esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Constitutive Modeling for Numerical Investigations of the TemcoreTM Process
This amounts to computing the stresses \(\boldsymbol{\sigma}\) from the elastic strains \(\boldsymbol{\epsilon}^{\rm e}\) as \[\boldsymbol{\sigma}=\mathbb{C}:\boldsymbol{\epsilon}^{\rm e}=\left(\frac{\
u E }{(1+\
u)(1-2\
u)}\mathbb{I}\otimes\mathbb{I}+\frac{E}{(1+\
u)}\mathbb{I} \right):\Big{(}\boldsymbol{\epsilon}-\boldsymbol{\epsilon}^{\rm th}- \boldsymbol{\epsilon}^{\rm P}\Big{)}\] (8) where \(\boldsymbol{\epsilon}^{\rm th}\) is the eigenstrain due to thermal expansion (and potentially phase transformations) and \(\boldsymbol{\epsilon}^{\rm P}\) is the plastic strain computed by integration of the (associated) flow rule \[\dot{\boldsymbol{\epsilon}}^{\rm P}=\gamma\frac{\partial f(\boldsymbol{\sigma})}{ \partial\boldsymbol{\sigma}}\] (9) subject to the constraints \(\gamma\geq 0\), \(f(\boldsymbol{\sigma})=\sqrt{\frac{3}{2}\boldsymbol{\sigma}^{\prime}: \boldsymbol{\sigma}^{\prime}-\sigma^{\rm Y}}\leq 0\) (von [PERSON] criterion) and \(\gamma f(\boldsymbol{\sigma})=0\)[16]. In these equations, \(\mathbb{I}\) and \(\mathbb{I}\) denote the identity tensors of rank 2 and 4, respectively, \(\boldsymbol{\sigma}^{\prime}\) the stress deviator, \(\gamma\) the plastic multiplier, \(\sigma^{\rm Y}\) the yield strength, and \(E\) and \(\
u\) [PERSON]'s modulus and the [PERSON]'s ratio, respectively. In order to apply the above IBVPs to the numerical simulation of heat treatment of a solid compound of more than one phase, the material parameters encountered in above equations are most commonly computed via a linear volume-fraction averaged mixture rule from the respective parameters of the individual phases [20]. For example, the yield strength \(\sigma^{Y}\) of a mixture of \(P\) phases, each constituting a volume fraction \(z_{i}=V_{i}V^{-1}\), \(i=1...P\), of the microstructure at the considered point \(\mathbf{x}\in\Omega\) of the continuum, may be assumed to adhere to  
\[\sigma^{Y}=\sum_{i=1}^{P}z_{i}\sigma_{i}^{y}\,, \tag{10}\]  
where \(\sigma_{i}^{Y}\) denotes the yield strength of phase \(i\). Furthermore, evolution equations describing the phase transformation kinetics in terms of the volume fractions \(z_{i}\) of each phase are required to evaluate such mixture rules. It is apparent from the above presentation that any numerical computation of process-induced residual stresses based on a thermo-mechanical constitutive model of comparable complexity as above relies on the accurate identification of at least the following parameters for each phase encountered during the process: density, heat capacity, and conductivity for the heat transfer problem and [PERSON]'s modulus, Poisson's ratio, and the yield strength for the mechanical problem. The aim of this paper is therefore to enable numerical heat-treatment simulations for rebars by experimental determination of the mechanical parameters for the alloy B500B.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Preparation of the Randomly Oriented Carbon Nanotube Films
#### 2.1.2 Pretreatment of the CNT Films before Transistor Fabrication  
Next, the yttrium oxide coating and decorating process was carried out [30]. First, 3 nm of yttrium metal (Y) was deposited on the CNT films by electron-beam evaporation (DE400) and then thermally oxidized at 250 \({}^{\circ}\)C in a hot plate for 0.5 h. The yttrium oxide layer was removed by immersion in a 1:20 volume solution of hydrochloric acid for 5 min (this ensured that there were no residual yttrium oxide residues on the carbon nanotube films). Finally, repeated rinses with deionized water and isopropanol were used to obtain cleaner CNT films. The main purpose of this process was to remove the excess polymer molecules wrapped around the CNTs.  
