esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Pacemaker  
A compact meander line planar implantable antenna for a pacemaker application operating at 402.5 MHz with a bandwidth of 33.5% is presented by [PERSON] et al. in Ref. [90]. The proposed antenna is implemented on a FR-4 substrate with \(\varepsilon_{r}=4.7\) and tan \(\delta=0.025\) with a size of 30.5 mm \(\times\) 21.02 mm \(\times\) 6.4 mm, as shown in Figure 9. The antenna performance is evaluated through simulation in a multi-layer human body model.  
Figure 10 shows a tiny and compact implantable planar antenna with the size of 3 mm \(\times\) 3 mm \(\times\) 0.5 mm as presented in Ref. [25] for a wireless cardiac pacemaker. Rogers 3010 is used as a superstrate and substrate where \(\varepsilon_{r}=10.2\) and tan \(\delta=0.0023\). The antenna is optimized and loaded with a defective slotted structure to improve the efficiency and overall performance of the antenna in an ISM frequency band of 2.4 to 2.48 GHz. The definite bandwidth of the antenna is 22%, with the peak gain of \(-24.9\) dBi.  
Figure 9: Compact meander line planar implantable antenna for a pacemaker [90]. Reproduced with permission from an open-access article from the _Indonesian Journal of Electrical Engineering and Computer Science_ (CC-BY).  
A triband spiral shaped implantable antenna is presented by [PERSON] et al. in Ref. [PERSON] et al. (2018) with slotted ground operating at 402 MHz, 1.6 GHz and 2.45 GHz for a leadless pacemaker system. The size of the antenna is 7 mm \(\times\) 6.5 mm \(\times\) 0.377 mm, where Rogers RT/Duroid 6010 with \(\varepsilon_{r}\) = 10.2 and tan \(\delta\) = 0.0035 is utilized as a superstrate and substrate, as shown in Figure 11. The gains of the antenna at the three different frequencies are \(-\)30.5 dBi, \(-\)22.6 dBi, \(-\)18.2 dBi, respectively, with bandwidths of 36.8%, 10.8% and 3.4%, respectively.
## 4 Discussion  
### Comparison with Other Methods  
To facilitate comparison with other methods, three other filtering methods were used in the Test 1-Test 3 of test data for experiments. These methods include two traditional methods, namely, the morphological filtering method (MF) [57] and the slope-based filtering method (SF) [34], as well as the recently proposed cloth simulation filtering method (CSF) [58]. The method results are compared in Table 7.  
Type I error divides the original ground points into a non-ground point set. The type II error is a wrong classification of non-ground points into the ground point set. The total error is computed as the proportion of type I and II errors to the total number of points. The smaller the total error, the more accurate the filtering result, so the result of the total error is the most important. According to the results of the total error, in Test 1 all methods perform well, while in Test 2 and Test 3, the total errors of MF and SF filtering methods are high, and the total errors of CSF and this method are low. This means that this method and the CSF method have higher accuracy and stronger terrain applicability.  
In addition, concerning overall filtering the total error, the average total error of CSF is 7.74%, the average total error of MF is 12.70%, the average total error of SF is 15.53%, the average total error of this method is 7.91%. Compared with the two filtering methods of MF and SF, the accuracy of this method was improved. Although the total filtering error of this method is 0.17% higher than that of CSF, the CSF method needs to set parameters according to different terrains. Compared with the CSF method, the advantage of this method is that there are fewer parameters to set, and there is no need to distinguish the type of terrain.  
Compared with other filtering methods, the proposed method shows certain advantages in all aspects and can achieve good filtering effects in three different terrains, thereby showing its high precision and strong universality.
