esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this research, the spatial distribution of events monitored by rock acoustic emission is analyzed. The 3D convex hull algorithm and the MVEE algorithm were adopted to evaluate the single-cracked space formed by the theoretical distribution model, and the following three conclusions were obtained:  
(1) AE events reflect the distribution of the real fracture, and it is considered that the microcracks expend with increasing stress in the stress direction. The theoretical distribution pattern space was proposed to characterize the crack space of a single-cracked fracture.  
(2) The 3D convex hull, MVEE, and TDPS methods were applied to calculate the characteristics of the crack space. The area and volume results for all three methods demonstrated similar evolutionary behavior as the spatial scale decreased. The evolution pattern of area and volume was established. It was found that the 85% position is the scale on which the theoretical distribution and the MVEE values became closer to each other in three methods.  
(3) The variations in the deviation angle between the MVEE and TDPS methods were compared. It was found that as the spatial interval decreases, there is a tendency for MVEE and TDPS methods to have smaller offset angles in the principal \(X\)-axis. Similar trends were found for the Y-axis and Z-axis results, but a tendency to increase was observed after 65%.  
Consistent patterns of variation were obtained under conditions with different microcrack numbers and distributions, indicating that the present method is feasible. By analyzing the variation in area, volume characteristics, and offset angle through the spatial scale, 65% to 85% was selected as a reliable characterization interval for the crack space in sandstone.  
Conceptualization, [PERSON] ([PERSON]); methodology, X.H. ([PERSON]); data curation, Z.B., B.Z.; writing--original draft preparation, X.H. ([PERSON]); writing--review and editing, C.W., H.Z., X.H. ([PERSON]), X.S., T.W., X.L.; funding acquisition, X.H. ([PERSON]), X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Fund of the Institute of Geophysics, China Earthquake Administration (grant number DQJB21K50), and Wuyi University introduced talents and started scientific research project (grant number YJ202202).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We thank the anonymous reviewers for their support to this study.
# A Compact Wideband Active Two-Dipole HF Phased Array
## 2 Theoretical Model of the Antenna Array
It is based on the integral equation method presented by [PERSON] and [PERSON] and by [PERSON] [15]. For the aforementioned, the effect of the mutual coupling on the radiation pattern between two parallel driven elements is modelled in Figure 4. Two isolated driven elements with 50 \(\Omega\) purely resistive impedance, installed as a phased array, now exhibit an active impedance of \(35-\mathrm{j}20\ \Omega\) and \(65+\mathrm{j}20\ \Omega\), respectively. In this case, both dipoles are in phase and terminated at the 50 \(\Omega\ 0^{\circ}\) combiner input ports. The reason for a \(0^{\circ}\) instead of \(90^{\circ}\) hybrid combiner is due to the phase error that is more evident on the oscilloscope screen. Based on Figure 4, the phasing error and the current imbalance can be calculated by the use of Equations (7) and (8):  
Figure 1:0  
Figure 1:1  
The simulation results are demonstrated in Figure 5 as computed at the input ports of the combiner. The first two waveforms, at the top, present the voltages at the input ports of the 50 \(\Omega\) combiner. Based on Figure 5, a current imbalance and a small phase shift occurs between the first two waveforms. This is caused by the coupling between the two driven elements of the array. The other two waveforms at the bottom of Figure 5 demonstrate the two driven elements when terminated into a very high input impedance port; therefore, no phase error and current imbalance occurs. Figure 1:2 The equivalent circuit of the phased array affected by mutual coupling. Figure 1:3 Radiation patterns of two isotropic sources spaced from 0.05 \(\lambda\) to 0.5 \(\lambda\) with a \(90^{\circ}\) phase difference feed. The impact of the above is presented in Figure 6 by applying the current values and phase shift in Equation (5). The brown color cardioid radiation pattern reflects ideal conditions. The mutual coupling between the two driven elements of the array affects the radiation pattern as presented by the blue color diagram. It has a small back lobe and a slightly different form. As a result, if the current imbalance and phase error goes far beyond ideal values, then the radiation pattern of the array is expected to be less directional and the F/B ratio will decrease.
