esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The scale distribution parameter (\(\eta\)) of the Weibull statistics showed statistically significant differences between the number of cycles of in-and-out movements of the 20.06 and 25.06 apical diameter study groups (\(p<0.001\)), 20.06 and 30.06 apical diameter study groups (\(p<0.001\)), and 25.06 and 30.06 apical diameter study groups (\(p<0.001\)). In addition, the shape distribution parameter (\(\beta\)) of the Weibull statistics showed statistically significant differences between the number of cycles of in-and-out movements of the 20.06 and 25.06 apical diameter study groups (\(p<0.001\)) and 20.06 and 30.06 apical diameter study groups (\(p=0.002\)); however, it did not show statistically significant differences between the number of cycles of in-and-out movements of the 25.06 and 30.06 apical diameter study groups (\(p=0.656\)) (Table 6, Figure 10A). The Weibull statistics of the number of cycles of in-and-out movements for the apical diameter and taper study groups. The scale distribution parameter (\(\eta\)) and the shape distribution parameter (\(\beta\)) of the Weibull statistics showed statistically significant differences between the number of cycles of in-and-out movements of the 25.02 and 25.04 taper study groups (\(p<0.001\)), 25.02 and 25.06 taper study groups (\(p<0.001\)), and 25.04 and 25.06 taper study groups (\(p<0.001\)) (Table 6, Figure 10B). The ANOVA test did not show statistically significant differences between the mean length of the fractured files regarding the apical diameter study groups (\(p=0.344\)) (Figure 11A) and taper study groups (\(p=0.344\)) (Figure 11B). The scale distribution parameter (\(\eta\)) and the shape distribution parameter (\(\beta\)) of the Weibull statistics did not show statistically significant differences for either the apical diameter (\(p>0.05\)) (Figure 12A) or taper (\(p>0.05\)) (Figure 12B) study groups. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Weibull Shape (\(\beta\))**} & \multicolumn{4}{c}{**Weibull Scale (\(\eta\))**} \\ \hline  & **Estimate** & **St Error** & **Lower** & **Upper** & **Estimate** & **St Error** & **Lower** & **Upper** \\ \hline
20.06 & 26.22 & 6.75 & 15.83 & 43.43 & 369.77 & 4.70 & 360.67 & 379.09 \\
25.02 & 37.02 & 9.08 & 22.89 & 59.86 & 416.37 & 3.76 & 409.05 & 423.81 \\
25.04 & 32.42 & 7.98 & 20.01 & 52.52 & 299.13 & 3.09 & 293.14 & 305.25 \\
25.06 & 22.37 & 5.53 & 13.78 & 36.31 & 231.14 & 3.46 & 224.45 & 238.02 \\
30.06 & 7.62 & 1.84 & 4.74 & 12.23 & 109.38 & 4.81 & 100.34 & 119.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The Weibull statistics of the number of cycles of in-and-out movements for the apical diameter and taper study groups. Figure 10: Weibull probability plots of the number of cycles of in-and-out movements for the (**A**) apical diameter and (**B**) taper study groups.
## 4 RF Concepts and Principles  
Using [PERSON]'s equation concept to fully comprehend electromagnetic waves, it is possible to implement the physics that underlie microwave antennas [35]. [PERSON] characterized the properties of magnetic fields and electricity in terms of electromagnetic waves and described the antenna functions for transmitting and receiving. Classical electromagnetism predicted that electromagnetic waves with infinite frequencies travel at the speed of light, which indicates the primary sign of the presence of the electromagnetic spectrum [36]. The existence of the electromagnetic spectrum was demonstrated by the behavior of a valence electron that simultaneously loses energy and shifts to a lower energy state from its previously higher energy state [37; 38]. According to the principles of energy conservation, the electron radiates energy known as photon energy. The photon continues to travel indefinitely until it comes into contact with another electron in an atom along its path of movement.  
When the energy-carrying photon hits the electron, the electron may absorb the energy and become excited. During the photon process, the energized electron would move from the valence band to the conduction band. Once it transfers its energy, it disappears. Hence, the waves formed by the photons are described as electromagnetic waves, and they can exist in all forms of medium (liquid, gas, or solid). Interestingly, the RF energy is able to be transferred via EM [28]. The nature of the ambient RF energy overcomes the necessity for self-sustained. powered multipurpose electronic components by extracting nearby wireless RF energy sources, such as broadcast stations, cell phone towers, Wi-Fi hotspots, and RF emitting devices [39; 40; 41; 42]. The constant, abundant supply of RF energy in nature provides an edge over other conventional energy sources, including thermal, solar, vibration, mechanical, and wind [43; 44; 45; 46; 47; 48].  