## 5 Conclusions  
In this paper, three emerging swarm intelligence algorithms, namely the GWO, the WOA, and the SSA were investigated. To this end, several well-known test functions were utilized to assess the performance of the selected algorithms. Moreover, two different design cases, the design of a 50-element linear antenna array and the design of an aperture-coupled E-shaped patch antenna, were carried out to evaluate the operation of the SI algorithms. To further estimate their effectiveness, two independent statistical tests were applied, the Friedman test and the Wilcoxon signed-rank test. Numerical results demonstrated that the WOA outperforms the other algorithms in terms of average ranking as well as in 8 out of 10 well-known test functions. From the design case of the linear antenna array, we concluded that the best pSLL value was achieved by the GWO algorithm; yet the other SI algorithms exhibited satisfactorily competitive results. The employment of the WOA to the optimization problem of an aperture-coupled E-shaped antenna revealed the capability of the algorithm to design complex (a large number of parameters to be optimized) and compact (small size) structures as applications in antenna design optimization problems.  
Figure 11: Surface current distribution plot of the aperture-coupled E-shaped antenna at (**a**) 26.4 GHz and (**b**) 38.5 GHz (color scale in dB).  
Figure 10: 3D pattern of the antenna gain (realized gain) at (**a**) 26.4 GHz and (**b**) 38.5 GHz (color scale in dB).  
**Author Contributions:** The conceptualization of the paper was done by [PERSON] and [PERSON]. [PERSON] and [PERSON] performed the theoretical analysis and the simulations. [PERSON], [PERSON] and [PERSON] validated the theoretical analysis and the simulation results. [PERSON], [PERSON], [PERSON], [PERSON] and S.K.G. supervised the process. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the results and contributed to writing the manuscript. All authors contributed to reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Quadrotor Cascade Control System Design Based on Linear Active Disturbance Rejection Control
## 3 Controller Design
### Outer-Loop Sliding Mode Control  
The SMC algorithm is easy to implement, insensitive to disturbance, with a fast response, etc. Under certain conditions, it can rise up and down with small amplitude along the specified state trajectory, so SMC has a good robust performance. LESO solves the problem of jitter in SMC by observing the error of the system and treating the chattering of SMC as interference, which is compensated off by the sliding mode external loop control.  
In the mathematical model of the position channel, the tracking error is defined as:  
\[\delta_{1}=z_{1}-u_{d} \tag{12}\]  
where \(u_{d}\) is the expected signal of the system. \(z_{1}\) is the real-time estimation of the state \(u\) of the quadrotor control system by LESO.  
The design of the sliding surface [28] is shown below:  
\[\left\{\begin{array}{c}S_{1}=\alpha_{1}\delta_{1}+\delta_{2}\\ \dot{\delta}_{1}=\delta_{2}\end{array}\right. \tag{13}\]  
where \(I=x,y,z,\theta,\phi,\psi\) represents six channels of quadrotor; \(\alpha_{1}\) is an adjustable parameter.  
Derivation of the designed sliding surface gives:  
\[\dot{S}_{I}=\alpha_{1}\dot{\delta}_{1}+\dot{\delta}_{2} \tag{14}\]  
The commonly used convergence rates are equal convergence rate, exponential convergence rate, power convergence rate, and general convergence rate. When selecting the convergence rate, the dynamic quality of the convergence motion can be guaranteed by using a suitable control method.  
\[\dot{S}_{I}=-\lambda\text{sgn}(s)-\eta s \tag{15}\]  
where \(\lambda>0\), \(\eta>0\).  
