esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A novel aero-engine RUL probability prediction framework based on BiGRU-IVBI is proposed in this paper, which takes into account the uncertainties during the degradation process of aero-engines. The representative features are first extracted by the PCA algorithm. Then, the BiGRU network is trained for RUL prediction with prediction uncertainties quantified by the proposed IVBI method, where the Gaussian mixture distribution is introduced to improve the generalization capability and prediction accuracy of the traditional variational Bayesian inference approach. The proposed method can give not only the point estimate of RUL prediction but also the CI of the estimate, which is of great importance for real-world application. Finally, the performance of the proposed method is verified on the CMAPSS data set, and the prediction accuracy is higher than other comparative models, which proves the effectiveness and superiority of the proposed framework.  
Further research will be committed to finding more effective uncertainty prediction methods to obtain a higher RUL prediction accuracy. At the same time, exploring the application of the proposed method to the RUL prediction in situations where multiple degradation mechanisms are in place is also the direction of our future research.  
Figure 12: Comparison results of different \(\rho\).  
Author Contributions: Conceptualization, methodology, writing--review and editing: [PERSON]; Methodology, software, validation, and writing--original draft: [PERSON]; Writing--review and editing: [PERSON]; Methodology and writing--review and editing: P.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under grants 62273038 and U21A20483.  
Not applicable.  
Data Availability Statement: [[https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/)]([https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/)) (accessed on 13 December 2022).  
The authors would like to thank the National Natural Science Foundation of China for grants 62273038 and U21A20483 and the anonymous reviewers and the editor for their valuable and insightful suggestions.  
The authors declare no conflict of interest.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multirow{2}{*}{**Side**} & \multicolumn{2}{c}{**PRE**} & \multicolumn{2}{c}{**POST**} & \multicolumn{2}{c}{**ANOVA Effects**} \\ \cline{3-8}  & & \(\mathbf{M\pm SD}\) & **Shapiro–Wilk** & **M \(\pm\) SD** & **Shapiro–Wilk** & **Time** & **Side** & **Time \(\times\) Side** \\ \hline \multirow{3}{*}{AT} & \multirow{3}{*}{R} & 9.41 \(\pm\) 2.85 & \multirow{3}{*}{0.97 (0.01)} & 10.53 \(\pm\) 2.70 & \multirow{3}{*}{0.81 (<0.001)} & \multirow{3}{*}{0.81 (<0.001)} \\  & & (8.11, 10.71) & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 9.18 \(\pm\) 1.38 & \multirow{3}{*}{0.97 (0.74)} & 9.43 \(\pm\) 1.53 & \multirow{3}{*}{0.96 (0.46)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (8.55, 9.81) & & & & \\ \hline \multirow{3}{*}{BF} & \multirow{3}{*}{R} & 17.64 \(\pm\) 1.71 & \multirow{3}{*}{0.95 (0.40)} & 17.77 \(\pm\) 1.57 & \multirow{3}{*}{0.93 (0.11)} & \multirow{3}{*}{0.93 (0.11)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (16.86, 18.42) & & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 18.08 \(\pm\) 1.76 & \multirow{3}{*}{0.94 (0.22)} & 18.18 \(\pm\) 1.50 & \multirow{3}{*}{0.94 (0.22)} & \multirow{3}{*}{0.94 (0.22)} & \\  & & (17.27, 18.88) & & & & & \\ \hline \multirow{3}{*}{S} & \multirow{3}{*}{R} & 18.03 \(\pm\) 1.48 & \multirow{3}{*}{0.96 (0.52)} & 18.34 \(\pm\) 1.77 & \multirow{3}{*}{0.97 (0.69)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (17.35, 18.70) & & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 17.53 \(\pm\) 1.41 & \multirow{3}{*}{0.98 (0.88)} & 17.80 \(\pm\) 1.55 & \multirow{3}{*}{0.99 (0.99)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (16.88, 18.17) & & & & & \\ \hline \multirow{3}{*}{RF} & \multirow{3}{*}{R} & 17.98 \(\pm\) 1.87 & \multirow{3}{*}{0.96 (0.48)} & 19.50 \(\pm\) 1.43 & \multirow{3}{*}{0.97 (0.81)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (17.13, 18.83) & & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 17.87 \(\pm\) 1.88 & \multirow{3}{*}{0.95 (0.39)} & 19.51 \(\pm\) 1.68 & \multirow{3}{*}{0.96 (0.56)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (17.02, 18.73) & & & & & \\ \hline \multirow{3}{*}{MOS} & \multirow{3}{*}{R} & 20.07 \(\pm\) 4.02 & \multirow{3}{*}{0.97 (0.77)} & 20.09 \(\pm\) 3.85 & \multirow{3}{*}{0.94 (0.17)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (18.24, 21.90) & & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 19.88 \(\pm\) 3.31 & \multirow{3}{*}{0.97 (0.77)} & 21.59 \(\pm\) 2.32 & \multirow{3}{*}{0.97 (0.63)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (18.37, 21.38) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics and ANOVA results for RT variable.
