esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Case Study II: According to Different Fault Location
#### 4.2.2 The Result of the Diagnosis  
The diagnostic accuracy of the three methods for the two sets of data is shown in Figure 18. It can be seen that SAFC is more accurate and stable than SAE an SF. The specific calculation results are shown in the Table 4. In the diagnosis of the two sets of data, the diagnostic accuracy is generally higher than the diagnosis of each component, which is because the characteristics of different components  
Again, these three methods were used to extract features from the two sets of data, and t-SNE dimension reduction was used to obtain a two-dimensional view. The feature mapping results of the three methods are shown in Figures 19 and 20. It can be seen that in the proposed method, characteristic samples of the same health status are clustered in the corresponding clusters, and test samples of different health status are well separated. However, in SAE, not only in the group 1, some samples of planet wheel 3 were not well aggregated, but also in the group 2, some groups of characteristics were not very well separated. While in the SF, although the characteristics of different health conditions were generally separated, uniform health conditions at different speeds were also separated, which obviously did not meet the classification requirements.  
Figures 21 and 22 show the visualization of the feature distribution of the two groups under the three methods. It can be seen that in SAFC, the characteristics of different fault types were different with obvious differences, while in SAE, the difference was relatively fuzzy. In SF, subdivision occurred in each of the same characteristics, that is, the same health condition showed different characteristics,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SAFC**} & \multicolumn{2}{c}{**SAE**} & \multicolumn{2}{c}{**SF**} \\ \cline{2-7}  & **Accuracy (\%)** & **Time (s)** & **Accuracy (\%)** & **Time (s)** & **Accuracy (\%)** & **Time (s)** \\ \hline Group 1 & 99.91 \(\pm\) 0.04 & 43.35 \(\pm\) 1.46 & 96.84 \(\pm\) 0.49 & 25.16 \(\pm\) 0.76 & 98.32 \(\pm\) 0.12 & 62.37 \(\pm\) 2.57 \\ Group 2 & 99.62 \(\pm\) 0.08 & 43.64 \(\pm\) 1.81 & 92.03 \(\pm\) 0.66 & 25.84 \(\pm\) 0.89 & 97.27 \(\pm\) 0.60 & 62.49 \(\pm\) 2.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time and accuracy of diagnosis.  
Figure 19: Signal feature dimension reduction for group 1 using the three methods.  
Figure 20: Signal feature dimension reduction for group 2 using the three methods.  
which was consistent with the results in t-SNE. In conclusion, the proposed method can well extract domain invariant features and classify them.
## 3 Proposed Method
Mathematically, this can be expressed as  
\[b_{EDF}(p,q)=b_{k}(p,q) \tag{8}\]  
and  
\[I(x,y)=\overset{pq-(K/M-1)}{\underset{pq=0}{\cup}}b_{EDF}(p,q) \tag{9}\]  
where \(k=D(p,q)\), and the block \(b_{EDF}(p,q)\) that is selected is referred to as an extended depth of field block. The symbol \(\cup\) denotes the union operator that is used to compose the EDF image by patching  
Figure 3: Partitioning an image \(S_{j}(x,y)\) into non-overlapping square blocks of size \(M\times M\). it with the extended depth of field blocks (i.e., \(b_{EDF}(p,q)\)). However, as we shall show later, an EDF image reconstructed in this manner will exhibit a blocky, visual unpleasant appearance. _Stage 3: Filtering_  
To reduce the blocky appearance of the EDF image, we propose to smooth the depth map with a simple low-pass filtering process that can be realized as follows. First, we compute the mean intensity for each EDF block as  
\[\Delta z=z_{j+1}-z_{j}=\frac{Z}{N} \tag{4}\]0  
Next, a corresponding status flag \(status(p,q)\) is determined as  
\[status(p,q)= \begin{cases}1&if\ mean(p,q)\geq T\\ 0&otherwise\end{cases}  \tag{11}\]  
where \(T\) is a small fixed threshold value corresponding to the minimum intensity that is visible to the human eye. An EDF block with an average intensity higher than \(T\) is referred to as a \"visible block\". Subsequently, a low-pass filtered depth map is obtained by averaging the depth values of visible blocks within a \(3\times 3\) window as  
\[D_{L}(p,q)=\frac{1}{9}\underset{s=-1}{\overset{1}{\sum}}\underset{t=-1}{ \overset{1}{\sum}}[D(p+s,q+t)]\times status[m(p+s,q+t)] \tag{12}\]  
