esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 4 Simulation Analysis
### Combined Cycle Power-Plant Power-Output Forecast  
The above two tests are tested based on generic benchmark experiments, and in this experiment, we use data from a combined-cycle power plant [40] for testing. The data were obtained from the UCI machine learning dataset with temperature (T), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (EV) as the input variables and electrical energy output (EEO) as the output variable. After removing the missing values, 850 samples are selected from the dataset, of which the first 800 samples are used for training, and the remaining 50 are used for testing. Since the input and output data have a wide range of values, they need to be normalized. The parameters of SOFNN-CA in this example are empirically set as follows: md-addth is 0.2, e-min is 0.2, den-th is 10, and S-th is 8. The training results are shown in Figure 10.  
As shown in Figure 10a, the number of neurons is six in the initial condition, and SOFNN-CA increases and merges neurons under the action of a self-organization mech  
Figure 10: (**a**) Neuron change plot; (**b**) k-means clustering plot; (**c**) training RMSE plot; (**d**) training result plot; (**e**) test result plot; (**f**) test error plot.  
anism, to finally obtain a compact network structure of three neurons. The k-means initialization algorithm, as shown in Figure 10b, generated six clusters with different colors. The final test results are shown in Figure 10e, with errors in the [\(-\)10 10] interval.  
The performance comparison between SOFNN-CA and the other algorithms is shown in Table 3. The proposed SOFNN-CA implements the simplest network structure (3 fuzzy rules) and the best test RMSE (3.6034). Among all the competitors, SOFNN-HPS adopts a hierarchical pruning self-organization method and has a good test RMSE (3.7469), but the number of neurons is twice that of SOFNN-CA due to the low efficiency of neuronal merging. DG-FNN and D-FNN have more fuzzy rules and a higher training accuracy, but the test accuracy becomes low, and the over-fitting phenomenon exists. Due to more parameters needing to be set, the test RMSE of GD-FNN is higher than that of D-FNN.  
Compared with GEBF-OSFNN, the proposed SOFNN-CA can obtain better performance regarding the number of rules and test accuracy. This indicates that the proposed self-organization scheme can effectively merge similar fuzzy rules and achieve high generalization of neural networks.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 2 Background
### Phishing Techniques and Attack Vectors  
#### 2.2.1.  
Spear phishing is a targeted form of phishing targeted at specific individuals or groups. It often includes malicious attachments or links to compromised websites within the email content [4]. Although spear phishing is more resource intensive than mass phishing, its effectiveness lies in the use of personalized information to craft highly tailored and convincing messages [5].  
Human factors play a significant role in the success of these attacks. Research has shown that individual behaviors--such as impulsivity, risk-taking, sociability, compliance, and anxiety--can be exploited by attackers to increase susceptibility [6].  
Due to their personalized and persuasive nature, spear phishing attacks are notoriously difficult to detect [7]. Addressing them requires a combination of technical defenses and human-centered strategies. Cybersecurity training and awareness programs are crucial to minimizing user error, which is responsible for approximately 60% of data breaches. In 2019 alone, phishing-related incidents caused an estimated SAR 3.5 millionin financial losses [8].
## 3 Comparison of Fetal Heart Rate Monitoring Techniques
### Doppler Sound  
The Doppler principle is used in fetal heart rate monitoring by analyzing the mechanical activity of the heart, such as movement of valves of the heart of the fetus in the cardiac cycle [63; 64]. The use of Doppler ultrasound to monitor the FHR, as shown in Figure 6[64], was proposed in the 1960s, but it is not suitable for continuous monitoring due to discomfort and irritation caused to the patients due to the gel and conduction paste used during the procedure.  
The procedure is non-invasive and deploys the ultrasound transducer placed on the maternal abdomen. The ultrasound with the frequency range of 1-2.3 MHz penetrates the tissue and the reflected sound wave generated by the internal structure of the body or fetus is received by the ultrasound transducer, which analyzes the fetal heart rate [65; 66].  
Figure 5: (**a**) Non-invasive FECG measurement. (**b**) Invasive FECG measurement [45] (Reprinted with permission from [PERSON] (2012)).
