esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
In order to compare the anticipated strength values at an age of 28 days after concrete pouring and the measured concrete compressive strength values, the following equation from the EN1992-1-1 standard [20] for estimating the development of the mean compressive strength of concrete (f\({}_{\rm cm}\)(t)) was used:  
\[\rm{f}_{\rm cm}(t)=\exp\left(s\cdot\left(1-\left(\frac{28}{t}\right)^{\frac{1 }{2}}\right)\right)\cdot f_{\rm cm.28}, \tag{2}\]  
where t is the concrete age after pouring (in days), s is a coefficient that depends on the type of cement (equal to 0.2 for the used cement), and f\({}_{\rm cm.28}\) is the mean compressive strength of the concrete at age 28 days after pouring. Considering the age of the fiber-reinforced concrete cubes at testing (37 days), the mean compressive strength at an age of 28 days after pouring was estimated to be 44.17 MPa for cubes and 35.34 MPa for cylinders. A value of 1.25 was considered for the ratio of the compressive strength determined on cubes and cylinders (at a concrete age of 37 days after pouring, the estimated \"cylinder\" mean compressive strength was 36.26 MPa). The characteristic compressive strength determined on cylinders was assumed to be 27.34 MPa (8 MPa difference was considered for the difference between concrete's characteristic and mean compressive strength). The used relations between the characteristic, mean, \"cube\" and \"cylinder\" compressive strength values were from the EN1992-1-1 standard [20]. It can be concluded that the chosen concrete composition (Table 2) resulted in a slightly higher compressive strength as anticipated by the mix-design.
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## 1 Introduction  
Quantum key distribution (QKD) presents information-theoretically secure communication by employing quantum mechanical laws to reveal the presence of an eavesdropper [1]. For the past two decades, the majority of research has focused on the proposing of QKD protocols involving just two legitimate users, conventionally named [PERSON] and [PERSON], who want to establish a secret shared key [2; 3; 4; 5; 6]. The rapid development in information processing technologies has led to the emergence of quantum networks [7; 8; 9] aimed at realizing quantum information tasks among multiple users. Quantum Cryptographic Conferencing (QCC) or multipartite QKD, which allows the distribution of information-theoretical secure keys among multiple remote users, is one of the most promising applications in quantum networks. Consequently, several QCC protocols have been proposed recently [10; 11; 12; 13; 14; 15; 16; 17] using various quantum resources and techniques to prove the security of multipartite QKD. Despite these achievements, a large gap remains between the theoretical assumptions in security proofs of QCC protocols and actual implementation. For instance, challenges exist in relation to secret key rate, transmission distance, size, cost, imperfect physical devices, signal-to-noise ratio, and practical security [3; 18; 19]. Moreover, other challenges are associated with imperfections in communication channels, for instance, quantum data communications and networking, underwater communication, satellite communication, andfiber-optic communication [20; 21; 22; 23; 24]. A QCC protocol is ideally secure only when it employs perfect single-photon sources and detectors. Unfortunately, ideal devices are impractical to realize. Consequently, device imperfections may open up the possibility of security loopholes or side channel attacks by an eavesdropper, which can compromise the security of practical QCC. This brings about the need to design protocols that can be made secure against device imperfections, such as decoy-state QKD. Another bottleneck to large scale deployment of QCC is high channel loss and decoherence, which lead to a relatively low secret key rate. Therefore, in order to realize full-scale practical QCC for secure everyday communications, it is of utmost importance to develop efficient methods and models which address the aforementioned challenges. Except for this introduction, we organize this article as follows. Section 2 provides the literature review, while Section 3 outlines the operation of the proposed protocol. Section 4 evaluates the security bounds for the proposed protocol. In Section 5, we provide the key rate formula of the three-party RFI QKD based on the decoy-state theory. Section 6 provides a numerical simulation of the secret key rate based on fiber implementation and discusses the results. Lastly, Section 7 provides our conclusions.
