esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wetting and Drying
Figure 7 shows a direct relationship between price and amount of cement for each of the mixes. It can also be seen that, for the same cement content, mix M1 has the highest price, followed by M2 and M3. This is because mix M3 contains the highest proportion of by-products, which are cheaper than the clay used in mixes M2 and M1. The prices obtained are similar to those reported by [PERSON] and [PERSON] [46], who found costs of 4.16 USD/t and 4.81 USD/t for SRE materials stabilized with 2.5% and 5% cement, respectively. Regarding the carbon footprint, it is again observed that an increase in cement content leads to a rise in the carbon footprint for all mixes. Although the data on price and carbon footprint are interesting, it is more relevant to analyze the price/UCS ratio and carbon footprint/UCS ratio in order to make a direct comparison between the combinations. Figure 8 shows the price/UCS ratio and the carbon footprint/UCS ratio for all the combinations studied in this research. Figure 8 showed that, for each mix, the price/UCS ratio decreased significantly from the unstabilized combination to the one stabilized with 2.5% cement, and then leveled off, in accordance with the observations made by [PERSON] et al. [47]. For mix M1, the M100 combination had a price/UCS ratio of 4.41 EUR/tMPa, which dropped to 1.63 EUR/tMPa for  
Figure 8: (**a**) Price/UCS ratio and (**b**) carbon footprint/UCS ratio for all the combinations. M1C2.5 and to 1.51 EUR/tMPa for M1C5. In the case of mix M2, this reduction was smaller, as the M2C0 combination already had a much lower ratio of 1.44 EUR/tMPa. M2C2.5 and M2C5 reached ratios of 0.73 EUR/tMPa and 0.66 EUR/tMPa, respectively. M3C0, M3C2.5, and M3C5 showed values of 1.05 EUR/tMPa, 0.56 EUR/tMPa, and 0.46 EUR/tMPa. As observed, the combinations that used the by-product-based mix (M3) achieved the lowest price/UCS ratios among all the combinations studied in this experiment and they also obtained a lower ratio compared to other studies [47], demonstrating the potential of this material for SRE building. A similar pattern was observed for the carbon footprint/UCS ratio, where combinations using mix M1 showed a higher carbon footprint/UCS ratio compared to those with M2 and M3. Combinations M3C2.5 and M3C5 had the lowest carbon footprint/UCS ratios, just as they did for price/UCS ratio.
## 9 Dispersion Compensation
### Group Delay Dispersion  
When a pulse propagates through a dispersive material, its spectral phase, \(\phi(\omega)\), is affected. By expanding \(\phi(\omega)\) in a Taylor series around the pulse central frequency, \(\omega_{0}\), one can identify three major effects, corresponding to the first three terms in the series: (1) overall phase accumulation; the propagation phase added to all frequencies; (2) group delay (GD); the entire pulse is delayed compared to a pulse propagating in free space; and (3) group delay dispersion (GDD); a frequency-dependent group delay of the different spectral components of the pulse. Since GDD (not GD) causes a temporal broadening of the pulse in every round trip through the cavity [57], it must be compensated for to sustain the pulse in the cavity over time. In terms of the wavelength, \(\lambda\), the GDD of a medium is given by:  
\[\mathrm{GDD}=\frac{d^{2}\phi}{d\omega^{2}}=\frac{\lambda^{3}}{2\pi c^{2}}\frac {d^{2}}{d\lambda^{2}}\mathrm{OP}(\lambda) \tag{15}\]  
where \(\mathrm{OP}(\lambda)=\mathrm{n}(\lambda)\mathrm{L}(\lambda)\) is the optical path in the cavity of wavelength \(\lambda\). Note that pulse broadening can also be measured in terms of the group velocity dispersion (GVD) which is the GDD per millimeter  
Figure 8: CW and ML astigmatism (defined as \(w_{s}/w_{t}\)) on the end mirror, \(EM1\), as a function of the crystal position for hard aperture mode-locking at \(\delta\lesssim\delta_{1}\). A linear astigmatism of \(\approx\)\(1.24\) is pre-introduced into the CW mode, allowing for the stronger Kerr effect in the sagittal plane to “catch up” with the weaker tangential plane, resulting in the astigmatically-compensated ML mode at \(Z\approx 0.08\) for a specific value of \(K=0.1\).  
of the corresponding material. To calculate the GDD, due to a window with thickness \(L\) inside the cavity, we can assume \(L\) to be constant for all wavelengths (this is not exact for Brewster windows, but a very good approximation). For the Ti:sapphire crystal in the NIR range of the spectrum, Equation (15) results in a positive GDD for material components. For simplicity, higher order terms in the phase expansion, which correspond to third-, fourth- and fifth-order dispersion effects, are neglected so far; yet, the higher order terms (along with the overall gain bandwidth) are eventually the limiting factor for the shortening mechanism of the pulse duration. Expressions for higher order dispersion terms can be found in [58].
