esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Assessment and Repair Method of Runway
## 3 Numerical Simulation (LS-DYNA M&S)
### Construction of Simulated Runway Pavement  
For the modeling of materials, it is necessary to combine and analyze the Lagrangian method and the Eulerian method; the former is for numerical analysis of solid materials in order to interpret the multi-layered airfield pavement system; the latter is for numerical analysis of explosive pressure [18]. The modeling of solid materials consists of concrete slabs, subbase, and continuum for bedrock, in order to model the multi-layered airfield pavement. The explosion in the current numerical model is symmetrical to the spherical explosion, meaning that a pressure pulse occurs. In order to reduce the time for analysis, the point of explosion, as shown in Figure 2, was chosen to be the center as well as a quarter of the entire model, in order to confirm the form of the explosion. The RHT Concrete Model was applied for the concrete materials, and the Mohr-Coulomb Model was applied for the materials used for a base course, such as crushed stone. In addition, the Simplified Johnson-Cook Model and the High Explosive Burn Model were applied to model the penetration of a warhead and the explosion, respectively. For the EOS of the explosion, Jones-Wilkins-Lee (JWL) was applied [19; 20; 21].  
The field test is an experiment to confirm the scale of damage by TNT, which has the same weight level as the CBU warhead, penetrating 33 cm below the pavement in accordance with the formula, under the assumption that the freefalling cluster bomb unit does not penetrate the concrete pavement. The numerical simulation for modeling also installs and detonates the same size of PBXN-109 33 cm below the pavement. The 3D numerical model applied in this simulation only models a quarter of the concrete slab,  
Figure 1: Crater Diameter by LS-DYNA M&S.  
considering the symmetry. The material properties of the concrete slab are as follows in Table 4.  
For the material properties of the explosive device, the Density (\(\rho\)) was 1680 kg/m\({}^{3}\), and the Speed of Explosion (v) was 7650 m/s, which is the average explosion speed of PBXN-109 [22].
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 6 Limitations and Future Work
### Limitations  
One of the main operational constraints of SIBERIA lies in its reliance on persistent Internet connectivity. Core operations such as DID creation, credential issuance, and credential verification require continuous network availability. In industrial environments with intermittent or constrained connectivity, such as remote facilities, this dependency may hinder operational continuity.  
Additionally, while the mobile wallet adheres to privacy-by-design principles, its current implementation is limited to Android-based platforms. This restricts its applicabilityin contexts where other platforms such as iOS are prevalent and highlights the need for cross-platform alternatives.  
From an infrastructure perspective, the large number of operations involved in the authentication flow, such as multiple HTTP requests for presentation verification and credential status retrieval, can introduce significant latency under heavy system load. This challenge is further exacerbated by the reliance on blockchain interactions, where slow or unstable connectivity may degrade system responsiveness and user experience.  
Finally, the behavioral biometrics module is designed for deployment within the backend of the protected service, ensuring that sensitive data remains local. While this approach supports privacy and low-latency operation, it presumes the presence of a compatible backend infrastructure, which may limit integration in legacy or resource-constrained environments.
## 5 Discussion  
### Key Insights  
From the preliminary screening, it is evident that there is a notable lack of research focusing on the utilization of machine learning models for chronic kidney disease diagnosis and monitoring using planar scintigraphy and SPECT. The broader screening reveals that classic and novel deep learning methods have been widely explored in other nuclear imaging applications, such as cardiac and neurological SPECT images and whole-body planar scans. The limited research addressing renal scans and CKD diagnosis indicates an underexplored opportunity for leveraging deep learning techniques, such as convolutional neural networks, to enhance image quality, automate kidney segmentation, improve functional parameter estimation, and refine CKD classification. Moreover, the emerging development of new models capable of generating synthetic data and employing transfer learning enables the expansion of studies on previously underutilized datasets constrained by older methodologies.
