esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The findings highlighted a significant relationship between the anticipated and measured values, with a correlation factor of 0.99447 [13]. However, a number of friction systems have employed ANN to design, forecast, and optimize friction parameters. By combining Elman's recurrent configuration with the ANN approach, [PERSON] and [PERSON] established several friction characteristics. The outcomes led to an improvement in material formulation, which was supported by evidence from research trials and experiments [14]. Additionally, ANN was employed by [PERSON] to predict the wear rate for friction materials. In his study, a number of different factors were considered, including (i) the formulation of the friction material (18 ingredients), (ii) the most critical manufacturing conditions for the friction material (5 parameters), (iii) the applied load and sliding velocity of the friction material (with both factors being represented by the work carried out through brake application) and (iv) the temperature of the brake interface [15]. In order to predict the corrosion potential and corrosion rate for the samples using the friction stir processing process parameters, [PERSON] et al. proposed an ANN model containing a feed-forward backpropagation network and the Levenberg-Marquardt training algorithm [16]. With a coefficient of determination of unity, the ANN model was found to be in line with the experimental results. Moreover, [PERSON] et al. investigated the prediction ability of tribological properties of nuclear grade graphite using the ANN model [17]. Meanwhile, Alyuda Neuro's intelligence software was employed to test the ANN model under different measured parameters, including time, temperature, sliding distance, and friction force. The findings revealed that the proposed ANN model and back-propagation Alyuda Neuro intelligence was able to effectively predict the frictional features of nuclear grade graphite with a correlation coefficient of 0.9995 and a mean absolute error of 0.0030%. Erosion wear was also predicted using various computational methods [18]. Although many studies in the literature, as presented above, have been accomplished for predicting the tribological behavior of different materials using the ANN approach. However, to our knowledge, no one has investigated the forecasting of the tribological characteristics for ductile cast iron under dry conditions. The key objective of this research is to examine the wear rate and friction coefficient for ductile cast iron. In order to carry out the experiments under the influence of sliding duration, load, and speed, three different microstructures were selected. These samples consisted of bainitic, ferrite, and perlite plus ferrite. Additionally, the ANN model based on the obtained data for the experimental testing was constructed to estimate the coefficient of friction using two structures.
## 4 Edge Cloud-based Next-generation Internet  
Our proposed next-generation Internet model, following the key rationales of Cloud computing and CDN, transfers computing load from the client to the Cloud while bringing intelligence to the edge. The proposed Edge Cloud-based next-generation Internet architecture is shown in Figure 2. Latency, security and privacy, high bandwidth requirement for data-intensive applications are some of the major challenges Cloud computing is facing [14]. On the other hand, CDNs have overcome many limitations by pushing intelligence to the edge network. The core of the Internet demands simplicity of operation and remains dedicated to forwarding IP packets: any kind of additional inspection or caching of IP packets may severely increase end-to-end delivery time. Therefore, instead of implementing a Cloud infrastructure at remote, core-deep locations, many small Clouds at the edge would be implemented in partnership with the edge ISPs.
## 4 Engineering Example Analysis
### Scheme Design  
In this section, three CLNVD schemes are designed. Based on the optimization design method, Scheme 1 is obtained. In comparison with Scheme 1, the position of the CLNVD in Scheme 2 is \(\beta=0.2\). In contrast to Scheme 2, Scheme 3 does not have the NSD. The layout of Scheme 1 to Scheme 3 in the floors is described in Figure 14. The CLNVD's parameters for each scheme are shown in Table 4. The lever is made of Q235 steel. The box-shaped cross-sections corresponding to the support and lever arm are 300 mm \(\times\) 140 mm \(\times\) 20 mm \(\times\) 20 mm, \(\times\) 20 mm, and 200 mm \(\times\) 200 mm \(\times\) 25 mm \(\times\) 25 mm, respectively, with a lever arm's length of 1000 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Period (s) & 1.60 & 1.58 & 1.38 & 0.56 & 0.54 & 0.48 \\ Direction & X (E-W) & Y (N-S) & Rotation & X (E-W) & Y (N-S) & Rotation \\ Effective Mass (\%) & 77 & 76 & 76 & 11 & 12 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic properties of the structure.  
