esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Quantitative Analysis of the Effect of Various Factors
### SVR-DSWA Parameterization Setting
The prediction model is trained with the optimal parameters, the model is used to predict the output values for the (n-N) test samples, and the model's output values are obtained. Step 5: The predicted results are then analyzed. The predicted output data are denormalized, and it is compared with the experimental data. The MRE, mean absolute error (MAE), mean square error (MSE), RMSE, and squared correlation coefficient (R\({}^{2}\)) between the predicted values and the experimental data are calculated. the predicted results are analyzed. Step 6: The training sample's input matrix and support vector matrix are constructed. All training samples corresponding to non-zero coefficients \((a_{i}-a_{i}^{*})\) are found, which are the support vectors. The training sample input matrix \(\text{X}_{\text{N}\times\text{K}}\) and the support vector matrix \(\text{V}_{\text{Ns}\times\text{K}}\) are constructed, where N is the number of training samples, K is the number of influencing factors, \(\text{N}_{\text{S}}\) is the number of support vectors, and \(\text{N}_{\text{S}}\leq\text{N}\). Step 7: the partial derivative of the decision function is computed. Based on the optimal kernel parameter \(\gamma\) of the Gaussian radial basis function and the coefficients \((a_{i}-a_{i}^{*})\) of the support vectors, the partial derivative \(\partial f(x_{i})/\partial x_{ik}\) of the decision function for the input variable is calculated. Step 8: The significance coefficient and weight are calculated. The significance coefficient S(k) is calculated. The average absolute value of the partial derivatives of the decision function for all training samples corresponding to that factor is selected. Weight C(k) r is calculated based on S(k). The parameter setting is shown in Table 13.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 9 StarBurst
### Instrument Design  
StarBurst relies heavily on the heritage of the GBM, Glowbug, and BurstCube instruments, consisting of an array of 12 NaI(TI) scintillator detectors that utilize new, low-mass, and low-voltage SiPMs to cover an energy range from 30 keV to 2 MeV. The StarBurst detectors are arranged to form a half cube, with two detectors mounted side-by-side to form four sides of the cube and four detectors comprising the top, as shown in Figure 15. This configuration maximizes the available active surface area of the instrument given the volume constraints imposed on ESPA-Grande Rideshare Payloads and provides coverage of the entire unoculted sky. Each of the individual StarBurst detectors consists of a 24 cm \(\times\) 24 cm \(\times\) 1.6 cm NaI(TI) scintillator read out by an array of SiPMs and enclosed in a housing with a thin (1 mm) aluminum window transparent down to 30 keV and a beryllium-copper back shield to provide strong attenuation below \(\sim\) 200 keV. The scintillation light is read out by a 2 \(\times\) 38 linear array of 6 mm \(\times\) 6 mm J-Series SiPMs from ON Semiconductor (formerly SensL) optically coupled through an elastomeric silicone optical pad to a single edge of the NaI(TI) crystal. Edge readout of the NaI(TI) exploits the planar geometry of the crystal to pipe scintillation light to the SiPM array. Each of the detectors has a dedicated bias voltage supply and Multi-Channel Analyzer (MCA) for independent operation. The MCA is a Commercial-off-the-Shelf (COTS) Bridgeport Instruments slimMorpho with an on-board 20 MHz oscillator and flash ADC.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.2 GPS + Laser Coupling Measurement Technology  
One of the technical solutions for the composite relative measurement of a GPS + laser is to use laser measurement as an auxiliary measurement. This provides sub-millimeter laser data to complement the CDGPS data. By increasing the determination accuracy of the GPS's integer ambiguity, the measurement accuracy is improved [62]. Furthermore, the measurement range of the laser, CDGPS, and the CDGPS + laser with the highest accuracy are determined through experiments, and an algorithm is designed to select the measurement method that offers the highest accuracy within the corresponding distance. The composite method has the best accuracy at 10 km.  
The aim of the second solution of the GPS + laser composite relative measurement technique is to improve the problem of large altitude-estimation errors found in laser measurements over long distances. The method smooths the relative state measurement results via extended Kalman filtering. Furthermore, the laser azimuth measurement model is replaced by a GPS pseudo-range measurement single-difference model to deal with the problem of low altitude-determination accuracy. Compared with only using laser measurement, this method improves the accuracy and robustness of long-distance measurement [63].
