esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Blockchain-Based E-Bidding System
## 2 Preliminaries
### Hyperledger Fabric
The HLF maintains a business network that connects the different businesses of stakeholders through the Fabric software development kit (SDK) in the bidding process. Then, each bidder needs to be connected to the framework through client nodes. Biddingorganizations collect and submit documents through the client to the SDK within the framework. Submitted documents are automatically checked by chaincode. The client from the tenderee and the bidder and peer node members must be registered with the MSP and require a digital certificate from the CA. At the commencement of the transaction, the bidder will collect tender documents, review requirements and prepare bidder documents. Figure 1 shows the process framework and its different components of the E-bidding system with a typical HLF:  
The discussion will follow the following brief steps:  
1. The bidder initiates the transaction by sending a request to the HLF-based application client. 2. The client invokes SDK to submit the transaction proposals to the endorsing peer. 3. These endorsement peers receive the transaction proposals, simulate the transaction by executing chaincode specified by the transaction, and verify it with business logic. 4. After verification, the endorsing peer executes the transaction in the ledger and returns the response result to the Fabric platform. The bidder receives and verifies the response of the endorsing peer. 5. At the same time, Fabric platform combines transaction with endorsement, and broadcasts it to the ordering peer. 6. The ordering peer checks the endorsements and orders the transactions sequentially, packs a transaction block for each channel through the consensus mechanism and delivers it to the committing peer [22]. 7. The committing peer verifies the transaction in the block by checking the signature and version information, and then submits the transaction to the ledger and notifies the Fabric platform. In the transaction process, Steps 1, 2, 3, and 4 represent the endorsement process, Steps 5 and 6 are similar to the ordering process, Step 7 is similar to the verification process [23], and the transaction is attached to the blockchain. Finally, only authorized bidders will be able to open the bidding documents and select the best bidder for the tendering agency through the prescribed processes and algorithms controlled by chaincode and MSP. The framework takes advantage of the characteristics of HLF to provide flexibility and adaptability, which can be reused in different bidding processes by introducing the required chaincode and peer. In addition, failed bidders, managers, auditors or any other relevant parties can access the framework according to their requirements to check the quality and compliance of the  
Figure 1: The framework and process of E-bidding system with HLF. tendering process. Even anyone inside or outside the organization can monitor the process in the right time with the appropriate consent.
## 3 Results and Discussion
### Squalene Content of Teff and Amaranth Varieties  
Table 7 presents the squalene oils content from teff and amaranth grains. The content obtained from white amaranth (486.54 mg/100 g DM) was significantly higher than that found in brown and red amaranth (327.54-340.81 mg/100 g DM), and in teff varieties (0.85-2.33 mg/100 g DM). [PERSON] et al. [12] reported lower contents of squalene (197-335 mg/100 g) in cultivated _Amarantthus hypochondriacus_ and _Amarantthus cruentus_. The results show that the squalene content in amaranth was higher than other pseudo-cereals, such as quinoa (58.4 mg/100 g DM) and buckwheat (1.9 mg/100 g DM), and most other cereals and legumes [24]. Squalene is a plant secondary metabolite and an intermediate product of cholesterol biosynthesis, which takes place in humans under the skin and inside the adipose tissue. Squalene has several health benefits, such as anticancer and hypocholesterolemic effects [38]. Hence, the high level of squalene determined in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fatty Acids**} & \multicolumn{3}{c}{**Amaranth Varieties**} \\ \cline{2-4}  & **White** & **Brown** & **Red** \\ \hline SFA & 28.16 \(\pm\) 0.34  & 28.52 \(\pm\) 0.09  & 27.74 \(\pm\) 0.17  \\ UFA & 71.64 \(\pm\) 0.66  & 71.46 \(\pm\) 0.10  & 72.20 \(\pm\) 0.17  \\ MUFA & 35.16 \(\pm\) 1.19  & 34.16 \(\pm\) 0.62  & 24.71 \(\pm\) 0.23  \\ PUFA & 36.47 \(\pm\) 1.04  & 37.30 \(\pm\) 0.67  & 47.48 \(\pm\) 0.20  \\ Omega 6/Omega 3 & 68.63 \(\pm\) 13.66  & 64.17 \(\pm\) 4.48  & 54.90 \(\pm\) 6.09  \\ SFA/UFA & 0.39 \(\pm\) 0.01  & 0.40 \(\pm\) 0.00  & 0.38 \(\pm\) 0.00  \\ PUFA/SFA & 1.30 \(\pm\) 0.04  & 1.31 \(\pm\) 0.03  & 1.71 \(\pm\) 0.01  \\ TI & 0.70 \(\pm\) 0.03  & 0.69 \(\pm\) 0.00  & 0.65 \(\pm\) 0.01  \\ AI & 0.31 \(\pm\) 0.01  & 0.31 \(\pm\) 0.00  & 0.31 \(\pm\) 0.01  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Proportions of fatty acid groups (% of total fatty acids) and thrombogenicity and atherogenicity indices of amaranth.  
