esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models and Methods
### Mathematical Model
#### 2.2.2 Governing Equations  
In the models developed, forced convection heat transfer was assumed. As the Reynolds number exceeds \(10^{6}\), the fluid is turbulent and, therefore, the k-\(\epsilon\) turbulence model was used. Equations for three-dimensional unsteady-state flow distribution and porous medium were employed, including three-dimensional continuity equation, momentum equation and energy equation, which are as follows [28; 31; 32; 33; 34].  
1. Three-dimensional continuity equation: \[\frac{\partial\rho}{\partial t}+\overset{\rightarrow}{\
abla}\Big{(}\rho \overset{\rightarrow}{v}\Big{)}=0\] (1)
2. Momentum equations: \[\frac{\partial}{\partial t}(\rho v_{i})+\frac{\partial}{\partial x _{i}}\big{(}\rho v_{i}v_{j}\big{)} =\frac{\partial}{\partial x_{i}}\bigg{[}-p\cdot\delta_{ij}+\mu \bigg{(}\frac{\partial v_{i}}{\partial x_{i}}+\bigg{(}\frac{\partial v_{j}}{ \partial x_{i}}\bigg{)}\bigg{)}\bigg{]}+\rho\cdot g_{i}\] (2) \[\frac{\partial}{\partial t}(\rho v_{i})+\frac{\partial}{\partial y _{i}}\big{(}\rho v_{i}v_{j}\big{)} =\frac{\partial}{\partial y_{i}}\bigg{[}-p\cdot\delta_{ij}+\mu \bigg{(}\frac{\partial v_{i}}{\partial y_{i}}+\bigg{(}\frac{\partial v_{j}}{ \partial y_{i}}\bigg{)}\bigg{)}\bigg{]}+\rho\cdot g_{i}\] (3) \[\frac{\partial}{\partial t}(\rho v_{i})+\frac{\partial}{\partial z _{i}}\big{(}\rho v_{i}v_{j}\big{)} =\frac{\partial}{\partial z_{i}}\bigg{[}-p\cdot\delta_{ij}+\mu \bigg{(}\frac{\partial v_{i}}{\partial z_{i}}+\bigg{(}\frac{\partial v_{j}}{ \partial z_{i}}\bigg{)}\bigg{)}\bigg{]}+\rho\cdot g_{i}\] (4)
3. Energy equation: \[\frac{\partial}{\partial t}\Big{(}\epsilon\cdot p_{f}\cdot E_{f} +(1-\epsilon)p_{f}\cdot E_{p}\Big{)}+\
abla\cdot\left(\overset{ \rightarrow}{v}\Big{(}\rho_{f}\cdot E_{f}+p\Big{)}\right)\] (5) \[=\
abla\bigg{[}k_{ef}\cdot\
abla\cdot T-\bigg{(}\sum h_{i}\overset{ \rightarrow}{j}\bigg{)}\bigg{]}+S_{e}\] (6) \[S_{e} =-\bigg{(}\sum_{j=1}^{3}D_{ij}\mu v_{j}+\frac{1}{2}\sum_{j=1}^{3} C_{ij}\bigg{|}\overset{\rightarrow}{v}_{j}\bigg{|}v_{i}\bigg{)}\] (7) \[D =\frac{150(1-\xi)^{2}}{d^{2}\xi^{3}}\] (8) \[C =\frac{3.5(1-\xi)^{2}}{d^{3}}\] (9)
## 2 The CDMA RoFSO System with Spatial Diversity
### Spatial Diversity Receivers  
A spatial diversity scheme is employed on the receivers' side, along with a maximum ratio combining (\(MRC\)) scheme. Multiple copies of the same radio frequency (RF) CDMA signal are transmitted from an array of \(M\) optical laser sources towards a set of \(M\) photo-detector apertures at the receiver. The spacing between the laser sources at the transmitter and the receive apertures at the receiver is larger than the spatial coherence length of the atmospheric channel, i.e., a few centimeters, in order to achieve uncorrelated and independent diversity branches. Thus, the signals from each diversity branch are weighted with a gain factor \(G_{m}\), which is proportional to the signal strength in the specific diversity branch. They are co-phased and summed coherently. The output of the \(MRC\) combiner is given as [2; 20]:  
\[CNIR_{k,MRC}(I)=\sum\limits_{m=1}^{M}CNIR_{k,m}(I_{m})=r(t), \tag{8}\]  
The output of the \(MRC\) combiner is sent to a \(K\)-dimension matched filter receiver, where the desired signal of the 1 st user, denoted as \(y_{1}(n)\), is given in [18] (Equation (6.40)). The specific process can be implemented in both directions of the forward and the reverse link. The details of the spatial diversity is shown in Figure 2.