Note that in the filtering process, blocks with too low intensity are discarded as they are likely associated to empty space in the scene, and may not carry reliable depth information. _Stage 4: Reconstruction of the EDF image_  
Finally, we apply Equation (9) to obtain the EDF image, with the variable \(k=D_{L}(p,q)\), i.e.,  
\[b_{EFD}^{\prime}(p,q)=b_{k}(p,q)\Big{|}_{k=D_{L}(p,q)} \tag{13}\]  
and  
\[I^{\prime}(x,y)=\underset{pq\
eq-0}{\overset{pq=(K/M-1)}{\cup}}b_{EFD}^{\prime }(p,q) \tag{14}\]  
In comparison with the method in [18], our proposed scheme has a significantly decrease in the computational loading of the entropy values as they are evaluated on a block-by-block, instead of a pixel-by-pixel basis. With our approach, the computation time for deducing the entropy values has been reduced by \(M^{2}\) times. The computation time for the reconstruction of the hologram into different planes are similar for both methods. As will be shown later, our proposed method is around 60 times faster than the method in [18].
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### CCC Retrieval Model
#### 2.6.2 Implementing the Random Forest Regression Approach  
Random forest is an ensemble learning method that combines multiple decision trees for classification or regression [60]. It runs efficiently on large datasets with excellent performance and accuracy [38; 61]. In this study, recursive partitioning was employed to divide the simulated dataset into 100 homogeneous subsets (100 trees), and the results of all the trees were then averaged. A random forest regression (RFR) was implemented in Matlab with the use of the MTCI and LAI-VIs as input features for estimating CCC. The CCC models were validated using ground spectral measurements and MERIS satellite data.
## 2 Introduction  
Nowadays, \(AM\) represents a very promising way to obtain high-quality, cheap, low time consumption, and low pollution/waste products (unique components or spare parts,as well). Rapid Prototyping served to obtain the first complex products [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Later, the technology was applied not only for plastics, but also for plastic-metal, specifically only the metal parts [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].  
Fuse Deposition Modeling (_FDM_), representing the authors' main field of analysis/investigation, was one of the earlier technologies. It was followed by Power Bed Fusion processes, which were widely analyzed and applied [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. Sheet Laminations processes, which followed them, gained a huge and efficient application [40; 42; 55; 56; 57; 58; 59; 60; 61]. Nowadays, Directed Energy Deposition, specifically Automated Fiber Placement techniques, are widely and successfully applied [62; 63; 64; 65; 66; 67; 68; 69; 70]. The accepted common casting technologies for metals, combined with _AM_, offer several facilities and advantages, both in obtaining the complex final products and in improving their initial manufacturing process from the point of view of cost, time, accuracy, and waste amount, as well as durability.  
An efficient combination of the plastic and metal layers, mainly in the cases of the casting molds and cores manufactured using AM, offers new research directions. Taking into consideration the complexity of these final products, obtained using a combination of plastic and metal layers, there are several significant problems, such as their geometric accuracy, their obtained surface quality, their stiffness against the thermal loading, as well as their mechanical loading, their durability, and their dimensional stability. In order to solve these significant problems, dimensional methods were successfully introduced by specialists. These dimensional methods have demonstrated their effectiveness in solving complex problems related to the manufacture of these casting molds.  
By applying these dimensional methods instead of testing the real structural element (here: the casting mold, specifically the core, i.e., the prototype), one can perform high-accuracy tests on an attached reduced-scale element, named the model [71; 72; 73; 74; 75; 76]. It is known that performing experimental measurements on the real parts, that is, on the so-called prototypes, usually involves serious difficulties. Compared with this, on the attached model, made on a reduced scale, they become much simpler, safer, and repeatable, with significantly lower material and human expenses.  
