esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C Derivation of Equation (8)  
Let us substitute Field Representation (5) together with its Hermitian conjugate into the commutator of the operators. We have  
\[\begin{array}{l}\big{[}\hat{\mathcal{U}}_{k}(\mathbf{x}),\hat{U}_{k^{  
For the third term in the right-hand-side of Equation (A21), we obtain by a similar argument  
\[f_{3}\Big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}}\Big{)}=v\int\limits_{S}\Gamma^{*} \Big{(}\mathbf{x}^{{}^{\prime}},\boldsymbol{\rho};k\Big{)}\text{Im}(g( \boldsymbol{\rho},\mathbf{x}))d\boldsymbol{\rho}=f_{2}^{*}\Big{(}\mathbf{x}^{{}^ {\prime}},\mathbf{x}\Big{)}.\] (A25)  
For the last term in Equation (A21), using Equations (A22)-(A24), we obtain  
\[\begin{array}{c}\int\limits_{S}\int\limits_{S}\Gamma(\mathbf{x}, \boldsymbol{\rho};k)\Gamma^{*}\big{(}\mathbf{x}^{{}^{\prime}},\boldsymbol{\rho }^{{}^{\prime}};k\big{)}\text{Im}(g(\boldsymbol{\rho},\boldsymbol{\rho}^{{}^{ \prime}}))d\boldsymbol{\rho}d\boldsymbol{\rho}^{\prime}=\\ \sum\limits_{n}\sum\limits_{m}\frac{1}{(v_{n}-v)(v_{m}^{*}-v)}u_{n}(\mathbf{x })u_{m}^{*}\big{(}\mathbf{x}^{{}^{\prime}}\big{)}\int\limits_{S}\int u_{n}( \boldsymbol{\rho})u_{m}^{*}(\boldsymbol{\rho}^{\prime})\text{Im}(g( \boldsymbol{\rho},\boldsymbol{\rho}^{{}^{\prime}}))d\boldsymbol{\rho}d \boldsymbol{\rho}^{\prime}.\end{array}\] (A26)  
Using the representation of the Green function in the eigenmode basis (Equation (A4)), as well as their orthogonality (Equation (A3)), we transform Equation (A26) to  
\[\begin{array}{c}\int\limits_{S}\int\limits_{S}\Gamma(\mathbf{x}, \boldsymbol{\rho};k)\Gamma^{*}\big{(}\mathbf{x}^{{}^{\prime}},\boldsymbol{\rho }^{{}^{\prime}};k\big{)}\text{Im}(g(\boldsymbol{\rho},\boldsymbol{\rho}^{{}^{ \prime}}))d\boldsymbol{\rho}d\boldsymbol{\rho}^{\prime}=\\ \sum\limits_{n}\sum\limits_{m}a_{mn}u_{n}(\mathbf{x})u_{m}^{*}\big{(}\mathbf{x }^{{}^{\prime}}\big{)},\end{array}\] (A27)  
where  
\[\alpha_{mn}=\frac{1}{2i(v_{n}-v)(v_{m}^{*}-v)}\bigg{(}\frac{1}{v_{n}}-\frac{1 }{v_{m}^{*}}\bigg{)}\int\limits_{S}u_{n}(\boldsymbol{\rho})u_{m}^{*}( \boldsymbol{\rho})d\boldsymbol{\rho}\] (A28)  
with \(\alpha_{mn}=-\alpha_{nm}^{*}\). Combining in Equation (A21) all obtained relations and using Hilbert-Schmidt Relation (A8), we obtain Equation (8) after some elementary transformations.