## 2 Materials and Methods
### Comet Assay  
Comet assay was performed according to the method of Singh et al. [33] with slight modifications. Cell suspension was prepared by homogenizing approximately 40 mg of hatchlings in 1 mL of PBS (pH 7.4). The glass slides were coated with 1% normal melting point agarose 12 h prior to second layer. The second layer of 35 uL of the cell suspension mixed with 120 uL of 0.5% low melting point agarose (LMPA) was applied over the base layer and covered with a coverslip. Slides were again coated with 150 uL of 0.5% LMPA. After complete gel solidification, slides were immersed in lysis buffer (pH 10) for 3 h at 4\({}^{\circ}\)C in dark. Then, the slides were subjected to electrophoresis for 20 min in electrophoresis buffer at 300 mA and 20 V. Finally, the slides were neutralized with Tris buffer (pH 7.4) for 15 min and stained with ethidium bromide. A total of 300 cells per exposure group (100 per replicate) were scanned under a fluorescent microscope (Nikon ECLIPSE E200) at 40\(\times\) and photographed with a Nikon D5300 camera. Casp Lab software was used to  
Figure 1: Photographs showing viable cells (green), necrotic cells (red) and apoptotic cells (yellow).  
evaluate the extent of DNA damage in the form of tail length (TL), tail moment (TM), olive tail moment (OTM) and percent tail DNA (%TDNA).
###### Abstract  
Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate.  
**Keywords:** quantum memory; continuous variable-quantum key distribution; CHMAC-EPQH; 2E-HRIO; technology; system; process
## 6 Experimental Results and Discussion
### Displacement Components  
Table 9 summarizes the individual deformation components at various drift ratios for specimens S1A, S1B, and S5. \(\Delta_{\text{f}}\), \(\Delta_{\text{s}}\), and \(\Delta_{\text{ext}}\) are the deflection contributions at the section of load application of flexure, shear, and fixed-end rotation due to bar extension, respectively. Figure 17 shows the hysteretic loops corresponding to the flexural and shear-deformation components. The contributions of flexure and shear deformations to the overall beam deflection were derived from the LVDT displacement measurements. Fixed-end rotations due to bar extension (i.e., strain penetration) at the beam-column interface were estimated from the local strain measurements closest to the fixed-end section on the top and bottom main longitudinal reinforcement. The total deflection values presented in Table 9 were measured with a single-string potentiometer connected to the softf of each beam specimen at the section of load application. Hence, these values do not equate to the sum of the three individual deflection components, estimated from different sensor readings, as described above. Data outliers shown as a dash are related to sensors located within the hinging region whose readings were not reliable or were disconnected during the test. The details on the computation of each individual deflection component can be found in a previous publication by the authors [4].  
Figure 16: Normalized hysteretic energy (NHE) dissipation.  
Figure 17: Overall load vs. flexural displacement (**top**) and load vs. shear displacement (**bottom**) at the section of load application.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Drift**} & \multicolumn{2}{c}{**Displacement**} & \multicolumn{4}{c}{**SIA**} & \multicolumn{4}{c}{**S1B**} & \multicolumn{4}{c}{**S5**} \\ \cline{3-14}  & \multicolumn{2}{c}{**Component**} & \multicolumn{2}{c}{**Down (+)**} & \multicolumn{2}{c}{**Up (-)**} & \multicolumn{2}{c}{**Down (+)**} & \multicolumn{2}{c}{**Up (-)**} & \multicolumn{2}{c}{**Down (+)**} & \multicolumn{2}{c}{**Down (+)**} & \multicolumn{2}{c}{**Up (-)**} \\ \hline \multirow{4}{*}{1\%} & \(\Delta_{  
Although flexural deformations dominate the specimens' response, the contribution of shear deformations is found to be significant for the tested MRF beams (ranging between 16% and 23% for S1A, 18% and 28% for S1B, and 7% and 40% for S5). This finding is consistent with the observed inclined cracks and the pinching of the hysteretic loops. In this regard, Figure 14 confirms that shear deformations are the sole cause behind pinching of the hysteretic loops prior to reaching peak capacity. As also discussed by the authors in [4], the displacement component analysis also reveals that the contribution of shear deformations increases with the applied drift level and with the number of cycles, even with repeated cycles at a constant drift level, as also described in [PERSON] and [PERSON] (1973) [40].
# Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect
## 4 Testing Scenarios
### Form of Roughness Excitation  
The excitation parameter for runway roughness evaluation has been simplified to harmonic vibrations, with the cosine function adopted as input. The function expression is shown in Equation (3).  
\[z=\frac{H}{2}\Big{[}1-\cos\Big{(}2\pi\times\frac{x}{\lambda}\Big{)}\Big{]} \tag{3}\]  
where \(x\) is the horizontal coordinate of the runway, \(z\) is the elevation coordinate of the runway, \(H\) is twice the amplitude, that is, the vertical distance between the peak and valley, \(\lambda\) and is the wavelength.  
In order to eliminate the interference of the initial disturbance and ensure that the aircraft maintains a smooth taxiing state before encountering excitation, we set a 500-m buffer zone in front of the single cosine excitation. Then, we calculated the elevation data of the runway profile and input those data into the pavement file for simulation. Figure 7 presents a schematic diagram of a single bump and multiple bumps excitation (bulge) represented by cosine functions.  
Previous study shows that the aircraft vibration caused by the runway roughness is sensitive to the runway longitudinal wavelength, the longest wavelength could be 120 m [9]. In this study, multibody dynamics analysis ADAMS was used to simulate five typical aircraft models with various wavelength excitation. The range of the wavelength is 0-120 m. We first divided the entire range into discrete steps, and then generate the corresponding longitudinal profile to perform multibody dynamics analysis. Such process can be time consuming. Through several trials, we have determined the optimum step size to be 1 m. Using step size larger than 1 m makes the corrected BBI curve not smooth enough and affect the result accuracy, while using step size smaller than 1 m significantly increases the simulation cost.
# 3D Documentation with TLS of Caliphal Gate (Centa, Spain)
## Materials and Methods
### Methods  
#### 2.2.1 Previous Works  
The possibility to work with a properly-referred system of coordinates has a critical importance for any project of heritage analysis or enhancement. This reference framework offers benefits such as allowing for the junction of any planimetric or altimetric work related to the process of enhancement.  
Considering the case of Spain, there are planimetric (national geodesic network) and altimetric (high precision network) official reference frameworks. Hence, it is easy to obtain a local network of topographic vertices, and to characterize its points with (x,y,z) coordinates in accordance with these two reference systems. This eases the management of any surveying activity that could be required within the area. The adopted system can be summarized as it follows:  
* European Terrestrial Reference System 1989 (ETRS89). ETRS89 replaced the regional reference system ED50 in the year 2015, after a period of coexistence of both systems (2012-2015);
* Planimetric Universal Transverse Mercator (UTM) coordinates, the official projection system since July 1970;
* Altimetric coordinates referred with respect to the mean sea level of Alicante (NMMA), which were obtained by means of GPS and previous junction with vertices within the precise leveling network.  
The main objective of these previous works was the implementation of a network of vertices outside the archaeologic complex, so that it allows for an adequate coordinate translation of the TLS point-clouds. Hence, its use provides an accurate geometric definition of the spaces that are included within the area of interest not only in relative coordinates (which would be necessary for the development of most of the measurements and observations that would be required by activities related to archaeological surveying, engineering or construction), but also in absolute coordinates according to the referred system. The sequence of activities that was followed to fulfill these objectives is listed next:  
* Analysis of any existing network;
* Physical location and signaling of outside vertices;
* Observation with GPS of the outside vertices;
* Analysis, compensation, and determination of final coordinates;
* Digital identification and documentation of each outside vertex.
## 2 Technical Implementation
### Parameterization Method  
CST parameterization method is a frequently used airfoil parameterization method [16; 17; 18] firstly proposed by [PERSON] [19] in 2008. Its core idea is to modify the class functions by shape functions which is constructed by Bezier polynomials to describe the airfoil geometric shape, as shown in Equation (2).  
\[\begin{array}{lcl}y_{u}&=&C(x)\cdot S_{u}(x)+x\cdot y_{TEu}\\ y_{l}&=&C(x)\cdot S_{l}(x)+x\cdot y_{TEl}\end{array} \tag{2}\]  
where \(y_{u}\) and \(y_{l}\) are the vertical coordinates of the upper and lower surface of airfoil respectively, the class function \(C(x)\) is: \(C(x)\ =\ x^{N1}\cdot(1-x)^{N2}\), for normal airfoils \(N1\) and \(N2\) are taken as 0.5 and 1. The shape function \(S(x)\) is defined as shown in (3), \(A_{ui}\) and \(A_{li}\) are the coefficients to be determined, \(S_{i}(x)\) are Bernstein polynomials as shown in Equation (3).  
