esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Intercomparison
The implementation of \(R^{2}\) that we selected is important to note, as its results are purely correlative and quantify only dispersion, ranging in value between zero and one. This has some desirable properties in that no negative or large values are produced, and that it is insensitive to differences in scale. Regardless of the correlation metric used, complementary metrics are needed to quantify the direction (i.e., bias) and/or magnitude of error. We rely on RMSE and MAE to provide information on error or residual magnitude, and ME to provide information on bias. We utilize a visual analysis to assess error directionality over time, as this can be poorly characterized by a single coefficient, masking periodicity. We compute \(R^{2}\), RMSE, MAE, and ME for time-series of the metrics described in Table 5 on page 13. These include NEE, above- and below-ground biomass, C, and N, soil organic C and N, soil respiration (\(r_{soil}\)), aboveground net primary production (ANPP), and, species aboveground biomass and relative abundance. All of these metrics are pools with the exception of NEE, \(r_{soil}\), and ANPP fluxes. Finally, we diagnose the ability of both models to meet a range of logistical criteria related to deployment: Model usability, performance, and transferability. Model usability is assessed per four criteria:  
1. Ease of installation
2. Ease of parameterization
3. Ease of program operation
4. Ease of parsing outputs  
Model software performance is assessed per a single metric: The speed of program execution for each site for the predefined simulation duration. The durations are 11 years and five years for the HF-EMS and JERC-RD EC flux tower sites, respectively. Simulation results are output at annual temporal resolution, the standard resolution for both models, while NECN operates on a monthly timestep, and most other modules of LANDIS-II are annual. Finally, model transferability is assessed per the following five criteria:  
1. Model generalizability
2. Availability of parameterization data
3. Size of the program
4. Cross-platform support
5. Ease of training new users  
Each of these logistical criteria are compared in a qualitative analysis, with the exception of software performance.
## 5 Results and Discussion
### The Proposed Predictive Model  
Based on the obtained results, both ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). These models also have the highest accuracy, sensitivity, and specificity compared to the previously proposed model, QDA model with 80% training data. Therefore, both ANN models developed using logistic and tanh activation function with 70% training data were the proposed predictive models in this research. We displayed the proposed predictive models' performances in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Data Training Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline H3-O2 & \(-8.726450\) & \(8.371778\) & \(5.738516\) \\ \hline H4-O2 & \(4.006490\) & \(-9.933384\) & \(9.385994\) \\ \hline H5-O2 & \(10.649440\) & \(11.691794\) & \(2.076983\) \\ \hline H6-O2 & \(14.583526\) & \(-8.421655\) & \(8.594578\) \\ \hline B2-O3 & \(-4.4530081\) & \(7.2697955\) & \(1.294068\) \\ \hline H1-O3 & \(9.7557132\) & \(-0.7564020\) & \(8.395259\) \\ \hline H2-O3 & \(-3.0576919\) & \(-2.9147165\) & \(-1.187068\) \\ \hline H3-O3 & \(13.5887864\) & \(-10.5569871\) & \(4.191767\) \\ \hline H4-O3 & \(-0.8085856\) & \(-0.6266332\) & \(-8.351199\) \\ \hline H5-O3 & \(0.8808544\) & \(-9.8940081\) & \(5.886176\) \\ \hline H6-O3 & \(-12.4993457\) & \(9.8877980\) & \(-8.863369\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Data Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline _Accuracy_ & 90.91\% & 78.26\% & 72.73\% \\ \hline _Sensitivity_ & 91.11\% & 81.39\% & 72.22\% \\ \hline _Specificity_ & 95.51\% & 88.19\% & 86.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance measurements of the ANN model developed using logistic activation function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Data Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline _Accuracy_ & 90.91\% & 78.26\% & 72.73\% \\ \hline _Specificity_ & 91.11\% & 79.68\% & 72.22\% \\ \hline _Specificity_ & 95.51\% & 89.10\% & 86.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance measurements of the ANN model developed using tanh activation function.