## 3 Results
SWC: smallest worthwhile change. \(\mathrm{P_{max}}\): maximal horizontal power. \(\mathrm{F_{0}}\): theoretical maximal horizontal force. \(\mathrm{V_{0}}\): theoretical maximal horizontal running velocity. \(\mathrm{V_{max}}\): actual maximal running velocity. \(\mathrm{\tau}\): acceleration time constant. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**CV Inter-Trial Mean \(\pm\) SD (\%)**} & \multicolumn{4}{c}{**Change in the Mean \(\pm\) SEM**} & \multicolumn{4}{c}{**MDC (95\% CI)**} \\ \hline  & **Radar** & **GPS 1** & **GPS 2** & **Radar** & **GPS 1** & **GPS 2** & **Radar** & **GPS 1** & **GPS 2** \\ \hline \(\mathrm{P_{max}}\) (W\(\cdot\)kg\({}^{-1}\)) & \(3.1\pm 2.9\) & 4.6 \(\pm\) 4.5 & 4.2 \(\pm\) 3.1 & 0.38 \(\pm\) 0.94 & \(-0.29\pm 1.4\) & 0.2 \(\pm\) 1.2 & 2.61 & 3.88 & 3.33 \\ \hline \(\mathrm{F_{0}}\) (N\(\cdot\)kg\({}^{-1}\)) & \(3.3\pm 2.9\) & 5.1 \(\pm\) 5 & 4.7 \(\pm\) 3.2 & 0.16 \(\pm\) 0.42 & \(-0.19\pm 0.69\) & 0 \(\pm\) 0.58 & 1.16 & 1.91 & 1.61 \\ \hline \(\mathrm{V_{0}}\) (m\(\cdot\)s\({}^{-1}\)) & \(0.96\pm 0.7\) & \(1.4\pm 1.1\) & 1.1 \(\pm\) 0.8 & 0.01 \(\pm\) 0.15 & 0.11 \(\pm\) 0.22 & 0.07 \(\pm\) 0.17 & 0.42 & 0.61 & 0.47 \\ \hline \(\mathrm{V_{max}}\) (m\(\cdot\)s\({}^{-1}\)) & \(0.91\pm\) 0.64 & 1.2 \(\pm\) 1 & 1.0 \(\pm\) 0.69 & 0.01 \(\pm\) 0.13 & 0.09 \(\pm\) 0.18 & 0.07 \(\pm\) 0.14 & 0.36 & 0.50 & 0.39 \\ \hline \(\mathrm{\tau}\) (s) & \(3.7\pm 3.2\) & 5.6 \(\pm\) 5.7 & 4.9 \(\pm\) 3.7 & \(-0.02\pm 0.08\) & 0.05 \(\pm\) 0.13 & 0 \(\pm\) 0.11 & 0.22 & 0.36 & 0.30 \\ \hline \end{tabular}
\end{table} 
Table 3: Inter-trial reliability. Coefficients of variation (CV), standard deviations (SD), changes in the mean, standard errors of measurement (SEM) and minimal detectable change (MDC) were computed for each variable. \(\mathrm{P_{max}}\): maximal horizontal power. \(\mathrm{F_{0}}\): theoretical maximal horizontal force. \(\mathrm{V_{0}}\): theoretical maximal horizontal running velocity. \(\mathrm{V_{max}}\): actual maximal running velocity. \(\mathrm{\tau}\): acceleration time constant.
## 5 Simulation Results  
### Parameters Setting  
In simulations, the value of the Boltzmann constant is \(1.380649\times 10^{-23}\) J/K, the speed of light is \(3\times 10^{8}\) m/s, the Earth's radius is 6371 km, and the GEO satellite altitude is 35,786 km. The noise temperature is same with [17], implying 300 K for the GEO UT. The remaining system parameters are detailed in Table 1. Simulation results are the expected results of channels following 1000 random generations.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 4 Simulation Results and Discussion
### Simulation Setup  
In simulations, we consider an uplink SC-GFDMA system, described in Section II, with a bandwidth of 5-MHz and 512 subcarriers. The center frequency is 2 GHz. Unless otherwise specified, the number of total users is 8, and the distances between the BS and the users are set to deterministic values: {1, 8/7, 9/7, 10/7, 11/7, 12/7, 13/7, 2} m, having the same intervals. The 512 subcarriers are grouped into \(M=16\) chunks each of which consists of \(K=32\) consecutive subcarriers. Perfect uplink channel estimation is assumed at the BS. A convolutional code with a constraint length of 3 is considered. For the QoS constraint, \(\Gamma_{th}\) is set to the value that corresponds to the target BER of \(10^{-5}\). The channels between the BS and the users are frequency-selective fading channels. We assume that each channel is a six-tap typical urban channel [29] and that the normalized Doppler frequency is 0.001. The maximum number of iterations of SNR-variance density evolution is 20. The iteration is stopped when the sum of SINRs for all the users stops increasing. Unless otherwise specified, the values from Table 2 are used for the proposed algorithm.  