Combining Equations (14) and (15) it is shown:  
\[\begin{array}{c}\dot{S}_{I}=\alpha_{1}\dot{\delta}_{1}+\tilde{z}_{1}-\ddot{ u}_{d}\\ =\alpha_{1}\dot{\delta}_{1}+z_{3}+b_{0}v-\ddot{u}_{d}\end{array} \tag{16}\]  
where \(z_{1}\), \(z_{2}\), \(z_{3}\) are the LESO implementation estimate of the state of the quadrotor system; \(b_{0}\) is an adjustable parameter; considering Equation (16), the design of the outer loop control rate of the sliding mode is shown as follows:  
\[u_{I}=-\alpha_{1}\delta_{1}-\alpha_{2}\dot{\delta}_{1}-z_{3}+\ddot{u}_{d} \tag{17}\]  
where \(\alpha_{1}\), \(\alpha_{2}\) are an adjustable parameter. When suitable parameters are chosen, the tracking error \(\delta_{1}\) can be shown to converge, ensuring that the actual signal can be accurately tracked for the given signal.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sedentary Group** & **Active Group** & \\  & **(n = 46)** & **(n = 44)** & **\(p\)** \\ \hline Sex. n Male (\%) & 27 (66.6) & 18 (46.1) & 0.13 \\ Age (years) & 62.8 \(\pm\) 17.5 & 56.1 \(\pm\) 15.5 & 0.001 * \\ BMI & 27.3 \(\pm\) 6.3 & 26.1 \(\pm\) 5.3 & 0.125 \\ Charlson Index & 3.6 \(\pm\) 2.7 & 3.4 \(\pm\) 2.5 & 0.428 \\ Hospital stay (days) & 6.7 \(\pm\) 3.4 & 7.5 \(\pm\) 4.2 & 0.063 \\ ICU n (\%) & 0 (0) & 0 (0) & 1 \\ Pulmonary Ward n (\%) & 46 (100) & 44 (100) & 1 \\ CURB-65 & 1.7 \(\pm\) 2.7 & 1.8 \(\pm\) 2.3 & 0.812 \\ FINE & 88.8 \(\pm\) 32.3 & 87.8 \(\pm\) 39.8 & 0.823 \\ Borg Dyspnea & 3.2 \(\pm\) 2.8 & 2.6 \(\pm\) 2.2 & 0.079 \\ NRS Fatigue & 4.8 \(\pm\) 3.2 & 4.5 \(\pm\) 2.9 & 0.495 \\ \hline \hline \multicolumn{4}{c}{EUROQOL 5D-5L} \\ \hline Mobility & 1.9 \(\pm\) 0.6 & 1.6 \(\pm\) 1.2 & 0.001 * \\ Activity Daily Living & 2.4 \(\pm\) 1.1 & 1.7 \(\pm\) 1.3 & \textless{}0.001 ** \\ Self-Care & 1.4 \(\pm\) 0.6 & 1.2 \(\pm\) 0.4 & 0.002 * \\ Pain & 2.8 \(\pm\) 1.2 & 2.2 \(\pm\) 1.1 & 0.391 \\ Anxiety/depression & 2.1 \(\pm\) 0.9 & 1.8 \(\pm\) 0.8 & 0.055 \\ VAS score & 47.2 \(\pm\) 19.6 & 58.2 \(\pm\) 24.6 & 0.002 * \\ \hline \hline \multicolumn{4}{c}{WHODAS 2.0} \\ \hline Cognition & 9.6 \(\pm\) 4.1 & 9.5 \(\pm\) 5.2 & 0.78 \\ Mobility & 9.9 \(\pm\) 5.9 & 7.9 \(\pm\) 3.9 & 0.003 * \\ Self-care & 6.1 \(\pm\) 3.5 & 5.4 \(\pm\) 2.7 & 0.092 \\ Relations & 7.4 \(\pm\) 3.7 & 6.8 \(\pm\) 3.4 & 0.141 \\ Activity Daily Living & 9.0 \(\pm\) 5.7 & 7.4 \(\pm\) 4.7 & 0.011 * \\ Participation & 16.5 \(\pm\) 7.9 & 12.9 \(\pm\) 5.1 & \textless{}0.001 ** \\ Total & 62.5 \(\pm\) 26.6 & 53.4 \(\pm\) 17.6 & 0.002 * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of CAP patients. Concerning the health-related quality of life scores, the active group showed significant among-group improvement in activity of daily living (\(p<0.001\)) and VAS (\(p=0.039\)) scores. The sedentary group showed a significant among-group decline in the self-care subscale (\(p=0.002\)). Significant differences between groups at 3 months follow-up were observed for mobility, activities of daily living, self-care, pain, and global scores (\(p<0.001\)), with better results in the active group. Significant differences between groups were observed, with higher functional levels in the active group for all WHODAS 2.0 scores (\(p<0.001\)). The sedentary group had significant differences between discharge and 3-month follow-up, with a significant improvement in cognition (\(p<0.001\)) and activity of daily living (\(p<0.001\)), and a significant worsening in mobility (\(p=0.048\)), self-care (\(p<0.001\)) and total score (\(p=0.049\)). The active group significantly improved mobility (\(p<0.001\)), self-care (\(p=0.002\)), participation (\(p<0.001\)) and total score (\(p=0.002\)), but significantly worsened activity of daily living score (\(p<0.001\)).