## 2 Materials and Methods
### PWV Calculation  
PWV calculations were performed using the time series extracted from 1000 timepoints obtained using both vertical and radial projection methods. First, the time series were smoothed to reduce noise and ensure accurate peak point detection. The findpeak function in MATLAB R2020b was used to identify the peak points in the time series. _PWV_ was calculated by applying the difference between the peak points of _ICA_ and _CCA_ within the same hemisphere, as in Equation (1):  
\[PWV\ (\mathrm{m}/\mathrm{s})=\frac{\mathit{distance}\ \mathit{of}\ \mathit{two}\ \mathit{slices}\ (\mathit{\Delta d})}{\mathit{( ICA\ peak\ point-CCA\ peak\ point)\times temporal\ resolution}} \tag{1}\]  
Figure 3: Method for selecting the projection angles in the radial projection method. Templates with the time series of the internal and common carotid arteries are constructed from the ground truth image (red dashed box). These templates are then Radon transformed across all angles from 1\({}^{\circ}\) to 180\({}^{\circ}\). The resulting sinograms for each angle are used to select the optimal projection angles to avoid signal interference between vessels. The number of selected projection angles can range from a minimum of one to a maximum equivalent to the number of ROIs. Note that each angle line in the right panel shows the selected vessel being isolated, not the center of the image. TOF MRA, time-of-flight magnetic resonance angiography; SMS, simultaneous multislice; ROI, region of interest.
## 2 Materials and Methods
### Laser-Cleaning Experiment  
Figure 1a presents a schematic representation of the pulsed laser-cleaning apparatus, comprising a laser source, a galvanometer scanning system, and a control card. During the experimental procedure, the field mirror's focal length was set at 180 mm with a laser spot diameter of 0.05 mm and a repetition frequency of 60 kHz. The efficiency of the laser-cleaning process is primarily influenced by the laser energy density and the rate of spot overlap. Additionally, the power density of the laser and the diameter of the laser beam spot are critical determinants in the efficacy of the cleaning process [18]. The equation for laser energy density is as follows:  
\[\varepsilon=\frac{4\text{P}}{\pi D2f} \tag{1}\]  
The laser energy density \(\varepsilon\) is a function of the laser spot diameter \(D\) and the laser pulse repetition frequency \(f\). When \(D\) and f are held constant, the laser power P can be modulated by adjusting the laser's operating current. This adjustment enables precise control over the laser energy density \(\varepsilon\), thereby influencing the outcomes of the laser-cleaning process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element** & **C** & **Si** & **Mn** & **P** & **Ni** & **Cr** & **Cu** \\ \hline Mass faction & 0.17 & 0.17 & 0.35 & 0.035 & 0.30 & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal chemical composition of 20 steel (wt%).  
Figure 1: Laser-cleaning experimental system: (**a**) equipment diagram, (**b**) laser spot overlap diagram, (**c**) Schematic diagram of the electrochemical workstation.  