## 2 Instrument Overview
### Operational Principle of the Microwave Antenna  
The operational principle of the GeoMWR is shown in Figure 2. Since the GeoMWR is designed to operate in GEO (where the relative motion of the Earth and the satellite are nulled), it requires a two-dimensional (2-D) beam scanning mechanism to realize the  
Figure 1: The geometry of the GEO microwave antenna that was used in this study.  
narrow-beam coverage in a regional viewed scene. The GeoMWR implements 2-D beam scanning in a combination of 1-D local fast scanning (LFS) and 2-D overall slow scanning (OSS) [18]. In the process of 1-D LFS, the moving part in the QOF system rotates at a high speed around its rotating shaft under the control of the servo-drive system, driving the antenna beam to conduct conical scanning around the cone axis that is parallel to the focal axis of the MR. Throughout the entire scan cycle, the antenna beam receives the radiation from the viewed scene, the hot calibration source, and the cold cosmic background in turn (Figure 2a). Along with the 1-D LFS, the satellite platform performs 2-D OSS along the east-west and north-south directions. The GEO satellite scans the antenna beam in the east-west direction to produce an east-west longitudinal swath. At the end of a swath, the GEO satellite steps in the north-south direction to a new scan swath (Figure 2b) [29]. Since ABP determination outside the antenna is much simpler than inside the antenna, the 2-D OSS effect on the ABP is not considered in developing the ABP determination model.  
In the real case, the broadband antenna beam transmitted from the viewed scene is first reflected from the MR to the FSR. From the FSR, the antenna beam is reflected again to the SSR and finally enters the QOF system, where it is divided into separate beams and received by the feed horns working at different frequency bands. To facilitate the development of the ABP determination model, we assume that the propagation direction of the antenna beam is reversed: the antenna beam is emitted from the QOF system (path L1) and is reflected off the SSR (path L2), the FSR (path L3), and the MR (path L4) in turn (as shown in Figure 3, where the thick purple lines represent the pointing vectors of the beam (i.e., the maximum gain direction)). When the moving part in the QOF system is rotated, the emergent beam generates a cone-shaped scan pattern, as previously discussed.  
Figure 3: The reverse propagation path of the beam within the GEO microwave antenna.  
Figure 2: The operational principle of the GEO microwave antenna in this study. (**a**) 1-D LFS. (**b**) 2-D OSS.
## 3 Case Studies
### Consequence Analysis & Design Scenarios  
To estimate the consequences, different characteristics of the various fires are analyzed. This includes exits blocked by a fire, fire in concealed spaces, smouldering fires, challenging fires and a robustness check. These characteristics are described in further detail below.  
The FSVM associates each performance requirement that has been individuated with the hazard identification process to a certain number of design scenarios to be tested (see Table 1.2 of FSVM Handbook [21]). In the specific case, the performance requirement is the DP6 (Paths of Travel to Exits), which requires only 4 design scenarios to be modeled (BE, CS, CF, RC). The SF scenario in our model is assumed to produce no casualties and has been not modelled. The RC scenario is required to be one where a safety measure (e.g., detection, sprinkler, alarm) is not working as expected; in the T-H-O-Risk model the RC event is included in the analysis of the DTS and performance solution. The details of the numerical experiments are shown in Table 3 and the simulations yield the results as presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Scenario** & **Numerical Experiment \#** & **Solution** & **Fire Spread** \\ \hline Fire blocks evacuation route & BE1 & Performance & Yes \\  & BE2 & Performance & No \\  & BE3 & DTS & Yes \\  & BE4 & DTS & No \\ Fire starts in concealed space & CS1 & Performance & Yes \\  & CS2 & Performance & No \\  & CS3 & DTS & Yes \\  & CS4 & DTS & No \\ Robustness Check & RC1 & Performance & Yes \\  & RC2 & Performance & No \\  & RC3 & DTS & Yes \\  & RC4 & DTS & No \\ Challenging fire & CF1 & Performance & Yes \\  & CF2 & Performance & No \\  & CF3 & DTS & Yes \\  & CF4 & DTS & No \\ Fire in a normally unoccupied room threatens & UT & Not required \\ occupants of other rooms & SF & Not required \\ Smouldering fire & SF & Not required \\ Internal surfaces & IS & Not required \\ Structural stability and other properties & SS & Not required \\ Horizontal fire spread & HS & Not required \\ Vertical fire spread involving cladding or & VS & Not required \\ arrangement of openings in walls & & Fit & Not required \\ Fire brigade intervention & UF & Not required \\ Unexpected catastrophic failure & UF & Not required \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of experiments for FSVM design scenarios.
