esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## Appendix A
### Experimental Results After Applying Electrical Interference Noise
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **SNR** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{FCN} & 27 dB & 0.9816 & 0.9876 & 0.984 & 0.9858 \\ \cline{2-6}  & 17 dB & 0.9735 & 0.9854 & 0.9775 & 0.9813 \\ \cline{2-6}  & 11 dB & 0.9469 & 0.974 & 0.9509 & 0.962 \\ \cline{2-6}  & 3 dB & 0.8782 & 0.9474 & 0.8861 & 0.9145 \\ \hline \multirow{4}{*}{ResNet} & 27 dB & 0.9816 & 0.9876 & 0.9849 & 0.9862 \\ \cline{2-6}  & 17 dB & 0.9704 & 0.9851 & 0.9739 & 0.9793 \\ \cline{2-6}  & 11 dB & 0.9312 & 0.97 & 0.9373 & 0.953 \\ \cline{2-6}  & 3 dB & 0.8712 & 0.9475 & 0.8837 & 0.9136 \\ \hline \multirow{4}{*}{InceptionTime} & 27 dB & 0.9786 & 0.9879 & 0.981 & 0.9843 \\ \cline{2-6}  & 17 dB & 0.9691 & 0.9847 & 0.9724 & 0.9784 \\ \cline{2-6}  & 11 dB & 0.934 & 0.9673 & 0.9399 & 0.953 \\ \cline{2-6}  & 3 dB & 0.9012 & 0.9574 & 0.9113 & 0.9334 \\ \hline \multirow{4}{*}{XceptionTime} & 27 dB & 0.9777 & 0.9842 & 0.9819 & 0.9829 \\ \cline{2-6}  & 17 dB & 0.9674 & 0.9841 & 0.9709 & 0.9773 \\ \cline{2-6}  & 11 dB & 0.931 & 0.9664 & 0.9361 & 0.9508 \\ \cline{2-6}  & 3 dB & 0.8859 & 0.9527 & 0.8965 & 0.923 \\ \hline \multirow{4}{*}{XCM} & 27 dB & 0.9811 & 0.9864 & 0.9846 & 0.9855 \\ \cline{2-6}  & 17 dB & 0.9675 & 0.983 & 0.971 & 0.9769 \\ \cline{2-6}  & 11 dB & 0.9367 & 0.9716 & 0.9432 & 0.9568 \\ \cline{2-6}  & 3 dB & 0.8799 & 0.9491 & 0.8929 & 0.9192 \\ \hline \multirow{4}{*}{LSTMAttention} & 27 dB & 0.9802 & 0.987 & 0.9822 & 0.9845 \\ \cline{2-6}  & 17 dB & 0.9733 & 0.9833 & 0.97 & 0.9811 \\ \cline{2-6}  & 11 dB & 0.9394 & 0.9721 & 0.9463 & 0.9588 \\ \cline{2-6}  & 3 dB & 0.8889 & 0.9508 & 0.903 & 0.9256 \\ \hline \multirow{4}{*}{GRUAttention} & 27 dB & 0.9816 & 0.9868 & 0.9837 & 0.9852 \\ \cline{2-6}  & 17 dB & 0.9699 & 0.9842 & 0.9737 & 0.9788 \\ \cline{2-6}  & 11 dB & 0.9416 & 0.9728 & 0.9454 & 0.9586 \\ \cline{2-6}  & 3 dB & 0.8689 & 0.9511 & 0.8779 & 0.9115 \\ \hline \multirow{4}{*}{TSSequencer} & 27 dB & 0.98 & 0.9868 & 0.9843 & 0.9855 \\ \cline{2-6}  & 17 dB & 0.9664 & 0.9818 & 0.971 & 0.9763 \\ \cline{2-6}  & 11 dB & 0.9424 & 0.9701 & 0.9487 & 0.959 \\ \cline{2-6}  & 3 dB & 0.8918 & 0.9531 & 0.8994 & 0.9247 \\ \hline \multirow{4}{*}{PatchTST} & 27 dB & 0.9805 & 0.9888 & 0.982 & 0.9852 \\ \cline{2-6}  & 17 dB & 0.9724 & 0.9862 & 0.9758 & 0.9809 \\ \cline{2-6}  & 11 dB & 0.943 & 0.9743 & 0.9495 & 0.9615 \\ \cline{2-6}  & 3 dB & 0.8959 & 0.9553 & 0.9043 & 0.9283 \\ \hline \multirow{4}{*}{MultiPatchTST} & 27 dB & 0.9852 & 0.9906 & 0.9867 & 0.9886 \\ \cline{2-6}  & 17 dB & 0.9735 & 0.9866 & 0.9772 & 0.9819 \\ \cline{2-6}  & 11 dB & 0.9478 & 0.9762 & 0.9527 & 0.9615 \\ \cline{2-6}  & 3 dB & 0.9021 & 0.9582 & 0.9114 & 0.9337 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont.