## 4 Results and Analysis of Three-Dimensional Forward Modeling  
### Full-Space Model  
The cross-hole grounded-wire-source TEM model, employed for advanced exploration, is typically located at depths of up to 500 m below the surface, which can be approximated as a full-space environment. Consequently, this study modeled a 100 \(\Omega\cdot\)m full-space background, with the emitting source wires deployed along the x-direction, extending 100 m in length. Figure 5 illustrates the electric field diffusion, where the electric field components are represented by a color bar, with arrows indicating their directions. At early times, the electric field was stronger near the wire, originating from the positive terminal  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Elements** & **DOFs** & **Time Channel** & **Relative Error (\%)** & **Time (s)** & **Max Memory (GB)** \\ \hline
270,270 & 316,559 & 1100 & 0.70 & 550 & 41 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the full-space model using the FE method.  
Figure 4: A comparison between the results obtained using our FE and FD methods for a half-space model. Specifically, (**a**) shows the EMF parameters, while (**b**) displays the relative errors compared to the 1D semi-analytical solution.  
and returning to the negative terminal in a vortex-like pattern. As time progressed, the center of the electric field remained at the source, but its strength gradually diminished. Furthermore, the direction of the electric field evolved over time. At a later time, the x-component of the electric field near the source became the dominant component, while the y- and z-components approached zero. This indicates that the electric field diffusion was more prominently aligned with the wire direction. In the TEM geoelectric model of the cross-hole grounded-wire source, the borehole was not aligned with the model coordinate axes but was tilted at an angle. Therefore, the final forward modeling calculations required transforming the electric field components into the receiver's directional coordinates.
# Group Pile Effect on Temperature Distributions inside Energy Storage Pile Foundations
## 2 Research Background  
### Thermodynamic Cycles  
CAES technology is designed to use an advanced adiabatic cycle [20]. This cycle consists of 4 steps: compression, cooling, heating, and expansion (see Figure 1).  
Figure 1: Thermodynamic processes of compressed air energy storage (CAES) (modified from [7]).  
Firstly, the renewable energy generated from solar panels is used to compress the air with initial temperature (T\({}_{1}\)). During this process, the compressor is activated by the power generated from the solar panels. As a result, the temperature and pressure of the air increase considerably, making it impossible to directly store the compressed air inside the concrete piles. After this stage, the temperature (T\({}_{2}\)) of air can exceed 1000 \({}^{\circ}\)C. Thus, cooling is needed to reduce the temperature. In this step, the heat is extracted and placed inside another storage medium. This stored heat will then be used during release of the compressed air to heat it up before expansion. During the cooling stage, the efficiency of the cooling process prevents the temperature (T) from returning to the initial value (T\({}_{1}\)). Although the storage temperature can be as high as 150 \({}^{\circ}\)C, it is still possible to be stored in the pile foundation since such a temperature has a negligible impact on the strength and stiffness of the concrete [21]. Next, as the power supply from the solar panels becomes unavailable, the energy stored as compressed air is released from the piles passing through the heating and expansion stages.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 4 Results
### Correlation Coefficient  
The datasets for each month with the in situ depth values together with the band ratios were processed to obtain depth values by each model and, subsequently, a measure of accuracy. In particular, the correlation coefficient was used to measure the performance. The results are presented in Table 2. RF proved to have the best correlation coefficient in all three months, while the other three algorithms, namely, the MLP, K*, and LR, all varied throughout the months.
## 3 Unit Cell Analysis
### 2 \(\times\) 4 Arrays  
In Figure 6, the electromagnetic characteristics of the proposed unit for the 2 \(\times\) 4 array configuration are demonstrated. Negative permeability (\(\upmu\)) exists from 6.65 GHz to 12.21 GHz, covering 5.56 GHz of the bandwidth shown in Figure 6b. In Figure 6c, the magnitude of permittivity (\(\upvarepsilon\)) is negative from 2.857 GHz to 5.759 GHz and 8.264 GHz to 9.974 GHz. The real magnitude of the refractive index (\(\upeta\)) is depicted in Figure 6d. The refractive index (\(\upeta\)) exhibits negative peaks from 8.258 GHz to 14.533 GHz.  
