esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Detection and Response via Optimized Alerts: Usability Study
## 4 Discussion  
### Discussion of the Results  
This study sought to determine if there is a difference between the adoption of VAO and TAO generated via data science/machine learning methods as predicted by the TAM. The related hypothesis tested for significant differences in the level of acceptance of alert outputs between those preferring VAO in all scenarios and those preferring TAO in some or all scenarios, as predicted by the TAM. The null hypothesis was rejected. A non-parametric test, the Mann-Whitney test, indicated a significant difference in the level of acceptance of output between those preferring visual alerts in all scenarios, and other preferences (\(U=863.5\), \(p=0.023\)). This result was repeated in the between-subjects element of a mixed ANOVA, \(F\) (1, 79) = 4.111, \(p=0.046\), np2 = 0.049. The within-subjects element of the mixed ANOVA, relating to different responses to each scenario, was also statistically significant, \(F\) (1.455, 114.915) = 5.634, \(p=0.010\), np2 = 0.067. These results indicate a statistically significant difference in perception that favors VAO.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### The Weighting Mechanism of the Graph, Calculations of the Weights of Nodes and Edges
#### 4.1.1 The Energy Demand Calculation of the Node  
To evaluate the energy demand of the node, the following conditions are taken into account: the orientation of the drone (see: yaw/pitch/roll--Equations of motion (6), (7)) [21]; altitude changing (see: rise/fall--Equations of motion (5)); air resistance and wind directions (see: \(E_{move}\)--calculation, Equation (11)). Basically, in \(E_{motors}\) Equation (10), all these calculations can be found. Consequently, the final energy demand of graph-node (\(i\)) can be calculated as follows:  
\[E_{(i)}=E_{motors}+f(yaw,\,pitch,\,roll)+f(rise,\,fall,\,hover) \tag{13}\]  
**Description of n(1)**: This is the node where the drone starts from, accelerates to the desired velocity, reaches the desired altitude (rise), and changes its orientation by an angle of \(\alpha(1)\) (rotate around \(x\)-axis). After this, the drone will cover the distance \(l(1)\) in the given orientation at the given altitude. The tailwind is considered in \(E_{motors}\) equation.  
**Description of n(2)**: In this node, the drone maintains its altitude, it only has to change its orientation around \(z\)-axis by an angle of \(\alpha(2)\). In \(E_{motors}\) equation, a crosswind has to be considered. After this, the drone will cover the distance \(l(2)\).  
**Description of n(3)**: In this node, the drone has to change its altitude (drop to the given altitude), change the orientation by the angle \(\alpha(3)\) around the \(z\)-axis, and the headwind (contra wind) is considered in \(E_{motors}\) equation.
## 3 Results and Discussion
### Effects of Gasification Temperature  
The gasification temperature \(T\) is a very important operating parameter for the coal/MSW gasification, which has a significant influence on the composition of syngas product and system performance [44]. Therefore, to investigate the effects of gasification temperature, some simulations were carried out from range of 750 to 1100 \({}^{\circ}\)C at a certainly operating condition of \(R_{\rm{O}}\) = 0.2 and \(R_{\rm{c}}\) = 0.5. The raw syngas composition and system performance for the individual BC, MSW, and blend of \(R_{\rm{M}}\) = 0.6 are displayed in Figure 3. Based on Figure 3a, the mole fraction of CO gradually increased, whereas that of CO\({}_{2}\) and H\({}_{2}\) all decreased with increasing gasification temperature. It may because the Boudouard reaction (R4) and steam reforming reaction (R5) are enhanced which are benefited from high temperature. In addition, the syngas composition of CO, H\({}_{2}\), and CO\({}_{2}\) tended to be in a stable state at gasification temperature higher than 900 \({}^{\circ}\)C. This stability indicates that all reactions are close to the equilibrium at a certain amounts of gasifying agent (O\({}_{2}\) and CO\({}_{2}\)) when the gasification temperature is \(\sim\)900 \({}^{\circ}\)C [45]. This can be further confirmed by the variety of the CGE and OEE, which also perform the increasing trends and reach the plateau at 900 \({}^{\circ}\)C.  