## 2 Design Requirement  
The array investigated in this paper is considered to be a representative configuration for radar systems, and its specifications are as follows:  
* Working band: Ku band;
* Operational fractional bandwidth: 6% (0.98\(f_{0}\)\(\sim\)1.03\(f_{0}\));
* Element space: 0.53\(\lambda_{0}\) (\(\lambda_{0}\) is free space wavelength at\(f_{0}\));
* Scanning coverage: 2D;
* Scanning region: \(-45^{\circ}\) to \(+45^{\circ}\);
* No scan blindness in the scan angle;
* Gain fluctuation: \(\leq\)3 dB;
* Polarization: linear polarization.  
Figure 1 illustrates two antenna structures. Antenna 1 (Ant-1) represents the conventional SIW cavity-backed patch antenna (CBPA), while antenna 2 (Ant-2) is the proposed modified SIW CBPA. For the sake of simplicity, the single-layer SIW cavity-backed patch antenna is designed as the array element. Notably, both antennas consist of a single substrate. To enhance isolation between adjacent elements, vias encircling the antenna element are employed to form the SIW cavity. In Ant-2, vias along the E-plane are strategically relocated towards the center by a distance denoted as 'S'. Figure 2 shows the exploded view of the proposed antenna element. As shown in Figure 2, the patch at the top metal layer is fed by the coaxial probe. The feed probe is directly connected to the patch through the probe pin. In engineering, we use a 50 \(\Omega\) SMP connector as coaxial probe. As we know, the transmit/receive (TR) channels also use 50 \(\Omega\) SMP connectors, so it is easy to connect the two structures using KK connectors. These models are simulated and optimized by using ANSYS HFSS.  
Figure 3 shows the simulated \(\mid\)S\({}_{11}\)\(\mid\) characteristics of both SIW CBPAs. Notably, the simulated \(\mid\)S\({}_{11}\)\(\mid\) of Antenna 1 is similar to that of Antenna 2, ranging from 0.95\(f_{0}\) to 1.05\(f_{0}\) with a 10 dB IBW of 10%.  
Figure 1: Evolution diagram of the proposed antenna element.  
Figure 2: Exploded view of the proposed antenna element.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Model Performance with Different Sets of Input Features
#### 3.2.2 Vegetation Indices  
We predicted corn yield using time-series NDVI and EVI data and reported the R\({}^{2}\) and RMSE of the models in Figure 6. The derived R\({}^{2}\) ranges from 0.636 to 0.849. The R\({}^{2}\) and RMSE of GWRFR are 0.849 and 0.944 MT/ha, respectively. The results also illustrate that GWRFR outperforms other models such as MLR, PLSR, SVR, DTR, and RFR by 0.10-0.21 in terms of R\({}^{2}\). DTR has the lowest performance (R\({}^{2}\) = 0.636 and RMSE = 1.466 MT/ha). GWFR improves the yield prediction by 0.109 in terms of R\({}^{2}\) and 0.294 MT/ha in terms of the RMSE when compared with its non-spatial version, RFR (R\({}^{2}\) = 0.74 and RMSE = 1.238 MT/ha). The scatterplots of the observed and predicted yield values are presented in Figure 6. The performances of all machine learning algorithms including GWFR decrease in this case when compared with that of the models using full-length features. More cases of underestimations and overestimations can be observed in the scatterplots; however, GWRFR can still perform better than other models.  
Figure 5: The scatterplots of the actual yield and the predicted yield derived from (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using full-length features.  
