esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Presentation and Analysis of Prediction Results  
In an attempt to verify the effectiveness of the six intelligent prediction models constructed for predicting the weir life, as well as to explore the performance advantages and disadvantages of each model in comparison, this section systematically compares and analyzes the prediction results of different models from two levels, namely, the training set and the test set. The fitting relationship between the predicted and actual values of each model is demonstrated through intuitive visual graphs, and the prediction accuracy and generalization ability of the models are further quantified by combining the key evaluation indexes.  
As can be seen in Figure 6, the IBKA-CNN-Transformer model has the best performance, with R\({}^{2}\) of 0.99 and 0.98 for the training set and test set, respectively, showing very high fitting accuracy and excellent generalization ability. The BKA-CNN-Transformer model is the second best, with R\({}^{2}\) of 0.99 and 0.97 for the training set and test set, respectively, and with only a small deviation in the test set. The predicted and actual values are highly consistent with each other, with only a small deviation in the test set. CNN-Transformer performs slightly worse than the first two models, but its test set R\({}^{2}\) still reaches 0.94, indicating that it is able to effectively capture the nonlinear features in the data. In contrast, the prediction performance of the traditional machine-learning models is relatively weak. LightGBM and MLP have R\({}^{2}\) of 0.95 and 0.95 on the training set, but their R\({}^{2}\) on the test set drop to 0.90 and 0.88, respectively, with some of the predicted values deviating from the actual values. SVR has the most limited performance, with R\({}^{2}\) of 0.94 and 0.79 on the training and test sets, respectively, with significantly large errors, especially on the test set. SVR has the most limited performance, with R\({}^{2}\) of 0.94 and 0.79 in the training and test sets, respectively, and the error is significantly large, especially in the test set, where there are more points that deviate from the \"perfect fit curve\". Overall, the deep-learning models outperform the traditional models in terms of prediction accuracy and generalization ability, especially the IBKA-CNN-Transformer, which shows excellent performance in all the indicators, proving its applicability and advantages in solving this research problem.  
Figure 5: Heat map of correlation.
## 6 Conclusions  
State observer is very important in the control theory. In the past, the study on the state observer have focused on linear system. For the design of observer of nonlinear systems, the linear design methods are often used after linearization of nonlinear systems. When unknown state initial value, output noise and uncertain model parameters are considered, state observation will become more difficult. To address the challenge, a intelligent adaptive observer based on intelligent optimization algorithm is proposed in this paper. As a simple and efficient heuristic for global optimization over continuous spaces, chaotic DE is used to construct a adaptive scheme based on dynamic optimization which allow the direct estimation of the states and parameters.When the necessary observability condition is satisfied, the observer can realize the robust estimation of system parameters and states with less measurement information.The simulation proves the effectiveness of the algorithm. In addition, we found that fitness function plays an important role on the performance of observer. Two fitness functions are used in this paper. Finally, it is found that the two fitness functions have different robustness under the same algorithm control parameters. The simulation verified the difference on performance. On the whole, the method based on the residual norm is better than the one based on correlation coefficient. Therefore, it is necessary to select an appropriate fitness function before designing the proposed observer. Further study on this problem is valuable, as it can improve the performance of the observer. In conclusion, this paper expands the application of DE in practical engineering problems. In the future, it is interesting to combine more intelligent optimization algorithms with the practical application.  
Methodology, X.Y. and Z.L.; validation,X.Y.; writing--original draft preparation, Z.L. and X.Y.; writing-review and editing, B.D.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [No.61963009], Collaborative Foundation of Guizhou Province [No.7228(2017)], Platform Talent Project of Guizhou Province [No.5788(2017)], Science and Technology Planning Project of Guizhou Province(No.2154[2019]) and (No.2302 [2016]) and Special fund project of provincial governor for outstanding science and technology education talents in Guizhou Province, grant number 4[2010].  
The authors declare that they have no conflicts of interest.
## 2 Materials and Methods
### Design of the DC-AC Controller
#### 2.3.4 Current Control  
From the transfer function found in Equation (32) of Section 2, it can be seen that the system input is \(Vi-V_{LN}=dV_{bus}-V_{LN}\), and as \(V_{LN}\) is an external input that cannot be controlled, it acts as a disturbance for the current control. To avoid this problem and to be able to calculate the duty cycle (\(d\)), a feedback linearization technique is used under the assumption that the cut-off frequency of the current loop is much higher than the grid frequency. Consequently, \(V_{LN}\) can be treated as a DC parameter compared to the current dynamics of the inductor \(i_{L}\)[19]. The proposed scheme is shown in Figure 16.  