However, the effects of gasification temperature for individual MSW showed a great difference compared with that of BC from the Figure 3b. Although the change trends of all syngas composition mole fraction were similar to the increasing of the gasification temperature, the amount of H\({}_{2}\) component was the largest for the MSW gasification and the change in amount of H\({}_{2}\) was also larger compared with that of BC gasification, approximately 6%. This is mainly because the MSW consists of more moisture component, and the H\({}_{2}\)O released by the MSW drying process can act as an extra gasifying agent to improve the H\({}_{2}\) generation reaction. Moreover, the OEE of the MSW gasification system was between 13.8% and 14.6%, as the gasification temperature increase was much lower than that of BC. This might be because the carbon content and heating value of MSW is lower than that of BC, resulting in lower syngas (CO+H\({}_{2}\)) production. In addition, for the co-gasification process of \(R_{\rm{M}}\) = 0.6 blending ratio, it can been seen that the syngas composition, CGE, and OEE of this system is between that of BC  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Composition** & **Present Work** & **[PERSON] [43]** & **[PERSON] [18]** & **Error1** & **Error2** \\ \hline T = 800 \({}^{\circ}\)C & & & & & \\ H\({}_{2}\) & 30.21\% & 30.70\% & 30.45\% & \(-\)0.49\% & \(-\)0.24\% \\ CO & 61.01\% & 60.00\% & 63.20\% & 1.01\% & \(-\)2.19\% \\ CO\({}_{2}\) & 8.12\% & 9.80\% &  
and MSW, which indicates that the addition of BC is beneficial to improve gasification performance of MSW.
## 2 Model Construction and Algorithm Enhancement
### Validation and Evaluation
#### 2.3.3 Random Forest Model  
The Random Forest [28] is an ensemble learning method based on multiple decision trees, commonly used for classification and regression. Unlike logistic regression, it does not assume a linear relationship between variables. Instead, it builds multiple trees using Bootstrap sampling, and each tree selects a random subset of features at each split to reduce feature dominance. The final prediction is made by aggregating the outputs of all trees--majority voting for classification and averaging for regression.  
The classification formula is as follows:  
\[\hat{y}=argmax_{k}\sum_{i=1}^{T}I(f_{i}(x)=k) \tag{3}\]  
where \(\hat{y}\) is the predicted class label, T is the number of trees, \(f_{i}(x)\) is the output of the \(i^{th}\) tree, and I is the indicator function.
## 3 Materials and Methods  
### Data Collection  
The VOC concentrations data series were collected during three experimental campaigns from 25 September to 24 October 2023, 20 November-19 December 2023, and 18 January-17 February 2024, corresponding to the revision periods of some tanks in zones 1-3 from Figure 1. The measurements were done at a high of 1.4 m from the ground every 36 h. The first measurement was done on the first day at 7 a.m., the second one on the second day at 7 p.m., and so on, due to the restrictions imposed by the maintenance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & **Access Road** & **No. of Sampling Points** & **No. of Tanks Under Repair** & **Tank Capacity (m\({}^{3}\))** & **Transported Product** \\ \hline
1 & R20 & 15 & 1 & 5000 (\(\times\)4) & gasoline/rafined product \\
2 & R4 & 14 & 1 & 10,000 (\(\times\)4) and 3500 (\(\times\)2) & gasoline \\
3 & R9 & 12 & 2 & 2000 (\(\times\)2) and 1000 (\(\times\)2) & toluene and gasoline \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the experimental area.  
Figure 1: Sampling area. The dots represents the sampling points in Zones 1, 2, and 3.  
schedule. We worked with the average series from the three campaigns to ensure the data series' reliability. Therefore, there are 20 values for each measurement point in each zone. Thus, the datasets comprise 15 series \(\times\)\(20=300\) values in Zone 1, 14 series \(\times\)\(20=280\) values in Zone 2, and 3 by 13 series \(\times\)\(20=260\) values in Zone.  