Figure 6: The scatterplots of the actual yield and the predicted yield derived from (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using only vegetation indices derived from RS data.
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 3 Results
### Evaluation of the Relation between the Axial Contraction and the Input Pressure
A specially developed computer programme based on the experimentally determined polynomial equations was loaded to a Programmable Logic Controller (PLC). The PLC sends an electric signal to the proportional pressure regulator, whose voltage is continuously modified according to the experimentally obtained polynomial function. This affects the desired variation of air pressure in the pneumatic muscle and, consequently, the desired contractions and forces [39]. Figure 15 shows an example of positioning an object with a mass of 2.55 kg using the control diagram proposed above. The red line shows the motion paths obtained under these conditions. The movement paths obtained using the diagram in Figure 1b is drawn in blue. It can be seen that the accuracy of positioning by controlling the pressure with the help of a proportional regulator is very good, with the proposed system offering a viable and efficient alternative. Figure 14: Supply pressure adjustment with a proportional pressure regulator. A significant disadvantage of the pneumatic muscles used for precise positioning systems is that, for each actuator, experiments must be performed, then it is necessary to determine the dependencies (\(p=f\left(\Delta I\right)\)) in both the inflation and the deflation phase. These relationships must then be loaded into the PLC. The transposition into practice of the working method presented above, i.e., that of obtaining high positioning accuracies for work equipment actuated with pneumatic muscles, has been shown by the author in several previously published articles [40; 41]. In [40], a rehabilitation equipment of the lower limb joints (hip, knee, and ankle) is presented. Its actuation is carried out with the help of a pneumatic muscle for which the hysteresis curves were experimentally determined. Based on these, the PLC was programmed, with the results obtained validating the used working method. In [41], a hand joint rehabilitation equipment driven by a pneumatic muscle is described. Using the same working methodology, the results obtained confirmed its validity.
## 3 Results
### TR Propagation Process and Model Verification of Battery Modules  
The TR propagation process in the battery unit is composed of five main stages: exhaust, initial jet fire, jet fire diffusion, buoyancy fire, and extinction. In the battery module test process, when the TR occurs, it is usually observed that an intense jet flame from the cell is emitted. This phenomenon is particularly evident in the first cell to experience TR. However, when the exhaust phase occurs in the first cell, it is difficult to observe the exhaust phase in detail due to the extremely fast temperature rise in the other cells. This is mainly because as the temperature rises, the newly generated gas is immediately ignited by the flame of the previous cell. In addition, when the TR of the current cell has not fully entered the buoyancy fire stage, the initial flow fire of the adjacent cell begins to form due to the propagation of heat and flame from one cell to another. This chain reaction of TR leads to the possibility of TR in the cells inside the battery module, which further increases the safety risk. The TR propagation process of the battery module with different insulation materials is shown in Figure 2c.  
The experiment failed to show gas production in the cell before TR. When the initial cell experienced TR, a large amount of smoke could be observed, accompanied by intense combustion. This is because there was an assembly force between the cells; the cell broke easily due to its coating material, an aluminum-plastic film. The gas production of the internal component materials was discharged with the initial phase of the flow of fire and did not accumulate in the battery; the occurrence of bulges resulted in the intense and very quick combustion of the battery. Therefore, in the TR propagation of the battery module, the initial time of the TR of a single cell was determined by observing the emergence of the jet fire. Figure 2d shows the change process of the temperatures of various surfaces of the cell over time.  
The comparison of the simulation and test results is shown in Figure 2e, which indicates that the simulation results are basically consistent with the experimental results at the time when the battery triggers TR; this proves that the simulation model can accurately capture the behavior and propagation process of the battery TR and verifies the reliability and accuracy of the simulation model. The temperature field distribution of the model in the process of TR was observed using numerical simulation images, as shown in Figure 2f.
