esa-sceva/satcom-chunk-collection huggingface dataset:
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 2 High-Throughput Satellite Communication System Model  
### High-Throughput Satellite Communication System Architecture  
The high-throughput satellite communication system is divided into three main segments: the ground segment, the space segment, and the user segment. All the user terminals covered by all the spot beams of the satellite constitute the user segment. The ground segment is mainly composed of gateway stations, which are responsible for sending signals to and receiving signals from satellites. The space segment includes the satellites and all the ground facilities for monitoring and controlling them. The definition of the link between different segments of the high-throughput satellite communication system is different. The feeder link is the wireless link connecting the satellite and the gateway station, and the user link is the link connecting the user terminal and the satellite. The forward uplink from the gateway station to the satellite and the forward downlink from the satellite to the user constitute the forward link of the high-throughput satellite system. The reverse uplink from the user to the satellite and the reverse downlink from the satellite to the gateway station constitute the reverse link of the high-throughput satellite system, which is shown in Figure 1.
## 5 Test Protocol
### Measurement with the Sensor Inserted in the Soil  
Next, the sensor was inserted in a clayey red latosol with the following composition: 0.65 kg kg\({}^{-1}\) clay, 0.21 kg kg\({}^{-1}\) silt, and 0.14 kg kg\({}^{-1}\) sand; and a second test was carried out to measure its output as a function of small differences in matric potentials. When the water retention curve of this soil was measured for small matric potentials between 1 kPa and 35 kPa, a practically linear behavior was observed [23].  
An inverted Richards chamber [24] was used, and the soil was submitted to pressures between 1 kPa and 35 kPa, to see how the sensor behaved for small changes in the matric potential in the soil. A photograph of the inverted Richards chamber and the equivalent schematic drawing are shown in Figure 7.  
Figure 5: Plot of the measured temperature \(T\) and the temperature difference \(\Delta T\), as a function of time, for several values of \(\theta_{p}\).  
Figure 6: Plot of \(\Delta T\) as a function of \(\theta_{p}\), measured at \(t=10\) s (calculated from the measured values of \(V_{BE}\)).  
A minimum time interval of 12 h between measurements was used, to guarantee that an equilibrium between the water in the porous sensor and the in soil was reached before each measurement. From the results presented in Figure 8, we observed a practically linear behavior as a function of the matric potential. From Figure 8, we can see that, in this small region, the sensor presented a sensitivity of approximately \(S=0.015\)\({}^{\circ}\)C/kPa.
###### Abstract  
The calibration of three-dimensional (3D) coordinates in augmented reality systems is a complex activity. It involves the recognition of environmental characteristics and technology that models the 3D space of the device, determining its position and orientation. Single markers suffer from numerical instability, particularly when they are small within the camera image. In the industrial environment, it is common for augmented reality applications to cover large spaces, making it difficult to maintain attributes such as precision and accuracy. To address this issue, our study proposes a two-step calibration model that leverages multiple markers for accurate localization in a larger indoor environment. We developed the calibration model using Unity3D, Mixed Reality ToolKit, and Vuforia and evaluated it in terms of precision and accuracy in a proof of concept using the MS Hololens device. Our findings reveal that employing two markers significantly reduces angular discrepancies between points in the real and augmented environments. Moreover, our results underscore that registration accuracy improves as the number of calibration points increases. The results show improvements in determining the axes that define the 3D space, with a direct influence on the position of the points observed in the experiment.  