## 3 Simulated Results and Discussion
Now, we express two LP-CP reflection coefficients (\(r_{RHCP-y}\) and \(r_{LHCP-y}\)), PCR and PER [17], as the following equations:  
\[r_{RHCP-y}=\frac{\sqrt{2}}{2}\left(r_{xy}+ir_{yy}\right)\text{ and }r_{LHCP-y}=\frac{\sqrt{2}}{2}\left(r_{xy}-ir_{yy}\right) \tag{7}\]  
\[PCR=\frac{\left|r_{RHCP-y}\right|^{2}}{\left|r_{RHCP-y}\right|^{2}+\left|r_{ LHCP-y}\right|^{2}}\text{ and }PER=20\log\left(\frac{\left|r_{RHCP-y}\right|}{\left|r_{LHCP-y}\right|}\right) \tag{8}\]  
Figure 5a illustrates the calculated reflection coefficients for the left-and right-handed circularly polarized (\(r_{LHCP-y}\) and \(r_{RHCP-y}\)) waves in dB. The magnitude of \(r_{RHCP-y}\) in the frequency range 10-40 GHz is below \(-3\) dB (approximately equal to 0 dB), and \(r_{LHCP-y}\)  
\[r_{yx}=\frac{E_{yr}}{E_{xi}}\text{ and }r_{xy}=\frac{E_{xr}}{E_{yi}} \tag{2}\]0 The calculated results for the proposed meta-atom using a \(y\)-polarized wave: (**a**) phase difference; (**b**) axial ratio (dB) (blue dashed line represents 3 dB line); (**c**) elliptical angle with elliptical ratio; (**d**) ideal characteristics \(\left(R=|r_{xy}|/|r_{yy}|\right)\). in the multiband is below \(-20\) dB, respectively. It indicates that the reflected wave at the \(y\)-polarized incidence wave has entirely converted into multiple bands. The additional components of the polarization reflection coefficient include the polarization conversion ratio (_PCR_) and the polarization extinction ratio (_PER_). The _PER_ is an approach that shows potential for evaluating LP-CP conversion. The proposed metasurface performs perfect LP-CP conversion at five frequencies, namely 12.46, 16.88-23.11, 26.1-32.44, 34.97-38.49, and 39.75 GHz, respectively. The acquired PCR, shown in Figure 5b, also demonstrates that the PCR can be sustained at greater than 95.0% in the abovementioned frequency bands, covering the 3 dB AR band, indicating that the proposed converter has significantly greater efficiency. The polarization conversion performance of an LP-CP converter is also expressed through the PER, which defines the ratio of a wave's RHCP component to its LHCP component. Figure 5 illustrates that RHCP is obtained over three frequency bands, 16.88-23.11, 26.1-32.44, and 34.97-38.49 GHz, since the PER magnitude is greater than +20 dB. Moreover, the LHCP mode is achieved by covering two frequencies, 12.46 and 39.76 GHz, because the PER value is smaller than \(-20\) dB, as illustrated in Figure 5b. So, the highly efficient metasurface converts the incident LP wave into a CP wave at different frequencies.
# The Quanta Image Sensor: Every Photon Counts
## 4 Read Noise and Counting Error Rates  
### Read Noise and Readout Signal Probability  
Counting photon or photoelectrons requires deep sub-electron read noise (DSERN), that is, read noise less than 0.50 e\(-\) r.m.s. It has been suggested that 0.30 e\(-\) r.m.s. read noise is sufficient for many photon-counting applications [47; 56; 57] however accurate counting with low error rate under low exposures (e.g., \(H\) < 0.2) requires read noise less than 0.15 e\(-\) r.m.s. [21].  