First, we verify the approximation of \(\mu_{u,m}\) in (25), which is expected to be close when \(K\) is small. When the proposed power control scheme is applied, the RMSEs of approximating \(\mu_{u_{1},m}\) by using \(\frac{\text{am}[S]}{\text{am}[T]}\), at \(i=1\), are depicted in Figure 6. The RMSEs are generally small and can be reduced by choosing a small chunk size as expected.
## 1 Introduction
(1) There exists a randomized algorithm, such that there are positive constants, \(c_{0}\) and \(c_{1}\), such that, if the alphabet size is at least four, the number of sequences is at least \(c_{1}\log n\), the motif length is at least \(c_{0}\log n\) and each character in the motif region has a probability of at most \(\frac{1}{(\log n)^{2+\mu}}\) of mutation for some fixed \(\mu>0\), then the motif can be recovered with a probability of at least \(\frac{3}{4}\) in \((O(\frac{n}{\sqrt{h}}(\log n)^{\frac{7}{2}}+h^{2}\log^{2}n),O(\log n))\) time, where \(n\) is the longest length of any input sequences and \(h=\min(|G|,n^{\frac{2}{5}})\). The algorithm's total time is sublinear if the motif length, \(|G|\), is in the range \([(\log n)^{7+\mu},\frac{n}{(\log n)^{1+\mu}}]\). This is the first sublinear time algorithm with rigorous analysis in this model. (2) There exists a randomized algorithm, such that there are positive constants, \(c_{0},c_{1}\) and \(\alpha\), such that if the alphabet size is at least four, the number of sequences is at least \(c_{1}\log n\), the motif length is at least \(c_{0}\log n\) and each character in the motif region has a probability of at most \(\alpha\) of mutation, then the motif can be recovered with a probability of at least \(\frac{3}{4}\) in \((O(\frac{n^{2}}{|G|}(\log n)^{O(1)}),O(\log n))\) time. (3) There exists a deterministic algorithm, such that there are positive constants, \(c_{0},c_{1}\) and \(\alpha\), such that if the alphabet size is at least four, the number of sequences is at least \(c_{1}\log n\), the motif length is at least \(c_{0}\log n\) and each character in the motif region has a probability of at most \(\alpha\) of mutation, then the motif can be recovered with a probability of at least \(\frac{3}{4}\) in \((O(n^{2}(\log n)^{O(1)}),O(\log n))\) time. The research in this model has been reported in [19, 20, 21]. In [19], [PERSON] _et al._. developed an algorithm that needs the alphabet size to be a constant that is much larger than four. In [20], our algorithm cannot handle all possible motif patterns. In [21], [PERSON] _et al._ designed an algorithm that runs in \(O(n^{3})\) time and lacks rigorous analysis about its performance. The motif recovery in this natural and simple model has not been fully understood and seems to be a complicated problem. This paper presents two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is as small as four, giving them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The algorithm for motif detection is named Recover-Motif(.). The entire Recover-Motif(.) is described in Section 4.2. We analyze Algorithm Recover-Motif (.) in Section 6.
## 4 Conclusions  
In summary, we have successfully synthesized an efficient heterogeneous catalyst comprising hydroxyapatite-supported Pd nanoparticles (HAP-Pd). The prepared HAP-Pd catalyst was characterized using various techniques such as field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. The designed catalyst displayed high catalytic efficiency for the reduction of nitroarenes to the corresponding amines with aqueous NaBH4, and similarly the oxidation of alcohols using H2O2 in water with high yield and selectivity. Additionally, the catalyst could be reused multiple times for the aforementioned reactions without significant loss of activity. Remarkably, several significant advantages were offered by the HAP-Pd catalyst in these greener transformations, which deliver high product yields in benign solvent--water.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/19/4183/s1](http://www.mdpi.com/2076-3417/9/19/4183/s1)]([http://www.mdpi.com/2076-3417/9/19/4183/s1](http://www.mdpi.com/2076-3417/9/19/4183/s1)), Figure S1: TEM (a) and (b) HRTEM images of HAP-Pd catalyst synthesized by green coffee utilization. TEM (c) and (d) HRTEM images of Pd NPs decorated on HAP synthesized by NaBH4 utilization, Figure S2: TEM image of (a) carbon and (b) carbon-Pd catalyst synthesized by green coffee utilization, Figure S3: FT-IR spectra of (a) aniline, (b) 4-chloro aniline, (c) 4-bromo aniline, (d) 2,4-dimethyl aniline, (e) 4-methyl aniline. Table S1: A comparison study of catalytic activity with previously reported nanocatalysts.  
