esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### VHIopt Accuracy Evaluation  
#### 3.2.1 Evaluation Based on SMA  
Since soil moisture is a crucial variable for monitoring drought condition, SMA was used to access the performance of VHIopt [36]. Figure 3 indicates a significant improvement in the correlation coefficient between VHIopt and SMA across all vegetation types compared to VHIori, with an overall increase of 0.03 in the vegetation area. Specifically, R increased by 0.09, 0.02, and 0.03 in forest, grassland, and changing vegetation areas, respectively. The R for farmland did not improve due to the limited extent of farmland vegetation in the YRB and its susceptibility to human influence. Additionally, both RB and RMSE showed significant decreases, RB between VHIopt and SMA decreased by 0.04, 0.04, 0.05, 0.01, and 0.03 in forest, grassland, farmland, varied vegetation types, and all vegetation types, respectively. RMSE of VHIopt and SMA decreased by 0.02, 0.03, 0.01, 0.04, and 0.02 in forest, grassland, farmland, changed vegetation type, and all vegetation types, respectively. Taking into account the accuracy of various SM data, we compared VHIopt with the SM data from the global land evaporation Amsterdam model (GLEAM). The verification results demonstrated that VHIopt outperforms VHIori overall (Figure 11), which is consistent with the findings presented in Figure 3. It can be seen that VHIopt can better reflect the abnormal situation of soil moisture than VHIori.  
Figure 2: Correlation between VHI during the growing season and SPEI at multiple timescales under different weight factors.  
Figure 3: Scatter plots of SMA versus VHIori and SMA versus VHIopt under different vegetation types (forest (**a**,**b**), grassland (**c**,**d**), cropland (**e**,**f**), changing vegetation types (**g**,**h**), and all vegetation types (**i**,**j**)) and the red dashed lines in all subfigures represent the lines of linear fit.
## 3 Methodology
### Loss Functions  
In order to make the simulation results conform to the given conditioning data constraints, this paper designs a joint loss function that includes both the original adversarial loss and a conditional loss term. This approach optimizes the network parameters of the generator and discriminator, respectively. Specifically, an improved hinge loss is introduced as the adversarial loss to penalize the generation of unreasonable data distributions, and to enforce the learning of geologic pattern knowledge inherent in the real data distribution [39,40]. Compared to KL divergence and JS divergence, hinge loss offers distinct advantages. Firstly, it heightens the discriminator's sensitivity to data distribution and improves its discriminative ability by setting specific scoring thresholds for real and generated samples. Secondly, the effective gradient is still maintained even when the classification accuracy of discriminator is high, thus mitigating the gradient vanishing issue. The loss functions for the discriminator and generator can be sequentially expressed as follows:  
\[\begin{array}{rl}\mathcal{L}(D)&=-E_{x_{con}  
generated image and the corresponding observation data. In other words, it measures the reconstruction accuracy of the conditioning data locations. The conditional loss can be expressed as follows:  
\[\mathcal{L}(G)_{cont}=E_{x_{cont}\sim P(x_{cont}),x\sim P_{data}(x),z\sim P(z)}Dist \Big{(}M\bigotimes(G(z,x_{cont})-x)\Big{)} \tag{10}\]  
where \(\bigotimes\) represents the element-wise product, \(Dist\) denotes the Frobenius norm, and \(M\) denotes the binary mask, where positions corresponding to the conditioning data are labeled as 1, and all other positions are labeled as 0. This allows for the quick filtering of attribute information at the conditional point positions from the simulation results and the reference model, enabling an effective loss measurement.
## 3 Results and Discussion
### Experimental Envelope  
For the analysis of the dynamic test, it is important to consider the relationship between monotonic curves and hysteretic curves. This was considered according to the provisions of Chapter 2 of FEMA 355D, \"Effect of load and deformation history\"; [3]. That is, monotonic tests provide an approximate envelope to that obtained from hysteretic curves in cyclic load tests [16].  
The envelope curve is obtained by plotting the maximum points in each cycle extracted from the curve obtained from the monotonic or cyclic tests. This envelope is of special importance for defining the inelastic response of the connection to the action of dynamic loads for structural design and numerical modeling. The envelope representing the results obtained in the cyclic test is shown in Figure 10. The figure also shows the equation defining the curve. It is a third order polynomial trend line with a correlation coefficient of 0.9949. This polynomial represents a good correlation up to 0.06 rad, which is the point of maximum resistant moment in the connection.  