## 7 The Influence of the Constraint Range on the Optimization Results
### The Influence of the Constraint Range for Cable Force Uniformity on the Optimization Results  
Holding all of the other constraints constant, the effect of varying the cable force uniformity constraint from 1600 \(\mathrm{kN}\leq T_{\mathrm{i}}\leq 0.4T_{\mathrm{pdi}}\) to 2000 \(\mathrm{kN}\leq T_{\mathrm{i}}\leq 0.4T_{\mathrm{pdi}}\) on the optimization outcomes is investigated. The variations in the structural states resulting from decreasing the cable force uniformity constraint range are presented in Figure 20. Variations in the cable force uniformity constraint significantly affect the main beam's maximum deflections, the maximum bending moments, the maximum tensile stresses at the top and bottom edges, the maximum bending moments in the pylons, and the maximum cable force distribution parameter. The impact on the other structural characteristics is less than 5%. In particular, the maximum positive bending moment in the main beam, the maximum tensile stress at the bottom edge of the main beam, and the maximum cable force distribution parameter all increase with a decreasing range of constraints on the cable force uniformity. The maximum negative bending moment in the main beam decreases with a decreasing range. The main beam's vertical displacement, the maximum tensile stress at the top edge of the main beam, and the maximum positive and negative bending moments in the pylons initially decrease and then increase with a decreasing range. Crucially, the results indicate that a cable force uniformity constraint of \(|P_{\mathrm{i}}|\leq 300\)\(\mathrm{kN}\) yields a smoother structural profile, smaller maximum positive and negative bending moments in the main beam and pylons, and a lower maximum cable force distribution parameter, suggesting the suitability of this constraint range.  
Figure 20: Variation in values of selected structural responses with decreasing constraint range of cable force uniformity. (**a**) Structural bending energy; (**b**) structural displacement; (**c**) structural bending moment; (**d**) structural normal stress; (**e**) cable force; (**f**) vertical support reaction.  
Based on the foregoing analysis, for the Xiangshan Port cable-stayed bridge with steel box girders, the vertical displacement of the main girder, the positive and negative bending moments, and the normal stresses at the top and bottom edges, as well as the positive and negative bending moments in the towers, are significantly influenced by the constraint ranges. The horizontal displacement of the pylon roof is significantly affected by the constraint ranges of both the main girder's vertical displacement and the pylon's horizontal displacement, while the effects of the other two constraint ranges are comparatively minor. The four constraint ranges exhibit a limited effect on the stresses on both sides of the pylon.
_Applied_  
_sciences_  
_Article_  
**Potential Use of Time-Lapse Surface Seismics**  
**for Monitoring Thawing of the Terrestrial Arctic**  
**[PERSON] 1,2,*(r), [PERSON] 1,2,3(r) and [PERSON] 1,2(r)**  
1 Department of Earth Science, University of Bergen, P.O. Box 7803, 5020 Bergen, Norway; [EMAIL_ADDRESS] (T.A.J.); [EMAIL_ADDRESS] (B.O.R.)  