## 3 Results
### Reliability Analysis  
Data reliability was seen from the Cronbach alpha value to measure each construct's internal consistency. Table 4 shows that the Cronbach alpha value is between 0.705 and 0.889. According to [PERSON] et al. [73], the Cronbach alpha value must be greater than 0.70. Therefore, the questionnaire items have high reliability and internal consistency between variables. The next step checked the loading factor, composite reliability (CR), and AVE value to analyze convergent validity. The loading factor, CR, and AVE must be higher than 0.5, 0.7, or 0.5 [74]. In this study, the lowest factor loading, CR, and AVE values were 0.75, 0.90, and 0.76, respectively. Therefore, the proposed model has good convergent validity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **Excess Kurtosis** \\ \end{tabular} &
\begin{tabular}{c} **Skewness** \\ \end{tabular} \\ \hline PU1 & 4.221 & 0.791 & 2.437 & \(-\)1.167 \\ \hline PU2 & 4.129 & 0.752 & 2.136 & \(-\)1.090 \\ \hline PU3 & 4.202 & 0.800 & 2.113 & \(-\)1.109 \\ \hline PEU1 & 3.638 & 0.813 & 1.435 & \(-\)0.351 \\ \hline PEU2 & 3.798 & 0.800 & 1.832 & \(-\)0.487 \\ \hline PEU3 & 4.031 & 0.779 & 2.902 & \(-\)0.841 \\ \hline SN1 & 3.699 & 0.800 & 2.145 & \(-\)0.491 \\ \hline SN2 & 3.681 & 0.7
###### Abstract
Moreover, GA, BFA, FA, LFA, and BPSO are applied with hybridized EHO [7] for the Scheduled Routing Mechanism (SRM) to design Scheduling Routing Algorithm for Localization (SRAL). To reduce delays and an unnecessary long routing path, these hybrid schemes help by optimizing the route from source to destination. Moreover, EHO can potentially be used for scheduling and routing mechanisms, as it is a general optimization algorithm that can be applied to a wide range of problems. However, the performance of EHO for scheduling and routing problems specifically would depend on the specific problem and its fitness function and its error value. Scheduling and routing problems typically involve finding the optimal order or sequence of tasks or routes based on certain constraints and objectives [8]. These problems can be formulated as optimization problems, and EHO can be used as an optimization algorithm to find an optimal or near-optimal solution. All of the above techniques have latency issues when only used for optimization [9] but the optimization with hybridization of metaheuristic approaches such as EHO with GA, FA, LFA, BFA, and BPSO have enough potential on those areas where the large-scale network needs to be tackled, such as an underwater environment. All of these algorithms are hybridized with EHO for particular tasks so that EHO-BFS is used for selection population update, EHO-BFA emphasizes the selection of the best, EHO-GA is used for the selection of flight (routing) path, BPS-FA is used for select positions, BFA-LFA is used to calculate directions (best selection), and BFA-BPSO provides scheduled positions updated for beacon node localization. After that, the architecture of scheduled beacon node broadcasting and fitness functions of metaheuristic techniques are analyzed to identify the impact of scheduled/unscheduled nodes before going to extensive simulation setup with state-of-the-art benchmark techniques. The proposed model, SRAL, is finally compared with other competing schemes such as Range-based Directional Beacon Forwarding (RDBF) [10], Energy Efficient Probabilistic Depth Based Routing (EEPDBR) [11], Using Directional Beacon (UDB) routing [12], Three-Dimensional Underwater Localization (3 DUL) [13], Underwater Localization (UL) [14], and Range-free scheme based on Mobile Beacon (RSMB) [15]. The rest of this paper is structured as follows: Section 2 deals with related work while Sections 3 and 4 discuss the system and proposed model. In Sections 5-10, the hybridization of nature- and bio-inspired techniques is presented. Moreover, the beacon node broadcasting is presented in Section 11 while simulation results are described in Section 12. Finally, the conclusion is presented in Section 13.
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
###### Abstract  
Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2-3 m, under the light condition of 279 h and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than \(\pm\)10 mm, and the absolute proportion error is not more than \(\pm\)4%.  
plant phenotype; plant height; multiple plant height measurement; scaleless; monocular image; computer vision +
Footnote †: journal: Applied sciences
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Generate the Canonical Image  
#### 3.3.1 Train the Model Based on Video Frames  
When dealing with video data, we are faced with the following task: Given a video \(V\) composed of multiple frames, and its frame sequence is represented as \(I_{1},I_{2},I_{3}\). Our goal is to train an implicit deformable model that can accurately fit the features and change patterns of these frames.  