When both read noise and conversion gain variation is considered for an ensemble of jets that are read out, the probability distribution of readout voltages is given by:  
\[P\left[U\right]=\sum_{k=0}^{\infty}\frac{\mathbb{P}[k]}{\sqrt{2\pi\sigma_{k} ^{2}}}exp\left[-\frac{\left(U-k\right)^{2}}{2\sigma_{k}^{2}}\right] \tag{9}\]  
where \(U\) is the readout signal normalized by mean conversion gain (in electron number), \(u_{n}\) is the read noise (in e\(-\) r.m.s.), and where \(\sigma_{k}\) is given by:  
\[\sigma_{k}\triangleq\sqrt{u_{n}^{2}+\left(k\sigma_{CG}/\overline{CG}\right)^ {2}} \tag{10}\]  
and \(\sigma_{CG}\) is the standard deviation of conversion gain in the ensemble, and \(\overline{CG}\) is the mean conversion gain in the ensemble.  
An example of the distributions arising from different levels of read noise is shown in Figure 7 Electron number quantization is seen for read noise in the deep sub-electron range. Using a fine resolution ADC, plots of frequency of occurrence vs. readout voltage can be made for experimental jet devices under illumination and are called photon-counting histograms (PCH). The ratio of valley amplitude to peak amplitude, called valley-peak modulation (VPM) can be used to experimentally determine read noise from the PCH, the peak spacing can be used to determine conversion gain, and the relative peak heights can be used to determine exposure [22].
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation  
In this section measurements are presented to analyze the sensitivity of the PCPA to two practically important error sources. The first error source is reflections: the beacon signals may be reflected from various surfaces near the PCPA, thus not only the line of sight signals, but other disturbing components will be measured, causing error. The second possible error source is the tilting of the sensor: in practical scenarios the sensor's alignment may not be perfect, thus the not homogenous vertical sensitivity of the applied PDs may cause error.  
In the subsequent sections, first the reference measurement is introduced. The impact of the error sources will be analyzed on  
* raw measurements, i.e., the measured light intensities (Section 4.2),
* bearing estimations (Section 4.3),
* and finally, localization results in some illustrative scenarios (Section 4.4).  
The generic test setup can be seen on Figure 7. The center piece is the PCPA itself, which was placed on a turntable. With this turntable the measurement device could be accurately rotated around the vertical axis. Ideally the PCPA's normal vector is vertical, but to investigate the effect of tilting, the PCPA may be mounted on the turntable with various tilting angles, denoted by \(\alpha\) in Figure 7. The turntable allowed the measurement of the radiant intensity characteristics automatically with a resolution of \(1^{\circ}\).  
Figure 6: Photo of the PCPA deployed on the turntable, used in the automatic measurements.  
Figure 7: The measurement setup with the light sources, PCPA, and reflective surfaces.  
The beacons were deployed on one side of the PCPA, around direction zero, as shown in Figure 7. On the other side of the PCPA (in direction 180\({}^{\circ}\)) various reflective surfaces were placed at different distances from the PCPA. The reflective materials, including white paper, black paper, aluminum foil, and mirror, were placed on \(80\times 80\) cm wooden surfaces.  
It is important to note, that the measurements were carried out in a 5 m \(\times\) 4 m office, which was furnished and had several large white areas on the walls. Thus some of the measurements may include the influence of the reflections of the environment.
## 2 Materials and Methods
### Adaptation and Marginal Integrity Assessment  
The crown adaptation and marginal misfit were assessed in micrometer (\(\upmu\)m). Internal adaptation and marginal integrity of interim crowns were analyzed on the master die with Bruker micro CT (Skyscan 1173 high-energy spiral scan micro-CT; Skyscan NV, Kontich, Belgium) to detect microgaps at selected points. Microgaps were detected at buccal, mesial, distal, and lingual locations between the tooth preparation for the adaptation and CEJ points at the margin of 4 different surfaces. Each sample was mounted and positioned inside the specimen chamber with the following scanning parameters: 130 kV of X-ray source energy, 60 \(\upmu\)A of source current, and 300 ms of exposure time using a brass 0.25 mm filter with 0.2\({}^{\circ}\) rotation step for a 360\({}^{\circ}\) angle; the amount of frame averaging was 4 to create a good image of the structure. The specimen-tooth complex was placed in a fabricated mold to keep the position constant for optimum outcomes. The average time for a scan was 1.5 h. A 3D reconstruction of images was performed after scanning using N-Recon@software (program version 1.6.1.3, Bruker Skyscan, Kontich, Belgium). Parameters were adjusted to enhance image quality, as follows: post-alignment compensation, 6% of artifact reduction, 25% of hardening correction, and smoothing of 2 with a Gaussian kernel. A slice thickness of 14 \(\upmu\)m with 10,890 slices was used to ensure that the specimen would be included. A 16 bit tagged image file format was selected for saving the images.  
