esa-sceva/satcom-chunk-collection huggingface dataset:
# Fragility Assessment of Traffic Embarkments Exposed to Earthquake-Induced Liquefaction
## 2 Methodology for the Derivation of Fragility Curves  
In general, vulnerability analysis is the process of systematically evaluating natural hazards and other threats that may potentially cause damage to an observed object. Among all available engineering tools, the fragility function is used to quantify the level of vulnerabilities to the object under consideration in terms of the probability of exceeding a certain damage state under a given IM.  
The fragility function is commonly defined as a lognormal cumulative distribution function (Equation (1)), where \(P(ds|IM=x)\) is the probability that a GM with \(IM=x\) will exceed the selected damage state, \(\Phi()\) is the standard normal cumulative distribution function, \(\theta\) is the median of the fragility function (the IM level with a 50% probability of exceeding the damage state) and \(\beta\) is the standard deviation of ln\(IM\) (sometimes referred to as the dispersion of IM) [19].  
\[P(ds|IM=x)=\Phi\left(\frac{ln(x/\theta)}{\beta}\right) \tag{1}\]  
Different procedures for performing numerical simulations to assemble the results needed for the derivation of fragility curves are available in the literature (e.g., [19]). One common approach is incremental dynamic analysis (IDA) [20], in which a set of GMs is scaled to find IM levels at which each GM causes a selected damage state. Alternatively, multiple stripe analysis can be used, in which the analysis is performed at a specified set of IM levels, each of which has a unique GM set [21; 22].  
In this study, the fragility analysis was performed by scaling a set of GMs at multiple IM levels. The employed procedure is conceptually consistent with IDA because the seismic response is obtained by the scaling of GMs. However, this approach does not involve an explicit estimation of the IM levels at which the GMs cause selected damage states. This was expected to yield a reduction in the number analyses compared to the conventional IDA.
## 1 Introduction: Principles of L-Band Microwave Radiometry and GNSS-R over the Ocean
The DDM is the square of the absolute value of the correlations of the reflected GNSS signals with local replicas of the transmitted signal, but shifted in delay and Doppler, and it is given by [6]:  
\[\left|\mathrm{DDM}\left(\Delta\tau,\Delta f_{{}_{d}}\right)\right|^{2}=T_{{}_{i }}^{2}\iint\frac{\left|R\right|^{2}D^{2}\left(\tilde{\rho}\right)\Lambda^{2} \left(\Delta\tau\right)\left|S\left(\Delta f_{{}_{d}}\right)\right|^{2}}{4R_{{} _{0}}^{2}\left(\tilde{\rho}\right)R^{2}\left(\tilde{\rho}\right)}\frac{q^{4} \left(\tilde{\rho}\right)}{q_{z}^{4}\left(\tilde{\rho}\right)}P_{v}\left(- \frac{\vec{q}_{{}_{\perp}}}{q_{{}_{z}}}\right)d^{2}\vec{r} \tag{1}\]  
where \(T_{{}_{i}}\) is in the integration time, \(R\) is the Fresnel reflection coefficient, \(D\) is the directivity of the receiving antenna, \(\chi\left(\Delta\tau,\Delta f_{{}_{d}}\right)\approx\Lambda\left(\Delta\tau \right)\cdot S\left(\Delta f_{{}_{d}}\right)\) is the Woodward ambiguity function, that can be approximated by the product of a triangle function \(\Lambda\left(\Delta\tau\right)=\left(1-\left|\Delta\tau\right|\right/T_{{}_{c }}\right)\) if \(\left|\Delta\tau\right|\leq T_{{}_{c}}\), being \(T_{{}_{c}}\) the chip period, and a sinc function \(S\left(\Delta f\right)=\exp\left(-j\;\pi\;\Delta f\;T_{{}_{i}}\right)\cdot \sin\left(\pi\;\Delta f\;T_{{}_{i}}\right)/\left(\pi\;\Delta f\;T_{{}_{i}} \right)\), \(R_{{}_{0}}\left(\tilde{\rho}\right)\) and \(R\left(\tilde{\rho}\right)\) are the distances between the transmitter and receiver to the scattering point, \(q\) is the amplitude of the scattering vector \(\vec{q}\triangleq k(\widehat{n}-\widehat{m})\) where \(k\) is the wavenumber, \(\hat{n}\) and \(\hat{m}\) are the unit vectors of the incident and scattered waves, respectively, \(q_{z}\) is the \(z\) component of \(\vec{q}\), and \(\vec{q}_{{}_{\perp}}\) is the perpendicular component, and \(P_{v}\) is the sea surface slopes probability density function, and the integral is performed over the whole glistening zone. Measuring the whole DDM, avoids the loss of information due to the asymmetry of the DDM originated by the relative orientation of the transmitter, the receiver and the wind direction, that is not captured when only the so-called _waveforms_ (time domain correlation when the Doppler shift is compensated) are measured (_waveform_ = \(\mathrm{DDM}\left(\Delta\tau,\Delta f_{{}_{d}}=0\right)\)). The volume of the normalized DDM (peak amplitude equal to 1) above a given threshold (Figure 4) has been found to provide a measurement of the area over which the GNSS signals are scattered, and it can be related to geophysical variables such as the sea surface roughness, without need of any intermediate model, either numerical to compute the scattering or for the sea surface spectrum [7, 8]. Figure 2: Sun glint over the sea under: (a) calm and (b) windy conditions.