By implementing the above concept, microwave antennas are regarded as the essential element in RF-EH systems [49; 50; 51]. Whereas the RF-EH system is based on freely available RF energy in the environment, electromagnetic waves have different characteristics based on frequency, distance, and the conducting environment [52; 53]. The design of the antenna should take into consideration the selection of potential traits based on the specifications of the application in perspective for the characterization of the electromagnetic waves. Determining the free space path loss, which defines power dissipation in space, requires knowledge of the transmitting wave frequency, gain, and distance between the transmitting and receiving antennas [54; 55]. In order to accomplish RF-EH, antennas that can receive EM energy from the environment are used. These antennas are available to existing services that use ambient RF energy sources. There are two types of electromagnetic power sources: near-field and far-field.
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 1 Introduction  
The phenomenon of crowds has garnered considerable academic interest in recent years owing to the proliferation of events that attract large gatherings [1]. The safety concerns associated with such events, particularly religious and sporting events, have underlined the importance of detecting crowd anomalies, which entails examining the actions and interactions of individuals in large groups. The study of crowd anomalies presents a formidable challenge due to the crowd dynamics' intricate and unpredictable nature. Fortunately, recent breakthroughs in computer vision, machine learning, and deep learning have opened up new possibilities for crowd behaviour analysis. Crowd anomaly detection (CAD) [2] involves identifying unusual or abnormal behaviour within a crowd. This capability is crucial in ensuring public safety, preventing accidents, and managing the flow of people in public spaces, especially at crowded events like religious or sporting events, where emotions can run high.  
By detecting anomalies in real time, it is possible to respond quickly and effectively to potentially dangerous situations, preventing them from escalating. Local feature extractionmethods are pivotal in accurately representing crowd behaviour. These methods facilitate the identification of unique patterns of movement and behaviour within a crowd, simplifying the detection of abnormal or anomalous events. However, the effectiveness of these methods depends on several factors, including the features used and the specific anomaly being detected. This study aims to provide insights into the strengths and limitations of various methods and contribute to developing more robust and practical approaches to crowd anomaly detection. This study's findings are expected to contribute to the crowd behaviour analysis field and help to improve public safety in crowded spaces.  
The rest of this paper is organised as follows. Section 2 briefly reviews recent works in crowd anomaly detection. Section 3 outlines the methodology employed in this study, including the extraction of local features, the utilisation of sparse feature tracking, and the spatial representation achieved through Delaunay triangulation. Additionally, the descriptors used for individual and interactive behaviours and their calculation using graph notations are also explained. Furthermore, we elaborate on this research's neural network-based classification method. Section 4 presents the details regarding the datasets utilised for the analysis and a thorough analysis of the results, including a comparison of the three local features with and without dimensionality reduction using the autoencoder and PCA techniques. Finally, Section 5 concludes the paper and outlines potential avenues for future research.
## 7 Net Axial Stress
### Net Axial Stress Distribution  
The net axial stress, given as a three-part sum in Equation (3a), may be interpreted most simply as the sum of three separate distributions within which the sign changes across the four quadrants are accounted for. Referred to the fixed end (\(z=0\)), this stress summation becomes  
\[\sigma_{z}=-[(\omega-\varpi)T/\mu\Gamma_{1}](1-\mathrm{e}^{-2\mu L})/(1+ \mathrm{e}^{-2\mu L})+M_{x}y/I_{x}+M_{y}x/I_{y} \tag{14}\]  
where the counter-clockwise direction taken for \(s\) within the first (constraint) term must start and finish at the flange ends. As with each analysis, when equal free end loads \(F_{x}=F_{y}=F=100\) N are applied at G, the three distributions may be added. Taking the corresponding stress levels from Figure 9 for the first term and from Figure 8 for the sum of the second two terms (net bending stress), Table 9 is constructed for the net axial stress summation \(\sum\sigma_{z}\) in each channel section. Linear interpolation over points 1, 2, and 3 and 5, 6, and 7 is applied when required to make this sum.  