Keywords:HoloLens; calibration; industrial; augmented reality; indoor positioning +
Footnote †: journal: _Applied Sciences_
## 4 Policy Optimization
### Noisy Optimization of Crane Dispatching Policy
The result of comparison \(p_{c}=\max(p(y\prec x),\,(1\,-\,p(y\prec x)))\), therefore, is always greater than or equal to 0.5. If \(p_{c}\) is close to 1, we believe \(x\) and \(y\) are significantly different, in which case we determine the winner without hesitation. If \(p_{c}\) is smaller than a predetermined threshold \(\theta\), we try a more careful comparison by adopting the idea of accumulative sampling [11]. If the competitor \(x\) from \(P\) looks no worse than the newly born \(y\) (i.e., \(p(y\prec x)\geq 0.5\)), we give \(n_{a}\) additional samples to \(x\) and update its evaluation by averaging all the samples, including the additional samples and the existing ones. Otherwise, the additional samples are given to \(y\) and its evaluation is updated. The more samples allotted to an individual, the more reliable its evaluation becomes. The notable features of N-RTS are that it saves samples by giving additional samples only to a better looking one during the survival selection and no sample at all when one looks decisively better than the other, while NTGA unconditionally gives additional samples to \(x\), regardless of the quality difference between \(x\) and \(y\). The level of decisiveness can be controlled by adjusting the threshold value \(\theta\). As one wants a higher confidence for that decision, he or she needs a higher value of \(\theta\). The sizes of \(P\), \(T\), and \(M\) were empirically set to 100, 50, and 2, respectively. The sizes of \(n_{i}\) and \(n_{a}\) were set to 2 and 1, respectively. The _binary tournament selection_ was used to form a mating pool (\(M\)) from the population (\(P\)). The confidence threshold \(\theta\) was empirically set to 0.98. Each individual chromosome is represented as an array of real values. The crossover operator used is the _simulated binary crossover_[29], with a crossover rate of 0.9 and a crowding control parameter of 2. The mutation operator used is the _polynomial mutation_[29], with a mutation rate of \(1/L\) (\(L\) is the length of the chromosome) and a crowding control parameter of 2.
## 4 Experimental Results  
To test the CpVMI spectrometer, we have performed (1) photoionization of Ar 2p at the beamline BL02B at the Shanghai Synchrotron Radiation Facility (SSRF), and (2) dissociative ionization of O\({}_{2}\) by 800 nm femtosecond laser. They showed experimentally resolution for the electron arm and ion arm, respectively.
## 2 Operation Principle/System Design
### Out-Coupling of a Wide FOV from a Curved Waveguide in an EMB of Finite Dimensions
In the calculation, we assume that the pupil does not go beyond the EMB, so the range of its motion is limited to 8 mm. The magnitude of the error is shown by a colored histogram. As can be seen from Figure 10, before the optimization, the center of the angular field is out-coupled without aberrations for all positions of the pupil, and the error increases towards the edges of the FOV. The maximum error for the system with selected parameters is about 2 arcmin. After the law of variation of the period of the out-coupling DOE was optimized, the out-coupling error for the extreme rays of the angular field decreased significantly (from 1.2-2 arcmin to 0.2-0.8 arcmin). However, in this case, the error for the central part of the field was increased (up to \(\approx 1.4\) arcmin). Thus, we can conclude that the optimization procedure made it possible to obtain the out-coupling image with an average error in the angular field and EMB of the order of 1 arcmin, which is comparable to the angular resolution limit of human eye. Figure 9: A scheme of ray (blue lines) diffraction on the out-coupling DOE in the \(YOZ\) meridional plane. 1—the inner surface of the curved waveguide in the meridional plane; 2—the EMB in the meridional plane; 3—the center of the curvature radius of the waveguide surface.
## 2 Materials and Methods
### Deep Learning Models
#### 2.5.3 GRU Cell  
A gated recurrent unit (GRU) cell is a simplified version of the LSTM cell; see the set of equations in Equation (13).  
There are some simplifications that are done by the GRU cell with respect to the LSTM cell: The long-term state vector \(c_{(t)}\) is merged into the short-term state vector \(h_{(t)}\). Then, \(z_{(t)}\) controls both the input gate \(i_{(t)}\) and forget gate \(f_{(t)}\). The output gate \(o_{(t)}\) is replaced by \(r_{(t)}\) which shows the information that will be shown to the main gate \(g_{(t)}\).  
The process starts on the left-hand with the previous state \(h_{(t-1)}\) and the data \(x_{(t)}\), which is used to calculate both \(r_{(t)}\) and \(z_{(t)}\) in Equations (13b) and (13a), respectively. The \(\sigma(x)\) function is used to control the amount of past information input to the main gate \(g(t)\) by \(r_{(t)}\), and the proportion of information that would be forgot and learned by \(z_{(t)}\) and its complement \((1-z_{(t)})\). In this sense, \(g_{(t)}\) is the output of new information learned, calculated by \(r_{(t)}\) and \(h_{(t-1)}\), and applying tanh in Equation (13c). The current output \(h_{(t)}\) is then calculated in Equation (13d) using both the past information \(h_{(t-1)}\) and the present information \(g_{(t)}\), regularized by \(z_{(t)}\) in order to forget and learn at a complementary rate of information, such as \((z_{(t)}+(1-z_{(t)})=1)\), because \(0<\sigma(x)<1\).  
