esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 5 Discussion  
### BLE Signal Propagation in Underground Mines  
In underground mines, there are several structural and environmental factors that cause diffraction, reflection, and interference of BLE signal propagation. For example, there exist excavations with crossings inclined at 90 degrees and curved sections. These structures make stable line-of-sight propagation impossible. The mine wall has a high roughness that causes diffraction and reflection of the signal. In addition, radio signal attenuation occurs due to the rock mass. All mine areas have high relative humidity, and dust particles are suspended in the air. Electrical installations for power supply exist throughout underground mines. As a result, signal interference may occur due to electromagnetic fields. Therefore, it is necessary to perform BLE signal testing to understand the effects of disturbing factors that interfere with stable BLE signal propagation in underground mines.
## 3 Results and Discussion  
Equation (7) represents the condition for the existence of a stable, compact layer of ligands on the two \(xy\) facets of a 3D cuboid crystal. This means that the third dimension, \(z\), of the crystal is fixed, i.e., the crystal is not growing in the \(z\)-direction. In contrast, the reverse condition \((x-1)\cdot(y-1)<C\) means that the \(xy\) facet does not support a ligand layer, and the crystal grows in the \(z\)-direction. Using these inequalities, we can analyze the conditions that lead to growing nanoplatelets and nanowires. The growth of nanoplatelets requires continuous growth in the \(x\)- and \(y\)-directions, which implies the following conditions  
\[\left\{\begin{array}{ll}(x-1)\cdot(y-1)\geq C\\ (y-\alpha)\cdot(z-\alpha)<\alpha C,\ \alpha=L_{yz}/L_{xy}>1\\ (x-\beta)\cdot(z-\beta)<\beta C,\ \beta=L_{xz}/L_{xy}>1\end{array}.\right. \tag{8}\]  
Without loss of generality, we have chosen a \(xy\) facet with \(L_{xy}\leq\min\bigl{\{}L_{xz},L_{yz}\bigr{\}}\). It is useful to rewrite the inequalities in Equation (8) in the form  
\[\mathbf{y}(z)>y\geq\mathbf{y}(x),\ z<\mathbf{z}(x) \tag{9}\]  
where the _boundary functions_  
\[\mathbf{y}(x)=1+\frac{C}{x-1},\ \mathbf{y}(z)=\alpha\cdot\biggl{(}1+\frac{C}{z- \alpha}\biggr{)},\ \mathbf{z}(x)=\beta\cdot\biggl{(}1+\frac{C}{x-\beta}\biggr{)} \tag{10}\]  
define the boundary lines between the two states of the corresponding facet: (i) a facet covered with the compact layer of ligands and (ii) a free facet without any ligand layer. As can be seen from the second and third relations in Equation (10), the asymptotic values \(\mathbf{y}(z\to\alpha)\to\infty,\mathbf{y}(z\to\infty)=\alpha\), \(\mathbf{z}(x\to\beta)\to\infty\) and \(\mathbf{z}(x\to\infty)=\beta\) reflect the anisotropy in LCN sizes.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 3 Proposed Approach
### Evaluation Metrics  
Machine learning and statistics use confusion matrices to evaluate the performance of classification models. By summarizing how well a model has classified instances, the model can provide a clear picture of how well it has classified instances [36]. An actual class label and predicted class label of a classification problem are organized in a performance matrix. True positives (_TP_), true negatives (_TN_), false positives (_FP_), and false negatives (_FN_) represent the four main components of the performance matrix.  
In classification tasks, the following performance metrics are commonly used [37]:  
1. The accuracy of a classifier is measured by calculating the percentage of correctly classified instances (true positives and true negatives) divided by the total number of instances (5).  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
2. Precision: Out of all occurrences anticipated to be positive (including true positives and false positives), precision is the percentage of accurately predicted positive cases (true positives) (6). This parameter measures how well the classifier avoids false positives.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{6}\]  
3. Recall (Sensitivity): Recall, also known as sensitivity or true positive rate, is the percentage of real positive cases (also known as true positives and false negatives) that the classifier properly classifies (7). It evaluates how well the classifier can identify instances of success.  
\[\text{Recall (Sensitivity)}=\frac{TP}{TP+FN} \tag{7}\]  
4. F1 Score: The harmonic mean of recall and precision is the F1 score. The performance of the classifier is balanced by combining precision and recall into a single statistic. When there is an imbalance between the positive and negative cases in the dataset, it is especially helpful (8).  
\[\text{F1 Score}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{8}\]  
These metrics are obtained from the confusion matrix, which summarizes the predictions made by the classifier about the labels that represent the ground truth.