In monitoring VOC emissions from the tank park designated for storing highly volatile petroleum products, both FID (Flame Ionization Detectors) and PID (Photoionization Detectors) analyzers were used as follows:  
Portable Device TVA 1000B, manufactured by Thermo Scientific (Waltham, MA, USA), contains a dual detection system (FID and PID). The technical specifications of the measurement equipment are measurement range (0.5-2000 ppm isobutylene (PID)/0.5-5000 ppm methane (FID)); response time (3.5 s); accuracy 2.5 ppm (FID and PID); linearity 0.5-500 ppm isobutylene (PID)/0.5-10,000 ppm methane (FID); repeatability (\(\pm\)1% (PID); \(\pm\)2% (FID)); flow rate 1000 mL/min under environmental conditions; operating time--8 h; standard calibration--FID (methane)/PID (isobutylene).  
Portable Device Drager Multi-PID 2, manufacturer Ion-Science (Fowlmere, UK), with the following technical specifications: minimum resolution (1 ppb); range of 0-20,000 ppm; response time \(<\)2 s; standard calibration 100 ppm with customizable calibration capability using isobutylene; flow rate--220 mL/min; PID lamp of 10.6 eV (standard) and two other lamps of 11.7 eV available.  
At the beginning of each working day, the FID is calibrated with methane, and the PID is calibrated with isobutylene to ensure the manufacturer's specified accuracy for the TVA 1000B portable device.
## 4 Methodology
### Multi-Collinearity Test  
Before implementing the MaxEnt method, a multi-collinearity test was performed to determine the collinearity rate among the selected predisposing factors. Consequently, the highly correlated factors were removed to avoid bias in the susceptibility model [50]. The multi-collinearity can be defined as the linear correlation between two or more variables in a dataset [51]. In order to test the multi-collinearity between the 12 predisposing factors chosen for gully erosion susceptibility analysis, the variance inflation factor (VIF) and tolerance (TOL) index were calculated [52]. In general, values of VIF > 10 and TOL < 0.1 indicate collinearity problems between the variables [53]. In this study, multi-collinearity analysis was made by means of the IBM SPSS software, version 29.
## 4 Results of Stability Calculations
It can, therefore, be concluded that cohesion plays an important role in the stability calculations (Figure 11). Slight differences between the values of the factor of safety of each material within calculations using the same cohesion value may result from small differences in the angle of internal friction and bulk density. Figure 10: Changes in the factor of safety with decreasing inclination at a given load on the embankment. Numerous publications (among others: [22; 51; 52; 53; 54; 55]) indicate that the problem of slope stability in industrial waste landfills is an important engineering issue. The results of tests on shear strength parameters of materials stored there are often carried out either at low values of their moisture content or at optimum moisture content and indicate that these materials usually have high values of shear strength parameters, although the height of the slopes or their too-steep inclination are the reason why they are destroyed (a landslide occurs). These failure causes are an indication that detailed studies of geotechnical properties, reduction of the amount of wastes deposited on landfills, or changes in the slope inclinations should be done. Another important element of waste management is using mixtures with other wastes or mineral soils. As the presented research indicate and, among others, research by [PERSON] et al. [57], the addition of fly ash increases the shear strength of natural soils. They stated that the factor of safety of a 37\({}^{\circ}\)  
Figure 11: Changes in the factor of safety with increasing cohesion at different loads on the embankment. slope, obtained from calculations in the FLAC/Slope program, increased with the increase in the fly ash addition [57], indicating that the optimal addition of fly ash was 30% for a slope up to 12 m high. A particular problem when using waste materials is the difficulty in interpreting their shear strength related to the occurrence of additional friction on contact between coarser grains manifested as the occurrence of so-called apparent cohesion [4; 19], which is accepted or omitted in further analyses. The interpretation of shear strength is especially important at low normal stresses and, depending on the assumed shear criterion, can lead to significant discrepancies [58; 59]. This is important because in case of stability calculations, cohesion plays an important role (Figure 11). It should also be mentioned that during operation there are different weather conditions so slopes of the earth embankments are alternately dry and wet. If these processes occur, it should be expected that coal wastes will disintegrate, which will lead to changes in their geotechnical properties. While comparing the results of tests on unburnt and burnt waste, it seems right to state that the strength of the material due to disintegration and thermal transformations has a positive effect on its properties. On the other hand, if only the second process occurs, it can lead to a decrease in cohesion. Therefore, it is justified to use reduced cohesion in stability calculations.
