esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer
## 3 Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer (DHSMEO)
### Heterogeneous Hybrid Search-Based Concentration-Updating Strategy  
With the adaptive grouping strategy established in the previous subsection, the entire population is restructured into a dynamic dual-subpopulation structure. In this subsection, a heterogeneous hybrid search-based concentration-updating strategy is introduced into the dynamic dual-subpopulation structure to enhance the optimization efficiency. In the heterogeneous hybrid search-based concentration-updating strategy, the equilibrium search strategy derived from the standard EO is adopted in the kernel subpopulation, while the auxiliary subpopulation adopts the hunting coordination search strategy derived from the reptile search algorithm (RSA) [11]. The process of particle concentration updating using the hunting coordination search strategy is denoted as  
\[\mathbf{X}_{(i,j)}(l+1)=\mathbf{X}_{\text{eq}(\alpha,j)}(l)\mathbf{P}_{(i,j)}(l)r_{\text{R}}, \tag{14}\]  
\[\mathbf{P}_{(i,j)}(l)=\alpha+\frac{\mathbf{X}_{(i,j)}(l)-\mathbf{M}_{\text{X}(i)}(l)}\mathbf{{X}_{\text{eq}(\alpha,j)}(l)(\mathbf{X}_{\text{max}(j)}-\mathbf{X}_{\text{min}(j)})+ \epsilon}, \tag{15}\]  
\[M_{\text{X}(i)}(l)=\frac{1}{D}\sum_{j=1}^{D}\mathbf{X}_{(i,j)}(l), \tag{16}\]  
where the following applies: \(\mathbf{P}_{(i,j)}(l)\) indicates a percentage difference between the optimal equilibrium candidate and the \(i\)-th particle in the \(j\)-th dimension concentration value during the \(l\)-th iteration; \(r_{\text{R}}\) represents a rand value within \([0,1]\); \(M_{\text{X}(i)}(l)\) represents the average value obtained via the \(i\)-th particle at iteration \(l\); \(D\) indicates the dimension within each particle, and \(j=1,2,...,D\); \(\alpha\) is a sensitivity control parameter whose value is set to 0.1 according to the parameter setting of original RSA; and \(\epsilon\) is a small value.  
In the constructed heterogeneous hybrid search-based concentration-updating strategy, the hunting coordination search strategy adopted in the auxiliary subpopulation has a stronger local search capability compared to the equilibrium search strategy adopted in the kernel subpopulation, which contributes to assisting the kernel subpopulation in ulteriorly raising the optimization accuracy. Meanwhile, the search results of the auxiliary subpopulation can assist the kernel subpopulation in constructing the equilibrium pool, while the search results of the kernel subpopulation can provide the potential optimization direction for the auxiliary subpopulation. This mode further facilitates effective information sharing across the kernel subpopulation and auxiliary subpopulation. Therefore, under the dynamic dual-subpopulation adaptive grouping strategy, the heterogeneous hybrid search-based concentration-updating strategy can further strengthen the optimization efficiency by exchanging information obtained from different search strategies.
## 2 Methodology  
### Generalized Radial Flow Model  
A typical double-packer hydraulic test in fractured bedrock is done by injecting water between two packers into a section of a borehole under a constant head. One of the fundamental questions that arises is, where did that water in the borehole flow during the hydraulic test? The geometry of a fractured system, which represents the spatial distribution of fractures around a borehole, may dominate the flow geometry. Fracture geometric parameters, such as aperture, orientation, trace length, and spacing, may affect the hydraulic conductivity of the fractured rock formation and even the groundwater flow pattern, resulting in different flow models. When the density of fracture geometry is large, a spherical flow model (flow dimension \(n=3\)) might be applicable; while the density of fracture geometry is low, a linear (flow dimension \(n=1\)) or cylinder model (flow dimension \(n=2\)) would possibly be suggested. Indeed, most of the flow dimension in nature is a non-integral value. Due to the unknown flow dimension of a borehole site and its non-integral character, [PERSON] [23] developed a generalized radial flow model to determine this parameter from hydraulic testing data. In addition to obtaining flow dimension, the advantage of this model can improve the estimation accuracy of hydraulic conductivity (K).  