Then, the annealing process was carried out [20]. Annealing allowed us to further remove the polymer residues from the CNTs. The substrate with the CNTs was placed in a tube furnace (Thermo Scientific Lindberg/Blue Moldatherm 1100 \({}^{\circ}\)C), and the excess air was vented from the tube by passing 1000 sccm of argon gas. The annealing temperature was 600 \({}^{\circ}\)C, and the flow rates of argon and hydrogen were 300 sccm and 50 sccm, respectively, for 1 h.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
###### Abstract  
The present study proposes a method for model updating using measurements from sensors installed in arbitrary positions and directions. Modal identification provides mode shapes for physical quantities (acceleration strain, etc.) measured in specific directions at the location of the sensors. Besides, model updating involves the use of the mode shapes related to the nodal degrees-of-freedom of the finite element analytic model. Consequently, the mode shapes obtained by modal identification and the mode shapes of the model updating process do not coincide even for the same mode. Therefore, a method for constructing transform matrices that distinguish the cases where measurement is done by acceleration, velocity, and displacement sensors and the case where measurement is done by strain sensors was proposed to remedy such disagreement among the mode shapes. The so-constructed transform matrices were then applied when the mode shape residual was used as the objective function or for mode pairing in the model updating process. The feasibility of the proposed approach was verified by means of a numerical example in which the strain or acceleration of a simple beam was measured and a numerical example in which the strain of a bridge was measured. Using the proposed approach, it was possible to model the structure regardless of the position of the sensors and to select the location of the sensors independently from the model.  
Keywords:model updating; modal identification; measured data; measured mode shape +
Footnote †: journal: Applied Sciences
## 1 Introduction
Then, the reference UWB antenna is modified for the proposed quintuple band-notched UWB antenna to reject the 3.5, 4.5, 5.25, 5.7, and 8.2 GHz bands for WiMAX, INSAT, lower WLAN, upper WLAN, and ITU 8 GHz frequency bands, respectively. To optimize the dimensions of the RSRR and RCSRR, a commercially available 3D-EM software, Ansoft High Frequency Structure Simulator (HFSS) is used. The proposed antenna with quintuple rejection bands is fabricated, and the measurements are presented for the verification. Finally, the design process in relation to the reflection coefficient, VSWR, antenna gain, radiation efficiency, and radiation pattern are described in detail. The proposed antenna performance has been checked for tumor detection, and the simulated results are presented. The geometrical analysis and simulation results of the antenna are discussed in detail in Section 2 with corresponding results. The measurement setup and experimental results of the proposed and the reference UWB antenna are explained in Section 3. Section 4 deals with the comparison of the proposed antenna with another recent state-of-the-art design reported in the literature. Section 5 provides the application of the proposed antenna in microwave sensing, and the conclusion then follows.
## 2 Lubrication Model and Algorithm of the Regional Flow Pattern
### Algorithm  
As shown in Figure 3, a multiple iterative numerical algorithm for the partitioning flow pattern is used to solve the model. We input the initial film thickness and tilt parameters of the pad and calculated the deformation coefficient \(G\)(\(r\),\(\theta\),\(i\),\(j\)) using the FEM. The Reynolds equation and the energy equation were solved using the Finite Difference Method (FDM) [22]. In order to accelerate the convergence, the over-relaxation iteration method was used. The calculation process included the iteration of the pressure field and temperature field. In addition, the minimum film thickness was corrected in the load-carrying capacity iteration. In the iteration of the pad balance position, the tilt parameter of the pad was modified.  
Figure 3: Flow chart of partitioning flow pattern.  