\[z_{(t)}=\sigma(W_{xz}^{T}x_{(t)}+W_{hz}^{T}h_{(t-1)}+b_{z}) \tag{13a}\] \[r_{(t)}=\sigma(W_{xr}^{T}x_{(t)}+W_{hr}^{T}h_{(t-1)}+b_{r})\] (13b) \[g_{(t)}=\tanh\left(W_{xg}^{T}x_{(t)}+W_{hg}^{T}(r_{(t)}\otimes h_ {(t-1)})+b_{g}\right)\] (13c) \[h_{(t)}=z_{(t)}\otimes h_{(t-1)}+(1-z_{(t)})\otimes g_{(t)} \tag{13d}\]
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 7 Results and Discussion  
The simulation results have been obtained to validate the effectiveness of the proposed optimization techniques in determining the optimal values of the capacitance and the parameters of the DC voltage regulator. Moreover, to validate the accuracy of the those obtained from the results, these values have been applied to the system under study under the integration of nonlinear loads and the condition of three-phase imbalance. The performance of the MMC STATCOM in the two study cases has been introduced in the simulation results.
## 4 Interleaver Communication System Analysis in the Presence of Fading  
### Single Correlator Receiver  
The fading increases significantly BER inside communications systems. It is assumed that a particular fading coefficient affects each sequence/symbol transmitted. If interleaver/deinterleaver blocks are included into the transceiver structure, as shown in Figure 1, the effects on fading can be spread in a symbol interval. In this paper, it will be assumed that the block interleaver of \(2\beta\times 2\beta\) size is employed. Thus, the chips for each symbol are written into the interleaver row wise and taken out column wise at the transmitter side. The opposite operation is performed at the receiver side to re-order the chips and return them to be in the corresponding symbol interval. Thus, the samples at the output of receiver multiplexer (MUX) are the chips affected with independent fading coefficients expressed as  
\[z_{i}=\alpha_{i}\sqrt{E_{c}}c_{i1}+\sqrt{E_{N}}n_{i},\text{for}\ i=1,2,3,...\,2\beta. \tag{41}\]  
The received chip samples are applied to the chip sequence correlator input. The output sample of the first correlator is  
\[w_{1}=\sum_{i=1}^{2\beta}z_{i}c_{i1}=\sqrt{E_{c}}c_{i1}^{2}\sum_{l=1}^{2\beta }\alpha_{i}\ +\sqrt{E_{N}}n_{i}c_{i1}\sum_{l=1}^{2\beta}\alpha_{i}\, \tag{42}\]  
with its mean  
\[\eta_{w1}=E\{w_{1}\}=\sqrt{E_{c}}E\{c_{i1}^{2}\}\sum_{i=1}^{2\beta}\alpha_{i} \tag{43}\]  
and variance  
\[\sigma_{w1}^{2}=E_{c}[E\{c_{i1}^{4}\}-E^{2}\{c_{i1}^{2}\}]\sum_{i=1}^{2\beta }\alpha_{i}^{2}+2\beta E_{N}\sigma_{ni}^{2}E\{c_{i1}^{2}\}. \tag{44}\]  
The probability of bit error can be calculated as  
\[p_{be}=\tfrac{1}{2}\,erfc\left(\frac{\
u_{-1}}{\beta}+\frac{E_{b}}{N_{0}}{ \left(\Sigma_{l=1}^{2\beta}\,\alpha_{i}\right)}^{2})^{-1}\right)^{-1/2}.\]  
If the source generates binary symbols and the spreading sequence is in binary form, we may have  
\[p_{be}=\tfrac{1}{2}\,erfc\left(\sqrt{\tfrac{E_{c}}{2\beta N_{0}}{\left(\Sigma _{i=1}^{2\beta}\,\alpha_{i}\right)}^{2}}\right). \tag{45}\]  
This probability depends on a random function which is a sum of \(2\beta\) Rayleigh distributed random variables. The proabability of error can be calculated as the mean value of Equation (45) in respect to this random function.  
This expression was derived by finding the mean values and variance for \(\alpha_{i}\) as independent and identically distributed random variables, as it was done in [15]. The probability of error is derived in this closed form  
\[p_{be}=\frac{1}{2}\,erfc\left(\frac{2(4-\pi)}{\beta\pi}+\frac{4}{\pi}(\frac{E_{ b}}{N_{0}})^{-1}\right)^{-1/2}. \tag{46}\]  
As can be seen from this expression, the probability of error will decrease as the spreading factor \(2\beta\) increases.