## 3 Parameter Sensitivity Analysis Based on Orthogonal Design
### Orthogonal Design of Calculation Scheme
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Levels** & \begin{tabular}{c} **Depth** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Water Pressure** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Elasticity** \\ **Modulus (GPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Saturated** \\ **Density (kN/m\({}^{3}\))** \\ \end{tabular} \\ \hline Reference value (1) & 300 & 0.35 & 2.84 & 0.31 & 0.42 & 32.78 & 25.4 \\ \hline \hline \multirow{2}{*}{1} & 240 & 0.28 & 2.272 & 0.248 & 0.336 & 26.224 & 20.32 \\  & (2) & (10) & (18) & (26) & (34) & (42) & (50) \\ \hline \multirow{2}{*}{2} & 255 & 0.2975 & 2.414 & 0.2635 & 0.357 & 27.863 & 21.59 \\  & (3) & (11) & (19) & (27) & (35) & (43) & (51) \\ \hline \multirow{2}{*}{3} & 270 & 0.315 & 2.556 & 0.279 & 0.378 & 29.502 & 22.86 \\  & (4) & (12) & (20) & (28) & (36) & (44) & (52) \\ \hline \multirow{2}{*}{4} & 285 & 0.3325 & 2.698 & 0.2945 & 0.399 & 31.141 & 24.13 \\  & (5) & (13) & (21) & (29) & (37) & (45) & (53) \\ \hline \multirow{2}{*}{5} & 315 & 0.3675 & 2.982 & 0.3255 & 0.441 & 34.419 & 26.67 \\  & (6) & (14) & (22) & (30) & (38) & (46) & (54) \\ \hline \multirow{2}{*}{6} & 330 & 0.385 & 3.124 & 0.341 & 0.462 & 36.058 & 27.94 \\  & (7) & (15) & (23) & (31) & (39) & (47) & (55) \\ \hline \multirow{2}{*}{7} & 345 & 0.4025 & 3.266 & 0.3565 & 0.483 & 37.697 & 29.21 \\  & (8) & (16) & (24) & (32) & (40) & (48) & (56) \\ \hline \multirow{2}{*}{8} & 360 & 0.42 & 3.408 & 0.372 & 0.504 & 39.336 & 30.48 \\  & (9) & (17) & (25) & (33) & (41) & (49) & (57) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Orthogonal computational samples for sensitivity analysis.
# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 4 Experimental Results
### Performance Evaluation against State-of-the-Art Supervised Algorithms  
In the sequel, we will evaluate the performance of the RNNs, which were trained with WCRNN algorithm against the state-of-the-art supervised algorithms, naive Bayes [25], Support Vector Machines (SVM) [26], 3 NN [27], and random forest [28]. These classification models constitute some of the most effective and widely-used data mining algorithms for classification [29].  
Table 1 presents the performance comparison of WCRNN\({}_{1}\) and WCRNN\({}_{2}\) against state-of-the-art classification algorithms, relative to \(F_{1}\)-score and accuracy. Notice that the best performance is highlighted in bold for each metric. Random forest exhibited the best performance among state-of-the art classifiers, which is probably due to the fact that random forest is the only classifier based on an ensemble methodology. Nevertheless, the RNNs trained with WCRNN\({}_{1}\) and WCRNN\({}_{2}\) exhibited the highest average \(F_{1}\)-score, relative to all problems. Regarding the classification accuracy, the RNNs (WCRNN\({}_{2}\)) exhibited the best performance for bank marketing and German credit approval datasets,  
Figure 2: Log\({}_{10}\)-scaled performance profiles for the German credit approval classification problem. The performance profile plots, for every \(\tau\geq 1\), the proportion \(P(\tau)\) of simulations for which any training algorithm has a performance within a factor \(\tau\) of the best algorithm. (**a**) \(F_{1}\)-score. (**b**) Accuracy.  
followed by RNN (WCRNN1). Moreover, random forest reported the highest accuracy for banknote authentication dataset, slightly outperforming the RNN (WCRNN2).  
Summarizing, it is worth mentioning that the RNNs trained with both versions of WCRNN outperformed all state-of-the-art classifiers in terms of \(F_{1}\)-score. Furthermore, their classification performance was superior to all single classifiers and competitive with the ensemble-based random forest, regarding all benchmarks.