The core of the arithmetic lies in the partitioning of regions and the choice of Reynolds equations. The basic procedures of the algorithm are as follows: (1) divide the pad into \(m\)\(\times\)\(n\) grids, where the vertices of each grid represent nodes, (2) compare the nodes' Reynolds numbers \(R_{e}\)(_i,j_) with the critical Reynolds number \(R_{\alpha}\) to find out the nodes that are approximately equal to each other, defining them as \(B\)\(J\)\(n\)(\(i\)), and (3) connect all \(B\)\(J\)\(n\)(\(i\)) into a line as the junction line between the laminar and turbulent flow areas.  
\(R_{\alpha}\) can be used to define the transition from laminar to turbulent flow as the fluid flow rate increases. It can be found in [23] that \(R_{\alpha}\) equals 1600. \(R_{\textit{emax}}\) is the maximum node Reynolds number. For the turbulent flow pattern (single flow pattern), the average Reynolds number can be calculated using the average film thickness, average viscosity, and average linear velocity. The flow coefficients can be obtained by inputting the average Reynolds number into Equation (3). The bearing performance is obtained by solving the turbulence model. For the node mixing flow pattern, \(R_{e}\)(_i,j_) is compared with \(R_{\alpha}\). If it is less than \(R_{\alpha}\), the flow coefficient of the node is taken to be 1/12. Otherwise, the flow coefficient can be calculated through Equation (3). The distribution of the flow pattern coefficient is substituted into the Reynolds equation and energy equation. The bearing lubrication performance is obtained by solving the node lubrication model. For the regional mixing flow pattern, it combines the features of the two above patterns. The pad interface is divided into a laminar and turbulent flow area by comparing \(R_{e}\)(_i,j_) with \(R_{\alpha}\). The laminar and turbulent lubrication models are used for each region, and the bearing performance is obtained after a comprehensive solution.
## 4 Simulation Results
### Case III  
This example is to demonstrate the performance of the proposed scheme with respect to branch outage contingency following several loads increase condition. In this case, the loads at bus #12, #24, and #29 are increased by 20% from the time instant 0.5 min to 2 min, and the transmission line connected between bus #27 and #28 is tripped in a contingency at 2 min.  
As can be expected, the system has poor voltage profile and unsecure margin due to the disturbances of both load increase and line outage. Figure 8 shows the simulation result of case III. One can observe that the proposed scheme works satisfactorily in this case.  
Figure 8: \(Cont.\)  
Figure 7: Simulation result of case II. (**a**) The trace of \(|V_{3}|\); (**b**) The trace of \(|V_{12}|\); (**c**) The trace of VSM.
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 2 Improved CTM and Parameter Calibration, Validity Verification
### Update Entrance Ramp Status  
When there is congestion on the ramp, the arrival rate on the detection line will decrease, which makes the CTM wrongly judge this phenomenon as a decrease in ramp traffic demand. At the same time, the ramp cell \(M\) only serves as a state update tool for the merging cell \(i\) and loses track of its own traffic state. Based on the above analysis, this paper proposes improvements to the defects of ramp cells.  
