esa-sceva/satcom-chunk-collection huggingface dataset:
# Fail-Safe Topology Optimization Using Damage Scenario Filtering
## 4 Discussion  
To enhance the computational efficiency of the fail-safe topology optimization method, a density-based filter has been introduced to determine whether additional finite element analyses and sensitivity calculations are necessary when traversing local damage scenarios. A comparative analysis of the optimization results reveals that this approach can significantly reduce computational efforts, especially in scenarios involving extensive damage. The locally simplified damage scenario method based on density filtering ensures optimization precision, thereby improving the computational efficiency of the fail-safe structural topology optimization algorithm. Additionally, considering some prior knowledge of the optimization model, such as the symmetry of the structure or load during the optimization process, further enhances the computational efficiency of the fail-safe topology optimization algorithm.  
However, this study is limited to the research on structural fail-safe topology optimization algorithms in the elastic phase, without considering fail-safe optimization designs after structural plastic deformation. Determining the regularization parameter poses a challenging question. In fail-safe topology design, unreasonable regularization parameters often lead to more unnecessary details or the appearance of numerous intermediate density elements in the optimization results. Therefore, for the efficient and rational selection of regularization parameters in plastic phase fail-safe topology optimization algorithms, further in-depth research and exploration are needed.  
Conceptualization, Y.L. and W.S.; methodology, Y.L. and Y.Z.; software (MATLAB R2021a), Y.L.; validation, Y.L. and K.C.; formal analysis, Y.L. and F.C.; investigation, Y.L. and W.S.; resources, W.S.; data management, Y.L.; writing--original draft preparation, Y.L.; writing and editing, Y.L. and W.S.; visualization, Y.L. and Y.Z.; supervision, W.S.; project administration, W.S.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Outstanding Young Researcher Fund from the Department of Science and Technology of Jilin Province (20190103067 JH). This work was supported by the National Natural Science Foundation of China (No. 11217020530) and the National Key Research and Development Program of China (No. 2020 YFA0713604).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Hyperparameter Optimization Using Successive Halving with Greedy Cross Validation
## 2 Materials and Methods
### Evaluative Experiments
Since there was no \(ex\)_ante_ reason to expect the distributional variances of the performance metrics generated by each competing algorithm to be equal, [PERSON]'s \(t\)-tests provided an appropriate statistical foundation for comparing the experimental results. The experiments themselves were run sequentially using a fixed hardware configuration on the Google Cloud Platform [28]. Since the hardware resources used to conduct the experiments were identical for each experimental condition, any statistically significant differences in wall-clock times or model performance values would be attributable solely to differences between the standard successive halving algorithm and the greedy successive halving algorithm. The results of the experiments are presented in the following section. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**ML Algorithm**} & \multicolumn{2}{c}{**250 Candidate**} & \multicolumn{2}{c}{**500 Candidate**} & \multicolumn{2}{c}{**1000 Candidate**} \\  & & **Models** & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} \\  & & **\(k\) = 5** & \(k\) = 10** & \(k\) = 5 & \(k\) = 10** & \(k\) = 5 & \(k\) = 10 \\ \hline \multirow{2}{*}{California Housing \({}^{\ddagger}\)} & Passive Aggressive & 0.573 & 0.570 & 0.555 & 0.558 & 0.548 & 0.548 \\  & Tweedie & 0.588 & 0.584 & 0.574 & 0.571 & 0.571 & 0.568 \\ \hline \multirow{2}{*}{Diabetes \({}^{\ddagger}\)} & Passive Aggressive & 44.482 & 44.345 & 44.380 & 44.253 & 44.320 & 44.218 \\  & Tweedie & 44.016 & 43.982 & 43.988 & 43.954 & 43.978 & 43.940 \\ \hline \multirow{3}{*}{Wine Recognition \({}^{\ddagger}\)} & Bernoulli Naive Bayes & 0.914 & 0.920 & 0.917 & 0.923 & 0.918 & 0.924 \\  & Decision Tree & 0.923 & 0.935 & 0.929 & 0.939 & 0.934 & 0.946 \\  & Deep Neural Network & 0.979 & 0.983 & 0.980 & 0.985 & 0.982 & 0.986 \\ \hline \multirow{3}{*}{Wisconsin Diagnostic Breast Cancer \({}^{\ddagger}\)} & Bernoulli Naive Bayes & 0.940 & 0.940 & 0.940 & 0.942 & 0.941 & 0.942 \\  & Decision Tree & 0.953 & 0.955 & 0.955 & 0.958 & 0.957 & 0.958 \\ \cline{1-1}  & Deep Neural Network & 0.982 & 0.982 & 0.983 & 0.983 & 0.984 & 0.983 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Values indicate the average MAE for the corresponding experiments’ ground truth optimal models. \({}^{\ddagger}\) Values indicate the average classification accuracy for the corresponding experiments’ ground truth optimal models.  
