esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Observations and Discussions
### Modeling of \(k_{s}\)  
The values of \(k_{sc}\) for the raft models with different dimensions were obtained to show their relationships with the shear wave velocities of the soils. Figure 11 presents the plots of \(k_{sc}\) vs. \(V_{s}\). The medians of \(k_{sc}\) were used to suggest the interpolation functions in representing the relationships. The resulting functions are presented in Table 4. It should be pointed out that the functions can yield more accurate \(k_{sc}\) in cases where foundation dimension is greater than 20 m. The coefficient of subgrade reaction underneath the raft \(k_{s}\) can be calculated using the following equation:  
\[k_{s}(x,y)=k_{sc}\times h(x,y)=k_{sc}\times f(x)\times f(y), \tag{4}\]  
where \(h(x,y)\) is a 2D function formed by multiplying the normalized function \(f(x)\) with \(g(y)\). \(g(y)\) has the same expression as \(f(x)\) replacing \(x\) by \(y\). Note that \(x\) is the distance between the center and point of interest in the width direction, and \(0\leq x\leq B/2\) (for square raft foundation, \(B=L\)). Similarly, \(y\) is the distance between the center and the point of interest in the length direction; \(0\leq y\leq L/2\). The normalization function \(f(x)\) values are listed in Table 5. By knowing the averaged shear wave velocity of the soils at the site and the designed width of the raft foundation, engineers can use Equation (4) with the values of \(k_{sc}\) from Table 4 and the normalized functions from Table 5 to compute the coefficients of subgrade reactions \(k_{s}\) for foundation analysis.  
\begin{table}
\begin{tabular}{c c c} \hline
**Soil** & \(\mathbf{k_{sc}}\) **(Units in kN/m/m2)** & \(\mathbf{V_{s}}\) **(Units in m/s)** \\ \hline
**Clay** & \(k_{sc}=0.9956(V_{s})^{2}-190.378V_{s}+9888.4\) & \(120\leq V_{s}\leq 180\) \\
**Sand** & \(k_{sc}=0.3721(V_{s})^{2}-7.6883V_{s}+588.467\) & \(120\leq V_{s}\leq 180\) \\ \hline \end{tabular}
\end{table}
Table 4: The suggested functions of coefficient of subgrade reaction at the center of the foundation based on shear wave velocity of the soil.  
Figure 11: Interpolation function of the coefficient of subgrade reaction \(k_{sc}\) at the foundation center based on the shear wave velocity of soils: (**a**) rafts in clays, (**b**) rafts in sands.
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 3 Concepts and Evaluation Methods
### Public IPFS-Filecoin  
IPFS can be implemented in both a private and a public setup. Each paradigm involves different intricacies that need to be considered. On the one hand, public IPFS solutions offer a broad decentralization and may spread all around the globe while, on the other hand, private setups do not require a financial incentive to be run and also allow for generally better performance.  
Filecoin [18] is a Blockchain-based P2P network that uses such a financial incentive to guarantee a persistent and reliable data storage. By using Filecoin, each client will pay a small fee for the data storage to the peers hosting the storage space. Those peers are essentially storage servers connected to the underlying IPFS network. After registering to the Filecoin network, they can offer their free available storage space to any client willing to pay them. Thus, a digital marketplace for decentralized data storage, which is backed by a Blockchain keeping track of the Filecoin transactions, is established.  
Additionally, storage providers can promote special benefits like lower cost, higher redundancy, or enhanced access speed. After a client chose to store his data on a given provider's hardware, the provider needs to execute a _Proof-of-Replication_. This scheme, being beyond the scope of this article, is responsible for eliminating the _double-spending_ problem and essentially ensures a proper storage operation that is provably correct.
## 3 Life Cycle Impact Assessment: Results and Interpretations
### Insight into the Life Cycle Assessment of Different NMC Chemistries  
The upstream production of battery materials as a whole incurs more energy and environmental burdens than the cell production and pack assembly processes [21]. The most notable contributors are active cathode materials, Copper, Aluminium, and the energy demand. It seems reasonable to suppose that a change in the chemical composition of the cathode could lead to different LCA results.  
Therefore, a study was made of the effect of the cathode chemistry by comparing the impacts of NMC111 with those of NMC811 and NMC622. The latter two represent the Nickel-rich cathode composition category, as can be inferred from their BOMs listed in Table 2. The comparison was conducted assuming that the three battery packs had the same energy capacity, that is, of 35 kWh, but different energy densities and thus different masses (e.g., NMC622 had the highest energy density and thus the lowest mass).  