In [PERSON]'s theory, the generalized radial flow model was derived utilizing an n-dimensional spherical surface system focalized on a common source point. The areas (A) of these surfaces change with radial distance from the source (\(r\)). The area can be given by the following:  
\[\text{A}=\alpha_{n}r^{n-1} \tag{1}\]  
in which \(\alpha_{n}\) is defined as follows:  
\[\alpha_{n}=2\pi^{\frac{n}{2}}/\Gamma\left(\frac{n}{2}\right) \tag{2}\]  
where \(\Gamma\left(\frac{n}{2}\right)\) is the gamma function; \(n\) is the flow dimension that determines the changes in the through-flow area with distance from the source. Furthermore, Equation (1) also describes the power relation of the through-flow area to the radial distance.  
Based on Equations (1) and (2), the generalized groundwater flow equation can be expressed as follows:  
\[\frac{K}{r^{n-1}}\frac{\partial}{\partial r}\left[r^{n-1}\frac{\partial h}{ \partial r}\right]=S_{s}\frac{\partial h}{\partial t} \tag{3}\]  
where \(K\) is hydraulic conductivity, \(h\) is hydraulic head, and \(S_{s}\) is specific storage.  
The flow dimension \(n\) in Equation (3) can be determined from hydraulic test data with [PERSON]'s generalized radial flow model. The following results were used to recognize fracture network properties around a borehole.
## 4 Discussion
### Critical Review of the Emission Factors  
A general point of criticism of the development of prospective emission factors is the quality of the data basis and the underlying assumptions of the scenarios and the development of the future power supply. The focus here is not necessarily on the influence of a possible earlier coal phase-out, a different expansion of renewable energies, or the development of energy carrier prices, but rather on changes in energy-specific laws, assumptions on dispatch rules, grid restrictions, exchanged trading volumes, and storage management. The authors are thus aware that future emission factors can and will develop differently due to the actual evolution of power supply in Germany and Europe. Therefore, the focus of the analysis was on the calculation method for hourly prospective emission factors and the consideration of trading and storage effects. The scenarios used can always be updated and improved. This only changes the data basis, and the calculation model can still be applied.  
An inaccuracy in the calculation method is the exclusive consideration of the trade balance. Due to the export of virtual emissions and the import of relatively low-emission power from other European countries, the emission factor could reach even slightly lower values. To generate trading volumes between different countries in hourly resolution as endogenous model parameters requires very sophisticated energy system models with long calculation durations and is therefore not met in full detail in any publication known to the authors for prospective emission factors.  
Another possible inaccuracy is the determination of virtual emissions of the charged power. The created discharge factor \(E/S_{iO,y}\) is an annual average of the virtual stored emissions. This is necessary, because otherwise it would not be possible to assign an emission value to the power usage at the corresponding hour. Average storage durationsand intervals could perhaps be used as an indicator and the annual discharge factor could be differentiated more precisely.  
The MEF is strongly influenced by the assumptions made on the dispatch of the power plants. In the future power generation system in Germany, bioenergy could become a marginal power plant. The Renewable Energy Sources Act-tariff ensures that a high capacity factor of renewable energy plants is most economical. This is also probably the most likely way to reduce GHG emissions at the present time. That is why bioenergy power plants in Germany are still inflexible, even though there is a flexibility bonus. Because of the increased need for flexibility due to a decrease and negative residual load and the integration of renewables, the currently typical dispatch of power plants could be changing. A marginal change in demand can be compensated by the availability of a flexibility option rather than by an adjustment in load by conventional power plants. Furthermore, the method shown does not take into account the occurrence of non-grid related excess power but could be subject to future research.