Figure 14: Three-dimensional schematic diagram of the structure and layout of the CLNVD: (**a**) 3D structural schematic diagram; (**b**) plane layout of the CLNVD; (**c**) scheme 1 (B-axis); (**d**) scheme 2 (B-axis); (**e**) scheme 3 (B-axis).  
The structure's section design with the CLNVDs is on the basis of the internal forces of the response spectrum design under the circumstance of fortification earthquakes. As per Chinese code [55], 7 seismic waves are selected, and 7 seismic waves' response spectra are presented in Figure 15. The average of all the waves' analysis results is taken as the relevant analysis result. As for fortification earthquakes, elastic dynamic analysis is carried out through adopting SAP2000 software (Version 23.1.0); as for rare earthquakes, elastic-plastic dynamics is implemented by means of utilizing PERFORM-3d software (Version 8.0). In elastoplastic analysis, fluid damper elements are used for VD, and a nonlinear elastic gap hook bar is used for NSD. For steel bars, a double-line model is used; for unconstrained concrete and restrained concrete, the trilinear model and Mander model are applied, respectively; for the beam and column components, a nonlinear fiber section model is adopted. Structures without CLNVD are forced to maintain elasticity. In this way, only the CLNVD's control effect on the structure's seismic response can be considered, and the changes caused by the structure's plastic deformation are not taken into account additionally. Meanwhile, this section only analyzes the CLNVD's damping effect in the structure's north-south direction.
## 5 Results and Discussion
### Qualitative Results
These results verify the effectiveness of the MHCFIN method in learning transferable complementary knowledge through multi-hierarchical feature interactions. Figure 11 shows the trend that the loss values of each reconstruction method gradually decrease as the number of epochs increases during the training process. The analysis results show that our proposed method first achieved the lowest loss value and showed stable convergence characteristics. It was followed by the DuDoCAF method, which had a slightly higher loss value than our method but also showed good performance. In contrast, the loss value of MINet fluctuated greatly during the training process, which reflects its instability when processing the fastMRI dataset, and thus shows that its application effect on this dataset may not be as ideal as other methods. Figure 10: Bar charts for **(a)** PSNR, **(b)** SSIM, and **(c)** RLNE depicting the performance of various reconstruction methods using an \(8\times\) acceleration factor mask on the fastMRI brain dataset. The black arrows represent the standard deviation (mean \(\pm\) standard deviation) for each method. Figure 9: Qualitative comparison with different reconstruction methods using 1D random undersampling pattern with acceleration factor \(10\times\) on fastMRI knee datasets.