## 3 Ontology Modeling and Quality Measures
### Quality Metrics
#### 3.1.2 Usability Metrics  
The usability factor can be investigated from many different aspects. The usual categorization involves the following elements:  
* Human aspects, i.e., the readability and understandability of the ontology;
* Compatibility aspects, i.e., are the instances derived from the same schema definition compatible with each others or not;
* Technical aspects, that is, applicability of the ontology must be measured considering the available software tools.  
In [35], the following practical measures were proposed to evaluate the quality of ontology models:  
* Computational efficiency (the size of the model);
* Adaptability (involving cohesion and coupling);
* Clarity (unambiguous naming);
* Accuracy (which shows the agreement between the constructed model and expert knowledge about the domain);* Completeness (all relevant information is covered by the ontology model);
* Consistency (the ontology does not include or allow for any contradictions).
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 5 Parametric Study on the Proposed Connection
### Connection under Axial Tension
The ultimate load carrying capacity of specimen 01 and specimen 02 compared to their counterpart connections Cp1 and Cp2 was higher as illustrated in Figure 8e, whereas the proposed connection Cp1 and Cp2 were composed of hot rolled hollow sections and shown up local deformation around the bolt heads. In all the connections, the failure occurred due to the yielding of bolts and local deformation at connections.
## 3. Results and Discussion
### Experimental Results
#### 3.1.7 Corneal Hysteresis  
Viscoelastic corneal hysteresis is characterized by different behaviors under load and unload conditions. It is a measure of the energy absorbed during the cycle and represents a combination of this tissue's elastic and viscous properties. It is useful for understanding corneal resistance to intraocular pressure [31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Surfaces** & **E (MPa)** & **Random Uncertainty (MPa)** & **R2** \\ \hline Anterior & 0.22475 & 0.00152 & 0.9996 \\ \hline Posterior & 0.14084 & 0.00428 & 0.9908 \\ \hline Mean & 0.18804 & 0.00143 & 0.9995 \\ \hline \hline \end{tabular}
\end{table}
Table 2: [PERSON]’s modulus values for the anterior, posterior and mean corneal surfaces.  
Figure 6: The average corneal stress-strain curves obtained for the increasing pressure data calculated for Rm (the mean curvature radius of the cornea), Ram (the mean curvature radius of the anterior cornea surface) and Rpm (the mean curvature radius of the posterior cornea surface).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surfaces** & **E (MPa)** & **Pressure (kPa)** \\ \hline [PERSON] 2008 [22] & 0.1449–0.2209 & 0–2.67 \\ \hline [PERSON] 2008 [29] & 0.05–0.24 & 1.60–3.33 \\ \hline [PERSON] 2012 [21] & 0.0945–0.2873 & 0.40–4.00 \\ \hline [PERSON] 2016 [30] & 0.247 & 2.00–4.00 \\ \hline Mean cornea of this work & 0.18804 & 1.60–3.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Young’s modulus values reported for the inflation tests with porcine corneas.  
Hysteresis can be determined as the ratio of the area contained in the cycle divided by the area under the load curve. The corneal hysteresis values obtained in this work varied between 15.64% and 44.21%, with an average value of 26.22% and a standard deviation of 13.36%.
## 4 Capture of Holograms: Interference of Object and Reference Waves  
Interference is an essential concept used in physical implementations of holography. It enables to record off-axis holograms [45] where the image of the light source, the conjugated, and the real image are laterally separated in space upon back-propagation of the hologram. While the optical wavefront may only be partially characterized in specific setups, solely four types of reference waves, denoted in Equation (1) as \(E_{R}\), are common.  
**Diverging spherical wave** This is the wavefront of an ideally isotropic (reference) point-source. It is used for rigorous propagation within the confines of scalar diffraction theory. An exemplary phase space footprint is shown in Figure 3c.  