**Author Contributions:** M.S. and R.S.V.; conceptualization. M.S., S.M.-G.Y., M.N. investigation; S.M.-G.Y., M.N. and A.K. formal analysis. All authors contributed to writing of original draft. M.S. and R.S.V. writing-review and editing.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Reusability of HAP-Pd catalyst in heterogeneous reduction of nitrobenzene (gray columns) and oxidation of cycloheptanol (black columns).
# Quantitative Model for the Value of the 3D Product Model Use in Production Processes
## 3 Results
### Less Time Updating Work Instructions  
In another application of the 3D product model in the production process, an agricultural equipment manufacturer transitioned from using Microsoft Excel to reconcile engineering change orders with work instructions to using the 3D product model in combination with Proplanner's Assembly Planner. In the original process using an Excel sheet, the company spent almost seven hours updating work instructions (\(W_{m}\)) following an engineering change order. In the new process, the company could complete the same update, with the 3D product model included in the production process work instructions, in just over two hours. This resulted in a savings of over four and a half hours per engineering change order and with around 245 engineering change orders per line processed each year (\(K_{m}\)) that is over 1000 man-hours saved per year per production line. The company has industrial engineers with an average fully loaded cost of USD 60 per labor hour (\(\overline{\Gamma}\)) updating the work instructions manually. By integrating the 3D product model into the production process, and automating that through Proplanner's Assembly Planner, the organization is able to save USD 60,000 per year per line (\(P_{W_{m}}\)) as a result, reducing the cost associated with updating wok instructions by over 67%.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## 3 Results and Discussions  
Substituting (6)-(10) into Equations (3) and (4) and Equations (11)-(13) into Equation (5), we obtain the momentum equations in the vectorial form (here we have assumed that both components are incompressible) and the transport equation for the platelets in the scalar form.  
The momentum equation for plasma is:  
\[\begin{split}(1-\phi)\rho_{f}&\Big{[}\frac{9\
u_{ f}}{\partial t}+\Big{(}grad\ \mathbf{v}_{f}\Big{)}\mathbf{v}_{f}\Big{]}\\ &=-grad((1-\phi)\ p)+div\ \Big{(}2\mu_{f}(1-\phi)\mathbf{D}_{f}\Big{)}+ \rho_{f}(1-\phi)\mathbf{b}_{f}\\ &+\frac{9\mu_{f}}{2\alpha^{2}}f(\phi)\Big{(}\mathbf{v}_{s}-\mathbf{v}_{f }\Big{)}+\frac{3(6.46)\big{(}\rho_{f}\mu_{f}\big{)}^{1/2}}{4\pi a}\phi\Big{(}2 tr\mathbf{D}_{f}\Big{)}^{-1/4}\mathbf{D}_{f}\Big{(}\mathbf{v}_{s}-\mathbf{v}_{f}\Big{)}\end{split} \tag{14}\]  
And for the RBCs,  
\[\begin{split}\phi\rho_{s}&\Big{[}\frac{9\
u_{s}}{ \partial t}+(grad\ \mathbf{v}_{s})\mathbf{v}_{s}\Big{]}\\ &=-grad(\phi p)+div\left(\left(\mu_{\infty}(\phi)+(\mu_{0}(\phi)- \mu_{\infty}(\phi))\frac{1+ln\Big{(}1+k\big{(}2 tr\mathbf{D}_{s}{}^{2}\big{)}^{\frac {1}{2}}\Big{)}}{1+k\big{(}2 tr\mathbf{D}_{s}{}^{2}\big{)}^{\frac{1}{2}}}\right) \mathbf{D}_{s}\right)\\ &+\rho_{s}\phi\mathbf{b}_{s}-\frac{9\mu_{f}}{2\alpha^{2}}f(\phi) \Big{(}\mathbf{v}_{s}-\mathbf{v}_{f}\Big{)}-\frac{3(6.46)\big{(}\rho_{f}\mu_{f}\big{)} ^{1/2}}{4\pi a}\phi\Big{(}2 tr\mathbf{D}_{f}{}^{2}\Big{)}^{-1/4}\mathbf{D}_{f}\Big{(} \mathbf{v}_{s}-\mathbf{v}_{f}\Big{)}\end{split} \tag{15}\]  
The transport (convection-diffusion) equation for the platelets is,  
\[\begin{split}\frac{9C}{\partial t}+div&(\mathbf{v}_{m} C)\\ &=div\big{(}\big{(}D_{B}+\xi\phi\dot{\gamma}\big{)}\
abla(1+q( \phi))C\big{)}+div\big{(}\big{(}D_{B}+\xi\phi\dot{\gamma}\big{)}(1+q(\phi)) \
