esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Environment and Results  
### Experimental Environment  
This experiment was conducted at the corridor on the 3 rd floor of the IT-1 building and the hall on the 2 nd floor of the IT-1 building at Kyungpook National University (KNU) as shown in Figures 6 and 7. Figure 8 shows the schematic diagram of the experimental environment of the IT-1 building, which is a 2.5 m by 10 m corridor environment. Figure 9 shows the schematic diagram of the experimental environment of the IT-2 building, which is an 8 m by 10 m hall environment. In the collection of sample data, there are 10 reference points between AP and reference point (RP), each of which collects 1000 samples, and the interval between each reference point is 1 m.  
In this paper, we use a laptop with a modified wireless network card and CSI tools [31; 32] to obtain CSI data. The equipment required was: (1) a laptop with Intel 5300 for which the Ubuntu 14.04 operating system is installed with the customized kernel and wireless card drivers; (2) wireless AP based on 802.11n. We connect to the laptop through the Wi-Fi hotspot provided by the wireless AP. Since the 5 GHz wireless signal has a strong ability to penetrate the wall, 5 GHz was selected as the experimental frequency band. Currently, the commercial wireless network card supporting the IEEE 802.11n standard can provide the amplitude and phase difference information of different subcarriers in the form of the CSI matrix. The OFDM system of the IEEE 802.11n standard contains 56 subcarriers.  
In this paper, the modified device can extract 30 subcarriers. Therefore, CSI is a numerical matrix of 3 \(\times\) 3 \(\times\) 30. Figure 3 shows that the antenna c did not work as expected. Thus, we use a 2 \(\times\) 3 \(\times\) 30 CSI numerical matrix for ranging. The collected CSI subcarrier information is an imaginary number that contains the amplitude and phase values of the subcarriers between different channels. Analysis of Figure 4 shows that the phase information measured at the receiving end is chaotic. There is no way to use it directly.
## 3 Numerical Model
### Approach A--Complete  
The first model, approach A, described the geometry considering all the elements of the PCM tank. Since the tank was symmetrical, only the left half was simulated. The values obtained in the simulations for columns B', D', and F' (Figure 4) were equivalent to the results measured experimentally for columns B, D, and F (Figure 1). The temperature interface between the PCM and the hot surface had to be set at 67.15 \({}^{\circ}\)C to simulate the experimental heating. The EP method was used for modeling the phase-change process. Solid and liquid PCM are part of a single volume of the same material (fixed grid). The thermophysical properties of lauric acid for the solid and liquid phase were assumed constant, according to the values in Table 1. However, for the phase-change temperature range (361.65 K-321.35 K), each property had a linear variation as a function of temperature, from the solid to the liquid value. The thermophysical properties of the other materials in the system were fixed, according to the values attached in Table 2.  
Heat losses due to the external convection of the container were modeled as a boundary condition in order to calculate the convection coefficient (\(h_{c}\)), obtained from the Nusselt number (\(Nu\)) from Equation (1):  
\[Nu=\frac{h_{c}\cdot l_{c}}{k} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \(\rho\) **(Kg/m3)** & \(C_{p}\) **(J/kg\(\cdot\)K)** & \(k\) **(W/m\(\cdot\)K)** & **Ref.** \\ \hline \hline Vinyl ester resin (VER) & 1110 & 1400 & 0.21 & [36; 37] \\ Aluminum (Al) & 2719 & 871 & 202.4 & ANSYS Fluent defaults \\ Extruded polystyrene (XPS) & 30 & 1280 & 0.031 & [38; 39] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical properties of the materials of the container.  
Figure 4: Diagram of the geometries used in each approach.  
where \(l_{C}\) is the characteristic length of the surface and \(k\) is the thermal conductivity of the material.  
Several experimental correlations were found to estimate natural convection within different geometries and systems. The implemented correlations, attached in Table 3, were defined in the Fluent software using a user-defined function (UDF).  
