esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## 2 Transmittance and Security Analysis
### Transmittance Analysis
With this ellipse hypothesis, the transmittance can be approximated as  
\[T=T_{0}\text{exp}\Big{\{}-\Big{[}\frac{r/a}{R(\frac{2}{W_{eff}(\Phi-\Phi_{0})} )}\Big{]}^{\lambda[2/W_{eff}(\Phi-\Phi_{0})]}\Big{\}}, \tag{4}\]  
where \(a\) denotes the aperture radius, \(W_{eff}(\blacksquare)\) represents the effective point radius, \(T_{0}\) denotes the transmission coefficient maximum for the centered beam, and \(R(\blacksquare)\) and \(\lambda(\blacksquare)\) are scale and shape functions, respectively. The above parameters will be explained in detail in \"Appendix A\". On the basic of the above equations, the probability distribution of atmospheric transmittance can be calculated by the Monte Carlo method. It is evident that the transmittance \(T\) is at rest with five parameters \(\{x_{0},y_{0},\Theta_{1},\Theta_{2},\varphi\}\), where \(W_{i}^{2}=W_{0}^{2}\text{exp}\Theta_{i}\). Parameter \(\varphi\) is uniformly distributed and irrelevant to the other parameters. The vector \(\mathbf{v}=(x_{0},y_{0},\Theta_{1},\Theta_{2})^{T}\) is a Gaussian random vector. The above-mentioned parameters are elaborated in \"Appendix A\". The density distribution of the horizontal link transmittance is simulated by the Monte Carlo method, which is shown in Figure 2. It is obvious that the distance and refraction index structure parameter \(C_{n}^{2}\) affect the transmittance. With the increase of distance and \(C_{n}^{2}\), the transmittance declines. The average value of the PDT could be represented by the simulated transmission value [25]  
\[\langle f(T)\rangle\approx\frac{1}{N}\sum_{i=1}^{N}f(\chi_{\text{ext}}T( \mathbf{v}_{i},\varphi_{  
where \(T(v_{i},\varphi_{i})\) is acquired from Equation (4), and the absorption and scattering losses describe with extinction factor \(\chi_{\text{ext}}\in[0,1]\) which is a stochastic variable [25]. Hence, the fading transmittance's average value has access to acquire  
\[\langle T\rangle = \chi_{\text{ext}}\frac{1}{N}\sum_{i=1}^{N}T(\mathbf{v}_{i},\varphi _{i}), \tag{6}\] \[\langle\sqrt{T}\rangle = \sqrt{\chi_{\text{ext}}}\frac{1}{N}\sum_{i=1}^{N}\sqrt{T(\mathbf{ v}_{i},\varphi_{i})}. \tag{7}\]
## 5 Discussion
### Single-Number Quality, \(L^{\prime}{}_{i,Fmax}\) of Bang Machine and Rubber Ball
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{3}{c}{**Bang Machine**} & \multicolumn{3}{c}{**Rubber Ball**} \\ \hline \multicolumn{3}{c}{**1/1 Octave Band**} & \multicolumn{3}{c}{**1/3 Octave Band**} & \multicolumn{3}{c}{**1/1 Octave Band**} & \multicolumn{3}{c}{**1/3 Octave Band**} \\ \hline \multirow{2}{*}{**Division**} & **Mean Contri-** & \multirow{2}{*}{**Division**} & **Mean Contri-** & \multirow{2}{*}{**Division**} & **Mean Contri-** & \multirow{2}{*}{**Division**} & **Mean Contri-** \\ \cline{3-3} \cline{5-6}  & **bution Rate** & & & & & 50 Hz & 20.0\% \\ \hline \multirow{4}{*}{63 Hz} & \multirow{4}{*}{78.4\%} & 63 Hz & 32.9\% & \multirow{4}{*}{63 Hz} & 63 Hz & 63 Hz & 12.8\% \\ \cline{3-3} \cline{5-6}  & & 80 Hz & 8.9\% & & & 80 Hz & 9.0\% \\ \cline{3-3} \cline{5-6}  & & Sum & 75.6\% & & & Sum & 41.8\% \\ \hline \multirow{4}{*}{125 Hz} & \multirow{4}{*}{15.4\%} & 100 Hz & 9.0\% & \multirow{4}{*}{125 Hz} & 100 Hz & 13.9\% \\ \cline{3-3} \cline{5-6}  & & 125 Hz & 2.6\% & & & 125 Hz & 5.6\% \\ \cline{3-3} \cline{5-6}  & & 160 Hz & 4.8\% & & & 160 Hz & 16.8\% \\ \cline{3-3} \cline{5-6}  & & Sum & 16.4\% & & & Sum & 36.3\% \\ \hline \multirow{4}{*}{250 Hz} & \multirow{4}{*}{4.3\%} & 200 Hz & 2.1\% & \multirow{4}{*}{250 Hz} & 200 Hz & 8.9\% \\ \cline{3-3} \cline{5-6}  & & 250 Hz & 1.7\% & & & 250 Hz & 4.2\% \\ \cline{3-3} \cline{5-6}  & & 315 Hz & 1.6\% & & & 315 Hz & 4.5\% \\ \cline{3-3} \cline{5-6}  & & Sum & 5.4\% & & & Sum & 17.6\% \\ \hline \multirow{4}{*}{500 Hz} & \multirow{4}{*}{1.9\%} & 400 Hz & 0.9\% & \multirow{4}{*}{500 Hz} & 400 Hz & 2.4\% \\ \cline{3-3} \cline{5-6}  & & 500 Hz & 0.9\% & & & 500 Hz & 1.2\% \\ \cline{3-3} \cline{5-6}  & & 630 Hz & 0.8\% & & & 630 Hz & 0.7\% \\ \cline{3-3} \cline{5-6}  & & Sum & 2.6\% & & Sum & 4.3\% \\ \hline \end{tabular}