By adequately deducing the model law (_ML_), constituted strictly from the dimensionless variables, one can extend the obtained results from the model to the prototype, forecasting the prototype's behaviors [77; 78; 79; 80; 81; 82; 83; 84]. In the following, the authors briefly review the main dimensional methods, together with their advantages and limitations.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 4 System Design and Methodology  
In this section, we introduce the image storage and verification schemes in detail. SEAIS combines satellite network-based imaging and blockchain storage and ensures image data integrity and authenticity through homomorphic encryption and zero-knowledge proof.
## 5 U-Pb Geochronology Results  
_Chitarwatta Formation (Age-34 Ma)_  
The sample DZ-8 was collected from the lower contact zone (Figure 2). One hundred detrital zircons were analyzed, which yielded 98 usable concordant ages. The PDP shows that the major population (~70%) is clustered between \(\sim\)391 Ma and \(\sim\)1150 Ma. The peak ages in this range are present at ~459 Ma, ~485 Ma, ~574 Ma, ~688 Ma, ~741 Ma, ~803 Ma, ~856 Ma, and ~918 Ma (Figure 5A). A minor age cluster (~12%) is present between the ages ~1498 Ma and ~1808 Ma, with peaks at ~1500 Ma and ~1775 Ma (Figure 5A). Another minor population of ages exists between ~2237 Ma and ~2535 Ma. A few zircons yielded younger ages, in a range between ~110 Ma and 130 Ma (Figure 5).  
Figure 5: (**A**) Probability density plots of the U–Pb ages of detrital zircon samples representing the Cenozoic sequence, Mughalkot section, Sulaiman fold–thrust belt. (**B**) The binary plot showing the U–Pb detrital zircon ages and Th/U ratio, reflecting igneous and metamorphic zircons.  
The sample DZ-9 is representative of the upper portion of the Chitarwatta Formation. One hundred analyses yielded 93 concordant ages. A significant younger age cluster (~30%) exists between the ages ~40 Ma and ~170 Ma, with age peaks at ~60 Ma, ~63 Ma, and ~83 Ma (Figure 5A). The major cluster (~57%) is present between the ages ~390 Ma and ~1100 Ma (Figure 5A). A minor age population exists between the ages ~1285 Ma and ~1720 Ma (Figure 5A). In addition, a few scattered ages exist between ~1900 Ma and ~3100 Ma (Figure 5A).
## 2 Materials and Methods  
### Experimental Fish and Culture Conditions  
Largemouth bass juvenile (5.64 \(\pm\) 0.6 in length, 4.05 \(\pm\) 0.6 in weight) were purchased from a local fishery in Shunde (Guangdong, China), and acclimatized in a circulating breeding system in South China Agricultural University (Guangzhou, China). For the acclimatization period, fish were kept in tanks (50 cm \(\times\) 60 cm \(\times\) 60 cm) with air-pumped circulating diluted water at least two weeks prior to the experiment. Fish were fed twice per day using commercial fish feeds (Fenghua Feed Industrial Co., Ltd., Dongguan, China) with a ratio of 5% fish body weight. Formal experiment was carried out in the same tanks with 90 L water and 40 largemouth bass per tank. Three experimental groups and one control group were set up with three replicates in each group, giving a total of 12 tanks and 480 fish. Weight and length information of largemouth bass are shown in Supplementary Table S1. The culture conditions during fish adaptation period were: 12 h light, 12 h dark photoperiod, dissolved oxygen concentration from 5.5 to 6.5 mg/L, temperature 26 \(\pm\) 1.5 \({}^{\circ}\)C, pH 7.1 and ammonia and nitrite levels of 0.01-0.02 mg/L.