The spot overlap rate equation is as follows [19]:  
\[\lambda=(1-\frac{\
u}{Df})\times 100\%=(1-\frac{L}{D})\times 100\% \tag{2}\]  
The scanning speed of the galvanometer \(\
u\), the laser spot diameter \(D\), the laser pulse repetition frequency \(f\), and the distance between adjacent spot centers \(L\) were key parameters. The non-energy density and spot overlap rate were regulated by varying the laser's loading current and scanning speed. For these experiments, the selected spot overlap rates were 25%, 50%, 75%, and 80%. Correspondingly, the laser energy densities were set at 1.42 J/cm\({}^{2}\), 2.84 J/cm\({}^{2}\), 4.26 J/cm\({}^{2}\), and 5.68 J/cm\({}^{2}\).
## 4 Experiment and Discussion
### Optimization of RF Power  
The RF power corresponds to the RF magnetic field. The Rabi frequency \(\Omega\) is proportional to the RF magnetic field. From Equation (11), when the RF power (the Rabi frequency) increases, the signal amplitude reaches a maximum value. In this case, the sensitivity of the magnetometer can be optimized. In our experiments, the RF magnetic field is produced by a solenoid connected in series with a resistance and a voltage source. The RF magnetic field is changed by changing the output of the voltage source. The measured amplitude of the Larmor signal and the output noise floor varying with the amplitude of RF signal are shown in Figure 11. When the amplitude of RF signal increases from small values to larger ones, the signal amplitude increases first and then decreases. Thus, by optimizing the parameters, the maximum signal amplitude can be found. As the noise and relaxation rate are independent of the RF power [10; 28], the output SNR and the sensitivity are the best when the signal amplitude reaches the maximum. The measurements of the output noise floor and the output SNR are shown in Figure 11b,c.  
Figure 11: The measured amplitude of the Larmor signal, the output noise floor and the output signal-to-noise ratio (SNR) varying with the amplitude of RF signal. (**a**) The measured amplitude of the Larmor signal. (**b**) The output noise floor. (**c**) The output SNR.  
Figure 9: Photo of the electronic unit.
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
## 2 Materials and Methods
### Experimental Design
#### 2.4.4 Electrochemical Impedance Spectroscopy (EIS)  
The Electrochemical Impedance Spectroscopy was carried out by using a computer controlled potentiostat (VersaSTAT 3F, AMETEK SI, Oak Ridge, TN, USA) with 3.5 wt % NaCl solution at room temperature. The experimental setup consisted of three electrode cell containing graphite as counter electrode, 3D printed sample as working electrode and saturated Ag/AgCl electrode (saturated with KCl, Accumet Glass Body (Fisher Scientific, Dallas, TX, USA)) was used as the reference electrode. Working electrodes, the electrical contact between a copper wire and the sample was made at the back of the sample with silver conductive epoxy. The whole assembly was protected from the solution by mounting it in epoxy resins, which makes it leave only one face of the electrode exposed to the solution. The exposed surface area was 0.77 cm\({}^{2}\).
Sensors_**2012**, _12_, 5850-5871; doi:10.3390/s120505850  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**EBDC: An Energy-Balanced Data Collection Mechanism Using a Mobile Data Collector in WSNs**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Computer Science and Information Engineering, Tamkang University, No. 151,  
Yingzhuan Road, Tamsui District, New Taipei City 25137, Taiwan;  
E-Mails: [EMAIL_ADDRESS] (C.-Y.L.); [EMAIL_ADDRESS] (C.-H.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-2-2621-5656 (ext. 2749); Fax: +886-2-2620-9749.  
_Received: 13 March 2012; in revised form: 20 April 2012 / Accepted: 2 May 2012 / Published: 7 May 2012_  
**Abstract:** The data collection problem is one of the most important issues in Wireless Sensor Networks (WSNs). Constructing a tree from all sensor nodes to the sink node is the simplest way, but this raises the problem of energy unbalance since the sensors closer to the sink node would have much higher workloads from relaying data. To cope with the energy unbalance problem, a number of mobile-sink mechanisms have been proposed in recent years. This paper proposes an Energy-Balanced Data Collection mechanism, called _EBDC_, which determines the trajectory of a mobile data collector (or mobile sink) such that the data-relaying workloads of all sensors can be totally balanced. Theoretical analysis and performance evaluation reveal that the proposed _EBDC_ mechanism outperforms the existing approaches in terms of network lifetime and the degree of energy balancing.  