\[\begin{array}{lcl}S_{u}(x)&=&\sum\limits_{i\ =\ 0}^{N}A_{ui}\cdot S_{i}(x)\\ S_{l}(x)&=&\sum\limits_{i\ =\ 0}^{N}A_{li}\cdot S_{i}(x)\\ S_{i}(x)&=&\frac{N!}{i!(N-i)!}x^{i}(1-x)^{N-i}\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 12 \\ Crossover rate & 0.9 \\ Number of population generations & 200 \\ Probability of variation & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of algorithm.  
Figure 3: Verification of pressure and surface friction distribution. (**a**) Pressure distribution; (**b**) Friction coefficient distribution.  
It distinguishes the orders according to the different orders of Bernstein polynomials. The eighth-order method is used in this paper, and its' fitting results to NLF-0215 airfoil and fitting accuracy are shown in Figures 4 and 5, respectively. It can be seen that the differences between the fitting airfoil and the original airfoil are less than \(1.0\times 10^{-3}\) times the chord length, which is an acceptable range.
* This scheme exhibits substantial performance enhancements over traditional methods, especially in complex network environments. It provides a superior fault protection rate, thereby ensuring a more reliable and stable network operation. Further details are elaborated in Section 5. The rest of this paper is structured as follows. Section 2 introduces the related work. Section 3 presents the related network model and gives the description of specific problems;1 Section 4 analyzes the steps required in the algorithm in detail. Section 5 presents the MCDBP. Section 6 evaluates some experimental results of this algorithm, Finally, Section 7 concludes the paper.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### IRI Model  
The IRI is an empirical model designed to provide standardized specifications of plasmaspheric content in the ionosphere. The IRI relies on measurements from rockets, radiowave sounders, and a global network of incoherent scatter radars called \"ionosondes\" to generate values for the ionospheric composition (e.g., electron densities, temperatures, velocities, ion temperatures, and ion compositions) and Vertical Total Electron Content (VTEC) globally throughout the whole ionosphere and at an altitude range from 60 to 2000 km. One drawback of the IRI model is the irregular distribution of the ionosondes, which can decrease the accuracy of the measurements outside the coverage area of ionosondes, such as the areas over the oceans and across the poles [22]. We refer the readers to [20; 46] for a deeper understanding of the model's architecture and formulation. The IRI-2016 model is taken as one of the comparison benchmarks in our validation analysis.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 2 Compression Aspects of PDE Solvers
### Error Metrics and Error Propagation Affect Compression Accuracy Needs  
The notion of \"compression error\" is not well-defined, but needs to be specified in view of a particular application. The impact of compression on data analysis has been studied empirically [30; 41] and statistically [42; 43]. Ideally, the compression scheme is tailored towards the desired error metric. In lack of knowledge about the application's needs, general error metrics such as pointwise error (maximum or \(L^{\infty}\) norm) and mean squared error (MSE, \(L^{2}\) norm) are ubiquitous, and generally used for compressor design. Integer Sobolev semi-norms and Hausdorff distances of level sets have been considered by [PERSON] et al. [44] for sorting level and bitplane contributions in wavelet compression. Broken Sobolev norms have been used by [PERSON] [45] for multilevel decimation.  
If intermediate values are compressed for storage, in addition to the final simulation results compressed for analysis and archival, errors from lossy compressions are propagated through the following computations. Their impact on the final result depends very much on the type of equation that is solved and on the position in the solution algorithm where the compression errors enter. For example, inexact iterates in the iterative solution of equation systems will be corrected in later iterations, but may lead to an increase in iteration count. The impact of initial value or source term errors on the solution of parabolic equations is described by negative Sobolev norms, since high-frequency components are damped out quickly. In contrast, errors affecting the state of an explicit time stepping method for hyperbolic equations will be propagated up to the final result. Suchanalytical considerations are, however, qualitative, and do not allow designing quantization tolerances for meeting a quantitative accuracy requirement.  
Fortunately, a posteriori error estimates are often available and can be used for controlling compression errors as well as for rate-distortion optimization. As an example, error estimators have been used in [46] for adaptive selection of state compression in the adjoint computation of gradients for optimal control.