Figure 10 shows each channel's net axial stress distribution in which \(\sum\sigma_{z}\) applies as calculated for positions 1, 2, 3... 7 within Table 9. Table 9 applies in the absence of any further contribution from the _trans-moment_ (see Section 9.8). The latter raises the bending stress under \(M_{y}\) from \(M_{y}=F_{x}L\) to \(M_{y0}=F_{x}(L+e_{x}+X^{\prime})\), i.e., in the ratio \(M_{y0}/M_{y}=1+(e_{x}+X^{\prime})/L\) equalling 1.027, 1.017 and 1.025 for channel sections A, B and C, respectively. With each ratio being near unity and with no transfer value for moment \(M_{x}\), since \(F_{x}\) passes through E, the trans-moment contribution may be ignored. Hence, the analyses given above are judged to be sufficient to place a limit upon the loading giving stress levels that remain elastic and non-crippling.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Channel A & 37.95 & 4.12 & \(-7.04\) & \(-23.89\) & \(-7.04\) & 4.12 & 37.95 \\ \hline Channel B & 10.36 & 1.14 & \(-2.22\) & \(-7.09\) & \(-2.22\) & 1.14 & 10.36 \\ \hline Channel C & 39.04 & 4.34 & \(-4.34\) & \(-18.80\) & \(-4.34\) & 4.34 & 39.04 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fixed-end axial stress (MPa) due to constraint in three different channel section cantilevers.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 5 Adsorption Isotherm
### Langmuir Isotherm  
The Langmuir adsorption isotherm was explained on the basis of subsequent pre-sumptions  
* Adsorbent surface has fixed vacancy on the adsorbent surface with the same energy.
* Only monolayer sorption occurs on the surface of sorbent which is reversible.
* Available empty sites having the same size and shape on the adsorbent surface [91].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Langmuir Isotherm** & **Plot** \\ \hline Non-linear form & \(Q_{e}=\frac{Q_{e}\,K_{i}C_{e}}{1+K_{i}C_{e}}\) \\ \hline Type-I & \(\frac{C_{e}}{Q_{e}}=\left(\frac{1}{Q_{e}}\right)\,C_{e}+\left(\frac{1}{n_{e}Q_ {e}}\right)\) & \(\frac{C_{e}}{Q_{e}}\,Vs\,C_{e}\) \\ \hline Type-II & \(\frac{1}{Q_{e}}=\left(\frac{1}{B_{e}\,\mathrm{O}\mathrm{e}}\right)\,\frac{1}{C _{e}}+\frac{1}{Q_{e}}\) & \(\frac{1}{Q_{e}}\,Vs\,\frac{1}{C_{e}}\) \\ \hline Type-III & \(Q_{e}=Q_{m}-\left(\frac{1}{B_{e}}\right)\,\frac{Q_{e}}{C_{e}}\) & \(Q_{e}\,Vs\,\frac{Q_{e}}{C_{e}}\) \\ \hline \hline
**Freundlich Isotherm** & \\ \hline Non-linear form & \(Q_{e}=K_{F}\,C_{e}\,^{1/n}\) \\ \hline Linear form & \(\ln\left(Q_{e}\right)=\ln\left(K_{F}\right)+\left(\frac{1}{n}\right)\,\ln\left( C_{e}\right)\) & \(\ln\left(Q_{e}\right)\,Vs\ln(C_{e})\) \\ \hline \hline
**Adsorption kinetics** & \\ \hline (i) Pseudo-first order & \(\mathrm{dqt/dt=K_{1}\left(q_{e}-q_{e}\right)}\) \\ \hline (ii) Pseudo-second order & \(\mathrm{dqt/dt=K\left(q_{e}-q_{e}\right)}\,^{2}\) \\ \hline \hline
**Thermodynamics** & \\ \hline \hline \end{tabular} \(\mathrm{K_{C}=C_{A}/Ce}\)  
\(\mathrm{\Delta G^{\circ}=\Delta H^{\circ}\,-\,T\Delta S^{\circ}}\)  
\(\mathrm{\Delta G^{\circ}=-RTlinKc}\)  
\end{table}
Table 1: Equilibrium modelling equations.  
\[\mathrm{q_{e}}=\mathrm{q_{0}}\;\mathrm{K_{L}}\;\mathrm{C_{e}}/(1+\mathrm{K_{L}}\; \mathrm{C_{e}}) \tag{7}\]  
The term \(\mathrm{q_{e}}\) is the quantity of solute in milligrams sorbed by 1 g of sorbent at equilibrium (mg/g), \(\mathrm{C_{e}}\) is an equilibrium amount of adsorbate in solution after sorption (mg/L), \(\mathrm{q_{0}}\) represents highest sorption value (mg/g), and also known as the experimental Langmuir constant and \(\mathrm{K_{L}}\) is the experimental Langmuir constant (L/mg) The \(\mathrm{q_{0}}\) represents the presence of the highest number of vacancies on the sorbent. The constant \(\mathrm{K_{L}}\) represents the Langmuir constant indicates the same adsorbate attraction for all surface sites.  
