esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.2.1 Review of Selected PLA Technique-based Radio Frequency Fingerprinting  
Radio frequency fingerprinting is an effective and direct authentication technique that can enable ubiquitous computing to minimize authentication latency. However, the primary mission of authentication is to identify which device sent the unknown signal. To address this, **(S6)** used a one-against-one method that extended the SVM classifier to calculate the statistical features of many signals sent from one device. Furthermore, they utilised the Hilbert transform for feature extraction, which is considered the best of many straightforward methods to extract features from the original amplitude signal. The findings showed that using only the amplitude feature without any transforms, the classification accuracy was less than 40%, but the accuracy can reach 94% when using the Hilbert transform. In this regard, S5 proposed a radio frequency fingerprint extraction method based on fractional Fourier transform (FrFT) to distinguish different wireless network devices. To recover the useful low-rank matrix in the data and eliminate the sparse matrix of noise under certain conditions, they used robust principle component analysis (RPCA). RPCA is considered an excellent dimensionality reduction method that can achieve this goal. The proposed method results showed that when the SNR is 20 dB, the recognition rate was close to 100%. In **(S11)**, the authors presented a radio ID verification-based IoT security approach using RF distinct native attribute (RF-DNA) fingerprints. The following are the eight feature selection techniques that were investigated in this study: dimensional reduction analysis (DRA), linear discriminant analysis (LDA), principal component analysis (PCA), neighbourhood component analysis (NCA), probability of error plus average correlation coefficient (POEACC), Bhattacharyya coefficient (BC), \(t\)-test, and relief-F. Selecting a model that is effectively adapted not only to verify the authorized radio's ID but also to reject rogue radios masquerading as the authorized radio without having access to the rogue radios' RFF during model building is an issue in ID verification. Setting a user/administrator-defined threshold is not required for ID verification and rogue radio rejection. Threshold-based
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 3 Methods and Validation  
The optical transmission performance in this study is primarily assessed based on the flowchart depicted in Figure 5 below. Initially, a model similar to THAAD is created and meshed, followed by precise calculation of its flow field. Once the fine outflow field is obtained, the aero-optical program accesses the grid refractive index matrix of the study area. By inputting the coordinates of the initial incident point and incident angle, the principal program of geometric optics determines whether or not the incident point falls within the calculation area and calculates its corresponding grid number based on its coordinates. Interpolation is then used to calculate refractive indices (\(n_{0}\) and \(n_{1}\)) at both incident light and refracted light locations within their respective grids, while applying refraction law to determine if light reaches the surface of an optical window by crossing into another grid boundary. If it fails to reach said surface, the tracing process for the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & \multicolumn{3}{c}{**Flow Field Parameter**} & \multicolumn{3}{c}{**Blowing**} & \multicolumn{3}{c}{**MVGs**} & **Suction** \\ \hline  & \(Ma_{\infty}\) & \(P\) (Pa) & \(T\) (K) & \(P_{j}\) (kPa) & \(m_{0}\) (kg/s) & \(\theta\) & & \\ \hline
3 & 5 & 5529 & 216 & 19 & 0.05 & 15\({}^{\circ}\) & without & without \\
4 & 5 & 5529 & 216 & 19 & 0.05 & 90\({}^{\circ}\) & without & without \\
5 & 5 & 1197 & 227 & 4.1 & 0.05 & 15\({}^{\circ}\) & without & without \\
6 & 5 & 1197 & 227 & 4.1 & 0.05 & 90\({}^{\circ}\) & without & without \\
7 & 5 & 5529 & 216 & 19 & 0.01 & 15\({}^{\circ}\) & without & without \\
8 & 5 & 1197 & 227 & 4.1 & 0.01 & 15\({}^{\circ}\) & without & without \\
9 & 5 & 1197 & 227 & 4.1 & 0.01 & 90\({}^{\circ}\) & without & without \\
10 & 3 & 5529 & 216 & - & 0 & - & with & without \\
11 & 3 & 5529 & 216 & 11 & 0.05 & 15\({}^{\circ}\) & with & without \\
12 & 5 & 5529 & 216 & - & 0 & - & with & without \\
13 & 5 & 5529 & 216 & 19 & 0.05 & 15\({}^{\circ}\) & with & without \\
14 & 3 & 5529 & 216 & 11 & 0.05 & 15\({}^{\circ}\) & without & with \\
15 & 3 & 5529 & 216 & 11 & 0.05 & 15\({}^{\circ}\) & with & with \\
16 & 5 & 5529 & 216 & 19 & 0.05 & 15\({}^{\circ}\) & without & with \\
17 & 5 & 5529 & 216 & 19 & 0.05 & 15\({}^{\circ}\) & with & with \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._aforementioned light will be repeated accordingly. Finally, once it does reach said surface, the tracing process concludes with outputting the evaluation parameters pertaining to aero-optical effects. A detailed introduction to the evaluation parameters for aero-optical effect assessment, as well as the flow field calculation method are provided below.
