esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Performance Analysis for CSIDH-Based Cryptosystems
## 3 CSIDH-Based Schemes
### Csurf  
Since CSIDH protocol used a prime of the form \(p\equiv 3\mod 8\), the Montgomery curves \(M_{a}(\mathbb{F}_{p})\) has no \(\mathbb{F}_{p}\)-rational 2-torsion point except for \((0,0)\). Using only odd-degree isogenies without 2-isogenies resulted in the inefficiency of computing the class group action. To overcome this problem, [PERSON] and [PERSON] presented a new hard homogeneous space using tweaked Montgomery curves in [14]. The CSURF protocol uses a prime of the form \(p\equiv 7\mod 8\) and the tweaked Montgomery curves \(M_{a}^{t}/\mathbb{F}_{p}\). Thus, the \(\mathbb{F}_{p}\)-endomorphism ring of \(M_{a}^{t}/\mathbb{F}_{p}\) is isomorphic to \(\mathbb{Z}[(1+\sqrt{-p})/2]\) and every curve in this setting has three \(\mathbb{F}_{p}\)-rational 2-torsion points. Hence, CSURF can now exploit horizontal 2-isogenies with the ideal \(\mathfrak{l}_{0}=(2,\frac{\sqrt{-p}-1}{2})\) to help compute the class group action. For a supersingular Montgomery curve \(M_{a}\), the Montgomery coefficient \(a\) and \(\mathbb{F}_{p}\)-isomorphism class of \(M_{a}\) are one-to-one correspondence when the base prime \(p\) is of the form \(p\equiv 3\mod 8\). Likewise, for a supersingular tweaked Montgomery curve \(M_{a}^{t}\), the tweaked Montgomery coefficient \(a\) and \(\mathbb{F}_{p}\)-isomorphism class of \(M_{a}^{t}\) are also one-to-one correspondence when the base prime \(p\) is of the form \(p\equiv 7\mod 8\). This is summarized in Table 3. Thus, [PERSON] and [PERSON] can construct well-defined free and transitive group action. Finally, they used a secret exponent \((e_{0},e_{1},\ldots,e_{n})\) to compute class group action \([a]M_{a}^{t}\coloneqq[\mathfrak{l}_{0}^{e_{1}}\cdots\mathfrak{l}_{n}^{e_{n}} ]M_{a}^{t}\).  
In [14], [PERSON] and [PERSON] used the prime \(p_{3}\), which have 74 odd primes. For a secret exponent, they used \((e_{0},e_{1},\ldots,e_{74})\in[-137,137]\times[-4,4]^{3}\times[-5,5]^{46} \times[-4,4]^{25}\) with the class group action \([\mathfrak{l}_{0}^{e_{0}}\mathfrak{l}_{1}^{e_{1}}\cdots\mathfrak{l}_{74}^{e_{ 74}}]E\), where \(\mathfrak{l}_{0}=(2,\frac{\sqrt{-p_{3}}-1}{2})\). This leads to about \(275\cdot 9^{28}\cdot 11^{46}\approx 2^{255,995}\) distinct exponents.
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 3 Parametric Design of Primary Mirror
### Sensitivity Analysis of Primary Mirror Parameters  
Sensitivity analysis refers to the extent to which the changes in structural design parameters affect the performance of the objective function. The design of experiments (DoE) studies the relationship and trend between input and output parameters. This study has many parameters, and DoE can improve efficiency. F(t) is selected as the objective function of the sensitivity analysis. DoE results usually provide an approximate model between the input factors (\(X_{i}\)) and the output response (Y) in the form of a lower-order polynomial. In formula (18), the magnitudes of the coefficients in this equation reflect the strength of the \(X_{i}\) effects of factors on model output response Y. Each factor contains three levels. On the basis of orthogonal arrays (L\({}_{81}\)), this method is an efficient, fast, and economical experimental design method. This method has the advantages of balanced collocation of factor level and uniform distribution of data points, which greatly reduces the number of experimental analyses [28; 29; 30].  
\[Y=C_{0}+\sum_{i=1}^{m}C_{i}X_{i}+\sum_{i=1,j=1}^{m}C_{ij}X_{i}X_{j} \tag{18}\]  
As shown in Figure 6, according to the sensitivity analysis of each parameter to F(t), 22 parameters, namely ribs1, ribs2, ribs3, ribs4, ribs5, ribs6, ribs7, ribs8, ribs9, ribs10, ribs11, ribs12, ribs13, ribs14, ribs16, ribs17, ribs18, ribs20, ribs21, ribs25, ribs26 and ribs29, which have great contributions, are taken as the variables of the parametric design.