2 ARCEx, UiT The Arctic University of Norway, P.O. Box 6050 Langnes, 9037 Tromse, Norway  
3 Department of Arctic Geology, The University Centre in Svalbard (UNIS), P.O. Box 156, 9171 Longyearbyen, Norway  
* Correspondence: [PERSON]  
Received: 9 February 2020; Accepted: 5 March 2020; Published: 9 March 2020  
**Featured Application: We investigate the feasibility of using seismic data acquired in repeated active experiments for monitoring changes in the degree of freezing in unconsolidated sediments.**  
**Abstract: The terrestrial Arctic is warming rapidly, causing changes in the degree of freezing of the upper sediments, which the mechanical properties of unconsolidated sediments strongly depend upon. This study investigates the potential of using time-lapse surface seismics to monitor thawing of currently (partly) frozen ground utilizing synthetic and real seismic data. First, we construct a simple geological model having an initial temperature of \(-5\)\({}^{\circ}\)C, and infer constant surface temperatures of \(-5\)\({}^{\circ}\)C, \(+1\)\({}^{\circ}\)C, \(+5\)\({}^{\circ}\)C, and \(+10\)\({}^{\circ}\)C for four years to this model. The geological models inferred by the various thermal regimes are converted to seismic models using rock physics modeling and subsequently seismic modeling based on wavenumber integration. Real seismic data reflecting altered surface temperatures were acquired by repeated experiments in the Norwegian Arctic during early autumn to mid-winter. Comparison of the surface wave characteristics of both synthetic and real seismic data reveals time-lapse effects that are related to thawing caused by varying surface temperatures. In particular, the surface wave dispersion is sensitive to the degree of freezing in unconsolidated sediments. This demonstrates the potential of using surface seismics for Arctic climate monitoring, but inversion of dispersion curves and knowledge of the local near-surface geology is important for such studies to be conclusive.**  
**Keywords: geophysics; permafrost; rayleigh wave; heat flux modeling; seismic modeling; rock physics modeling; seismic experiments; frozen ground; saline**
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 2 Theoretical Background
### Proportional Hazard Models  
Proportional hazard models are regression models used to incorporate the effect that a set of external variables has on the base hazard function of the process or system analyzed [19]. There are two types of proportional hazard models, parametric and semi-parametric. Parametric models are suitable for describing the behavior of lifetime data. In the literature, there are exponential, Weibull, gamma and lognormal models. However, only the exponential and the Weibull models can be considered as proportional hazard models and accelerated life models at the same time [6]. Since the Weibull model has been suggested as an accelerated life model [20] and proportional as well [21], it was applied in this investigation, and the following power function gives its hazard rate [20]:  
\[h(t,z_{t})=\frac{\beta}{\eta}\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta-1}\exp( \gamma\times z_{t}) \tag{4}\]  
where \(t\) is the time, \(z_{t}\) represents a row vector at time \(t\), \(\gamma\) is a column vector of the variables, \(\beta\) is the shape parameter, and \(\eta\) is the scale parameter of the Weibull distribution. This represents the hazard function of the Weibull proportional hazard model. Now, let us present the generalities of the Weibull distribution model.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 3 Bragg Fibers
The tedious deposition of numerous layers from solution or vapor phase in a tube can be avoided by making a sheet of dual- or triple-layer foil of the materials required, and winding it the required number of times around a smooth central rod [15]. The rod is later removed, and the jacketing tube applied. Individual layers of foil are consolidated when the preform is heated enough for melting or softening of one or both materials. The researchers at OmniGuide Communications Inc. published, in 2002, a paper [17] in which they stated that confinement loss can be as low as 0.003 dB/km at 1650 nm for a 17 layer Bragg reflector made of materials with refractive indices of \(n\) = 1.6 and \(n\) = 4.6. This study, unfortunately, omitted absorption and scattering resulting from contamination, irregularities, and inclusions in the reflector materials. The lowest loss measured in a hollow-core Bragg fiber at a typical telecom wavelength (1570 nm) was \(\approx\)5500 dB/km [15], and the interest of most researchers shifted to HC-PBGF and ARF fibers.
# Flavone Hybrids and Derivatives as Bioactive Agents
## 2 Flavone Hybrids with Different Pharmacophores
### Aryl Derivatives of Flavones  
The aryl-substituted flavones are summarized in Figure 19. Compound **54** was prepared via Suzuki cross-coupling between the corresponding 3-bromoflavone and 3,4,5-trimethoxybenzeneboronic acid under usual reaction conditions and using the required protecting groups. Derivative **55** was obtained via Knoevenagel condensation of the 2,3-dihydroavone (i.e., the appropriate flavanone) with 3,4,5-trimethoxybenzaldehyde in the presence of a piperidine base. Compounds **54** and **55**, together with some similar derivatives, presented moderate tubulin polymerization-inhibitory activity [62].  