## 3 Results and Discussion
### Four Quality Assessment
In flours from grain from the other years of the study, amylolytic enzyme activity was higher (means 290 and 309 s). The influence of meteorological conditions on FN is also indicated by other authors [20,48]. Our research also showed cultivar variation in this parameter. The significantly highest value of FN was characterised by flours from grain of the Serenada cultivar (mean 426 s), slightly lower from the Mandaryna cultivar (mean 390 s). The values of this parameter for flours from grain of the other cultivars (Harenda and Kandela) were similar (means 326 and 329 s). A significant effect of cultivar on FN was also shown by [PERSON] et al. [11], [PERSON] et al. [31], and [PERSON] et al. [32]. A significant effect of farming system on FN values was also found in this research. Flours from grain from CONV (mean 399 s) had the highest significant FN value. Flours from grain of the other farming systems were characterised by similar FN values (means 349 and 355 s). In a study by [PERSON] [27], there was no significant effect of farming system on the activity of amylolytic enzymes in flours from grain from the ORG and CONV systems, while [PERSON] et al. [32] showed that ORG flours were characterised by high activity of these enzymes (mean FN 90 s). Some authors [49, 50, 51] also point to the effect of nitrogen fertilisation and post-harvest grain storage conditions on flour FN. The optimum activity of amylolytic enzymes in flour for breadmaking should be at a medium level (FN in the  
Figure 1: Correlation between all study traits. Abbreviations: AC—ash content; PC—protein content; WG—wet gluten; GI—gluten index; FN—falling number; WA—water absorption; DT—development time; SD—stability; DS—degree of softening; QN—quality number; TBL—total baking loss; DY—dough yield; BY—bread yield; BV—bread volume; DP—Dallman porosity; CD—crumb density; RS—resilience; SP—springiness; HD—hardness. range 220-280 s). The use of flour with a high enzyme activity (FN below 150 s) for baking will result in a small volume and a flaky crust, while bread baked from flour with too low enzyme activity (FN above 300 s) has a compact crumb, showing a tendency to crumble and a spherical shape [48; 52]. Not all of the flours tested met the quality requirements for the FN parameter. The values of this parameter ranged from 185-599 s. The lowest FN, and thus the highest amyloid enzyme activity, was characterised by flour from wheat grain of the Mandaryna cultivar grown in ORG in 2019 (185 s, Table S1). In contrast, the highest FN was obtained for flour from wheat grain of the Serenada cultivar from ORG harvested in 2020 (599 s, Table S1). At the same time, flours from grain of this wheat cultivar, irrespective of the year of grain harvest and plant farming system, were characterised by low amyloid enzyme activity (FN \(\geq\) 300 s).
## 1 Introduction
### The Need for Therapies  
The large number of RV/EV strains and their antigenic diversity are significant obstacles in broad-spectrum vaccine development. Several clinical trials, evaluating the efficacy of small molecules, have been carried out in recent years, but there are no approved therapies targeting these viruses. Some of the reasons for these failures include viral resistance, single-targeted approaches, and lack of clinical efficacy, which pose significant obstacles in drug development [18; 19]. Moreover, the importance of combination therapies involving different therapeutic mechanisms to control a disease is well-known in clinical practice. Indeed, the current treatment recommendations against hepatitis C virus (HCV) mainly involve four different combinations of Direct-Acting Antiviral Agents DAAs [20]. For instance, combinations of NS5A inhibitors and/or NS5B polymerase and/or NS3/4 polymerase inhibitors (Sofosbuvir/Velpatasvir, Glecaprevir/Pibrentasvir, Grazoprevir/Elbasvir, Sofosbuvir/Velpatasvir/Voxilaprevir) are recommended.  
The treatment for HIV involves taking a combination of HIV drugs every day. Approved antiretroviral (ARV) HIV drugs are divided into eight drug classes based on how each drug interferes with the HIV life cycle. These eight classes are nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), fusion inhibitors, CCR5 antagonists, post-attachment inhibitors, integrase strand transfer inhibitors (INSTIs), and capsid inhibitors. Three or four classes of drugs are currently used per day according to the patient's individual needs, the resistance profile of the virus, adverse effects, interaction with other drugs, and the previous treatment failures.  
The main goal of this work is to identify promising compounds and to discuss multi-target directed ligand (MTDL) systems, from combination therapies to chimera design. The workflow is organized into two distinct parts. The first is a literature survey highlighting previous clinical trials involving NPEV inhibitors, and the second highlights selection criteria for potentially synthesized MTDL.
# Analysis of Bullet Impact Locations in the 10 m Air Pistol Men's Competition Based on Covariance
## 3 Methods
### Feature Indicators  
The feature indicators used in this study include x-variance, y-variance, covariance, x-mean, y-mean, root mean square error, x-mean score, and y-mean score. These key indicators carry the following meanings.  