# Nonlinear Dynamics of an Electromagnetically Actuated
## 6 Analysis of the Stability for the Primary Resonance
### Global Stability by Lyapunov Function  
The governing equation of an electromagnetically actuated cantilever beam (19) can be written by adding the control input \(U\) in the following form:  
\[\begin{array}{l}\dot{z}_{1}=z_{2}\\ \dot{z}_{2}=-cz_{2}-\Omega_{0}^{2}z_{1}-(k-8a_{0})z_{1}^{3}+12a_{0}z_{1}^{5}+ 16a_{0}z_{1}^{7}+f\cos\omega\tau+U\ \,\ \ \ \omega=\Omega_{0}+\delta\varepsilon\end{array} \tag{68}\]  
The tracking errors \(e_{1}\) and \(e_{2}\) are as follows:  
\[e_{1}=z_{1}-\overline{z},\ \ \ e_{2}=z_{2}-\dot{\overline{z}}+\theta e_{1} \tag{69}\]  
where \(\dot{\overline{z}}\) is the approximate analytical solution of Equations (19) and (20) above that obtained by OAFM, \(\theta\) is a positive parameter, and the control \(U\) will be defined later.  
To define the errors of the parameters and the control \(U\) that appear in Equation (69), we introduced the following parameters:  
\[\alpha_{1}=k-8a_{0};\ \ \ \alpha_{2}=12a_{0};\ \ \ \alpha_{3}=16a_{0} \tag{70}\]  
If \(\overline{c}\), \(\overline{\Omega}^{2}\), \(\overline{\alpha}_{1}\), \(\overline{\alpha}_{2}\), \(\overline{\alpha}_{3}\) and \(\overline{f}\) are so-called estimated parameters [19], then the estimated errors of parameters are defined as follows:  
\[\widetilde{c}=\overline{c}-c;\ \ \ \ \widetilde{\Omega}_{0}^{2}=\overline{\Omega}_{0 }^{2}-\Omega_{0}^{2};\ \ \ \ \widetilde{\alpha}_{1}=\overline{\alpha}_{1}-\alpha_{1};\ \ \ \ \widetilde{\alpha}_{2}=\overline{\alpha}_{2}-\alpha_{1};\ \ \ \ \widetilde{\alpha}_{3}=\overline{\alpha}_{3}-\alpha_{3};\ \ \ \ \widetilde{f}=\overline{f}-f \tag{71}\]  
The Lyapunov function is chosen using the Pontryagin principle in the following form:  
\[V(e_{1},e_{2},\widetilde{c},\widetilde{\Omega}_{0}^{2},\widetilde{\alpha}_{1 },\widetilde{\alpha}_{2},\widetilde{\alpha}_{3},\widetilde{f})=\frac{1}{2}( \lambda_{1}e_{1}^{2}+\lambda_{2}e_{2}^{2}+\lambda_{3}\widetilde{c}^{2}+ \lambda_{4}\widetilde{\Omega}_{0}^{2}+\lambda_{5}\widetilde{\alpha}_{1}^{2}+ \lambda_{6}\widetilde{\alpha}_{2}^{2}+\lambda_{7}\widetilde{\alpha}_{3}^{2}+ \lambda_{8}\widetilde{f}^{2} \tag{72}\]  
Figure 13: The determinant det[\(J\)] given by (63).  
Since \(e_{1}\) and \(e_{2}\) are known, from Equation (77), the estimation parameters \(\widetilde{c}\), \(\widetilde{\Omega}\), \(\widetilde{\alpha}_{1}\), \(\widetilde{\alpha}_{2}\), \(\widetilde{\alpha}_{3}\), and \(\widetilde{f}\) are known, and therefore, the input control \(U\) is known from Equation (74).
# Development of a Practical Evaluation Method for Tsunami Debris and Its Accumulation
## 1 Introduction
Empirical data have also been used to develop fragility functions for building damage (e.g., [13]), and debris drift processes have been analyzed by using numerical models of debris diffusion (e.g., [7]). All of these models require various parameters related to debris formation and debris drift, and they generally need to be tuned to target terrain conditions and debris characteristics. Therefore, at present, they are not general-purpose models suitable for practical use. Real-time tsunami inundation predictions based on DONET tsunami observations [14] have been used by local governments in Japan (e.g., Wakayama Prefecture, [15]). To implement predictions of debris accumulation as an additional function in a real-time tsunami inundation prediction system, a huge number of tsunami scenarios must be considered. However, the methods of [PERSON] and [PERSON] [11] and [PERSON] et al. [12]  
Figure 1: Tsunami debris and fire damage around Kadonowaki, Ishinomaki City—from [PERSON] et al. [6]. are not appropriate for effective use of conventional tsunami inundation data because of high economic and computational costs. A practical evaluation method for estimating the amount and thickness of accumulated debris, that can effectively use the results of inundation computations for multiple tsunami scenarios, would enable efficient prediction of tsunami debris accumulation and evaluation of the tsunami-related fire hazards, which depends on the distribution of accumulations of tsunami debris. In this study, we focused on the role of tsunami debris in the spread of tsunami-related fires and estimated production of debris by using conventional tsunami analysis results and empirically obtained building fragility functions. We then applied a previously developed practical evaluation method for tsunami debris accumulation [6] to the urban area of Owase City, Mie Prefecture. The prediction results, including the distribution of tsunami debris and its implications for estimating tsunami hazards, are discussed.