In Figure 3, it is assumed that there is a virtual initial cell \(D\) in front of the on-ramp cell \(M\), which can be regarded as a collection of roads connected to the on-ramp and the urban roads along it. Therefore, the flow rate \(q_{D,k}\) that leaves the cell \(D\) and flows into the cell \(M\) within the \(k\)th time step is the vehicle arrival rate \(\delta_{M,k}\) detected by the on-ramp detection line, and the conservation law of the on-ramp cell \(M\) can be defined by referring to Equation (1):  
\[\rho_{M,k+1}=\rho_{M,k}+\frac{\Delta t}{\lambda_{M}\cdot I_{M}}(q_{D,k}-q_{M,k}) \tag{10}\]  
The formula is the state update formula of the ramp cell, in which \(q_{D,k}\) has been calibrated by the vehicle arrival rate \(\delta_{M,k}\) detected on the detector. Therefore, in order to update the implementation (10) in each time step, the \(q_{M,k}\) value needs to be determined. From Equation (5) to (6), it can be seen that \(q_{M,k}\) is determined by the transmission capability \(s_{M,k}\) of cell \(M\), the transmission capability \(s_{UL}\) of cell U and the receiving capability \(r_{i,k}\) of cell \(i\), where \(s_{UL}\) and \(r_{i,k}\) can be calibrated by Equations (2) and (8) respectively. For the transmission capability \(s_{M,k}\) of ramp cell, the actual traffic demand of ramp is used to replace the detection arrival rate so as to ensure the flow conservation on cell \(M\) and avoid the misjuddment of ramp traffic demand by CTM. The expression is as follows:  
\[s_{M,k}=\min\Bigl{\{}\lambda_{M}v_{f}{}^{\prime}\rho_{M,k},\lambda_{M}q_{m}{} ^{\prime}\Bigr{\}} \tag{11}\]  
Figure 3: CTM merging segment improved by merging cells.  
where \(\lambda_{M}\) represents number of lanes on ramp cell \(M\). \(v_{f}\) represents free-flow speed on ramp cell \(M\) (km/h). \(\rho_{M,k}\) represents average traffic density of cell \(M\) at the beginning of \(k\)th time step (pcu/km/ln). \(q_{m}\)\({}^{\prime}\) represents single lane capacity of cell \(M\).
###### Abstract  
Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO\({}_{2}\) dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO\({}_{2}\) and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm\({}^{-2}\)year\({}^{-1}\)a\({}^{-1}\), 12.34 gCm\({}^{-2}\)year\({}^{-1}\)a\({}^{-1}\), 24.91 gCm\({}^{-2}\)year\({}^{-1}\)a\({}^{-1}\), and 39.62 gCm\({}^{-2}\)year\({}^{-1}\)a\({}^{-1}\). There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future.  
g rong primary productivity; LUE-RE model; attribution analysis; future projection Article
## 3 Enhanced SoC-Balancing Strategy with Saturation  
In the strategy in Section 2, the high current deviations only last for a short time. While the discharge process continues, the SoC differences become low, and then the approaching speed decreases rapidly. Although this strategy is brief and clear, it sacrifices more time to reach the balanced point. To accelerate the balancing process, current saturations are further utilized to enhance the aforementioned strategy.  
For parallel scenarios, the preprocessed current reference is set as \(i_{\text{satu}}\) if it exceeds \(i_{\text{satu}}\). Moreover, the rest of \(i_{\text{sum}}\) is redistributed by the other unsaturated units according to their own SoC levels. As the equalization process continues, the units with a higher SoC will desaturate one by one. As for series scenarios, \(i_{o}\) provides an inherent lower limit for the phase currents in boost converters. Therefore, \(i_{k}\) is increased to \(i_{\text{satt}}\) if it is lower than \(i_{\text{satt}}\).  
This improved strategy enables the usage of a larger \(n\). During the initial stage, the high current deviations are maintained for a longer duration, so the eliminating rate of SoC deviations is high. When the ESUs are no longer saturated, a larger \(n\) leads to a faster balancing process that resembles a power function. In view of these two factors, current saturations can expedite the entire balancing process.  
Figure 5: Ideal curves without saturation using different accelerating factors: (**a**) Current; (**b**) SoC.
# Effect of Tree Quantity and Distribution on the Electric Field under Transmission Lines
## 2 Charge Simulation Method Solving Electric Field near Transmission Lines
### Field Equation
The three-dimensional line charge units are shown in Figure 2.  
