esa-sceva/satcom-chunk-collection huggingface dataset:
# Incoherent Digital Holography: A Review
## 4 Special Topics and Applications
### Coded Aperture Correlation Holography (COACH)  
As a method advancing from FINCH, a remarkable single-path incoherent DH has been recently proposed, called coded aperture correlation holography (COACH) [80]. An optical setup initially proposed for COACH is the same as that of FINCH, as illustrated in Figure 4. In COACH, instead of a quadratic phase distribution, a coded phase-modulation (CPM) distribution is displayed on SLM. Before recording a hologram of an object, multiple phase-shifted holograms of a point object such as a pinhole are recorded. CPM generates a random complex amplitude distribution, and an object wave on the image sensor plane is the PSF of a COACH system. The PSF does not change against the position in the in-plane direction but changes against that in the depth direction. The PSFs of a CPM in each depth are measured in advance to obtain the information of the PSFs in 3D space. After that, incoherent light of an object in 3D space is recorded as multiple phase-shifted holograms, and correlations between PSFs and recorded images are calculated. Cross-correlations are used to obtain a 3D image, because each point in an object cannot be refocused by diffraction integrals, as a result of the random phase distribution of CPM. As a result, a 3D image of incoherent light is reconstructed without calculating  
Figure 8: Single-shot in-line phase-shifting digital holography (DH) with Fresnel incoherent correlation holography (FINCH). (**a**) Schematic of a constructed optical setup. (**b**) Transitions of the states of polarizations.  
Figure 9: Experimental results. (**a**) Recorded hologram; (**b**) magnified image of a part of (**a**) four phase-shifted holograms, which are generated from (**a**), with (**c**) 0, (**d**) \(\pi/2\), (**e**) \(\pi\), and (**f**) \(3\pi/2\) phase shifts; (**g**) amplitude, and (**h**) phase images on the image sensor plane, which are extracted by phase-shifting interferometry; (**i**) reconstructed image of a 1 mm aperture object illuminated by incoherent light.  
diffraction integrals. By the adequate design of CPM and calculations of the cross-correlations, the depth resolution is improved in comparison to FINCH. To improve the depth resolution, one should make the spatial frequency distribution of CPM uniform. With COACH, incoherent 3D imaging [80] and incoherent color 3D imaging with a diffractive lens [81] have been demonstrated. It is worth noting that a diffractive lens is not needed to separate wavelength information by using a holographic technique [67; 82]. Moreover, a stack of the PSFs is obtained without interference of incoherent light, and interferenceless incoherent 3D imaging for a scattering object has been performed [83]. In a most recent development, single-shot COACH with space-division multiplexing [84] has been proposed and demonstrated.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning
### Support Vector Machine (SVM)  
It is a family of supervised algorithms applied to a broad range of tasks, such as regression or classification, where it is usually called Support Vector Classification (SVC). In the case of binary classification, SVC defines a hyperplane, so that the distance to the closest point of each cluster (support-vectors) to such hyperplane is maximized. This methodology can be extended to multi-level classification following the strategy \"one-against-one\", where various classifiers are trained for each pair of levels. This way, for \(n\) clusters, we would need \(n\times\frac{n-1}{2}\) classifiers, each trained in a binary fashion. This algorithm has been used for comparison purposes to know how far the unsupervised learning solutions performances are from the supervised version (benchmark). In all cases, the SVC implementations have been trained with the 15% of the samples, while the remaining 85% have been used for evaluation.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 1 Introduction
### Related Work
#### 1.2.1 Thermal-Based Attacks and Countermeasures  
The related work contains attacks which are constructed based on the different types of measuring the thermal dissipation of the CPU. The attacks are measured from hardware or software, and they use side channels or covert channels. RSA private key was recovered in [10], using a fault side-channel attack which risen the temperature at a very high rate on AVR and PIC microcontrollers. Covert-channels were investigated in the context of IoT in [11]; however, they did not conduct any further experiment regarding the thermal sensors. Correlation Power Analysis (CPA) and Deep Learning (DL) were adapted for heat dissipation in [12], where the private key for the RSA algorithm was fully recovered; however, this kind of study is a hardware-based one, and physical access to the targeted device is needed, a procedure which is out of our scope. In [5], two versions of covert channel attacks were implemented on a multi-core computing system, reaching a peak data rate of 12.5 bps. The attackers utilized the _coretemp_ filesystem in Linux to monitor the temperature of one core, which is affected by the temperature of an adjacent one. By doing so, they achieved a high correspondence between the temperatures of the cores while executing the RSA algorithm. In [13; 14], the authors try to improve the error rate of the covert channel in [5] by solving the thermal interference barrier. However, the covert channel limitation was defeated in ThermalBleed [1], which constructed the first software-based thermal side-channel attack from Linux userspace. It leverages Intel's digital thermal sensors on each CPU core to measure temperature under various conditions. Using this method, hits and misses in cache together with TLB address translations were identified, allowing for the breaching of the KASLR (Kernel Address Space Layout Randomization). Another side-channel attack on mobile devices was created in [15], wherein a CNN which was based on computer vision was employed to foretell the passwords of different mobile devices. Thermal traces were used while the passwords were encrypted using an AES algorithm and then they were converted into images. By inputting the resulting images into the CNN network, they were able to predict the passwords. Other techniques test the predisposition of GPUs to leak information via temperature, power or frequency, by mounting a hybrid side-channel attack [16].