\end{table} 
Table 2: Mean performance of ground truth optimal models for each experimental condition, as identified by an exhaustive search.
## 5 Simulation Results
### Transmission Latency Analysis  
As we analyzed in the previous sections, HCR-1 uses relay selection in \(\mathcal{R}(1)\) to balance the energy consumption and prolong the network lifetime. However, the relay selection in \(\mathcal{R}(1)\) could lead to the  
Figure 11: Network lifetime comparison with different node densities.  
Figure 10: Network lifetime comparison with different network areas.  
growth of transmission latency. In this section, we use simulations to investigate the impacts of HCR-1 on the transmission latency. The hop count from the node to the sink is used as the metric to evaluate the transmission latency. In this scenario, the network area is set as 200 \(\times\) 200 m\({}^{2}\), and the number of nodes is \(400\). The clustering range \(R_{c}\) is set as \(70\) m.  
The simulation results are classified by different gradients in Figure 12. As shown in Figure 12, the average hop counts in both HCR and HCR-1 have a tight relation to the gradient of the nodes. It is worth noting that the gradient of each node indicates the minimum transmission hop count from the node to the sink. In HCR, the average hop count is about \(k+1\), which proves that HCR can generate a connected and sub-optimal topology, as analyzed in [19].  
On the other hand, the average hop counts in HCR-1 are approximately \(0.7\) larger than those in HCR. This is because the inter-cluster routing algorithm used in \(\mathcal{R}(1)\) adds one hop for the CHs with low residual energy (NEXT = 0) and their CMs. Nevertheless, the growth of hop counts in HCR-1 is tightly bound by one hop. Therefore, HCR-1 can improve the network lifetime with the reasonable growth of transmission latency.
## 2 The Proposed Fusion Approach
### qshiftN Dual-Tree Complex Wavelet Transform  
Highly sampled DWT exhibits change invariance issues in 1-D and directional sensitivity in N-D. The DTCWT approach is shift-invariant, economical, and directionally selective. The DTCWT [33; 34] is an improved wavelet transformation that generates actual and imagined transformational coefficients. The DTCWT uses two 2-channel FIR filter banks. Output is the actual component of one filter bank (Tree A), whereas yield is the imaginary component (Tree B).  
For a d-dimensional object, the DTCWT uses two significantly sampled filter banks with a 2\({}^{d}\) redundancy. The three stages of a 1-D DTCWT filter bank are shown in Figure 2. While DWT-fused images have broken borders, DTCWT-fused images are soft and unbroken. When compared to DWT, which only delivers constrained directions in (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\)), DTCWT produces 6 subbands in each of the three (\(\pm\)15\({}^{\circ}\), \(\pm\)45\({}^{\circ}\), \(\pm\)75\({}^{\circ}\)), both real and imaginary, which improves transformational correctness and preserves more detailed features.  
The odd/even filter approach proposed by DTCWT, however, has a number of drawbacks:  
1. There is no clear symmetry in the sub-sampling structure;
2. The frequency reactions of the two trees vary slightly;
3. Otherwise, since both terms denote linearity, the filter sets must be biorthogonal rather than orthogonal. It demonstrates that energy efficiency does not apply to signals and fields.  
Each of them is reduced and solved using the DTCWT qshiftN as illustrated in Figure 3, with all filters above level 1 much shorter. It is possible to achieve a sample gap above levels 1, and 1/2 during a test period by using delayed filters of 1/4 and 3/4 rather than the DTCWT original's 0 and 1/2. An asymmetric equal-length filter and the time it takes will be used to accomplish this.  
Wavelet orthonormality can be perfectly transformed because of the asymmetry. When it comes to reverse filters, Tree-A filters are used, but Tree-B filters are used for both reverse and reconstruction filters because they are all part of the same orthonormal array. All trees have the same response in terms of their natural frequency. Individual effects are symmetrical around their midpoints, but the total complex impulses are asymmetric. Asymmetrical extension continues to work on the frame's edges because of this.  
