esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of Dynamic Torque-Coordinated Control That Considers Engine Starting Conditions
### Active Damping Feedback Compensation Control Considering System Resonance
#### 4.3.2 Wheel Speed Estimation  
A wheel-speed observer is designed to obtain an accurate, real-time wheel speed \(\hat{W}_{ob}\). In the following formula, \(\mathbf{X}\) and \(\mathbf{U}\) are selected as the state variables and control variables, respectively.  
\[\mathbf{X}=\left[\begin{array}{ccc}\dot{\theta}_{L}&\dot{\theta}_{R}&\theta _{L}&\theta_{R}\end{array}\right]^{T} \tag{27}\]  
\[\mathbf{U}=\left[\begin{array}{ccc}T_{L}&\hat{T}_{R\_g\_est\_f}\end{array} \right]^{T} \tag{28}\]  
The discretization state equation is expressed as follows:  
\[\left\{\begin{array}{c}\overset{\wedge}{\mathbf{X}}(k+1)=\left[\begin{array} []{ccc}\mathbf{M}&0\\ 0&\mathbf{I}\end{array}\right]^{-1}\!\left[\begin{array}{ccc}\mathbf{C}& \mathbf{K}\\ \mathbf{I}&0\end{array}\right]^{\overset{\wedge}{\mathbf{X}}}\!\!(k)\\ \qquad\qquad+\left[\begin{array}{ccc}\mathbf{M}&0\\ 0&\mathbf{I}\end{array}\right]^{-1}\!\left[\begin{array}{ccc}\mathbf{N}\\ 0\end{array}\right]\mathbf{U}\!(k)\\ \overset{\wedge}{\mathbf{W}}_{ob}(k)=\mathbf{I}_{4\times 4}(1,1)\mathbf{X}\!(k)/i_{0} \end{array}\right. \tag{29}\]  
with  
\[\mathbf{M}= \left[\begin{array}{ccc}I_{L}&0\\ 0&I_{R}\end{array}\right],\mathbf{N}=\left[\begin{array}{ccc}-1&0\\ 0&1\end{array}\right]\!,\] \[\mathbf{C}= \left[\begin{array}{ccc}-C_{TI}&C_{TI}\\ C_{TI}&-C_{TI}\end{array}\right]\!,\text{and }\mathbf{K}=\!\left[\begin{array}{ccc}-k_{TI}&k_{TI}\\ k_{TI}&-k_{TI}\end{array}\right]\!,\text{where }\overset{\wedge}{\mathbf{X}}\!\!\text{ is the estimation state variables; }\mathbf{I}\text{ is the unit matrix; and }k\text{ is the sample number.}\]
## 5 Results
### Dispersive Example  
The materials are here described by the dielectric models in Section 3, although in general any dielectric model or dispersive data table may be used. For this demonstration, a healthy breast fat tissue is simulated in the waveguide. The GNPs are localized within the layer of width \(2z_{0}\), where the concentration of particles gradually increases toward the center \(z=0\). The background material \(\varepsilon_{\text{G}}(\omega)\) is given by the four-term Cole-Cole Equation (18) with parameters describing breast fat as in [27], while the thin dielectric layer \(\varepsilon_{\text{L}}(\omega)\) is given by the Drude function (19) with breast fat as its host medium.  
The parameters for the Drude function (19) were selected based on the parameter study in [18], with some modification to account for a different host medium. The radius of GNPs including ligands were \(a=2.5\,\text{nm}\) with gold core radius \(a_{\text{Au}}=0.75\,\text{nm}\). The mass of a GNP is then \(m=(4\pi a_{\text{Au}}^{3}/3)\rho_{\text{Au}}+[4\pi(a^{3}-a_{\text{Au}}^{3})/ 3]\rho_{\text{L}}\), where \(\rho_{\text{Au}}=19,300\) kg m\({}^{-3}\) and \(\rho_{\text{L}}=1000\) kg m\({}^{-3}\) are the mass densities assumed for gold and ligands, respectively. The net charge of GNPs is modeled as \(q=(3a_{\text{Au}}+0.5a_{\text{Au}}^{2})\epsilon_{0}+n_{\text{L}}\epsilon_{0}\), as in [11, 18], where \(\epsilon_{0}\) is the electron charge and \(n_{\text{L}}=250\) is in this case the chosen net electron count in the ligand shell. The particle density has been calculated as \(\mathcal{N}=\phi/(4\pi a^{3}/3)\) where \(\phi=2.5\cdot 10^{-2}\) denotes the volume fraction of GNPs within the layer. The dynamic viscosity of the adipose host medium is assumed to be \(\mu_{f}=3\cdot 10^{-2}\) Nsm\({}^{-2}\).  
