esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### Watermarking Method  
The proposed watermarking algorithm is based on calculating an HMAC, which provides both integrity protection and data authenticity [34]. However, using HMAC also makes the embedded watermark in the model fragile. This is because the HMAC code is based on hash functions, which means that changing even one bit in the input data causes a significant change in the bits in the resulting hash [35].  
The developed method consists of four main steps, shown in Figure 3. The first step involves determining the vertices where the watermark data will be hidden. The second step involves zeroing, for the appropriate number of vertices in the 3D model, the specified number of bits in the selected vertex attribute, reassembling the modified model and saving it in a new temporary glb file. The next step is calculating the HMAC code based on the entire newly created glb file containing the model after the zeroing bits step. The calculated HMAC is then appended to the model by replacing the previously zeroed bits with the bits of the generated HMAC, then saving the marked model to the glb file. This constitutes the final step in the process. As a result, the model is marked with a fragile, invisible watermark that makes it possible to detect a change of even a single bit in the entire file and thus allows confirmation or denial of the authenticity of the data.  
Figure 3: Scheme of the watermarking algorithm.  
Figure 2: Structure of the glb file. Retrieved from [[https://www.khronos.org/files/glt20-reference-guide.pdf](https://www.khronos.org/files/glt20-reference-guide.pdf)]([https://www.khronos.org/files/glt20-reference-guide.pdf](https://www.khronos.org/files/glt20-reference-guide.pdf)) (accessed on 18 June 2025).
## 4 Discussion of Simulation Results
### Self-Propulsion Simulation
#### 4.1.2 Evaluation of Propeller Performance in Self-Propulsion Point
From the analysis of Figure 22d, it can be seen that, when the diamond wing underwater glider is operated at 6 kn, 5 kn, 4 kn, and 3 kn speed self-propelled point conditions, in addition to the conventional propeller Kap00, the propulsion efficiency \(\eta\) gradually increases with the increase in the bending degree of the end plate of the series of Kappel propellers. Among these, the propellers Kap04 and Kap05 perform the best, which once again proves that the bending of the  
Figure 21: Schematic diagram of wake section location. propeller end plate can effectively improve the efficiency of the propeller. It can improve the overall performance of hybrid-driven underwater gliders.
# Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
## 4 Conclusions  
In this paper, a fast numerical reconstruction of II based on a determined interval mapping is proposed for reducing computation time. The proposed method does not require the magnification process by interpolation and large memory size during the numerical reconstruction of II. Accordingly, the computation time can be reduced significantly. To achieve the proposed method, the displayed EIs through the corresponding virtual pinhole are first transformed into flipped EIs. These flipped EIs are shifted to match the determined interval calculated depending on the reconstruction plane and superimposed together. After this superimposed image is divided by the number of the superposition, the position error between the location of the shifted EI and the pixel position of the reconstruction plane is corrected by interpolation. Finally, the refocused image is reconstructed based on the relatively short computation time. To validate the feasibility of the proposed method, two numerical experiments related to the computation time and the quality comparison of each refocused image were carried out. By conducting an experiment to measure computation time, the proposed method can yield a refocused image faster than the conventional method. Furthermore, the computational time of the proposed method varies slightly depending on the type of interpolation, such as the nearest, linear, and bicubic methods. To accurately measure the reconstruction quality, the SSIM was used to compare the quantitative quality measurement between the reference image and each reconstructed image. Through the experimental result, we confirmed that the proposed method outperforms the conventional method in terms of reconstruction quality, regardless of the type of interpolation used. From both numerical experiments, we confirmed that the proposed method can improve the computation time and the numerical reconstruction quality.  
Conceptualization, H.C.; methodology, H.C., N.K. and H.K.; software, H.C.; validation, H.C., N.K. and H.K.; formal analysis, H.C.; investigation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, N.K. and H.K.; visualization, N.K. and H.K.; supervision, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1 IIA3071771). This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-00343).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 SECHash Proof-of-Concept
### SECHach Evaluation and Discussion
#### 4.3.2 Data Security Analysis  
SECHash achieves data integrity and authority based on the security of blockchain technology. The security of the SECHash system is based on the LBRY blockchain [53]. Data integrity is achieved by leveraging the technologies and concepts used in the LBRY. When documents are recorded in the blockchain network, changing or modifying them is difficult. This is due to the consensus mechanism in approving and confirming each transaction within each new block. Thus, any tiny change in the approved documents needs massive power of calculations. At the same time, it is easy to validate every document saved on the network using hashing algorithms. SECHash also achieves data authority. Since all the documents are hashed, and the data are encrypted, only authorised users can access these documents. In addition, using OCR technology will reduce the potential risk of human errors and data entry. Furthermore, the security features of SECHash are unaffected in the application domain.  