The comparison is reported in Figure 5 for the selected impact categories: GWP (Figure 5a), ADP (Figure 5b), POP (Figure 5c), and AP (Figure 5d). These categories were chosen since GWP is the most commonly used impact category and ADP represents resource exploitation, and it is linked to possible issues concerning the future demand for materials for batteries. POP and AP were chosen since their chemistries present significant differences. The contributions to the LCA were split following the same rationale already described in the comment to Figure 4. Solid black circles indicate the net impact of each category (i.e., environmental credits are taken into account).  
GWP shows no significant difference between the NMC chemistries (Figure 5a). NMC111 is the most GHG intensive battery, whereas Nickel-rich batteries show slightly lower GHG emissions and higher recycling benefits. Moreover, NMC811 is slightly more GHG intensive than NMC622, because of the higher impact due to Nickel production. This in line with [44]. ADP shows a different trend and more significant differences (Figure 5b). Nickel-rich cathodes show a reduced ADP and greater benefits, linked to the amount of avoided Nickel production. Thus, although NMC622 is the least GHG intensive, NMC811 contributes less to abiotic resource exploitation. These results are in line with those reported in [45], where the comparison was carried out with the same energy density for the different cathode chemistries.  
Both POP and AP show significant differences between the NMC chemistries (Figure 5c,d) and NMC811 shows the lowest impacts for both categories.  
In what follows, the study only analyses the NMC111 battery that has been used to equip the case-study LDCV (NMC-BEV).
## 3 Results
### Heat Pump Outlet and Inlet Temperature and COP  
The outflow water temperature from the heat pump decreased by 7.44, 14.74, and 11.83 \({}^{\circ}\)C during the weaning, growing, and finishing phases, respectively, compared to the inflow water temperature. The maximum and minimum COP calculated were 4.50 during the early finishing and 4.10 during the weaning phase, respectively (Table 3).  
Particulate Matter (PM\({}_{2.5}\)), Formaldehyde, NH\({}_{3}\) and H\({}_{2}\)S Concentrations  
The concentration of FA in the pig house was not affected (\(p>0.05\)) by the cooling system during the experiment (Table 4). Similarly, the concentration of PM\({}_{2.5}\) remained unaffected (\(p>0.05\)), except during the early finishing phase. On the other hand, the concentration of noxious gases (NH\({}_{3}\) and H\({}_{2}\)S) decreased (\(p<0.05\)) in the GHP-connected pig house compared to the control (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Outside** & **Control** & **GHP** & **SEM** & \(p\)**-Value** \\ \hline Weaning & 28.70 & 25.85 & 25.32 & 0.21 & 0.0495 \\ Growing & 27.31 & 24.31 & 23.85 & 0.21 & 0.2134 \\ Early Finishing & 31.12 & 27.76 1  & 22.93  & 0.15 & \(<\)0.0001 \\ Late Finishing & 26.49 & 22.20 1  & 20.28  & 0.17 & \(<\)0.0001 \\ Average & 28.40 & 25.07 1  & 23.84  & 0.22 & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of the GHP cooling mode on the inside temperature of the pig house.  
Figure 4: Effect of the GHP cooling mode on the temperature–humidity index (THI). The data are presented as the mean \(\pm\) SEM. 1, 2 Values with different superscripts within the same bars differ significantly (\(p<0.05\)).
## 4 Conclusions  
Common super-resolution reconstruction algorithms usually pre-process low-resolution images; that is, interpolation is enlarged to the required size, and then super-resolution reconstruction is carried out. This greatly increases the complexity of reconstruction. The image super-resolution reconstruction algorithm based on sub-pixel convolution directly takes low-resolution images as input, which reduces the complexity and improves the speed. However, due to the low resolution of the infrared image, the unclear gray level and the centralized gray distribution, the texture is not obvious, and the reconstruction effect is not ideal. In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution is proposed. The algorithm regularizes the image before convolution. Compared with the traditional regularization algorithm, the algorithm firstly enhances the multi-scale details of the image, and adds another regularization term to the regularization part of the algorithm to enhance the edge of the image, and adds a constraint fidelity. Compared with other algorithms, the PSNR and SSIM values of the proposed algorithm are significantly improved, and the enhancement of image details and edge preservation are significantly better. Moreover, the proposed algorithm can effectively reduce noise and enhance the overall brightness of the image, and the subjective visual effect is better than other algorithms.  
