esa-sceva/satcom-chunk-collection huggingface dataset:
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 3 Simulation and Measurement  
### Return Loss  
Return loss (S11) is usually used to describe the reflection characteristics of components such as circuits or antennas. In microwave and radio frequency engineering, S11 is an important parameter that represents the reflection of the input signal by the component.  
Figure 6 illustrates the comparison results of the Horn antenna on chip. Return loss (1S111) between the measured and simulated. Return loss (1), where PR and PI are reflected power (W), and incident power (W) respectively. The results show that although the frequency changes toward low frequency, the simulation results and the measurement results have a very similar trend. The cause of the frequency shift may be due to minor manufacturing differences, such as differences in material properties, size, arrangement, etc. when manufacturing the chip antenna. When making chips, due to DRC (Design rule check) restrictions in the manufacturing process, the density will be filled with dummy, so there will be a slight error in the measurement. The measurement technology used in actual measurement may have certain limitations, such as limitations in measurement accuracy, measurement frequency range, etc. These factors can have an impact on the performance of the antenna, and these effects are difficult to fully account for in simulations.  
\[\text{Reflection Coefficient}_{\text{dB}}=10\times\text{log}_{10}\bigg{(}\frac{ \text{P}_{\text{R}}}{\text{P}_{\text{I}}}\bigg{)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value (Unit: \(\upmu\)m)** & **Bandwidth (GHz)** & **Return Loss Lowest Point (dB)** \\ \hline
10 & 7.77 & \(-\)25.4172 \\
16 & 6.52 & \(-\)18.2411 \\
22 & 5.31 & \(-\)14.7682 \\
28 & 4.35 & \(-\)12.9665 \\
34 & 3.68 & \(-\)12.0275 \\ \hline \hline \end{tabular}
\end{table}
Table 2: L\({}_{6}\) parameter analysis results (Bandwidth, Return loss lowest point).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value (Unit: \(\upmu\)m)** & **Center Frequency (GHz)** & **Return Loss Lowest Point (dB)** \\ \hline
455 & 180.99 & \(-\)25.4172 \\
475 & 174.61 & \(-\)18.2411 \\
480 & 173.09 & \(-\)14.7682 \\ \hline \hline \end{tabular}
\end{table}
Table 3: W\({}_{3}\) parameter analysis results (center frequency, Return loss lowest point).
# Numerical Evaluation of Rotordynamic Coefficients for Compliant Foil Gas Seal
## 2 Methods
### Governing Equation  
The relevant non-dimensional Reynolds equation for the compressible flow of the cylindrical gas seal can be presented as Equation (1):  
\[\frac{\partial}{\partial\theta}\bigg{(}\overline{p}\overline{h}^{3}\frac{ \partial\overline{p}}{\partial\theta}\bigg{)}+\frac{R^{2}}{L^{2}}\frac{ \partial}{\partial\overline{z}}\bigg{(}\overline{p}\overline{h}^{3}\frac{ \partial\overline{p}}{\partial\overline{z}}\bigg{)}=\Lambda\frac{\partial}{ \partial\theta}\bigg{(}\overline{p}\overline{h}\bigg{)} \tag{1}\]  
where \(\overline{z},\overline{p},\overline{h},\Lambda\), and \(\theta\) are, respectively, the dimensionless axis distance from inlet to the outlet, pressure, gas thickness, compressible number, and circumferential angle with the following dimensionless variables, given by Equation (2):  
\[\overline{z}=Z/R,\overline{p}=p/p_{a},\ \overline{h}=h/C,\ \Lambda=6\mu\omega R ^{2}/(C^{2}p_{a}) \tag{2}\]  
Figure 1: Schematic of the compliant foil gas seal.  