\end{table} 
Table 7: Contribution rate on the difference in 1/1 and 1/3 octave bands of \(L^{\prime}{}_{iA,Fmax}\) during the bang machine and rubber ball measurement. effect compared to the previous evaluation method. Additionally, previous research has shown that the conventional \(L^{\prime}_{i,Fmax}\), AW evaluation method did not account for the reduction effects in the 250 Hz to 500 Hz range, which were often perceived subjectively. The current evaluation method appears to take the 250 Hz band into consideration, unlike the evaluation method used before the post-certification system. Due to the introduction of the post-certification system, the method for evaluating heavy-weight impact sound has changed, making it difficult to simply compare performance using a single number quantity. However, it is considered an important area for future research to continuously monitor the performance of multi-family housing constructed before and after the system change, and to assess both the objective and subjective effects of the system change on performance.
## 1 Introduction
In the frame of the FP7 European funded research project \"RESearch on COrenoise ReDuction\" (RECORD), the research HPT installed in the high-speed close-loop test rig of the Politecnico di Milano was operated in four different conditions, ranging from an almost incompressible flow regime to transonic conditions, by imposing the same mean (absolute) flow direction at the stage exit. This latter constraint matches with relevant technical requirements of the whole system, with the aim to feed the intermediate turbine duct and the intermediate/low pressure turbine as close as possible to their design condition. Some studies discuss the time-mean effects of expansion ratio on the aerodynamics of linear cascades [26] or those of rotational speed in turbines operated in low subsonic conditions [27]; however, the impact of the expansion ratio on the unsteady aerodynamics of a full turbine stage, and in particular on the stator-rotor interaction, is still to be investigated. The present paper aims at filling this gap by comparing different operating conditions with the same stage geometry featured by different flow regimes, and considering both time-averaged and unsteady flow features. Moreover, as the change in expansion ratio has not only \"direct\" implications on the Reynolds and Mach number level, but has also \"indirect\" impact on the rotor aerodynamic loading, the experimental data-base was enriched by a set of tests on the stage operated with different rotational speed at the same expansion ratio. This further set of data, which highlights the impact of the change in aerodynamic loading only, are instrumental for properly decoupling the direct from the indirect effects of expansion ratio. The paper is organized as follows. First, a description of the test rig layout and of the instrumentation is provided. Second, the variation in the stator flow field with the flow regime is analyzed by means of pitch-wise averaged profiles of the flow quantities and by maps on the secondary plane. Then, the rotor-exit flow field and the vane-rotor interaction effects are discussed for different conditions. Finally, a discussion on the impact of the operating conditions on the flow released by the stage, on the contribution of the different effects and on the stage performance concludes the paper.