With the above permittivities for the thin layer and surrounding medium, the TE\({}_{10}\) response of the graded waveguide with layer width \(z_{0}=0.5\,\text{cm}\) can be found in Figure 4, plotted for frequencies above the cutoff frequency \(f_{c}=1.08\,\text{GHz}\). The relative error between analytical solution and computational results is under 0.1%. Figure 5 illustrates the data processing accomplished to obtain the simulation results found in Figure 4. The output from COMSOL is given in S-parameters measured at the ports at either end of the waveguide, i.e., power reflection and transmission throughout the entire waveguide of 17 cm in length.  
Figure 3: Numerical and exact analytical results for \(R_{P}\), \(T_{P}\) and \(C_{\text{abs}}\) for complex permittivities \(\varepsilon_{\text{L}}=4-0.81\) and \(\varepsilon_{\text{G}}=2-0.1\,\text{i}\), as functions of (**a**) operating frequency with \(z_{0}=1\,\text{cm}\), and (**b**) layer width \(z_{0}\) with fixed frequencies \(f\in\{2.1,2.5\}\,\text{GHz}\).  
Since we are only interested in the power coefficients within the central layer, the S-parameters are multiplied by the scaling factor (17) plotted in Figure 5b.
## 6 Conclusions
\begin{tabular}{l l} BWB & Blended-Wing-Body \\ BLI & Boundary Layer Injection \\ DOC & Direct Operational Costs \\ DP & Distributed Propulsion \\ DEP & Distributed Electric Propulsion \\ MCRDO & Multidisciplinary Conceptual Robust Design Optimisazion \\ \end{tabular} \begin{tabular}{l l} FRIDA & Framework for Integrated Design of Aircraft \\ BEM & Boundary-Element-Method \\ HEP & Hybrid-Electric-Propelled \\ SPL & Sound Pressure Level \\ OASPL & OverAll Sound Pressure Level \\ H2020 & Horizon 2020 \\ ARTEM & Aircraft noise Reduction Technologies and related Environmental iMpact \\ DLR & Deutsches Zentrum fur Luft- und Raumfahrt \\ BPF & Blade-Passage Frequency \\ NACA & National Advisory Committee for Aeronautics \\ DFT & Discrete Fourier Transform \\
**variables** & \\ \(w_{\phi},w_{r},w_{z}\) & Wake induced velocity components \\ \(\alpha_{i}\) & Induced angle of attack \\ \(\alpha_{ef}\) & Effective angle of attack \\ \(C_{l}\) & Lift coefficient \\ \(C_{d}\) & Drag coefficient \\ \(C_{m}\) & Moment coefficient \\ \(n_{b}\) & Number of blades \\ \(r_{0}^{H}\) & Hub radius \\ \(c\) & Blade chord \\ \(r_{0}\) & Blade root \\ \(d\) & Rotor diameter \\ \(\lambda\) & Blade taper ratio \\ \(\dot{\theta}\) & Pitch angle [deg] \\ \(T\) & Period of the tonal noise \\ \(\Omega\) & Rotational speed \\ \(\mathbf{v}\) & Velocity \\ \(\varphi\) & Potential \\ \(\varphi_{I}\) & Incident potential \\ \(\varphi_{S}\) & Scattered potential \\ \(S_{B}\) & Body surface \\ \(S_{W}^{N}\) & Near wake surface \\ \(S_{W}^{P}\) & Far wake surface \\ \(G\) & Fundamental solution \\ \(v_{n}\) & Normal velocity \\ \(\mathbf{v}_{B}\) & Body velocity \\ \(\mathbf{n}\) & Outward unit normal \\ \(u_{n}\) & Induced normal velocity \\ \(\mathbf{u}_{I}\) & Velocity induced by the far wake \\ \(\Delta\varphi_{S}\) & Potential jump across the wake surface \\ \(p_{T}^{\prime}\) & Thickness noise \\ \(p_{L}^{\prime}\) & Loading noise \\ \(\mathbf{r}\) & Distance between observer and source \\ \(\mathbf{x}\) & Observer position \\ \(\mathbf{y}\) & Source position \\ \(c_{0}\) & Undisturbed speed of sound \\ \(\rho_{0}\) & Undisturbed medium density \\ \(p_{0}\) & Undisturbed medium pressure \\ \(M\) & Mach number \\ \(\tau\) & Emission time \\ \(t\) & Time \\ \(\theta\) & Delay \\ _dist_ & Tip-clearance \\ \end{tabular}
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 5 Teleoperation Assistant Control Strategy Based on Collision Risk Assessment