## 5 Effect of RAS on Performance Properties of Asphalt Binders and Mixtures
### Fatigue Performance of RAS-Modified Asphalt Binders and Mixtures  
Fatigue failure is one of the main issues in flexible pavements due to the cumulative damage induced by traffic load repetitions. [PERSON] et al. found that RAS-modified mixtures had almost the same fatigue resistance as neat mixtures. Moreover, in a statistical analysis, it was noticed that an RAS-modified binder had relatively higher failure stress and lower failure strain [40]. [PERSON] et al. concluded that 3% RAS-modified asphalt mixtures had almost better fatigue resistance than a control mix [41]. [PERSON] et al. used both RAP and an asphalt shingle modifier in asphalt mixtures and noticed a significant improvement in the fatigue resistance of the modified mixtures [42]. [PERSON] et al. used the strain energy values (Jc) to assess the fatigue performance and found that the Jc values for the control mixes were larger than those for the RAS-modified mixtures. Moreover, rejuvenated mixtures had lower (Jc) values than RAS-modified mixtures [43]. [PERSON] et al. found that the addition of 5% RAS and 5% RAP in PG 64-22 was the optimum value to increase the fatigue resistance of the modified mixtures [1].  
[PERSON] et al. determined that a 2.5% RAS-modified PG 46-34 binder had relatively better fracture energy and fatigue resistance than the PG 58-28 one [45]. [PERSON] et al. used both stress and strain control modes to assess the fatigue resistance of RAS-modified mixtures. The results showed that an increase in RAS content decreased the fatigue resistance in strain control mode and improved the fatigue resistance in stress control mode [46]. [PERSON] et al. performed semicircular bending tests and found that rejuvenator addition in RAS-modified mixtures decreased the strain energy even below the minimum threshold value (0.5 kJ/m\({}^{2}\)), i.e., modified mixtures had less intermediate temperature crack resistance [43]. [PERSON] et al. used RAS/RAP mixtures and found that an increase in the asphalt binder content above the optimized value could increase the fatigue crack resistance of the modified mixtures [60]. [PERSON] et al. suggested the addition of 40% RAP and up to 5% shingles in order to improve the fatigue resistance of conventional asphalt mixtures [36].
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 6 Resulting Design Methodology and Case Studies
### Design Flow and Methodology  
Early in the design process, a SN designer would follow the steps shown in the design flow graph presented in Figure 21 in order to make high-level decisions about the right COTS components to use and the possibility to meet the reliability, energy, and latency requirements. At this point, all of the model inputs, which are outlined in Table 1, should be available. Then, the designer needs to determine the system's noise density by measuring the background noise in the deployment site and taking into account the receiver's noise figure. Before estimating the energy consumption per measurement, it should be made sure that an acceptable communication range can be achieved. Therefore, starting by using the highest data rate and the lowest output power level on the transceiver, the designer estimates the range and iterates between the two parameters while always maximizing the data rate. Once a good communication range is achieved for a given BER target value, the energy consumption per measurement while using different reliable data transfer schemes is estimated. Finally, given the application requirements, if a good trade-off between energy, latency, and reliability is found, the designer would proceed with the implementation phase. Otherwise, other COTS components need to be selected.  
Figure 21: SN design flow graph.
## 2 Analytical Representation  
### Coprime Sensor Array Structure  
In this part, the formation of the coprime array structure is discussed briefly, we then perform the derivation of the received signal. The coprime array comprises two coprime uniform linear arrays with \(P\) and \(Q\) antenna elements as shown in Figure 1. The inter-element distance between two consecutive antennas of the first sub-array is \(Qd\), and in the second sub-array is \(Pd\), which is more significant than \(\lambda/2\) in both arrays. By merging these two sub-arrays, we obtain the coprime antenna array with the shared the first antenna element. \(P\) and \(Q\) are coprime integers, \(d\) is equal to the wavelength of the received signal and \(P>Q\) for the sake of generality. \(M_{P}\) and \(M_{Q}\) shows the indexes of sub-array 1 and 2 while \(M_{coprime}\) demonstrates the positions of the coprime array. This coprime array consists of \(P+Q-1\) antenna elements having non-uniform inter-element distance among them, as depicted in the following equations.  