Aryl derivatives coupled with the 2-phenyl group via an amide bond (**56**) were synthesized using linear synthetic methods. The amide group was formed by the reactions of the corresponding carboxylic acids and amines [63; 64; 65]. Compounds **56a,b,c,d** showed potent anticancer activity in several cancer cells, and proved to be potential lead molecules for further investigations [66]. It is important to emphasize that compound **56b,d** exhibited \(IC_{50}\) values above 100 uM in two non-tumor cell lines (L132 (human lung epithelial cell)) and IOSE-80 PC (human ovarian epithelial cell)) using MTT cells viability assay. This suggests that these derivatives are selective for cancer cells.  
Further effects were also obtained for **56e,f,g** (where **g** is the same as **d**), showing a promising possibility for the development of anti-inflammatory agents on the base of biological investigations of proinflammatory mediators [33]. However, it should be noted that no positive control was used during the determination of the \(IC_{50}\) values in connection with PGE\({}_{2}\) production inhibition.  
In the course of changing the methoxy substituents to methyl or halogen, especially chloro atom, the hit compounds obtained (**56h,i**) proved to have a potential inhibitory effect against the STE20/GCK-IV kinase family [63].  
Figure 19: Aryl derivatives of flavones coupled directly or with a short linker.  
Molecule **57** was selected from the many similar compounds [64] as the most potent derivative.  
Arylamino flavone **55** exhibited nanomolar antiproliferative activity in the MCF7 cell line (breast cancer, \(GI_{50}\) = 30 nM) and the HCT-15 cell line (colon cancer, \(GI_{50}\) = 60 nM).
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 3 Results and Discussion
### Effect of Heat Input on Tensile Strength
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Power (\(P\))**} & \multirow{2}{*}{**Welding Speed (\(v\))**} & \multirow{2}{*}{**Heat Input (\(q\))**} & \multicolumn{2}{c}{**Before Quenching**} & \multicolumn{2}{c}{**After Quenching**} \\ \cline{5-8}  & & & & **Tensile Strength** & **Fracture Position** & **Tensile Strength** & **Fracture Position** \\ \hline
**Unite** & **W** & **cm/min** & **J/cm** & **MPa** & **-** & **MPa** & **-** \\ \hline
1\({}^{\#}\) & 2200 & 75 & 1760 & 632.1 \(\pm\) 1.6 & BM & 1616 \(\pm\) 20.2 & Joint \\
2\({}^{\#}\) & 2600 & 75 & 2080 & 633.6 \(\pm\) 3.3 & BM & 1607 \(\pm\) 28.6 & Joint \\
3\({}^{\#}\) & 3000 & 75 & 2400 & 630.7 \(\pm\) 2.2 & BM & 1545 \(\pm\) 37.2 & Joint \\
4\({}^{\#}\) & 2600 & 150 & 1040 & 631.2 \(\pm\) 2.8 & BM & 1446 \(\pm\) 43.7 & Joint \\
5\({}^{\#}\) & 2600 & 125 & 1248 & 628.8 \(\pm\) 2.5 & BM & 1535 \(\pm\) 26.8 & Joint \\
6\({}^{\#}\) & 2600 & 100 & 1560 & 625.5 \(\pm\) 3.6 & BM & 1587 \(\pm\) 34.9 & Joint \\
7\({}^{\#}\) & 1800 & 75 & 1440 & 630.3 \(\pm\) 1.2 & BM & 1644 \(\pm\) 18.6 & Joint \\
8\({}^{\#}\) & 3400 & 75 & 2720 & 627.3 \(\pm\) 2.4 & BM & 1585 \(\pm\) 24.5 & Joint \\
9\({}^{\#}\) & 2600 & 65 & 2400 & 624.9 \(\pm\) 3.2 & BM & 1607 \(\pm\) 32.4 & Joint \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tensile test results of all the samples before and after quenching.