studied amaranth varieties could encourage its exploitation in the functional food market and cosmetics industry.
## 3 Results
### Analysis of Experimental Results
To comprehensively evaluate the impact of different backbone networks on model performance, this paper replaces the backbone network of GCAFF-Net with MobileNetV2 [34] and compares their performance across three scenes. MobileNetV2, as a lightweight convolutional neural network, achieves a substantial reduction in computational costs and boasts a parameter count of just 3.5 million; meanwhile, it maintains a high accuracy through its innovative inverted residual structure and linear bottleneck layers. Table 5 shows that although GCAFF-MobileNetV2 has potential advantages regarding its computational efficiency, its accuracy performance in all scenes is lower than that of  
Figure 8: The detection results for water bodies in Scene III using various networks. (**a**) The third SAR image for independent testing. (**b**) The ground truth. (**c**) The extracted water result for U-Net; (**d**) for MF2 AM; (**e**) for DeepLabV3+; and (**f**) for GCAFF-Net. The blue color indicates correct detections of water, the green color highlights missed detections, and the red color marks false alarms. GCAFF-ResNet. For example, in Scene I, GCAFF-ResNet achieves a PA of 95.24% and an IoU of 91.63%, significantly higher than GCAFF-MobileNetV2's 94.01% and 87.16%. This gap further widens in more complex scenes, such as Scene III: GCAFF-ResNet achieves a PA of 89.59% and an IoU of 82.10%, while GCAFF-MobileNetV2 only reaches 85.54% and 79.47%, respectively. These results indicate that ResNet, leveraging its deep residual structure, can more effectively extract multi-level features, thereby maintaining a significant advantage in high-precision segmentation tasks. Especially in complex scenes where target boundaries are blurred or background interference is severe, GCAFF-ResNet's Recall and F1-Score also outperform those of the lightweight model, further validating its superiority in detail preservation and missed detection control. Although the adoption of ResNet entails greater computational demands, the resultant marked improvement in accuracy underscores its effectiveness. In future work, we will focus on refining the backbone network's architecture to enhance both the accuracy and computational efficiency.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix A Coefficient's Derivation and Error Probability Analysis of Theorem 1