Table 2 summarizes the effective electromagnetic properties of the proposed metamaterial unit cell and different array configurations, such as 1 \(\times\) 2, 2 \(\times\) 2 and 2 \(\times\) 4. From Table 2 it can be found that the LHM MTM unit cell and array configurations have different resonances in the negative index frequency region. Finally, the proposed unit cell structure shows negative metamaterial characteristics at 8.50 GHz in all the configurations of the 1 \(\times\) 2, 2 \(\times\) 2 and 2 \(\times\) 4 array.  
Figure 5: 2 \(\times\) 2 array characteristics (**a**) Simulation setup (**b**) Permeability (**c**) Permittivity (**d**) Refractive index.  
Figure 6: 2 \(\times\) 4 array configuration characteristics (**a**) Simulation setup (**b**) Permeability (**c**) Permittivity (**d**) Refractive index.
## 7 Conclusions  
In this paper, a reliability evaluation model for TWTA, which has two performance degradation characteristics based on the Copula function and bivariate hybrid stochastic processes, was developed to overcome the limitations and shortcomings of the existing methods. The degradation characteristics of the space TWTA were analyzed, and the degeneracy models with single degradation and bivariate degradation were established, respectively. The main innovations of this paper are its viewpoints of a space TWTA's performance degradation model based on the multi-degradation variables of the bivariate hybrid stochastic processes, taking into account random effects compared with the existing methods, and depicting the correlation with the different performance parameters by the Copula function. Besides, a two-step method was proposed to evaluate unknown parameters in the model, where the edge distributions of the helix current and anode voltage were modeled through the Wiener and Gamma processes, and then the Copula function was applied to calculate the joint distribution. The evaluation results obtained using this paper's method compared with the traditional methods that only consider a one-dimensional parameter or multiple independent parameters were presented. The mean time to failure (MTTF) evaluated by the model proposed in this paper was 127.86 months, or 10.655 years. This shows that it is necessary to consider the correlation between the helix current and anode voltage of TWTA. Therefore, the method proposed in this paper is more applicable and more accurate than other traditional methods when dealing with the reliability evaluation of multi-parameter degradation products.  
In summary, this research provides a new insight for the reliability modeling and analysis of the space TWTA, and the proposed method can be further applied in complex systems with long life, high reliability, and measurable degradation data. However, this paper showed a correlation between the helix current and anode voltage as a preliminary study, and there is still a lot of work to be carried out because it is assumed that the initial degradation state of TWTA is consistent. In the future, we would like to continue carrying out the reliability analysis of TWTA, such as unfixed failure threshold, multi-source fusion, online prediction, etc.  
Conceptualization, X.-N.W. and X.-B.S.; methodology, X.-N.W.; software, D.-D.M. and X.-N.W.; validation, D.-D.M. and X.-N.W.; formal analysis, W.-L.W.; investigation, R.Z.; resources, G.-X.M.; writing--original draft preparation, X.-N.W.; writing--review and editing, X.-N.W. and X.-B.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. The authors declare no conflict of interest.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**IP**} & \multicolumn{2}{c}{**PU**} & \multicolumn{2}{c}{**HU**} \\ \cline{2-10}  & **OA\%** & **AA\%** & **KAPPA\%** & **OA\%** & **AA\%** & **KAPPA\%** & **OA\%** & **AA\%** & **KAPPA\%** \\ \hline SFFNet & **94.44** & 93.89 & **94.41** & **99.76** & **99.95** & **99.97** & **97.87** & **99.87** & **99.89** \\ CNN & 89.22 & **95.28** & 90.71 & 99.16 & 99.71 & 99.60 & 96.71 & 98.65 & 98.60 \\ GCN & 80.03 & 81.74 & 77.48 & 95.83 & 95.14 & 94.81 & 94.61 & 96.53 & 96.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy (%) on the Houston dataset of the different methods. SFFNet model, but as can be seen from Section 4, the final accuracy of SFFNet is higher after increasing the number of training iterations. The accuracy of the model has been demonstrated in order to show the superiority of the fusion framework. Figure 11 illustrates the change in the loss of the three models during the training iterations. All three models exhibit a decreasing trend on all three datasets, but the decrease rate of SFFNet is significantly faster than the other two single-classification models. During subsequent iterations, the loss of SFFNet converges almost infinitely to 0, reflecting its favorable achievement of high classification accuracy. Figure 12 shows the overall classification accuracy of the three