## 7 Simulation Results Discussion: Analysis and Evaluation
### Results Summary: Pre-Signature Scheme in Rural Vehicular Communication Areas  
The study on vehicular communication systems in rural settings places significant emphasis on the effectiveness of the Pre-Signature scheme, particularly in enhancing reputable communications in environments with sparse or non-existent RSU support. This scheme emerges as a pivotal solution for maintaining reliable and secure vehicular communication channels, especially in offline scenarios prevalent in rural areas.  
The experiments show the effectiveness of our scheme with different parameters and in different ways. In particular, we showed to what extent reputation disseminates over time in a 24 h period and how this affects the number and proportion of reputable  
Figure 19: Analysis of Communication Types over Overnight Percentage in High RSU Range = 900 m for 1 RSU (**Left**) and 3 RSUs (**Right**).  
Figure 20: Analysis of Communication Types over Overnight Percentage in Low RSU Range = 300 m for 5 RSUs (**Left**) and 10 RSUs (**Right**).  
communications. We then investigated how adoption of RSU units affects how useful our approach is, which showed that, in rural environments, increasing RSUs typically has a positive effect. We finally quantified the impact of drivers obtaining a Pre-Signature before entering the road, showing that this is an extremely powerful way to boost the effectiveness of our approach.  
A noteworthy aspect of the Pre-Signature scheme is its ability to uphold the integrity and trustworthiness of communications, regardless of RSU density. It ensures a consistent level of reputable communications, both online and offline. This is particularly vital in situations where vehicles operate outside the RSU range or in locations completely devoid of RSU presence. The study's findings highlight the Pre-Signature scheme as a key enabler for robust and dependable communication in rural vehicular networks, successfully addressing the challenges posed by limited infrastructure.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 4 Parameters Selection and Empirical Design
### Setting of the Initial Parameters of the BA-SVR Model  
According to the implementation steps of the BA-SVR algorithm, the model parameters need to be set before the data iteration of the training set. Considering the total sample data dimension and data volume of the model in this paper, the initial parameters are set in this paper, including the maximum number of iterations \(maxgen=30\), search dimensions (that is, the number of parameters that need to be optimized) \(\dim=3\), population size \(n=30\), loudness attenuation coefficient \(\alpha=0.98\), rate enhancement coefficient \(\gamma=0.98\), and minimum loudness \(A_{min}=0\), the minimum and maximum sound wave frequency \(f_{min}=0\), \(f_{max}=2\), the minimum and maximum bat flight speed \(v_{min}=-1\), \(v_{max}=1\). In addition, in order to ensure the stability of the data, before the algorithm starts to run, both the training set and the test set data are normalized before the algorithm starts. After the initialization settings in this section, the following describes the specific process of the empirical research in detail.
# Reverse Coarsening and the Control of Particle Size Distribution through Surfactant
## 2 Results and Discussion  
Thermodynamic Theory of Reverse Coarsening. Following the classical approach [13; 14; 15], we used a mean-field approximation in our analysis. This approximation assumed that the particles are sufficiently far from each other so that they only interact through the averaged characteristics of the solution, and that their occupied volume is negligible compared to the entire volume of the system. First, we analyzed the thermodynamic stability of a single particle in the solution with a given concentration of identical molecules (_A-molecules_) in the presence of surfactant molecules (_B-molecules_). Then, by writing the molecular chemical potential as a function of particle size, we obtained the conditions for RC by considering the mass exchange between particles in an ensemble. Second, we confirmed our prediction of RC by direct numerical simulations of the particle ensemble evolution using the Kinetic Monte Carlo technique.
## 2 Materials and Methods
### Ground Tests  
In the ground tests, the STIL system emitted laser pulses horizontally from a window at an elevation of approximately 40 m within the building, aiming at the surface of the target building. The locations of the STIL system and the target building, which is 6 km away, are shown in Figure 3a. Figure 3b illustrates that the STIL system's laser pulse formsan elliptical Gaussian spot on a building target 1 km away, with the large-angle detection laser reflecting off the unobstructed target to generate a fully reflected echo spot.  