# Generation of Stereo Images Based on a View Synthesis Network
## 3 The Proposed Approach
### Semantic Segmentation  
Our model predicts semantic labels from the input image in addition to estimating the depth map. In semantic segmentation [26], the PSPNet [19; 23] can achieve high performance using various data sets and can also outperform the network proposed by [PERSON] et al. [9]. We used the PSPNet to predict semantic labels because of its high accuracy.  
The PSPNet first uses ResNet [27] to generate a feature map from the input image, and then produces different subregional representations using the pyramid pooling module. After upsampling, concatenation and convolution layers, semantic labels that consider the global and local context information are predicted. [PERSON] et al. [23] trained several models using different data sets to measure the performance of the PSPNet. In our model, because the network and pretrained model that estimate the depth map are trained using the NYUDepth indoor dataset, we chose the model trained using the ADE20K [28] dataset for the PSPNet. It is suitable for our model and can be applied to both outdoor  
Figure 2: Upscaling the depth map: (**a**) the input image (\(558\times 501\)); (**b**) the extended depth map (\(157\times 119\)); and (**c**) the upscaled depth map (\(558\times 501\)).  
and indoor scenes. The ADE20K data set contains 150 object categories. Although correctly identifying the category of each object is not essential to our model, the ability of PSPNet to accurately color most objects in a scene is helpful in subsequent processes. Also, the predicted labels and the input image are of the same size. Thus, we can directly use the labels to segment the image.  
To compute the translation and rotation of each object, we must segment the input image into several objects. After segmentation was conducted, we separated regions that were unconnected or belonged to different categories. Our algorithm identifies the cluster of each region using the semantic labels. After clustering, we generated a cluster list that is then filtered with four conditions: (1) the labels of the region are the background categories, such as wall, sky, floor, ceiling, road and sidewalk; (2) the size of the region is too small; (3) most pixels of the region are at the borders of the image; and (4) the average depth of the region is too deep, which means that the object is too far away to result in a significant rotation in human vision.  
If the region of a cluster meets any aforementioned conditions it will be removed from the cluster list, because in our model, we only focus on those regions or objects that are important, sufficiently large, and noticed easily by the human eye. After regions were separated, we segmented the input image into several objects, as presented in Figure 3. However, we eroded the background image because of the inaccuracy of the semantic labels along the borders of objects.
## 3 Results and Discussion  
### Structure and Morphology  
The XRD patterns of MgCo\({}_{2}\)O\({}_{4}\)samples synthesized by the microwave-assistant process and subsequent calcination at different temperatures (500, 550, 600, 650 \({}^{\circ}\)C) are shown in Figure 1. The diffraction peaks of MgCo\({}_{2}\)O\({}_{4}\) calcinated at different temperatures agreed with the standard JCPDS card No. 81-0667 [25]. No other additional phases or impurities can be observed, indicating that MgCo\({}_{2}\)O\({}_{4}\) with cubic spinel structures were successfully synthesized. As the calcination temperatures increased, the diffraction peaks become stronger and sharper, which could be clearly observed in the samples calculated at 500 \({}^{\circ}\)C or above, indicating that the crystal size appears larger with the increase in the crystallinity [24].  