The duty cycle of the PWM that controls the MOSFETs of the inverter is calculated from the feedback linearization as follows:  
\[d=\frac{u_{i}+V_{LN}}{V_{bus}}, \tag{65}\]  
where \(u_{i}\) corresponds to the current control's output, and \(V_{LN}\) and \(V_{bus}\) are the measured variables of the inverter.  
The following controller is implemented for the 3P3Z current control [19]  
Figure 16: Current-control loop with feedback linearization.  
\[G_{c}(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+b_{3}z^{-3}}{1-a_{1}z^{-1}-a_{2}z^{-2}- a_{3}z^{-3}}, \tag{66}\]  
where  
\[b_{0}=0.2866,\ b_{1}=-0.3173,\ b_{2}=0.338,\ b_{3}=-0.2616,\] \[a_{1}=1.584,\ a_{2}=-0.6978,\ a_{3}=0.1137.\]  
The current reference \(i_{Lref}\) is calculated by multiplying the outputs of the PI controllers and the active and reactive power control, that is:  
\[i_{Lref}=\Big{[}i_{4}^{*}\cos(\theta)-i_{q}^{*}\sin(\theta)\Big{]}u_{v}. \tag{67}\]
## 3 Results and Discussion
### Ground-Based Measurements of Vegetation  
The different amounts of fresh phytomass of lucerne, sampled at 22 locations across the different plots reflect the spatial heterogeneity of naturally occurring site properties (terrain and soil). Although all plots were treated equally, the averaged fresh phytomass of each permanent observation site ranges between 440 g\(\cdot\)m\({}^{-2}\) and 2080 g\(\cdot\)m\({}^{-2}\). The overall mean is 1469 g\(\cdot\)m\({}^{-2}\) and the coefficient of variation (CV) is 34%. The amount of dry phytomass ranges between 158 g\(\cdot\)m\({}^{-2}\) and 426 g\(\cdot\)m\({}^{-2}\). The overall mean is 329 g\(\cdot\)m\({}^{-2}\) with a CV of 23%. The variation in the corresponding water content ranges between 282 g\(\cdot\)m\({}^{-2}\) and 1709 g\(\cdot\)m\({}^{-2}\). With a CV of 38%, the variation is similar to the variation in fresh phytomass. The statistic evaluation of the data shows a strong linear correlation (\(R^{2}\) = 0.89) between fresh and dry phytomass with a RMSE of 24 g\(\cdot\)m\({}^{-2}\) (Figure 15).
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
###### Abstract  
The motor is the primary impetus source of most mechanical equipment, and its failure will cause substantial economic losses and safety problems. Therefore, it is necessary to study online fault diagnosis techniques for motors, given the problems caused by shallow learning models or single-sensor fault analysis in previous motor fault diagnosis techniques, such as blurred fault features, inaccurate identification, and time and manpower consumption. In this paper, we proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion. Firstly, a correlation adaptive weighting method is proposed in this paper, and it is used to integrate the collected multi-source homogeneous sensor information into multi-source heterogeneous sensor information through the data layer fusion. Secondly, the 1D-CNN is used to carry out feature extraction, feature layer fusion, and fault classification of multi-source heterogeneous information of the motor. Finally, the data of seven states (one healthy and six faulty) of the motor are collected by the motor drive test bench to realize the model's training, testing, and verification. The experimental results show that the fault diagnosis accuracy of the model is 99.3%. Thus, this method has important practical implications for improving the accuracy of motor fault diagnosis further.  