Assuming the length of the basic unit of linear charge is \(L\), the starting point of the unit is \(P_{1}\), the ending point is \(P_{2}\), and the proportional calculation parameter is denoted as  
Figure 1: The flow chart of the charge simulation method. Figure 2: The three-dimensional line charge units. \(u\) (\(0\leq u\leq L\)), the coordinates \(P\) (\(X\), \(Y\), \(Z\)) of any point on the basic unit can be expressed as a function of \(u\). \[ \begin{cases}X(u)=x_{1}+\frac{l}{L}u\\ Y(u)=y_{1}+\frac{u}{L}u\\ Z(u)=z_{1}+\frac{u}{L}u\end{cases}  \tag{5}\]  
where \(l=x_{2}-x_{1}\), \(m=y_{2}-y_{1}\), and \(n=z_{2}-z_{1}\). If the charge densities at the two ends of a line charge unit are \(\tau(0)=\tau_{1}\) and \(\tau(L)=\tau_{2}\), the charge density of the entire unit can be characterized by the charge densities at both ends. \[\tau(u)=\tau_{1}+\frac{\tau_{2}-\tau_{1}}{L}u=b+au \tag{6}\]  
where \(b=\tau_{1}\), and \(a=(\tau_{2}-\tau_{1})/L\). According to the numerical calculation formula in the electromagnetic field calculation method, the potential of a line charge unit \(Q\) (\(x\), \(y\), \(z\)) at any point in the field can be expressed as  
\[\varphi=\frac{1}{4\pi\epsilon_{0}}\int_{0}^{L}\frac{\tau(u)}{D}du \tag{7}\]  
where is the distance \(D\) between the simulated charge point and the calculation point. Assuming \(u=Lt\) (\(0\leq t\leq 1\)), then \(D\) is  
\[D=\sqrt{\left(x_{1}+lt-x\right)^{2}+\left(y_{1}+mt-y\right)^{2}+\left(z_{1}+ nt-z\right)^{2}} \tag{8}\]  
After substituting Equations (6) and (8) into (7), they can be simplified as  
\[\varphi=\frac{L}{4\pi\epsilon_{0}}\int_{0}^{1}\frac{At+B}{\sqrt{Et^{2}+Ft+G}}dt \tag{9}\]  
where \(A=aL\), \(B=b\), \(E=l^{2}+m^{2}+n^{2}\), \(F=-2[l(x-x_{1})+m(y-y_{1})+n(z-z_{1})]\), and \(G=(x-x_{1})^{2}+(y-y_{1})^{2}+(z-z_{1})^{2}\). Substitute the linear charge density into Formula (9), and then perform integration simplification according to the integration formula, expressed as  
\[ \begin{array}{l}\varphi=\frac{L}{4\pi\epsilon_{0}}\left[\frac{2E+F}{2\sqrt{ E}}\ln\frac{2E+F+2\sqrt{E(E+F+G)}}{F+2\sqrt{EG}}-\frac{1}{E}\left(\sqrt{E+F+G}- \sqrt{G}\right)\right]\tau_{1}\\ +\frac{L}{4\pi\epsilon_{0}}\left[\frac{-F}{2\sqrt{E}}\ln\frac{2E+F+2\sqrt{E(E+ F+G)}}{F+2\sqrt{EG}}+\frac{1}{E}\left(\sqrt{E+F+G}-\sqrt{G}\right)\right]\tau_{2} \end{array}  \tag{10}\]
# A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
## 4 Hardware Implementation
It should be mentioned that our design consumes the fewest memory resources in the above designs, which is suitable for low-cost portable healthcare devices. Compared with commercial chips such as MAX30003 [20], our design can not only monitor HR in real-time but also rapidly detect cardiac arrhythmias with low power and classify abnormal beats into five classes, which is important for the early prevention of heart diseases. We will improve this ECG processor ASIC in terms of three aspects in the future. (1) Improve configurability: In order to adapt the algorithm model to different application scenarios, the hardware can be improved to achieve a configurable number of neurons in  