Figure 10: Experimental envelope of the momentum-rotation curve.  
Figure 9: Final state of the specimen during the test seen from different angles.  
The momentum-rotation envelope curve characterizes the inelastic behavior of the connection in terms of its resistance; see Figure 11. According to the cyclic test described, the flexural capacity of the connection measured from the column face is greater than the established minimum of 0.8 \(M_{\text{P}}\) at a distortion level of 0.04 rad. By meeting these conditions, this connection can be implemented in moment-resistant porches with special power dissipation capacity in areas of high seismic threat.
## 4 Discussion of Results
### Influence of Filler Contents and Binder Types  
#### 4.2.1 TTS Test Results  
Mastics with different filler contents and binder types were prepared and the aging time was set to be 8 h. The TTS test was conducted on both virgin mastics and mastics after 8 h of aging. Figure 6 shows the master-curve of the complex modulus of different mastics. As Figure 6 illustrates, when the filler contents increase, the complex modulus of the mastics increase significantly. As Figure 6 shows, binder type could also affect the master-curve of the mastics. The complex modulus G* at intermediate temperature of 15 \({}^{\circ}\)C and loading frequency of 10 Hz was proposed to evaluate the durability of the mastics. Figure 7 shows the G* @ 15 \({}^{\circ}\)C & 10 Hz of different mastics before and after aging. It could  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Origin** & **Aging 2 h** & **Aging 4 h** & **Aging 8 h** & **Aging 12 h** & **Aging 16 h** & **Aging 20 h** \\ \hline \(\mathrm{G_{e}}\) & \(3.9\times 10^{3}\) & \(4.3\times 10^{3}\) & \(5.5\times 10^{3}\) & \(6.5\times 10^{3}\) & \(9.2\times 10^{3}\) & \(9.5\times 10^{3}\) & \(9.8\times 10^{3}\) \\ \hline \(\mathrm{G_{g}}\) & \(2.7\times 10^{8}\) & \(3.0\times 10^{8}\) & \(3.1\times 10^{8}\) & \(3.9\times 10^{8}\) & \(5.2\times 10^{8}\) & \(5.4\times 10^{8}\) & \(5.5\times 10^{8}\) \\ \hline \(\mathrm{f_{c}}\) & 55.50 & 56.58 & 50.28 & 31.01 & 30.88 & 29.86 & 28.55 \\ \hline k & 0.33 & 0.32 & 0.31 & 0.32 & 0.33 & 0.34 & 0.33 \\ \hline \(\mathrm{m_{e}}\) & 0.95 & 0.93 & 0.91 & 0.85 & 0.85 & 0.85 & 0.84 \\ \hline R & 0.88 & 0.87 & 0.87 & 0.79 & 0.77 & 0.75 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CAM model fitting results of master-curves from mastics with different aging times.  
Figure 5: Master-curve of the mastics with filler content of 80% after aging 8 h with or without the fillers: (**a**) complex modulus; (**b**) phase angle.  
be observed that the G* @ 15 \({}^{\circ}\)C &10 Hz after 8 h of aging is much larger than no aging. The G* @ 15 \({}^{\circ}\)C & 10 Hz increased with the increasing filler contents at both no-aging and 8 h aging conditions. The mastics with SBS modified binder had higher G* @ 15 \({}^{\circ}\)C &10 Hz than mastics with neat binder at both aging conditions.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 2 Seismic Response Analysis of Site-Embedded NPP Structural Systems
### Containment Structure
#### 2.2.3 Relative Displacement  
Figure 21 shows the relative displacement distribution of the safety shell structure along the floors, and Figure 22 shows the peak relative displacement of the safety shell structure. It can be observed that as the embedment depth increases, the relative displacement of the safety shell structure gradually decreases. When the embedment depth exceeds 22.6 m, the influence on the structure's floor relative displacement by different embedment depths becomes minimal. Furthermore, the structural peak displacement response gradually decreases with increased embedment depth. Compared to the aboveground condition, embedment can reduce the peak displacement response by up to approximately 44.8%.  
Figure 21: Safety shell structure’s floor relative displacement.  
Figure 20: Safety shell structure’s floor response spectrum.  