\[M_{P}=\{0,Qd,2 Qd,3 Qd,...,(P-1)Qd\} \tag{1}\]  
\[M_{Q}=\{0,Pd,2 Pd,3 Pd,...,(Q-1)Pd\} \tag{2}\]  
\[M_{coprime}=\{0,Qd,Pd,2 Qd,2 Pd,...,(Q-1)Pd,(P-1)Qd\} \tag{3}\]
## 4 Discussion
[22] pointed out that the assessment of postural regulation depends on the level of athletes within a sport. Accordingly, the results of top athletes in a sport are hardly comparable to recreational athletes of the same sport. Within this consideration, only the highest national level athletes were examined in the technical-coordinative sports of ski jumping, diving, and gymnastics. In the team sport of basketball, our subjects trained at least within professional structures (first national league). [PERSON] et al. [23] reported that the level of postural control depends significantly on the qualification level (national vs. international). Consequently, it can be assumed that the technical-coordinative sports have a better internal representation of their posture as well as a more efficient cerebellar integration of the different afferent subsystems (visual, peripheral-vestibular, and somatosensory) than the athletes of the team sports. Gymnasts were one of the most powerful groups in terms of postural control. This group demonstrated the highest performance level for stability indicator in the more demanding test positions. Similar findings were reported by [PERSON] [21] and [PERSON] et al. [2], particularly when compared to team sports (basketball, soccer). A possible explanation for these findings is that a key physical characteristic of these team sports is strong dynamic performance, and the test conditions applied in this study were more static and may not have accurately replicated their more sport specific conditions. This is mainly done by flexing the knee and hip joints and is a strategy to lower the body's center of gravity. Body height and weight are well known to be factors affecting Center-Of-Pressure (COP) parameters [21; 24]. In this respect, [PERSON] et al. [24] observed a significant correlation between these physical characteristics during more challenging test conditions among young adults (eyes open (EO) eyes closed (EC)). In comparison, the divers showed greater heterogeneity in the test positions that did not require the visual system (NO vs. NC, PO vs. PC). More specifically, the sway path in anterior-posterior direction was influenced by the aforementioned anthropometric parameters (body height: EO: r = 0.39, EC: r = 0.70; body weight: EO: r = 0.44, EC: r = 0.67). Furthermore, [PERSON] et al. [25] reported that the physique in women, but not in men, correlates with the area of sway. [PERSON] et al. [26] suggested adjusting factors like activity level, injury, and health history, for better comparability of individuals with different anthropometric characteristics. Therefore, the interpretation of absolute values has to be done with caution. However, the ski jumpers showed little differences in postural regulation during positions with eyes open or closed and seemed largely homogeneous.
## 4 Simulations
### Simulation Results  
#### 4.2.1 Non-Encapsulated MAPbI3 Samples  
The IV curves obtained by the simulation software for the non-encapsulated MAPbI3 samples, before and after the irradiation process, are shown in Figure 11. These results indicate an efficiency decrease taking place in the samples. This decrease is slightly higher in the samples exposed to solar irradiation than in the ones exposed to the UV lamp. These results are mainly due to exposure to higher levels of irradiation after 70 min: 50,432 W/m\({}^{2}\) under full solar irradiation versus 47,820 W/m\({}^{2}\) under UV lamp. In particular, it can be seen that the degradation affects the current intensity of the device, showing a notable reduction in the short-circuit current, going from 21.66 mA/cm\({}^{2}\) (pristine) to 19.07 mA/cm\({}^{2}\) (exposed to UV lamp) and 19.28 mA/cm\({}^{2}\) (exposed to full solar irradiation), while the open circuit voltage remains constant, around 1.08 V (Table 2).
## 4 FWA Design Considerations, Challenges, and Solutions
### Regulatory Policy  
There is a significant need for favorable policy from governments at all levels, as this will play a vital role in the 5G FWA realization. Similarly, the regulatory bodies should ease related restrictions in an attempt to facilitate the operator's potential for the radio units' deployment in the neighborhoods [9; 178; 327; 328]. In addition, as a notable advantage of the 5G system is the capability for supporting different spectrum bands, the government regulators should open up new spectrum bands and allocate more spectrum for the network deployments [6]. For instance, to be able to launch 5G FWA commercially, Orange is anticipating that Romania's government will auction a 26 GHz in the second half of 2019 [329]. This will enable the operators to have access to a wide range of spectrum bands that will facilitate high-speed broadband connectivity for customer devices such as smartphones, laptops, and TVs [6; 329]. Furthermore, there have been notable concerns about the probability of regulation and license regarding the narrowband. This can be attributed to the growing use of the unlicensed spectrum for different applications and services, which may attract government attention in regulating and issuing the license for it. Consequently, these could have notable effects on the existing communication systems [4; 254].
