esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Experimental Results and Analysis
#### 5.2.3 Experimental Results  
TAPP uses deep reinforcement learning to complete the prediction of the pipeline parallel training task allocation method, which effectively avoids the imbalance load problems caused by the difference of node computing power and network bandwidth in improving the acceleration of model training. The pipeline parallel training experiment results showed that the single-step training time of TAPP in the three models is better  
Figure 6: The impact of different computing power: the training-loss curve of the pipeline parallel task allocation scheme generated by the prediction network for the VGG-16 model.  
Figure 7: The impact of different batch sizes: the batch size affects the convergence speed of the prediction network with the smaller batch size having the faster convergence speed.  
than that of data parallel, which was increased by 1.33 times, 1.30 times, and 1.49 times, respectively. The training time of TAPP was the shortest among the three models, which was shortened by 1.5 h, 10 h, and 1.6 h, respectively. The training accuracy of TAPP was consistent with the data-parallel BSP. The evaluation experiment of the TAPP algorithm explored the influence of different computing powers and different batch sizes on the time required to train the prediction network.  
Experimental results showed that high-performance computing nodes can effectively improve the prediction network convergence time, and a smaller batch size requires a shorter convergence time. In the experiment, the sum of the predicted network convergence time and the pipeline system parallel training time was less than the time required for data parallelism, obviously in the VGG-16 model. This experiment proves that the TAPP method achieves a good training acceleration effect in DNN model training, and the greater the number of the model parameter, the more obvious the lifting effect achieved.
# Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces
###### Abstract  
The major objective of the present work is to investigate into the appropriate tilt angles of south-oriented solar panels in Saudi Arabia for maximum performance. This is done with the estimation of the annual energy sums received on surfaces with tilt angles in the range \(15^{\circ}\)-\(55^{\circ}\) inclined to south at 82 locations covering all Saudi Arabia. The analysis shows that tilt angles of \(20^{\circ}\), \(25^{\circ}\) and \(30^{\circ}\) towards south are the optimum ones depending on site. These optimum tilt angles define three distinct solar energy zones in Saudi Arabia. The variation of the energy sums in each energy zone on annual, seasonal and monthly basis is given; the analysis provides regression equations for the energy sums as function of time in each case. Furthermore, the spatial distribution of the annual global inclined solar energy in Saudi Arabia is shown in a solar map specially derived. The annual energy sums are found to vary between \(1612\) kWhm\({}^{-2}\)year\({}^{-1}\) and \(2977\) kWhm\({}^{-2}\)year\({}^{-1}\) across the country. Finally, the notion of a correction factor is introduced, defined, and employed. This factor can be used to correct energy values estimated by a reference ground albedo to those based on near-real ground albedo.  
solar potential; maximum energy; inclined surfaces; south orientation; Saudi Arabia 2021
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 4 Comparison and Results
### Median Filtering and Moving Average Method  
Aiming at the non-stationary wire rope detection signals with multi-source characterizations, a nonlinear signal processing method based on median filtering and a linear signal processing method of moving average technique were also applied to these three typical weak signals. The median filtering method was first used regarding to the big liftoff weak signals by replacing the signal point with the median of a certain time series. When every  
Figure 5: Weak signals processed by lowpass filtering and wavelet denoising. (**a**) Lowpass, FFT spectrum and wavelet denoising of wire rope signal S1. (**b**) Lowpass, FFT spectrum and wavelet denoising of wire rope signal S2. (**c**) Lowpass, FFT spectrum and wavelet denoising of wire rope signal S3.  
20 signal points was processed through median filter, the preliminary processing results are shown in Figure 6, and the original weak signals of 1 to 3 correspond with the testing signals of S1 to S3 in Figure 5.  