**Parabolic wave** This is an approximation to a spherical wave in the Fresnel or paraxial approximation. It is used for Fresnel propagation. An exemplary phase space footprint is shown in Figure 3c.  
**Planar wave** (also: plane wave) These form a dual basis to spherical waves, in the sense that they can be interpreted as spherical waves with infinite curvature. This wavefront is used for accurate propagation with the angular spectrum method. In phase space the footprint will be line parallel to the spatial axis. A planar wave incident with an angle \(\theta\
eq 0^{\circ}\) will appear as a line at frequency \(f\) given by Equation (5).  
**Other** Other forms can arise e.g., in shearing interferometry when the object wave is used to interfere with itself. The phase space footprint will depend on the object.  
In case the individual object and reference wavefronts are known, one can study the interference in phase space with PSRs--where it is understood intuitively as a sum of the corresponding instantaneous frequencies. We showcase the phase space footprint of exemplary holograms obtained from the interference of an object and multiple reference waves in Figure 7. As object wave we use the scene of Figure 6, recorded in the paraxial limit, after complex conjugation and up-sampling it by a factor of 4. Whilst the former modification merely aids visualization, the second avoids the occurrence of aliasing after interference.
## 3 Result and Discussion
### Particle Size Distribution Using SMPS
The outside of the tunnel manifested the mode of particle diameter of 14 nm in the interval from 11 PM to 6 AM, while the mode of 68 nm appeared in the interval from 6 AM to 11 PM. Figure 7a represents the diurnal pattern of the variation of the mode of particle diameter in the inside of the tunnel in winter and spring. Figure 7b,c represents the correlation of the mode of particle diameter with temperature in winter and spring. The mode of particle diameter according to temperatures in respective seasons exhibited high correlation together with rapid changes thereof at temperature above the breakthrough point. Further, the mode of particle diameter increased rapidly to 62 nm at the level of temperature 10-11 \({}^{\circ}\)C, contrary to the slight increase from 20 nm to 28 nm at the level of temperature from 5 \({}^{\circ}\)C to 10 \({}^{\circ}\)C inside of the tunnel in winter. For the case of spring time, the mode of particle diameter of 10-30 nm appeared at higher level of temperature of 17-21 \({}^{\circ}\)C [32]. Figure 5: Overall averaged seasonal particle size distribution in the inside and outside of the tunnel estimated by using SMPS. According to the previously conducted study, the condensation of particles reported that the growth of particle diameter would be limited by the formation of competitive relationship with moisture in the atmosphere [32; 33; 34; 35]. In the present study, the temperature acted as a major cause of determination of final condensation diameter after emission, which is also dependent on diverse environmental effects comprising particle number concentration including humidity. The particles in the discharged exhausts in spring time, wherein the temperature are relatively higher than winter time, were estimated to be involved in the competitive reaction with potential moisture to reach the final condensation diameter after emission. Figure 6: Diurnal patterns of seasonal particle size distribution in the inside- and outside of the tunnel estimated by using SMPS.