models in the interval of niter from 0 to 300, with classification data taken every 10 intervals. The local accuracy can provide a clearer indication of the classification trends. It can be observed that the final accuracies of all the models achieve high-precision classification. However, the CNN exhibits stronger fluctuations across the different datasets. In contrast, SFFNet demonstrates relatively greater stability, with faster speed. We observed a greater variation in accuracy across the different datasets, which may be attributed to the variability of the datasets. For instance, the data in the Indian Pines dataset are more concentrated and block-distributed, whereas the data in the Pavia University and Houston datasets are scattered. Consequently, the classification accuracy and stability of the models under the Pavia University and Houston datasets are better than those under the Indian Pines dataset. Therefore, it is reasonable to speculate that SFFNet is  
Figure 11: Loss of the model with the three datasets. Figure 12: OA% of the model with the three datasets. more suitable for classifying large scattered datasets, aligning with our current practical application environment.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 2 Materials and Methods
### Ai-Qca
#### 2.5.4 Stenosis Detection  
According to the reference diameters, the rate of stenosis at each position on the CAT can be calculated. To detect stenosis, we first removed data with reference diameters less than 1 mm, because these vessels are typically located at the end of the vessel and are of relatively little medical significance for PCI. Then, the vessels are segmented by specific points, including the maximum, minimum, bifurcation, and adjacent points for reference diameters. This allows us to extract multiple effective segments and detect the narrowest position of each segment.  
The segments are then merged to accurately determine the proximal and distal areas of the stenosis. A stenosis uniqueness validation is performed to ensure that the current position is the narrowest among the potential stenosis areas nearby. It is not sufficient to only consider the narrowest points at similar candidate segments, but it is also important to evaluate the distance from existing stenosis to avoid missing any detection positions. Additionally, the DBSCAN [41] algorithm is used to eliminate duplicated coordinates of detection results by clustering within small segments. The potential stenosis candidate segments may have overlapping areas with adjacent segments, resulting in close proximity of the coordinates in these areas. However, the coordinates may not be exactly the same. To address this issue, DBSCAN can be utilized to eliminate the redundant coordinates and retain the validity of the remaining coordinates. After the stenosis coordinates are determined, the segments that contain the stenosis are merged, and the DS, MLD, RVD, and LL at the coordinates are calculated as the output of the AI-QCA.  
Figure 5: Branch separation and vessel fitting. (**a**) denotes the whole CAT, and (**b–d**) denote the separated branches of CAT. (**e**) denotes the whole fitting vessel, and (**f–h**) denote the separated branches of fitting vessels, while red lines represent the fitting reference vessel edges, and yellow areas represent the differences in the actual vessels.
## 3 Flexible Substrate Antenna Design
### Measurements
#### 3.5.2 Measurements of the \(S\)-Parameters  
As discussed previously, the effects of bending flexible polymer-based antennas have an impact on radiation characteristics such as resonant frequency, reflection coefficients, and gain. The impact on the \(S\)-parameters, which indicates the percentage shift, or the deviation, of the resonant frequency and the loss of signal strength, is discussed in our recent investigation on polymer based flexible antennas in [1]. The reflection coefficient \(S_{11}\) of the three polymer substrate antennas operating at 2.45 GHz is presented in Figure 9.  
Figure 7: Actual aspects of fabricated E-Shaped microstrip antennas operating at 2.45 GHz on substrates PVC (**left**), Teflon (Centre), and PET (**right**).  
The measured results of the reflection coefficients \(S_{11}\) of the PET, Teflon, and PVC substrate antennas operating at 2.45 GHz in the chamber show the resonant frequencies of 2.426, 2.438, and 2.417 GHz, respectively. The reflection obtained for the PET-based antenna is \(-35\) dB, the Teflon-based antenna is \(-39\) dB, and the PVC-based antenna is \(-32.5\) dB. Similarly, the \(S_{11}\) parameters of the antennas designed for the operating frequency of 4.25 and 7.45 GHz are presented in Figures 10 and 11, respectively.  
Figure 8: Testing arrangements in the Frankonia anechoic chamber.  
Figure 9: Reflection coefficients of E-shaped flexible polymer substrate antennas on PET, Teflon, and PVC substrates operating at 2.45 GHz.