## 3 Instrument Performance
### Errors Based on Counting Statistics  
In addition to the velocity errors based on the finite resolution of the instrument, the detection of particles also introduces uncertainties due to the counting statistics. The number of counts \(N_{i}\) that the sensor measures in pixel \(i\) follows a Poisson distribution:  
\[P(N_{i})=e^{-C_{i}}\frac{C_{i}^{N_{i}}}{N_{i}!}, \tag{35}\]  
where \(C_{i}\) is the expected number of counts for velocity \(v_{i}\) presented in Equation (27). The standard deviation of the Poisson distribution is \(\sigma=\sqrt{C_{i}}\). The number of counts for each pixel with a confidence interval of \(2\,\sigma\) is presented in Figure 4. We use an acquisition time of 5 ms with a disk aperture of 1 mm radius and a pixel size of 1 mm in width. The solar wind is modeled with a Maxwellian VDF with a density of 5 cm\({}^{-3}\), as represented in Figure 5. Due to the high number of counts per pixel, the Poisson errors are low over a wide range of velocities around the center of the VDF. We obtain less than 10% of relative error for speeds ranging from 440 to 560 km/s, which corresponds to \(\pm\)60 km/s around the central velocity of the VDF. The Poisson errors are, however, linked to the size of the pixels. The larger the pixels are, the lower the relative Poisson errors are due to the larger \(N_{i}\) for each pixel, and vice versa.  
Figure 4: Evolution of the counting errors with particle speed. The top plot shows the \(2\sigma\) error bars on the expected counts for each pixel. The bottom plot represents the relative error (\(e_{r}\)) in percent made on the determination of the speed. The 10% (green) and 20% (red) levels are represented.
## 2 Modeling of the PDSH Circuit  
The PDSH circuit based on the OTA chip consists of OTA, diode, sampling capacitor, and voltage Buffer, as shown in Figure 2. OTA can amplify and convert the difference between the non-negative input voltage \(V_{i}\) and the negative input voltage \(V_{o}\) into the output current \(I_{o}\). Therefore, if the input voltage \(V_{i}\) is greater than the output voltage \(V_{o}\), the output current of OTA charges the sampling capacitor \(C_{th}\) through diode. While \(V_{i}\) is smaller than \(V_{o}\), the diode is reverse-biased, and the PDSH circuit is forced into a hold state. Subsequently, the output voltage of the PDSH circuit is determined as the peak value of pulse signal, and it can be acquired by using a low-speed ADC.  
The voltage-current characteristic curve of OTA is commonly classified into two main regions, namely the linear region and the saturation region. When the differential input voltage is less than the threshold voltage \(V_{th}\), the OTA operates in the linear region, and the output current \(I_{o}\) can be described by (1), where \(g\) is the transconductance of OTA.  
\[I_{o}=g(V_{i}-V_{o}) \tag{1}\]  
When the differential input voltage is greater than the threshold voltage \(V_{th}\), the OTA operates in the saturation region, and the output current remains at the maximum output current \(I_{om}\), expressed as (2).  
\[I_{om}=gV_{th} \tag{2}\]  
The actual OTA devices exhibit a linear offset in the output current in the saturation region, denoted by \(k\). Therefore, the output current \(I_{o}\) in the saturation region can be described (3) and (4).  
\[I_{o}=k(V_{i}-V_{o})+b \tag{3}\]  
\[b=gV_{th}-kV_{th} \tag{4}\]
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 4 Simulation and Numerical Analysis  
In the simulation experiments, it is evident that numerical results illustrated the performance of the proposed algorithm for the rechargeable wireless sensor network. The goal of the simulation is to assess the performance of the proposed algorithm. To evaluate the performance of existing algorithms--such as GR protocol and the grid clustering algorithm--that utilize a multi-hop network scenario, the comparative analysis conducted on the performance metrics that include energy consumption, vacation time, and remaining energy of the network. The maximum energy (E\({}_{max}\)) of each sensor node at the initial stage of the simulation is considered to be 10,800 J and fully charged. The minimum energy (E\({}_{min}\)) of a sensor node is supposed to be 540 J, and the sensor node with less energy than the minimum energy (E\({}_{min}\)) is deemed to be exhausted. The details of proposed simulation parameters represent in Table 2. The corresponding simulations were performed in MATLAB 2012a using a desktop PC with an Intel (i5-2520) Processor @ 2.50 GHz with 8 GB of RAM.