**Keywords:** wireless sensor networks; data collection; mobile sink; energy balancing
# Low-Cost Automation for Gravity Compensation of Robotic Arm
## 3 State of Technology
### Gravity Compensation  
The field of stability control of manipulative robots has become more important since its proposal in 1981, as this only applied to robotic arms that only have rotating joints, for mathematical modeling equations are used that are nonlinear, however, [PERSON]'s theory is often used as an auxiliary means [23].  
The development of the PD control with gravity compensation for manipulators is based on the assumption that the dynamics of the actuators do not exist, so it should be considered that the control signal is not the torque but the voltage that is applied to the motors, where the torque comes as the result of the electrical interaction of the actuators [24].  
Figure 2: Geometry of the KukaTM youBot Arm in the plane.  
Without considering external disturbances, the mathematical model of a rigid robot that has a number n of links is described with Equation (10):  
\[M\left(q\right)\ddot{q}+C\left(q,\dot{q}\right)\dot{q}+G\left(q\right)+F(\dot{q})=\tau \tag{10}\]  
where \(M\left(q\right)\ddot{q}\) represents matrix of symmetric manipulator inertia, \(C\left(q,\dot{q}\right)\dot{q}\) is matrix of centripetal and coriolis forces, \(G\left(q\right)\) is gravitational pairs vector, \(F(\dot{q})\) is friction pairs and \(\tau\) represents input torque vector.  
The derivative proportional control algorithm with gravity compensation is defined by the correction of the joint positioning error presented by Equation (11):  
\[\tau=K_{p}\widetilde{q}-K_{d}(\dot{\widetilde{q}})+G(\dot{q}) \tag{11}\]  
where \(K_{p}\widetilde{q}\) is the Proportional gain multiplied by the position error, \(K_{d}(\dot{\widetilde{q}})\) is the Derivative gain multiplied by the speed of movement and \(G\left(q\right)\) represents the Gravitational torque compensation.  
Gravitational compensation is defined as a term proportional to the position error, to which a damping consisting of the speed of movement is added, this part of the control consists of a mechanical retention that absorbs the impulses resulting from the response, suppressing peaks and oscillations, for the stationary stage it is estimated that the position of the effector or terminal tool is constant, assuming a value of zero at the speed of movement [25].  
According to what is expressed in [26], the control loop with gravity compensation for robots is established as an equation that needs a constant measurement of position and speed, Equation (11), determines the dynamic model for PD control with gravitational compensation for manipulative robots.  
Additionally, Figure 4 shows the block diagram equivalent to the PD control law with gravity compensation, where Kp and Kd are defined as positive symmetric matrices, however this type of optimization requires a partial knowledge of the manipulator, considering values of length, mass and rotational parameters of the components of the robotic arm.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 5 Experimental Study
### Signal Processing for Feature Extraction  
Current data were collected from the three-phases of the motors at the different operating conditions and cleaned. Figure 4 shows a visualization of the current signals collected from the induction motors at the different operating conditions, with red, blue, and green representing phases 1, 2, and 3, respectively.  
Figure 3: Fault conditions to replicate for the failure modes: (**a**) rotor misalignment, (**b**) broken rotor bar, and (**c**) inter-turn short circuit winding.  
Figure 2: A pictorial view of the experimental setup.  
As shown, the signals reveal some similarity in waveforms across the different operating conditions apart from the phase 2 signal (in blue) of Figure 4c, whose maximum amplitude is about 2 amperes while the rest have a magnitude of 1 ampere. Next, the signals were processed for feature extraction using the method proposed in Section 4. Figure 5 shows the respective FFT, PSD, and ACF visualizations of the signals under the different operating conditions.  