Conceptualization, L.Y. and X.Z.; methodology, L.Y.; software, X.Z.; validation, Y.C.; formal analysis, X.Z.; investigation, Y.C.; resources, L.Y.; data curation, L.Y.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; visualization, X.Z.; supervision, L.Y.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
Figure 9: (**a**) and (**b**) are comparison of reconstruction effects of different algorithms.  
**Funding:** The work is supported by NSFC (61771155) and Fundamental Research Funds for the Central Universities.  
**Acknowledgments:** The work is supported by NSFC (61771155) and Fundamental Research Funds for the Central Universities. And thanks a lot to [PERSON] for her brilliant help to the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Attributes and Characteristics of FL
### Privacy Challenges and Limitations in FL  
Model inversion attacks pose a risk by allowing adversaries to reverse-engineer training data from model outputs. Despite protecting the raw data, the model parameters or outputs in FL may still be vulnerable. Section 8 will discuss these attacks and countermeasures in detail.  
Balancing user privacy with model accuracy is challenging. Techniques like differential privacy [66], which introduce noise to protect individual data points, can obscure genuine data patterns and reduce the predictive precision. This raises questions about the value of an extremely private model if its accuracy is compromised, and vice versa. This balance is crucial in sectors like healthcare, where both privacy and precise predictions are essential [38]. Additionally, privacy safeguards can introduce computational and communication burdens, potentially slowing model training and increasing the resource demands in constrained environments.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 6 Conclusions  
Aiming to solve the mosaic problems of multiple large-area spaceborne ScanSAR images, a new method was presented that is an improvement over the traditional method. Six GF-3 ScanSAR images were stitched quickly and accurately based on the proposed method. The main findings of this study can be summarized as follows:  
1. Images were preprocessed by the improved Wallis filter. The mosaic result had no obvious intensity inconsistency so that the stitching seams can be eliminated and the transitions are very smooth.  
Figure 12: Correct matching rate and RMSE at the different scaling coefficients.  
2. Extracting overlapping areas using the geolocation results was a crucial improvement in the registration. On the one hand, a large number of redundant keypoints were eliminated to improve the efficiency of mosaics, and the number of matched keypoints could also be guaranteed. On the other hand, the interference of similar keypoints from nonoverlapping areas was avoided, and the false matching rate was reduced, which improved the accuracy of matched keypoints.  
3. Homography matrix scaling compensation can greatly improve the efficiency of image mosaics but causes some stitching errors. Therefore, it is not suggested to downsample blindly but to make a tradeoff between the efficiency and accuracy of mosaics according to the specific requirements.  
4. The speed of image mosaicking was much faster when a parallel operation was used in equally divided overlapping areas, especially for large-area images.  
The performance of the novel image mosaic method based on homography matrix compensation was verified by GF-3 spaceborne ScanSAR images. The experimental results indicate that the proposed method is more effective, more efficient and more robust than the traditional method. In addition, the degree of optimization can be adjusted within the range of permissible error to achieve a shorter mosaicking time. In summary, this new method is suitable for multiple large-area spaceborne ScanSAR image mosaics to obtain a wide image for subsequent applications, such as information extraction, object detection and target recognition, and has a strong value in both theoretical research and practical applications.  
Conceptualization, W.Y. and H.F.; methodology, J.T., Y.W. and Y.C.; software, J.T.; investigation, J.T., Y.W. and Y.C.; resources, W.Y. and H.F.; writing--original draft preparation, J.T.; writing--review and editing, Y.W., Y.C., W.Y. and H.F.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61901442.  
The authors declare no conflict of interest.
# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
## 2 System Overview
### Framework of Pose Estimation Utilizing GRU Network
#### 2.3.2 Framework with Classical VO
\begin{split} R_{corr}=\left(\begin{array}{ccc}R_{11}&R_{12}& R_{13}\\ R_{21}&R_{22}&R_{23}\\ R_{31}&R_{32}&R_{33}\end{array}\right)\\ =\left(\begin{array}{ccc}cos\delta cos\phi&sin\psi_{corr}sin\delta cos\phi- cos\psi_{corr}sin\phi&cos\psi_{corr}sin\delta cos\phi+sin\psi_{corr}sin\phi\\ cos\delta sin\phi&sin\psi_{corr}sin\delta sin\phi+cos\psi_{corr}cos\phi&cos \psi_{corr}sin\delta sin\phi-sin\psi_{corr}cos\phi\\ -sin\theta&sin\phi cos\theta&cos\phi cos\theta\end{array}\right)\end{split}  \tag{16}\]  
where \(\psi_{corr}=NCC\psi_{VO}+(1-NCC)\psi_{GRU}\) The NCC for the image \(I_{1}\) centered on \((u,v)\) and image \(I_{2}\) centered on \((u^{\prime},\,v^{\prime})\) is calculated with \(m\times n\) patch, where \(m=2a+1\), \(n=2b+1\) are odd integers, as follows  
\[NCC=\frac{\sum_{k=-a}^{a}\sum_{l=-b}^{b}[I_{1}(u+k,v+l)-\mu_{1}][I_{2}(u^{ \prime}+k,v^{\prime}+l)-\mu_{2}]}{\sqrt{\sum_{k=-a}^{a}\sum_{l=-b}^{b}\left[I _{1}(u+k,v+l)-\mu_{1}\right]^{2}+\sum_{k=-a}^{a}\sum_{l=-b}^{b}\left[I_{2}(u^{ \prime}+k,v^{\prime}+l)-\mu_{2}\right]^{2}}} \tag{17}\]  
where  
\[\mu_{1}=\frac{1}{(2a+1)(2b+1)}\sum_{k=-a}^{a}\sum_{l=-b}^{b}I_{1}(u+k,v+l), \mu_{2}=\frac{1}{(2a+1)(2b+1)}\sum_{k=-a}^{a}\sum_{l=-b}^{b}I_{2}(u^{\prime}+ k,v^{\prime}+l).\]  
The pseudo-code of the MVO based on the proposed pose estimation using the GRU network is shown in Algorithm 2. The 6-th line represents the condition for determining if the camera is in a cornering section and the 7-th line represents the condition for checking if the yaw angle from the VO deviates significantly from the previous rotation trend. In addition, in the 7-th line, the yaw angle is predicted by the GRU network and corrected using NCC and as a result the rotation matrix \(R\) is reconstructed. Figure 8: Camera yaw angle calculated by MVO and Normalized Cross-Correlation (NCC) of each pair of frames for the KITTI 07 sequence. The left vertical axis represents the yaw angle and the right vertical axis is the NCC.
## 4 Proposed Algorithm
### Uniqueness Score with Local Search Algorithm (ULA)
#### 4.4.2 Local Search  
After finding a solution based on the uniqueness score, the local search algorithm is applied to improve the current solution by determining better solutions at the cost-of-coverage level. The purpose of this algorithm is to cut the total camera cost while satisfying the minimum level of coverage, because the algorithm based on uniqueness scores tends to select expensive cameras that provide higher coverage. In this phase, the applied local search procedure can be described as follows:  
1. Specify a seed solution and select a camera placement \(s\) with the lowest total uniqueness score (\(US_{s}\)) in the solution.
2. Generate neighborhoods by interchanging \(s\) with another camera having a lower cost and then add these neighborhoods to a neighborhood set \(N\).
3. Compute the total cost and coverage level of all neighborhoods in \(N\), and then rank the neighborhoods in a non-increasing order of the total cost.
4. If the total cost of a neighborhood solution that satisfies the minimum coverage level is lower than the seed solution, update the seed solution with the neighborhood solution. If there is no improvement of cost for any neighborhood, do not update the seed solution.