## 1 Introduction
In [37; 38; 39], platform-based DF system designs based on the theory of characteristic modes were presented. First, in [37], the chassis of the aircraft was used as the main radiator of an antenna array for DF purposes. The performance of the system suggested in [37] was enhanced in [38] by introducing a dynamic selection of the excited modes. In [39], a further improvement was introduced to the platform-based DF system by reducing the area on which the DF array was distributed. The last three publications had the 3-300 MHz frequency band in common. In contrast, our work does not use the chassis as the main radiator, and we aim to operate in the ACAS and Mode S frequency range between 1030 MHz and 1090 MHz. In related work, ref. [40] presented an innovative design of an M\({}^{3}\)PA antenna specific for utilization in aerial application within the aforementioned ACAS and Mode S frequency range. The authors provided design guidelines from an antenna development point of view. However, the evaluation of the designed M\({}^{3}\)PA antenna is based only on the deterministic properties of the antenna. In other words, the suitability of the suggested design is evaluated independently of the statistical parameters that are associated with DF algorithms and their theoretical bounds such as the Cramer-Rao bound (CRB). Contrarily, the objective of this paper is to test and verify the DF capabilities of the M\({}^{3}\)PA designed in [40] based on fundamental DF methods. The contributions of this paper can be outlined as follows: (i) A MUSIC-based DoA estimation scheme utilizing the investigated M\({}^{3}\)PA is introduced. It is based on measured patterns of a prototype of the mentioned M\({}^{3}\)PA. (ii) The proposed approach is analyzed and evaluated by means of numerical simulations. (iii) The theoretical results are experimentally validated in an antenna measurement chamber using the M\({}^{3}\)PA prototype and a software-defined radio (SDR). Accordingly, this paper is organized as follows: Section 2 provides an overview of the M\({}^{3}\)PA under investigation. Section 3 describes the assumed system briefly. The applied DoA estimation algorithm is introduced in Section 4. Numerical results are presented in Section 5. Section 6 delivers the measurement setup. The experimental results are shown in Section 7. A discussion is provided in Section 8. Finally, Section 9 provides a discussion and concludes the paper.
## 2 Proposed Algorithm
### Wavelet Transform  
The wavelet transform (WT) is a mathematical tool with which to analyse the power system transient signals. It extends a single prototype function to break up a signal into different scales with different levels of resolution. It shows how the signal looks locally in both the time and frequency domains. This wavelet transform capability is used to locate, classify, and detect fault conditions. The basic idea behind wavelet analysis is to choose an appropriate wavelet function known as the \"mother wavelet\" and then analyse it using shifted and dilated versions of this wavelet [12]. The fault signals are transformed into different frequency bands using the discrete wavelet transform and the Daubechies wavelet transform in this method, which can then be used to identify the faults. These signals can be represented in terms of both the scaling and wavelet function, as shown in the equation below;  
\[f(t)=\Sigma_{m}CJ^{n}\Phi^{t-n}+\Sigma_{n}\Sigma_{j=0}^{J}d_{j}(n)2^{\frac{j}{ n}}\Psi(2^{j}t-n), \tag{2}\]  
where \(c_{j}\) represents the \(J\) level scaling coefficient and \(d_{j}\) represents the \(j\) level wavelet function. \(\Phi(t)\) is the scaling function, \(\Psi(t)\) is the wavelet function, \(J\) is the highest wavelet level, and \(t\) is the time. Each wavelet is created by the scaling and translation operations of the mother wavelet [12]. The continuous WT for a given signal \(x(t)\) to the parent wavelet \(\Psi(t)\) is shown in Equation (2):  
\[CWT_{\Psi}x(a,b)=W_{x}(a,b)=\int_{-\infty}^{\infty}x(t)\Psi a,b^{(t)}dt, \tag{3}\]  
where \(\Psi a,b(t)=\mid a\mid^{\frac{1}{2}}\Psi(\frac{t-b}{a})\), \(a\) (scale) and \(b\) (translation) are real numbers. For discrete-time systems, the discretion process leads to the time-discrete wavelet series given as  
\[DWT_{\Psi}x(m,n)=\int_{-\infty}^{\infty}x(t)\Psi m,n^{(t)}dt, \tag{4}\]  
where \(\Psi m,n(t)=a_{0}\frac{-\pi}{2}\Psi\left(\frac{t-nb_{0}a_{0}^{m}}{a}\right)\), \(a=a_{0}{}^{m}\) and \(b=nb_{0}a_{0}{}^{m}\).