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulations Results
#### 5.2.1 Cache Hit Ratio
This situation changes in a more complex network as shown in Figure 3(b). Increasing network traffic affects the CHR. Limitation in terms of the available hardware restrained the simulation of all scenarios with a higher request rate. One scenario (i.e., Topology 1, with 100% cache nodes) was selected and simulated with a request rate of 1000 packets/s. Figure 5 shows the result of the aforementioned simulation, which is similar to the result achieved earlier and presented in Figures 1(b) and 3. Figure 4: Sensibility of policies with variable number of CS, NDN testbed with content request rate of 100 packets/s. Figure 3: Cache hit ratio evolution with variable number of CS, for scenarios with 32 consumers and 10 producers. The results presented in this section are particularly inconsistent with those achieved in [5], where the network performance in terms of CHR increases when the number of CS increases up to about 40% and then decreases with the number of CS above this threshold. However, the results are consistent with ones achieved in [6], where the CHR increases with the continuous increasing in the number of CS, or in [7; 8; 9; 10; 11; 12; 13; 14; 50] which evaluate the network performance with a constant number of CS but different sizes.
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 4 Results and Discussions
### Stereo Matching
#### 4.1.2 KITTI Dataset
Pixels with significant errors are mainly located at occluded regions as they have no matching pixels in the other view. Comparing the error maps of these two kinds of features, the average end-point error of pixels in occluded areas of handcrafted features is smaller than that of deep ones. The reason is that the matching costs of handcrafted features are less distinctive than those of deep features since it is easier to propagate reliable information from non-occlusion regions to occluded areas. Table 6 presents the results of different approaches using the handcrafted feature to compute the matching cost on the KITTI 2015 dataset [26]. Our method achieves the best results in most metrics. Compared with FCGF, the average end-point errors of the initial disparity images in all and non-occluded regions are decreased by 0.63 px and 0.66 px, and the error rates in these regions are reduced by about 4.3% and 4.4%, respectively. Although the error rates of GF are lower than ours after refinement, we achieve the lowest average end-point error among these methods. It means that the end-point error of most outliers of our approach is smaller than that of GF. Table 7 lists the results of typical methods on the KITTI 2015 dataset using deep features extracted by PSMNet [24] to compute the matching cost. Our approach surpasses all other methods in all metrics. Comparing our statistics with those of other non-local cost aggregation methods on both KITTI 2012 [25] and KITTI 2015 [26], our method achieves the best performance. Thus, we can conclude \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Initial Disparity**} & \multicolumn{4}{c}{**Final Disparity**} \\ \cline{2-10}  & **Out\_a** & **Avg\_a** & **Out\_n** & **Avg\_n** & **Out\_a** & **Avg\_a** & **Out\_n** & **Avg\_n** \\ \hline GF [13] & **11.61** & **3.32** & **9.33** & **1.99** & **9.30** & 1.80 & 9.33 & **1.59** \\ FCGF [18] & 17.76 & 4.34 & 15.67 & 3.02 & 13.33 & 2.21 & 12.02 & 1.94 \\ NL [16] & 13.87 & 3.80 & 11.67 & 2.45 & 11.57 & 1.97 & 10.26 & 1.75 \\ ST [17] & 15.66 & 3.86 & 13.50 & 2.53 & 13.44 & 2.15 & 12.16 & 1.93 \\ GAM [32] & / & / & / & / & 18.63 & 3.20 & 17.22 & 2.80 \\ ADS\_RS [33] & / & / & / & / & 10.05 & 2.70 & 8.71 & 2.10 \\ HASR [1] & / & / & / & / & 10.33 & 2.90 & 8.15 & 1.90 \\ Ours & 12.17 & 3.41 & 9.91 & 2.02 & 10.64 & **1.78** & **9.29** & 1.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparison of typical cost aggregation approaches using handcrafted features to compute matching cost on the KITTI 2012 dataset [25]. Avg\_n and Avg\_a are the end-point errors in non-occluded and all regions, respectively (/px). “/”: the results are not available. The bold is the best result in each metric.