abla C\big{)}\end{split} \tag{16}\]  
With the above mathematical model, we built our computational fluid dynamics (CFD) solver based on the libraries provided by OpenFOAM(r) [PERSON] et al. (2017). For all the cases studied in this paper, we use the following values for the material properties: \(\mu_{f}=0.96\) cP, \(\rho_{f}=1027\) kg/m\({}^{3}\) and \(\rho_{s}=1093\) kg/m\({}^{3}\). The body forces, \(\mathbf{b}_{s}\) and \(\mathbf{b}_{f}\), refer to gravity, which is ignored due to the large Froude number of the cases studied in the following. We first study the blood flow and platelets transport in a benchmark problem, namely a straight vessel, with different hematocrit. We then study a more practical problem, which is flow in two different aneurysal vessels. For all cases studied in current work, the hexahedral meshes are generated using ICEM (Version 14.0. ANSYS Inc., Canonsburg, PA, USA). In each of these cases, mesh-dependence studies were performed.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Spectrograms  
Spectrograms are time-frequency representations that provide insights into frequency variations over time, making them particularly useful for detecting EEG changes during events such as epileptic seizures [12]. Figure 1 'Spectrogram' shows an example of the spectrogram used in this study. The X-axis in spectrograms represents time (0 to 5 min), while the Y-axis represents frequencies between 0.1 and 100 Hz. The color intensity indicates the power or magnitude of each frequency component at a given time, with hotter colors (red, yellow) showing higher power and cooler colors (blue, green) indicating lower power.  
The spectrogram is generated by computing the Fast Fourier Transform (FFT) of short overlapping segments of the signal. The mathematical formula for the STFT is as follows:  
\[X(t,f)=\int_{-\infty}^{\infty}x(\tau)\,w(t-\tau)e^{-j2\pi f\tau}\,d\tau \tag{1}\]  
where:  
* \(x(t)\) is the signal in the time domain.
* \(w(t-\tau)\) is the window function (in this case, a Hamming window).
* \(f\) is the frequency.
* \(t\) is the time index for the center of the window.
* **Hamming Window Function**:  
\[w(t)=0.54-0.46\cos\biggl{(}\frac{2\pi t}{N-1}\biggr{)} \tag{2}\]  
Figure 1: Examples of the signal image, spectrogram, and scalogram used as inputs in this study are presented here. The signal images display the EEG signals from channels T5 (top channel) and O1 (bottom channel), with the X-axis representing time (0 to 5 min) and the Y-axis indicating amplitude (\(-0.0003\)\(\mu\)V to \(0.0003\)\(\mu\)V). In the spectrograms, the X-axis represents time (0 to 5 min), while the Y-axis depicts frequencies ranging from 0.1 to 100 Hz. For the scalograms, the X-axis corresponds to time (0 to 5 min), and the Y-axis represents wavelet scales that align with different frequency bands (0.1 to 100 Hz).  
where:  
* \(N\) is the length of the window.
* \(t\) is the time index within the window.
* **Spectrogram**: The spectrogram is the squared magnitude of the STFT: \[\text{Spectrogram}(t,f)=|X(t,f)|^{2}\] (3)
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 5 Assessment of Samples' Repeatability
### Odometrical Response  
#### 5.2.1 Experimental Setup  
The repeatability in terms of the mechanical behavior of the sand-clay mixtures prepared according to the S1 protocol was verified by performing classical one-dimensional oedometer tests. The goal of this type of test is to provide a first insight into understanding what will happen when using the designed preconsolidation cell. The results of the oedometer tests performed hereafter also give an understanding on S1 samples' mechanical behavior and the response when the vertical stress reaches 800 kPa (maximum limit of the device).  