5. Update \(s\) with the next lowest \(US_{s}\) and go to step 2. If \(s\) is the camera placement with the highest \(US_{s}\), then return the seed solution.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Performance Comparison of SSIM  
Afterwards, to further deeply reidentify the performance of the 10 filtering models, this study uses the second measurement indicator (i.e., SSIM value) for processing the noise density of images. Thus, Table 17 and Figure 18 show the SSIM average value at different noise densities from the 10 filter models for the five experimental images. Regarding the empirical results, it is found that the ranking order is AFWMF \(\rightarrow\) ASMF \(\rightarrow\) DWM \(\rightarrow\) ROR \(\rightarrow\) SDOOD \(\rightarrow\) SBF \(\rightarrow\) FRDFM \(\rightarrow\) EAIF \(\rightarrow\) BDND \(\rightarrow\) EBDND in terms of the performance of the SSIM average value. From Tables 4 and 17, two interesting and meaningful findings are defined and determined. (1) The proposed AFWMF model is the best one for processing impulse noise density regardless of the PSNR and SSIM values. (2) It is also important to find an appropriate filtering method for use in a variety of images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Density** & **30\%** & **50\%** & **70\%** \\ \hline \(m_{c}=3,m_{f}=1\) & 26.72 & 25.28 & 22.44 \\ \(m_{c}=6,m_{f}=4\) & 26.05 & 25.03 & 22.44 \\ \(m_{c}=9,m_{f}=7\) & 25.76 & 24.81 & **22.65** \\ \(m_{c}=1,m_{f}=1\) & 26.20 & 24.41 & 20.62 \\ \(m_{c}=12,m_{f}=12\) & 24.65 & 24.80 & 22.17 \\ PSRNR (AFWMF) & **26.98** & **25.43** & **22.65** \\ \hline \hline \end{tabular}
\end{table}
Table 16: The PSNR value for setting parameters by referring to the PSRNR formula (Plane image).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise Density** & **30\%** & **50\%** & **70\%** \\ \hline \(m_{c}=3,m_{f}=1\) & 26.63 & **26.50** & 22.58 \\ \(m_{c}=6,m_{f}=4\) & 26.50 & 24.80 & **22.64** \\ \(m_{c}=9,m_{f}=7\) & 25.97 & 25.01 & **22.64** \\ \(m_{c}=1,m_{f}=1\) & 25.79 & 24.07 & 20.92 \\ \(m_{c}=12,m_{f}=12\) & 24.89 & 23.51 & 22.17 \\ PSRNR (AFWMF) & **26.88** & 25.11 & 22.61 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The PSNR value for setting parameters by referring to the PSRNR formula (Boat image).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Density** & **30\%** & **50\%** & **70\%** \\ \hline \(m_{c}=3,m_{f}=1\) & 26.63 & **26.50** & 22.58 \\ \(m_{c}=6,m_{f}=4\) & 26.50 & 24.80 & **22.64** \\ \(m_{c}=9,m_{f}=7\) & 25.97 & 25.01 & **22.64** \\ \(m_{c}=1,m_{f}=1\) & 25.79 & 24.07 & 20.92 \\ \(m_{c}=12,m_{f}=12\) & 24.89 & 23.51 & 22.17 \\ PSRNR (AFWMF) & **26.88** & 25.11 & 22.61 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The PSNR value for setting parameters by referring to the PSRNR formula (Peperps image).
## Materials and Methods
### Preparation of Samples  
Supercritical Carbon Dioxide Extraction and Dispersion in NADES  
Supercritical CO\({}_{2}\) extraction was carried out using a high-pressure extraction system (HPEP, NOVA-Swiss, Effretikhon, Switzerland) with the following main specifications: gas cylinder with CO\({}_{2}\), diaphragm-type compressor (with a pressure range up to 1000 bar), extractor vessel with heating jacket (internal volume 200 mL, maximum operating pressure 700 bar), separator with cooling jacket (internal volume 200 mL and maximum operating pressure 250 bar), pressure control valve, temperature regulation system, and regulation valves. The extractions were performed under the following conditions: pressure 300 bar, temperature 40 \({}^{\circ}\)C, CO\({}_{2}\) flow 0.194 kg/h, and extraction time 4.5 h. The parameters of extraction were selected based on the previously published work [14]. Obtained extracts were placed in glass bottles and stored at 4 \({}^{\circ}\)C prior to further analysis. Each extraction was performed in triplicate.  
After 4.5 h of extraction, to separate the CO\({}_{2}\) and extract, depressurization was performed, and the extract was directly dispersed into the prepared NADES mixture. The mixture of CO\({}_{2}\) extract and NADES was homogenized by mixing in a vortex for 1 min. The extract: NADES ratio was 0.05 g \(\pm\) 15% extract: 1 mL NADES. This ratio was chosen because it enabled easy homogenization of the mixture. The control and CO\({}_{2}\) extracts that dispersed into NADES (CO\({}_{2}\)-NADES systems) were analyzed at the beginning (month 0), after 3 months, and after 6 months of storage in transparent containers at room temperature in the dark.