In comparison, systems were proposed in previous studies and attempted to be applied to be effective in a specific field. At the same time, these systems lose their effectiveness and ability to save documents as required if used in other areas.
# Seismic Effect of Marine Corrosion and CFRP Reinforcement on Wind Turbine Tower
## 4 Shaking Table Test
### Comparison of FE Simulation and Test Results  
According to the shaking table test model, the finite element model of the wind turbine with the same size as the experiment is established using ABAQUS2020. The towers are  
Figure 30: Comparison of structural dynamic response under El Centro earthquake: (a) Acceleration Peak comparison; (b) Displacement peak comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**Wall**} & \multicolumn{2}{c}{**35 Gal**} & \multicolumn{2}{c}{**70 Gal**} & \multicolumn{2}{c}{**Rate of**} \\ \cline{3-8}  & & **Without** & **With** & **CFRP** & **Change** & **Without** & **With** & **Change** \\ \hline \multirow{4}{*}{El Centro} & 2.5 & 0.0625 & 0.072 & 15.20\% & 0.14 & 0.165 & 17.90\% \\  & 2.65 & 0.071 & 0.081 & 14.10\% & 0.16 & 0.18 & 12.50\% \\  & 2.8 & 0.08 & 0.09 & 12.50\% & 0.182 & 0.22 & 20.90\% \\  & 3 & 0.125 & 0.125 & - & 0.319 & 0.319 & - \\ \hline \multirow{4}{*}{Nihonkai-Chubu} & 2.5 & 0.088 & 0.093 & 5.70\% & 0.179 & 0.19 & 6.10\% \\  & 2.65 & 0.095 & 0.1 & 5.30\% & 0.2 & 0.23 & 15.00\% \\  & 2.8 & 0.115 & 0.12 & 4.30\% & 0.228 & 0.26 & 14.00\% \\  & 3 & 0.134 & 0.134 & - & 0.355 & 0.355 & - \\ \hline \multirow{4}{*}{Hyogoken-Nanbu} & 2.5 & 0.063 & 0.075 & 19.00\% & 0.129 & 0.15 & 16.30\% \\  & 2.65 & 0.08 & 0.09 & 12.50\% & 0.151 & 0.18 & 19.20\% \\  & 2.8 & 0.098 & 0.106 & 8.20\% & 0.192 & 0.23 & 19.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of structural acceleration response.  
Figure 29: Dynamic response of structure under the Hyogoken-Nanbu earthquake: (a) Comparison of acceleration peak; (b) Comparison of displacement peak.  
connected by flanges and bolts. The tower adopts the S4R element, and other components adopt the C3D8R element. The finite element model is shown in Figure 31.  
The dynamic response characteristics of the structure under three different earthquake actions are simulated, respectively. The peak acceleration and displacement at the top of the tower are extracted and compared with the experimental results. As shown in Figures 32-34, where EX and FE represent experimental values and finite element simulation values, respectively.  
Figure 31: Scale finite element model.  
Figure 32: Comparison under the El Centro earthquake: (**a**) Comparison of acceleration peak; (**b**) Comparison of displacement peak.  
Comparing the experimental results and finite element simulation results under three different earthquakes. It can be seen that the difference value of peak acceleration is 2.4-16.8%. Three of these data sets exceed 15%. Other difference ranges are within the allowable range of the project. The range of peak displacement differences is 0.6-16.7%. There are also three groups of data exceeding 15%. Other difference ranges are within the allowable range of the project. The proportion of numerical differences exceeding the limit is 6.25%. It can be concluded that the finite element simulation results have good relativity with the experimental results.
## 1 Introduction  
Forest ecosystems contain the highest organic carbon stock among terrestrial ecosystems [1; 2]. Forest systems cover more than 4.1 \(\times\) 10\({}^{9}\) hectares of the Earth's land area [3]. In these ecosystems, organic carbon accumulates in the biomass of trees, shrubs, and herbaceous plants, as well as in the soil horizons that form the soil profile. It is estimated that the carbon contained in soil constitutes 75% of the total organic carbon stock stored in terrestrial ecosystems, and it is twice the amount of carbon stock in the atmosphere [3; 4]. Soil carbon (C) stock is influenced by several environmental factors, such as the topography, slope, exposure, elevation, climate, parent material, and vegetation [5; 6; 7; 8]. Site properties (altitude, exposition, slope) influence the soil conditions and soil properties. Above all, vegetation affects soil parameters because it supplies organic matter that varies in quantity and quality [9].  