motor; fault diagnosis; multi-sensor information fusion; deep learning; convolutional neural network +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Chromatographic Analysis  
The UHPLC-HRMS (ultra-high performance liquid chromatography coupled with a high-resolution mass spectrometer--Q Exactive Orbitrap Focus MS, Thermo Fisher Scientific, Waltham, MA, USA) technique was used to determine the free amino acids concentration. The analytes were separated on the Cortecs UPLC C18 column (2.1 \(\times\) 100 mm, 1.6 \(\upmu\)m, Waters, Milford, MA, USA). Liquid phases consisted of LC-MS-grade water and LC-MS-grade acetonitrile (90:10 phase A, 10:90 phase B, Witko, Lodz, Poland). Each phase contained 0.1% of LC-MS-grade formic acid (98-100%, Chem-Lab, Zedelgem, Belgium) and 5 mM of ammonium formate for LC-MS (\(\geq\)97%, Chem-Lab, Zedelgem, Belgium). The flow rate was set at 0.3 mL/min. The following gradients for the liquid phases were applied: A:B (%) 0-2 min--90:10, 2-22 min--0:100, 22-25 min--0:100, 25-26 min--90:10, and 26-28 min--90:10. The injection volume was set at 2.5 \(\upmu\)L. Analyses were performed in a positive polarization mode. The MS (mass spectrometer) used for analyses was equipped with a HESI (heated electrospray ionization) source. The specific MS parameters were as follows: spray voltage: 3 kV, capillary temperature: 256 \({}^{\circ}\)C, sheath gas flow rate: 48, auxiliary gas flow rate: 11, sweep gas flow rate: 2, probe heater temperature: 413 \({}^{\circ}\)C, S-lens RF level: 50, and resolution: 70,000 simultaneous scan/35,000 all-ion fragmentation. Xcalibur 4.2.47 software was used for data acquisition. The representative LC-MS/MS chromatograms showing peaks of analyzed free amino acids are presented in Supplementary Figure S1.
# Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture
###### Abstract  
The combination of a high-speed wireless network with passive optical network technologies has led to the evolution of a modern integrated fiber wireless (FiWi) access network. Compared to broadband wireless networks, the FiWi network offers higher bandwidth with improved reliability and reduced maintenance costs due to the passive nature of passive optical network (PON). Since the quality of service (QoS) is a baseline to deploy high-speed FiWi broadband access networks, therefore, it is essential to analyze and reduce the typical problems (e.g., bandwidth and delay) in the high-speed next-generation networks (NGANs). This study investigates the performance of a fiber wireless architecture where a 10-Gigabit-capable passive optical network (XGPON) and fifth generation of wireless local area network (WLAN) (i.e., IEEE 802.11 ac) are integrated. Both technologies take benefits from each other and have pros and cons concerning the QoS demands of subscribers. The proposed work offers a very flexible QoS scheme for the different types of services of 5G WLAN and XGPON with the help of the highest cost first (HCF) algorithm, which leads to reduced upstream delays for delay-sensitive applications. The simulation results show that the HCF algorithm boosts the performance of the dynamic bandwidth assignment (DBA) scheme and results in up to 96.1%, 90.8%, and 55.5% reduced upstream (US) delays for video: VI(T2), background: BK(T3), and best effort: BE(T4) traffic in enhanced-distributed-channel-access (EDCA) mode. Compared to earlier work, the HCF and immediate allocation with the colorless grant (IACG) DBA combination results in the reduction of up to 54.8% and 53.4% mean US delays. This happens because of 50% to 65% better bandwidth assignment by the IACG DBA process due to efficient mapping by the HCF algorithm.  
Keywords:FiWi access network; QoS mapping; XGPON; 802.11 ac; DCF; EDCA; 5G-WLAN; QoS; US Traffic +
Footnote †: journal:
## 2 Related Works and the Key Contribution of This Paper
### HQAM and IL-FEC  
This paper integrates previous findings on hierarchical QAM schemes, such as those discussed in [16; 17]. Hierarchical QAM offers a flexible approach to balancing data rate and robustness by organizing data hierarchically. This method is beneficial for transmitting MVD content, as it allows for prioritizing more critical information, such as color data, over less critical data, like depth maps. In addition, the proposed approach uses IL-FEC, which has been studied extensively in previous research [13; 14]. IL-FEC provides targeted error protection within each hierarchical layer, thereby mitigating the impact of channel impairments and preserving the integrity of critical data. This technique has been used effectively to improve the reliability of video transmissions by reducing the BER in scenarios where multiple layers of video data need to be protected.