## 4 Methodology and Setup
### Radio Calibration  
The PDR is evaluated with respect to SIR between the transmit power of the data and the interferer signals. According to the AT86 RF215 radio transceiver datasheet (specifically Figure 11-2), there is a 6 dB offset between the value of the transmit power configuration register (RFn_PAX.TYPWR) and the actual transmit power of the radio transceiver depending on the selected modulation (OQPSK-DSSS and SUN-OFDM).  
Such difference in transmit power between different modulations can have an impact on the obtained results. For instance, an uncontrolled higher transmit power of the data signal results in a better PDR result for that particular modulation. Similarly, an uncontrolled lower transmit power of the interferer signal also results in a better PDR for that particular modulation. To validate the offset and obtain the configuration register values that ensure the same transmit power for both modulations, we calibrate using a Rigol DSA-815 spectrum analyzer as the Test Equipment (TE), and an OpenMote-B as the Device Under Test (DUT).  
The DUT is connected to TE using an RF cable and the radio calibration procedure is performed as follows. For each modulation, the DUT is configured to transmit with a known power configuration register and the TE is configured to measure the power in the band taking into consideration the parameters of the modulation under test (i.e., center frequency, occupied bandwidth and span). The procedure is repeated for all the values of the power configuration register (from 0 to 30 with  
Figure 4: OpenMote-B board. The CC2538 (MCU) and the AT86 RF215 (radio transceiver) are interconnected using the SPI bus, whereas the CC2538 (MCU) is connected to the computer using the USB-UART bridge (FTDI FT2232).  
a 3-unit step) to obtain the relation between those values and the actual transmit powers that are generated by the radio transceiver, as shown in Table 3.  
Overall, results are in accordance with the data provided in the datasheet and allow for empirically determining the transmit power configuration register values for the OQPSK-DSSS and the SUN-OFDM modulations. In particular, we have decided to use a transmit power between \(-9\) dBm and \(+9\) dBm, as these are the values that are common to both modulations. We then define the default transmit power of \(+3\) dBm, and let the interferer power range from \(-9\) dBm to \(+9\) dBm, corresponding to the SIR between \(-12\) dB and \(+6\) dB described earlier.
## 2 Models for Voltage Distributions and Measurement Data  
In this section, we discuss some basic properties of flash memories and introduce the notation. Furthermore, we consider a model to estimate the threshold voltage distributions. This curve fitting approach is used to determine the optimal read voltages.  
Flash memory consists of transistors which have an insulated floating gate below the control gate. The information is stored as electric charges in the floating gate. The charge levels affect the threshold voltage of the transistor. The cell can be charged by applying a high program voltage to the control gate and erased by applying a high erase voltage to the substrate. The information can be read out by applying a reference threshold to differentiate erased and charged cells. The cells are organized in arrays of blocks and pages, where the control gates of the cells inside a page are connected using the so-called word line. Multiple pages constitute a block by connecting the drain of one cell to the source of the next cell. These connections form the bit lines.