Arrange the Equation (7) we obtain:  
\[\mathrm{C_{e}}/\mathrm{q_{e}}=1/\mathrm{q_{0}}\mathrm{K_{L}}+\mathrm{C_{e}} \;\mathrm{q_{0}}(4) \tag{8}\]  
the graph between \(\mathrm{C_{e}}/\mathrm{q_{e}}\) vs. \(\mathrm{C_{e}}\) gives a straight-line having slope \(1/\mathrm{q_{0}}\) and intercept \(1/\mathrm{q_{0}}\mathrm{K_{L}}\).
## 2 Methodology
### Fatigue Damage Evolution
#### 2.2.2 Analysis of Critical Parameters  
By definition, there is no damage at the beginning of fatigue loading, and complete damage (i.e., \(D_{\text{f}}\) = 1) occurs when the cycle ratio reaches 1. The parameter \(b\) in Equation (20) can thus be interpreted as a function of parameters \(a\) and \(c\) by simple transformation, and the correlation is expressed as in Equation (21):  
\[b=a^{c}+1. \tag{21}\]  
Since Equation (21) is a power or exponential function of \(a\) and \(c\), parameter \(b\) is called the composite factor. The analysis of critical parameters can be simplified to the impact analysis of \(a\) and \(c\), whose effects on the damage variables (Figure 3) are explored by changing one parameter while keeping the other parameter constant.  
Figure 3a shows the effect of various values of \(a\) on the damage evolution curve with a constant \(c\) = 4. As \(a\) increases, the slope of stage I of the evolution curve grows more rapidly, and stage I accounts for a more significant proportion of the entire curve. Stage III of the evolution curve develops in the opposite direction; with an increase of \(a\), stage III approaches unity more slowly, and the characteristics may gradually disappear. Since \(a\) exhibits significant influence on the development rate and proportion of stages I and III, \(a\) is therefore called the stability factor of the beginning and end stages.  
Based on previous analysis, Figure 3b similarly explores the effect of \(c\) on the damage evolution curve while setting \(a\) = 0.4. The three-stage evolution trend becomes more apparent as \(c\) increases; unlike the effect of \(a\), an increase in \(c\) results in an increased development rate of both stages I and III. However, compared with the influence of \(a\) on  
Figure 3: Effect of parameters on fatigue damage evolution (Equation (20)): (a) Influence of parameter \(a\); (b) Influence of parameter \(c\).  
stages I and III, \(c\) shows a more significant impact on the damage stabilization development stage. Since increasing \(c\) decelerates the slope of stage II, \(c\) can be regarded as a development rate factor.  
With various combinations of \(a\) and \(c\), a family of evolution curves that could reflect the three-stage fatigue damage development are generated and plotted in Figure 4.  
The fatigue damage variable and its evolution equation in Equation (20) can be obtained from maximum fatigue strains of fatigue tests under different loading conditions. It should be noted that only a few references have reported the original strain data collected during the whole process of fatigue loading. Specimens from axial compression and flexural tension fatigue tests in the literature [31; 32] are taken as examples to derive typical fatigue damage evolution equations.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### PRESENT Block Cipher
#### 2.1.1 AddRoundkey of PRESENT Block Cipher  
Given the 64-bit round key \(RK_{i}=rk_{63},...,rk_{0}\) is exclusive-ored to the 64-bit block \(B_{i}=b_{63},...,b_{0}\) for \(1\leq i\leq 32\). The notation \(\oplus\) means XOR operation.  
\[b_{j}\gets b_{j}\oplus rk_{j},\ \ j=0,...,63 \tag{1}\]
# Theoretical Study on Geometries of Electrodes in Laboratory Electrical Resistivity Measurement
###### Abstract  
Electrical resistivity tests have been widely conducted in multiple scales, from a few centimeters to kilometers. While electrode spacing is used to define field resistance, laboratory measurements in a limited space need to consider electrode geometry. However, there are no studies that theoretically explore the effects of the geometries of electrodes and container size on laboratory electrical resistivity measurements. This study formulates a theoretical electrical resistance for the geometry of cylindrical electrodes and the size of a non-conductive container with the method of image charges. As a complementary study, experimental tests were conducted to verify the derived equations. The discussion includes the concepts of the spherical equivalent electrodes and a simple design method for container size.  