## 1 Introduction
BO in dc biased lattices is due to wave interference, and have been observed in a number of quite different physical systems: a few interacting atoms in optical lattices [23; 24], ultracold atoms [25; 26; 27; 28; 29; 30], light intensity oscillations in waveguide arrays [31; 32; 33; 34; 35; 36; 37], acoustic waves in layered and elastic structures [38], atomic oscillations in Bose-Einstein condensates [39], among others. Several recent studies have investigated the dynamics of cold atoms in optical lattices subject to ac forcing; the theoretically predicted renormalization of the tunneling amplitudes has been verified experimentally. Recent observations include global motion of the atom cloud, such as giant \"super-Bloch oscillations\" [40]. As a result, BO was transformed from a partial physical effect, to the universal phenomenon of oscillatory motion of wave packets placed in a periodic potential, when driven by a constant force [24; 41]. Recently, there have been several theoretical studies of the ultrastrong light-matter interaction regime [42; 43; 44; 45], where the Rabi frequency becomes comparable with the frequency of the inter-zone quantum transition [43]. This regime has been realized experimentally with intersubband transitions coupled with plasmon waveguides in the infrared frequency range [46], and metallic microcavities in the THz [47; 48; 49], magnetoplasmons of two-dimensional electron gas [50], superconducting qubits [51], and molecular transitions [52]. It was shown that the system behaves like a multi-level quantum bit with non-monochromatic energy spacing, owing to the inter-particle interactions. One of the general tendencies in modern physics consists of the synthesis of different physical mechanisms in the network of one physical process, with their strong mutual influence. One of such examples have been demonstrated in [53], where the quantum chain of the coupled two-level fermion systems driven simultaneously by ac and dc fields was considered. It was shown that in the case of resonant interaction with an ac-component, the particle dynamics exhibits itself in the oscillatory regime, which may be interpreted as a combination of RO and BO, with their strong mutual influence. This type of quantum dynamics was named in [53] as Rabi-Bloch oscillations (RBO). Such a scenario dramatically differs from the individual picture of both types of oscillations due to the interactions. This novel effect is counterintuitive because of the strongly different frequency ranges for two such types of oscillation existences. In this paper, we consider the RBO in the regimes of strong and ultrastrong coupling. We present the approximate analytical solution, and results of the numerical modelling. We study both the temporal dynamics and spectra, identify the spectral lines, and compare the spectra behavior in the strong and ultrastrong regimes. Conclusively, we discuss the promising applications of our results in the novel types of spectroscopy.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC-AES Hybrid PKE Protocol  
The hybrid protocol leverages the strengths of both post-quantum key exchange (using a KEM like CRYSTALS-Kyber) and a well-established symmetric cipher (AES-256). This defense-in-depth approach enhances overall security.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification
### General Instruction for User-Defined Parameters  
The initial and unavoidable step before the implementation of SSI is to select the user-defined parameters. These parameters can be roughly categorized into three parts: the size of the Hankel matrix, the estimation of system order, and the criterion of assured modes. Fortunately, researchers have carried out comprehensive studies and provided valuable suggestions in the past few years [23; 41].
## 2 Materials and Methods
### Description of MOD17A2H Algorithm  
The MOD17A2H algorithm is based on light-use efficiency (LUE) approach [49; 50], which provides global GPP estimates of 8 day temporal and 500 m spatial resolution [15]. The MODIS GPP product is calculated from the following equation:  
\[\text{GPP}=\epsilon_{\text{max}}\times 0.45\times\text{SW}_{\text{rad}}\times \text{FPAR}\times\text{f}(\text{T}_{\text{min}})\times\text{f}(\text{VPD}) \tag{1}\]  
where \(\epsilon_{\text{max}}\) is the maximum LUE obtained from the Biome-specified Parameters Look Up Table (BPLUT) on the basis of vegetation type. The BPLUT contains values specifying minimum temperature and VPD limits, specific leaf area and respiration coefficients for the standard land cover classes [48]. \(\text{SW}_{\text{rad}}\) is shortwave solar radiation of which 45% is photosynthetically active radiation (PAR), FPAR is the fraction of PAR absorbed by vegetation and the scale factors f(Tmin) and f(VPD) reduce \(\epsilon_{\text{max}}\) under unfavorable conditions of low temperature and high VPD. The forcing data such as \(\text{SW}_{\text{rad}}\), Tmin and VPD in the MOD17A2H GPP product were implemented by the Global Modeling and Assimilation Office (GMAO) Reanalysis data. The MODIS GPP algorithm is described in detail in previous literature [12; 15; 18].
## 3 Model Evaluation and Implementation  
### Walk-Forward Validation  
Cross-validation is the most commonly used ML method for comparing algorithms [42]. Nonetheless, in scenarios where the order of data is essential, like time series, this approach is inappropriate [43]. In this instance, the walk-forward validation approach, noted in [44], is implemented, which is especially beneficial for time-series models.  
In this approach, the training set size is fixed but shifts one observation forward in each iteration, thus preserving the temporal sequence. This method enables the evaluation of the model's capacity to forecast outcomes using data that adhere to a natural time sequence. In this study, 75% of the data were used for training and 15% for validation. Further details are provided in Section 4. Figure 3 displays the data split utilized in this validation process.  