Explanatorily, all the original defect signals in the left side are very weak, and the defect features become more and more difficult to recognize from signal 1 to signal 3. Although the processed signals shown in the right side manifest that the burr noise features are abridged and the SNR is improved in some extent, especially for the median filtering signal 1, the weaker signals of 2 and 3 are still difficult to identify judged by the median filtering signal of 2 and 3. Namely, the nonlinear signal processing method of median filter could denoise the wire rope signals to some extent, the defect features of weak wire rope signals are still overwhelmed by multi-source noises. Therefore, the single median filtering method is also incapable of weak wire rope signal denoising as well as the feature identification. Nevertheless, a linear signal processing method based on the moving average technique is presented. When the moving average span is set as 200, the typical three kinds of original signals and the processing results by moving average technique are shown in Figure 7. Similarly, the SNR of these weak signals are improved to some extent, but the defect features still could not be effectively identified, especially for the big liftoff wire rope inspection signals expressed in moving average signal 2 and 3.  
Combining the single nonlinear and linear weak signal processing results aforementioned through median filter and moving average technique, three typical weak wire rope detecting signals still could not be well recognized because of the mixed interference features of multi-source noise and wire rope strand signals, which not only demonstrated the incapability of these single weak signal processing methods, but also indicated that the weak wire rope signals' processing are full of challenges and difficulties.  
Figure 6: Weak signals processed by median filter. The original weak signals of 1 to 3 correspond with the testing signals of S1 to S3 in Figure 5.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 3 Dam Crack Detection Based on Underwater Robots
### Application of Underwater Robots in Dam Crack Detection
In addition, the water environment near the dam is complex, and the underwater steel bars, rocks, etc., threaten the safety of underwater robots and increase the difficulty of underwater image acquisition. Therefore, it is necessary to carry out targeted research of underwater robots in combination with the actual needs in future work, and, at the same time, it is necessary to study more practical underwater robot detection systems and develop intelligent real-time detection technology of dam surface cracks with multivariate data, so as to improve the efficiency and accuracy of underwater crack detection.
###### Abstract  
Deep learning plays a highly essential role in the domain of remote sensing change detection (CD) due to its high efficiency. From some existing methods, we can observe that the fusion of information at each scale is quite vital for the accuracy of the CD results, especially for the common problems of pseudo-change and the difficult detection of change edges in the CD task. With this in mind, we propose a New Fusion network with Dual-branch Encoder and Triple-branch Decoder (DETDNet) that follows a codec structure as a whole, where the encoder adopts a siamese Res2 Net-50 structure to extract the local features of the bitemporal images. As for the decoder in previous works, they usually employed a single branch, and this approach only preserved the fusion features of the encoder's bitemporal images. Distinguished from these approaches, we adopt the triple-branch architecture in the decoder for the first time. The triple-branch structure preserves not only the dual-branch features from the encoder in the left and right branches, respectively, to learn the effective and powerful individual features of each temporal image but also the fusion features from the encoder in the middle branch. The middle branch utilizes triple-branch aggregation (TA) to realize the feature interaction of the three branches in the decoder, which enhances the integrated features and provides abundant and supplementary bitemporal feature information to improve the CD performance. The triple-branch architecture of the decoder ensures that the respective features of the bitemporal images as well as their fused features are preserved, making the feature extraction more integrated. In addition, the three branches employ a multiscale feature extraction module (MFE) per layer to extract multiscale contextual information and enhance the feature representation capability of the CD. We conducted comparison experiments on the BCDD, LEVIR-CD, and SYSU-CD datasets, which were created in New Zealand, the USA, and Hong Kong, respectively. The data were preprocessed to contain 7434, 10,192, and 20,000 image pairs, respectively. The experimental results show that DETDNet achieves F1 scores of 92.7%, 90.99%, and 81.13%, respectively, which shows better results compared to some recent works, which means that the model is more robust. In addition, the lower FP and FN indicate lower error and misdetection rates. Moreover, from the analysis of the experimental results, compared with some existing methods, the problem of pseudo-changes and the difficulty of detecting small change areas is better solved.  