Seismic Response Analysis of Site-Embedded Nuclear Power Structure Systems with Different Liquefaction Layer Positions  
Seven reference points were selected for the auxiliary buildings, with their spatial distribution illustrated in Figure 23a, while three reference points were configured for the containment structure, as shown in Figure 23b. A comprehensive evaluation of the effects of varying liquefaction layer positions on the seismic response of embedded nuclear power plant structures under non-rock site conditions was conducted using three metrics: floor acceleration, floor spectral acceleration, and inter-story displacement.  
Concurrently, site liquefaction phenomena are explicitly shown in Figure 24. Based on the liquefaction analysis results, the site responses under different liquefaction layer configurations in non-bedrock conditions were systematically assessed, with structural response characteristics induced by site liquefaction conclusively summarized. Monitoring points A, B, and C are strategically positioned at the center of the structure's base, the apex of the internal containment structure, and the centroid of the containment shell, respectively.  
Figure 23: Schematic diagram of structural monitoring point locations.  
Figure 22: Safety shell structure’s floor peak relative displacement.
###### Abstract  
This article presents the results of an acoustic characterization of fibers obtained from Posidonia Balls (scientific name: Aegagropiles), produced by a marine plant (_Posidonia oceanica_) that is widespread in the Mediterranean Sea and can be found on beaches in large quantities, particularly following storm surges. The aim of this research is to evaluate the possible use of these fibers as eco-sustainable sound-absorbing materials and to define an acoustic model for the optimization of sound-absorbing panels made from these fibers. Experimental tests were conducted to measure airflow resistivity and sound absorption for different densities of loose fiber samples. From these experimental tests, the five physical parameters of the Johnson-Champoux-Allard model were calculated to obtain an analytical formulation of the acoustic behavior of the fibers depending on their density. To the author's knowledge, this is the first time that an article has been published on acoustic data relating to the sound-absorbing performance of loose _Posidonia oceanica_ fibers and that an analytical model has been presented that allows for the acoustical design of panels of different thicknesses and densities made with this material. An interesting aspect of this material is that the lignin fibers are ready for acoustic application due to the natural cleaning process of the waves and salt water. Furthermore, the methodology consists of a hybrid method between the experimental characterization of some parameters (i.e., different densities) and the numerical inversion of the acoustic data for other parameters. This is an effective solution that has rarely been adopted in other studies on sustainable materials.  
Posidonia fibers; natural fibers; sustainable porous material; sound absorption; acoustical modeling 2023
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 2 Related Work
### Particle Filter  
The idea of particle filtering is based on the Monte Carlo method, which uses sets of particles to represent probabilities of things and can be used for any form of state-space model. The core idea is to express the distribution through random state particles drawn from posterior probabilities through a sequential importance sampling method. In simple terms, particle filtering is the process of approximating a probability density function by finding a set of random samples propagated in a state space and replacing the integration operation with the sample mean to obtain the state minimum variance distribution.  
Although the probability distribution of the algorithm is only an approximation of the true distribution, it is unaffected by the constraint according to which random quantities must satisfy a Gaussian distribution when solving non-linear filtering problems due to its non-parametric nature, and it can express a wider range of distributions than a Gaussian model, in addition to having a greater ability to model the non-linear properties of the variable parameters. Thus, particle filtering can more accurately express the posterior probability distribution based on the observed and control quantities and can be used to solve SLAM problems. New variants of the MCMC have been developed as improvement strategies for particle filters, unscented particle filters, Rao-Blackwellized particle filters, and Gaussian particle filters.
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 19 Description of Modern Industrial Methods Based on the Process of Direct H\({}_{2}\)S Oxidation
Figure 19: Catalytic segment after 2500 h of continuous operation. Figure 20: Semi-industrial installation for the direct oxidation of hydrogen sulfide via monolithic catalyst with a honeycomb structure. Tail-gas of the Claus process. Additionally, APG is a source of the propane-butane fraction for petroleum chemistry companies in Russia. This fraction is often in short supply. In order to address the issue of the primary removal, sorption facilities for amine treatment have been developed to remove hydrogen sulfide and transport hydrocarbon components to the appropriate sites of further treatment. However, the problem is addressed only partially, as the hydrogen sulfide released is burnt with flares. Typical examples of the implementation of such an approach are the Bavlinsky gas workshop, PJSC Tatneft, Shkapovskiy, and Tuymazinskiy gas processing plants of PJSC ANK Bashneft. In 2011, an industrial installation with a fluidized catalyst bed for the removal of hydrogen sulfide from acid gases from the amine treatment of oil-associated gases was created and put into operation by the Boreskov Institute of Catalysis SB RAS [155; 156; 157; 158] at the PJSC Tatneft Bavly gas shop (Figures 21-23). Figure 21: Bavly gas shop of PJSC Tatneft. Purification of associated oil gas. Amine treatment and direct oxidation. Figure 22: Bavly gas shop of PJSC Tatneft. Purification of oil associated gas. Amine treatment and direct oxidation. The main feature of the initial feed is the extreme instability of the input gas parameters; see Figure 24.  