Figure 3: Walk-forward validation schema.  
Figure 2: The same stacking architecture is trained separately to predict the maximum and minimum prices, resulting in two distinct models.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Earthquakes  
Seismic phenomena are a rich area of research and ubiquitous at substantial scales throughout the terrestrial body, in particular together with the propagation and coupling of seismic waves with the environment. We investigate four large earthquakes with moment magnitudes M \(\geq\) 6.5 accessible with CSES and _Swarm_ magnetic field data in a time period a few days around the event in order to get pre- and post-background behaviour and at best foreshock signatures, see Table 2. For common investigations and statistical analysis,  
Figure 2: Distribution of _Swarm_ A/B/C (**top/centre/bottom** panel) VFM magnetic field fluctuations (box plots) after wavelet transform grouped in individual frequency bands for the August 2014 volcanic event, BarBarbunga, Iceland. The numbers of pre- or post-crossings for _Swarm_ A/B/C are [3-4/2-2/2-3]. The colour coding [pre/post] is [blue/red] with gradually desaturation [blueish/reddish] for larger \(\Delta\)t (interval between crossing and event).  
according to the United States Geological Survey (USGS) database, 59 earthquakes occurred in the period 1 March 2018 to 31 July 2019.
## 2 Materials and Methods
### Common Details and Training
As neglecting bone, soft-tissue and fat are the main issues when synthesizing a pseudo-CT [38], we computed the results in two regions of interest: (i) the whole anatomy of the subject (including all tissues) and (ii) only the bone. To this end, a mask for soft tissue (between \(-100\) and \(100\) HU), fat (lower than \(-100\) HU) and bone (greater than 100 HU) was obtained by thresholding the Houndsfield Units (HU) in the ground truth CT. The measures calculated to compare the performance of the networks and schemes are the following:  
* Mean Average Error (_MAE_): \[MAE=\frac{\sum_{i=1}^{n}|y_{i}-x_{i}|}{n}\] (1)
* Peak Signal-to-Noise ratio (_PSNR_): \[PSNR=20\cdot\log_{10}\frac{MAX}{\sqrt{MSE}}\] (2) \[MSE=\frac{\sum_{i=1}^{n}(y_{i}-x_{i})^{2}}{n}\] (3)
* Pearson Correlation Coefficient: \[PearsonCoef=\frac{\sum_{i=1}^{n}(x_{i}-m_{x})\cdot(y_{i}-m_{y})}{\sum_{i=1}^{n }(x_{i}-m_{x})^{2}\cdot\sum_{i=1}^{n}(y_{i}-m_{y})^{2}}\] (4)  
In Equations (1)-(4), \(y\) represents the ground truth CT voxel value and \(x\) the generated pseudo-CT voxel value. In Equation (2) the max value depicts the range of possible values for the measured signal. In our case the HU units range goes from \(-1024\) to \(3072\), therefore the max range value is \(4096\). In the Pearson correlation coefficient, \(m_{y}\) and \(m_{x}\) represent the mean of the voxel values in the ground truth CT and in the pseudo-CT, respectively. Additionally, to validate the statistical differences among the architectures and schemes, we decided to perform various statistical test over the cross-validation results. To decide which test would be the most appropriate, we tested if the results of the cross-validation tended to be Gaussian or not using a Shapiro test and a D'Agostino's test. The results tended to be normal, thus we decided to perform several ANOVA test and Student's \(t\)-test for paired data with a statistically significant difference defined as \(p<0.05\) to verify whether certain network or scheme results were significantly different or not.