## 5 Experiments Results
### Effect of the Model Structure  
To validate the effectiveness of the proposed parallel double-branch structure, we do another two experiments, i.e., only with the 1D spectral branch and only with the 2D spatial branch. In these  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **SVM-RBF** & **3D CNN** & **SSRN** & **Proposed** \\ \hline
1 & 98.68 & 98.56 & 98.75 & 98.73 \\
2 & 98.90 & 98.08 & 98.21 & 98.06 \\
3 & 99.64 & 98.76 & 99.59 & 98.90 \\
4 & 94.31 & 89.28 & 73.17 & 88.48 \\
5 & 99.77 & 95.76 & 98.71 & 99.95 \\
6 & 62.26 & 75.79 & 79.22 & 80.66 \\
7 & 95.35 & 96.55 & 96.62 & 99.26 \\
8 & 98.33 & 95.77 & 98.78 & 95.02 \\ AA & 93.41 & 93.57 & 92.88 & 94.88 \\ OA & 96.84 & 95.25 & 96.26 & 95.28 \\ Kappa & 94.65 & 91.85 & 93.70 & 91.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results (%) of different methods with 100% training samples.  
Figure 10: Classification maps of different methods. (**a**) The false-color map. (**b**) The label image. (**c**) Support vector machine with a radial basis function kernel (SVM-RBF). (**d**) 3D convolutional neural network (3D CNN). (**e**) Spectral-spatial residual network (SSRN). (**f**) The proposed hybrid DenseNet. Please zoom in to see the details.  
experiments, PCA is used to reduce the spectral bands from 174 to 40, and 100% training samples are used to train the models. The results are shown in Figure 11. From the figure, we can see that the model with only 2D spatial branch weakens the performance a lot, which ignores the spectral features. It implies that spectral features of active HSI are the key elements for classification. Adding the 1D spectral branch, the classification performance increases a lot as expected. In the other hand, for the model with 1D spectral branch only, it can achieve relatively good classification results for most objects, but for Class 6, its classification accuracy is very low. As it shows, considering spatial features is helpful to decline the misclassification of the Class 6, which means the spatial branch is indispensable to obtain more robust classification models for active HSI.  
In conclusion, for the active HSI data, the spectral-based classifiers, whatever SVM-RBF or 1D deep-learning-based classifier, could distinguish most objects, since the spectral differences between different objects are significant. However, reasonable utilization of spatial information is helpful for improving the classification performance. Taking advantages of the two kinds of features, the hybrid DenseNet achieves better classification results with the parallel double-branch structure.
## 3 Results
### Cost Analysis of AM vs. CM for GIS Aluminum Components
#### 3.2.1 Cost Analysis for Series Production  
Cost analysis for short-series production needs to consider multiple parameters to compare the cost of printed and current products accurately. This evaluation should take into account all costs including storage, transportation, necessary tools for production, etc. Since the comparison in this study is based on the outsourcing of both production methods, warehousing and transportation can be considered as the constant parameters. The cost of the printed parts is estimated by the weight of the part, and it is considered USD 0.8 per gram for the selective laser melting (SLM) method, one of the PBF techniques, and the type of powder is AlSi10 Mg alloy. This estimation is based on received quotations from multiple AM service providers. The final cost of the current parts manufactured by CM methods is also due to the outsourcing to Hitachi Energy manufacturing suppliers. Table 5 shows a comparison of the costs of the candidate parts with the AM and CM methods.  
The results indicate that the price-determining parameter for AM is weight, and on the other hand, the cost of machining is defined by geometry complexity. Therefore, in the case of short series production with AM, the best candidates are the smallest ones with the highest complexity in design. Considering the parts in GIS products, small and complex aluminum parts that can be printed without any additional post-processing are not available.  
Current CM methods are different because of the size, weight, geometry, and material requirements. Table 5 shows different parts with different current manufacturing methods to cover all aspects of cost. Part numbers 5 and 6 are manufactured by the casting process and machining. Although the price of special mold design and production will be added to the final cost, for series production, this cost will be divided into 2000 to 10,000 parts, and the share of the cost per part will be negligible.