These expressions are a function of the dimensionless Rayleigh number (\(Ra\)), given by Equation (2):  
\[Ra=\frac{g\cdot\Gamma_{a}\cdot(T_{s}-T_{\infty})\cdot l_{C}^{3}}{{v_{a}}^{2}}Pr \tag{2}\]  
where \(g\) is gravity, \(\Gamma_{a}\) is the coefficient of thermal expansion of the air, \(T_{s}\) is the surface temperature, \(T_{\infty}\) is the temperature of the flow away from the wall, \(Pr\) is the Prandtl number, and \(v_{a}\) is the kinematic viscosity of the air. \(\Gamma_{a}\), \(v_{a}\), and \(Pr\) were calculated using the average temperatures of \(T_{s}\) and \(T_{\infty}\). The experimentally recorded temperature from each thermocouple was compared with the simulated temperature of a given point for the evaluation of the simulation results.
## 1 Introduction  
Bacteria and Archaea are frequently exposed to stresses such as infection from bacteriophages (phage) and other genetic elements. These events can result in horizontal gene transfer (HGT), which is mediated by transduction, transformation or by conjugation of mobile elements such as plasmids [1,2]. HGT can be beneficial for adaptation and survival by rapidly promoting the transfer of genes encoding antibiotic resistance, virulence factors, or the ability to degrade toxic compounds [1]. However, in the absence of any selective advantage, foreign genetic elements can have a fitness cost. This is most evident when prokaryotes are infected by virulent phages. The sheer scale of viral abundance (\(\sim\)10\({}^{31}\)), diversity and rates of infection (\(\sim\)10\({}^{25}\)/s) [3,4] has resulted in the evolution of prokaryotic defenses to maintain the balance in this arms race [5].  
Bacteria possess multiple methods to regulate genetic flux and resist phage infection. These mechanisms include the mutation or masking of cell surface receptors, restriction-modification, abortive infection and the clustered regularly interspaced short palindromic repeats (CRISPR) systems [5,6]. CRISPR systems are a widespread mechanism that equips bacteria with a sequence-specific heritable 'adaptive immune system' that has a genetic memory of past genetic incursions (for recent reviews see [7, 8, 9]).  
CRISPRs use small non-coding RNAs for defense and function in conjunction with CRISPR associated (Cas) proteins. The mechanism of CRISPR/Cas interference involves three phases (Figure 1). Firstly, resistance is acquired via the integration of short sequences from foreign genetic elements (termed spacers) into repetitive genetic elements known as CRISPR arrays. Secondly, CRISPR arrays are then transcribed and processed into small RNAs (crRNAs) by Cas proteins. In the third and final step, targeting of the invading phage or plasmid is mediated by a Cas protein complex that contains crRNAs. During this stage, the crRNA-Cas protein complex then interferes, in a sequence-specific manner, with the foreign nucleic acids.  
Despite sharing mechanistic similarities, there is significant diversity amongst CRISPR/Cas systems. There are three major CRISPR/Cas system types (I-III), which are characterized by a signature protein [10]. The main types are further divided into subtypes (e.g., I-A to I-F) based mainly on the presence of a subtype-specific set of Cas proteins and for some types the repeat sequence of the associated CRISPR array is also taken into account. The last five years have seen rapid advances in our mechanistic/molecular understanding of CRISPR/Cas systems. In this review, we provide an historical context, highlight the current models for the adaptation, processing and interference stages and finally, discuss the regulation of the CRISPR/Cas systems.
## 3 Methods
### Application Use and Data Acquisition Flow  
Figure 1 shows the flow and data acquisition process for the mobile application. Participants first input their departure location and destination to find suitable routes to their intended destinations (Route search). Then, the application displays four options: \"Depart now\", \"Depart after 1 hour\", \"Depart after 2 hours\", and \"Depart after 3 hours\", along with their respective estimated travel times, taking into account the hourly traffic congestion estimates from the time of route search, as shown in Figure 2a. Then, participants selected an option (Route choice). If an option other than \"Depart now\" was selected, suggestions for stopovers in the surrounding tourism areas were displayed with the return route as map markers. These stopovers were classified as \"restaurants\", \"shopping\", \"sightseeing spots\", and \"hot springs\", as shown at the top of Figure 2b (Spot check).  