where \(L\), \(R\), \(C\), \(Z\), and \(h\) are, respectively, the seal length, rotor radius, mean gas thickness, axial length, and gas thickness. Moreover, \(\omega\) is the rotor speed, \(\mu\) is the gas viscosity. \(p_{a}\) the is standard pressure, and \(p\) is the operation pressure. \(\varepsilon\) is the eccentric ratio. As given by Equation (3), the gas forces of \(F_{x}\) and \(F_{y}\) are integrated by the dynamic pressure along the \(X\) and \(Y\) radial directions of the seal [5]. \(F_{x}\) and \(F_{y}\) are the gas force in the \(X\) and \(Y\) directions  
\[F_{y,x}=\int_{-L/2}^{L/2}\int_{0}^{2\pi}(p-p_{a})\Bigg{\{}\begin{array}{c}cos \theta\\ sin\theta\end{array}\Bigg{\}}Rd\theta dz \tag{3}\]
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 2 Materials and Methods
### Alignment Camera
#### 2.2.4 Evaluation Metrics  
To evaluate the performance of the camera-based SpO\({}_{2}\) estimates, we computed the root-mean-square error (RMSE) and the bias (mean difference) metrics, which are calculated as:  
\[RMSE =\sqrt{\frac{\sum\limits_{i=1}^{L}\left(SpO_{2}^{Cam}(i)-SpO_{2} ^{Ref}(i)\right)^{2}}{L}},\] \[Bias =\frac{\sum\limits_{i=1}^{L}\left(SpO_{2}^{Cam}(i)-SpO_{2}^{Ref}( i)\right)}{L},\]  
where \(SpO_{2}^{Ref}\) is the median of all four contact-probes to improve the reliability of the reference. The RMSE is used as metric in the ISO standard (80601-2-61, 2019) [21] for pulse-oximeters when compared to a reference obtained by arterial blood gas analysis (SaO\({}_{2}\)). The ISO standard requires an accuracy with an RMSE <4% in the range 70% to 100% SaO\({}_{2}\).
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## Program Design of Online Monitoring System  
### Communication Program Design  
The communication program is designed to send the basic information of the detection equipment and the monitoring data in JSON format to the cloud platform through the GPRS/4G DTU module. The cloud platform will check the information from monitoring equipment, and if the information is correct, communication will be established. The design of the program is based on the MQTT protocol, which can upload the collected data to the cloud platform completely and safely using a small amount of code and low bandwidth. The flow chart of the data sending program is shown in Figure 10.  
Figure 8: Block diagram of the data collector.  
Figure 9: Photo of the IoT wireless development module.  
When the cloud platform receives the data in JSON format, it connects to the host computer database through the data stream transfer 4G DTU module and stores the collected data into the database.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 2 Overview of Structural Control Systems
### Passive Control System  
Passive control systems are the basic structural control systems that do not need any external power to operate. The passive systems do not generate energy within the structure, thereby maintaining the stability of the control system. Passive energy-dissipating systems are external add-on damping devices commonly used to dissipate energy from structural vibrations. Passive devices generate control forces in response to the structure's motion. Passive control devices are broadly classified into two categories: passive isolation devices and energy-dissipating devices [22]. Some of the most widely used passive devices are viscoelastic dampers, viscous fluid dampers, friction dampers, metallic dampers, tuned mass dampers, liquid column dampers, and base isolators. Researchers have conducted state-of-the-art reviews on the application of passive isolation [15; 16; 17]. [PERSON] et al. [7] presented a critical review of the application of tuned mass dampers for the structural vibration control of structures subjected to wind and earthquakes, providing a comparison of their efficiency and the comparative advantages and disadvantages. The potential of TMDs for improving the wind and seismic behaviors of prototype civil structures was also reviewed by [PERSON] et al. [24]. The review highlights the dynamic characteristics and unique features of different systems, namely single-tuned mass dampers (STMDs), multiple-tuned mass dampers (MTMDs), and spatially distributed MTMDs (d-MTMDs), which have been explored both theoretically and through experimental investigations.  
Figure 1: Classification of control systems.
## 4 Power Consumption Models
### Model for LTE User Device  
The power consumption (mW) when receiving data in a connected state is estimated as [18]:  
\[P_{\text{rx}}=P_{\text{on}}+P_{\text{rxBB}}(R_{\text{rx}})+P_{\text{rxRF}}(S_{ \text{rx}})+\beta_{\text{rx}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**BS Type** & \(N_{\text{trx}}\) & \(P_{\text{max}}\) **(W)** & \(P_{\text{0}}\) **(W)** & \(\Delta_{p}\) & \(P_{\text{sleep}}\) **(W)** \\ \hline Macro & 6 & 39.8 & 130.0 & 4.7 & 75.0 \\ Remote radio & 6 & 20 & 84.0 & 2.8 & 56.0 \\ head & 6 & 6.3 & 56.0 & 2.6 & 39.0 \\ Pico & 2 & 0.13 & 6.8 & 4.0 & 4.3 \\ Femto & 2 & 0.05 & 4.8 & 8.0 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Base station power consumption parameters. Data from [22]; BS: Base station; \(N_{\text{trx}}\): number of radio frequency (RF) chains; \(P_{\text{max}}\): Maximum transmission power; \(P_{\text{0}}\): Minimum active power consumption; \(P_{\text{sleep}}\): Power consumption in the sleep mode.  