## 3 Results
### Water Supply and Soil Water Content  
In order to have all the treatments at the same soil moisture conditions at the beginning of the experiment, three and two full irrigations were provided regardless of the treatments in 18 May, 29 May and 5 June in 2015, as well as in 6 June and 14 June, in 2016. In 2015, the seasonal water supply was 1557 m\({}^{3}\) ha\({}^{-1}\) for T and F, and 815 m\({}^{3}\) ha\({}^{-1}\) for M, S. The irrigation season lasted about 4 months (Figure 2A). In 2016, T and F received 1810 m\({}^{3}\) ha\({}^{-1}\) while M and S 1023 m\({}^{3}\) ha\({}^{-1}\) and the duration of the irrigation season was about 3 months (Figure 2B). M and S received about 50% less water than T and F in both years. Soil water content remained between the field capacity (FC) and the intervention threshold (IT) till the end of July in T, M and F during the two seasons. In the same period, S showed SWC lower than IT for several days (Figure 3, June-July). From the beginning of the irrigation season till the end of July, M and F showed the highest SWC values (average SWC of 0.32 and 0.33 m\({}^{3}\) m\({}^{-3}\) in 2015 and 2016, respectively, for M; 0.31 and 0.32 m\({}^{3}\) m\({}^{-3}\) for F). In the same period, T revealed SWC similar to M and F, in 2015 (average of 0.31 m\({}^{3}\) m\({}^{-3}\)), while in 2016, it decreased at an average value of 0.29 m\({}^{3}\) m\({}^{-3}\). The average SWC of S in the same period was 0.29 and 0.26 m\({}^{3}\) m\({}^{-3}\) in 2015 and 2016, respectively (Figure 3). Due to local watershed restrictions occurring during the period August-September for both the years, the irrigation frequency (number of peaks in Figure 3) was reduced for all the treatments. In this period, the SWC of T, F and S fell below the intervention threshold several times and in S it reached the wilting point; M showed the highest soil water content, rarely below IT (Figure 3). In this period, the comparison between T and F, receiving almost the same water volume in each irrigation, showed that in August-September, after water supply SWC declined faster in F than in T, reaching values lower than T and close to WP at the end of the irrigation season (Figure 3).
## 5 Discussion
### Evaluation of the Hypotheses  
H1 states that in precision-oriented XR applications, smaller 3D objects relative to the virtual hand will improve task accuracy and user satisfaction. The results obtained support this hypothesis by showing that a ratio of 1:0.5 between the size of the virtual hand and the 3D object resulted in the most favorable user experiences in precision-based tasks. Participants reported higher accuracy and greater satisfaction when interacting with smaller objects at this scale.  
H2 states that visual distractions reduce task performance and user satisfaction. The results partially support this hypothesis. It has been shown that the presence of visual distractions, such as additional 3D elements or complex backgrounds, negatively affects task performance in time-limited scenarios and leads to longer processing times. This supports the claim that distractions are detrimental under time pressure. However, in scenarios without a strict time limit, these distractions were not detrimental to performance, and in some cases, they can be neutral or even beneficial if users can proceed at their own pace. This result contrasts with the second hypothesis.  
H3 states that indirect interaction methods lead to better task efficiency, accuracy, and satisfaction than direct interaction. The data strongly support this hypothesis. Indirect interaction techniques, such as the use of sliders, resulted in better task efficiency, higher accuracy, and greater user satisfaction compared to the direct manipulation of 3D objects. Participants reported that the indirect method was more intuitive and provided better control, which contributed to a shorter task completion time.
## 3 Methods
### Topographic Mapping of Tidal Flats by the Waterline Method  
The waterline method, which is currently considered to be the most useful approach [12], makes use of the ever-shifting boundary between tidal flats and adjacent water areas, whose positions can be regarded as a quasi-contour line of the topography (not a true contour line because the water level of the tidal wave varies horizontally). The waterline method has been previously applied to the Jiangsu coast. In this study, 21 waterlines were extracted from remote sensing images (partly listed in Figure 2a). Then, based on a 2D hydrodynamic model, the waterlines were given elevation values. The DEM was constructed by the interpolation method using these heightened waterlines (Figure 2b). More details can be found in the work of [PERSON] et al. (2017) [13].  
Figure 2: Height assignment to tidal creeks. (**a**) Waterlines extracted from remote images; (**b**) initial topographic map generated from the waterline method; (**c**) linear interpretation in tidal creeks; (**d**) topographic map of Tiaozini area.  
The tidal creeks, however, lack data. Thus, in this study, in order to gain seamless DEM data, we interpolate the topographic data using the linear method. A previous study [32] showed that along a tidal creek, the elevation presents a linear attenuation from its head to its mouth. Firstly, the center lines of the tidal creeks were extracted based on the waterline and remote sensing image from head to mouth (that is, from upstream to downstream along one creek). Secondly, the centerline was given an elevation value by using the linear interpretation method as shown in Figure 2c. The elevation of the head (\(Z_{\textit{upstream}}\)) was the height value of the lowest waterline, and that of the mouth (\(Z_{\textit{downstream}}\)) was set in advance based on known survey sections and charts. The middle points (\(Z_{\textit{Target}}\)) along the centerline of the tidal creek were calculated based on the linear interpolation formula shown in Figure 2c. Lastly, we added the centerline with height information, and the seamless DEM shown in Figure 2d was generated by Natural Neighbor spatial interpolation using ArcGIS software. This DEM will be used as training data in the output layer of the ANN model in the next section.