### Graphical User Interface of Teleoperation  
The top left corner of the interface is the predictive-simulation interface, which is built using the OSG based on remote environment information. In this virtual interface, the operator can control the virtual robot in real time through the wearable positioner. The \"Status\" sign in the lower right corner indicates whether the robot arm is malfunctioning: the green color indicates normal, and the red color indicates collision and other failures. The relative shortest distance between the robot and the surrounding obstacles can be obtained in real time during the movement by the proposed bounding box method. Moreover, the input parameters required for the dynamic collision risk assessment model, such as operation speed and delay time, can also be obtained in real time. The calculated collision risk assessment value is displayed in the information display column (lower left corner), which displays the angle information of each joint of the teleoperation robots at the same time. This column also contains several function buttons on the upper side, such as the start and end of the control program, the update of the simulation interface, etc. The video information of the remote environment acquired from the depth camera is displayed in the bottom right corner of the interface to facilitate the operator to grasp the real environment.
## 3 Proposed BICORN-RNN  
In this section, we formally propose an ingenious BIdirectional COnvolutional RaNdom RNN (BICORN-RNN) structure to solve the long-standing gradient problems in TSC processing.  
A distinctive feature of BICORN-RNNs is an alternative combination of one or more improved measures. This sophisticated architecture has a probable contribution to discriminating different time series classes. From the different aspects and perspectives of input space, feature extraction, and transmission mode, different improvement methods are adopted correspondingly, and coordinate with each other. (1) The principal part of this novel model, RaNdom RNNs (RN-RNNs), is to project the classification prediction of previous time-steps into the current input, namely to modify the input space in order to easily catch important information; (2) There is a hierarchical part, COnvolutional RNNs (CO-RNNs), which extracts indescribable local features to enhance the ability of discrimination in different hierarchies, especially fine structures; (3) A symmetric part, BIdirectional transmission (BI-RNNs), provides access to the contexts on both sides of the series, so that it tends to gain a better performance because of its compensation for gradient problems.  
Figure 2: Deriving the gradients according to the back-propagation through time (BPTT) method. We denote by \(\varepsilon(t)\) the error obtained from the output at the \(t\)-th time-step.