where \(P_{\text{on}}\) is the power consumption when the cellular subsystem is active, \(\beta_{\text{rx}}\) is the additional power consumption of a receiver being active. Parameter \(P_{\text{rxRF}}\) defines radio frequency (RF) block power consumption that is dependent on the received power \(S_{\text{rx}}\) and \(P_{\text{rxBB}}\) is the baseband power consumption, dependent on the received data rate \(R_{\text{rx}}\). These parameters are given as  
\[P_{\text{rxRF}}=\begin{cases}-0.04\times S_{\text{rx}}+24.8,&S_{\text{rx}} \leq-52.5\text{ dBm}\\ -0.11\times S_{\text{rx}}+7.86,&S_{\text{rx}}>-52.5\text{ dBm}\end{cases}\]  
\[P_{\text{rxBB}}=0.97R_{\text{rx}}+8.16\]  
Equivalent power consumption (mW) when transmitting data in the connected state is given as:  
\[P_{\text{rx}}=P_{\text{on}}+P_{\text{txBB}}(R_{\text{tx}})+P_{\text{txRF}}(S_{ \text{tx}})+\beta_{\text{tx}} \tag{4}\]  
where same parameters are defined for the transmitter side, respectively. Transmission power \(S_{\text{tx}}\) primarily affects the RF block power consumption:  
\[P_{\text{txRF}}=\begin{cases}0.78\times S_{\text{tx}}+23.6,&S_{\text{tx}} \leq 0.2\text{ dBm}\\ 17.0\times S_{\text{tx}}+45.4,&0.2\text{ dBm}<S_{\text{tx}}\leq-11.4\text{ dBm}\\ 5.90\times{S_{\text{tx}}}^{2}-118\times S_{\text{tx}}+1195,&11.4\text{ dBm}<S_{\text{tx}}\end{cases}\]  
The data rate does not affect baseband power consumption in the uplink, i.e., \(P_{\text{txBB}}\) is constantly 0.62 mW. Other parameters are \(P_{\text{on}}\) = 853 mW, \(\beta_{\text{rx}}\) = 25.1 mW and \(\beta_{\text{tx}}\) = 29.9 mW.
## 4 Case Study Description
### Cleaning of the GPS Traces  
The raw GPS traces include a lot of noise. The first filter step is to detect outliers and discard those. Sudden changes in speed and direction are used to determine outlier data points. In order to allow state estimation, frequent location information of one individual is essential within the Mysteryland event terrain. That is, enough data points to allow one to determine the full route of a visitor through the network and have an estimate of the speeds that were travelled by the pedestrian on each link. This translates into three filtering criteria, namely: (1) all data points outside the Mysteryland event terrain are discarded, (2) if the absolute speed between two consecutive data points is larger than 5 m/s, these points are discarded, (3) if a trajectory contains a gap which is larger than 10 min, the trajectory is split into two separate parts.
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
## 2 Design and Fabrication of the Photonic Structure
### Test of the Device  
We then characterized the coupling efficiency of the waveguides with a setup similar as in Reference [25]. Two focusers oriented with an angle of 30\({}^{\circ}\) were used to inject and collect the signal at the input and output gratings, respectively. The injection of light emitted by an amplified spontaneous emission (ASE) source spanning from 1900 to 2050 nm was adjusted (thanks to two cameras operating in the visible and 2-\(\upmu\)m ranges (model Xeva 2.35-320, Xenics nv, Leuven, Belgium). After transmission in the waveguide and decoupling by the grating, the output light was collected by a lensed-fiber focuser and recorded with an optical spectrum analyzer (OSA) (model AQ6375B, Yokogawa Electric Corporation, Musashino, Japan). Figure 4 shows the total insertion losses as a function of the injected wavelength for the two waveguides under test. Note that those transmission spectra are normalized with respect to a reference spectrum obtained by optimizing the light collected by the output focuser after specular reflection of the incident light onto a gold mirror [25]. It is noted that, in Figure 4, oscillations of high amplitude and short period are particularly marked for the large waveguide. These spectral oscillations are attributed to a Fabry-Perot effect, the spectral range between two maxima being in agreement with what can be expected from the roundtrip distance. The experimental loss should be twice as large as the value expected from our numerical simulation (Figure 1) performed for one facet, given that we neglect the propagation loss. Then, the discrepancy between the numerical simulations and the maximum experimental efficiency is about 3 dB, mainly attributed to the strong dependency on the width of the grating lines and also probably due to fabrication issues (in particular related to the roughness). Even though, for the MM waveguide configuration, the TE mode was expected to be as efficient as the TM mode for a grating line width of 700 nm, here it was the case for \(w_{1}=630\) nm. Regarding the TE waveguide, as expected, the coupling efficiency is similar to the one obtained for the large waveguide. Let us once again recall that a (6 dB) better efficiency can be reached for optimized parameters, but our goal here was to establish a fair comparison between the MM and SM components.  