* X-variance  
The x-variance represents the distribution of data in the x-axis direction, indicating the range of left-right shot impacts. A large x-variance indicates that the data are widely spread in the left-right direction.  
* Y-variance  
The y-variance represents the distribution of data in the y-axis direction, indicating the range of shot impacts in the up-down direction. A large y-variance indicates that the data are widely spread in the up-down direction.  
Figure 2: Example of collected ISSF image data (6 target results per player).  
The covariance represents the relationship between x-variance and y-variance variables, indicating the correlation between left-right aiming and up-down aiming. If the covariance value is positive, it means there is a tendency for left-right aiming and up-down aiming to increase or decrease together, whereas if it is negative, it indicates a tendency for up-down aiming to decrease when left-right aiming increases.  
* X-mean  
X-mean represents the average of the x-axis data, indicating the central position of the overall left-right shot impact data. A higher x-mean value indicates deviation from the central value 0, reflecting the accuracy of left-right aiming.  
* Y-mean  
Y-mean represents the average of the y-axis data, indicating the central position of the overall up-down shot impact data. A higher y-mean value indicates deviation from the central value 0, reflecting the accuracy of up-down aiming.  
* Root mean square error (RMSE)  
RMSE is a metric for evaluating the precision of bullet impact locations, with a smaller RMSE value indicating more precise bullet impact locations.  
* X-mean score  
X-mean score represents the accuracy of scores in the left-right direction, calculated as the average value of scores based on the x-axis. A higher x-mean score value indicates greater accuracy in the left-right direction.  
* Y-mean score  
Y-mean score represents the accuracy of scores in the up-down direction, calculated as the average value of scores based on the y-axis. A higher y mean score value indicates greater accuracy in the up-down direction.  
Through correlation analysis between specific indicators and rankings, we aim to identify the key factors influencing rankings and formulate strategies to improve athletes' performance.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions  
To demonstrate the effectiveness and superiority of our scheme, experiments were conducted on six test images sized \(512\times 512\): Baboon, Beeflower, Goldgate, Lena, London, and Peppers (see Figure 5). The choice of free parameters is tabulated in Table 2. For simplicity of calculation, \(\alpha\) was set to 1 and \(\beta\) was set to 8. Thus, \(n=\alpha\times\beta=8\). \(q\) was 4 because we embedded four bits to each block. The environment of our experiments was based on a personal computer with a 3.2 GHz Intel i5 processor, 4 GB memory, Windows 10 operating system, and Matlab R2016a. In the following subsections, five aspects of experimental results are presented: (1) reversibility; (2) security; (3) embedding capacity; (4) visual quality; and (5) computational complexity. Finally, we give the feature comparisons with related schemes.  
To view the result image of each procedure intuitively, we take Figure 5b as an example. Figure 6a shows the original halftone image, and Figure 6b is the corresponding encrypted result \(I\). We can observe that principal contents of original image \(O\) were effectively masked after encryption. Figure 6c shows the stego image \(C\), which is embedded with watermark and displays at 50%. Figure 6d is the recovered image \(O^{\prime}\), which is exactly the same as the original image \(O\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Parameter & \(\alpha\) & \(\beta\) & \(q\) & \(n\) \\ \hline Value & 1 & 8 & 4 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of free parameters.  
Figure 4: The diagram of watermark extraction and image recovery.  
Figure 5: Original image \(\mathbf{O}\): (**a**) Baboon; (**b**) Beeflower; (**c**) Goldgate; (**d**) Lena; (**e**) London; and (**f**) Peppers.  
Figure 6: The whole images of every procedure with Beeflower of blocks size \(1\times 8\).