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 5 Dynamic Public Transit Labeling  
In this section, we introduce Dynamic Public Transit Labeling (d-ptl, for short), a new technique that is able to maintain the ptl data structure under delays occurring in the given transit network. In particular, we first show a dynamic algorithm (referred to as basic d-ptl) to update the basic ptl framework, that is how to maintain both a red-te graph \(G=(V,A)\), the corresponding 2 hc-r labeling L and stop labeling SL under delays affecting connections, and then discuss on how to extend this procedure to the multi-criteria setting. Formally, a _delay_ is an increase in the departure time of an elementary connection of a finite quantity \(\delta>0\). Hence, it is easy to see how a delay can induce an arbitrary number of changes to both the graph and labelings [2; 13], depending on the structure of the trip the connection belongs to, thus in turn inducing arbitrarily wrong answers to queries. A general strategy to achieve the purpose of updating both \(G\), the 2 hc-r labeling L and the stop labeling SL, after a delay, while preserving the correctness of the queries, is to first update the graph representing the timetable (via, e.g., the solutions in References [2; 9; 10]) and then reflect all these changes on both L and SL by: (i) detecting and removing obsolete label entries; and (ii) adding new updated label entries induced by the new graph, as done in other works on the subject [21; 24]. However, this results in a quite high computational effort, as shown by preliminary experimentation we conducted. In order to minimize the number of changes to both L and SL, we hence exploit the specific structure of the red-te graph and design a dynamic algorithm that alternates phases of update of the graph with phases of update of the labeling L through the procedures given in Reference [24]. At the end of such phases, changes to L are reflected onto its compact representation sl through a dedicated routine. In particular, our algorithm is based on the following observation: a delay affecting a connection of a trip might be propagated to all subsequent connections in the same trip, if any. Hence, the impact of a given delay on both the graph and the labelings strongly depends on \(\delta\), on the structure of the trip and, in particular, on the departure times of subsequent connections. Therefore, d-ptl processes connections of a trip incrementally, and in order with respect to departure time. In details, d-ptl comprises two sub-routines, called, respectively, removal phase (Algorithm rem-d-ptl, see Algorithm 1) and insertion phase (Algorithm ins-d-ptl, see Algorithm 2) that update L along with the graph. Such phases are then followed by a bundle update of sl by a suitable procedure (Algorithm UpdateStopLab, see Algorithm 3).
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 2 Advanced Sliding Mode Control Framework for UAV Security
### Adaptive and Fault-Tolerant Mechanisms
#### 2.3.2 Fault-Tolerant Control  
To enhance the robustness and reliability of the helicopter control system, particularly in the presence of actuator/sensor faults or external disturbances, the fault-tolerant control (FTC) strategies are incorporated into the existing SMC framework. These strategies are designed to detect, isolate, and compensate for faults, ensuring the helicopter maintains stable operation when the control system reconfigures itself by:  
* Fault detection and isolation (FDI): Implementing FDI algorithms to detect and isolate faults, ensuring system integrity.
* Reconfiguring control parameters: Adjusting \(\lambda\) and other control parameters to compensate for faults.  
Utilizing redundancy: Leveraging redundant actuators and sensors to maintain control despite component failures.  
These enhancements ensure the SMC framework can robustly handle unexpected disturbances and maintain stable control of the helicopter's attitude, which are explained next.