# Effectiveness of LRB in Curved Bridge Isolation:
## 5 Sensitivity of Response Parameters
### Uni-Directional Loading
The maximum decrease (about 74%) in pier torsional moment was found for Kobe's ground motion. In the case of Imperial Valley and Turkey motions, the decrease in pier torsional moment is about 68% and 60%, respectively. However, the variation of pier torsional moment is almost insensitive for all the damping ratios, especially for Kobe and Turkey ground motions.
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 3 Results
### Laser Scan Path Interval  
The laser scan path interval is the interval between lines, as shown in Figure 3, which affects the overlap rate in the pick-feed direction. If the overlap ratio is low, an interconnection between metal powders becomes difficult. Conversely, if the overlap ratio is excessively high, thermal damage to the entire specimen may occur. In this study, because a beam shaper was not separately used, the laser spot followed a Gaussian distribution so the energy distribution of the entire patterning area could only be evenly made when an appropriate overlap ratio was applied. Therefore, we conducted the experiment whilst varying the scan path interval from 1.25 to 50 \(\upmu\)m.  
When the overlap ratio was low and the scan path interval was 50 \(\upmu\)m, only the machined shape was observed, as shown in Figure 6, because there was almost no sintering effect by heat and only the polymer binder ablation occurred neatly. Even when the path interval was reduced to 25 \(\upmu\)m, a sufficient interconnection between the aluminum powders was not formed and irregularly blown-away shapes were observed. When the path interval was reduced to 10 \(\upmu\)m or less, the ablation of the polymer binder and sintering between the aluminum powders were simultaneously well-performed, enabling a smooth conductive pattern formation. However, as the path interval decreased, the process time also increased and, as shown in Figure 6, when excessive light energy was irradiated unnecessarily, we observed that the heat-affected zone at the line edge increased. The IV curves were measured, as shown in Figure 7, to accurately measure the conductivity change tendency for each condition. When the path interval was greater than 25 \(\upmu\)m, as shown in the SEM image, an interconnection between the powders was not formed so the currents were measured as 0, making it impossible to measure the resistance. On the other hand, when  
Figure 4: SEM images of laser irradiated areas with different laser spot sizes.  
the path interval was smaller than 10 \(\upmu\)m, the calculated resistance value based on the IV measurement for each process condition was 0.33 to 0.42 \(\Omega\cdot\)cm\({}^{-1}\). Although the difference in the resistance value by the process condition was not large, the resistance slightly increased as the path interval decreased. This indicated that the minimum resistance that could be made through the sintering process at a given aluminum powder content was limited to an extent due to the voids between the powders. In addition, it was found that excessive thermal energy caused damage to the surface of the interconnected aluminum powder and adversely affected the resistance reduction. Therefore, in this experiment, the scan path interval for excellent conductivity and low thermal damage was determined to be 10 \(\upmu\)m.  
Figure 5: SEM images of laser irradiated areas with different laser repetition rates.  
Figure 6: SEM images of laser irradiated areas with different laser scan path intervals.
# Several Cases for the Validation of Turbulence Models Implementation
## 2 Physical Model
### Turbulence Models
#### 2.2.1 Shear Stress Transport Turbulence Model  
The Shear Stress Transport turbulence [4] model is a two-equation turbulence model that is a combination of a high Reynolds number \(\kappa-\epsilon\) turbulence model and the \(\kappa-\omega\) turbulence model by [PERSON] [6]. \(\kappa\) denotes the kinetic energy of the turbulent fluctuations, \(\epsilon\) is the turbulence dissipation per unit mass, and \(\omega\) is the root mean square fluctuating vorticity (or the rate of dissipation of energy in unit volume and time). The goal of combining the two models is to retain the best attributes of both models while fixing their weaknesses. For example, as the \(\kappa-\omega\) model is sensitive to the freestream values of \(\omega\), the Shear Stress Transport model switches to the \(\kappa-\epsilon\) model to resolve this issue. The ShearStress Transport model is defined by the transport equations of the kinetic energy of the turbulent fluctuations, \(\kappa\),  
\[\frac{\partial(\rho\kappa)}{\partial t}+\frac{\partial(\rho v_{j}\kappa)}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\left[(\mu_{L}+\sigma_{\kappa} \mu_{T})\frac{\partial\kappa}{\partial x_{j}}\right]+\tau_{ij}^{F}\frac{ \partial u_{i}}{\partial x_{j}}-\beta^{*}\rho\omega\kappa \tag{5}\]  
and of the dissipation \(\omega\),  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial(\rho v_{j} \omega)}{\partial x_{j}} =\frac{\partial}{\partial x_{j}}\left[(\mu_{L}+\sigma_{\omega} \mu_{T})\frac{\partial\omega}{\partial x_{j}}\right]+\tau_{ij}^{F}\frac{ \partial u_{i}}{\partial x_{j}}\frac{C_{\omega}\rho}{\mu_{T}}-\beta\rho \omega^{2}+\] \[+2(1-f_{1})\frac{\rho\sigma\omega_{2}}{\omega}\frac{\partial \kappa}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}}. \tag{6}\]  
\(f_{1}\) is the blending function between the \(\kappa-\epsilon\) and the \(\kappa-\omega\) turbulence models. This blending allows the Shear Stress Transport turbulence model to switch between the two turbulence models depending on (i) the distance from the wall and (ii) the region of the turbulent boundary layer is being analyzed. Variables \(\beta^{*}\) and \(\sigma_{\omega 2}\) are constant coefficients. Variables \(\sigma_{\omega}\), \(\sigma_{\kappa}\), \(C_{\omega}\), and \(\beta\) are non-constant coefficients that utilize the blending function, \(f_{1}\)[4].