## 4 Conclusions  
High-entropy BNKLBT and BNKMBT ceramics were fabricated successfully. XRD and Rietveld refinement confirmed their cubic structure with the space group \(Pm\overline{3}m\). Tolerance factors showed that the perovskite structure of the ceramics is highly tolerant for various ions, which supports expanded compositional space and practical applications. With the elemental complexity of BNKLBT and BNKMBT ceramics, temperature dependent relative dielectric analysis showed diffuse phase evolution for a wide temperature range with diffusion coefficients of 1.617 and 1.753, respectively. BNKLBT and BNKMBT ceramics had \(P_{max}\) values of 21.24 \(\mu\)C/cm\({}^{2}\) and 17.71 \(\mu\)C/cm\({}^{2}\), \(W_{mc}\) values of 0.8525 J/cm\({}^{3}\) and 0.7295 J/cm\({}^{3}\), and \(\eta\) values of 80.27% and 82.38%, respectively. Ferroelectric analysis performed at a high temperature of 120 \({}^{\circ}\)C confirmed the energy storage efficiencies of the ceramics at \(>\)80%. The high values of energy efficiencies of the high-entropy ceramic samples indicate the potential of the samples to be used in pulsed power dielectric capacitors.  
Conceptualization, N.J. and A.H.; methodology, M.A.Q.; software, G.A.A.; validation, J.A., A.R. and V.T.; formal analysis, M.A.Q. and A.H.; investigation, N.J.; resources, N.S.; data curation, J.A.; writing--original draft preparation, A.H. and N.J.; writing--review and editing, V.T. and G.A.A.; visualization, A.R. and J.A.; supervision, A.H.; project administration, N.J.; funding acquisition, N.S. and G.A.A. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work supported through the research groups program under grant number (GRP/328/43).  
Not applicable.  
Figure 6: Temperature-dependent \(P-E\) analysis (**a**) for the BNKLBT ceramic and (**c**) for the BNKMBT ceramic. Energy density and energy storage efficiency as a function of temperature (**b**) for the BNKLBT ceramic and (**d**) for the BNKMBT ceramic.
## 5 Conclusions  
Non-invasively classified accurate morphological and quantitative information have significant advantages in agriculture for diagnostic evaluations of plant diseases. Hence, low coherence interferometry based SS-OCT was employed here to classify the morphological structures of rice leaf specimens as an initial feasibility investigation. Although the work demonstrates an OCT imaging attempt for a different plant specimen resembling existing reports, the conceptual breakthrough of this study is the OCT-based analysis of acute angular characteristics of the external angle between the lower epidermis layer and lower mid-vein, and the internal angle of the lower mid-vein that associates with leaf rolling cytological mechanism, which is potentially beneficial for the pre-identification of leaf tissue necrosis and atrophy along with subsequent investigation of rice leaf morphological properties, such as bulliform cells; aerenchyma, parenchyma and collenchyma layer; and vascular bundle. The examined acute angle information provided a robust comparison between healthy and abnormal leaf specimens, which can be employed for the pre-identification of leaf mutations. Although rice is one of the crops playing an important role in world economy, non-destructive investigations using OCT have not been descriptively reported to date. Therefore, this proposed study for rice leaf specimens based on OCT can be beneficial and lead to new discoveries in agricultural research. The acquired qualitative and quantitative evaluations sufficiently confirm the applicability of OCT as a potential agricultural tool that can be well-utilized for future studies involving leaf growth and plant growth through new technological enhancements.  
Conceptualization, K.-M.K., M.J. and J.K.; validation, B.-J.Y. and M.J.; formal analysis, S.K.; investigation, H.K., S.K. and X.D.; data curation, H.K. and X.D.; writing--original draft preparation, H.K. and R.E.W.; writing--review and editing, B.-J.Y. and R.E.W.; visualization, P.K.; supervision, K.-M.K., M.J. and J.K.  
This study was supported by BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011). This research was supported by the Bio and Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2017M3A9E2065282). This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government, MSIP (No. 2018R1A5A1025137). Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1D1A1B07043340).  