## 3 Results and Discussion
### Single Fiber Tensile Strength  
Table 3 reports the mechanical properties obtained from the single fiber tensile test of the IPF AR-glass fibers, unsized and after application of sizing and coating SB-MMT and CSB. It can be observed that the most significant increase in the tensile strength was obtained after application of the sizing. Compared to the unsized fibers, PTMO fibers have a 50% higher tensile strength. This is in accordance with results obtained in other studies and can be attributed to the ability of the sizing to increase the fiber tensile strength by mitigating the effect of existing surface defect and preventing the formation of new surface flaws [70].  
This phenomenon is usually called _healing effect_ and depends on the ability of the sizing to penetrate in the surface defects. Therefore, the _healing effect_ is correlated with the dimension of the sizing particles. Pure silane dispersions have been proved to enable healing a great part of the defect content [71], which is in accordance to the results obtained for unsized and PTMO fibers. The results obtained for SB-MMT and CSB suggest that the application of coatings on sized fibers does not lead to a further reduction of the number and severity of the surface flaws.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Signaling Required for Proposed PMIP6 Extension  
The main aim of the provided scheme is to achieve the low signaling requirement, handover efficiency and required buffering in a single solution. For this purpose, the scheme is based on location information to eliminate the additional signaling during RSS error and the multi-casting of IHR messages by sharing the target MAG-ID with LMA. As  
Figure 1: Working and signaling procedure of Loc-PMIPv6.  
aforementioned, the MAG shares its location information with its MNs. Therefore, the signaling requirement for handover is the location sharing of MAG with MNs. Furthermore, in the case of the RSS event, MN shares its information with pMAG using the L2 report. Finally, the signaling completed with LMA communicated the IHR message with nMAG.  
Based on the working of the proposed scheme (Loc_PMIPv6), MN will share its location information with its current MAG (pMAG) if an RSS event occurs. Subsequently, pMAG identifies target MAG and informs LMA regarding target MAG by sending a message that contains De-Reg PBU and nMAG-ID. With the help of nMAG-ID, LMA communicates with the target MAG using an IHR message. The signaling required for proposed Loc_PMIPv6 extensions is computed using Equation (1), where N is the number of MNs, the cost for sharing MAGs information with MN is represented by \(\text{C}_{\text{loc\_MAG}}\).  
\[\text{C}_{\text{LH-PMIPv6}}=\text{N}\cdot\text{C}_{\text{loc\_MAG}}+\mu_{ \text{c}}\cdot\text{T}\cdot\delta.\ \big{\{}\text{C}_{\text{L2 report}}+(\text{C}_{\text{IHR}}\cdot\text{C}_{\text{ LMA\_MAG}})\big{\}} \tag{1}\]  
In Equation (1), the cost of the L2 report is represented by \(C_{\text{L2 report}}\), \(\delta\) is the number of MNs attached to its associated MAG. \(\text{C}_{\text{IHR}}\) is showing the cost of IHR messages, \(\text{C}_{\text{LMA\_MAG}}\) is the number of hope between LMA and MAG. \(\text{M}_{\text{c}}\) is the cell crossing rate and it is calculated by using Equation (2). In Equation (2), cell radius, and velocity is represented by \(R\) and \(v\), respectively.  
\[\mu_{\text{c}}=\frac{2\,\
u}{\pi\,\text{R}} \tag{2}\]
## 2 Fault Identification Method for Thermal Generator Sets
### Fault Feature Extraction Method
#### 2.3.2 Short Time Fourier Transform  
Blade crack fault diagnosis is a problem that troubles scholars and engineers at home and abroad. Online and offline condition monitoring systems are widely used in rotating machinery. The key problem is how to choose the method to process the signal from the industrial field and obtain the fault characteristics. The short-time Fourier transform (STFT) is one of the earliest and the most basic methods used for time-frequency analysis [28]. The STFT is one of the most widely used algorithms in signal processing and fault diagnosis based on a detailed Fourier transform centered at each time point. In STFT, the signal is compared with window functions that are concentrated in both the time and  
Figure 2: The work flow diagram of the research framework.  