## 3 Problem Formulation
The constraint \(C2\) limits packet size (\(\psi\)). This ensures that the queue does not exceed the capacity and limits the utilization of the UAV's resources. The constraint \(C3\) introduces a control parameter for the transmitted data. The constraints \(C4\) and \(C5\) restrict the UAV's trajectory within the vehicle's coverage range such that the UAV traverses a minimum distance (\(d_{min}\)) and receives packets from at least one vehicle in a TTI. The maximum velocity of the UAV is restricted to (\(v_{max}\)). In constraint \(C6\), the UAV's trajectory is constrained to an elliptical path. In constraint \(C6\), the variables \(a\) and \(b\) indicate the major axis and minor axis of the UAV's elliptical path. We introduce a variable \(\alpha\) as a tuning factor to expand or contract the elliptical trajectory. The selection of an elliptical trajectory over a circular trajectory was motivated by the results demonstrated in [84; 85; 86]. The authors in these works postulate that, for UAVs, an elliptical path is a common trajectory for surveillance, target tracking, and other applications. Through extensive experiments and analysis, the authors have illustrated that an elliptical path provides optimal obstacle avoidance and motion stability compared to a circular path [84]. Moreover, a rectangular or a square path has abrupt discontinuities and may lead to a UAV becoming stuck in a specific segment of the path. The variable \(\alpha\) is a tuning factor between 0 and 1. When \(\alpha=0\), the UAV is stationary at height (\(\mathcal{H}\)) and does not move along the \(x\) and \(y\) axes. When \(\alpha=1\), the largest possible elliptical trajectory is realized. For other values of \(\alpha\) between 0 and 1, the area of the elliptical path gradually increases as \(\alpha\) is increased from 0 to 1. The constraint \(C7\) implies that the weighting coefficients in Equation (6) should add to unity. In this paper, we use block coordinate descent (BCD) to decompose the optimization problem \(P1\) into three subproblems. BCD optimizes a subset of variables in a subproblem while keeping others constant. This process is repeated until convergence by optimizing smaller blocks of variables in each TTI. To account for the underlying hardware's computing limitations, it is important to achieve a positive semi-definite matrix and to have the rank of the variables' matrices as less than or equal to one. This allows the non-convex optimization problem to be transformed into a convex optimization problem. If the weighting coefficients \(w_{1}\), \(w_{2}\), and \(w_{3}\) in Equation (6) do not add to unity or are large integers, it takes a comparatively longer time to solve all the subproblems.
## 4 Capacity Analysis for Each Topology
### Application to the Design Proposed by Korea Airports Corporation  
The capacity analysis algorithm was applied to the verithub in Figure 15 presented by the Korea Airports Corporation. Each floor of this verithub consists of a total of 5 TLOF pads and 51 gates as shown in Figures 41 and 42, and an aircraft can be moved to another floor through an elevator. The capacity was analyzed by assuming that a double-decker verithub is the same as a single floor.  
As shown in Figure 43 and Table 12, the total number of passengers who can be accommodated at the Gimpo Airport verithub is 448 for 15 min operation time. Converting  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sections** & **Standard** & **Applied Value** \\ \hline Approach/departure surface & 1:8 (12.5\%) & 1:8 (12.5\%) \\ \hline TO & Safety area + CD & 50.6 m \\ \hline FATO & 2 \(\times\) CD & 27.6 m \\ \hline h\({}_{1}\) & - & 3 m \\ \hline h\({}_{2}\) & \(\geq\) h\({}_{1}\) & 30.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 11: The dimensions of the approach/departure surface for the pier topology at the current Gimpo Airport parking lot that comply with the EASA’s new regulations.  