# Long-Term and Seasonal Changes in Sources of Urban Atmospheric Particulates in the Western Pacific
## 2 Materials and Methods
### Source Contribution Calculations  
The NP method for calculating combustion source contributions to TSP is briefly described. TSP was divided into combustion-derived particulate matter (P\({}_{\text{c}}\)) and non combustion-derived particulate matter (P\({}_{\rm n}\)), and P\({}_{\rm c}\) was further divided into particulates from combustion sources with high-temperature (P\({}_{\rm h}\)) and low-temperature (P\({}_{\rm l}\)). \(x\) is defined as [P\({}_{\rm h}\)]/[P\({}_{\rm c}\)], i.e., the proportion of P\({}_{\rm c}\) in the atmosphere due to P\({}_{\rm h}\) (\(0<x<1\)), and \(y\) as [P\({}_{\rm c}\)]/([TSP]), i.e., the fraction of TSP in the atmosphere due to P\({}_{\rm c}\) (\(0<y<1\)). The atmospheric concentrations of P\({}_{\rm c}\)-bound 1-NP ([1-NP]) and Pyr ([Pyr]) are expressed by the following equations:  
\[[\rm 1\text{-}NP]=[\rm 1\text{-}NP_{h}][P_{c}]x+[\rm 1\text{-}NP_{h}][P_{c}](1-x) \tag{1}\]  
\[[\rm Pyr]=[\rm Pyr_{h}][P_{c}]x+[\rm Pyr_{l}][P_{c}](1-x) \tag{2}\]  
where [1-NP\({}_{\rm h}\)] and [Pyr\({}_{\rm h}\)] are concentration of 1-NP and Pyr in P\({}_{\rm h}\), respectively, and [1-NP\({}_{\rm l}\)] and [Pyr\({}_{\rm l}\)] are the concentrations of 1-NP and Pyr in P\({}_{\rm l}\), respectively. Our previous studies found that the [1-NP\({}_{\rm h}\)]/[Pyr\({}_{\rm h}\)] ratio was nearly constant at 0.425, and the [1-NP\({}_{\rm l}\)]/[Pyr\({}_{\rm l}\)] ratio was nearly constant at 0.0013 [18]. These values were substituted in Equations (1) and (2), since they depend on combustion temperatures of the major sources as described below. Among the P\({}_{\rm c}\) sources, only vehicle engines use high combustion temperatures in the range 2700-3000 \({}^{\circ}\)C, resulting in a large [1-NP]/[Pyr] ratio. In the Western Pacific, coal, oil, and natural gas are the main energy sources [21]. Among them, coal and oil are used in industry, power production, and heating, especially during the winter. The combustion of coal occurs at temperatures in the range 1100-1200 \({}^{\circ}\)C. Due to this relatively low combustion temperature, NPAH formation is lower than with vehicles and therefore the [1-NP]/[Pyr] ratio of coal combustion is comparatively low. Natural gas emits the smallest amount of PM among the above energy sources. Moreover, there was little post-harvest field burning, whose combustion temperature is in the range 500-600 \({}^{\circ}\)C, during the air sampling periods in this study [22]. Therefore, vehicle emissions and coal combustion were respectively used as representative combustion sources with high and low temperatures.  
Atmospheric concentrations of TSP, Pyr, and 1-NP were obtained (Table S2) from previous studies [10; 11; 12], and were used to calculate the source contribution parameters \(x\) and \(y\) in the above equations. Using the definitions \(x=[\rm P_{h}]/[\rm P_{c}]\) and \(y=[\rm P_{c}]/[\rm TSP]\), [P\({}_{\rm c}\)], [P\({}_{\rm n}\)], [P\({}_{\rm h}\)] and [P\({}_{\rm l}\)] were obtained.