Detailed information such as business hours, photos, and ratings are also displayed for each location upon selection, as shown in Figure 2b. Only facilities currently open were displayed to improve convenience. If the \"depart now\" option was selected, the final route confirmation screen was displayed. If a participant decided to stopover, they could choose the expected additional time required (Spot choice). Finally, the travel time from the departure location to the stopover, total stay time, and the required travel time to the final destination, considering the estimated traffic congestion, are displayed as shown in Figure 2c (Route check). When a participant selects the \"Start navigation\" button, the \"Drive Supporter\" application (a Japanese car navigation application by Navitime Japan Co., Ltd.) initiates, obtaining driving trajectories using mobile GPS and providing voice navigation (Start navigation).
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 4 Results
### Energy Requirements and Redarging
#### 4.3.2. Recharging RSTAR  
As in Nature, the parent robot can also provide its child robot with energy. A recharging point can be attached to the tail of the BSTAR to recharge the battery of the RSTAR. Because the charging point is in the center of the tail and the RSTAR is in contact with both walls of the tail, the docking is very simple. The RSTAR simply has to approach the charging point and adjust its height to plug to its connector into the charging point magnetically (Figure 20 and video).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Surface** & **Sprawl** & **Speed [m/s]** & **Current [A]** & **Power [W]** & **COT** \\ \hline
**Tile** & 30 & 1.13 & 4.57 & 33.8 & 0.299 \\ \hline
**Tile** & 45 & 1.17 & 4.31 & 31.9 & 0.272 \\ \hline
**Tile** & 60 & 1.25 & 3.86 & 27.8 & 0.221 \\ \hline
**Dirt** & 30 & 1.05 & 5.16 & 38.2 & 0.333 \\ \hline
**Dirt** & 45 & 1.05 & 6.30 & 46.6 & 0.443 \\ \hline
**Dirt** & 60 & 1.05 & 5.23 & 38.6 & 0.367 \\ \hline
**Grass** & 30 & 0.8 & 9.66 & 71.5 & 0.892 \\ \hline
**Grass** & 45 & 1.03 & 6.69 & 49.5 & 0.477 \\ \hline
**Grass** & 60 & 1.11 & 5.07 & 37.5 & 0.337 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The power requirements of BSTAR.  
Figure 19: The current consumption of BSTAR over three different surfaces (tiles, dirt and grass) for three sprawl angles (30, 45 and 60 degrees) at its maximum output speed.
## 4 Findings
### Street Connectivity Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Model 1**} \\ \hline B & Std. Error & Beta & T & P \\ \hline Constant & \(-1277.51\) & \(1035.43\) & & \(-1.23\) & 0.21 \\ Age & \(405.85\) & \(396.38\) & \(0.044\) & \(1.02\) & 0.30 \\ Daily activity & \(1716.35\) & \(516.80\) & \(0.142\) & \(3.32\) & 0.001 \\ Monthly household income & \(42.67\) & \(410.63\) & \(0.004\) & \(0.104\) & 0.917 \\ \hline \multicolumn{5}{c}{**Model 2**} \\ \hline Constant & \(-4229.23\) & \(1344.52\) & & \(-3.146\) & \(0.002\) \\ Age & \(215.15\) & \(395.30\) & \(0.023\) & \(0.544\) & \(0.586\) \\ Daily activity & \(1333.32\) & \(553.88\) & \(0.110\) & \(2.40\) & \(0.016\) \\ Monthly household income & \(-209.68\) & \(412.62\) & \(-0.021\) & \(-0.508\) & \(0.612\) \\ Number of commuting trips per week & \(862.80\) & \(424.84\) & \(0.086\) & \(2.031\) & \(0.043\) \\ Commuting distance & \(0.016\) & \(0.032\) & \(0.023\) & \(0.511\) & \(0.610\) \\ Frequency of public transport use & \(939.08\) & \(465.07\) & \(0.093\) & \(2.019\) & \(0.044\) \\ Private motor vehicle use for commuting trips & \(996.17\) & \(474.71\) & \(0.099\) & \(2.098\) & \(0.036\) \\ Private motor vehicle use for non-commuting trips & \(-304\) & \(403.33\) & \(-0.032\) & \(-0.754\) & \(0.451\) \\ \hline \multicolumn{5}{c}{**Model validation**} \\ \hline  & \multicolumn{5}{c}{Sum of Squares} & Df & Mean Square & F & P \\ \hline  & \multicolumn{5}{c}{**Model 1**} \\ \hline Regression & \(3\) & & \(4.71\) & \(0.003\) \\ Residual & \(562\) & \(21145974\) & & \\ Total & \(565\) & & & \\ \hline  & \multicolumn{5}{c}{**Model 2**} \\ \hline Regression & \(8\) & \(90803772\) & & \\ Residual & \(577\) & \(20568106\) & & \\ Total & \(565\) & & & \\ \hline  & \multicolumn{5}{c}{**Model summary**} \\ \hline Model & R & R-Square & Adjusted R-Square & F Change & Df1 & Df2 &
\begin{tabular}{c} Sig. F \\ Change \\ \end{tabular} \\ \hline \(1\) & \(0.157\) & \(0.025\) & \(0.019\) & \(0.025\) & \(4.71\) & \(3\) & \(562\) & \(0.003\) \\ \(2\) & \(0244\) & \(0.060\) & \(0.046\) & \(0.035\) & \(4.15\) & \(5\) & \(557\) & \(0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HMR model for street-length density around homes. \(p\)-value above 0.05. All significant variables showed highly significant associations in the model. According to our calculations, one unit increase in street-length density correlated with a 21% and 22% decrease in private motor vehicles. Street-length density has a negative correlation with the frequency of public transport use. This means that residents who work in sprawled areas use public transportation less than those who work in compact areas. Table 3 shows HMR model for street-length density around workplaces.
## 5 MPPT Algorithms
### Artificial Neural Network (ANN)
#### 5.1.2 Training Results of ANN  
Figure 9 shows the training results of the ANN. The performance plot (Figure 9a) highlights a best validation performance of 0.94349 at epoch 266, indicating effective convergence. The regression plot (Figure 9b) reveals a strong correlation between predicted and actual values, with a regression coefficient of \(R=0.99575\) for the test dataset, confirming the model's ability to generalize. A dataset containing one million samples was used for each variable (irradiance, temperature, \(V_{mp}\), and \(P_{mp}\)), ensuring a broad representation of different operating conditions and enhancing the generalization capability of the ANN.  
The trained network is subsequently integrated as a Simulink block within the MPPT control system, as illustrated in Figure 10.  
Figure 8: Temporal evolution of environmental variables used in the dataset generation: (**a**) temperature profile and (**b**) irradiance profile.  
Figure 9: Performance of the ANN during training: (**a**) training performance (best validation = 0.94349 at epoch 266) and (**b**) training regression (\(R=0.99575\) for test data).  
It is worth noting that the performance saturation can be primarily attributed to the size of the dataset used for training. With approximately one million samples covering variables such as irradiance, temperature, voltage, and power, the dataset is sufficiently large to ensure that the network effectively learns the mapping between input and output variables. As a result, increasing or decreasing the number of neurons in the hidden layers did not lead to significant improvements in prediction error since the network already achieves good generalization.