The SEM images of MgCo\({}_{2}\)O\({}_{4}\) samples acclinated at 500, 550, 600, and 650 \({}^{\circ}\)C in air for 4 h at a heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\) are shown in Figure 2. As shown in Figure 2a-c, the as-synthesized MCOs exhibit geometrical sheet-like 2D structures, and the thickness of the nanosheets is about 15-30 nm. Specifically, according to Figure 2a, MCO-500 sample exhibits shaggy and compact nanosheets. When the calcination temperatures increased to 550 and 600\({}^{\circ}\)C, as shown in Figure 2b,c, MgCo\({}_{2}\)O\({}_{4}\) samples display unilaminar porous, cross-linked nanosheets with certain dispersity, which could ensure sufficient space between nanosheets for infiltration of electrolyte and buffering volume changes during electrochemical charge-discharge cycling. By contrast, when the calcination temperature continued to increase to 650\({}^{\circ}\)C, there were more macropores on nanosheets; nanosheets changed into nanoparticles, as shown in Figure 2d.  
To further explore the microstructure of MgCo\({}_{2}\)O\({}_{4}\) nanosheets, TEM, HRTEM, and selected area electron diffraction (SAED) of the MCO-600 sample were carried out, which are shown in Figure 3a-c, respectively. To be specific, as shown in Figure 3a, the TEM image indicates that as-prepared MCO-600 has porous nanosheets morphology with many micropores, and the size of the micropore is 30-150 nm.  
Figure 1: X-ray diffraction (XRD) patterns of MCO-500, MCO-550, MCO-600 and MCO-650.  
The HRTEM image shows regular crystal orientation, as shown in Figure 3b, and the lattice spacing is \(-0.290\) nm, which is corresponding to (220) plane in standard JCPDS card No. 81-0667. Figure 3c shows the SAED image of MCO-600, which indicates that the crystalline structure of as-prepared MCO-600 is spinel structure that is well consistent with the XRD pattern shown in Figure 1.  
Figure 3: (**a**) Transmission electron microscope (TEM) image of MCO-600; (**b**) High resolution transmission electron microscope (HRTEM) image of MCO-600; (**c**) Selected area electron diffraction (SAED) pattern of MCO-600.  
Figure 2: SEM images of (**a**) MCO-500; (**b**) MCO-550; (**c**) MCO-600; (**d**) MCO-650.
## 1 Introduction  
### Motivation and Objectives of the Study  
Challenges in the transport industry worsen the logistics of material flow, which can lead to crises in various sectors of the economy. As material production is the foundation of economic development in any country, the significance of the transport industry in ensuring the smooth operation of agricultural enterprises, the mining and processing industries, construction, and energy is exceptional. During rapid economic growth, the limited capacity of transportation logistics can become a constraining factor.  
Thus, identifying the quantitative relationships among these factors is highly relevant and represents a significant area of interest. In the context of sustainable development, transport logistics play a crucial role in balancing economic efficiency with environmental responsibility. Within this evolving ecosystem, the shift towards sustainable transport solutions is essential to minimize the carbon footprint, optimize resource use, and ensure long-term resilience in economic growth management. Green logistics, thoughtful infrastructure planning, and the integration of digital technologies are key priorities in modern transport management.  
Data-driven management in sustainable transport logistics involves the use of digital platforms for monitoring transport and cargo flows. This can enable Route Optimization, which will lead to reduced mileage and, as a result, reduced CO2 emissions and fuel savings.  
Therefore, analyzing sustainable transport logistics in Poland and Ukraine allows a deeper understanding of how these countries can align their logistics strategies with global sustainability goals [1,2]. Building a resilient logistics ecosystem also requires the integration of innovative tools such as BIM technology, which can facilitate cooperation among specialists working on construction projects, including architects, engineers, project managers, and transportation logisticians. Central communication hubs, the construction of which is proposed to develop transport infrastructure, can include airports, rail and bus stations, and hotel and restaurant infrastructures. Such hubs serve as nodes within the broader transport ecosystem, supporting connectivity and operational synergy.  
The development of central communication hubs can increase freight and passenger transportation speeds, making the transportation system more efficient and attractive for businesses and investors. Building high-precision transportation network models is another important application of BIM technology in transportation logistics. This can be used to solve various infrastructure and transportation-planning tasks. For example, BIM can be used to forecast road and rail traffic loads, plan transportation routes, address transportation safety issues, and improve the efficiency of transportation companies.  