20 April 2023 13 May 2023 13 May 2023 14 May 2023 18 May 2023  
convolutional neural network (CNN); change detection (CD); remote sensing (RS); multibranch; feature fusion
## 3 Experimental Results and Discussion
### Experiment on Adaptive Focus Window  
To verify the effectiveness of the adaptive window, this window was applied to three types of representative images summarized in 80 sets of human eye focus data. As shown in Figure 5, the proposed window can avoid irrelevant image contents, and the selected region basically covers all visible fundus details, which meets the selection criteria of ideal windows.  
To compare with traditional methods, full window (entire image), inverted T window [25] (fixed region), manual window [21] (expert annotation), and proposed window were applied to image stacks of representative examples in Figure 5 (sampling interval is half DOF). The sharpness evaluation curves were calculated using the criteria of [11; 21; 22; 23], as shown in Figure 6. For a fair comparison, all calculation results are divided by the  
Figure 4: Self-developed autofocus system for NMFP.  
corresponding window area (the number of pixels included), and then normalized as the sharpness evaluation values (SEV).  
In the ideal imaging condition, evaluation curves of three automatic windows accurately indicate the actual focus position. However, because the adaptive window can  
Figure 5: Three examples of adaptive focus window: (**a**) ideal imaging conditions (including ORI), (**b**,**c**) non-ideal imaging conditions (including ORI and different degrees of SL). The input images, window masks, and processing results are shown from top to bottom.  
Figure 6: Normalized sharpness evaluation curves of automatic and manual windows, where (**a**–**c**) correspond to representative cases in Figure 5. Black dotted lines indicate the actual focus position marked by experts, and \(F_{M}\), \(F_{A}\), \(F_{T}\), and \(F_{L}\) indicate [11; 21; 22; 23] sharpness evaluation criteria. An ideal evaluation exhibits a Gaussian-like distribution, which is unimodal and unbiased.  
actively exclude the contribution of image invalid features, the curve shows better unimodality, facilitating fast focus search convergence. In non-ideal imaging conditions, the proposed method maintains a curve distribution that is similar to the ideal case, without obvious local extrema. In contrast, the other two automatic windows regard the grayscale mutation of SL and ORI as effective sharpness characteristics, leading to evaluation curves that fail to reflect the 'defocus-focus-defocus' trend. These results demonstrate that adaptive window enhances robustness and focus accuracy for sharpness evaluation criteria.  
It can also be concluded from Figure 6 that the evaluation curves of the adaptive window are comparable to those of the manual window, although the peak sensitivity [17] of the latter is slightly higher, which does not affect the focus accuracy. In this case, the proposed window only needs 2 ms to complete the calculation of a single image, which is 1000 times faster than the manual window (taking about 2 s), thereby significantly shortening the fundus examination time.
## 4 Influence of Pile Length and Pile Diameter  
### Pile Length  
Considering the ratios of the pile length to the structure height for most pile-supported RC frames are between 0.1 and 0.5 in reality, the ratios of the pile length to the structure height of the 3-story, 6-story, and 9-story RC-frames (F3, F6, and F9) were set as 0.1, 0.2, 0.3, 0.4, and 0.5 to investigate the influence of the pile length on the SSI effect. To sufficiently capture the influence of the pile length, the seismic records RSN-587 and RSN-445 with a peak acceleration of 100 gal were employed as seismic inputs, and the values of vs. were, respectively, set as 150 and 250 m/s. It can be seen from Figure 13 that with the increase in the pile length, the values of \(e_{bs}\) generally increase with the increase in the pile length while the values of \(e_{sd}\) may increase or decrease. Moreover, the maximum influence of the pile length on \(e_{bs}\) and \(e_{sd}\) is only about 10%. The above finding indicates that in general, the maximum base shear of the structure considering SSI increases with a larger pile length while the influence of the pile length on the structural base shear and story drift is within 10%.