Figure 41: The lower deck design of the Gimpo Airport verithub [21].  
Figure 42: The upper deck design of the Gimpo Airport verithub [21].  
to one hour of operation time, it can accommodate a total of 472 passengers with 118 arrivals and 118 departures.  
The gate size of this vertihub is 18 m in diameter, which is different from this research. The proposed gate sizes and the total number of gates are different because of the size gap between aircraft designated by the Korea Airports Corporation and S-A1.
## 1 Introduction
Therefore, it is necessary to introduce new theories and methods to study the multi-objective joint regulation of hydropower and photovoltaic from the practical point of view, so as to provide an effective decision-making scheme for the optimal dispatching of the power system and reduce the gap between theoretical research and actual dispatching. Therefore, hedging theory is applied to the hydropower and photovoltaic joint scheduling problem in this paper. Considering the reservoir profit benefit, flood control risk, and output stability of the combined hydropower and photovoltaic system as the objectives, the daily optimal dispatching model of hydropower and photovoltaic is constructed, in order to allow better multi-objective decision-making in the combined hydropower and photovoltaic dispatching. The structure of this paper is as follows. The second section briefly introduces the theoretical methods, including the application of hedging rules in the joint scheduling and the analysis of the two-stage decision-making process, and establishes the hydropower and photovoltaic power joint scheduling model and solution method based on the hedging theory. Section 3 establishes the improved solution. method, Section 4 verifies the model with application examples, and Section 5 presents the conclusion.
# Neural Network Exploration for Keyword Spotting on Edge Devices
## 4 Evaluation and Deployment Results
### Evaluation Results
#### 4.1.3 Effect of Quantization on Accuracy  
To evaluate the impact of quantization schemes on accuracy, the inference accuracy versus model size for each neural network architecture after training for 100 epochs were plotted under the four different quantization schemes in Figure 7.  
Overall, the models with the 16-bit floating point, the dynamic range, and the integer-float quantization schemes closely mimicked their nonquantized counterparts in terms of accuracy, with reduced model sizes. For 16-bit floating-point quantization, this is likely due to the simplicity of converting 32-bit floating-point values to 16-bit floating-point values with minimal error. The ability of the dynamic range and the integer-float quantization models to provide the benefits of 8-bit integer quantization with best effort post training or at inference while avoiding the associated drop in accuracy was a pleasant surprise. For model sizes above 30 kB, most architectures, except FFCN, still exceeded 90% accuracy. For model sizes smaller than 30 kB, however, a drop in accuracy was observed. Compared with the nonquantized models, the accuracy achieved with 16-bit floating-point quantization and dynamic range quantization is about the same, whereas the drop in accuracy is more pronounced for models with integer-float quantization. For example, the smallest ResNet model achieves 76.67% accuracy under the integer-float quantization, which is more a than 1.5% reduction from 78.34% with no quantization. For the smallest DenseNet model, the accuracy dropped from 61.83% without quantization to 54.24% with integer-float quantization.  
The full-integer quantization scheme greatly impacted the performance of the models, with an approximate 20% drop in accuracy. The drop in accuracy was likely caused by the limitations of PTQ. While PTQ aims to maintain network performance using a representative dataset for calibration, some inaccuracies are inevitably introduced by converting all 32-bit floating-point values to 8-bit integers. QAT could achieve better results, but the model would have to be completely retrained.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Quantization Scheme** & **FFCN** &
\begin{tabular}{c} **DS-** \\ **CNN** \\ \end{tabular} & **ResNet** & **DenseNet** & **Inception** & **CENet** \\ \hline
16-bit floating point & 50.72\% & 62.29\% & 58.31\% & 65.95\% & 63.24\% & 81.20\% \\ Dynamic range & 25.81\% & 48.22\% & 39.51\% & 52.41\% & 50.55\% & 39.51\% \\ Integer-float & 26.01\% & 50.94\% & 37.23\% &  
Although the FFCN models with the quantized schemes have lower accuracy than all the CNN-based architectures, they have other advantages, such as less memory consumption, lower inference latency, and greater size reduction with quantization. It has also been discovered that FFCN accuracy can be improved with pooling [15]. Therefore, enhanced FFCN models may still be considered for certain keyword spotting applications on embedded systems.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 2 Basic Theory and Methods  
### Definition of Aerodynamic Correlation  
The correlation coefficient is an index that reflects the degree of correlation between variables and the variation range of which is [\(-\)1, 1]. When the absolute value of the correlation coefficient is close to 1, the correlation is large and when it is close to 0, the correlation is small.  