\begin{split}&\rho_{\uparrow}^{2}=\frac{\big{(}A_{\uparrow}( \rho_{\uparrow})\big{)}^{2}}{\phi}\times\\ &\left(1+\frac{\Big{(}h_{sd}\sqrt{\frac{P}{\phi}}+h_{sr}h_{rd} \beta_{\uparrow}\sqrt{P}\rho_{\uparrow}\Big{)}^{2}}{h_{sr}^{2}h_{rd}^{2}\beta _{\uparrow}^{2}P\Bigg{(}\Big{(}1-\frac{1}{\phi}\Big{)}-\Big{(}1-\frac{1}{A_{ \uparrow}(\rho_{\uparrow})}\Big{)}^{2}\rho_{\uparrow}^{2}\Bigg{)}+F_{ \uparrow}}\right)^{-1},\end{split}  \tag{103}\]  
where  
\[A_{\uparrow}(\rho_{\uparrow})=1+\frac{Ph_{sd}\Big{(}h_{sd}\sqrt{\frac{1}{ \phi}}+h_{sr}h_{rd}\beta_{\uparrow}\rho_{\uparrow}\Big{)}\Big{(}\sqrt{\frac{ 1}{\phi}}-\sqrt{\phi}\Big{)}}{h_{sr}^{2}h_{rd}^{2}\beta_{\uparrow}^{2}P\Bigg{(} \Big{(}1-\frac{1}{\phi}\Big{)}-\Big{(}1-\frac{1}{A_{\uparrow}(\rho_{\uparrow} )}\Big{)}^{2}\rho_{\uparrow}^{2}\Bigg{)}+F_{\uparrow}}. \tag{104}\]  
Let \(\rho_{\uparrow}^{*}\) be defined as the unique solution within \([0,1]\) of (103). Then, according to (100), the variance of the final estimation error \(\varepsilon_{N}\) is as follows  
\[\alpha_{N}=\alpha_{2}\Bigg{(}1+\frac{\Big{(}h_{sd}\sqrt{\frac{P}{\phi}}+h_{sr} h_{rd}\beta_{\uparrow}\sqrt{P}\rho_{\uparrow}^{*}\Big{)}^{2}}{\Gamma_{ \uparrow}}\Bigg{)}^{2-N}, \tag{105}\]  
where  
\[\Gamma_{\uparrow}=h_{sr}^{2}h_{rd}^{2}\beta_{\uparrow}^{2}P\Bigg{(}\bigg{(}1 -\frac{1}{\phi}\Big{)}-\Bigg{(}1-\frac{1}{A_{\uparrow}\Big{(}\rho_{\uparrow} ^{*}\Big{)}}\Bigg{)}^{2}\Big{(}\rho_{\uparrow}^{*}\Big{)}^{2}\Bigg{)}+F_{ \uparrow}. \tag{106}\]  
Next, according to (119) and (103), we have  
\[ \begin{split}\Pr\Big{(}\widetilde{C}_{N}\Big{)}&= \Pr\Big{\{}\varepsilon_{N}\
otin\bigg{[}-\frac{0.5}{2^{NR(N\varepsilon)}}, \frac{0.5}{2^{NR(N\varepsilon)}}\Big{)}\Big{\}}\\ &=2Q\Bigg{(}\frac{0.5}{2^{NR(N\varepsilon)}\sqrt{\pi_{N}}}\Bigg{)} =\frac{\varepsilon}{2}.\end{split}  \tag{107}\]  
Substituting (105) and (106) into (107), we obtain  
\[\mathcal{R}_{\text{GRC}}^{\text{NFB}}(N,\varepsilon)=\mathcal{R}_{\text{GRC} }^{*}(N,\varepsilon)-\mathcal{R}_{\text{GRC}}^{\diamondsuit}(N,\varepsilon), \tag{108}\]  
where \(\mathcal{R}_{\text{GRC}}^{*}(N,\varepsilon)\) and \(\mathcal{R}_{\text{GRC}}^{\diamondsuit}(N,\varepsilon)\) are, respectively, given in (9) and (10). It should be noted that \(\mathcal{R}_{\text{GRC}}^{*}(N,\varepsilon)\) represents the maximum achievable rate when blocklength \(N\) is sufficiently large. And \(\mathcal{R}_{\text{GRC}}^{\diamondsuit}(N,\varepsilon)\) represents the achievable rate loss caused by given blocklength \(N\) and decoding error probability \(\varepsilon\).