To investigate the far-field characteristics of the detection laser and verify the system's long-range ranging accuracy, the target was set as a building located 6 km away. Due to obstructions and spatial constraints at this distance, the detection beam could only reflect off a portion of the target, specifically the upper section of the building. Figure 3c shows the incomplete reflected laser spot on the surface of the target building.
## 4 Results
### The 2014-2015 Flood Assessment  
The final aspect of the GPM IMERG products hydro-meteorological assessment was to evaluate their reliability during the 2014-2015 flood event from 14 December 2014 to 3 January 2015. Table 1 illustrates that the overall statistical metric values of the daily precipitation measurement during the 2014-2015 flood period are better than the entire period, except for the RMSE and MAD values. For example, the CC and POD values of the IMERG_E for the flood period improved 0.17 and 0.14, respectively, compared to the entire period. As shown in the box plots of Figure 8, the statistical metric values distribution of all three GPM IMERG products are similar, showing that the IMERG_F displays no advantage in daily precipitation measurement during the 2014-2015 flood events. The precipitation detection ability of the IMERG products increased dramatically during the flood period because better score values were found in most of the categorical statistical metrics. For instance, FAR values of the IMERG products decreased from 0.39-0.41 to 0.05-0.06, showing only about 5% of the SPPs detected precipitation events were not observed in the precipitation gauges. CSI and ACC values indicated that about 90% of the precipitation and non-precipitation days were successfully detected by the IMERG products during the 2014-2015 flood period.  
The statistical metrics and temporal variability of the SWAT daily streamflow executed by the three IMERG products are summarized in Figure 9. Interestingly, hydrological assessment showed a contradictory result compared to meteorology assessment, where the performance of all three GPM IMERG products performed comparatively poorer in the 2014-2015 flood period compared to the entire period. A possible explanation for these results may be the NSE and R\({}^{2}\) statistical metrics are oversensitive to high extreme values [34; 38]. However, the SWAT performance of the IMERG_E and IMERG_F was still regarded as a satisfactory performance based on the NSE and R\({}^{2}\) values. In addition, Figure 9 shows that IMERG_E and IMERG_F accurately simulated the peak flow day on 24 December 2014. Meanwhile the peak flow simulated by the IMERG_L was found a day earlier. These findings indicate that the IMERG_E and IMERG_F can be alternative precipitation sources to monitor the flood event of the basin. One thing that needs to be considered is the peak flow rate, as all the IMERG products as well as the precipitation gauges underestimated the peak flow from 23 to 28 December 2014.  
Figure 7: Comparison of cumulative streamflow simulated by the IMERG products and gauge at the Jambatan Guillermard from 12 March 2014 to 31 December 2016.
## 4 Conclusions  
In summary, the proposed virtual prediction model utilizes the ANN methodology to predict the Ms temperature for medium-carbon steels. The effect of ten potential alloying elements on the Ms temperature is also predicted. The ANN model predictions are in close agreement with experimental Ms temperature values, surpassing previous empirical formulae in accuracy and convenience. For the total 313 samples, the model exhibits an AAE of \(-0.93\) K and an MPE of \(-0.097\%\). Notably, the model reveals the quantitative influence of alloying elements on the Ms temperature, marking an unprecedented finding. As previous empirical formulae require complex calculations and are not universally applicable to all alloying additions, the developed ANN model serves as an ideal replacement for these formulae. This model can aid in material design, and its predictive capabilities could be extended to a wide range of material properties in the future.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/a18020116/s1](https://www.mdpi.com/article/10.3390/a18020116/s1)]([https://www.mdpi.com/article/10.3390/a18020116/s1](https://www.mdpi.com/article/10.3390/a18020116/s1)): Table S1: Calculation formulae of Ms temperature for medium-carbon steel; Table S2: Medium-carbon steel set database. References [38; 39; 40; 41] are cited in the Supplementary Materials.  