The spanwise correlation coefficient of an aerodynamic force of a cylinder in a uniform flow field is defined by the following formula [18]:  
\[R_{Ci}(\delta/D)=\mathrm{cov}[C_{i}(\delta/D)]/\mathrm{var}[C_{i}]=\frac{ \sum\left(X-\overline{X}\right)\left(Y-\overline{Y}\right)}{\sqrt{\sum\left(X -\overline{X}\right)^{2}\cdot\sum\left(Y-\overline{Y}\right)^{2}}} \tag{1}\]  
where: \(C_{i}\)--aerodynamic parameters (pressure coefficient and three component coefficient), \(\delta\)--spanwise spacing, \(D\)--characteristic length of section, \(X,\overline{X},Y,\overline{Y}\)--a time-history curve of aerodynamic parameters at different positions of a cylinder.  
The spanwise correlation coefficient of aerodynamic force of stationary mean beam in a uniform flow field defined by the following formula [18]:  
\[R_{C_{i}}(\delta/D)=\mathrm{exp}\left[-c_{i}\frac{\delta}{D}\right] \tag{2}\]  
where: \(c_{i}\)--the exponential attenuation coefficient corresponding to the aerodynamic coefficient \"\(C_{i}\)\".  
The degree of spanwise correlation of aerodynamic coefficients of the main beam in static state can also be expressed in terms of the correlation length \(L_{i}\)[13]:  
\[L_{i}/D=\int_{0}^{\infty}R_{C_{i}}(\delta/D)\mathrm{d}(\delta/D)=1/c_{i} \tag{3}\]  
The aerodynamic force of main beam sections which are under vibration is composed of external excitation force and self-excitation force related to vibration. The correlation coefficient is defined by the following formula [18]:  
\[R_{C_{i}}(\delta/D)=(1-d_{i})\,\mathrm{exp}[-C_{i}(\delta/D)]+d_{i} \tag{4}\]  
where: \(d_{i}\)--the horizontal asymptote of the attenuation function which represents the fully relevant part of the aerodynamic parameters; \(c_{i}\), \(d_{i}\) can be obtained by fitting the test data with the least square method.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Performance Analysis
#### 3.2.3 Qualitative Comparison between LWLC+GSME and Related Methods  
Finally, a quantitative analysis for the defect inspection after segmentation is given by Figures 12 and 13. The two figures further explain that the LWLC+GSME method is more suitable for detection of rail defects based on the UAV image.  
Although the GSTA method enhances image contrast, it can't repress interference of noise effectively. These three methods acquire poor segmentation effects because they are susceptible to noise and background points. In contrast, LWLC+GSME not only achieves contrast enhancement between defects and background, but also obtains the best segmentation effects. For example, LWLC+GSME achieves a precision of 88.63% for T-I defects and a precision of 90.32% for T-II defects. It should be noted that both of MLC+PEME and LWLC+GSME obtain similar effects for precision, because they see non-defect regions as defect under light disturbance (such as uniform illumination and low contrast).  
Figure 11: Examples of four defect segmentation methods for rail surface images. (**A**) Three examples of extracted rail images. (**B**) Three examples of defect segmentation by LWLC+ME method. (**C**) Three examples of defect segmentation by MLC+PEME method. (**E**) Three examples of defect segmentation by LWLC+GSME method. In Figure 10, the discrete defects on images have been marked by red rectangle to compare enhancement performance of LWLC+GSME method with related methods.  