However, the developed computer control system made it possible to rapidly adjust the air and coolant flows to maintain the preset temperature in the catalytic reactor. The quality of the resulting sulfur (Figure 25) surpassed the Russian National Standard #127.1-93 (commercial grade sulfur 9990). The main results of the operation of the installation in the Bavly gas shop are given below:  
* Over 1 billion m\({}^{3}\) of purified gas produced
* 6000 tons of hydrogen sulfide converted to elementary sulfur
* Emission of 12,000 tons of sulfur dioxide and sulfuric acid (340 railway tanks) into the atmosphere prevented;
* Environmental damage amounting to about 2.9 billion rubles avoided
* One-stage technology with computer control providing stable operation with variable parameters in terms of the acid gas (for example, hydrogen sulfide content). Figure 16:0 Industrial unit with a capacity of acid gas of up to 250 nm\({}^{3}\)/h, which has been in continuous operation since 2011. The hydrogen sulfide content is 30–65 vol.%. Figure 16:1 Fluctuations of H\({}_{2}\)S content in the feed gas subjected to purification.
## 2 Theoretical Background
### Parametric Lifetime Models
#### 2.4.4 Goodness of Fit  
There are several methods for measuring the goodness of fit of a Weibull model [22]. The methods used are log likelihood, Akaike information criteria (AIC), Bayesian information criterion (BIC) and Anderson-Darling goodness of fit statistic (AD). Log likelihood is the logarithm of the likelihood function, which is the probability of observing the data, given the parameters. Log likelihood is used to compare different models and to determine the best-fitting parameters for a given model [22].  
The AIC is a model-selection criterion that balances the goodness of fit of a model with the complexity of the model. It is defined as \(-2\) times the log likelihood of the model plus twice the number of parameters in the model [22]. The goal is to find the model with the smallest AIC. Lower AIC values indicate a better model [22].  
The BIC is similar to the AIC but also takes into account the number of observations in the data set. It is defined as \(-2\) times the log likelihood of the model plus the number of parameters in the model times the natural logarithm of the number of observations. The BIC also aims to find the model with the smallest value [22].  
The AD is a statistical test that compares the sample data to a specific probability distribution, such as the Weibull distribution [22]. It measures the difference between the CDF of the sample data and the CDF of the hypothesized distribution, providing a measure of the goodness of fit of the hypothesized distribution to the sample data [22].
## 5 Performance Analysis and Discussion
a larger \(M\), which has a greater ability to suppress PIN, obtain better BER than the ones with a smaller \(M\). Due to the increasing signal rate in networks, the decoder requires a large bandwidth to meet this demand. Figure 10 shows the relationship between BER and the electrical bandwidth for \(K=15\) and \(P_{\text{sr}}=-10\) dBm. The result shows that BER increases with \(B\), which reveals that a high signal rate is obtained at the expense of a large noise variance in the receiver's bandwidth. Similar to the tendency shown in Figures 8 and 9, the proposed labels show lower BER when the code length is fixed. Therefore, employing extended SAC labels for packet switching is more suitable for achieving high-speed transmissions in GMPLS. In Figures 8-10, the value of \(M\) is carefully chosen to achieve good BER performance. However, the stacked label number \(K\) or the LSP length may vary in different networks. Figure 11 investigates the impact of channel number on the system signal-to-noise ratio (SNR) for \(K=10\), \(20\), and \(40\). The lengths of extended labels \(L\) are set close to \(127\). The optimal set for designing extended SAC labels for \(K=40\) is (\(N\),\(M\)) = (7,64). Although increasing the channel number can reduce PINN variance, it lowers the photocurrent at the same time. When \(K\) is sufficiently small, and the PINN effect is not notable, a large value of \(M\) may lead to system degradation due to the low SNR. This analysis is very helpful in designing a label set for GMPLS networks of various sizes. Figure 10: BER versus electrical bandwidth for MLS and extended SAC labels. Figure 9: BER versus received optical power for MLS and extended SAC labels.