Conceptualization, N.G.S.R.; methodology, N.G.S.R. and A.K.M.; software, N.G.S.R.; validation, S.-G.K.; formal analysis, X.-S.W. and M.I.; investigation, X.-S.W. and M.I.; resources, N.G.S.R. and A.K.M.; data curation, X.-S.W. and M.I.; writing--original draft preparation, X.-S.W.; writing--review and editing, S.-G.K. and N.G.S.R.; visualization, N.G.S.R.; supervision, N.G.S.R.; project administration, N.G.S.R.; funding acquisition, S.-G.K. and N.G.S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, Further inquiries can be directed to the corresponding author.  
[PERSON] acknowledges YSJ-TV for the inspiration and Y.K.K. for helping with the model GUI development. The authors would like to acknowledge the support from the Engineering Research Institute, Gyeongsang National University.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
Ms Martensite start ANN Artificial neural network MAE Mean absolute error AAE Average absolute error MSE Mean squared error MPE Mean percentage error GUI Graphical user interface KDD Knowledge discovery in databases
## 4 Results and Discussion
### Composition of the Pond Water
[45] showed that existence of such saturated sections within waste rock pile as permanent features provides sulfide minerals oxidation during the entire hydrological cycle. As a result, dissolved forms of many elements accumulate in pore solutions, in particular arsenic--up to 29 g/L, antimony--up to 10 g/L [46]. The main mechanism for the metal release from solid tailings into solutions is the oxidative dissolution of sulfides and oxides, such as following. (1) Oxidation of arsenopyrite FeAsS with dissolved oxygen [47; 48]:  
\[4\text{FeAsS}+11\text{O}_{2}+6\text{H}_{2}\text{O}\text{$\rightarrow$}4\text{ Fe}^{2+}+4\text{H}_{3}\text{AsO}_{3}+4\text{SO}_{4}{}^{2-}\] \[4\text{Fe}^{2+}+\text{O}_{2}+10\text{H}_{2}\text{O}\text{$ \rightarrow$}4\text{Fe}(\text{OH})_{3}+8\text{H}^{+}\] \[2\text{H}_{3}\text{AsO}_{3}+\text{O}_{2}\text{$\rightarrow$}2\text{ H}_{3}\text{AsO}_{4}{}^{2-}+4\text{H}^{+}\] \[2\text{H}_{3}\text{AsO}_{3}+\text{O}_{2}\text{$\rightarrow$}2\text{ H}_{2}\text{AsO}_{4}{}^{2-}+1\text{H}^{+}\]  
(2) Bacterial oxidation of pyrite and arsenopyrite [49]:  
\[2\text{FeS}_{2}+7\text{O}_{2}+2\text{H}_{2}\text{O}\text{$ \rightarrow$}2\text{Fe}^{2+}+4\text{SO}_{4}{}^{2-}+4\text{H}^{+}\] \[4\text{Fe}^{2+}+\text{O}_{2}+\text{H}^{+}\rightarrow4\text{Fe}^ {3+}+2\text{H}_{2}\text{O}\] \[4\text{FeAsS}+13\text{O}_{2}+6\text{H}_{2}\text{O}\text{$ \rightarrow$}4\text{Fe}^{2+}+4\text{SO}_{4}{}^{2-}+4\text{H}_{3}\text{AsO}_{4}\]  
(3) Bacterial oxidation of antimony oxide or in the presence of the Fe\({}^{2+}\)/Fe\({}^{3+}\) (Mn\({}^{2+}\)/Mn\({}^{3+}\)) system with the transition to the mobile Sb(VI) form [50; 51]:  
\[\text{Sb}_{2}\text{O}_{3}+3\text{H}_{2}\text{O}\text{$\rightarrow$}2\text{Sb }(\text{OH})_{3}\] \[\text{Sb}(\text{OH})_{3}+2\text{H}_{2}\text{O}+0.5\text{O}_{2}\text{$ \rightarrow$}\text{Sb}(\text{OH})_{6}{}^{-}+\text{H}^{+}\]  
\[4\text{FeAsS}+11\text{O}_{2}+6\text{H}_{2}\text{O}\text{$\rightarrow$}4\text{ Fe}^{2+}+4\text{H}_{3}\text{AsO}_{3}+4\text{SO}_{4}{}^{2-}\]0 The geoelectric section through Komsomsomsk tailings pond along line A–B (Figure 1). The exchange of the pond water and the pore solutions from underlying horizons supports stable concentrations of elements in the pond.
# Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
## 4 Experiments
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Trigger Detection (\%)**} & \multicolumn{2}{c}{**Argument Recognition (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **F1\_Avg** \\ **(\%)** \\ \end{tabular} } \\ \cline{2-2} \cline{5-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline CNN-rand & 55.16 & 52.64 & 53.87 & 47.86 & 45.34 & 46.57 & 50.22 \\ CNN-pre-train & 61.86 & 63.28 & 62.56 & 54.24 & 58.61 & 56.34 & 59.45 \\ LSTM-rand & 58.69 & 50.67 & 54.39 & 45.16 & 54.64 & 49.45 & 51.92 \\ LSTM-pre-train & 60.85 & 59.24 & 60.03 & 55.40 & 59.63 & 57.44 & 58.74 \\ Bi-LSTM & 67.49 & 65.89 & 66.68 & 56.14 & 60.37 & 58.18 & 62.43 \\ CINO & 59.37 & 63.52 & 61.37 & 53.75 & 58.15 & 55.87 & 58.62 \\ TJEE & 70.57 & 70.36 & 70.47 & 61.67 & 64.38 & 62.99 & 66.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Baselines** & **Description** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{CNN-rand} & Convolutional neural network. The convolution kernel size is 3, the number of convolution kernels is 16, and the Tibetan syllable vector is randomly initialized. \\ \cline{3-3}  & & & Convolutional neural network; parameter settings are the same as CNN-rand. The difference is that the Tibetan syllable vector is pre-trained. \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{LSTM-rand} & Long short-term memory network. The number of hidden layers is 2, the hidden state dimension is 128, and the Tibetan syllable vector is randomly initialized. \\ \cline{3-3}  & & & Long short-term memory network, parameter settings are the same as LSTM-rand. The difference is that the Tibetan syllable vector is pre-trained. \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{LSTM-pre-train} & Long short-term memory network, parameter settings are the same as LSTM-rand. The difference is that the Tibetan syllable vector is pre-trained. \\ \cline{3-3}  & & & Bidirectional long short-term memory network. Other parameter settings are the same as LSTM-pre-train. \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Bi-LSTM} & Sequence labeling model, Tibetan syllable vectors are pre-trained. \\ \cline{3-3}  & & & pre-trained. \\ \hline \end{tabular}
\end{table} 
Table 5: Configuration of the baseline model. ing the local features of judicial entities, thus obtaining the global semantic features of the text. Compared with single pre-trained models or neural networks, the event extraction performance is significantly improved.
## 3 EPU Parameters Identification Procedure
### Identification Procedure  
The generated.csv files were loaded, processed, and graphically presented using the MATLAB software package. Regarding a relatively large number of measurements, this process was automated with a customized MATLAB script. The flowchart of the MATLAB script is shown in Figure 6. In the first step, the script finds all.csv files in the defined measurement root folder and in its subfolders.  
Figure 5: Setup script flowchart.  
All data from each.csv file is then read and appended into a common data array. Once the array is filled with data from all measurements, raw measurement data are plotted. By averaging measurement data obtained at the same PWM values (and with further data manipulation), output vectors required for EPU static maps were generated and plotted.  
Figures 7-9 show the plot of raw data measurement i.e., revolutions per minute (RPM) in relation to measurement time, and a plot of the static maps for the rotor speed measurement in relation to the input PWM signal. The results of other measurements are shown in the next subsection. Three series of measurements were conducted for selected EPU components (as shown in Table 1). The first series represents EPU setup with 1806 motors of different motor constants \(K_{v}\), the second series represents EPU setup with MN2214 motor and propellers of different geometric characteristics, while the third series represents EPU setup with MN4014 motor and 15\" to 17\" diameter propellers in combination with 4S and 6S LiPo batteries. It can be seen from the figures that lower speeds are expected for motors with lower \(K_{v}\), and that diverse ESC may differ in the control signal range.  
Figure 6: MATLAB script flowchart.  
Figure 7: Rotor RPM for setup with 1806 series of BLDC motor: (**a**) raw measurement; (**b**) RPM static maps.