This study conducts a comparative intergovernmental analysis of the dependence of economic development in Poland and Ukraine on the functioning of the transport sector to determine the prospects for their interaction based on interindustry connections.
## 5 Results and Discussion
### Experiment II: Litchi Spray Hydrocooling Performance
#### 5.2.2 Precooling Capacity and Precooling Rate  
Figure 12a shows that the precooling capacity increased simultaneously with the charging time, while the precooling rate did not change significantly. The more ice that is stored, the more TES capacity is stored, thus resulting in a higher precooling capacity. It was evident that the precooling rate of the fifth and sixth charging tests was faster than that of the third and fourth charging tests. Since the temperature of the ice-water mixture is 0 \({}^{\circ}\)C, the water temperature during the precooling process is higher than 0 \({}^{\circ}\)C. When there is more ice stored in the storage box, the contact area between the ice and water is larger, causing the ice to melt faster, the speed of heat release to increase, and the precooling speed of fruits and vegetables to be faster. The weight of precooled lithci reached 299 kg after 6 h of charging with one-third water of the TES tank. When the TES tank was full of water, the precooling capacity of lithci could exceed 900 kg. As the TES capacity increases, the relative thermal energy loss decreases. Figure 12b shows that the precooling capacity initially increased and then decreased with an increase in lithci load. The precooling rate significantly rose until the lithci load was more than 23 kg. The maximum precooling capacity was 207 kg, and the maximum precooling rate was 94.1 kg/h when the lithci load was 23 kg. With an increase in the single precooling load, the total precooling time decreases, which reduces energy loss in the precooling process, thus increasing precooling efficiency and rate. When the lithci load was the same, the precooling capacity increased. Figure 12c shows that the precooling capacity at 30 L min\({}^{-1}\) was higher than other spray flow rates. When the spray flow rate was 50 L min\({}^{-1}\), it had the minimum precooling capacity. This is because the cooling capacity stored in the storage box remains constant, and when the spray flow rate is higher, the power of the water pump increases, and the water circulation becomes faster, resulting in an increase in cooling capacity loss. The precooling rate at 50 L min\({}^{-1}\) was higher than other spray flow rates, while at 30 L min\({}^{-1}\) it had the minimum precooling rate. The precooling capacity and precooling rate for different spray flow rates had a small difference. The higher the flow rate, the more sufficient the contact between cold water and fruits/vegetables, thus accelerating the precooling speed. Due to the small temperature difference in the cold water, the precooling rate does not increase significantly. Overall, the precooling capacity clearly increased with an increase in charging time, and the precooling rate increased along with the lithci load until it reached less than 23 kg.  
Figure 11: The temperature curve of lithci spray hydrocooling in test 2.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 5 Discussion
### Performance on Different Size Nodules  
To validate the performance of our MGDFormer in different-size nodules, especially the classification results of hard samples, as shown in Figure 4, we test our MGDFormer with other state-of-the-art methods on three parts: small nodules (\(<\)5 mm), middle nodules (5-12 mm), and big nodules (\(>\)12 mm). We can observe that all the methods can obtain high accuracy in bigger and smaller parts. However, for middle nodules, the previous works mostly have poorer performance, and our MGDFormer has an obvious superiority to classify these hard samples, which proves that MGDFormer indeed can learn more discriminative local features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AUC** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** \\ \hline GP & 97.4 & 94.0 & 94.8 & 93.2 & 94.1 \\ DSP & 97.7 & 94.6 & 95.3 & 93.6 & 94.3 \\ LFS & 98.2 & 95.8 & 95.7 & 94.1 & 94.8 \\ GP+DSP & 97.9 & 95.2 & 95.3 & 93.8 & 94.5 \\ GP+DSP+LFS & **98.5** & **96.1** & **95.9** & **94.4** & **95.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of multi-granularity structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AUC** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** \\ \hline RA + Dilated Layer & 97.6 & 94.8 & 94.9 & 93.6 & 94.3 \\ RA + MLP & 97.3 & 94.1 & 94.4 & 93.1 & 93.8 \\ DA + MLP & 98.1 & 95.5 & 95.4 & 94.0 & 94.8 \\ DA + Dilated Layer & **98.5** & **96.1** & **95.9** & **94.4** & **95.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparisons of each component in the Deformable Dilated Transformer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **AUC** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** \\ \hline
2 & 97.7 & 94.2 & 94.9 & 93.6 & 94.2 \\
3 & 98.0 & 94.7 & 95.4 & 94.1 & 94.5 \\
4 & **98.5** & **96.1** & **95.9** & **94.4** & **95.2** \\
5 & 98.3 & 95.6 & 95.6 & 94.1 & 95.3 \\
6 & 98.0 & 94.8 & 95.2 & 93.8 & 94.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons of the different number of DDPormer blocks.