Mountain areas are characterized by climatic and topographic factors that are highly variable, and this variation results in the diversification of vegetation and, as a consequence, differences in soil properties among locations. Depending on the definition, mountain areas cover roughly 22-27% of the Earth's total land area [10]. According to [11], mountain soils are highly dynamic systems that are sensitive to environmental changes. Soil fertility and productivity depend on soil organic matter (SOM), which is a reservoir of nutrients and plays an important role in cycling nutrients and improving the physical, chemical, and biological properties of soils [12; 13]. The amount and quality of soil organic matter depend on forest land management. The amount of SOM in forest soils is determined by the balance between soil organic matter input by on the one hand, and the release of C during decomposition on the other hand [14]. Changing the land use from natural forest to plantations affects the C stored in the soil [15]. According to [16], ecosystem C stock in plant and soil pools was 284 Mg C ha\({}^{-1}\) in natural forests and decreased by 28% in plantations. In the study in [17], the land-use system and altitude were shown to be important factors in the regulation of SOM decomposition by altering the natural soil characteristics. Unmanaged or old-growth forests are important for carbon sequestration [18].  
The mechanisms responsible for carbon stabilization in soil have received much interest recently because they are crucial for understanding the global carbon cycle. Knowledge about the factors that shape the accumulation of organic carbon in the soil is important for understanding the carbon cycle in mountain forest ecosystems. The primary objective of this study was to quantify the soil organic carbon stock (T-SOCstock) in sites with different conditions in the Babia Gora National Park (BNP). We predicted that topographical factors influence the soil conditions and vegetation, leading to a diversity of carbon accumulation in the mountain soils of the BNP.
## 4 Results
### Comparison of Machine Learning to Queueing Theory Predictions
#### 4.2.1 Error Metrics Analysis  
Across each error-based indicator, the ML model surpasses the queueing theory approach:  
- MSE: 61.6% lower, marking a substantial jump in prediction accuracy.  
- RMSE: 38% improvement, showing a significant cut in overall error.  
- MAE: 38.4% smaller, reflecting tighter clustering around true values.  
- MAPE: Decline from 11.87% to 7.21%, a 39.26% surge in accuracy.  
Meanwhile, R\({}^{2}\) shows a 12.07% boost (0.938 vs. 0.837), which indicates that the ML model accounts for a larger share of total variance in usage. The heightened queue length accuracy (QLA: +8.19%) and waiting time accuracy (WTA: +8.48%) further highlight the ML model's capacity to anticipate service demands.  
As seen in Figure 12, differences in error metrics like MSE, RMSE, MAE, and MAPE are visually evident, highlighting how the ML approach narrows the gap between estimates and actual data. Figure 13 spotlights the percentages by which the ML model improves on each category (see Table 6).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Improvement** \\ \hline MSE & 61.6\% \\ RMSE, MAE, MAPE & \(\sim\)38–39\% \\ R\({}^{2}\), QLA, WTA & 8–12\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Error metrics.  
Figure 12: Error metrics: ML model vs. queueing theory.  
Figure 14 highlights the model's superior accuracy metrics--R\({}^{2}\), QLA, and WTA--relative to queueing theory outputs.  
Scenario-Specific Improvements  
The ML model also excelled in specialized circumstances:  
- Highly Variable Occupancy: Notable 52% drop in MAPE under fluctuating conditions.  
- Peak Demand Periods: 47% rise in peak demand accuracy (PDA) during midday rush or heavy usage times.  
- Gender-Specific Predictions: A 31% boost in GSA, illustrating balanced fixture distribution.  
- Special Events: 63% decrease in errors for non-standard usage spikes.  
In Figure 15, the greatest gains occur under scenarios involving special events (+63%) and highly variable occupancy (+52%).  
Computational Efficiency  
On standard desktop hardware, the ML model produces daily estimates in about 0.087 s, indicating scalability without excessive resource demands.  
Figure 14: Accuracy metrics: ML model vs. queueing theory.  
Figure 13: Improvement of ML model over queueing theory.