## 2 Mechanical Design and Working Environment Analysis
### Mechanism Design of CDTSR  
The CDTSR was designed in view of the above working environment. As shown in Figure 2, it mainly consisted of a tracked mobile platform, CDFM, and spray system. In addition, the manipulator and spray system were an integrated structure that can be directly installed on appropriate mobile platforms. The nozzle of the spray system was equipped at the end of the manipulator, the spray angle and distance of which could be adjusted.  
The CDFM was a concentric tubular manipulator with six degrees of freedom. As shown in Figure 3, it consisted of a control box, a proximal arm segment, and a distal arm segment. Each arm segment was controlled by a motor that was equipped with a reel. Two cables were wound clockwise and counterclockwise on each reel, respectively, and were fixed on the last connecting rod of the arm segment through the cable locking. The proximal arm segment and the distal arm segment were connected by a connecting rod with a torsion angle of 90\({}^{\circ}\) to achieve a bending motion in the horizontal plane and in the vertical plane, respectively. With the cooperation of these two arm segments, this manipulator could reach any position in three-dimensional space.  
Figure 1: Example of a single dwarf citrus tree.  
Figure 2: (**a**) The overall model of the CDTSR: (1) nozzle; (2) cable-driven manipulator control box; (3) pesticide box; (4) diaphragm pump; (5) inlet pipe; (6) track vehicle control box; (7) track vehicle; (8) outlet pipe; (9) cable-driven manipulator. (**b**) CAD drawings with different viewing angles of the CDTSR (the unit is mm).  
The manipulator had six joints, each with the same structure; a single joint structure is shown in Figure 4a. The adjacent connecting rod was connected using a spring and hinge structure, which could transfer force through the spring. Therefore, it only needed to apply tension on the last connecting rod in the arm segment to make each joint rotate, thereby reducing the number of driving motors required and the difficulty of control. The shaft hole and shaft of the support were connected with a graphite copper sleeve to reduce the friction at the joint. The CAD drawings of the connecting rod are shown in Figure 4b.
## 2 Methods and Data  
### Methods  
#### 2.1.1 Theil-Sen Trend and Mann-Kendall (MK) Test  
In this study, the interannual trends of satellite-observed LAI, GPP, SIF, and influencing factors (i.e., CO\({}_{2}\), SW, VPD, annual total precipitation (P), annual mean temperature (T), and solar radiation (SRAD)) were separately investigated by using the Theil-Sen method. This method is a nonparametric estimation for linear regression [47]. The method has been widely applied in trend detection analysis due to its advantages in easily calculating and strong robustness. The trend \(\beta\) can be calculated by Equation (1):  
\[\beta=Median\left[\frac{x_{j}-x_{k}}{j-k}\right],\ \forall k<j \tag{1}\]  
where \(x_{j}\) and \(x_{k}\) are the values in the _jth_ and _kth_ year in a long-time series. In this study, \(x\) represents the annually averaged value of multisource satellite-observed LAI, GPP, and SIF from 1982 to 2018. When \(\beta\) is higher than 0, it indicates that the analyzed factor exhibited an upward trend; otherwise, the time series of this factor shows a downward trend.  
The nonparametric MK test is frequently used to determine the significance level in long-term series data when the selected samples in the test need not follow a regular distribution and are not impacted by anomalous values [48]. The precise calculation steps of this method are not discussed in this study; instead, it makes references to these earlier publications [14,15,49]. This study employed the MK test to detect the significance level of the interannual trend in a long-term series of LAI/GPP/SIF, and the 95% confidence interval (\(p<0.05\)) was selected to determine whether the interannual trend was significant or not in this study.
## 3 Analysis of Dynamic Performance
### Optimization Model Based on Dynamic Performance  
The position vector and unit normal vector of the pinion modified tooth surface are as follows:  
\[\left\{\begin{array}{l}\mathbf{r}_{1}=\mathbf{r}_{1}(s_{p},\theta_{p},\varphi_{p}, \varphi_{1})\\ \mathbf{n}_{1}=\mathbf{n}_{1}(s_{p},\theta_{p},\varphi_{p},\varphi_{1})\end{array}\right. \tag{8}\]  
where \(s_{p}\) and \(\theta_{p}\) denote tool parameters, \(\varphi_{p}\) represents the angle of the shaking table, and \(\varphi_{1}\) represents the angle of the pinion.  