# Outpatient Total Knee Arthroplasty: A Meta-Analysis
## 3 Results
### Results of Syntheses  
The outpatient group evidenced a greater OKS (MD 4.77; 95%CI 1.06 to 8.49; \(p\) = 0.01). The inpatient group demonstrated a greater rate of revision (OR 5.06; 95%CI 1.18 to 21.75; \(p\) = 0.03), mortality (OR 15.07; 95%CI 2.58 to 88.07; \(p\) = 0.003), and DVT (OR 4.99; 95%CI 1.63 to 15.33; \(p\) = 0.005). No difference was found in the rate of readmission (_p_ = 0.3) and infection (_p_ = 0.4). The forest plot of each endpoint is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author, Year** & **Journal** & **Design** & \begin{tabular}{c} **Follow-up** \\ **(Months)** \\ \end{tabular} & **Treatment** & \begin{tabular}{c} **Patients** \\ **(_n_)** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Age** \\ \end{tabular} & \
\begin{tabular}{c} **Women** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **BMI** \\ \end{tabular} \\ \hline [PERSON] et al., 2019 [17] & _J. Arthroplasty_ & Retrospective & 3 & Outpatient & 858 & 61.0 & & \\ [PERSON] et al., 2018 [18] & _J. Arthroplasty_ & Retrospective & 1 & Inpatient & 2574 & 61.0 & & \\ [PERSON] et al., 2018 [18] & _J. Arthroplasty_ & Retrospective & 1 & Outpatient & 365 & 72.3 & 62 & 31.8 \\  & & & Inpatient & 45,738 & 72.8 & 63 & 31.8 \\ [PERSON] et al., 2019 [19] & _Acta Orthop._ & Prospective & 3 & Outpatient & 46 & 61.0 & 41 & 28.0 \\  & & & Inpatient & 134 & 62.0 & 43 & 28.0 \\ [PERSON] et al., 2019 [20] & _Acta Orthop._ & RCT & 3 & Outpatient & 16 & 58.0 & 8 & 28.0 \\  & & & Inpatient & 14 & 63.0 & 64 & 29.0 \\ [PERSON] et al., 2018 [21] & _J. Arthroplasty_ & Prospective & & Outpatient & 21 & 59.2 & 58 & 30.4 \\  & & & Inpatient & 61 & 64.1 & 28 & 32.7 \\ [PERSON] et al., 2009 [1] & _Clin. Orthop. Relat. Res._ & Prospective & 12 & Outpatient & 64 & 55.0 & 38 & 30.8 \\  & & & Inpatient & 64 & 55.0 & 38 & 30.8 \\ [PERSON] et al., 2014 [26] & _J. Arthroplasty_ & Retrospective & 24 & Outpatient & 454 & 68.0 & & \\ [PERSON] et al., 2016 & & & Inpatient & 71,341 & 71.0 & & \\
[27] & _J. Arthroplasty_ & Prospective & 3 & Outpatient & 309 & 64.0 & 56 & 30.0 \\ [PERSON] et al., 2016 [28] & _J. Arthroplasty_ & Prospective & 1 & Inpatient & 891 & 64.0 & 55 & 31.0 \\  & & & Outpatient & 379 & 65.2 & 58 & 32.5 \\ [PERSON] et al., 2016 & & & Inpatient & 35,870 & 67.3 & 66 & 32.9 \\
[29] & _Knee Surg. Sport Traumatol. Arthrosc._ & Prospective & 2 & Outpatient & 10 & 64.1 & 20 & 27.7 \\  & & & Inpatient & 10 & 66.9 & 50 & 29.2 \\ \hline  & Overall & & & 159,219 & 63.7 & 46.8\% & 30.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Generalities and patient baseline of the included studies.  
Figure 3: _Cont._
## 2 Mathematical Method
### Static and Perturbed Film Thickness for Tilting Pad Bearings  
The shaft whirling and the pads tilting around the static equilibrium position at small amplitude can be described by perturbation of eccentricity ratio, attitude angle (\(\widetilde{\epsilon}_{d}\), \(\widetilde{\phi}_{d}\)) and tilting angle \(\widetilde{\delta}_{d}\), respectively. Then the film thickness \(\overline{h}\) of the \(l\)th pad of tilting pad bearing is composed of the static \(\overline{h}_{0}\) between the shaft and the pads at equilibrium position and the perturbed film thickness \(\overline{h}_{d}\)[16].  