# Aerial Physical Interaction in Grabbing Conditions with Lightweight and Compliant Dual Arms
## 4 Control
### Force Control Based on Cartesian Deflection  
The force control of the compliant arm is formulated in the Cartesian space and based on the Cartesian deflection, defined as the position deviation of the TCP of the compliant arm w.r.t. the same point in an equivalent stiff joint arm. That is,  
\[\mathbf{\Delta}I_{TCPi}=\mathbf{F}\mathbf{K}\mathbf{\left(\theta^{i}\right)-\mathbf{F}\mathbf{K}\mathbf{ \left(q^{i}\right)}} \tag{18}\]  
This definition is useful for expressing the force at the end effector directly in the task space:  
\[\mathbf{i}\mathbf{F}_{TCPi}=\mathbf{K}_{C}^{i}\mathbf{\Delta}I_{TCPi}+\mathbf{D}_{C}^{i}\dot{ \mathbf{\Delta}}I_{TCPi} \tag{19}\]  
Figure 7: Control scheme of the aerial manipulator with grabbing arm (\(i=2\)).  
where \(\mathbf{K}_{C}^{i}\) and \(\mathbf{D}_{C}^{i}\) are the Cartesian stiffness and damping matrices, respectively, whose values can be obtained from the physical joint stiffness and damping through the Jacobian:  
\[\mathbf{K}_{C}^{i}=\left(\mathbf{J}^{T}\right)^{-1}\mathbf{K}_{p}^{i}\mathbf{J}^{-1} \tag{20}\]  
where \(\mathbf{K}_{p}^{i}=diag\Big{\{}k_{j}^{i}\Big{\}}\) is the physical joint stiffness matrix [17]. The Cartesian damping can be obtained analogously. The force controller, represented in Figure 7 relies on the inverse kinematics, giving as output an incremental position correction term for the TCP:  
\[\mathbf{i}^{ref}_{TCPi}=\mathbf{i}^{i}\mathbf{r}_{TCPi}+\mathbf{\Delta}\mathbf{r}_{TCP}^{i}=\bm {F}\mathbf{K}\Big{(}\mathbf{q}^{i}\Big{)}+\left(\mathbf{K}_{P}^{F}\mathbf{F}_{\epsilon,TCP}^{ i}+\mathbf{K}_{I}^{F}\int\mathbf{F}_{\epsilon,TCP}^{i}dt\right) \tag{21}\]  
Here, \(\mathbf{\Delta}\mathbf{r}_{TCP}^{i}\) is the position increment that should be applied in the grabbing arm to achieve the desired force reference; \(\mathbf{F}_{\epsilon,TCP}^{i}=\mathbf{F}_{ref}^{i}-\mathbf{F}_{TCP}^{i}\) is the force control error at the TCP; whereas \(\mathbf{K}_{P}^{F}=diag\Big{\{}K_{Px}^{F},\,K_{Py^{\prime}}^{F},\,K_{Pz}^{F} \Big{\}}\) and \(\mathbf{K}_{I}^{F}=diag\Big{\{}K_{lx^{\prime}}^{F}\,K_{ly^{\prime}}^{F},\,K_{lz} ^{F}\Big{\}}\) are the proportional and integral gain matrices, respectively, whose value is tuned experimentally knowing the nominal values of the forces (around \(1\)\([N]\)) and the Cartesian deflection (around \(0.02\)\([m]\)).
## 3 Results and Discussion
### Biomass Growth
Indeed, in field trials conducted by [25] with _Atriplex halimus_ over 16 months, organic amendments significantly increased shoot and root biomass. Similarly, [27] reported better growth of _Atriplex halimus_ after one year when manure was used as an amendment compared to pine bark compost. An increased shoot and root biomass was also reported after 95 days in contaminated soils amended with spent mushroom compost [26]. The water content analysis (Figure 2B) showed that roots in CP + CI had significantly higher water content than those in the other substrates. This differs from the findings of [10; 52], who reported no significant differences in root water content across treatments. In sprouts (Figure 2B), water content was higher in B and CP + CI compared to CP. This pattern may reflect the positive effect of compost on water retention, consistent with the known ability of compost to increase substrate moisture [53]. Increased water content in halophyte tissues under moderate salinity has been previously reported for _Atriplex halimus_[54; 55; 56]. Furthermore, [54] observed that moderate salinity stimulated rooting in _Atriplex halimus_, which could explain the improved root water content in CP + CI. Overall, the results suggest that _Atriplex halimus_ can successfully establish roots and produce new biomass in contaminated mine tailings. The presence of amendments--particularly compost--appears to enhance root development and water retention, although a longer growth period may be required to maximize shoot biomass production.
