, and are used consistently throughout the experiments to ensure a fair evaluation of the methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Processor** & **Intel Core i7 (2.30 GHz, 11 th Generation)** \\ \hline Operating System & Windows 10 \\ RAM & 32 GB \\ Language & Python \\ Matlab Version & Matlab R2022a \\ GPU & NVIDIA GeForce RTX 3060 \\ IDE & Jupyter Notebook \\ Numpy & 2.2.5 \\ PyTorch & 2.7.0 \\ Pandas & 2.2.3 \\ Scikit-learn & 1.6.1 \\ Batch Size & 1024 \\ Population Size & 20 \\ Maximum Iterations & 60 \\ Training Epochs & 80 \\ Hidden Layer Structure & [128, 64] \\ Learning Rate Scheduler & Cosine Annealing \\ Initial Learning Rate & 0.001 \\ Minimum Learning Rate & \(1\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental setup.
## 2 Materials and Methods
### Response Surface Methodology for Optimization of Furfural Production from Xylose Standard  
To maximize furfural production from the hydrolysate, the optimum conditions needed to be determined; optimization was conducted using a xylose standard solution. The xylose standard solution was adopted to investigate the relationship between reaction conditions and furfural yield, excluding the effect of impurities. Briefly, 10 mL of xylose standard solution containing a certain concentration of xylose was mixed with 20 mL of organic solvent in a Teflon-lined reactor. The sulfuric acid concentration of the xylose standard solution was adjusted to 4% (\(w/w\)); this was the same as the dilute acid hydrolysate used to obtain the optimum reaction conditions for the hydrolysate. The reactor was then sealed and soaked in an oil bath that had been pre-heated to a target temperature. The mixed hydrolysate was stirred during the reaction using a magnetic stirrer, and the temperature was maintained for a certain reaction time at the target temperature. After the reaction, the reactor was removed from the oil bath and immediately stored in the ice chamber to cool to room temperature and prevent undesirable reactions.  
An RSM was adopted to optimize furfural production using a xylose standard solution. The analysis was conducted based on a 2\({}^{3}\) factorial central design (CCD) using Design Expert 11.1.0.1 software (Stat-Ease, Inc., Minneapolis, MN, USA). Table 2 presents 17 sets of reaction conditions composed of six axial points and a duplication of the central point. The reaction temperature (X1, \({}^{\circ}\)C), reaction time (X2, min), and xylose concentration (X3, g/L) were designated as independent variables, while furfural yield (Y1, %) was the dependent variable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Concentration (g/L)** \\ \hline Sugars & \\ Glucose & 2.01 \(\pm\) 0.05 \\ Xylose+mannose+galactose (XMG) & 20.02 \(\pm\) 0.46 \\ Arabinose & 1.64 \(\pm\) 0.06 \\ Sugar derivatives & \\ Acetic acid & 6.17 \(\pm\) 0.13 \\ Formic acid & 0.03 \(\pm\) 0.00 \\ Furfural & 0.32 \(\pm\) 0.04 \\
5-Hydroxymethylfurfural (5-HMF) & 0.01 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of dilute acid hydrolysate of _Quercus mongolica_.  
The coded level of the CCD from each run was applied to real independent variables as follows:  
Variable = value of central point/variation of coded level per one point  
Reaction temperature (\({}^{\circ}\)C) = 160/20, reaction time (min) = 120/60, xylose concentration (g/L) = 20/10  
Following the optimization of reaction conditions based on the RSM, the extraction solvent was altered from THF to dimethyl sulfoxide (DMSO) or toluene in the optimum conditions to analyze the effects of the extraction solvent.
## 3 External Photon-Beam Radiotherapy  
The rarity of conjunctival and iris melanoma and the wider use of brachytherapy and proton therapy have limited the use of external photon-beam radiotherapy.  
In a recent meta-analysis on the role of radiotherapy in ocular melanomas [21], it is underlined that stereotactic radiation therapy (SRT) and stereotactic radiosurgery (SRS), mainly administered with gamma knife and cyber knife, represent an alternative therapy. Authors concluded that, for the small number of studies available on this topic and applied methodology, are not able to determine what the most effective radiotherapy technique is.  
For small and medium-sized lesions, external photon-beam radiotherapy has shown similar efficacy and side effects to proton therapy [21]. Stereotactic radiosurgery with charged-particle beams(carbon ions, protons) is effective in larger lesions with irregular margins, causing less adverse effect and less damages to surrounding organs at risk [22].  
External beam radiation is also proposed as palliative treatments for nonresponsive, recalcitrant carcinomas for patients in whom conventional therapy has not been effective. After failure of multiple standard treatments, [PERSON] et al. [23] treated a small patient's series with electron beam radiotherapy, but with curative doses (50 Gy in 2.5-Gy fractions or 60 Gy in 2.0-Gy fractions, approximately the same radiobiological equivalent dose). The local tumor control rate was 75%, with relatively few side effects; there were no second cancers.  
There are selected cases in which photon beam radiotherapy can be proposed: when patients refuse or cannot have surgery, positive margins, negative surgical margins with presumed residual microscopic disease, such as adjuvant therapy [24], extra tumoral perineural infiltration, post-exeneration radiotherapy in high-risk patients, lymph node involvement and previous recurrence.