As shown in Figure 5a,b, the FFT and PSD for each of the different operating conditions uniquely reflect different spectral bands of different magnitudes and frequency ranges, whereby PSD is more sensitive for the 1 SC-3 condition for phase 2 signals. In addition, the ACF results in Figure 5c reveals differing ACF amplitudes over the time delay for each of the phases. These were concatenated to form the feature set for discriminative modeling for diagnosis. To develop the feature set, the proposed peak detection algorithm extracted the ten (10) tallest peaks from the FFT, PSD, and ACF spectra, respectively, from each of the current signals collected from the different operating conditions.  
Figure 4: Current signals collected from the induction motors: (**a**) ARM-1, (**b**) BRB-2, (**c**) ISC-3, and (**d**) NOM-4.
# A Corrected Adaptive Balancing Approach of Motorized Spindle Considering Air Gap Unbalance
## 4 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Case 1**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Displacement (um)**} & \multicolumn{4}{c}{**Acceleration (g)**} \\ \hline
**Speed (rpm)** & **1500** & **1800** & **2100** & **2400** & **1500** & **1800** & **2100** & **2400** \\ \hline Before balancing & 28.39 & 28.50 & 27.50 & 28.23 & 0.00066 & 0.00134 & 0.00251 & 0.00544 \\ Conventional method & 26.40 & 25.32 & 23.46 & 22.53 & 0.00054 & 0.00124 & 0.00218 & 0.00485 \\ Reduction rate I (\%) & 7.00 & 11.16 & 14.69 & 20.19 & 18.18 & 7.46 & 13.15 & 10.85 \\ Proposed method & 23.89 & 23.64 & 21.53 & 21.54 & 0.00040 & 0.00123 & 0.00159 & 0.00362 \\ Reduction rate II (\%) & 15.85 & 17.05 & 21.71 & 23.70 & 39.39 & 8.21 & 36.65 & 33.46 \\ Rate difference (\%) & 8.85 & 5.89 & 7.02 & 3.51 & 21.21 & 0.75 & 23.50 & 22.61 \\ Average (\%) & \multicolumn{4}{c}{6.32} & \multicolumn{4}{c}{17.02} \\ \hline  & \multicolumn{4}{c}{**Case 2**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Displacement (um)**} & \multicolumn{4}{c}{**Acceleration (g)**} \\ \hline
**Speed (rpm)** & **1500** & **1800** & **2100** & **2400** & **1500** & **1800** & **2100** & **2400** \\ \hline Before balancing & 28.53 & 30.59 & 33.93 & 38.56 & 0.00091 & 0.00204 & 0.00616 & 0.00748 \\ Conventional method & 25.88 & 26.48 & 28.95 & 29.85 & 0.00084 & 0.00190 & 0.00551 & 0.00700 \\ Reduction rate I (\%) & 9.29 & 13.44 & 14.68 & 22.59 & 7.69 & 6.86 & 10.55 & 6.42 \\ Proposed method & 22.66 & 23.63 & 26.51 & 27.86 & 0.00062 & 0.00017 & 0.00311 & 0.00372 \\ Reduction rate II (\%) & 20.57 & 22.75 & 21.87 & 27.75 & 31.87 & 91.67 & 49.51 & 50.27 \\ Rate difference (\%) & 11.28 & 9.31 & 7.19 & 5.16 & 24.18 & 84.81 & 38.96 & 43.85 \\ Average (\%) & \multicolumn{4}{c}{8.24} & \multicolumn{4}{c}{47.95} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Vibration reduction rate using the proposed method compared to conventional method. Figure 7: Balancing results for Case 2: (**a**) #2 displacement sensor; (**b**) #2 acceleration sensor. As seen in Table 6, the corrected method proposed in this paper improved the vibration reduction rate by a maximum value \(\left(\Delta\xi\right)_{\text{max}}\) of 84.81% and a maximum average value \(\left(\overline{\Delta\xi}\right)_{\text{max}}\) of 47.95%, and the variation law of vibration reduction rates is not linear. The data in Table 6 show that the newly proposed adaptive balancing approach considering the air gap unbalance is effective for the improvement of the active balancing capacity of a motorized spindle, which positively contributes to high-speed precision machine tools.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 4 Analysis Results
### Probability of Failure of the Cement Mantle  
Generally, the peak stress in the distal region of the cement was primarily the result of stress concentration in the distal end of the metal stem and the bending effect of joint loading [39]. To avoid inappropriate evaluation of cement stress with the use of peak tensile stress, another approach based on the stressed volumes of the cement mantle associated with the failure probability at a specific stress level was suggested to provide a better illustration of the durability of the cemented fixations [36]. The stressed volume of cement was defined by the number of elements subjected to specific stress levels. The probability of failure of the cement under specific stress levels was evaluated according to the criterion proposed in a study conducted by [PERSON] and [PERSON] [40]. The probability of survival of cement specimens at 10 million loading cycles for hand-mixed cement can be expressed as a function of the applied stress by the following regression polynomial:  