## 4 Experimental Results
### Experimental Results on CIFAR-100  
The results of the test for the CIFAR-100 dataset are shown in Table 2. For ResNet-20, we obtain an 8.92% increase in accuracy compared to the Tucker decomposition method,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & **Compression** & \multirow{2}{*}{**Top-1 Acc (\%)**} & **Compression** \\  & & **Method** & & **Ratio** \\ \hline \multirow{8}{*}{Resnet-20} & Standard Tucker [10] & Tucker & \(-\)3.84 & 2.6\(\times\) \\  & Standard Tensor Train [47] & Tensor Train & \(-\)4.55 & 5.4\(\times\) \\  & Standard Tensor Ring [48] & Tensor Ring & \(-\)3.75 & 5.4\(\times\) \\  & PSTR-S [46] & Tensor Ring & \(-\)0.45 & 2.5\(\times\) \\  & PSTR-M [46] & Tensor Ring & \(-\)2.75 & 6.8\(\times\) \\  & Ours & CP & \(-\)0.31 & 2.6\(\times\) \\  & Ours & CP & \(-\)1.82 & 6.8\(\times\) \\ \hline \multirow{8}{*}{Resnet-32} & Standard Tucker [10] & Tucker & \(-\)4.79 & 5.1\(\times\) \\  & Standard Tensor Train [47] & Tensor Train & \(-\)4.19 & 4.8\(\times\) \\ \cline{1-1}  & Standard Tensor Ring [48] & Tensor Ring & \(-\)1.89 & 5.1\(\times\) \\ \cline{1-1}  & PSTR-S [46] & Tensor Ring & \(-\)1.05 & 2.7\(\times\) \\ \cline{1-1}  & PSTR-M [46] & Tensor Ring & \(-\)1.89 & 5.8\(\times\) \\ \cline{1-1}  & Ours & CP & \(-\)0.45 & 2.8\(\times\) \\ \cline{1-1}  & Ours & CP & \(-\)1.31 & 5.8\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with various tensor decomposition methods for ResNet-20 and ResNet-32 on CIFAR-10 dataset.  
while also achieving a higher compression ratio. When compared to the PSTR-S [46], our approach achieves 0.32% better accuracy and a 0.3\(\times\) higher compression ratio. In comparison to the original uncompressed model, this performance is even better by 1.05%. Even when the desired compression ratio is increased to 4.7\(\times\), our approach continues to surpass the PSTR-M [46] with 0.46% better accuracy. For ResNet-32, the proposed approach obtains an accuracy increase of 8.82% and 0.2%, respectively, over standard Tucker decomposition and PSTR-S [46]. Furthermore, with the same 5.2\(\times\) compression ratio, our method achieves a 0.52% increase in accuracy compared to PSTR-M [46].
## 4 Discussion  
Based on the results obtained with the SDEMDI method, it can be concluded that after each integration process, there is a certain degree of filtering in the signal. Moreover, the deviations are more significant when calculating the displacement. The selection of the degree of the polynomial used to calculate the trend that originates from the integration is somehow arbitrary and it is left to the researcher's discretion. For the analyzed cases, the final spectra for speed and displacement continue to show significant trends. It can also be noted that as trends are removed, a more symmetrical vibration is obtained. This method depends on the initial conditions, which are set to zero. It can also be concluded that for more complex vibrations, the largest trends are obtained. Consequently, the graphs showed a low density in the vibratory behavior, and the quantification of the recurrence result confirmed the above findings; however, it was possible to distinguish and quantify the effects due to the inclusion of friction both graphically and by RP.  
In contrast, the results obtained with the proposed PSEMDI method showed spectra with more symmetry, uniformity, and consistency. They practically did not show a low-frequency trend because the process does not apply the integration by finite differences as the first method does. The results do not depend on the initial values, nor on the subjective estimation of the degree of the polynomial used to eliminate the trend. The EMD is applied  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **U700** & **UF700** & **U1800** & **UF1800** \\ \hline \(RR\) & 0.06 & 0.06 & 0.06 & 0.05 \\ \hline \(DET\) & 0.15 & 0.17 & 0.15 & 0.17 \\ \hline \(L\) & 2.51 & 2.50 & 2.45 & 2.58 \\ \hline \(L_{max}\) & 8 & 9 & 7 & 9 \\ \hline \(ENTR\) & 0.95 & 0.95 & 0.89 & 1.02 \\ \hline \(LAM\) & 0.19 & 0.25 & 0.22 & 0.13 \\ \hline \(TT\) & 2.13 & 2.42 & 2.30 & 2.10 \\ \hline \(V_{max}\) & 5 & 7 & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantification of PSEMDI results.  
Figure 18: Quantification analysis of the experimental data: SDEMDI (M1); PSEMDI (M2).  
only once, and the speed and displacement are calculated with the same IMF spectra, considering only their respective phase shifts. Another fundamental characteristic of the proposed method is that it retains the exact nature of EMD and RP regarding their ability to analyze nonlinear and nonstationary systems. All these characteristics can be easily verified with the phase diagrams, the RP, and the quantification of the recurrence parameters; however, it was nonetheless possible to identify the effect of friction on the results.