Then, the normal deviation of discrete point \(i\) on the perfect pinion surface and the modified tooth surface is defined as:  
\[\mathbf{h}_{i}(\mathbf{d})=(\mathbf{p}_{i}^{*}-\mathbf{p}_{i})\mathbf{n}_{i}\quad(i=1,2,\ldots,k) \tag{9}\]  
where \(\mathbf{d}\) is the pinion machining parameter. The parameters \(\mathbf{p}_{i}\) and \(\mathbf{n}_{i}\) represent the position and unit normal vectors of the pinion modified surface, respectively.  
The normal deviation between the pinion modified surface and the perfect surface is as follows:  
\[\mathbf{h}(\mathbf{d})=[\mathbf{h}_{1}(\mathbf{d}),\mathbf{h}_{2}(\mathbf{d}),\ldots,\mathbf{h}_{k}(\mathbf{d })]\ ^{T} \tag{10}\]  
Finally, the optimal mathematical model is created by taking the minimum sum of squares of the normal deviation, the RMA as optimization objectives and the pinion  
Figure 3: The basic principle of structural dynamics.  
machining parameters as optimization variables. The modified pinion surface is obtained by the following genetic algorithm:  
\[\left\{\begin{array}{l}\begin{array}{l}\min f_{1}(\mathbf{d})=h^{\mathrm{T}}h\\ \min f_{2}(\mathbf{d})=R_{a}\\ a_{i},b_{i}\in[\chi_{1},\chi_{2}]\end{array}\end{array}\right. \tag{11}\]  
where \(\mathbf{d}\) are the variables. \(R_{a}\) represents the root mean square of the normal vibration acceleration of gear transmissions. \(\chi_{1}\) and \(\chi_{2}\) are the minimum and maximum variables, respectively.
## 3 Results
### Workability  
The results of the truncated cone measurements are presented in Table 2. The effect of changing the water dosage on workability is clearly observed, since for the same aggregate-cemetitious dosage, adding a higher percentage of water results in greater workability.  
The data in Table 2 show that the workability of the mortar changes when replacing the cement with an uncalcined clam shell. For MR-00, the cone slump is 1 cm; however, when replacing 5% of the cement with the clam shell in MR-05 specimen, the cone slump increases to 2 cm, indicating an improvement in workability. This same slump value is maintained when 10% of the cement is replaced in the MR-10 specimen. This suggests that the inclusion of uncalcined clam shell improves the workability of the mortar compared to conventional mortar [43], although the increase in the replacement percentage from 5% to 10% does not seem to have a significant additional effect on workability, with the slump remaining constant at 2 cm.  
The workability of the MRC specimens that include the replacement of cement by calcined clam shell decreases with increasing cement replacement. The conventional mortar MRC-00 has a cone slump of 3.5 cm, indicating considerably high workability. However, when 5% of the cement is replaced by a calcined clam shell in MRC-05, the slump decreases to 2.5 cm. This same value is maintained when the replacement is 10% in MRC-10.  
The results suggest that the incorporation of calcined clam shell reduces workability compared to conventional mortar, but increasing the replacement percentage from 5% to 10% does not produce a significant additional decrease in workability. It should be noted that the results of the MRC specimens are superior to the MR specimens and that this may be due to the increase in the amount of MRC in the mixture. The decrease in workability in MRC samples, where part of the cement is replaced by a calcined clam shell, can be explained due to its physical and chemical nature. A calcined shell tends to absorb more water due to its high specific surface area and porosity compared to conventional cement, which reduces the amount of free water available in the mix to lubricate the particles,  
\begin{table}
\begin{tabular}{c c} \hline
**Nomenclature** & **Cone Settlement [cm]** \\ \hline MR-00 & 1 \\ MR-05 & 2 \\ MR-10 & 2 \\ MRC-00 & 3.5 \\ MRC-05 & 2.5 \\ MRC-10 & 2.5 \\ \hline \end{tabular}
\end{table}
Table 2: Workability results.  
Figure 6: FTIR analysis results: (**a**) uncalcined clam shell and (**b**) calcined clam shell.  
thus decreasing the fluidity or workability. Furthermore, the chemical composition of the calcined shell, rich in calcium carbonate (CaCO\({}_{3}\)), can influence the interactions between particles and water, increasing the water demand in the mix [54; 55].