\[P_{s}=-0.0005\alpha^{3}+0.0202\alpha^{2}-0.3304\alpha+1.8365. \tag{2}\]  
The failure probability of the cement mantle can be determined from \(P_{f}=1-P_{s}\). It should be noted that the critical stress causing the cement to endure 10 million loading cycles without failure is about 3 MPa based on Equation (2). Using this approach, the percentages of the stressed volume of the cement mantle produced by stems with different surface finishes were obtained (Figure 8). The results revealed that, for roughened and polished stems, the majority of cement mantles (about 71%) were subject to lower stress of 4 MPa. The volume fraction of cement enduring 4-8 MPa stress was about 3.5-14%. According to Equation (2), the cement mantle would be stressed to fail completely after experiencing 10 million loading cycles at 10 MPa. Therefore, as presented in Figure 8, the volume fraction of cement mantle with survival life less than 10 million stress cycles is about 1.8% for roughened stem and 1.35% for polished stem, respectively. The volume fraction of damaged cement is not be significant in value, but differed by 75%. This seems to imply that the roughened stems tended to induce a higher cement failure rate than the polished stems. It can be expected that, if the long-term loading effects with debonded interface are involved [32], the difference in damaged rate of cement mantle for polished and roughened surface stems will be more significant.  
Figure 7: Distributions of maximum tensile stress and Von-Mises stress within stem, cement and femur bone.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model  
This paper considered four network configurations to analyze the \(P_{\text{max}}\) in an LTE uplink. As a reference case, the homogenous macro cell deployment was considered, assuming \(P_{\text{max}}=23\) dBm in the uplink direction. For the small cell uplink, a homogenous network deployment scenario with uniformly distributed small cells was considered at first and analyzed for different \(P_{\text{max}}\) limits, starting with the current standard limit of 23 dBm. Secondly, a HetNet deployment scenario was considered with uniformly distributed small cells within the homogenous macro cell environment mentioned above. Thirdly, the HetNet deployment scenario with small cells deployed near the macro cell eNodeB was considered, that is, the central area of the macro cell. Finally, the deployment scenario with small cell deployed away from the macro cell eNodeB was considered, that is, on the macro cell edge region.  
The uniform macro cell deployment and first scenario for small cell deployment are described in the following subsection together as the only difference among them was the cell size. This is followed by the subsections describing the three HetNet scenarios for small cell deployment separately. A fluid model in Reference [34] was employed to conduct the analysis, and was further extended for each network deployment, as the model was only available for a macro cell deployment scenario. The interference offered to the central small cell was evaluated in each scenario and the power control algorithm was optimized by selecting the optimal \(P_{\text{max}}\) limit. It was obvious that the transmit power for small cell UE must be reduced, since the cell size significantly decreased in the cases with small cells relative to the macro cell deployment. However, the \(P_{\text{max}}\) limit varied from one deployment scenario to the other, as the location of small cells within the macro cell plays a key role in the configuration of the network's parameters. This is described in detail in the following subsection.