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 1 Introduction
In some conditions, the instability of the separating shear layer cannot be captured by the models, even though resolved turbulence is described at the entrance because the models typically switch back to RANS mode after some boundary layer thicknesses. Nowadays, a new hybrid RANS-LES model is introduced, which is called the Embedded Large Eddy Simulation (ELES) approach. The ELES model has solved the above problem through the predefinition of interfaces in the geometric model. By this method, the ELES model can not only reduce the computational cost but can also obtain good predictive accuracy of the solution by distinguishing RANS and LES regions and creating synthetic turbulence at RANS-LES interfaces that have been predefined [22]. According to the literature [19], DDES, SAS, and ELES were compared, and it was revealed that better temperature predictions and stronger resolved turbulence activity were found by using the ELES approach than by using the two other hybrid models. In order to fully understand the flow characteristics and fatigue mechanism in tee pipes, the non-isothermal mixing flow characteristics were investigated using the ELES method with commercial computational Fluid Dynamics (CFD) software ANSYS FLUENT 17.0 in this paper. Firstly, three typical flow patterns of thermal mixing in tee pipes were simulated using the ELES method, and the results were compared with the experimental data in reference [5] to prove the validity of the ELES model. Secondly, the flow characteristics of the backflow upstream with different momentum ratios were studied by carrying out 12 groups of simulations using the ELES method, and some general rules about that were revealed. Thirdly, the temperature characteristics near the wall where the backflow appears were studied. It revealed that some regions were more prone to thermal fatigue at the intersection of the tee upstream under specified momentum ratios due to the mixing induced by the backflow. Moreover, in order to evaluate the thermal fatigue, a frequency analysis of the fluid temperature fluctuation at specified points near the wall of three typical flow patterns was conducted, and the results showed some long-period fluctuations of low frequencies upstream, which may be related to the characteristics of the backflow. The results of this paper can be helpful for the understanding of the thermal fatigue mechanism caused by the backflow, as well as the optimization and improvement of upstream thermal fatigue in tee pipes.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 3 Particle Filter
, \tag{11}\]  
where the three-dimensional position \(\mathbf{r}_{rx}\) and three-dimensional velocity \(\mathbf{v}_{rx}\) relate to the Earth Centered Earth Fixed (ECEF) reference frame. The time vector \(\mathbf{t}_{rx}\) contains the GNSS receiver clock error \(t_{rxClkErr}\) and clock drift \(t_{rxClkDrift}\). The replica signal code phase \(\tau_{n}\) and Doppler \(\omega_{n}\) for a single signal of satellite \(n\) are related to \(\mathbf{z}_{k}\) using  
\[ \begin{split}\text{geom. distance}\\ \tau_{n}=\frac{\overline{\left|\left|\mathbf{r}_{sat,n}-\mathbf{ r}_{rx}\right|\right|}}{c}+t_{rxClkErr}+T_{n}+I_{n},\end{split}  \tag{12}\] \[\omega_{n}=-(\underbrace{\mathbf{v}_{sat,n}-\mathbf{v}_{rx})^{T} }_{\text{rel. velocity}}\underbrace{\overline{\left|\mathbf{r}_{sat,n}-\mathbf{ r}_{rx}\right|}}_{\text{unit vector }\mathbf{u}_{i}}\frac{f_{c}}{c}+t_{rxClkDrift}f_{c}, \tag{13}\]  
where \(\mathbf{r}_{sat,n}\) and \(\mathbf{v}_{sat,n}\) correspond to the position and velocity of satellite \(n\), \(c\) to the speed of light, \(T_{n}\) to the tropospheric delay, \(I_{n}\) to the satellite and frequency dependent ionospheric delay and \(f_{c}\) to the carrier frequency. These relationships assume that the receiver position is known at epoch \(k\) and thus the ephemeris of the satellite \(n\) for the generated replica signal needs to be available. In a simplified simulated setup without ionosphere, troposphere and other errors, the terms \(I_{n}\) and \(T_{n}\) may reduce to zero and can be neglected. The simulations which are performed within this work do not consider any geometric relationship, troposphere and ionosphere for analysis, but the real-world examples fully consider the geometric relationship as well as the ionospheric and tropospheric terms. Basically the amplitude \(A\) and carrier phase \(\phi\) can also be estimated from the particle filter, but this further increases the dimensionality of the estimation problem form currently 8 to 10 dimensions. To avoid a further increase in dimensionality and thus computational complexity and be able to take the synthetically generated multi-correlation values which only depend on code-phase \(\tau\) and Doppler \(\omega\), the proposed approach is to make the probability function independent of the signal amplitude \(A\) and carrier phase \(\phi\), which is discussed in the next chapter.