## 2 Materials and Methods
### Design of Proposed Control System
#### 2.3.1 Design of the SGCMG-Integrated Satellite Model (SISM)
\begin{array}{c} \delta_{e}\\ \dot{\delta}_{e}\end{array} \right]+\left[ \begin{array}{c}\mathbf{0}_{4\times 4 }\\ \frac{1}{\tau}\mathbf{U}\end{array} \right]\dot{\delta}_{c} \tag{23}\]  
Consequently, the SISM combined Equations (5), (19), and (23) using \(\kappa\) is defined as follows:  
\[\left[ \begin{array}{c}\dot{\mathbf{q}}_{e}\\ \dot{\omega}_{e}\\ \dot{\delta}_{e}\\ \ddot{\delta}_{e}\end{array} \right]=\left[ \begin{array}{ccc}\kappa\mathbf{U}& \frac{1}{2}\mathbf{Z}_{e}&\mathbf{0}_{4\times 4}&\mathbf{0}_{4\times 4}\\ \mathbf{0}_{3\times 4}&-I^{-1}\big{(}\mathbf{W}+\mathbf{H}\big{)}&\mathbf{0}_{3\times 4}&-h_{0} \Gamma^{-1}\mathbf{A}^{\prime}(\delta_{e})\\ \mathbf{0}_{4\times 4}&\mathbf{0}_{4\times 3}&\kappa\mathbf{U}&\mathbf{U}\\ \mathbf{0}_{4\times 4}&\mathbf{0}_{4\times 3}&\mathbf{0}_{4\times 4}&-\frac{1}{\tau}\mathbf{U} \end{array} \right]\left[ \begin{array}{c}\mathbf{q}_{e}\\ \mathbf{\omega}_{e}\\ \delta_{e}\\ \dot{\delta}_{e}\end{array} \right]+\left[ \begin{array}{c}\mathbf{0}_{4\times 4 }\\ \mathbf{0}_{3\times 4}\\ \mathbf{0}_{4\times 4}\\ \frac{1}{\tau}\mathbf{U}\end{array} \right]\dot{\delta}_{c} \tag{24}\]  
where \(\mathbf{A}^{\prime}\) is another representation of \(A\) and is expressed as  
\[ \begin{array}{rl}\mathbf{A}^{\prime}(\delta_{e})&=A(\delta)\\ &=A(\ \delta_{e}+\delta_{r})~{}~{}(\because~{}\delta_{e}=\delta(t)-\delta_{r}) \end{array}  \tag{25}\]  
From the above, the optimal gimbal angular velocity input for an attitude maneuver is calculated by the SDLR of the SGCMG-integrated satellite, and this SISM is able to avoid a singularity by setting the target gimbal angle \(\delta_{r}\). As we mentioned above, the proposed SISM is able to explicitly design a gimbal angular velocity as a control input. In this model, controllability is guaranteed by \(\kappa\) even under the singularity. It is necessary to verify robustness against model uncertainty by Monte Carlo analysis or the like because a formal robustness analysis of SDRE does not yet exist [18]. However, as it is the same as solving the LQR for every control cycle, it is considered that the robustness against the uncertainty of the model and stability margin have a same or higher performance to LQR.
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 2 Materials and Methods
### Methods  
The analysis explores the impact of SHAP-based feature selection on PM\({}_{2.5}\) prediction models. Initially, an XGBoost Regressor was trained using all available features from the air quality dataset. Prior to training the model, missing values were imputed using the k-nearest neighbors (KNN) algorithm, with 5 neighbors. This imputation method made the data complete, allowing the features to have consistent values. SHAP values were calculated to assess features' importance, which formed a new refined feature set. The new refined set of features (applied to train the model) comprised features that made up a total of 95% of the SHAP values.  
Following the feature selection process, the models were retrained using the dimensionality reduced feature sets, and their performance was compared to the original model trained with all features. Evaluation metrics such as RMSE and R\({}^{2}\) were calculated to quantify performance changes. This comparison was made to highlight the extent of how much the exclusion of low-importance features influenced predictive accuracy, offering insights into the trade-off between model complexity and performance, therefore making the model more explainable and robust.  