# Current Research on the Effects of Non-Digestible Carbohydrates on Metabolic Disease
## 5 NDCs and MDs
### Diabetes
\begin{tabular}{c c c c c c c} \hline \hline
**Types** & **Model** & \
\begin{tabular}{c} **Dosage** \\ **(g/day or \%)** \\ \end{tabular} &
\begin{tabular}{c} **Duration** \\ **(weeks)** \\ \end{tabular} & **Related Disease** & **Physiological Effects** & **References** \\ \hline
**Polydextrose** & Human (men) & 12 & 15–75 min & Ob &
\begin{tabular}{c} \(\downarrow\) Energy intake in low protein group but not \\ high protein group \\ \end{tabular} & [117] \\ _Insoluble dietary fiber_ & & & & & \\
**Cellulose** & Rat & 10\% & 24 & G/M & \(\downarrow\) TG & [118] \\ \hline _Soluble/Insoluble dietary fiber_ & & & & & \\
**Mixed plant cell wall fibers (corn** & & & & & \\
**starchimeilloides)** & Human & 10 g/day & 48 & T2D &
\begin{tabular}{c} Improve insulin release, peripheral insulin \\ sensitivity, and blood glucose control \\ \end{tabular} & [106] \\ \hline _Non-idietary fiber NDCs_ & & & & & \\
**Resistance starch III** & Mice & 23\% & 4 & T2D &
\begin{tabular}{c} Improve disjudge and lipids profile (TC, TG, \\ LDL, MD) \\ \end{tabular} & [119] \\ \hline
**Resistance starch IV** & Human & 30\%/53.7\% & 12/6 & CVD/T2D &
\begin{tabular}{c} Improve dyslipidemia and body composition. \\ \(\downarrow\) HbAIc, improve glycaemic control and \\ cardiovascular risk without altering lipid \\ \end{tabular} & [84,85] \\ \hline
**Maltool oligosaccharides****(MOS)** & Mice & 6 g/kg & 11 & Ob/DB &
\begin{tabular}{c} \(\downarrow\) BW gain, adipose size, serum TC, TG, and \\ insulin resistance \\ \end{tabular} & [62] \\ \hline
**Chitosan**** oligosaccharides****(COS)** & Mice & 200 mg/kg & 21 & MD &
\begin{tabular}{c} \(\downarrow\) BG, TG, Hopplyosaccharides, and \\ adipose inflammation \\ \end{tabular} & [63] \\ \hline
**Bovine-milk oligosaccharides****(BMO)** & Mice & 6\% & 6 & Ob & \
\begin{tabular}{c} \(\uparrow\) glucose tolerance, insulin secretion and \\ HPLC-C \\ permeability, total fat, mass, and adipocyte \\ cell size \\ \end{tabular} & [64] \\ \hline \hline \end{tabular} 
\begin{tabular}{c} AIP, atherogenic index of plasma; ANOS, astrobis Darwin oligosaccharides; BC, blood glucose; BW, body weight; RRI, body mass index, CVD, cardiovascular disease; DB, diabetes; DM, methyl-self-attention; GC,M, substance metabolism; GC, genetic inhibitor polypeptide; COS, galactol
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 1 Introduction
* Studying a new optimization algorithm, which is the Heap-based optimizer (HBO) technique, while make a comparison with a three recent types of optimization methods namely, Franklin's and Coulomb's algorithm (CFA), the Sooty Tern Optimization Algorithm (STOA), and Grey Wolf Optimizer (GWO). * The study includes exploiting the capabilities of the proposed algorithms to optimize and minimize COE with increasing the reliability and efficiency of the suggested hybrid systems, and performs different sensitivity analyses on an optimal design to predict the upcoming system implementation. The suggested work is structured as follows: Section 2 explains the modeling of the suggested system units. Section 3 discusses the description of the studied area. Section 4 discusses the formulation of the optimization problem. Section 5 discusses a brief explanation of the optimization methodology of HBO, CFA, GWO, and STOA. Section 6 presents the results of the optimal sizing for the stand-alone hybrid power system. Finally, the conclusions are provided in Section 7.