# Antioxidative and Photop Protective In Vitro Potential of Lavandula Angustifolium
## 5 Conclusions  
Taking into account the results of our research, it has been established that the investigated alcoholic extracts of _Lavandula angustifolia_ Hidcote Blue variety have antioxidant and photoprotective potential. Better results were obtained if diluted alcohols were applied as solvents to obtain both the herb and the flower extracts using UAE. GC-MS analyses of _L_vandula angustifolia_ extracts showed the presence of 16 compounds with antioxidant activity. However, taking into account the content of these compounds in the extracts, the compounds mainly responsible for the antioxidant activity of the extracts from flowers should be assumed to be linalool and linalyl acetate, while the compounds responsible for the antioxidant activity of the extracts from herbs should be assumed to be 7-methoxycomarin and coumarin.  
A satisfactory correlation was observed between the antioxidant activity determined immediately after the preparation of extracts and twelve months later, which may indicate the stability of the prepared extracts. Similar results were found for the evaluation of the in vitro sun protection factor, which could confirm the above-mentioned conclusion. The obtained results could suggest the possible use of _Lavandula angustifolia_ extracts in the development of cosmetics. In the next stage, further studies of antioxidant activity using additional methods for the evaluation of the antioxidant potential of the lavender extracts, as well as their application to formulate cosmetic preparations, should be performed. Moreover, additional evaluation is required taking into account, among others, toxicological, antimicrobial, and allergy tests to assess the safety of such products.  
Conceptualization, M.S., A.K. and A.W.; methodology, M.S., A.K., A.W., D.O. and S.G.; software, A.K. and A.W.; validation A.K. and A.W.; formal analysis, M.S., A.K., D.O. and S.G.; investigation, M.S., A.K., D.O., S.G. and J.S.; resources, M.S. and A.K.; data curation, A.K. and A.W.; writing--original draft preparation, A.K., A.W. and J.S.; writing--review and editing, A.K. and A.W.; visualization, M.S., A.K., A.W. and S.G.; supervision, A.K. and A.W.; project administration, A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available upon request from the corresponding author. The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABTS & (2,2\({}^{\prime}\)-azino-bis(3-ehylbenzothiazazine-6-sulphonic acid) diammonium salt \\ DPPH & 2,2-diphenyl-1-picrylhydrazyl \\ SD & Standard deviation \\ SPF & Sun protection factor \\ TEAC & Trolox equivalent antioxidant capacity \\ \end{tabular}
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection  
We discuss the basics of UAV flight trajectory and RL-based approaches for solving problems of flight trajectory selection such as energy consumption [52], data fetching, QoS, quality of experience (QoE), coverage [53; 54], and obstacles [55]. In addition, we present a comparative analysis.  
As the existence of FANETs comes from the flying nodes, selecting the flying trajectory is a crucial factor in autonomous flying scenarios. There are various usages of FANETs, where flying trajectory selection plays a vital role. Using FANETs as portable interconnected aerial base stations (BSd) is one of the major commercial and civilian applications of FANETs. Because UAV base stations (UBSs) can be easily deployed to handle temporary traffic congestion, provide emergency coverage in disaster areas, to ensure the QoS, or collect data from remote internet of things (IoT) devices regardless of terrestrial territory as shown in Figure 12[14]. Moreover, UAVs can also be used to deliver products at the doorstep of people.  
Owing to the complex flying environment, limited data memory, limited power support, user mobility, and various QoS requirements, many researchers have proposed different trajectory designs that incorporate RL. The main reason for using RL is to obtain an optimal solution for the aforementioned challenges. The applications of RL in flight trajectory selection are summarized below and a comparative analysis is presented in Table 3.
# Image-Based Malware Detection Using \(\alpha\)-Cuts and Binary Visualisation
## 3 Materials and Methods
### Performance Evaluation Metrics  
To evaluate the performance of our proposed methodology, we employ the Accuracy, Precision, Recall, and F-score metrics, all of which are based on the following measures: True Positive (\(TP\)), True Negative (\(TN\)), False Positive (\(FP\)), and False Negative (\(FN\)).  