# Training from Zero: Forecasting of Radio Frequency Machine Learning Data Quantity
### RF Characteristics
#### 1.2.1 Real-World Degradation  
In practice, the problem becomes vastly more complex as relative motion between transceivers, multiple transceivers, environmental noise, environmental motion, unintended radio emissions from man-made devices, multipath interference, and the imperfect hardware that transmits and receives the waveform are introduced. An introduction to the effects of imperfect hardware is given by [PERSON] et al. in [43] by looking at the individual degradations the hardware can add to a system, as well as some mitigation strategies that can be applied; however, it is worth mentioning that these degradations are compounding and time-varying, so while the worst of the effects can be calibrated out, the effects persist and cause a separation from the ideals assumed in (8). For an example of how these degradations affect the ideal, here, the frequency-independent In-phase and Quadrature Imbalance (IQI) of the transceivers result in carrier modulation functions that are ideally expressed as \(\xi_{TX(ideal)}(t,f_{c})=\exp(j2\pi f_{c}t)\) for the transmitter for a carrier frequency \(f_{c}\) and \(\xi_{RX(ideal)}(t,f_{c})=\exp(-j2\pi f_{c}t)\) for the receiver as  
\[\xi_{TX}(t,f_{c})= \bigg{(}\bigg{(}\frac{1+g_{TX}\exp(j\phi_{TX})}{2}\bigg{)}\exp(j2 \pi f_{c}t) \tag{9}\] \[+\bigg{(}\frac{1-g_{TX}\exp(-j\phi_{TX})}{2}\bigg{)}\exp(-j2\pi f _{c}t)\bigg{)},\] \[\xi_{RX}(t,f_{c})= \bigg{(}\bigg{(}\frac{1+g_{RX}\exp(-j\phi_{RX})}{2}\bigg{)}\exp(- j2\pi f_{c}t)\] \[+\bigg{(}\frac{1-g_{RX}\exp(j\phi_{RX})}{2}\bigg{)}\exp(j2\pi f_{ c}t)\bigg{)},\]  
where \(g_{X}\) is the magnitude ratio imbalance and \(\phi_{X}\) is the phase difference between the quadrature mixer and the in-phase mixer [43]. The ideal carrier modulators are recovered when the magnitude ratio imbalance is unity, \(g_{X}=1\), and the phase difference is zero, \(\phi_{X}=0\). This results in an ideal received signal being changed from the ideal baseband transmitted waveform in (8) into  
\[r_{BB}(t)= (s_{BB}(t)\xi_{TX}(t,f_{c})+s_{BB}^{*}(t)\xi_{TX}^{*}(t,fc) \tag{10}\] \[+\
u_{BB}(t)e^{j2\pi f_{c}t}+\
u_{BB}^{*}(t)e^{-j2\pi f_{c}t})\] \[\cdot\xi_{RX}(t,f_{c})*h_{lp}(t)\] \[= s_{BB}(t)+\text{IQI}(s_{BB}(t),g_{TX},g_{RX},\phi_{TX},\phi_{RX})\] \[+\
u_{BB}(t)+\text{IQI}(v_{BB}(t),1,g_{RX},0,\phi_{RX})\]  
where the \(\text{IQI}(\cdot)\) is a function for the addition of IQI when both the transmitter's and receiver's parameters are known in the received signal as an additive interference, given as  
\[\text{IQI}(x(t), g_{TX},g_{RX},\phi_{TX},\phi_{RX})= \tag{11}\] \[x_{\text{re}}(t)\cdot(-jg_{RX}\sin(\phi_{RX}))\] \[+x_{\text{im}}(t)\cdot(-g_{TX}\sin(\phi_{TX})\] \[+j(g_{TX}g_{RX}\cos(\phi_{TX}-\phi_{RX})-1)).\]
## 3 Results
### Abundances and Morphological Characteristics of Microplastics  
A total of 224 microplastics were detected in 86 out of 110 samples (78.2%) of tap water, and 24 samples (21.8%) contained no microplastics (Figure 2). The values of microplastic abundance varied greatly from 0.000 to 8.605 n L\({}^{-1}\), with an overall mean (\(\pm\)SEM, standard error of the mean) of 2.181 \(\pm\) 0.165 n L\({}^{-1}\). The majority of microplastics were fibres (98.7%; Figure 3a), with only five films (2.2%; Figure 3b) found in the samples. Neither pellets nor fragments were observed (Figure 4a). Microplastics were found in transparent, blue, black and yellow colours. Black microplastics accounted for 42.4% of the total count, followed by blue (39.7%) and transparent (15.6%). Few yellowish microplastics (2.3%) were detected in the samples (Figure 4b). The identified microplastics varied greatly in size from 50 to 4830 um, with a mean of 949.9 \(\pm\) SD 913.2 um. The size distribution of microplastics displayed a positive skew (skewness = 1.774), with a peak at sizes \(<\) 1000 um (65.2% of the total microplastic count). Microplastics in the size fraction from 150-499 um were predominant, accounting for 30.8% of the total count. The relative abundances of particles in the other size categories of 2.7-149 um, 500-999 um, 1000-2499 um and 2500-5000 um were 8.9%, 25.5%, 27.2% and 7.6%, respectively (Figure 4c).  
