esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed an improved method for fault severity recognition. This paper, for the first time, proposed a noise reduction method based on ACLHC for processing bearing vibration signals. The main finding was that, compared with the frequency band selection methods, the ACLHC did not need to select the frequency band of the signal in the process of reducing the influence of noise and highlighting the impact components of signal. Therefore, the signals processed by ACLHC could not only reduce the influence of noise, but also contained all the fault information. Then, the modulation information of the processed vibration signal, which is an essential fault characteristic of bearing, was reflected by SAX. Finally, the complexity of the symbol sequence coded by SAX was calculated by the LZ indicator. Through the verification of the single-point fault dataset of KAt and that of DIRG, it was proven that the ACLHC can reduce the influence of noise and highlight the impact components of signal without losing the fault information. Also, the signals processed by ACLHC can effectively improve the recognition results of bearing fault severity. In conclusion, the proposed method can effectively improve the recognition results of fault severity by reducing noise while retaining all frequency band information, and can be used in different working conditions.  
Although some important problems associated with the proposed method have been investigated in this paper, there are still a few questions that are worthy of further consideration, such as the application scope of ACLHC.  
Figure 9: The complexity values of Dynamic and Identification Research Group datasets after different treatment. (**a**) 100 Hz; (**b**) 200 Hz; (**c**) 300 Hz; and (**d**) 400 Hz.  
**Author Contributions:** Conceptualization, J.Y.; Formal analysis, J.Y.; Investigation, J.Y., H.Z. and Y.Y.; Methodology, J.Y. and M.L.; Project administration, M.X.; Software, J.Y. and Y.Y.; Supervision, M.X.; Validation, H.Z.; Writing--original draft, J.Y.; Writing--review and editing, J.Y., H.Z., Y.L. and M.X.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 51805434 and 61203151.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Material Characterization
#### 2.3.2 Elemental Composition  
Both the raw and activated hydrochar samples were analyzed by X-ray fluorescence (XRF) spectroscopy in order to assess their elemental compositions using a SPECTRO-XEPOS XRF spectrometer (SPECTRO, Kleve, Germany). The instrument was equipped with a 10 mm\({}^{2}\) Si-Drift Detector with Peltier cooling and a spectral resolution (FWHM) at Mn Ka \(\leq\)155 eV for determination in the element range of Na-U (SPECTRO, 2014). The hydrochar samples were powdered (grain size \(<\)100 \(\upmu\)m) using a milling instrument, MM 400 (Retsch, Haan, Germany), for 5 min at a frequency of 25 s\({}^{-1}\). Then, sample pellets with a 32 mm diameter were prepared by mixing of 4 g hydrochloric powder with 0.9 g Licowax C micro powder PM (Clariant, Muttenz, Switzerland) and successive pressing under 15 tonnes pressure. The analysis was performed using the TurboQuant-screening method. Each side of the pellet was subjected to one exposure to X-ray radiation, and the mean results of both sides (two analyses) were recorded.  
Also, the carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) contents were measured based on the dry combustion method using a TruSpec analyzer (LECO, St. Joseph, MI, USA). The samples for the C, H, N, and O analysis were thoroughly dried at 105 \({}^{\circ}\)C and powdered using the mixer mill for 5 min at a frequency of 25 s\({}^{-1}\). Then, 100 mg of each sample was incinerated at 950 \({}^{\circ}\)C and recorded by a TruSpec CHN Macro Analyzer. Furthermore, the O content was assessed by combusting 3 mg of sample material at 1300 \({}^{\circ}\)C using the additional high-temperature pyrolysis furnace TruSpec Micro Oxygen Module. All of the samples were analyzed in duplicate, and the mean results were recorded.
## 4 Discussion
### Improvements in the Selection Process  
Research indicates that the heritability estimates for growth traits are generally high, allowing for genetic improvement through direct phenotypic selection [46]. After routine breeding in the studied population, the uniformity of growth traits improved. Despite fluctuations in the EBVs and GEBVs for BW over four consecutive generations, the overall trend was a decline. To optimize the growth performance and structure of the population, managers implemented early phenotypic selection to cull individuals with extreme body weights or disproportionate body and neck lengths, thereby enhancing the uniformity of the breeding population. This strategy not only improves population consistency but also ensures that breeding goals align better with market demands and production realities.  