In addition, based on the ME principle and characteristics of the defects mentioned above, a suitable segmentation threshold should be relatively small under the condition of complete retention of defects [30]. Three examples of the segmentation threshold with four segmentation methods are shown in Table 2. From this table, a relatively small threshold is obtained based on LWLC+GSME. It further illustrates that the proposed method can select a better segmentation threshold. It should be noted that thresholds based on LWLC+GSTA are the smallest. However, this method achieves poor performance for T-I and T-II defects, as shown in Figures 11 and 12; this is because this method uses Gaussian kernels to enhance object edges of high frequency areas in the wavelet domain, and UAV-based images contain a lot of irregular (noise) points. For this reason, the method also enhances these noises in rail images, thereby yielding poor defects detection performance.  
Figure 12: Comparison of defection precision for four detection methods. The blue block and orange block denotes T-I defect and T-II defect, respectively.  
Figure 13: Comparison of defection recall for four detection methods. The blue block and orange block denotes T-I defect and T-II defect, respectively.
## 4 Results and Discussion
### Comparison with Other Typical Shale Gas Reservoirs  
In North America, the commercial development of shale gas was first achieved in the Woodford, Barnett, and Haynesville shale reservoirs [56]. China has made significant breakthroughs in shale gas exploration in the Sichuan Basin and its periphery, with the largest shale gas field in China having been built in the Jiaoshiba area of the eastern Sichuan Province [38]. Studies on the reservoir characteristics and the main factors controlling shale gas enrichment in these areas are relatively complete and can thus be used as a reference for shale gas exploration and development in the Dalong Formation in Western Hubei.  
Comparing the characteristics of shale reservoirs in the Dalong Formation with those of the other typical shale reservoirs (Table 3) reveals that the Dalong Formation shales are currently buried at relatively shallow depths and contain more mature organic matter, generally at the over-maturation stage. This suggests that the Dalong shales experienced deep burial during early diagenesis. Compared to the organic matter contents of the Woodford and Barnett Shales in North America and the Longmaxi Formation shales in eastern Sichuan, those of the Dalong siliceous shales are twice as high. The brittle mineral  
Figure 16: Correlations between methane adsorption capacity and (**a**) Pore volume, (**b**) Pore-specific surface area, (**c**) Organic matter content, (**d**) Mineral contents in the Dalong Formation shales.  
contents of the Dalong shales are also higher than those of the other reservoirs, which is favorable for later exploitation by hydraulic fracturing. Although the total porosity of the Dalong Formation shales is relatively low, its micropore volume and combined mesopore + macropore volume are comparable to those of the Longmaxi Formation shales and are higher than those of the Woodford and Barnett Shales.  
Moreover, the desorption gas content of the Dalong Formation shales is slightly higher than that of the Longmaxi Formation shales. These comparisons reveal that siliceous shales are characterized by favorable hydrocarbon generation and enrichment conditions, making them a desirable target for shale gas exploration.
## 2 Constitutive Model and Calibration
### Calibration of Parameters and Model Validation  
The parameters of constitutive models of soils are often obtained by comparing experimental results and numerical simulations using finite elements at the element test level [31]. In this study, experimental data from an undrained cyclic triaxial test are used to calibrate the parameter \(C\), and two methods for calibrating the parameters \(\lambda\), \(\gamma_{o}\), and \(\sigma_{0}\) are presented. First, the curve of the undrained Young's modulus versus axial strain is drawn using the results obtained from the cyclic triaxial test and numerical simulations of the test to obtain the \(\lambda\) parameter. Second, data from a cyclic shear test and Ishibashi and [PERSON]'s equations [32] are used.  
The values of the parameters such as the undrained shear strength (\(S_{u}\)), maximum shear modulus (\(G_{max}\)) or shear wave velocity (\(V_{s}\)), and curve of the undrained Young's modulus versus axial strain (\(E_{u}\) Vs. \(\varepsilon_{1}\)) are required to calibrate the parameters. A series of undrained cyclic triaxial and monotonic tests of kaolin clay were conducted by [PERSON] [33] in both stress and strain control modes and under different level stress amplitudes.  
Figure 1: Simplified 1D representation of a kinematic hardening process.  
In this study, the results of monotonic and cyclic tests under the stress control condition with an effective mean pressure of 200 kPa are used to calibrate the constitutive model parameters. The process mentioned above, by which the parameters of the model are calibrated, is fully described below.