## 4 Assessment Procedure in Terms of Spectral Accelerations According to [PERSON]  
In the framework of capacity-demand checking [27, 37, 38, 39, 40, 41, 42, 43], an equivalent SDOF system replaces the MDOF actual system by means of the modal participation factor \(\Gamma\). Multiplying the multiplier \(\alpha\) with the design base shear, the capacity curve is reported in an \(F_{b}\)--\(d_{c}\) plane. The capacity curve must be reduced through the modal participation factor and represented in an \(F\)--\(d^{*}\) plane. The demand is estimated through the period \(T^{*}\) and the equivalent mass \(m^{*}\) according to European codes. The capacity in terms of spectral acceleration for the points A, B, C, and D is given as follows:  
* Point A--\"Fully Operational\" \[S_{aFO}(T^{*})\ =\ \frac{F_{FO}^{*}}{m^{*}}\] (25)
* Point B--\"Operational\" \[S_{aO}(T^{*})\ =\ \frac{F_{O}^{*}}{m^{*}}\] (26)
* Point C--\"Life Safety\" \[S_{aLS}(T^{*})\ =\ \frac{F_{LS}^{*}}{m^{*}}q_{LS}\] (27)  
\[q_{LS}\ =\ q_{0}(\mu,\ T,\ \gamma\ =\ 0)\ =\ \left[c(\mu_{LS}\ -\ 1)\ +\ 1\right]^{1/c}\] (28) where \(c\ =\ \frac{T*}{1\ +\ T*}\ +\ \frac{0.42}{T*}\) and \(\mu_{LS}\ =\ \frac{d_{LS}*}{d_{0}*}\).
* Point D--\"Near Collapse\" \[S_{aNC}(T^{*})\ =\ \frac{F_{NC}^{*}}{m^{*}}q_{NC}\] (29) \[q_{NC}\ =\ \frac{q_{0}}{\varphi}\] (30)  
\[q_{0}(\mu,\ T,\ \gamma\ =\ 0)\ =\ \left[c(\mu_{LS}\ -\ 1)\ +\ 1\right]^{1/c}\] (31) where \(c\ =\ \frac{T*}{1\ +\ T*}\ +\ \frac{0.42}{T*}\) and \(\mu_{NC}\ =\ \frac{d_{NC}*}{d_{0}*}\).  
\[\varphi\ =\ \frac{1\ +\ 0.62(\mu_{NC}\ -\ 1)^{1.45}\gamma}{(1\ -\ \gamma)} \tag{32}\]  
The coefficient \(\varphi\) is a function of the ductility \(\mu\) and the non-dimensional slope of the equilibrium curve \(\gamma\).
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Proof System for the Lattice Hash
#### 9.3.1 Range Proof  
The goal of the range proof is to demonstrate that all elements of \(v\) lie within the range \([0,\ b-1]\), i.e., \(\left\lVert v\right\rVert_{\infty}<b\). This is achieved using a lookup argument with a predefined table \(T=[0,\ b-1]\). The steps are as follows:  
1. Create a sorted vector \(z=v\|u\), where u contains all values in \(T=[0,\ b-1]\).
2. Prove that z is a permutation of \(v\|u\).
3. Prove that consecutive elements in \(z\) differ by 0 or 1, ensuring that all values in \(v\) are within the specified range.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Security Analysis of the Proposed Chaos-Based Cryptosystem
#### 3.3.1 Performance Analysis of the Proposed Key-Dependent S-Box  
A strong S-box should have some important properties, based on an information theory analysis. The main properties, besides the bijectivity, are nonlinearity, strict avalanche criterion (SAC), output bits independence criterion (BIC), equiprobable input/output XOR distribution, differential approximation probability (DP), and linear approximation probability (LP). To demonstrate the performance of the proposed S-box, we quantified these properties and compared the results obtained with those obtained from several S-boxes in the literature.  
From Table 6, we can observe that the constructed S-box met all the requirements of a robust S-box and the performance obtained were close to those obtained by [42; 43; 44; 49; 50].  
You can see from Table 2 that the constructed S-box had all the different output values of the interval \([0,\ 255]\), so it satisfied the requirement of bijectivity.  
By comparing the properties of our S-box with other S-boxes, we can claim that the proposed chaos-based S-box was better than others in some measures. Therefore, the proposed S-box was suitable for use in the proposed encryption scheme.