In terms of reproductive traits, particularly from the 13 th to 15 th generations, the EBVs and GEBVs for AFE decreased across generations, while those for EN25-44 WK increased. Specifically, each generation's AFE was on average one day earlier than the previous generation, and EN25-44 WK increased by one egg. This study separately evaluated the genetic gains for AFE and EN25-44 WK over multiple generations in different sexes and overall populations, using both pedigree and SNP information. The results showed that the selection progress for AFE and EN25-44 WK in males was consistent with that in females from the 13 th to 15 th generations.  
By integrating pedigree and SNP data, the genetic parameters can be estimated more accurately, thereby optimizing selection strategies for reproductive traits. Research has demonstrated that SNP-based selection can significantly enhance the accuracy and efficiency of genetic gain, particularly in large populations and long-term breeding programs [47, 48]. Additionally, this method helps to better understand the role of genetic variation in reproductive traits and provides reliable data support for future breeding plans.
## 3 Related Work and Methodology
### Categorization of Concentrated Activity Areas
#### 3.4.2 Kernel Density Estimation (\(K\))  
Kernel Density Estimation (\(K\)) is a statistical technique used to estimate the probability density function of a continuous random variable from a set of data points [47]. It is particularly valuable for visualizing data distribution in a non-parametric way [48]. The formula for Kernel Density Estimation is as follows [49]:  
\[\hat{f}(x)=\frac{1}{n}\sum_{i=1}^{n}K\left(\frac{x-x_{i}}{h}\right) \tag{10}\]  
where \(\hat{f}(x)\) represents the estimated probability density function at point \(x\). \(n\) is the total number of data points. \(x_{i}\) represents each data point. \(K(u)\) is the kernel function, which is a symmetric probability density function (such as the Gaussian or Epanechnikov kernel). \(h\) is the bandwidth parameter, which determines the width of the kernel and affects the smoothness of the estimated density. In this equation, the kernel function is centered at each data point \(x_{i}\) and scaled by the bandwidth \(h\). The kernel functions are summed up and normalized by \(n\) to create a smooth estimate of the underlying probability density function.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Link Quality Prediction Model Development
#### 2.3.3 Accuracy Test of Link Quality Prediction Model  
To evaluate the accuracy of the link quality prediction model, the coefficient of determination (R2) was calculated to assess the explanatory power of the independent variable of inter-node distance on the dependent variable of path loss. Nonlinear curve fitting was  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Number** & **26** \\ \hline Transmit power & 0 dB \\ \hline Frequency & 2.4 GHz \\ \hline MAC protocol & IEEE 802.15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment main hardware parameter settings.  
performed using OriginPro software (version 2022, OriginLab Corporation, Northampton, MA, USA). The expression for R\({}^{2}\) was calculated as  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\Bigl{(}y_{i}-\overset{\wedge}{y_{i}}\Bigr{)}^{2}}{ \sum_{i=1}^{n}(y_{i}-\overline{y})^{2}} \tag{3}\]  
where \(y_{i}\) is the actual measurement of the path loss of the dependent variable, \(\overset{\wedge}{y_{i}}\) is the predicted value of the model, \(\overline{y}\) is the mean value of the path loss of the dependent variable, and n is the sample size.
# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
## 3 Proposed FIS  
### Matching Table Approach  
In order to investigate the optimum solution, we first created a two-condition-based approach to leverage the possibility of \(P_{rk}\) value configurations. We call this approach a matching table condition. The first condition only required a single input variable, which was the vehicle distance to determine the final \(P_{rk}\) value on each data transmission. The second condition only required a single input variable, which was the CSI. The vehicle pairs were randomly selected on every transmission that occurs in the system. The matching table procedure can be explained as follows:  
Step 1: Every vehicle distance is stored and recorded in the matrix \(v_{all}\) with the respective vehicle ID \(v_{ID}\) pairs.  
Step 2: For every data transmission occurrence, the CSI matrix is recorded into the matrix \(v_{sense}\).  