Figure 2: DTCWT bank filter’s structure.
# A Generative Adversarial Network Structure for Learning with Small Numerical Data Sets
## 4 Experimental Study
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seeds**} & \multicolumn{2}{c}{**SVM\_Poly**} & \multicolumn{2}{c}{**SVM\_Rbf**} & \multicolumn{2}{c}{**Decision\_Tree**} & \multicolumn{2}{c}{**Naive\_Bayes**} \\ \cline{3-10}  & & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** \\ \hline \multirow{2}{*}{10 with 100 virtual samples} & average & 69.403\% & 74.532\% & 83.527\% & 85.720\% & 79.797\% & 79.180\% & 71.895\% & 74.907\% \\ \cline{2-10}  & Stddev & 9.701\% & 8.647\% & 7.425\% & 5.887\% & 10.215\% & 8.615\% & 11.355\% & 10.893\% \\ \cline{2-10}  & \(p\)-value & \({}^{*}\) & & ns & ns & & * & \\ \hline \multirow{2}{*}{15 with 100 virtual samples} & average & 72.785\% & 80.171\% & 87.303\% & 87.711\% & 82.018\% & 82.191\% & 81.837\% & 82.328\% \\ \cline{2-10}  & Stddev & 8.595\% & 7.638\% & 3.820\% & 3.616\% & 6.461\% & 5.002\% & 6.949\% & 6.801\% \\ \cline{2-10}  & \(p\)-value & \({}^{***}\) & & ns & & ns & & ns & \\ \hline \multirow{2}{*}{20 with 100 virtual samples} & average & 76.789\% & 81.981\% & 88.762\% & 89.563\% & 84.705\% & 85.561\% & 86.004\% & 85.372\% \\ \cline{2-10}  & Stddev & 8.024\% & 5.940\% & 3.159\% & 2.531\% & 3.668\% & 3.297\% & 3.791\% & 5.074\% \\ \cline{2-10}  & \(p\)-value & \({}^{**}\) & ns & & ns & & ns & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The result of generating 100 virtual samples by WGAN_MTD for the Seeds dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Wine**} & \multicolumn{2}{c}{**SVM\_Poly**} & \multicolumn{2}{c}{**SVM\_Rbf**} & \multicolumn{2}{c}{**Decision\_Tree**} & \multicolumn{2}{c}{**Naive\_Bayes**} \\ \cline{3-10}  & & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** \\ \hline \multirow{2}{*}{10 with 100 virtual samples} & average & 68.741\% & 78.703\% & 81.685\% & 87.648\% & 67.109\% & 72.515\% & 66.400\% & 73.216\% \\ \cline{2-10}  & Stddev & 10.536\% & 11.189\% & 9.278\% & 5.250\% & 8.610\% & 9.720\% & 12.741\% & 10.822\% \\ \cline{2-10}  & \(p\)-value & *** & & ** & ** & *** & & *** \\ \hline \multirow{2}{*}{15 with 100 virtual samples} & average & 73.159\% & 82.108\% & 92.584\% & 92.077\% & 74.849\% & 79.707\% & 85.165\% & 86.309\% \\ \cline{2-10}  & Stddev & 9.384\% & 9.739\% & 3.782\% & 3.546\% & 7.683\% & 4.691\% & 8.327\% & 7.292 \\ \cline{2-10}  & \(p\)-value & *** & & ns & & ns & & ns & \\ \hline \multirow{2}{*}{20 with 100 virtual samples} & average & 78.679\% & 88.526\% & 94.069\% & 93.775\% & 78.531\% & 83.132\% & 89.870\% & 86.309\% \\ \cline{2-10}  & Stddev & 7.410\% & 6.981\% & 2.639\% & 2.460\% & 6.229\% & 3.872\% & 6.169\% & 4.532\% \\ \cline{2-10}  & \(p\)-value & *** & & ns & & ns & & ns & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The result of generating 100 virtual samples by WGAN_MTD for the Wine dataset.