Figure 3: Images of (**a**) a subwavelength waveguide and (**b**) a large waveguide considered as a slab. Insets 1 show scanning electron microscopy (SEM) pictures of embedded metal grating on one end of the waveguides whereas insets 2 show optical images of the waveguides. Contrary to the slab, the subwavelength waveguide is equipped with tapers. Insets 3 correspond to SEM pictures of the subwavelength that has a width of 1.65 \(\upmu\)m (**a3**), and the slab which is actually 8.0 \(\upmu\)m wide (**b3**).
## Materials and Methods
### Adsorption of Ni\({}^{2+}\) by nZVI@ATP
#### 2.3.2 Effect of Initial Concentration on Ni\({}^{2+}\) Adsorption by nZVI@ATP  
After adding nZVI@ATP at an injection rate of 1 g/L, 30 mL of Ni\({}^{2+}\) solutions with initial concentrations of 100, 200, and 300 mg/L were added to the centrifuge tubes, and the pH was adjusted to 5. The solutions were then shaken for 0.5, 1, 2, 3, 4, 6, 8, and 12 h at 25 \({}^{\circ}\)C in a constant temperature water bath and removed and filtered through a 0.45 um microporous filter membrane. Subsequently, the remaining Ni\({}^{2+}\) concentration in the solution was measured.
## 2 Basic Structure and Key Technologies
### Special Structure of the 3D Stage  
Multi-dimensional stages can be constructed easily by stacking up several one-dimensional stages in series. However, the motion error is serious in this type of structure because the pitch angular errors of the bottom stage are magnified by the vertical offset between the guiding plane and the measurement point [11]. In order to reduce the motion error and the crosstalk between \(X\)- and \(Y\)-stages, a special mechanical structure is introduced as shown in Figure 2, where the \(X\) and \(Y\) bearings are almost co-planar and at the same height as the interferometer beam.  
In Figure 2, the \(Z\)-stage (9) is embedded in the \(Y\)-stage and can be moved by roller bearing in the vertical direction. The \(Y\)-stage, together with the \(Z\)-stage, can be moved in the \(Y\) direction along the \(Y\)-guideway. The \(X\)-stage, together with the \(Y\)- and the \(Z\)-stages, can be moved in the \(X\) direction along \(X\)-guideway. The \(X\)-guideway between the granite base (4) and the \(X\)-stage shares the same horizontal plane with the \(Y\)-guideway, forming a co-planar stage. The \(Z\)-stage can be moved up and down through the \(X\)-\(Y\) co-planar stage. In this arrangement of the 3D stage, Abbe errors as well as crosstalk moving errors among \(X\)-, \(Y\)- and \(Z\)-stages are minimized. The strokes of the 3D stage in \(X\), \(Y\) and \(Z\) directions are all 50 mm. The main body of the 3D stage is made of invar steel so as to reduce the thermal deformation of mechanical parts. The \(X\)-, \(Y\)-, \(Z\)-stages are driven respectively by three custom-made linear piezo-motors based on the type of N-310 provided by Physik Instrumente (Karlsruhe, Germany). The displacement resolution is about 1 nm.  
Figure 2: Schematic structure of 3D stage and schematic layout of metrological system. 1: \(X\)-interferometer (length reference), 2: \(Y\)-interferometer (length reference), 3: \(Z\)-interferometer (length reference), 4: Granite base, 5: \(X\)-guideway between \(X\)-stage and granite base, 6: \(X\)-stage, 7: \(Y\)-guideway between \(X\)- and \(Y\)-stages, 8: \(Y\)-stage, 9: \(Z\)-stage, 10: Reflection mirror of \(Y\)-interferometer, 11: Reflection mirror of \(X\)-interferometer.