Samples (50% clay by weight) in the oedometer tests were prepared with the S1 technique at an initial water content of 1.5 \(w_{\text{L}}\). After the specimens were prepared in the oedometer cell without any vertical stress loaded, the water contents were measured and the initial void ratio values were calculated, then an incremental loading oedometer device was used to perform the oedometer tests. The vertical stress loading steps were 12.5 kPa, 25 kPa, 50 kPa, 100 kPa, 200 kPa, 400 kPa, 800 kPa, then unloaded to 50 kPa. A loading step was started only when the vertical displacement in the previous step was stable. After that, the test was stopped and the water content of the specimen was measured. In total, three samples were prepared and performed on with the oedometer test.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 3 Gaofen-3 Data Experiment and Results
### Retrieved Ocean Surface Current  
In the first step of the proposed method, we extracted six sub-apertures for computation. Taking the processing result of label 1 SAR data as an example, after the first step of sub-aperture processing, one of the sub-aperture intensity images is shown in Figure 7. Obviously, the speckle noise of sub-aperture image is more serious and the resolution is reduced. In addition, the maximum azimuth angle of the sub-apertures is 0.3 degrees. In the second part, we divided each sub-aperture dataset into 30 \(\times\) 30 blocks, which resulted in a grid spatial resolution of 1 km \(\times\) 1 km for each block. Then, after the Doppler centroid estimation, the corresponding Doppler image is shown in Figure 7b.  
In the third step, the Doppler image results of each sub-aperture data are calculated according to the flow chart, and finally the two-dimensional current field results are obtained. Corresponding to the four Gaofen-3 SAR datasets in Figure 6, the retrieved ocean surface current field after the proposed method processing is shown in Figure 8, where the color ruler and the arrow point represent the velocity and direction of the ocean current, respectively.  
As can be seen from Figure 8, the ocean currents at panel (a), (c) and (d), that is, the positions at labels 1, 3 and 4 in Figure 5, are moving in a direction close to the northeast, which is consistent with the trend of the Kuroshio Current movement. Figure 8a is close to the Japanese islands, with the highest latitude, and the current velocity is the largest. In Figure 8b, the current velocity is the lowest and the direction changes, which is mainly due  
Figure 6: SAR intensity images corresponding to the four positions in Figure 5. (**a–d**) correspond to the labels 1, 2, 3, and 4, respectively.  
to the hindrance from the south of Japan island. The current field in Figure 8c is usually affected by the surrounding islands, making the velocity slower and the current direction closer to the east. Figure 8d has the lowest latitude, and the current field is closest to the equator, which is the source of the Kuroshio, so the current velocity is relatively high, and it moves northeast along the Taiwan island.
## 4 Performance Evaluation of Post-Installed Anchors
### Dynamic Loading Test  
#### 4.2.1 Presentation of Dynamic Load Protocol  
In this study, a load protocol for a seismic safety evaluation under a dynamic load was presented by referring to the ACI 355.2 standards of the United States for verification of the seismic performance of power generation facilities in power plants and the TR 049 Category C1 standards of Europe, which consider non-structural elements. Based on the average maximum loads derived from the static performance evaluation, a dynamic load protocol was presented, as shown in Table 6. The protocol was used to evaluate the dynamic performance of the M10 and M12 set anchors. Figure 13 shows the dynamic load protocol of the M10 set anchor, and Figure 14 shows that of the M12 set anchor. The seismic performance of the concrete anchors installed in power generation facilities was considered, verified if the maximum pull-out and shear load values of the dynamic tests derived under the presented cyclic loading met 80% of the performance presented in the static tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Anchor**} & \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Applied Load (kN)**} & \multirow{2}{*}{**Static Max. Average Load (kN)**} \\  & & **50\%** & **37.5\%** & **25\%** \\  & & **(10 Times)** & **(30 Times)** & **(100 Times)** & **(Refer to Tables 4 and 5)** \\ \hline \multirow{2}{*}{\(\Phi\)10} & Shear & 8 & 6 & 4 & 16.08 \\  & Pull-out & 13 & 9.5 & 6.4 & 26.03 \\ \multirow{2}{*}{\(\Phi\)12} & Shear & 13 & 9.8 & 6.5 & 25.44 \\  & Pull-out & 16 & 12 & 8 & 31.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Load protocol of post-installed anchors.  
Figure 12: Comparison of results of pull-out tests: (**a**) M10 set anchor; (**b**) M12 set anchor.