## 3 Signal Spatial Reconstruction Method for Relay Cooperative Networks
### Orthogonal Optimization of Signal Space
#### 3.2.2 Analysis and Improvement of Signal Spatial Direction Solving Order  
In the process of realizing orthogonal alignment of signal subspaces to solve the precoding vector, the sequence determined by the alignment direction of each signal space and the solution sequence of user nodes sending precoding vectors will directly affect the bit error rate performance of user pairs corresponding to interactive signals. When implementing the signal subspace orthogonal alignment algorithm, the spatial alignment direction that is first solved and determined has the largest degrees of freedom of selection. Therefore, the best bit error rate will be obtained. Then the spatial alignment direction is solved and determined. Due to the decreasing degrees of freedom of selection, the bit error rate performance decreases. The bit error rate performance of the interactive signal corresponding to the finally determined spatial alignment direction has the worst channel quality. Therefore, the performance bottleneck of the system is to ensure that the signal space alignment direction of the interactive signal pair with the worst transmission channel quality is determined first. Therefore, the idea to improve the performance is to determine the solution order of the spatial direction of the interactive signal formation alignment according to the quality of the upstream channel from each user node to the relay node. That is, the first alignment direction that is solved and determined is the interactive signal with the worst channel quality. Finally, the spatial direction of the interactive signal with the best channel quality is determined.  
In solving the precoding vector and determining the spatial alignment direction formed by relay nodes, the selection gain of channel quality is used to compensate for the disadvantages of poor channel quality to improve the overall system performance. Therefore, before solving the appropriate precoding vector, the channels between user nodes and relay nodes in the system should be sorted according to channel quality. Then, the spatial alignment direction and precoding vector of the interactive signals are solved according to the channel ordering.
## 2 Materials and Methods
### Models and Analysis
#### Ordinary Least Square and Geographically Weighted Regression
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MEAN** & **Tillering** & **Heading** & **Ripening** & **MEDIAN** & **Tillering** & **Heading** & **Ripening** \\ \hline Cl Green & 0.41 & 0.81 ** & 0.32 & Cl Green & 0.40 & 0.80 ** & 0.31 \\ Cl Red & 0.61 * & 0.79 ** & 0.46 * & Cl Red & 0.59 * & 0.76 ** & 0.44 * \\ GNDVI & 0.63 * & 0.82 ** & 0.41 & CNDVI & 0.60 * & 0.80 ** & 0.41 \\ MSAVI & 0.74 ** & 0.83 ** & 0.44 & MSAVI & 0.71 ** & 0.81 ** & 0.43 \\ MSR & 0.61 * & 0.82 ** & 0.49 * & MSR & 0.59 * & 0.82 ** & 0.47 * \\ NDVI & 0.65 * & 0.83 ** & 0.48 * & NDVI & 0.61 ** & 0.82 ** & 0.49 * \\ NDWI & 0.69 ** & 0.84 ** & 0.49 * & NDWI & 0.66 ** & 0.84 ** & 0.49 * \\ OSAVI & 0.66 ** & 0.83 ** & 0.47 * & OSAVI & 0.64 ** & 0.81 ** & 0.48 * \\ RDVI & 0.68 ** & 0.83 ** & 0.42 & RDVI & 0.67 ** & 0.82 ** & 0.44 \\ RVI & 0.59 * & 0.81 ** & 0.48 * & RVI & 0.57 * & 0.81 ** & 0.47 * \\ \hline
**MIN** & **Tillering** & **Heading** & **Ripening** & **MAX** & **Tillering** & **Heading** & **Ripening** \\ \hline Cl Green & 0.38 & 0.72 ** & 0.40 & Cl Green & 0.43 & 0.82 ** & 0.34 \\ Cl Red & & & & Cl Red & & & \\ Edge & & & Edge & & & & \\ GNDVI & 0.56 ** & 0.75 ** & 0.41 * & GNDVI & 0.63 * & 0.83 ** & 0.41 * \\ MSAVI & 0.63 ** & 0.81 ** & 0.40 * & MSAVI & 0.63 * & 0.84 ** & 0.40 * \\ MSR & 0.61 * & 0.77 ** & 0.39 & MSR & 0.60 * & 0.83 ** & 0.39 \\ NDVI & 0.62 ** & 0.77 ** & 0.38 * & NDVI & 0.65 * & 0.81 ** & 0.42 * \\ NDWI & 0.63 ** & 0.79 ** & 0.39 * & NDWI & 0.65 ** & 0.83 ** & 0.41 * \\ OSAVI & 0.62 ** & 0.79 ** & 0.41 & OSAVI & 0.64 ** & 0.84 ** & 0.45 * \\ RDVI & 0.63 * & 0.80 ** & 0.45 * & RDVI & 0.62 ** & 0.84 ** & 0.45 * \\ RVI & 0.53 * & 0.77 ** & 0.38 & RVI & 0.56 * & 0.83 ** & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multilinear stepwise equation estimating grain yield (GY) using two (MIN MSAVI and MAX RDVI) variables. Stepwise equation estimate, \(p\)-value and variance inflation factor (VIF) are shown together with the summarized equation. The model was developed with data from the 2018 wheat growing season at the indicated fields shown in Figure 1.