## 4 Time Series Analysis Method  
The main benefit of using the anharmonic spectral analysis method, described below, is that it suppresses the effect of the aliasing of possible periodic residues from imperfect models used for the elimination of systematic errors in the processing of GNSS data by PPP (see Section 3.1). The behaviour of the time series, which shows the features of repetition, can be observed in Figure 1. This shows the components, from W to E and Up, where the daily values of the coordinates show a near-periodic behaviour (which is often regarded as noise).
## 1 Introduction  
The continuous advancement of geophysical exploration technologies has established borehole hydrocarbon reservoir monitoring as a critical frontier in contemporary geophysical detection [1, 2, 3]. Conventional surface methods exhibit limitations in evaluating deep subsurface formations, presenting significant challenges including substantial burial depths of monitoring targets, geological complexity of borehole environments, interference sources, and insufficient resolution for target-scale characterization. These challenges necessitate high-precision, high-resolution monitoring solutions [4, 5, 6, 7]. The borehole-to-surface electromagnetic imaging method (BSEM) addresses this need as a geophysical electromagnetic monitoring technology that overcomes traditional surface electromagnetic depth limitations [8; 9; 10]. By directly establishing the source in target formations through well casings, this electrical excitation source enhances the current density distribution in deep reservoirs, showcasing application potential for dynamic subsurface monitoring [11; 12; 13]. This method is mainly applied in fine exploration of deep metal mines and hydraulic fracturing monitoring in horizontal wells, with promising application prospects in resource exploration and engineering geological survey fields in the future [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25].  
This study focuses on research into frequency-domain electromagnetic monitoring using the BSEM method (Figure 1), which is divided into two excitation modes [26]. One is using the metal casing of a well for excitation, that is, to inject low-frequency alternating current into metal casing well A and connect the negative pole B to another metal casing or ground it at infinity; alternatively, the positive and negative poles of the source can be simultaneously connected to two positions at different depths of the same metal casing (i.e., a vertical bipole source) and then receive the electromagnetic response signals on the surface. The other is placing an electric dipole (point or line) or a magnetic dipole in an open-hole well or a cased well, and receive the electric and magnetic fields on the surface. If the receiving electrodes are placed in another open-hole well, it becomes a crosswell electromagnetic observation method. Therefore, for simulation of the borehole-to-surface electromagnetic method, electromagnetic field simulation theory of dipole sources and line sources is of great significance. The simulation of the BSEM method is implemented via the finite volume method (FVM) [3; 7; 27].
## 3 Results
### Expression Analysis of ADGRG3 Gene in Various Cancers
#### 3.1.4 Expression of GPR97 in HNSC Based on Clinical Attributes  
In head and neck squamous cell carcinoma (HNSC), ADGRG3 (GPR97) expression was assessed across different clinical and demographic factors. Based on sample types (Figure 4A), GPR97 expression was significantly higher in primary tumor samples (\(n=520\)) compared to normal samples (\(n=44\)), indicating notable upregulation in tumor tissues. An analysis across individual cancer stages (Figure 4B) showed consistent GPR97 expression in Stages 1 (\(n=27\)), 2 (\(n=71\)), 3 (\(n=81\)), and 4 (\(n=264\)), with all stages demonstrating elevated levels compared with normal samples.  
Gender-based analysis (Figure 4C) revealed similar GPR97 expression levels in both male (\(n=383\)) and female (\(n=136\)) patients, suggesting no significant gender-based differences in expression. Age-based analysis (Figure 4D) showed elevated GPR97 expression across all age groups (21-100 years) compared to patients with normal samples, with no substantial age-related variation in expression levels.  
Figure 3: Expression of GPR97 in CHOL based on (**A**) sample types, (**B**) individual cancer stages, (**C**) patients’ gender, and (**D**) patients’ age. GPR97 expression was found to be significantly higher in primary tumor samples (\(n=36\)) compared to normal samples (\(n=9\)), expression peaked in stage 2 (\(n=9\)), suggesting increased expression in early-to-mid stages of CHOL. CHOL: cholangiocarcinoma.  
These findings indicate that GPR97 is upregulated in HNSC tumor tissues across all stages, genders, and age groups, suggesting a broadly applicable role for ADGRG3 in HNSC tumorigenesis. Further research could explore the specific pathways by which ADGRG3 contributes to HNSC progression and its potential as a therapeutic target.