###### Abstract  
The variability in soil hydrothermal conditions generally contributes to the diverse distribution of vegetation cover types and growth characteristics. Previous research primarily focused on soil moisture alone or the average values of soil hydrothermal conditions in the crop root zone (0-100 cm). However, it is still unclear whether changes in gross primary productivity (GPP) depend on the hydrothermal conditions at different depths of soil layers within the root zone. In this study, the soil hydrothermal conditions from three different layers, surface layer 0-7 cm (Level 1, L1), shallow layer 7-28 cm (Level 2, L2), and deep layer 28-100 cm (Level 3, L3) in the Qilian Mountains area, northwestern China, are obtained based on ERA5-Land reanalysis data. The Sen-MK trend test, Pearson correlation analysis, and machine learning algorithm were used to explore the influence of these three soil hydrothermal layers on GPP. The results show that soil moisture values increase with soil depth, while the soil temperature values do not exhibit a stratified pattern. Furthermore, the strong correlation between GPP and deep soil hydrothermal conditions was proved, particularly in terms of soil moisture. The Random Forest feature importance extraction revealed that deep soil moisture (SM-L3) and surface soil temperature (ST-L1) are the most influential variables. It suggests that regulations of soil hydrothermal conditions on GPP may involve both linear and nonlinear effects. This study can obtain the temporal and spatial dynamics of soil hydrothermal conditions across different soil layers and explore their regulations on GPP, providing a basis for clarifying the relationship between soil and vegetation in arid mountain systems.  
Qilian Mountains area; soil hydrothermal conditions; deep soil layer; gross primary productivity; ERA5-Land +
Footnote †: journal: _forests_  
0000-0002-319-22422
## 4 Experiment Results and Analysis
### Quantitative Evaluation  
We performed quantitative analysis by averaging precision (\(P\)) and recall (\(R\)) rates along with F-measures (\(F_{\beta}\)). Precision and recall rates could be obtained by comparing pixel-level ground truth images with the results of the proposed method and calculated as follows:  
\[P=\frac{T_{p}}{T_{p}+F_{p}} \tag{10}\]  
\[R=\frac{T_{p}}{T_{p}+F_{n}} \tag{11}\]  
where \(T_{p}\) denotes the number of true positives pixels that were obtained as salient by the proposed method and also labelled as salient in the ground truth image, \(F_{p}\) denotes the number of false-positive pixels that were obtained as salient by the proposed method but were not labelled as salient in the ground truth image, and \(F_{n}\) denotes the number of false-negative pixels that were not obtained as salient by the proposed method but were labelled as salient in the ground truth image. \(P\) is defined as the number of true-positive pixels over the number of true-positive pixels plus the number of false-positive pixels. \(R\) is defined as the number of true-positive pixels over the number of true-positive pixels plus the number of false-negative pixels. In other words, \(P\) is the fraction of salient pixels among all the obtained pixels by the proposed method, whereas \(R\) is the fraction of the total number of salient pixels that were actually obtained as salient by the proposed method. Worth noting are perfect precision (no false positive pixels) and perfect recall (no false negative pixels), as seen in Equations (10) and (11). The results showed that the proposed saliency cuts accomplished the highest precision rate of 0.94. RC_Cuts [17] and ATT_Cuts [16] yielded precision rates of 0.93 and 0.90, respectively. In addition, we computed the F-measure value, which balanced measurements between the mean of precision and recall rates. A higher F-measure meant higher performance and it was defined as follows:  
\[F_{\beta}=\frac{(1+\beta^{2})P\times R}{\beta^{2}\times P+R}. \tag{12}\]  
where \(\beta^{2}=0.3\) to weigh precision more than recall as in most of the existing methods [16; 17; 30; 31; 32], and others. The proposed approach achieved the highest F-measure value of 0.90, whereas the other two methods RC_Cuts and ATT_Cuts showed 0.89 and 0.88, respectively. The results of quantitative comparison among the three algorithms are shown in Figure 12 and Table 1. The proposed method produced saliency objects with the highest precision rate, whereas ATT_Cuts showed the highest recall rate. Although all three methods produced similar results, the proposed method proved to be most robust in complex images that contain foreground and background with similar appearances, as demonstrated in Figures 9 and 10.  
Figure 11: Failure cases: (1) input images, (2) ground truth, (3) saliency map, (4) saliency object segmentation using the proposed method.