## 4 Experimental Results and Analysis
### Physical Experiment Results and Analysis  
#### 4.2.1 Experiment System  
An experimental system was built and is shown in Figure 8. The system consists of a computer, a signal conditioning circuit, a DAQ card, and a probe. The sensor is an SL-106C, whose length and width are both 1.5 mm. The amplifier is AD620, and the magnification is 100. The DAQ card is ADLINK DAQ2208, and the sampling frequency of each channel is set to 4 kHz. The probe, which consists of two rows, eight sensors per row, and an excitation device, is mounted on a detection car that is placed on the rail. The detection speed was 2.0 m/s.  
Figure 8: MFL detection system.  
Figure 7: The x and z direction simulation results with different distance. (**a**) 1.8 mm; (**b**) 2.2 mm; (**c**) 2.6 mm.
## 3 Results  
### Boundary Layer  
The first major finding is that fluid velocity equals zero at the surface boundary (Figure 6), proving the concept of non-slip conditions claimed by other authors [13; 14; 15]; as the flowlines then distance from the O-ring, frictional forces decrease, enabling fluid flow to return to maximum velocity [13]. This region of varying velocity is classified as the boundary layer and incorporates a viscous sublayer, buffer layer, overlap layer and turbulent layer. Within internal, turbulent flow scenarios, the proximity near the wall can be stratified into distinct sublayers: the viscous sublayer, directly adjacent to the no-slip boundary, where molecular viscosity dominates, resulting in a linear velocity profile and high viscous shear stress; the buffer layer, transitioning from the viscous sublayer, where turbulent kinetic energy increases, leading to instability and the onset of turbulent fluctuations; the overlap (log-law) layer, further from the wall, exhibiting a logarithmic velocity profile governed by the balance between diminishing viscous shear stress and increasingly dominant turbulent shear stress, as described by the logarithmic law of the wall; and the outer layer, where turbulent shear stress significantly outweighs viscous stress, resulting in an inviscid turbulent flow characterised by momentum transport driven by large-scale eddies and approaching the free-stream velocity, with accurate representation in this layer reliant on turbulent kinetic energy cascade and eddy viscosity models [15; 16].
# Haptic Soft-Keyboard for Tablet-Sized Touchscreens
## 3 Materials and Methods
### Perception Study to Estimate the Sensitivity to Time Difference of Tactile Key Click
#### 3.2.5 Results of the Psychophysical Experiment  
Figure 5 shows the results of the psychophysical experiment that estimated temporal discrimination thresholds (3.96 ms for H and 2.27 ms for HA) of key clicks on a tablet touchscreen. The graph bars represent the thresholds for all participants for the two conditions, H and HA, and the error bars show standard deviations. In the left plot, it was observed that the thresholds for the H condition were all higher than those of the HA condition over all the participants. It was also apparent in the right that the average threshold for H was larger than that of HA. A one-way ANOVA with the condition (H, HA) showed that there was a significant difference between the two conditions [\(F(1,\,118)=160.1\), \(p<<0.0001\)]. The results demonstrated that (1) a tactile display of sequential two peaks with a smaller interval than 3.96 ms could be perceived as one peak, and (2) the perception of tactile key clicks could be more sensitive by adding a synchronized audio cue, which can serve as a guideline in both sound and touch feedback on touchscreens. The touch sensitivity (about 4 ms) to time difference measured in this study was slightly smaller than the 5 ms reported in [PERSON]'s book [21]. The increased touch sensitivity when combined with aural feedback was expected due to hearing dominance--the sensitivity to time difference was about 0.01 ms reported in [21]. In this study, the threshold estimated by the haptics only condition was adopted for the haptic soft keyboard.  
Figure 5: Discrimination thresholds (temporal sensitivity) estimated on tactile key click effects on tablet touchscreens: thresholds estimated per participant (left) and the average thresholds (right) for the haptics alone (H) and haptics with audible sound (HA) conditions.