## 3 Results and Discussion
### Analysis of Ester Content by GC  
The successful detection of ester bonds in the sample by FTIR analysis led to GC analysis to determine the FAME compound in the sample in detail. Table 4 shows the composition of the FAME ester content of the obtained biodiesel for each sample at different temperatures. Different concentrations of unsaturated fatty acids (FA) were observed for each biodiesel sample at different operating temperatures. It is to be noted that palmitic (C 16:0) and stearic (C 18:0) acids were the major components for both _Chlorella_ sp. and _Spirulina_ sp. biodiesel. The saturated FA percentage of both samples varies around 60-75%, whereas the unsaturated FA percentage varies around 25-40% of the FAME content. Palmitic and stearic acid contents were observed to be increased with the increase in temperature. The highest percentage of palmitic acid for both microalgae biodiesel as well as the highest stearic acid for _Chlorella_ sp. was obtained at 45 \({}^{\circ}\)C. However, for _Spirulina_ sp., the highest content of stearic acid was found at 40 \({}^{\circ}\)C, and the percentage decreased at 45 \({}^{\circ}\)C. [PERSON] et al. [66] also studied the ester content of _Spirulina_ sp. and pond water algae-based biodiesel. Palmitic, linolenic, and linoleic acids were the major FA components in those samples. [PERSON] et al. [55] studied the FAC of ultrasound-assisted _N. oculata_ microalgae-based biodiesel. They found only three fatty acids in the biodiesel sample viz. myristic, palmitic, and stearic acids. Similar to the previous study, palmitic acid was the dominant ester content in this biodiesel.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{_Chlorella sp._} & \multicolumn{4}{c}{_Spinlina sp._} \\ \cline{2-10}
**Fatty Acids** & **Lipid Chain** & **30 °C** & **35 °C** & **40 °C** & **45 °C** & **30 °C** & **35 °C** & **40 °C** & **45 °C** \\ \hline Hexadecenoic (Palmitic) & C 16:0 & 28.72 & 35.62 & 44.04 & 45.60 & 29.06 & 30.16 & 33.12 & 46.81 \\
7-Hexadeconic (Palmitic) & C 16:1 & - & - & - & 0.325 & - & - \\
7,10-Hexadeconic & C 16:2 & & & & & 2.41 & & \\
9-Octadeconic (Oleic) & C 18:0 & 18.22 & 14.67 & & & 16.34 & 20.46 & \\
11-Octadeconic & C 18:1 & - & - & 17.39 & - & & 26.50 & 23.50 \\
9,12-Octadeconic (Linoleic) & C 18:2 & 13.95 & 18.72 & 27.60 & 6.56 & & 13.45 & 9.66 \\ Octadeconic (Stearic) & C 18:0 & 27.82 & 28.05 & 28.23 & 30.45 & 25.51 & 26.35 & 26.93 & 20.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of ester content analyzed by GCMS at different transesterification operating temperatures.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 5 Results and Discussion
### Performance of ML Models  
Figure 8 illustrates actual \(V_{\text{s}}\) and predicted \(V_{\text{s}}\) using the optimized ML models, along with \(\pm 10\%\) error lines (red lines). The green lines show a match between actual and predicted \(V_{\text{s}}\) values. The results demonstrate that all ML models, except for the DT model, achieved excellent predictive accuracy on both training and testing datasets with high \(R^{2}\) and \(A10-I\) score values of 1. This shows that the RF, SVM, and XGBoost models can explain all the variance in the \(V_{\text{s}}\) using the given features. Furthermore, the scatter plots for these models show that many data points are closer to the error bounds, indicating that the models performed well. In contrast, the DT model achieved lower \(R^{2}\) and \(A10-I\) values ranging from 0.94 to 0.95 and from 0.77 to 0.78 on the testing and training data, respectively.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ML Models**} & \multicolumn{3}{c}{**Tuned Hyperparameters**} \\ \cline{2-4}  & **Names** & **Ranges** & **Optimized Values** \\ \hline \multirow{8}{*}{DT} & Complexity parameter, cp & 0.001–1 & 0.001 \\  & Maximum depth of trees & 1–30 & 20 \\  & Minimum number of splits & 2–20 & 5 \\  & Minimum number of & & \\  & observations & 2–20 & 6 \\  & at terminal node, minbucket & & \\  & Maximum number of splits at & 1–20 & 9 \\  & node, maxcompete & & \\ \hline \multirow{8}{*}{XGBoost} & Learning rate, eta & 0.01–1 & 0.26 \\  & Loss reduction term, gamma & 0.01–10 & 3.79 \\  & L2 regularization term, lambda & 0.01–1 & 0.38 \\  & L1 regularization term, alpha & 0.01–1 & 0.83 \\ \cline{1-1}  & Number of boosting rounds, & 1–100 & 84 \\ \cline{1-1}  & Maximum depth of trees & 2–10 & 9 \\ \cline{1-1}  & Fraction of samples for each tree, & 0.1–1 & 0.79 \\ \cline{1-1}  & subsample & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The residual plots of ML models are shown in Figure 9, illustrating a random distribution of points around the horizontal orange line at y = 0 (line of zero error). This indicates that the model's predictions are unbiased and have captured the underlying patterns in the data. Additionally, the frequency distributions of residuals (green bars) are shown in the figure. The distribution is approximately symmetric, indicating that the errors are normally distributed, a desirable property. To gain more insight into the performance of the ML models, a further comparison is carried out in the following subsection.  
Figure 8: Scatter plot illustrating the correlation between actual \(V_{S}\) and predicted \(V_{S}\).  