This model is primarily built from two coordinate-based multi-layer perceptrons (MLPs), namely the deformation field \(D\) and the canonical field \(C\). The deformation field \(D\) takes on the main responsibility of transforming and adjusting the coordinate information within video frames. By leveraging the input coordinate data, it is capable of outputting the corresponding deformed coordinates, thereby effectively depicting the shape-changes and displacements of objects or scenes across different frames in the video.  
On the other hand, the canonical field \(C\) is dedicated to extracting the canonical information from the video frames. This canonical information can be regarded as a representation encapsulating some common features, basic patterns, or internal structures inherent in the video content. It can harmonize and standardize the relevant information across different frames, enabling the model to better understand and learn the overall semantic and structural information of the video.  
Through the coordinated operation of these two key components, the implicit deformable model can effectively fit and analyze the video \(V\), laying a solid foundation for subsequent video-editing tasks.
## 3 Proposed Method
### Frequency Domain Similarity Gated Selection Vision Transformer
#### 3.1.2 Gsb  
In the lower branch, we introduce the SiLU activation function as a gate for each feature channel. The inclusion of Conv blocks enables adaptive feature selection, better capturing detailed information within images, and avoiding feature redundancy in tasks. Inside the Conv block, we adopt two kernel sizes, \(3\times 3\) and \(5\times 5\), for mixed-scale processing, and the specific structures are presented in Figure 2 as shown. After concatenation, we apply Channel Shuffle for mixed feature handling, addressing inter-group feature communication.  
SiLU, functioning as a gate for each feature channel, is applied to the feature vectors of each channel. It acts as a gating mechanism, activating elements within the feature vector to adjust the importance of each channel. This leads to saturation of feature vectors for each channel as they approach 0, enhancing non-linearity and adaptively adjusting the significance of feature channels based on their element values. Larger element values amplify the importance of the corresponding channel, focusing more on useful feature mappings, while smaller element values reduce the weight of the corresponding channel, minimizing the impact of irrelevant feature mappings on the model. Rejecting irrelevant features prevents feature redundancy. Elements with values below 0, after passing through the SILU function, are mapped to values close to 0. This implies rejection of feature mappings for that channel. For input feature \(X\), the branch is represented as:  
\[F_{3\times 3/5\times 5}=\text{ReLU}(\text{Conv}_{3\times 3/5\times 5}(\text{LN}(X))) \tag{10}\]  
\[F_{Conc}=CS(Concat(F_{3\times 3},F_{5\times 5})) \tag{11}\]  
\[\begin{array}{ll}GSB\_OUT=&F_{Conc}\otimes\text{SiLU}(FC(\\ &\text{ReLU}(FC(Pool(F_{Conc})))))\end{array} \tag{12}\]  
where \(F_{3\times 3/5\times 5}\) represents the feature vector obtained through mixed-scale processing, \(CS\) stands for Channel Shuffle operation, and \(FC\) represents the fully connected layer.  
Taking into consideration the introduction of both the \(FAB\) and the \(GSB\), the output of our FSGS-ViT can be represented as follows:  
\[FSGS\_OUT=FAB\_OUT+GSB\_OUT \tag{13}\]
## 3 Results and Discussion
### Fatigue Crack Growth  
Figure 6 shows the FCGR found for each series using two specimens in which the notches were cut on the underfill of the weld beads, seeking to agree with the sites where the failure occurred in the fatigue testing. As can be seen for the S1, S2 and S3 series, the specimens that were smoothed on both sides (two-sides-removed condition) showed a slightly slower crack growth rate than specimens smoothed only on one side (excess-weld-removed condition). In the case of the S5 series, even though the two specimens were prepared as the other series, the initial equality and subsequent greater difference (from the dotted line in the S5 series) observed in the crack growth rates could be explained once the crack paths and fractured surfaces were examined. For the specimen with faster crack growth rate, it was found that the direction of the growth of the cracks gradually deviated from the FZ towards the BM and therefore the crack growth rate of the upper part of the curve correspond to the BM. In what concerns the specimen with a slower crack growth rate, the change in the slope was due to a large difference between the crack sizes (exceeding the limit for the test validity) as one of the crack fronts was affected by porosity while the other was not.