\(TP\) is the number of all elements of a class that were correctly predicted to belong in that class or the number of malicious images that were correctly identified as malicious. \(TN\) is the number of all elements outside of a class that were correctly predicted to belong in a different class or the number of benign images that were correctly identified as benign. \(FP\) is the number of all elements outside of a class that were incorrectly predicted to belong in that class or the number of benign images that were incorrectly identified as malicious. \(FN\) is the number of all elements of a class that were incorrectly predicted to belong in a different class, or it is the number of malicious images that were incorrectly identified as benign. According to these definitions, we describe the following metrics:  
* Accuracy is the percentage of images (malicious and benign) that were correctly predicted: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (1)
* Precision is the percentage of correctly predicted malicious images over the total amount of predicted malicious images: \[Precision=\frac{TP}{TP+FP}\] (2)Recall is the percentage of correctly predicted malicious images over the total amount of malicious images:  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
F-score is the harmonic mean between Precision and Recall, and demonstrates the robustness of a model:  
\[F\text{-}score=\frac{2 TP}{2 TP+FP+FN} \tag{4}\]  
To assess model performance, we also plotted the learning curves of the training and validation datasets. Learning curves are a commonly used visual diagnostic tool in DL that indicate the performance of the model over time. More specifically, they provide insight into the adequacy of the datasets and the model's ability to generalise: after enough experience, two pairs of curves, the training and validation accuracies, and the training and validation losses should follow an increasing and a decreasing pattern, respectively, in a stable manner. The model is shown to be a good fit when the training and validation loss curves lower until they reach a stable value, with the latter being slightly higher than the former, or when the training and validation accuracy curves increase until they reach a stable value, with the latter being slightly lower than the former.
# Scaled Modeling and Measurement for Studying Radio Wave Propagation in Tunnels
## 6 Conclusions  
Evaluation of the radio wave propagation in an underground tunnel environment is important for the appropriate design of wireless links and networks. We demonstrated that link budget estimation for UHF and SHF links can be performed using multi-ray simulations and verified this experimentally by using down-scaled structures.  
In this paper, we have reported a series of experiments for measuring wave propagation in underground pedestrian tunnels as well as in a subscale laboratory model. We demonstrated that our simulations based on a modified ray tracing method were suitable for predicting the wave propagation in a tunnel for different frequencies and showed that a scale model in a laboratory environment can be a suitable replacement for field experiments.  
Conceptualization, Y.P., G.A.P., N.B., and J.G.; methodology, Y.P., G.A.P., N.B., and J.G.; software, G.A.P., J.G., and E.B.; validation, Y.P., G.A.P., N.B., and E.L.; formal analysis, Y.P., G.A.P., J.G., Y.R., and M.G.; investigation, J.G. and Y.P.; resources, J.G. and Y.P.; data curation, J.G., Y.R., and N.B.; writing, original draft preparation, J.G.; writing, review, and editing, J.G., E.L., Y.P., and G.A.P.; visualization, J.G., M.G., and Y.P.; supervision, G.A.P. and Y.P.; project administration, Y.P.; funding acquisition, Y.P. and G.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Proposed Methodology
### Data Preprocessing  
The first stage, text cleaning, handles data standardization by removing duplicates, user mentions (username), URLs, and special characters that might introduce noise into the classification process. This step ensures consistency in the input data format [54]. The second stage implements text normalization techniques. We convert all text to lowercase to maintain uniformity and apply tokenization to break the text into meaningful units. Also, stop words are removed to focus on content-bearing terms. This normalized representation enhances the quality of subsequent feature extraction. To visualize the most frequent terms in our dataset, we generated a word cloud of offensive class tweets shown in Figure 4, where larger term sizes indicate a higher frequency of occurrence.  