Keywords:electrical resistance; half-buried spherical electrode; cylindrical electrode; penetration depth; container size +
Footnote †: journal: Applied
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 2 Related Works
In order to model routing protocols and to integrate them into the dependability models, [PERSON] et al. [46] surveys the reliable routing protocols on WSNs and, more specifically, [PERSON] et al. [31] surveys' multipath routing protocols on Wireless Multimedia Sensor Networks (WMSNs), which is a generalization of WVSNs. In [47; 48], routing protocols are addressed considering energy consumption in order to preserve the network reliability. Those papers present relevant features from several routing protocols classified by suitable application scenarios, energy efficiency and real-time aspects, providing a basis to categorize them and model their behavior with respect to dependability. Additionally, [PERSON] et al. [49] present wireless link reliability models for energy harvesting and battery-powered sensor nodes, considering power consumption, noise, location uncertainty and wireless channel conditions. Differently from previous papers, in this work, we propose a methodology to analytically evaluate dependability metrics of wireless visual sensor networks, considering essential aspects in an integrated fashion, i.e., the influence of power consumption, battery discharging, hardware, link and coverage failures, as well as the behavior of routing protocols. To the best of our knowledge, our framework is the only one in the literature that puts together all of these features, providing an automated model generation approach.
## 3 Results and Discussion
### Simulation Results of Insulation Roof and Wall  
CASE-C: Thermal insulation in external walls and roofs has become a widespread energy-saving measure for village buildings in China. Extruded polystyrene and expansion polystyrene boards, commonly known as XPS and EPS boards, are generally used as insulation materials. An extruded polystyrene board was selected as the insulation material for the simulation because of its superior adhesive properties. Insulation materials (XPS) with different thickness (10-100 mm) were added to the roof (CASE-C roof) or walls (CASE-C wall) to assess their effect on annual energy consumption.  
As shown in Figure 13, thickening the insulation materials of the roof from 0 to 100 mm led to a decrease in winter energy requirements to 144.95 kWh/m\({}^{2}\), with an energy-saving rate of 23%. Accordingly, thickening the insulation materials of the walls from 0 to 100 mm  
Figure 11: Energy consumption of various equipment of the CASE-X.  
Figure 12: Working diagram of using solar energy and a sunroom in summer and winter.  
could also decrease the winter energy requirement to 130.58 kWh/m\({}^{2}\), for an energy-saving rate of 30.5%. It is evident that the insulation material of the walls was more effective because the exterior wall area was larger than that of the roof. More importantly, the energy-saving rate decreased slowly when the thickness of the insulation materials was increased beyond 30 mm, according to the trend shown in Figure 13. Considering the economic factors, it is recommended to choose 30 mm XPS, which was found to be the most efficient.  
By simulating the impact of different XPS thicknesses on the energy consumption of tubular houses, a curve regression simulation was performed on the data. The relationship between energy consumption and XPS material thickness was established using a highly fitting logarithmic curve. The specific energy consumption (EIR) and thickness of the roof insulation material XPS (N) were fitted to the following logarithmic function: EIR = 177.469 \(-\) 7.317 \(\times\) In(N), where N = 10, 20, 30, \(\ldots\), 90, 100 (see Equation (1) and Table 3), with a fitting index \(R^{2}\) = 0.969 and a Pearson correlation coefficient \(p\) < 0.0001, much lower than the limit of 0.01, indicating a high degree of fitness, as shown in Figure 14. The specific energy consumption (EIW) and thickness of the wall insulation material XPS (N) were fitted to the following logarithmic function: EIH = 194.114 \(-\) 14.173 \(\times\) In(N), where N = 10, 20, 30, \(\ldots\), 90, 100 (see Equation (2) and Table 4), with a fitting index \(R^{2}\) = 0.986 and a Pearson correlation coefficient \(p\) < 0.0001, much lower than the limit of 0.01, indicating a high degree of fitness, as shown in Figure 15. A highly fitting logarithmic curve may rigorously guide the choice of insulation material thickness in the energy-saving renovation of other tubular houses.