# Hamming Code Strategy for Medical Image Sharing
## 3 Hamming Codes
### Discussions
\begin{array}{c}15\\ 1\end{array} \right)+\left( \begin{array}{c}15\\ 2\end{array} \right)+\left( \begin{array}{c}15\\ 3\end{array} \right)=576\) ways to determine the positions) for (15,5) BCH code. For these two cases, we need 7 (\(\cdot\)\(\cdot\)\(\cdot\)\(27\geq 121\)) bits and 10 bits (\(\cdot\)\(\cdot\)\(210\geq 576\)) to determine the positions to flip the bits. There are two weakness for such a process: one is complex, and the other is that the probability of choosing the way for flipping bit is unequal because \(\left( \begin{array}{c}15\\ 0\end{array} \right)+\left( \begin{array}{c}15\\ 1\end{array} \right)+\left( \begin{array}{c}15\\ 2\end{array} \right)\
eq 2^{7}\) and \(\left( \begin{array}{c}15\\ 0\end{array} \right)+\left( \begin{array}{c}15\\ 1\end{array} \right)+\left( \begin{array}{c}15\\ 2\end{array} \right)+\left( \begin{array}{c}15\\ 3\end{array} \right)\
eq 2^{10}\). However, Hamming codes are perfect codes (i.e., \(\left( \begin{array}{c}7\\ 0\end{array} \right)+\left( \begin{array}{c}7\\ 1\end{array} \right)=2^{3}\) for (7, 4) Hamming code and for (15,11) Hamming code. Therefore, our approaches based on (7,4) and \(\left( \begin{array}{c}15\\ 0\end{array} \right)+\left( \begin{array}{c}15\\ 1\end{array} \right)=2^{4}\) for (15,11) Hamming codes have the same probability of choosing the way for flipping bit. Why not just use a scrambling algorithm to get a less distorted shadow in our scheme:  
Our goal is not just to let the shadow image look much different from the original one. The proposed scheme is a user-friendly (2,2) secret image sharing scheme. So, our scheme not only makes shadow different from the original one (i.e., providing user-friendly features) but also recovers the original medical image via two shadows (i.e., providing the threshold property of a (2,2) secret sharing scheme). However, any scrambling or related algorithm cannot achieve the same goal. There are many approaches to scramble bits in pixels. It should be carefully designed to achieve a threshold (2,2) scheme; rather than any scrambling tool can be easily applied on a (2,2) user-friendly secret sharing scheme. A well-known implementation of the secret sharing scheme is polynomial-based secret sharing. But the polynomial-based approach is more complex than our Hamming-code-based method. Because our scheme is a (2,2) secret scheme, one may adopt a bit-wise XOR operation. Careful design is required to achieve the threshold property of (2,2) scheme as well as to retain blurred versions of medical images on \(S_{1}\) and \(S_{2}\).
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## Inventory  
### Selection Criteria  
We have surveyed and assessed the ADCs among the catalog of the leading suppliers to the space market--Analog Devices, Atmel, Cobham Gaisler, ST Microelectronics, Teledyne e2V, Texas Instruments, etc.--and among the more relevant publications about radiation effects on components--2010-2019 NSREC/REDW's (and previous publications considered relevant) and all AMICSA's and RADECS' proceedings --. Candidates with degradation of electrical performance below 50 krad(Si) of TID or with a \(\text{LET}_{th}\) for latch-up below 50 MeV\(\cdot\) cm\({}^{2}/\text{mg}\) were discarded. It is important to remark that neither all qualified ADCs are compliant with these two requirements nor, just because an ADC is not qualified, it is non-compliant. In fact, several EMs (engineering models) and COTS were included in the assessment.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 2 Experimental Methodology
### Experimental Set Up and Procedure  
The uniaxial and triaxial loading tests were carried out on the prepared shale samples by using the TAW-2000 rock triaxial testing machine, as shown in Figure 3a, and the samples were fully dried before the test. In the test, the radial strain of the samples was collected by a chain extensometer, while the axial strain was collected by axial extensometers matched to the triaxial testing machine, which were set at the middle height position of the samples. In addition, a DS-5 acoustic emission (AE) instrument, as shown in Figure 3b, was used in the uniaxial test to record the AE signals during the uniaxial loading, and the AE recording probes were installed on the surface of the samples, as shown in Figure 3c. It is worth noting that due to the limitation of the space inside the confining pressure loading cylinder in the triaxial test, as shown in Figure 3d, the AE signals of the samples were not collected during the triaxial test.  
The confining pressures \(\sigma_{3}\) set in uniaxial and triaxial compression tests were 0, 10, 20, 30, and 40 MPa, respectively. During the experiment, the axial displacement was used to control the static loading rate of 0.1 mm/min, and the loading was terminated in the following two cases: the first case was that the bearing capacity of the samples dropped rapidly, causing the oil cylinder to fall back, and the second case was that the axial strain reached 104 \(\mu\)e. In the triaxial compression test, the axial compression pressure and confining pressure were applied to the design value at the rate of 0.05 MPa/s synchronously, and then axial displacement control loading was carried out. During the loading process, the stress and strain data of the samples were recorded simultaneously by the data acquisition system, and each group of loading conditions contained three shale samples. In the test, 15 shale samples of each bedding angle were prepared, and the IDs of shale samples with 5 bedding angles under different confining pressure \(\sigma_{3}\) are shown in Table 1.  
Figure 3: Instruments used in the experiment: (**a**) TAW-2000 rock triaxial testing machine; (**b**) DS-5 AE instrument; (**c**) AE recording probes of the samples; (**d**) Confining pressure loading cylinder.