Step 3: Use randomly selected \(v_{ID}\) pairs to extract the corresponding distance and CSI value for determining the \(P_{rk}\) value. We conducted two separate matching table scenarios, in which the first scenario only realized the distance value, and the second one realized the CSI value. The matching table based on the distance procedure can be explained by the following operations:  
\[\begin{array}{l}\textit{If distance}\,\in\,[3,\,30],\textit{ then }P_{rk}=0.8.\\ \textit{If distance}\,\in[30,\,60],\textit{ then }P_{rk}=0.7.\\ \textit{If distance}\,\in[60,\,90],\textit{ then }P_{rk}=0.6.\\ \textit{If distance}\,\in[90,\,120],\textit{ then }P_{rk}=0.5.\\ \textit{If distance}\,\in[120,\,150],\textit{ then }P_{rk}=0.4.\\ \end{array}\]  
As distance is relatively short, the \(P_{rk}\) value was set to high. This means that the vehicles will tend to choose new resources for the following data transmissions. The matching table based on the CSI procedure can be explained by the following operations:  
\[\begin{array}{l}\textit{If CSI}\in[0,\,0.25],\textit{ then }P_{rk}=0.2.\\ \textit{If CSI}\in[0.25,\,0.5],\textit{ then }P_{rk}=0.4.\\ \textit{If CSI}\in[0.5,\,0.75],\textit{ then }P_{rk}=0.6.\\ \textit{If CSI}\in[0.75,\,1],\textit{ then }P_{rk}=0.8.\\ \end{array}\]  
Similar to the distance condition, while the network condition is sensed to have a low quality, the \(P_{rk}\) value is set to low. Hence, this condition makes the vehicles keep the current Resource Block for the following data transmissions. Figures 1 and 2 show the comparison between the two matching tables with a fixed \(P_{rk}\) value for \(n_{V}\) = 200 and 400 vehicles, respectively. It can be seen that the performance of the matching table approach is still lacking from the fixed \(P_{rk}\) value. A possible explanation for this might be that the final \(P_{rk}\) was only determined by a single variable. Even though the \(P_{rk}\) value was dynamically adapting to the given conditions, the current observations indicated unsatisfactory results.
## 4 Experiments
### Viewpoint Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & **Car** & **Chair** & **Aero** & **Bike** & **Sofa** & **Boot** & **Bottle** & **Bus** & **Table** & **Train** & **Tv** & **Motor** & **Mean** \\ \hline \multirow{4}{*}{Median Error} & [PERSON] [17] & 9.1 & 14.8 & 13.8 & 17.7 & 13.7 & 21.3 & 12.9 & 5.8 & 15.2 & 8.7 & 15.4 & 14.7 & 13.6 \\  & [PERSON] [18] & 5.8 & 11.9 & 13.6 & 12.5 & 12.8 & 22.8 & 8.3 & 3.1 & 12.5 & 6.3 & 11.9 & 12.3 & 11.1 \\  & Zhou X & 6.5 & 11.0 & 10.1 & 14.5 & 11.1 & 30.0 & 9.1 & 3.1 & 23.7 & 7.4 & 13.0 & 14.1 & 10.4 \\  & Method & 6.3 & 10.8 & 10.4 & 14.6 & 10.9 & 23 & 8.9 & 3.2 & 14 & 7.1 & 12.2 & 13.9 & 10 \\  & proposed & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{Accuracy} & [PERSON] [17] & 0.89 & 0.80 & 0.81 & 0.77 & 0.82 & 0.59 & 0.93 & 0.98 & 0.62 & 0.80 & 0.80 & 0.88 & 0.806 \\  & [PERSON] [18] & 0.90 & 0.80 & 0.78 & 0.83 & 0.82 & 0.57 & 0.93 & 0.94 & 0.68 & 0.82 & 0.85 & 0.86 & 0.810 \\  & [PERSON] & 0.92 & 0.79 & 0.82 & 0.86 & 0.92 & 0.50 & 0.92 & 0.97 & 0.62 & 0.77 & 0.83 & 0.88 & 0.823 \\  & [PERSON] & 0.93 & 0.81 & 0.81 & 0.8 & 0.92 & 0.52 & 0.93 & 0.97 & 0.63 & 0.78 & 0.84 & 0.89 & 0.829 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of viewpoint estimation. Figure 10: Identification of mistaken keypoints. (**a**) and (**b**) are two image cases. Keypoints from the network are displayed as the first column, the mistaken points cannot be detected in [11]. The method proposed can identify mistaken keypoints though optimization process in the second column. Mistaken keypoints are shown as red points in the last column. It can be seen that as the number of error keypoints increases, the accuracy of [11] decreases faster than the method proposed. This is because 3D keypoints are used to estimate the viewpoint directly in [11], while our method conducts viewpoint estimation by deformable model matching, which is more robust. Finally, we test the performance of the method proposed for occluded object image cases. In practice, it is impossible for objects to be always visible. To evaluate our method, we artificially occlude the image of the car in Pascal3D+ dataset. The result is shown in Figure 12.  