Images were loaded into Dataviewer@software (Bruker Skyscan, Kontich, Belgium) to determine image quality, resizing, correct positioning, and visual inspection. Each scanned image of a sample was opened in the software, and measurements were taken at standardized location points. The measurements were preformed between the surface of the tooth preparation and the inner surface of the interim crown.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Infinite Repeated Game in the Unlicensed Spectrum
#### 3.4.2 Collusion between Two LTE Operators  
So far, the payoffs are ordered by \(U_{s}>U_{sh}>U_{f}>U_{w}\), and these inequalities generally hold when we observe the behavior of operators. Now we check whether the game with two operators in the unlicensed spectrum satisfies \(U_{sh}>(U_{s}+U_{w})/2\) or not. As long as the inequality holds, it prevents operators from choosing the strategy of _alternating cooperation and defection_ which results in greater utility than mutual cooperation in the repeated game. Unfortunately, if we regard payoffs of operators as their channel capacity, the inequality does not always hold. For two operators, the intuition is that \(U_{w}\) is simply zero but \(U_{sh}\) cannot even satisfy \(U_{sh}=U_{s}/2\), theoretically and experimentally. This is because sharing the unlicensed spectrum always causes medium access control (MAC) overhead and high collision probability due to CCA such as LBT schemes and CSMA/CA, unless a unified central scheduler can coordinate multi-RAT networks in every synchronous time slot. Moreover, as the number of WiFi networks increases in the co-channel of the unlicensed spectrum, LTE operators can notice that their portion of actual transmission time substantially decreases.  
The dense deployment of WiFi networks makes it possible for a few LTE operators to collude with each other under adverse circumstances as in Figure 4. As an example of the worst case, if two operators have \(U_{sh}\) that is almost equivalent to \(U_{f}\) due to highly busy channels, then \(U_{sh}\ll\ (U_{s}+U_{w})/2\) is enough to steer them towards disharmonious and corrupt behavior, e.g., in a country without LBT requirements, LTE operators are highly susceptible to collusion to exploit scarce spectrum resourcesby deceiving other networks into believing that channels are extremely busy. In a similar way to the always cooperation strategy, when \(U_{sh}<(U_{s}+U_{w})/2\) holds, the condition of \(\delta\) for collusion between two LTE operators can also be derived by using the grim trigger strategy.
## 4 Numerical Results
### Simulation Settings
#### 4.1.2 Reinforcement Learning Parameters  
The reinforcement learning parameters are mainly used to configure the training process, including reward function weights and training step limits. The specific parameter settings are shown in Table 4.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline System Scale and Deployment & & & \\ Number of UAVs & \(|U|\) & 5 & - \\ Number of Vehicles & \(|N|\) & 50 & - \\ Area Size & - & \(1000\times 1000\) & m\({}^{2}\) \\ UAV Height Range & \([H_{min},H_{max}]\) & \([50,200]\) & m \\ UAV Minimum Safe Distance & \(d_{min}\) & 10 & m \\ \hline Resource Configuration & & & \\ UAV Cache Size & \(Q_{u}\) & \(1\times 10^{9}\) & Bytes (1 GB) \\ Vehicle Computation Capacity & \(f_{n}\) & \(5\times 10^{9}\) & Cycles/s (5 G cycles/s) \\ UAV Computation Capacity & \(f_{u}\) & \(15\times 10^{9}\) & Cycles/s (15 G cycles/s) \\ UAV Core Limit & \(Core_{UAV}\) & 4 & - \\ \hline Communication Model & & & \\ Bandwidth & \(B_{n,u}\) & \(20\times 10^{6}\) & Hz (20 MHz) \\ Vehicle Transmission Power & \(P_{n}\) & 0.5 & W \\ Noise Power & \(\sigma^{2}\) & \(1\times 10^{-13}\) & W \\ Path Loss Exponent & \(\zeta\) & 2.7 & - \\ Reference Path Gain at 1 m & \(\eta_{0}\) & 1.0 & - \\ Communication Rate Threshold & \(R_{th}\) & \(1\times 10^{6}\) & bps (1 Mbps) \\ Backhaul Capacity & \(R_{b}\) & \(1\times 10^{8}\) & bps (100 Mbps) \\ \hline Tasks and Content & & & \\ Number of Service Types & \(|D|\) & 5 & - \\ Number of Content Types & \(|K|\) & 5 & - \\ Service Size & \(s_{d}\) & \(1\times 