In the final stage, we extract multiple types of features. TF-IDF vectors capture term importance, Word2 Vec embeddings represent semantic relationships, VADER provides sentiment context, and readability metrics (FKRA/FRE) quantify text complexity. These diverse features enable our models to capture different aspects of offensive language.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
###### Abstract  
In the field of structural health monitoring (SHM), with the mature development of artificial intelligence, deep learning-based structural damage identification techniques have attracted wide attention. In this paper, the convolutional neural network (CNN) is used to extract the damage feature of simple supported steel beams. Firstly, the transient dynamic analysis of the steel beam is carried out by finite element software, and the acceleration response signals under different damage scenarios are obtained. Then, the acceleration response signal is decomposed by wavelet packet decomposition (WPD) to extract the wavelet packet band energy ratio variation (ERV) index as the training sample of CNN. Subsequently, the vibration experiment of a simple supported steel beam was carried out, and the results were compared with the numerical simulation results. The characteristic indexes were obtained by making corresponding changes to the vibration signal, and then, the experimental data were input into the CNN to predict the effect of damage detection. The results show that the method can successfully detect the intact structure, single damage, and multiple damages with an accuracy of 95.14% under impact load, and the performance is better than that of support vector machine (SVM), with good robustness.  
simple supported steel beams; structural damage detection; convolutional neural network; wavelet packet decomposition; energy ratio variation +
Footnote †: journal: Applied sciences
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 6 Discussion
### Popular Algorithms  
There are many options available for RL algorithms, as shown in Figure 4. There is some overlap between policy gradient and actor-critic methods. For clarity, priority is given to algorithm naming conventions. It is common for papers to simply refer to their approach as DRL. These cases are categorized in the \"undisclosed\" label of the overall figure.  
Q-learning is a common choice, allowing the agent to estimate Q-values for given state-action pairs [73; 165; 175; 207; 296]. Other algorithms used in the same context include REINFORCE [178; 216; 288], value iteration [92], TD-learning [166] and Bayesian RL [110].  
Many approaches use deep Q-learning (DQN) [38; 52; 54; 121; 160]. Some common variations include double Q-learning [70; 80; 91; 149; 301] and dueling double DQN [168].  
Actor-critic methods [63; 105] are also used for option selection. Many variations are used, including advantage actor-critic (A2C) [20; 79; 122], soft actor-critic (SAC) [100; 103] and asynchronous advantage actor-critic (A3C) [173]. Proximal policy optimization algorithms (PPO) [104; 132; 197; 277; 285] and deep deterministic policy gradient (DDPG) [68; 227; 316; 317] are also very common.
## 3 Results and Discussion
### Recovery of Tomato Seed Oil
#### 3.2.3 Fatty Acid Content of Tomato Seed Oil
Similarly, conventional stirring, while simple and widely used, is limited by long processing times and inefficient mass transfer, making it less efficient for maximizing compound recovery compared to advanced extraction techniques like UAE-MAE and PLE. UAE-MAE, in particular, provides significant reductions in extraction time and energy consumption while maintaining high oil and lycopene yields, suggesting its suitability for large-scale applications. Moreover, PLE exhibits superior lycopene and fatty acid recoveries due to its pressurized environment. However, the high initial investment and operational costs associated with the moderate and high pressures utilized in PLE systems may pose challenges for its widespread adoption at an industrial scale. These advantages position UAE-MAE and PLE as sustainable extraction approaches, aligning with modern demands for environmentally friendly and efficient processing methods. Future research should focus on optimizing process parameters for large-scale industrial application and evaluating the economic feasibility of integrating these techniques into existing production lines.
###### Abstract  
Measurement-device-independent quantum key distribution (MDI-QKD) protocol has high practical value. Satellite-based links are useful to build long-distance quantum communication network. The model of satellite-based links for MDI-QKD was proposed but it lacks practicality. This work further analyzes the performance of it. First, MDI-QKD and satellite-based links model are introduced. Then considering the operation of the satellite the performance of their combination is studied under different weather conditions. The results may provide important references for combination of optical-fiber-based links on the ground and satellite-based links in space, which is helpful for large-scale quantum communication network.  
measurement-device-independent quantum key distribution; satellite-based links; quantum communication network +
Footnote †: journal:  
0021