## 7 Conclusions  
The paper presented the results of research on the application of a new research method for improving the performance of computing aircraft coordinates from GPS and GLONASS solutions for the PPP measurement technique. The paper addressed the problem of improving GPS and GLONASS positioning accuracy in the PPP method by developing a new computational algorithm based on a linear combination of individual GNSS solutions, i.e., GPS and GLONASS. The algorithm used a weighted mean model between the XYZ  
Figure 18: Position errors along the \(Z\)-axis.  
geocentric coordinates from the GPS and GLONASS. The mathematical relationship was based on the determination of six linear coefficients \((a,b,c,d,e,f)\), which linked the XYZ position coordinate readings together. The whole computational scheme was solved by the weighted mean model, taking into account different measurement weights. The measurement weights were a function of the number of GPS and GLONASS satellites and, also, as the inverse of the square of the mean error. Moreover, the whole algorithm was tested on GPS and GLONASS kinematic data from an airborne experiment carried out with a Seneca Piper PA34-200T aircraft at the Mielec airport. A dual-frequency dual-system GPS/GLONASS receiver was placed on-board the aircraft, which made it possible to record the GNSS observations, which were then used to calculate the aircraft's position in CSRS-PPP software. The calculated XYZ position coordinates from the CSRS-PPP software were used in the developed optimization algorithm and implemented in the Scilab programming language. The accuracy of the presented research method was checked and verified with the results obtained from the RTK-OTF solution. Moreover, the proposed solution's accuracy was better by 11-87% for the model with the weighting scheme as a function of the inverse of the square of the mean error. The performance of the algorithm was also tested for results from another navigation software, i.e., RTKLIB. In this case, the algorithm's accuracy for the improvement of PPP positioning was better by 45-82% for the model with the weighting scheme as a function of the inverse of the square of the mean error. The presented method's effectiveness was relatively high and could be used to improve the performance of GPS and GLONASS in PPP positioning method for air navigation.  
Conceptualization, K.K.; methodology, K.K. and D.W.; validation, K.K.; formal analysis, K.K.; investigation, K.K. and D.W.; resources, K.K.; writing--original draft preparation, K.K. and D.W.; writing--review and editing, D.W.; visualization, K.K.; supervision, D.W.; project administration, D.W. and funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Military University of Technology.  
Not applicable.  
The authors declare no conflict of interest.
# Second Harmonic Revisited: An Analytic Quantum Approach
## 3 Discussion  
We investigated the second-harmonic generation process starting from the usual perturbative approach, but aiming to achieve some analytic results. In particular, we focused on the statistics of the output field, and helped by the comparison with the well-known classical regime, we retrieved the autocorrelation functions of the SH field for different input states, which are, as far as we know, of experimental interest. In fact, among the nonlinear parametric processes, the SHG is widely explored also in view of its multiple applications, which range from the production of laser systems with multiple wavelengths to the matching of the operation spectral range of photodetectors [21]. Moreover, nonclassical states of light are an essential resource for quantum technologies. Thus, investigating the way in which quantum optical states can be obtained through the SHG process represents a fundamental step towards their tailoring and exploitation. For instance, only a quantum approach predicts the generation of a sub-Poissonian state starting from a coherent one, a fact that is also experimentally observable.  
In addition, we tried to reach a deeper understanding of the SHG process by approaching the quantum problem with different strategies and looked for the physical meaning of the perturbative terms in the expansion of the moments. In particular, we found that, at least in some cases, the conversion probability can be written in a closed form. Albeit that these latter results are not amenable for a direct experimental test, we think that they could be useful for further analytic predictions. On the one hand, an analytic formula up to a generic order \(k\) for the terms \(\alpha_{k}\) and \(\beta_{k}\) in Equation (37), specified in Equation (38) for the first three orders, would allow us to retrieve a general expression for the moments of the SH statistics, and on the other hand, further expansions of the output SH state will lead to computing the conversion probability in more complicated cases. These two issues are both non-trivial since they require a larger number of perturbative orders, which is a computationally hard task.  
The authors contributed to the work in the following ways: conceptualization, G.C., M.M.W., N.F., A.A., and M.B.; validation, M.B.; formal analysis, G.C. and M.B.; investigation, G.C., M.M.W., and N.F.; writing, original draft preparation, G.C.; writing, review and editing, G.C., A.A., and M.B. supervision, A.A. and M.B.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
SHG Second-harmonic generation  
SH Second-harmonic