###### Abstract  
Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API SL X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged from \(-6.33\%\) to \(2.39\%\) when compared to an unseen FEA dataset, with a correlation value (\(R^{2}\)) of \(0.9975\). A parametric study was subsequently performed using the equation to determine the effects of material property, defect depth, defect length, and longitudinal compressive stress on the remaining strength of pipelines with a single corrosion defect. Defect depth reduced the failure pressure by more than \(65\%\) on average, longitudinal compressive stress by more than \(20\%\) on average, and defect length by more than \(21\%\) on average.  
artificial neural network; remaining strength equation; corroded pipeline; single defect; combined loadings; finite element analysis +
Footnote †: journal: Applied sciences
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 6 Energy Efficiency  
As outlined in Section 1, in order to address problems of practical utility, quantum microprocessors need to protect the qubits against errors by adopting QEC codes. Nevertheless, these codes require fidelity that is better than a threshold value \(F_{threshold}\)[37; 50]. A QEC code implies merging \(N_{qubit}\) physical qubits into one logic qubit to achieve a desired logic error rate \(\epsilon_{logic}\), which decreases with increasing \(N_{qubit}\)[51]:  
\[\epsilon_{logic}=\textbf{{K}}_{1}\Bigg{(}\sqrt{\frac{\textbf{1}-F}{\textbf{1 }-F_{threshold}}}\Bigg{)}^{\frac{1}{2}(\sqrt{\frac{\textbf{{V}_{qubit}}^{ \textrm{TT}}}{2}+\textbf{1}})} \tag{69}\]  
with \(\textbf{{K}}_{1}\) being a suitable constant. By emphasizing the definition of the error rate of a quantum gate given in Section 3, Equation (69) can be reformulated as follows:  
\[\epsilon_{logic}=\textbf{{K}}_{1}\Bigg{(}\sqrt{\frac{\textbf{1}-F}{\epsilon_ {threshold}}}\Bigg{)}^{\frac{1}{2}(\sqrt{\frac{\textbf{{V}_{qubit}}^{ \textrm{TT}}}{2}+\textbf{1}})} \tag{70}\]  
where \(\epsilon_{threshold}\) is the error rate threshold required by the QEC code. From Equation (70), you can obtain:  
\[\textbf{{N}}_{qubit}=\textbf{2}\left[\textbf{1}-2\frac{log(\epsilon_{logic} )-log(\textbf{{K}}_{1})}{log\Big{(}\frac{1-F}{\epsilon_{threshold}}\Big{)}} \right]^{2}-\textbf{1}\cong\textbf{8}\left[\frac{log(\epsilon_{logic})}{log \Big{(}\frac{1-F}{\epsilon_{threshold}}\Big{)}}\right]^{2} \tag{71}\]  
where the approximation is based on \(\epsilon_{logic}\) being very small, in the order of \(\textbf{10}^{-\textbf{10}}\)[51]. By defining \(\textbf{{P}}_{qubit}\) the power dissipated to control each physical qubit, Equation (71) provides the total dissipated power \(\textbf{{P}}_{logical}\) for the logical qubit:  
\[\textbf{{P}}_{logical}=\textbf{{P}}_{qubit}\left[\frac{log(\epsilon_{logic})} {log\Big{(}\frac{1-F}{\epsilon_{threshold}}\Big{)}}\right]^{2} \tag{72}\]  
where \(\textbf{{P}}_{qubit}\) depends on the adopted qubit-driving method [52]. The power \(\textbf{{P}}_{logical}\) appears, magnified by the ratio between the room \(\textbf{{T}}_{room}\) and the qubit cryogenic \(\textbf{{T}}_{qubit}\) temperatures, in the power dissipated by the cryostat \(\textbf{{P}}_{cryo}\)[53]:  
\[\textbf{{P}}_{cryo}=\frac{\textbf{{T}}_{room}}{\textbf{{T}}_{qubit}}\textbf {{P}}_{qubit}\Bigg{[}\frac{log(\epsilon_{logic})}{log\Big{(}\frac{\textbf{1} -F}{\epsilon_{threshold}}\Big{)}}\Bigg{]}^{2} \tag{73}\]  
Equation (73) demonstrates the importance of achieving a quantum gate fidelity that is as high as possible, as this makes the ratio \((1-F)/\epsilon_{threshold}\) lower and reduces, in this way, \(\textbf{{P}}_{cryo}\). Equation (73) also shows that hot qubits may help in reducing \(\textbf{{P}}_{QCPU}\), because they reduce the temperature ratio \(T_{room}/\textbf{{T}}_{qubit}\). Interestingly, one- and two-qubit quantum gates encoded in semiconductor electron spins operating above 1 K have been recently demonstrated to produce fidelity, respectively, of 99.6% and 98.9% [54].