## 2 Materials and Method
### Remote-Sensing-Based Carbon Flux Model
#### 2.2.3 Temperature Scalar  
In many LUE models, temperature scalars are calculated using near-surface air temperature, because it is easy to obtain from site-based meteorological measurements. When estimating regional carbon fluxes using LUE models, site-observed near-surface air temperature is interpolated into gridded data, which is spatially and temporally consistent with remote sensing data. This process can lead to uncertainties when estimating the results. In this study, we attempt to use LST as the input for the LUE model, because high temporal and spatial resolution LSTs can be estimated using thermal infrared remote sensing images. The temperature scalar is estimated at each time step, using the equation developed for the Terrestrial Ecosystem Model [28]:  
\[f(T)=\frac{(LST-LST_{\rm min})(LST-LST_{\rm max})}{(LST-LST_{\rm min})(LST-LST _{\rm max})-(LST-LST_{opt})^{2}} \tag{6}\]where LST (\({}^{\circ}\)C) is the land surface temperature, LST\({}_{\text{max}}\) (\({}^{\circ}\)C), LST\({}_{\text{opt}}\) (\({}^{\circ}\)C) and LST\({}_{\text{min}}\) (\({}^{\circ}\)C) are the maximum, optimum, and minimum land surface temperature for photosynthesis, respectively. _f_(T) equals 0 when the LST is more than LST\({}_{\text{max}}\) or less than LST\({}_{\text{min}}\).
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
## 3 Results and Discussion
### Expanded OFC Generation Using MIL-GS Lasers
#### 3.1.1 Comb Expansion  
Figure 4a-c illustrates the phase modulator-induced comb expansion of the MIL-GS OFC, when the PM is driven at the fundamental and multiples of the FSR (6.25, 12.5, and 18.75 GHz). When driven at the fundamental frequency, 35 comb lines (comb bandwidth of 218.75 GHz) are generated, with the OCNR larger than 45 dB. The expansion factor is ~2.3 compared to the single EI-GS OFC (red spectrum, Figure 3c). The comb expansion is limited by the maximum allowable drive amplitude of the PM used. Therefore, to further increase the expansion, the PM is driven at multiples of \(f_{s}\) with the same drive voltage. The number of comb lines increases to 39 (comb bandwidth of ~243.75 GHz), when the PM is driven at \(2f_{s}=12.5\) GHz and 45 (~281.25 GHz bandwidth) when driven at \(3f_{s}=18.75\) GHz. It is worth noting that only the PM drive frequency is varied, while all other conditions, such as slave laser bias, gain-switching parameters, and PM drive amplitude remain unchanged. Table 2 summarizes and quantifies the OFC expansion achieved. Overall, with the proposed MIL-GS OFC technique, the number of comb lines is increased approximately 3 times, from 15, for individual EI-GS OFC, to 45 lines for the expanded OFC.  
Figure 4: Optical spectra of expanded MIL-GS OFC, PM driven at (**a**) 6.25; (**b**) 12.5, and (**c**) 18.75 GHz. The arrows (L: left, M: middle, R: right) and numbered tones are filtered for phase noise analysis.