The authors declare no conflict of interest.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 1 Introduction
Based on the first-phase project of Zhengzhou Rail Transit Line 1, this project conducts real-time monitoring of pore water pressure and water level under the subway vibration effect, studies the deformation law of the soil layer at the bottom of the tunnel caused by the change in pore water pressure under the train vibration load, and analyzes the response characteristics of pore water pressure in the soil layer around the tunnel caused by the subway vibration load in the small-radius curve section. The obtained data are integrated to provide a basis for the safety evaluation of subway train operation.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 3 Distributed DL Communication: Libraries and Architectures Compared
### PyTorch Environment
#### 3.2.1 Parameter Server Method  
In the PyTorch environment, the broadcast operations in each library are illustrated in Figure 10. For MPI and GLOO, the data are transmitted directly from the parameter server to the worker GPU memory. However, in the case of NCCL, only GPU-to-GPU communication is supported, so a GPU must be assigned to the parameter server. However, because the parameter server method has a central server-client structure, a worker role is not assigned to the central server; it goes from the parameter server GPU memory to the worker GPU memory for inter-GPU communication. This setup can lead to inefficient GPU resource usage when resources are limited. Figure 11 illustrates the architecture of each library for the gather operation. The gather subroutine involves transmitting gradient values calculated by each worker to the parameter server. Since allgather stores the entire gradient value in all worker nodes, MPI and GLOO use the gather subroutine to save memory. In contrast, NCCL generally employs the allgather function, so the process of applying allgather to the architecture is shown. Similar to broadcast, NCCL requires a dedicated GPU for the parameter server, resulting in one less available GPU for workers compared to the MPI and GLOO architectures.
## 2 Proposed Bearing Fault Diagnosis Method  
An overview of the proposed bearing fault diagnosis method is shown in Figure 1. First, the vibration signals are split into fixed-cycle 5 seconds segments. The Short-Time Fourier Transform (STFT) is then applied for each signal segment to produce the spectrogram. Then, spectrogram images generated from the vibration signals corresponding to the bearing faults are used to train network architecture CNN-VGG16, and the trained CNN-VGG16 model is used to classify bearing faults automatically in the test stage.
## 4 Coupled Dynamic and Control for Thermally Induced Vibration and Attitude Motion
### Dynamic Response of Rigid-Flexible Coupled Structure  
Generally, the communication satellite moves in a geosynchronous orbit and the antenna rotates uniformly around the central rigid body, we consider the \(\bar{\mathbf{R}}_{1}\) and \(\dot{\mathbf{\omega}}_{a}\) both equal to zeroes.  
By rearranging Equations (10) and (11) and omitting higher-order small variables, the coupling equation of motion of the antenna's vibration and the satellite's attitude motion can be expressed as:  
\[\left(\mathbf{I}_{b}+\mathbf{I}_{ab}^{b}\right)\dot{\mathbf{\omega}}=-\left( \mathbf{I}_{\omega}+\mathbf{I}_{\Omega ab}^{b}\right)\mathbf{\omega}+\mathbf{ H}_{ab}\mathbf{P}_{a}\vec{\mathbf{n}}_{a}+\mathbf{T}_{c} \tag{22}\]  
\[\mathbf{M}_{a}\mathbf{P}_{a}\vec{\mathbf{n}}_{a}+\mathbf{K}_{a}\mathbf{n}_{a} -(\mathbf{S}_{b}+\mathbf{S}_{ba})\mathbf{A}_{ab}\dot{\mathbf{\omega}}-( \mathbf{A}_{ab}\mathbf{\omega})^{X}(\mathbf{S}_{b}+\mathbf{S}_{ba})\mathbf{A }_{ab}\mathbf{\omega}=\mathbf{F} \tag{23}\]  
where thermal load \(\mathbf{F}\) acting on the antenna can be obtained by the modal matrix \(\mathbf{Q}\) and the thermal moment \(\mathbf{M}_{T}\) in Section 3. In the following part, we consider that the \(\mathbf{\eta}_{a}=[\mathbf{\eta}_{1},\,\mathbf{\eta}_{2},\,\mathbf{\eta}_{3}] ^{\mathrm{T}}\) which are the first three order modal coordinates.  
It is clear that the thermal-induced vibration of the antenna can affect the stability of the spacecraft attitude; thus, the attitude needs to be controlled.