## 2 Framework of Nonlinear Flutter Analysis
### Cubic Damping and Cubic Torsional Stiffiness  
Taking into account the structural nonlinearity of the bridge's stiffness and the possibility of obtaining strong nonlinear damping using tuned mass dampers, the vibration equations of the abovementioned flutter motion Equations (1) and (2) are expressed in the following form:  
\[m_{h}\ddot{h}+C_{h}\left(\dot{h}\right)+k_{h}h=F(L) \tag{4}\]  
\[I\ddot{\alpha}+C_{\alpha}\left(\dot{\alpha}\right)+k_{\alpha}(\alpha)=F(M) \tag{5}\]  
where \(C_{h}\left(\dot{h}\right)\) and \(C_{\alpha}\left(\dot{\alpha}\right)\) represent the nonlinear damping values of the flutter system in the vertical bending and torsional degrees of freedom, \(k_{\alpha}(\alpha)\) is structural nonlinear stiffness, and \(F(L)\) and \(F(M)\) are self-excited forces of flutter.  
The strong nonlinear damping in the vertical bending and torsional degrees of freedom is expressed as a function of vertical velocity \(h\) and angular velocity \(\dot{\alpha}\); then, the vertical and torsional damping value \(c_{h}\) and \(c_{\alpha}\) are written as follows:  
\[C_{h}\left(\dot{h}\right)=c_{h1}\dot{h}+c_{h2}\dot{h}^{3} \tag{6}\]  
\[C_{\alpha}\left(\dot{\alpha}\right)=c_{\alpha 1}\dot{\alpha}+c_{\alpha 2}\dot{ \alpha}^{3} \tag{7}\]  
In the equation, linear damping coefficients are given by \(c_{h1}=2\xi_{h}m_{h}\omega_{h}\) and \(c_{\alpha 1}=2\xi_{\alpha}^{2}l\omega_{\alpha}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(m\) & 65,649 (kg/m) \\ \(I\) & 7,808,150 (kg:m) \\ \(B\) & 41.7 (m) \\ \(\xi_{h}\) & 0.005 \\ \(\xi_{\alpha}\) & 0.005 \\ \(\omega_{h}\) & 0.45497 (rad/s) \\ \(\omega_{\alpha}\) & 1.33285 (rad/s) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of the suspension bridge.  
Figure 1: Motion model of bridge deck section.  
Considering the nonlinear stiffness in the torsional degree of freedom of the suspension bridge, the bending and torsional stiffness are expressed as follows:  
\[k_{h}h=m_{h}\omega_{h}^{2}h \tag{8}\]  
\[k_{\alpha}(\alpha)=k_{\alpha 1}\alpha+k_{\alpha 2}\alpha^{3} \tag{9}\]  
In the equation, stiffness \(k_{h}\) in the vertical degree of freedom is set as a linear function of vertical displacement \(h\). \(k_{\alpha}\), \(k_{\alpha 1}\) represent the first-order coefficient of torsional stiffness, while \(k_{\alpha 1}=I\omega_{\alpha}^{2}\), and \(k_{\alpha 2}\) represent the cubic term coefficient of torsional stiffness.  
The cubic damping coefficient and cubic torsional coefficient are determined in Table 2.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Monitoring Set-Up
#### 7.2.3 Behavior of a Defect during Time  
The behavior of a defect over time can be analyzed by using a deteriorating defect emulator, such as a glued mass. The reflection of a glued mass deteriorates quickly over time, thanks to the stresses caused by trains and to the progressive corrosion of the rail under the glue. A monotonically increasing defect, similar to a growing crack, can be simulated if the reflected signals recorded during the time window starting at the installation of the defect emulator and ending at its detachment are time reversed [9]. The size of the mass representing the crack has to be selected to provide a realistic reflection, such as a small transverse crack in the railhead. Reflections from rail welds can provide a useful reference during testing; furthermore, if these welds can be detected, it is believed that a crack can be found well before it reaches a critical size [44].