\[\overline{h}=\overline{h}_{0}+\widetilde{h}_{d}e^{i\Omega\overline{t}} \tag{4}\]  
\[\overline{h}_{0}=1-m\cos(\beta_{l}-\theta)+\varepsilon_{0}\cos(\theta-\varphi_ {0})+\widetilde{\delta}_{0,l}\sin(\beta_{l}-\theta) \tag{5}\]  
\[\widetilde{h}_{d}=\overbrace{\widetilde{\epsilon}_{d}\cos(\theta-\varphi_{0}) +\widetilde{\epsilon}_{0}\widetilde{\phi}_{d}\sin(\theta-\varphi_{0})}^{shaft \;whirling}+\overbrace{\widetilde{\delta}_{d,l}\sin(\beta_{l}-\theta)}^{pads\; tilting} \tag{6}\]where the \(\beta_{l}\) is the circumferential angle of the pivot position of the \(l\)th pad, as shown in Figure 1, and the subscript \(\_l\) in the \(\overline{\delta}_{0}\), \(\widehat{\delta}_{d}\) represents the \(l\)th pad as well. The preload factor \(m=1-C_{b}/C_{p}\) and the \(\overline{\delta}=\delta/\psi\). The dimensionless perturbation frequency \(\Omega\) is the ratio of shaft perturbation frequency \(v\) to the rotating frequency \(\omega\).
## 2 Materials and Methods
### Variable-Frequency Complex Demodulation Algorithm
#### 2.2.2 VFCDM  
In a scenario where the modulating frequency is not constant, as previously described, but varies as a function of time, the signal \(s(t)\) can be represented in the following manner:  
\[s(t)=V_{dc}+V(t)\text{cos}\int_{0}^{t}2\pi f(\tau)d\tau+\theta(t)\biggr{]} \tag{7}\]  
Following similar operations as those described in Equations (1) and (2), multiplying Equation (7) by \(e^{-j\int_{0}^{t}2\pi f(\tau)d\tau}\) results in the determination of both instantaneous amplitude \(V(t)\) and instantaneous phase \(\theta(t)\). This process yields:  
\[m(t)=s(t)e^{-j\int_{0}^{t}2\pi f(\tau)d\tau}=V_{dc}e^{-j\int_{0}^{t}2\pi f( \tau)d\tau}+\frac{V(t)}{2}\Bigl{(}e^{j\theta(t)}+e^{-j\int_{0}^{t}4\pi f(\tau )d\tau+\theta(t)}\Bigr{)} \tag{8}\]  
Expanding from Equation (8), if we filter the signal \(m(t)\) using an ideal LPF, the result will be the filtered signal \(m_{LP}(t)\) with identical instantaneous amplitude \(V(t)\) and phase \(\theta(t)\), as presented in Equations (4) and (5). The instantaneous frequency can be determined as provided in [28] and is given by:  
\[f(t)=f_{0}(t)+\frac{1}{2\pi}\frac{d\theta(t)}{dt} \tag{9}\]  
In this study, the EEG signal is represented by the signal \(s(t)\). The Hilbert transform method was used to compute the instantaneous frequency and amplitude [29]. Applying the Hilbert transform to the equation above for all time points associated with the obtained low-pass-filtered frequency components resulted in the derivation of the complete time-frequency spectrum. The use of CDM and Hilbert transforms enabled the generation of a high-resolution TFS. This facilitated the accurate and precise extraction of amplitude information.  
The procedure for implementing the TFS based on CDM is outlined as follows:  
1. Filter Design: Design a finite-impulse response LPF with a specified bandwidth (\(F_{w}\)) and filter length (\(N_{w}\)). Set center frequencies as (\(f_{0i}\)): \[f_{0i}=2\times F_{w}(i-1),\ i=1,2,3,\ldots,int\Bigl{[}\Bigl{.}\Bigl{.}\Bigl{.  
plitudes (as per Equation (4)) of each sinusoidal modulation component, using the Hilbert transform.
5. Time-Frequency Representation: Obtain the TFR of the signal by using the estimated instantaneous frequencies and amplitudes, providing a detailed depiction of signal variations across both time and frequency domains. For more details about the VFCDM algorithm, please refer to [21].