## 3 Results and Discussion
### Classification of Pepper Using Principal Component Analysis  
Further, for a visual inspection based on the fruit color, the assessment of the maturity level may be carried out in automated mode, e.g., if an automated sorting was envisaged. Before a quantitative measure of such assessment is established, a qualitative procedure using multivariate data analysis should be presented. To this purpose, principal component analysis (PCA) was chosen, cf. Figure 5.  
Figure 4: Raman band intensities at 1500 cm\({}^{-1}\) and their variation as measured at five spots of a single fully ripe pepper, cf. top picture and marked positions, using the mobile (blue) and benchtop (orange) Raman spectrometers.  
The scores plot visualizes the 1119 samples labeled according to their maturity category, cf. Table 3. For PCA, preprocessed Raman spectra, i.e., derivative spectra, served as the input matrix. From the representation of the first and second principal components, it can be seen that immature (green) and fully ripe fruit (red) are well separated due to the three major signals, cf. Figure 3. In particular, fully ripe fruits and, to some extent, very immature fruits appear centered on the first component. In contrast, immature and harvestable fruits are spread along the PC1 axis. However, ripening and harvestable fruits (yellow squares and orange circles) are clearly distributed between the early growth and the mature stage. This finding can be interpreted in that ripening fruits still have a higher chlorophyll content and thus are more similar to immature fruits, cf. Figures 1 and 5. From the loading vectors in Figure 5, it can be recognized that the carotenoid level exercises the biggest influence on the spectral and thus ripeness distinction. Chlorophyll and different caroten species are reflected by higher loadings. Determination of the optimum harvest time hence proved difficult when using qualitative PCA. An alternative method might be sought in a univariate method which relies exclusively on the three characteristic carotenoid signals and leads to a quantitative statement on the ripeness of the fruit.  
Figure 5: PCA of 1119 spectra from 122 hot pepper samples at different ripeness levels. Top: Scores plot representing the maturity categories fully immature (green spades), immature (yellow squares), harvestable (orange circles) and mature (red triangles). Middle: Averaged preprocessed derivative spectra of immature (green), harvestable (orange) and mature (red) fruits. Bottom: Loading vectors of PC1 (blue, 75.2%) and PC2 (orange, 6.1%).