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 7 Results and Discussion
Before that, Figure 12 shows Dynamic-RPL added to the time taken using the network to converge after being initiated. Compared to standard RPL, that was less than 6 s even in the relatively more complex and large-scale setup of S2. This extra time was taken using the global-DODAG establishment stage, which imposed additional communications after each completely-established-local-DODAG. In regard to how responsive it was, Dynamic-RPL did not need much time to resolve the stability and load issues as indicated in Figure 13. Only up to 23 s was required to restore the overall performance of the network. Figure 14 also shows that each dynamic operation imposed using Dynamic-RPL took as little as a maximum of 7 s to be completely performed. While Dynamic-RPL was able to adapt with a reasonably low delay, standard RPL continued to struggle with the challenging situations without being able to effectively settle down. Although they demonstrated similar performance variations for both experimental setups, all the observations were more significant in the case of S2. This is due to its relatively large-scale setup, which was developed in a more complex form with a multi-DODAG topology. Increasing the size and complexity of the network topology had noticeable performance impacts on both protocol variants. However, this is more evident in the case of standard RPL under unstable network conditions. Dynamic-RPL experienced less performance variation as the network scaled up in all the scenarios. The comparison provided in Table 5 demonstrates the efficiency and feasibility of Dynamic-RPL compared to relevant existing solutions. Although Refs. [20; 24] provided high PDR results, Dynamic-RPL achieved similar performance with the least network overhead rate. It succeeded in reducing network overhead by more than 13%. It is apparent that Dynamic-RPL provides a promising dynamic topology management solution. It effectively guarantees competitive overall performance, better adaptation to network deteriorations, and more dynamic and effective functionality. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Setup** & **PDR** & **Throughput** & **Delay** & **ETX** \\ \hline \multirow{2}{*}{RPL} & S1 & 91.37 & 203.62 & 199.14 & 287.9 \\  & S2 & 84.92 & 289.66 & 223.59 & 375.38 \\ \multirow{2}{*}{Dynamic-RPL} & S1 & 96.04 & 227.69 & 131.15 & 206.22 \\  & S2 & 91.44 & 323.78 & 138.61 & 266.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: QoS Performance Results During Adverse Network Conditions. Figure 14: Dynamic-RPL topological adjustment time. Figure 12: Network convergence results. Figure 13: Dynamic-RPL performance resolution time.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Adaptive Perturbation Control Strategy  
An adaptive perturbation control strategy is developed to enhance exploration in the multi-objective solution space. Since higher observation profits are often accompanied by larger perturbations, the adaptive perturbation control strategy gradually increases the maximum allowable perturbation to control the MODMSA to search for different multi-objective solution space regions. In the MODMSA, the perturbation mainly occurs during the multi-point crossover and greedy mutation and the profit-oriented local search algorithm. In addition, the maximize allowable perturbations are determined by the crossover probability \(P_{C}\), mutation probability \(P_{M}\), removal probability \(P_{D}\), and repair probability \(P_{R}\). Therefore, the adaptive perturbation control strategy expands the maximum allowable perturbation by linearly increasing these probabilities. The probabilities are updated as follows:  
\[P_{C}=\min\left(P_{C}^{m},\frac{n\cdot\epsilon_{C}}{N_{\max}}\right) \tag{17}\] \[P_{M}=\min\left(P_{M}^{m},\frac{n\cdot\epsilon_{M}}{N_{\max}}\right)\] (18) \[P_{D}=\min\left(P_{D}^{m},\frac{n\cdot\epsilon_{D}}{N_{\max}}\right)\] (19) \[P_{R}=\min\left(1,\frac{k\cdot\epsilon_{R}}{N_{\max}}\right) \tag{20}\]  
where \(n\) and \(N_{max}\) denote the current and maximum number of iterations of the MODMSA, \(\epsilon_{C}\), \(\epsilon_{M}\), \(\epsilon_{D}\), and \(\epsilon_{R}\) are the coefficients, and \(P_{C}^{m}\), \(P_{M}^{m}\), and \(P_{D}^{m}\) are the maximum crossover probability, maximum mutation probability, and maximum removal probability, respectively.