frequency domains. The STFT algorithm and the window function can be mathematically represented as follows:  
\[\begin{array}{rl}STFT_{x}(\tau,\ f)&=\int_{-\infty}^{+\infty}x(t)\ w^{*}(t-\tau) \ \mathrm{e}^{-\mathrm{i}2\pi/t}\mathrm{d}t\\ &=\int_{-\infty}^{+\infty}x(t)\left[w(t-\tau)\mathrm{e}^{\mathrm{i}\ 2\pi/t} \right]^{*}\mathrm{d}t\\ &=\langle x(t),\ w(t-\tau)\mathrm{e}^{\mathrm{i}\ 2\pi/t}\rangle\end{array} \tag{3}\]  
where, \(w(t)\) is the window function which has a user defined time duration; and \(x(t)\) is the waveform signal in the time domain.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## Data and Methodology
### IMERG Data  
Global Precipitation Measurement (GPM) mission was launched on 27 February 2014, as a successor of the Tropical Rainfall Measuring Mission (TRMM). GPM constellation incorporates passive microwave (PMW) and infrared (IR) satellites, providing global precipitation measurements within the range 60\({}^{\circ}\) N-60\({}^{\circ}\) S and better temporal and spatial analysis (see [25; 35; 36]).  
GPM consists of one Core Observatory and approximately 10 constellation satellites. The Core Observatory carries a Ku/Ka-band dual-frequency precipitation radar and a multi-channel GPM microwave imager, extending the measurement range of TRMM instruments. GPM provides three levels of precipitation-related products. The level-3 products are produced with the IMERG (Integrated Multi-satellite Retrievals for GPM) algorithm, which intercalibrates and merges precipitation estimates from all constellation microwave sensors, microwave-calibrated infrared satellite estimates, and monthly gauge precipitation data [37; 38]. It is important here to comment on the PMW sensitivity of retrieved rainfall, since the launch of GPM, the overland rainfall retrieval algorithm, is transitioning from an inversion technique based on rainfall-brightness temperature scattering relationships to a Bayesian framework consistent with the over-ocean algorithm [39]. GPM IMERG precipitation estimates are available from 12 March 2014 to present. The GPM IMERG products offer a relatively fine spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and high temporal resolution of 30 min, with a spatial coverage from 60\({}^{\circ}\) S to 60\({}^{\circ}\) N. The IMERG Final product [18] was chosen for our study and so, especially, was the IMERG (v05B) data for the period from April 2014 to June 2018.
# Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial
## 3 Results and Discussion
### Clinical Trial Results
#### 3.3.2 Results of Analysis of Pharmacokine Properties of the Tested Formulations
Similar to our study, the T\({}_{\text{max}}\) value obtained by [PERSON] and [PERSON] [31] for liposomal vitamin C was non-significantly higher than that obtained for the conventional form of this active ingredient (3.51 h vs. 3.42 h). Statistically insignificant differences in T\({}_{\text{max}}\) values were also reported in a randomized, single-dose, cross-over oral bioavailability study comparing powdered liposomal vitamin C obtained by nanofiber weaving (Zeal) technology (a dose of 150 mg of ascorbic acid) with its conventional counterpart (3.1 \(\pm\) 1.15 h vs. 3.6 \(\pm\) 0.62 h, \(p\) = 0.388) [50]. However, as noted by [PERSON] and [PERSON] [31], an analysis of the results of a number of published studies of liposomal vitamin C bioavailability shows significantdiscrepancies in the pharmacokinetic parameters reported. The authors also emphasize the difficulty of comparing these studies due to factors such as differing production methods, particle sizes, dosages, and the various forms of tested vitamin C, including ascorbic acid, sodium ascorbate, and calcium ascorbate. Indeed, the properties and quality of the liposomes obtained can vary significantly depending on the formulation [65]. The mere change in the length of the acyl chain and thus the phase transition temperature of phospholipids leads to carriers with different release profiles of the entrapped active substance, and thus different in vivo properties [66]. In turn, with the use of different production methods--impacting, among other things, the size of the carrier particles--the same chemical composition of liposomal products can translate into a lack of comparability in their in vitro properties and in vivo efficacy [67]. Not to be overlooked is the wide individual variability in vitamin C absorption [31; 68], including the existence of \"poor\" and \"good\" absorbers of ascorbic acid [68]. Differences in the analytical procedures used to measure plasma vitamin C concentrations are also not insignificant to the experimental results obtained [31]. With regard to the results of our clinical trial, it should be noted that they confirmed the preliminary conclusions derived from the properties of the developed liposomal product. As expected, the narrow size distribution of liposomes in the powder and their relatively small diameter and high absolute zeta potential value translated into improved pharmacokinetic properties of vitamin C and its increased in vivo bioavailability.