Figure 9: _Cont_.
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 4 Result
### Ablation Study  
The ablation study is conducted to confirm the effectiveness of the key modules of the MACS we proposed. Specifically, we use the four following modules to segment the LV\({}_{\text{endo}}\) and LV\({}_{\text{epi}}\):* ASDB, _w/o_ Domain Adaptation (DA);
* ASDB + Feature discriminator, _w/o_ Entropy Domain Adaptation (EDA);
* ASDB + Entropy discriminator, _w/o_ Feature Domain Adaptation (FDA);
* Our proposed method (MACS).  
In the ablation study, the self-acquired dataset and the CAMUS dataset are defined as the source and the target domain, respectively.  
Figure 4 displays the representative \(\mathrm{LV}_{\mathrm{endo}}\) and \(\mathrm{LV}_{\mathrm{epi}}\) segmentation predictions on the CAMUS. The ASDB can recognize the ROI of the cross-domain segmentation task, but the prediction masks are not ideal. The segmentation predictions of the ASDB without any discriminator have a large structural error. Either a single discriminator in the feature space or the output space corrects the structural mistake and encourages the segmentation network to obtain accurate results. The integrated MACS with multi-space discriminators further improves the segmentation performance of the boundaries and achieves the best segmentation result of all of these modules.  
Table 1 demonstrates the quantitative results of the segmentation performance with different modules. The addition of discriminators improves the mean DI by 2.2% for the segmentation result in the CAMUS dataset.  
For the full MACS model, the average running time of segmenting \(\mathrm{LV}_{\mathrm{endo}}\) and \(\mathrm{LV}_{\mathrm{epi}}\) on a single echocardiography is approximately 0.175 s.  
Figure 4: Representative visual segmentation results of the ablation study. Different model configurations are compared, including the ASDB, the ASDB + Feature discriminator (D\({}^{\mathrm{f}}\)), the ASDB + Entropy discriminator (D\({}^{\mathrm{e}}\)), and the full framework of the MACS. The green and blue contours indicate boundaries of the \(\mathrm{LV}_{\mathrm{endo}}\) and \(\mathrm{LV}_{\mathrm{epi}}\), respectively. The yellow and orange colors denote the ground truth.
## 1 Introduction
### Contributions  
Aiming at some technical difficulties in existing noise reduction methods, this paper proposes improved singular value decomposition (ISVD) and multivariate variational mode decomposition (MVMD) for PD signal noise reduction. Work is performed with respect to the following three aspects:  
(1) An adaptive method for accurately determining the amount of narrowband interference is proposed. The total amount of narrowband interference and partial discharge is determined by the good time-frequency aggregation of frequency slice wavelet transform (FSWT) and the small threshold method, and then the amount of narrowband interference is accurately determined by the deviation criterion, which solves the problem that it is difficult to determine the amount of narrowband interference in the method of SVD suppression of narrowband interference.  
(2) The improved Rife method is introduced into PD noise reduction research. By shifting the signal frequency to the center region of two adjacent quantization frequencies, the improved Rife method can effectively reduce the estimation error of the narrowband interference signal frequency. The guided signal constructed by this method can effectively suppress the narrowband interference signal in the noisy PD signal even when the narrowband interference amplitude is small.  
(3) The marine predator algorithm (MPA) considering minimum average envelope entropy (MAEE) is introduced to optimize the decomposition mode number \(k\) and penalty factor \(\alpha\) of MVMD. After MPA optimization, the multivariate variational mode decomposition algorithm can decompose the PD characteristic signal with the maximum degree of certainty as the goal, so as to accurately decompose the noisy PD signal.
###### Abstract  
Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn't been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.  
gross primary production; land cover; precipitation; temperature; sunshine; nitrogen deposition; carbon fixation; models; multiple linear regressions 2019 11, 1135; doi:10.3390/rs11091135 www.mdpi.com/journals/remotesensing