# Selected Applications of Terahertz Pulses in Medicine and Industry
## 5 Terahertz Imaging and Spectroscopy in Cultural Heritage
### Terahertz Spectroscopy
Studies of the hydration state have shown that there are many interchangeable hydration states of FeSO\({}_{4}\) two of which involve either four or seven molecules of water with terahertz spectra, which differ only by one absorption peak. Oxidation of these hydrates can occur, and the result is an even more highly corrosive compound. Consequently, detection is important to ensure document preservation [97; 98]. This type of ink produces a strong reflection in the terahertz region, whereas other types of ink, such as sepiia ink, do not produce a significant reflection. Herewith inks such as sepiia, transmission studies would be most appropriate if the sample permits it, although 3D imaging will be more problematic in transmission than in reflection. Terahertz spectra are still being accumulated from materials relevant to art restoration and studies, and given the relatively short time of the applications of the technology, knowledge lags behind infra-red. However, a terahertz data base has been assembled by NICT and RIKEN and is found at [[http://www.thzdb.org](http://www.thzdb.org)]([http://www.thzdb.org](http://www.thzdb.org)) (20 February 2022).
## 2 Materials and Methods
### Statistical Analysis  
Data are presented as the means \(\pm\) standard deviations (SD) and ranges. The data normality was assessed using the Shapiro-Wilk test. Differences between test and retests were analyzed by paired \(t\)-test; the comparison of the values of 1 RM and power between age groups were performed by one-way ANOVA, the effect size (f) was performed with G*Power 3.1.9.7 (Dusseldorf, Germany) [32], by F-Test for ANOVA Fixed Effects one-way, and post hoc tests with Bonferroni correction. The comparison of the average of the power values expressed at the different percentages of 1 RM, both for the LE and the SBP was performed by the repeated measures analysis of variance and the effects size were also reported as partial eta squared (\(\eta^{2}\)).  
To determine the confidence limits as measures of absolute reliability, the mean coefficient of variation (CV) from individual test-retest CVs was used, and the Bland-Altman method was used for visual evaluation of the reliability of measurements and agreement limit [34]. Repeatability is the closeness of the agreement between successive readings obtained by the same method for the same material and under the same conditions (same operator, same apparatus, same setting, and same time). The most commonly used method for the study of measurement repeatability is the intraclass correlation coefficient (ICC) [18]. This was calculated by determining the intraclass correlation coefficient (ICC) estimates and their 95% confident intervals based on two-way random effects, absolute agreement, and single-rater measurement (ICC2,1) [35]. In addition, [PERSON]'s proposal [38] is usually used to make a qualitative interpretation of the ICC result; thus, ICC < 0.4 indicates poor reliability, 0.4 \(\leq\) ICC \(\leq\) 0.75 indicates fair to good reliability, and ICC \(\geq\) 0.76 indicates excellent reliability. The absolute reliability was evaluated using the standard error of measurement (SEM). The minimal detectable change (MDQ95) was calculated both absolutely and as a percentage. The statistical significance level was set at 5%. All data were analyzed using SPSS statistical package version 25 (SPSS Inc., Chicago, IL, USA).
## 3 In-Situ Monitoring
### Monitoring Results
#### 3.2.3 Vertical Displacement of the Top of the Enclosure Wall  
The variation curves of the vertical displacement of the top of the enclosure wall at the monitoring points W1, W2, W3, W4, and W5, with the same time interval of observations, were recorded as AW1, AW2, AW3, AW4, and AW5 curves, respectively. The observation time was consistent with Figure 4, as shown in Figure 6.  
As the figure shows, the vertical displacement of the top of the enclosure wall increased significantly during the excavation process, and finally tended to be basically stable. The maximum vertical displacements of the top of the enclosure wall at the monitoring points W1, W2, W3, W