10^{6}\) & Bytes (1 MB) \\ Content Size & \(s_{k}\) & \(1\times 10^{6}\) & Bytes (1 MB) \\ Computation Cycles per Task & \(c_{n}\) & \(1\times 10^{9}\) & Cycles (1 G cycles) \\ Task Request Probability & - & 0.3 & - \\ \hline Energy Consumption Model & & & \\ Compute Efficiency Coefficient & \(\beta_{u}\) & \(1\times 10^{-27}\) & J/(Cycles/s)\({}^{3}\cdot\) s \\ Hovering Power Parameter & \(P_{0}\) & 79.86 & W \\ Flight Power Parameter & \(P_{1}\) & 88.63 & W \\ Blade Tip Speed & \(U_{r}\) & 120.0 & m/s \\ Average Rotor Induced Velocity & \(v_{r}\) & 4.03 & m/s \\ Fuselage Drag Ratio & \(d_{0}\) & 0.6 & - \\ Air Density & \(\rho\) & 1.225 & kg/m\({}^{3}\) \\ Rotor Solidity & \(s\) & 0.05 & - \\ Rotor Disc Area & \(A\) & 0.503 & m\({}^{2}\) \\ \hline Simulation Duration & & & \\ Time Slot & \(\tau\) & 0.1 & s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environment parameter settings.
## 4 Numerical Analysis
### Axial Force Analysis of CWR Fractures Using FEA
#### 4.3.2. CWR Axial Force Analysis Results  
The displacement of the welded joint caused by rail axial force was analyzed using temperature influence ranges derived from the CFD analysis, as shown in Figure 12.  
Figure 11: Numerical analysis of load conditions.  
Figure 12: Displacement analysis of the welded joint attributed to the rail axial force: (**a**) left-side displacement of the welded joint; (**b**) right-side displacement of the welded joint.  
The numerical analysis results are shown in Figure 13. The fracture was simulated by considering relative displacement differences, and a clearance of ~0.08 mm was observed, indicating that the rail had fractured.  
As illustrated in Figure 14, the numerical analysis results considering the rail axial force caused by temperature variations showed that a rail displacement of ~0.05 mm occurred at the welded joint when an axial force of about 535 kN was applied.  
The welded rails are highly sensitive to temperature variations. Increased axial force in the rail can lead to fractures if temperature management is inadequate near ventilation shafts. Therefore, proper temperature management at tunnel ventilation shafts is crucial for the maintenance and safety of CVR [10].
## 4 Results and Discussion
### Dataset and Evaluation Metrics  
We validated the effectiveness of the proposed model on the STL-10 dataset [45]. The STL-10 dataset contains 10 different classes of images, with 500 images per class, totaling 5000 labeled training images and 8000 unlabeled testing images. These images have a resolution of 96 \(\times\) 96 pixels. During training, we combined and trained the STL-10 dataset's train and test split datasets together. GDUI and its corresponding baselines are quantitatively evaluated for sample quality using the following metrics. We used the Frechet Inception Distance (FID) [46] as the primary metric to measure the overall sample quality, including both diversity and fidelity, because it is currently the de facto standard metric for evaluating generative models. FID combines the statistical feature differences between the samples generated by the image generation model and the distribution of real images. To ensure more accurate FID calculation, we sampled all 13,000 images from the STL-10 dataset as samples of real images. To ensure a fair comparison, the distribution of real images for calculating all the metrics was based on the 13,000 images from the STL-10 dataset. In addition, we also used sFID [47], another version of FID that uses spatial features instead of pooled features to capture spatial relationships, as a standard metric for evaluating image quality. For sample fidelity, we used Inception Score (IS) [48] as a supplementary metric, which evaluates the quality of generated images by utilizing a pre-trained Inception-V3 [49] network on ImageNet [50]. The Improved Precision and Recall [51] metrics can separately evaluate the fidelity and diversity of generated samples, where Precision measures the ratio of model samples falling into the data manifold, while Recall measures the ratio of data samples falling into the sample manifold.