Figure 11: Comparison of viewpoint estimation with error keypoints using data from Car in Pascal3D+ dataset. We artificially add error keypoints to test the method’s fault tolerance. Figure 12: Qualitative results for occluded object image cases. Three columns in the figure are input image, 2D keypoints detected and wireframe projection. The last instance is a failure case. The median error is 9.5, which is larger than cases without occlusion. It can be seen from the results that the method proposed can work with some keypoints missing caused by occlusion, although we have some failure cases for large occlusion. Because of the priori deformable model, our approach can tolerate the absence of some keypoints to a certain extent.
## 4 Results
#### 4.3.2 Correlation Between Rectifiers and Performance Metrics
Overall, the gain performance associated with each rectifier type offers useful insight into their optimal application domains. Topologies like Schottky, half-wave, and single-step rectifiers provide stable and predictable behavior ideal for communication-focused systems, while configurations such as multi-step and full-wave rectifiers, though more variable, offer flexibility for performance tuning in power-oriented designs. * Comparative insights Analysis of rectifier topologies reveals a spectrum of trade-offs in rectenna design, directly impacting antenna area, conversion efficiency, operating frequency, and gain. Microwave rectifiers are characterized by requiring the smallest and most consistent antenna area (\(\approx\)2500 mm\({}^{2}\)), while offering low but predictable efficiency and gain (\(\approx\)50% and  
Figure 23: Boxplot showing gain distribution of different rectifier designs for energy harvesting. \(\approx\)2 dBi, respectively). They also operate stably at moderately high frequencies (\(\approx\)2.5 GHz), making them ideal for compact systems where spatial economy is paramount. In contrast, half-wave and single-step rectifiers require the largest antenna areas (mean \(\approx\)6000 mm\({}^{2}\) and \(\approx\)4000 mm\({}^{2}\), respectively), while offering consistent gain (\(\approx\)5 dBi) and moderate efficiency. Notably, half-wave rectifiers exhibit high frequency stability (\(\approx\)2.5 GHz). Schottky and multi-step rectifiers feature moderate antenna areas (\(\approx\)2500 mm\({}^{2}\) and \(\approx\)2000 mm\({}^{2}\)), average efficiency (\(\approx\)60%), and stable gain (Schottky: \(\approx\)5 dBi; multi-step: \(\approx\)4 dBi), operating predictably at low to moderate frequencies (\(\approx\)2.4-2.5 GHz). Full-wave rectifiers, in turn, are characterized by a compact area (\(\approx\)1000 mm\({}^{2}\)), but show lower and more variable efficiency and gain (\(\approx\)50% and \(\approx\)3 dBi). Meanwhile, systems with undefined rectifier topologies exhibit the highest average gain (\(\approx\)6 dBi) and the greatest variability across all metrics, particularly in operating frequency, rendering them less suitable for applications requiring strict spectral control.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Domain-Decomposition Strategy for Overlapping Component Grids  
The parallel assembly algorithm for overlapping grids is closely related to the domain decomposition strategy of multiple component grids. Suppose \(np\) processes are utilized to perform the flow-field computation for overlapping grids containing \(M\) components. Figure 3 shows two strategies for the domain decomposition in the case of \(M=3\) and \(np=8\).  
* Strategy \(T_{1}\) (Figure 3a). All the \(M\) grids are regarded as a whole grid, and the load measured by the cell amount is distributed across the \(np\) processes. The domains are consequently decomposed according to the traditional greedy algorithm.
* Strategy \(T_{2}\) (Figure 3b). Based on the ratio \(\tau\) of the cell amount in \(G^{i}\) (\(i\in\{0,1,\cdots,M-1\}\)) to that in the total \(M\) grids, the required amount of processes for grid \(G^{i}\) is \(np\times\tau\), and the relevant workload is assigned equally among the processes.  
[PERSON] et al. [23] adopted the \(T_{1}\) strategy to guarantee a balanced load with regard to the amount of cells. However, the \(T_{1}\) strategy may result in fragmented partitions for each component grid, which can affect the parallel computation efficiency. Considering the frequent communications between the processes relevant to different component grids, a smaller number of processes for each component grid is preferable, since it can improve the parallel communication efficiency. Therefore, we adopt the \(T_{2}\) strategy in the current study.