## 4 Discussion
### Chemical Shift Resolution at LF  
The chemical shift, which we have shown can be measured at LF, is one of the primary metrics that delineate different electronic environments in a sample. Therefore, in the event of a mixture of \({}^{19}\)F containing compounds, the chemical shift could be used to differentiate between different molecules. The resolution (_Res_) of two peaks, assuming Gaussian line shapes, is defined as  
\[Res=1.18\frac{v_{1}-v_{2}}{\textit{FWHM}_{1}+\textit{FWHM}_{2}} \tag{13}\]  
where \(\
u\) and FWHM are the measured frequency and the full width at half maximum of the peak, respectively. With _Res_ = 0.2, two peaks, while overlapping, are still distinguishable. Assuming a conservative FWHM of 0.15, as demonstrated here for the fluorobenzene compounds, we could measure a frequency difference of 0.0508 Hz, which corresponds to a \({}^{19}\)F chemical shift of 24.56 ppm. Previous LF studies have reported linewidths as narrow as 0.05 Hz for \({}^{13}\)C-labeled methanol, with a Larmor frequency of \(\approx\)2060 Hz corresponding to \(\approx\)8 ppm at 20% resolution [15]. If peaks are fully resolved, (_Res_ = 1), a chemical shift difference of 41 ppm is discernable. Measurable chemical shift differences theoretically break the chemical equivalence of homonuclei (and by extension their magnetic equivalence) and can create a JCS without the presence of a heteronucleus. Therefore, differentiatingvarious electronic environments _via_ the chemical shift is feasible at these magnetic fields, but will ultimately be tied to the inherent relaxation mechanisms governing the linewidths of the samples. This is an additional parameter that may be used to obtain structural information, particularly for those nuclei that have higher Larmor frequencies and high atomic numbers, such as Th, Xe, Pb, and Pt.
## 3 Proposed Methodology
### Terminology and Introduction of the Improvement  
Building on existing studies, we developed a multicriteria stability mechanism called Multicriteria Weighted MPR (MCWMPR) for optimal MPR selection within the OLSR protocol. MCWMPR evaluates the mobility patterns, energy levels, and trustworthiness between nodes. Nodes with similar mobility, high energy, and mutual trust are more likely to maintain stable connections and stay cohesive over time. Table 2 defines the key terms associated with this improvement.
The tests for PD diagnosis are started with low voltages and then the voltage is gradually increased until the inception of discharge across the emulated PD source. The responses of the transformer before and after the inception of the PD are observed and analyzed in order to ascertain the usefulness of IFRA in diagnosing the potential sources of PD across the transformer insulation. The input and response waveforms for all these cases are observed while using a 2.5 G. Sa/second, 200 MHz Digital Storage Oscilloscope (DSO). The conventional FRA approaches were concentrated on the frequency response up to 10 MHz only [4; 6; 12; 13; 14]. Hence, the present work focuses on the frequency components up to 10 MHz only. The input and response waveforms are observed after fixing the DSO settings appropriately (time scale of 500 us, 25 M\(\cdot\)sa, 125 k points record length), so that the Fast Fourier Transform data (FFT) could be available directly from the DSO up to 12.5 MHz. The End to End Voltage Transfer Function (EEV TF) approach is used in the experimentation, which is convenient for both the delta and star connected windings of any practical transformer. Three Statistical parameters 'Comparative Standard Deviation (CSD), Min-Max ratio (MM), and Absolute Difference (DABS)' are used for further analysis and interpretation of the frequency response data. The analysis of the results confirms that the IFRA with the switching impulse response approach at moderately-high voltages can be useful in the preliminary diagnosis of the potential sources of PDs. The work becomes significant, as it offers insight into the technical aspects of extending the use of IFRA from inter-turn short diagnosis up to the preliminary diagnosis of the sources of PDs. A step by step procedure that can be followed for extending the IFRA based diagnostic process up to the PD diagnosis is also proposed in the discussion section.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?  
Some significant solutions to public blockchains scalability are discussed in this section based on the literature reviewed. Scalability challenges are also mentioned in this discussion. At macro level, researchers have attempted to address this issue in two ways: on-chain or off-chain. On-chain solutions tend to address scalability issues by working on elements within the blockchain, whereas off-chain prefers to process transactions outside of the chain. Along with these classifications, there are several application-based solutions designed to achieve the required scalability. For example, DAG [119] and NormaChain [86] for IoT, BlockTrail [40] for auditing, e-commerce EBCM [86; 109] and supply chain [55].