## 3 Results and Discussion  
### Pretreatment and Enzymatic Hydrolysis of Wood  
Beech wood material contained 44% \(\pm\) 2 glucose, 17% \(\pm\) 1 xylose and 20% \(\pm\) 1 Klason lignin as it was determined in our previous report, where wood particles were subjected to a diluted acid (3%) treatment (H2 SO4) [13]. However, solids collected after the diluted acid treatment were enzymatically hydrolys with low (<5%) saccharification rate, and concentration of produced glucose was insufficient as a feedstock for microalgae cultures (data not shown). Therefore, alkaline pretreatment, instead of diluted acid pretreatment, was applied before enzymatic hydrolysis. Alkaline pretreatment with the use of NaOH was reported as an effective method to remove hemicellulose and Klason lignin from birch [17], cedar [18] and eucalyptrus [18,19] wood, resulting in the increase in cellulose exposure and improvement of cellulose conversion to glucose during enzymatic hydrolysis [18,19]. Temperature of pretreatment and NaOH concentration are crucial parameters determining removal rate of hemicellulose and lignin from wood, and subsequent cellulose hydrolysis. For example, increase in NaOH concentrations from 1% to 8% improved release of hemicellulose and lignin from birch wood [17]. In another study, increase in temperature (from 25 \({}^{\circ}\)C to 100 \({}^{\circ}\)C) at 5 N [20%] NaOH, considerably improved the saccharification rate of cedar and eucalyptus [18]. Therefore, in our study, higher NaOH concentration (7%), at higher temperature (100 \({}^{\circ}\)C), was applied during pretreatment to facilitate hemicellulose and lignin removal, and to enzymatically produce glucose in the amount sufficient for glucose-based microalgal cultivation in a bioreactor. Hydrolysis of beech wood particles with the use of 7% NaOH, for 1 h at 100 \({}^{\circ}\)C caused 57% xylose removal and 11% removal of Klason lignin with no effect on glucose. Alkaline pretreated beech wood contained 63% glucose, 10% xylose and 24% Klason lignin (data not shown). Pretreated wood material was enzymatically hydrolyzed over 72 h to release approximately 10 g/L glucose, which corresponded to an approximately 21% saccharification rate (Figure 1).
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 8 Simulation Results
### Simultaneous Relative Pose Measurement Assessment
#### 8.2.1 Scenario 1--Effect of Other Vehicle Measurements on Single-Vehicle Localization
To demonstrate the importance of cross-covariances, we repeated the simulations in case 5 but without maintaining the cross-covariance updates as presented in [14]. The error norm statistics in the 100 simulations for vehicle 1 and all vehicles are listed as the last rows of Tables 4 and 5. It is clear that our method performs better for the overall network and the localization of individual vehicles. In addition, we plotted the time history of the error norms \(\Delta r\) and \(\Delta q\) for this method in Figures 7 and 8, respectively. It is observed that while initially the method in [14] performs better, the errors grow at much faster rate. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Position**} & \multicolumn{4}{c}{**Orientation**} \\ \cline{2-10}  & \(\mathbf{e_{r_{mean}}}\) & \(\mathbf{e_{r_{rad}}}\) & \(\mathbf{e_{r_{min}}}\) & \(\mathbf{e_{r_{max}}}\) & \(\mathbf{e_{q_{mean}}}\) & \(\mathbf{e_{q_{std}}}\) & \(\mathbf{e_{q_{min}}}\) & \(\mathbf{e_{q_{max}}}\) \\ \hline Propagate & 2.87 & 0.01 & 2.84 & 2.91 & 0.05 & 0.00 & 0.05 & 0.05 \\ Case 1 & 1.99 & 0.82 & 0.70 & 4.48 & 0.05 & 0.01 & 0.02 & 0.09 \\ Case 2 & 1.48 & 0.85 & 0.38 & 4.56 & 0.04 & 0.02 & 0.01 & 0.09 \\ Case 3 & 1.50 & 0.76 & 0.39 & 4.34 & 0.03 & 0.01 & 0.01 & 0.08 \\ Case 4 & 1.33 & 0.69 & 0.39 & 4.34 & 0.03 & 0.01 & 0.01 & 0.07 \\ Case 5 & 1.23 & 0.58 & 0.24 & 3.03 & 0.03 & 0.01 & 0.01 & 0.05 \\ Case 5 [14] & 1.27 & 0.01 & 1.16 & 1.19 & 0.04 & 0.00 & 0.03 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Position (m) and quaternion error statistics of _vehicle 1_ across 100 simulations for Scenario 1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Position**} & \multicolumn{4}{c}{**Orientation**} \\ \cline{2-10}  & \(\mathbf{e_{r_{mean}}}\) & \(\mathbf{e_{r_{rad}}}\) & \(\mathbf{e_{r_{min}}}\) & \(\mathbf{e_{r_{max}}}\) & \(\mathbf{e_{q_{mean}}}\) & \(\mathbf{e_{q_{std}}}\) & \(\mathbf{e_{q_{min}}}\) & \(\mathbf{e_{q_{max}}}\) \\ \hline Propagate & 14.97 & 0.02 & 14.91 & 15.03 & 0.43 & 0.00 & 0.43 & 0.44 \\ Case 1 & 13.30 & 1.61 & 10.80 & 18.37 & 0.42 & 0.03 & 0.37 & 0.49 \\ Case 2 & 10.58 & 2.58 & 7.16 & 19.59 & 0.36 & 0.04 & 0.28 & 0.50 \\ Case 3 & 9.33 & 2.77 & 5.40 & 19.83 & 0.30 & 0.05 & 0.21 & 0.44 \\ Case 4 & 7.82 & 2.91 & 3.50 & 20.95 & 0.23 & 0.05 & 0.13 & 0.40 \\ Case 5 & 6.15 & 2.59 & 1.67 & 14.13 & 0.15 & 0.06 & 0.05 & 0.33 \\ Case 5 [14] & 8.20 & 0.05 & 8.07 & 8.32 & 0.21 & 0.00 & 0.20 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Position (m) and quaternion error statistics across 100 simulations of _all vehicles_ for Scenario 1.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Methods and Parameters Description  
Our experiment measured various parameters for the different geological formations, including PCA (principal component analysis), geological and environmental parameters, ratio indices, and OLI and OR spectrometric parameters. These parameters were selected to influence the detection of geological features. Our experiments focused on the in-depth analysis of each parameter, evaluating their relevance and contribution to detection accuracy. After conducting these experiments, we conducted a rigorous calibration to identify and maintain the best parameters. This optimization process allowed us to refine our approach and guarantee better efficiency in detecting geological formations.  
We then merged the different selected parameters to evaluate their combined impact on the detection performance. This step was motivated by the idea that combining multiple data sources can capture complementary information, thereby increasing the model's accuracy. By merging parameters, we sought to maximize the representation of relevant geological features and improve the model's robustness to data variations. This approach was validated through experimental testing to observe whether fusion significantly improved detection results.  
The algorithms employed in this study include Gradient Boosting, Random Forest, and XGBoost, each selected for its ability to handle complex data and provide robust prediction models. Gradient Boosting improves accuracy by combining several weak models into a higher-performing model. The Random Forest, for its part, is a set of decision trees that reduces overlearning and improves the generalization of results. XGBoost, an optimized  
Figure 5: The overarching structure of our approach.  
version of Gradient Boosting, was chosen for its speed of execution and its ability to process large data sets efficiently.  
The cross-validation technique was applied to ensure better learning quality and validate the results. This method divides the data into subsets, where each subgroup trains and validates the model. This guarantees a better evaluation of the model's performance on all the data and reduces the risks of overfitting, thus contributing to the reliability of the results obtained within the study's framework.  
The parameters used are data from geographic parameters (DGPs), data from the ratio index parameters (DRIPs), data from PCA parameters (DPCAPs), data from OLI spectrometric parameters (DOLIPs), and data from OR spectrometric parameters (DORSPs).
## 4 Discussion of Test Results
### Energy Dissipation and Equivalent Viscous Damping
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Ascending Stage**} & \multicolumn{3}{c}{**Max State**} & \multicolumn{3}{c}{**Descending Stage**} & \multirow{2}{*}{\multirow{2}{*}{\(\xi_{eq}\)} **(\%)**} \\ \cline{2-3} \cline{5-10}  & \(\triangle\) & **DR** & \(E_{s,c}\) & \(\triangle\) & **DR** & \(E_{s,m}\) & \(\triangle\) & **DR** & \(E_{s,u}\) & \(\triangle\) & **DR** & \(E_{s,u}\) & \(\xi_{eq}\) \\  & **(mm)** & **(\%)** & **(kN/mm)** & **(mm)** & **(\%)** & **(kN/mm)** & **(mm)** & **(\%)** & **(kN/mm)** & **(\%)** & **(kN/mm)** & **(\%)** \\ \hline BMF & 4.0 & 0.22 & 171.6 & 16.0 & 0.89 & 1841.9 & 28.0 & 1.55 & 3381.5 & 10.8 \\ BMFP & 4.0 & 0.22 & 126.6 & 16.0 & 0.89 & 2070.2 & 28.0 & 1.55 & 4024.94 & 10.0 \\ BMFT & 2.5 & 0.14 & 93.9 & 10.0 & 0.56 & 1320.4 & 22.5 & 1.25 & 3133.15 & 8.23 \\ BMFTP & 2.5 & 0.14 & 142.7 & 10.0 & 0.56 & 1402.4 & 22.5 & 1.25 & 2832.45 & 8.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dissipated energy and equivalent viscous damping of all specimens.