Figure 14: The details of the power analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **[4]** & **[7]** & **[8]** & **[10]** & **[11]** & **This Work** \\ \hline Process (nm) & 90 & 180 & 180 & 40 & 65 & 55 \\ Frequency (Hz) & 40 M & 10 k-25 M & 12 k & 70 M & 5 M & 100 k \\ VDD (V) & 0.5–1.0 & 1.8 & 1.98 & 0.6 & 1 & 1.2 \\ Power (\(\mu\)W) & 20.1 & 13.34 & 8.75 & 2940 & 46.8 & 12.88 \\ Area (mm\({}^{2}\)) & 4.99 & 0.925 & 1.32 & 0.2123 & 1.74 & 0.33 \\ Algorithm Model & SVM & ANN & DNN & ELM & ANN & ANN \\ Energy Eff. (n)/Classifi.) & N.A. & 3210 & 2080 & 212 & 2250 & 234.4 \\ Memory (kB) & 20 & 64 & N.A. & 52 & 73 & 4.5 \\ Accuracy (\%) & 95.8 & 99.32 & 91.6 & 92 & 99.3 & 96.69 \\ Number of classes & 2 & 5 & 5 & 2 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with state-of-the-art ECG processors. the hidden layer and output layer; (2) Power optimization: By reducing the bit-width of weights or lowering the activation rate of the classification engine, further power reduction can be achieved; (3) Function expansion: Integrating this design with an analog front-end (AFE) to form an efficient cardiac sensor chip.
## 4 Conclusions  
The construction of an engineered bacterial strain, _E. coli_ BL21-7, was reported in this study. The constructed plasmid contained a strong promoter, _P16S_, a translation enhancer, _g10_, a strong terminator, _rpsT_, _Pseudomonas_ K-62 genes _merT_, _merP_, _merB1_ and _merB2_ and the _Klebsiella aerogenes_ polyphosphate kinase gene _ppk_. This construct provided sufficient transcripts for Hg tolerance and Hg accumulation. A mechanism for Hg detoxification by BL21-7 was proposed. This bacterial strain did not release Hg into the environment. It could accumulate both inorganic and organic Hg. When the engineered bacterial strain BL21-7 was cultured in LB liquid medium containing 10 \(\upmu\)mol/L mercurial derivatives (Hg\({}^{2+}\), PMC or MMC) in the presence of 10 \(\upmu\)mol/L Cd\({}^{2+}\), 10 \(\upmu\)mol/L Pb\({}^{2+}\) or 10 \(\upmu\)mol/L Cu\({}^{2+}\), the removal of mercurial derivatives by BL21-7 was not affected. After BL21-7 bacterial samples were added to wastewater, the Hg content in the wastewater could be reduced significantly. These characteristics make BL21-7 a good candidate for the removal of Hg from wastewater.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/9/1572/s1](http://www.mdpi.com/2076-3417/8/9/1572/s1)]([http://www.mdpi.com/2076-3417/8/9/1572/s1](http://www.mdpi.com/2076-3417/8/9/1572/s1)), S1: The sequence of the artificial operon _P16S-g10-merT-merP-merB1-merB2-ppk-rpsT_, S2: Construction of artificial operons and respective bacterial strains, S3: Hg remaining in medium after BL21-7 was cultured in LB liquid medium containing mercurial derivatives.  
[PERSON] and [PERSON] did data curation. They were equal contributors. [PERSON] did data curation. [PERSON] validated the data and wrote the manuscript.  
This research was funded by the National Natural Science Foundation of China [No. 41201309].  
The authors declare no conflict of interest.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Proposed Data Packet Format
#### 4.3.2 Location of the Producer  
This field carries the location of the producer when the data packet is generated. Using the location helps to find the direction of the critical data packet. The consumer vehicles will be able to take action according to the critical data packet using the location of the producer. In other words, if the alert comes in the direction of the vehicle, then prior action will be taken; otherwise, it will ignore and drop the alert.