To assess the effectiveness of the proposed feature selection technique, two evaluation strategies were used. In the first approach, all data prior to 2017 were used for model training, while a three-month time span from 2017 (Jan-March, which was only available) was used as the test set. This method ensured a temporally consistent evaluation, where PM\({}_{2.5}\) levels were predicted for each hour using all available features. In the second approach, an 80-20 training-test split was conducted randomly, with the additional condition that each month was represented in the test set. This condition emerged from the need to capture seasonal variability, which is inherently in the air quality data, ensuring the test set reflected a balanced temporal distribution and avoiding biases of specific months that lead to similar patterns. Both evaluation strategies were assessed using RMSE and R\({}^{2}\) to ensure consistency with the previous performance analysis. The analysis was conducted for each different station of our dataset both individually and collectively (by aggregating the errors and calculating R\({}^{2}\) in the total of 12 stations).
## 5 Conclusions  
To address the challenge of limited training data for the recognition of failure modes in STRC shear walls, this study proposes a GAN-based approach to augment the training data. The Blending-CRKL model is utilized to combine the advantages of multiple individual models, thereby improving the accuracy of failure mode prediction in STRC shear walls. Additionally, an analysis is conducted on the significant features of failure modes in STRC shear walls, and the parameter value ranges under different failure modes. The main conclusions are as follows:  
1. By augmenting the training dataset with GAN, we improved the predictive performance of STRC shear wall failure mode models. Compared to the initial dataset, the six models exhibited significant enhancements with average increases of 13% in accuracy, 81% in precision, 48% in recall, and 67% in F1-score. This approach effectively addresses the poor predictive performance issue in small sample databases, enhancing the accuracy of models. 2. The Blending-CRKL fusion model proposed in this study outperformed the other models in predicting the failure modes of STRC shear walls. It achieved an outstanding accuracy of 97% when trained with the GAN-augmented dataset, surpassing the performance of KNN, BPNN, RF, LightGBM, and Catboost models. Additionally, the Blending-CRKL model demonstrated good robustness and stability, making it highly suitable for accurately predicting the failure modes of STRC shear walls. 3. The Blending-CRKL fusion model was explained using SHAP. The results showed that the shear-span ratio (\(\lambda\)) is the most important feature for identifying the failure modes of STRC shear walls, followed by the horizontal bar index in the boundary element (\(\rho_{hc,c}\), \(f_{y4}\)/\(f_{c}\)) and the vertical bar index in the boundary element (\(\rho_{vc,s}\), \(f_{y1}\)/\(f_{c}\)). Additionally, the reinforcement configuration of the boundary elements plays a crucial role in the shear and flexural failure of STRC shear walls. 4. Analysis of the features value range of different failure modes of STRC shear walls reveals that as the shear-span ratio (\(\lambda\)) increases, the probability of flexural failure also increases. A larger value of the boundary element horizontal bar index (\(\rho_{hc,c}\), \(f_{y4}\)/\(f_{c}\)), the vertical bar index of the web (\(\rho_{vw,s}\), \(f_{y2}\)/\(f_{c}\)), and the web area ratio (\(A_{w}\)/\(A_{i}\)) indicates a higher likelihood of shear failure in the shear wall. On the other hand, a larger value of the boundary element vertical bar index (\(\rho_{vc,s}\), \(f_{y1}\)/\(f_{c}\)) and the compressive strength of concrete (\(f_{a}\)) indicates a higher likelihood of flexural shear failure in the shear wall. 5. In future research, the Blending-CRKL model proposed in this study can be further improved by expanding the experimental data and validating the predictive results of the model in engineering practice. These efforts will contribute to continuously enhancing the prediction accuracy of the model.