## 4 Experiments and Result Analysis
### Detail Experiments
Overall, Figure 10 demonstrates that the unsupervised branch is indeed beneficial, but only when balanced against the supervised loss, an \(\alpha\) between 0.10 and 0.20 delivers the optimal synergy for this task. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & \(\alpha\) & \(\beta\) & **Accuracy** \\ \hline
1 & 0.9 & 0.1 & 95.50\% \\
2 & 0.8 & 0.2 & 96.51\% \\
3 & 0.7 & 0.3 & 96.86\% \\
4 & 0.6 & 0.4 & 96.76\% \\
5 & 0.5 & 0.5 & 96.99\% \\
6 & 0.4 & 0.6 & 96.82\% \\
7 & 0.3 & 0.7 & 97.31\% \\
8 & 0.2 & 0.8 & 97.47\% \\
9 & 0.1 & 0.9 & 97.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: DBS-NET Weight Experiment. Figure 9: Exploring the optimal weight combination.
# Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data
## 2 Sample Data and Bayesian Matrix  
In the case data of diabetes [11], we mainly analyze the relationship between symptoms and medication. Assume that the medication regimens are written as \(T=(t_{1},\,t_{2},\,\ldots,\,t_{m})\), and the symptoms are written as \(S=(s_{1},\,s_{2},\,\ldots,\,s_{n})\). We write the two sets of events in a comparison matrix:  
\[B=\left(\begin{array}{cc}0&p(t|s)\\ p(s|t)&0\end{array}\right),\,t=\left(\begin{array}{c}t_{1}\\ \ldots\\ t_{m}\end{array}\right),\,s=\left(\begin{array}{c}s_{1}\\ \ldots\\ s_{n}\end{array}\right) \tag{1}\]  
Matrix B uses posterior probability (given the symptom, the probability of using different medications) and likelihood estimates (given the medication and the curative effect to different symptoms) to describe the relationship between symptoms and medications completely, which we call the Diabetes Medication Bayes Matrix (DTBM). The DTBM is a block matrix that contains four parts, among which the elements are represented by \(B_{ij}^{q}\), q = 1, 2, 3, 4. \(B_{ij}^{1}\) represents the Probability of correlation between medication regimens, \(i=1,\,2,\,\ldots,\,m,\,j=1,\,2,\,\ldots,\,m\). \(B_{ij}^{2}\) indicated the probability of different medication regimens received by patients with different symptoms. The elements in \(B_{ij}^{3}\) represent the effects of different medications on different symptoms in the case data, expressed by conditional probability, where \(i=m+1,\,m+2,\,\ldots,\,m+n,\,j=1,\,2,\,\ldots,\,m\); \(B_{ij}^{4}\) represents the probability of correlation between different diseases, where \(i=m+1,\,m+2,\,\ldots,\,m+n,\,j=m+1,\,m+2,\,\ldots,\,m+n\). Assuming that the medication regimens are independent and the symptoms of diabetic patients are independent. Therefore, \(B_{ij}^{1}=0,\,\,B_{ij}^{4}=0\).  
The DTBM fully demonstrates how should diabetic patients with certain symptoms choose medication regimens. Doctors can also directly recommend medication regimens for patients based on the probability in \(B_{ij}^{2}\). However, in the actual sampling and statistics process, due to the diversity and complexity of patient symptoms, there will be one or more missing columns. Currently, we need to do the completion.