## 3 Results and Discussion
### Structure-Activity Relationship Studies
Whereas the fluoro (**16a**) and methoxy (**18a**) analogs had diminished activity against _M. bovis_ BCG (IC\({}_{50}\) of 13 uM and 27 uM, respectively) and both were >25 uM against Mtb H37 Rv. The 7-position was explored with five different substituents-fluoro (**19a**), chloro (**20a**), promo (**21a**), trifluoromethoxy (**22a**) and methoxy (**23a**)--resulting in only two active compounds (**19a** and **21a**). The 7-fluoro analog (**19a**) displayed good activity against both mycobacterial strains (IC\({}_{50}\) of 0.8 uM and 7.6 uM; respectively) and the 7-bromo (**21a**) showed much better activity against _M. bovis_ BCG than Mtb H37 Rv (IC\({}_{50}\) of 7 uM and 20 uM; respectively). The most interesting SAR was observed with substitution of the quinazoline 2-position with methyl (**24a**) and cyclopropyl (**25a**) groups. Both compounds displayed good activity range (3-12 uM) with or without cyt-_bccaa3_ inhibition, suggesting possible dual modes of action. Lastly, one compound (**26a**) bore both 7-fluoro and 2-methyl quinazoline substituents which biased activity back towards cyt-_bd_ (IC\({}_{50}\) of 7 and 17 uM with Q203, and 28 uM and 24 uM without Q203; respectively)  
Our previous work had shown that the laboratory adapted Mtb H37 Rv strain over expresses cyt-_bd_ resulting in higher IC\({}_{50}\) values than those observed using a clinical Mtb isolate [24]. These higher IC\({}_{50}\) values render compound ranking more challenging. To gain greater insight on the activity of these compounds, they were re-screened against the clinical Mtb isolate N0145 strain. As observed previously with the thieno[3,2-_d_]pyrimidine-4-amine class of cyt-_bd_ oxidase inhibitors [13], these compounds displayed greatly improved activity against the Mtb clinical isolate likely due to lower expression of cyt-_bd_ within clinical isolates as compared to lab-adapted strains like H37 Rv [12]. Two compounds, **7a** and **12a**, from the first set were more active than or comparable in activity to aurachm D (0.1 and 1.1 uM vs. 1.5 uM, respectively). While six additional compounds (**6a**, **9a**, **10a**, **13a**-**15a**) had IC\({}_{50}\) values below 5 uM against N0145-Mtb. In the second set of compounds, only **19a** had activity superior to aurachm D (0.2 uM vs. 1.5 uM, respectively) and four additional compounds (**17a**, **21a**, **24a**-**26a**) had activity below 5 uM. The two most potent compounds, **12a** and **19a**, had slightly improved activity relative to ND-11992 which was active in the murine infection model of tuberculosis. However, preliminary metabolic stability assessment of these compounds against human and rat microsomes indicate much more rapid metabolism than that of ND-11992 (data not shown).
## 2 Three-Dimensional Finite Element Modelling and Validation
### Interactions  
This study's finite element model effectively captures the friction at the interface between segments. It models the interactions between adjacent segments and the connections between bolts and bolt sleeves through surface-to-surface contact. In the finite element software, this contact is represented either as interactions between two deformable surfaces or between a deformable and a rigid surface, as per reference [31]. The finite slip tracking method and surface-to-surface discretization are employed in this study. This method can effectively simulate the sliding friction between two surfaces. The friction coefficients are set at 0.5 for adjacent segments and 0.3 for bolts and bolt sleeves. Interactions between rebars and concrete segments are modeled using embedded constraints. Tie constraints are utilized for connections between the nut and bolt washer, the bolt washer and concrete, and the bolt sleeve and concrete [32]. Figure 4 presents a schematic illustration of the interactions among the various components. During the process of shield tunnel forces, the interactions include both the circumferential and longitudinal segments. The interactions between longitudinal segments and between circumferential segments are identical. These interactions encompass contact between segments, between washers and segments, between nuts and washers, and between bolts and bolt sleeves, as well as between bolt sleeves and segments.
# Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System
## 5 Simulation Results and Discussion
Figure 6: (**a**) One realization of the real phase noise and its estimations after the M-BPS and BD-PNC methods at \(\Delta v\cdot T_{S}=1.5\times 10^{-2}\) and OSNR = 20 dB for 30 Gbaud CO-FBMC 16-QAM BTB transmission systems with 1024 subcarriers. (**b**) Illustration of constellations before and after PNC employing M-BPS and BD-PNC at \(\Delta v\cdot T_{S}=5\times 10^{-3}\) and OSNR = 27 dB for 30 Gbaud CO-FBMC 64-QAM BTB systems with 1024 subcarriers. Figure 7: OSNR penalty versus \(\Delta v\cdot T_{S}\) at a BER of \(3.8\times 10^{-3}\) using M-BPS, the proposed schemes for CO-FBMC 4/16/64-QAM systems with 256 subcarriers (**a**), 512 subcarriers (**b**), and 1024 subcarriers (**c**).