## 4 Experimental Verification
### Test Results and Analyses
#### 4.2.5. Comparing the Creep Curves of Two-Layered and Three-Layered CGS  
The above triaxial creep test results of the one-layered, two-layered, and three-layered CGSs have proved the validity of increasing the degree of irregular pebble and height of interface-layer can decrease the creep deformation. For studying the effect of layer number on the creep curve of multi-layered CGS, take two-layered CGS (L1 + L1/L2 + L2 or L2 + L2/L3 + L2) and three-layered (L1 + L1/L2 + L2 + L2/L1 + L1 or L2 + L2/L3 + L3 + L2/L3 + L2) CGS compacted by the same gradation of one-layered CGS (L1 and L2 or L2 and L3) under the same total height (600 mm) for comparison.  
Figure 14 shows the predicted creep curves of the two-layered CGS (each layer height \(H\) = 300 mm and interface-layer height \(h\) = 0.3 \(H\) = 90 mm) and three-layered CGS (each layer height \(H\) = 200 mm and interface-layer height \(h\) = 0.3 \(H\) = 60 mm). It is seen that the three-layered CGS has more interface-layer and smaller creep deformation than the two-layered CGS, since the existence of interface-layer decreases the creep deformation (Figure 12). The more the interface-layer, the larger the total height of the interface-layer and thus the smaller the creep deformation. Obviously, the multi-layered CGS of more layer numbers can reduce settlement but also cause difficulty in field compaction and high cost. Both safety and economy must be considered in the design of a pavement or embankment.  
Figure 13. Tested and predicted creep curves of the three-layered CGS with different interface-layer numbers.
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 3 Results  
### Experimental Results  
The FRP specimen suddenly breaks when the tension reaches a certain value. The failure is brittle fracture, and the failure modes basically include three types: Central fracture, angled fracture and splitting failure, as shown in Figure 2. Most of the CFRP samples fail by central fractures and corner fractures, whereas the GFRP samples are accompanied by splitting failure in addition to fracture failure. The location of the fracture damage is relatively random and there is no obvious regularity. The failure modes of FRP samples at different corrosion times are also not significantly different.  
The test results of FRP's mechanical properties are listed in Table 1 and plotted in Figure 3. In which, the Young's modulus was computed between the longitudinal strain range of 1000-3000 \(\mu\epsilon\). The elongation has been computed as the ultimate tensile strength divided by the Young's modulus. It can be found that wet-dry cycles have almost no effect on the elastic modulus of FRP, but its standard deviation has an increasing trend with the increase of the number of wet-dry cycles. Under 360 times of wet-dry cycles, the elastic modulus of CFRP and GFRP are only reduced by 0.847% and 3.35%, respectively. It can also be seen that the wet-dry cycles have an adverse effect on the tensile strength of CFRP and GFRP. The tensile strength decreases approximately exponentially with the increase in the number of wet-dry cycles. The decreasing rate is fast in the early stage, and becomes slow in the following stage. Under 360 times of wet-dry cycles, the tensile strengths of CFRP and GFRP decreased by 9.32% and 11.98%, respectively. It also indicates that the corrosion resistance of CFRP is better than that of GFRP under wet-dry cycles. The elongation rates of CFRP and GFRP decreased under wet-dry  
Figure 1: Configuration of FRP samples: (**a**) Schematic diagram; (**b**) photos.  
cycles exposure, but only in the early stage, and the elongation of CFRP and GFRP did not change significantly in the later period.  
In summary, wet-dry cycles have a significant adverse influence on the tensile strength, have a certain adverse effect on the elongation, and a very limited influence on the elastic modulus of FRP. Moreover, GFRP is more susceptible to the wet-dry cycle exposure than CFRP.  
Figure 2: Typical failure modes of FRP samples: (**a**) CFRP-0, (**b**) GFRP-0, (**c**) CFRP-90, (**d**) GFRP-90, (**e**) CFRP-180, (**f**) GFRP-180, (**g**) CFRP-360, and (**h**) GFRP-360.