## 4 Test Results and Analysis  
### Unconfined Compressive Strength Test  
The unconfined compressive strength of 7 d is the only hard and fast rule regarding the design of cement stabilized macadam(JTG/T F20--2015). Specimens were used with curing ages of 7 days, 28 days, and 90 days, respectively, and the values of specimens with replacement amounts of RBA of 0%, 20%, 40%, 50%, 60%, and 80% are shown in Table 5.  
Combining Table 5, Figure 6, and Table 1, there are several features can be listed:(1) Replacing part of CBA with RBA in cement stabilized macadam fine aggregate will have a great effect on its strength, and the change in the law of strength at various ages slightly different from the change of RBA content.The strength of 7d and 28d decreases at first, andthen increases and decreases again with the increasing of RBA content. The 7d strength is slightly higher than that of the standard specimen (RBA 0%) when the RBA content is 50%, and the strength of other RBA contents is lower than that of the standard specimen. The 28d strength of 40%, 50%, and 60% RBA content is close to or slightly larger than the standard specimen, and the strength of other RBA contents is less than the standard specimen. However, the 90d strength of the specimens mixed with RBA is higher than that of the standard specimens.  
(2) The property of aggregate has a non-negligible influence on strength at an early stage. Strengths of 7d and 28d show that 20% is smaller than 0% because the pozzolanic reaction is weak; as the replacement ratio increases, the pozzolanic reaction becomes stronger and counteracts the weakening caused by the RBA aggregate, so the strength increases gradually. At the same time, the crushed stone value and the plastic index also increase, exceeding the reference values until the 60% RBA replacement ratio directly caused either the strength to decrease again or be maintained at a relatively stable level.  
(3) Since the increased percentage of strength was composed of the continuous hydration of cement and the activation of the potential activity of RBA, as the curing time increased, the strength of specimens containing RBA increased as well. The strength of the 90d kept increasing because the pozzolanic reaction started during the hydration reaction and enough hydration product was formed, leading to the higher strength of the RBA specimens.  
(4) According to the various strength characteristics at 7d, 28d, and 90d, the 50% RBA replacement ratio has the optimal strength, and it can be the recommended as the replacement percentage of RBA in cement stabilized macadam.
## 3 LCA of Pineapple Cultivation and Processing
### Life Cycle Inventory
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Agricultural Stage (FU: 1 kg of Fresh Pineapple)**} \\ \hline \multicolumn{2}{c}{**Sub-Stage**} & **Material** & **Quantity** & **Unit** \\ \hline \multirow{4}{*}{Nursery maintenance} & Water & \(1.94\times 10^{-1}\) & L \\  & Transport & \(3.82\times 10\) & kgkm \\  & NPK & \(4.09\times 10^{-4}\) & kg \\  & Pesticides & \(7.06\times 10^{-5}\) & kg \\ \hline \multirow{4}{*}{Land preparation} & Diesel & \(2.86\times 10^{-6}\) & kg \\  & Machinery & \(7.01\times 10^{-6}\) & kg \\  & Plastic & \(1.79\times 10^{-2}\) & kg \\ \hline \multirow{4}{*}{Sowing} & Water & \(1.47\times 10^{-2}\) & L \\  & Transport & \(9.88\times 10^{-1}\) & kgkm \\  & Pesticides & \(9.82\times 10^{-5}\) & kg \\ \hline \multirow{4}{*}{Plant growth} & Water & \(8.08\times 10\) & L \\  & Diesel & \(6.32\times 10^{-7}\) & kg \\  & Transport & \(8.82\times 10^{2}\) & kgkm \\  & Machinery & \(4.45\times 10^{-6}\) & kg \\  & NPK & \(6.91\times 10^{-2}\) & kg \\  & Pesticides & \(1.70\times 10^{-3}\) & kg \\  & Plastic & \(3.34\times 10^{-3}\) & kg \\  & Steel & \(1.79\times 10^{-3}\) & kg \\ \hline \multicolumn{4}{c}{**Industrial Stage (FU: 1 kg of Packed Pineapple)**} \\ \hline \multicolumn{4}{c}{**Sub-Stage**} & **Material** & **Quantity** & **Unit** \\ \hline \multirow{4}{*}{Packaging} & Cardboard & \(8.04\times 10^{-2}\) & kg \\  & Electric energy & \(8.09\times 10^{-3}\) & kWh \\  & Wax & \(5.51\times 10^{-4}\) & kg \\  & Water & \(1.67\times 10^{-1}\) & L \\ \hline \multicolumn{4}{c}{**Industrial Stage (FU: 1 kg of in Syrup Pineapple)**} \\ \hline \multirow{4}{*}{In syrup} & Cans & \(1.78\times 10^{-1}\) & kg \\  & Fuel oil & \(3.17\) & MJ \\  & Electric energy & \(5.91\times 10^{-3}\) & kWh \\  & Sugar & \(1.43\times 10^{-2}\) & kg \\  & Water & \(4.55\times 10^{-1}\) & L \\  & Glue & \(1.05\times 10^{-3}\) & kg \\ \hline \multicolumn{4}{c}{**Industrial Stage (FU: 1 kg of Dehydrated Pineapple)**} \\ \hline \multirow{4}{*}{Dehydrated} & Water & \(1.25\times 10^{-1}\) & L \\  & Electric energy & \(6.73\times 10^{-2}\) & kWh \\ \cline{1-1}  & Plastic bags & \(2.72\times 10^{-3}\) & kg \\ \cline{1-1}  & L.P. Gas & 4.56 & MJ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Foreground data collection from the main stages of the product system.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 3 Study of the Deformation and Instability Modes of the Slope After Excavation and Backfilling
### Displacement Response Analysis  
Figure 4 presents the permanent total displacement contour plot of the slope under the excavation post-condition. Under the influence of the self-weight load of the soil and rock mass, the maximum displacement of the slope without a contact surface is 7.7 mm, while the maximum displacement of the slope with a contact surface is 27.0 mm. In both cases, the maximum displacement occurs at the upper edge of the slope. The slope without a contact surface exhibits a larger range of displacement at the top and shows a distinct displacement discontinuity between the soil layers, which is mainly attributed to the poor mechanical properties of the strongly weathered mudstone-siltstone at the slope crest. Additionally, the steep slope at the top further increases the susceptibility to instability and failure.  
Table 4 presents the displacement data in the X and Z directions for each monitoring point. As shown in Table 4, the displacements in both the X and Z directions at these five monitoring points are generally negative, with larger displacements observed at higher locations on the slope. It is also noted that the Z-direction displacement is greater than the X-direction displacement, and this difference becomes more pronounced as the slope crest is approached. This suggests that, under natural conditions, deformation is primarily influenced by normal stress, with shear stress playing a lesser role [33]. On-site monitoring data indicate that displacement results from numerical simulations that incorporate interface interactions more closely match the actual measurements compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Natural State**} & \multicolumn{2}{c}{**Saturated State**} \\ \cline{2-5}
**Rock Mass Type** & \begin{tabular}{c} **Shear Modulus** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Bulk Modulus** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Shear Modulus** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Bulk Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline Highly weathered argillaceous siltstone & 32.7 & 70.8 & 0.12 & 6.6 \\ Moderately weathered argillaceous siltstone & 403.2 & 641.0 & 315.0 & 579.7 \\ Moderately weathered conglomerate rock & 2362.2 & 4347.8 & 1908.4 & 4386.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rock mass modulus parameters.  
to those that exclude them. This highlights the significant impact of weak rock layers on slope stability and demonstrates that including contact surfaces offers a more accurate representation of the slope's failure mode.
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 2 UWB Positioning Method Based on Unscented Kalman Filter
### Principle of UWB Positioning
If erroneous data is used for positioning, the positioning accuracy of the positioning system will decline, so in order to achieve high-precision positioning, the clock synchronization between the label and the BTS must be guaranteed, though that is difficult to achieve in practice.