Figure 4: Shape (**a**), colour (**b**), and size (**c**) compositions of microplastics detected in the tap water samples.  
Figure 3: Stereomicroscopic images showing (**a**) blue (left) and black (right) fibres and (**b**) a transparent film (black arrow).  
Figure 2: Mean microplastic abundance (n L\({}^{-1}\)) of Hong Kong tap water samples sourced from surface waters. The box plot shows the median (centreline within the box), 25 th and 75 th percentiles (box edges), excluding outliers (in circles). Outliers are values of at least 1.5 times the value of the interquartile range (IQR).
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 4 Design Example: Super Class AB QFG Amplifier in a 130 nm CMOS Process
### Measurement Results  
A 130 nm CMOS technology was employed to fabricate a unity-gain, closed-loop version of the proposed super class AB amplifier of Figure 8a. Transistor aspect ratios W/L (in \(\mu\)m/\(\mu\)m) were 72/0.24 (M\({}_{3A}\), M\({}_{4A}\)), 24/0.24 (M\({}_{5}\), M\({}_{6}\)), 12/0.12 (M\({}_{7}\), M\({}_{8}\)), 6/2 (M\({}_{R1}\), M\({}_{R2}\)), 0.5/5 (M\({}_{R3}\), M\({}_{R4}\), M\({}_{R5}\)) and 12/0.24 for the rest. Three metal-insulator-metal (MiM) capacitors C\({}_{BAT}\) of 1 pF were employed. A microphotograph of the circuit is shown in Figure 8b, where the layout plot is also shown, since the surface of the die is opaque.  
Supply voltages were \(\pm\)0.5 V and \(I_{B}\) = 3 \(\mu\)A. Cascade bias voltages \(V_{CP}\) and \(V_{CN}\) were \(-\)0.34 V and 0.3 V, respectively, and \(V_{RES}\) was set to 480 mV. The measurement setup used is shown in Figure 9.  
For transient response, the input signal was a 1 MHz 0.4 Vpp square wave, with a \(-\)0.2 V DC level generated by an Agilent 33522A arbitrary waveform generator, and the output was displayed in a Tektronix TDS5052B oscilloscope. Total load capacitance was 140 pF. Both the input and transient response are plotted in Figure 10. The measured SR values for the rising and falling edge were 5.7 V/\(\upmu\)s and \(-\)7.1 V/\(\upmu\)s, respectively. Figure 11 shows the measured total harmonic distortion (THD) using a 30 kHz input tone, whose amplitude varies from 100 mVpp to 350 mVpp. Note that THD is below 1% in all this range.  
The frequency response was also measured (Figure 12) using a Keysight N9320B spectrum analyzer. Since an off-chip buffer was employed to avoid loading the analyzer, total load capacitance was \(C_{L}\) = 120 pF. The cutoff frequency of the proposed OTA was 4.48 MHz, which corresponds approximately to the GBW, because of the dominant pole design.  
Figure 9: Measurement setup.