## 3 Results
### Surface Soil Moisture and FAPAR
For the'sm+fapar (multi)' case, the combination of SMOS soil moisture data with JRC-TIP FAPAR data resulted in a large cost function value for the parameters (\(J_{p}\) is 855.25). However, improvements in FAPAR in the'sm+fapar(multi)' case were visible at most sites. For example, at site US-Kfs (Figure 3), when soil moisture and FAPAR were simultaneously assimilated at multiple sites, the FAPAR was constrained much better than the prior model simulation in matching remotely sensed FAPAR. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Prior**} & \multirow{2}{*}{**sm**} & \multirow{2}{*}{**sm**} & \multirow{2}{*}{**fapar**} & \multirow{2}{*}{**fapar**} & \multirow{2}{*}{**sm+fapar**} & \multirow{2}{*}{**sm+fapar**} \\  & & **(Single)** & & **(Multi)** & **(Single)** & **(Multi)** & **(Single)** & **(Multi)** \\  & & & & & & & & \\  & & \(J_{ippar}\)/\(J_{p}\) & \(J_{ippar}\)/\(J_{p}\) & \(J_{ippar}\)/\(J_{p}\) & \(J_{ippar}\)/\(J_{p}\) & \(J_{ippar}\)/\(J_{p}\) & \(J_{ippar}\)/\(J_{p}\) \\ \hline AU-DaS & 0.01 & 39.14/ & 0.08/ & 8.32/ & 71.56/ & 69.26/ & 27.77/ \\  & & 101.43 & 218.50 & 5.47 & 92.26 & 103.94 & 855.25 \\ CA-Gro & 25.76 & 41.80/ & 39.71/ & 13.81/ & 28.31/ & 57.46/ & 18.61/ \\  & & 51.88 & 218.50 & 4.83 & 92.26 & 73.95/ & 855.25 \\ CN-Sw2 & 782.10 & 779.11/ & 3283.79/ & 13.28/ & 22.16/ & 293.45/ & 107.82/ \\  & & 8.32 & 218.50 & 6.52 & 92.26 & 25.65 & 855.25 \\ DK-Sor & 172.55 & / & 220.26/ & 21.88/ & 39.55/ & 36.13/ \\  & & 218.50 & 9.98 & 92.26 & / & 855.25 \\ ES-LMa & 59.12 & 412.50/ & 127.60/ & 11.09/ & 18.33/ & 33.13/ & 18.66/ \\  & & 31.25 & 218.50 & 12.61 & 92.26 & 68.65 & 855.25 \\ FI-Sod & 274.73 & / & 709.31/ & 37.04/ & 38.60/ & / & 67.75/ \\  & & 218.50 & 3.44 & 92.26 & & 855.25 \\ FR-Pue & 2.90 & 2.96/ & 2.76/ & 15.85/ & 24.23/ & 11.59/ & 57.18/ \\  & & 0.96 & 218.50 & 4.18 & 92.26 & 68.82 & 855.25 \\ NL-Loo & 17.38 & 13.90/ & 14.17/ & 13.99/ & 31.05/ & 9.10/ & 33.07/ \\  & & 24.80 & 218.50 & 4.09 & 92.26 & 671.33 & 855.25 \\ US-Kfs & 383.22 & 394.85/ & 438.06/ & 20.05/ & 71.26/ & 73.91/ & 37.10/ \\  & & 13.71 & 218.50 & 6.40 & 92.26 & 51.93 & 855.25 \\ US-Wkg & 1.86 & 2.99/ & 33.66/ & 24.79/ & 21.85/ & 25.39/ & 28.07/ \\  & & 46.55 & 218.50 & 0.23 & 92.26 & 21.13 & 855.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cost function values for FAPAR (\(J_{fapar}\)) and parameters (\(I_{p}\)) for prior and different assimilation experiments: ‘Sm’ with soil moisture only, ‘fapar’ with FAPAR only, ‘sm+fapar’ with soil moisture and FAPAR data combined, ‘multi’ denotes multi-site assimilation, and ‘single’ denotes single-site assimilation. Figure 2: Comparison of simulated surface soil moisture (0–4 cm) at site US-Wkg against in-situ soil moisture observations. The blue dot-dash line denotes the simulation with the prior parameters, the red line denotes multi-site assimilation results with both soil moisture and FAPAR used for assimilation (‘sm+fapar (multi)’), and the black dot denotes in-situ observations from the FLUXNET sites at the top layer.