## 3 Methodology  
To assess the performance of the aforementioned variations of grounding grids and to compare them with the safety measures and criteria mentioned in [9], several scenarios were taken into consideration. To further elaborate, extensive simulations were undertaken, encompassing grounding grids with diverse configurations (as depicted in Figure 6), alongside variations in soil models and resistivity. In more detail:  
* A uniform soil structure and a horizontal two-layer soil model with h = 1 m the depth of the first layer was applied to all three variations;
* This study investigates the variability of soil resistivity across power distribution grid routes. Although power distribution grid routes traverse mainly farmland regions, characterized by resistivity values typically ranging from 100 to 300 \(\Omega\)m, for research purposes, a wider range of soil resistivity scenarios were studied. These scenarios included uniform resistivity values ranging from 15 \(\Omega\)m to 1000 \(\Omega\)m, as well as horizontal two-layer soil models [27; 28]. In the latter, the resistivity of the first layer varied from 100 \(\Omega\)m to 1000 \(\Omega\)m, while the resistivity of the second layer ranged from 100 \(\Omega\)m to 2000 \(\Omega\)m;
* The total resistance of the grounding grid was calculated for each soil resistivity using the SES CDEGS software tool [27; 28];
* This study explores potential installation sites for the MV concrete pole, assessing three distinct scenarios: near the HV/MV substation (L = 0 km), at the middle of the MV line (L = 10 km), and at the end of it (L = 20 km);
* For each installation location mentioned above, the maximum single-line-to-ground fault current was determined using the PowerFactory 2022 software tool; the magnitude of the single-line-to-ground fault is contingent upon four resistances arranged in series, as illustrated in the Figure 7 and described in Equation (4) [26]: \[I_{1f}=\frac{V_{f}}{\sqrt{\left(R_{f}\cdot l+R_{N}+R_{G}\right)^{2}+\left(X_{f }\cdot l+X_{T}\right)^{2}}}\] (4) where: \[I_{1f}\] : Single-line-to-ground fault \[V_{f}\] : Line-to-ground voltage \[R_{G}\] : Grounding grid's total resistance (depending on the soil resistivity) \[R_{N}\] : Neutral point of the HV transformer resistance \[R_{f}\] : Equivalent single-line-to-ground fault resistance of 95 ACSR Conductor \[X_{f}\] : Equivalent single-line-to-ground fault impedance of 95 ACSR Conductor \[X_{T}\] : HV transformer impedance \[\begin{array}{c}\includegraphics[width=142.26378 pt]{Fig1}\\ \includegraphics[width=142.26378 pt]{Fig2}\end{array}\] (5)  
Figure 7: Single-line-to-ground fault current path.  
* Regarding fault clearing time, the instantaneous operation of the circuit breaker (CB) with a time of 0.15 s for the MV line was selected. The adopted procedure for the analysis is presented in Figure 8.
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Signal Processing Methods  
Figures 15-18 show comparison diagrams of the bearing vibration signals after RB processing: normal, inner ring fault, rolling body fault, and outer ring fault signals, along with their respective RB-treated versions. The abscissa represents the sample points, while the ordinate represents the amplitude. To facilitate the comparison of fault state signals, the ordinate interval is uniformly set to (\(-\)3, 3), and the abscissa represents 1000 sample points.  
Comparing the vibration signals before and after RB processing shows that the output characteristics of the data treated by RB closely resemble the input features, with the output data dimension remaining the same as the original. Although this randomized convolution operation alters the local characteristics of the sample, the overall signal information remains unchanged. Sample diversity is achieved by randomizing the size of k. In this paper, k \(=[1,3,5,7]\) is selected for analysis.  
As shown in the figure, the random convolution layer enhances the model's resistance to disturbances by altering the local features of the sample, enabling the model to prioritize global information during training. This, in turn, improves its robustness against small perturbations. Generating diverse samples increases the dataset's diversity, thereby improving the model's generalization performance across different domains and enhancing its robustness. Following RB treatment, the signal becomes more stable while retaining its fault characteristics. In wind turbine spindle bearing fault diagnosis, the RB-processed signal is ideal for fault feature extraction, thereby improving diagnostic accuracy. Experimental results confirm that RB is an effective method for processing the vibrational signals used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline
**Transfer Learning Tasks** & **Source Domain** & **Target Domain** \\ \hline
1 & A & B \\
2 & A & C \\
3 & A & D \\
4 & B & A \\
5 & B & C \\
6 & B & D \\
7 & C & A \\
8 & C & B \\
9 & C & D \\
10 & D & A \\
11 & D & B \\
12 & D & C \\ \hline \end{tabular}
\end{table}
Table 3: Task division in transfer learning.  
Figure 16: Comparison diagram of the bearing inner ring fault signal and the same signal after RB treatment.  
Figure 15: Comparison diagram of the normal bearing vibration signal and the same signal after RB treatment.  
Figure 17: Comparison diagram of the bearing rolling body fault signal and the same signal after RB treatment.  
Figure 18: Comparison diagram of the bearing outer ring fault signal and the same signal after RB treatment.