esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources and Processing  
#### 2.2.1 GPP Datasets  
Gross primary production (GPP) data for Northern China from 2001 to 2020 were obtained from the MODIS MODIS MOD17A2 HV006 product, which is generated using a photosynthetic efficiency model and provides 8-day composite GPP data at a 500 m spatial resolution. To ensure consistency in spatial resolution across different data sources, the dataset was annualized by summing and rescaled to a 1 km resolution [26]. Additionally, GPP measurements from four flux towers in Northern China were sourced from the China Flux Observation and Research Network (ChinaFLUX) via the National Ecosystem Science Data Center [27]. The specific information of the site is shown in Table 1. Carbon fluxes were measured using the eddy covariance (EC) system (LI-7500, LI-COR, Lincoln, NE, USA; and CSAT3, Campbell Scientific Inc., Logan, UT, USA) and processed following the standard procedures of ChinaFLUX. The EC technique is widely recognized as a standard method for observing carbon flux [28]. Detailed descriptions of the flux sites are available in [PERSON] et al. (2006) [27]. Our assessment indicates a high level of agreement between the MODIS GPP product and the GPP measurements from flux towers in Northern China (Figure 2), with a strong correlation (R\({}^{2}\) > 0.8) and good RMSE confirming the reliability of MODIS GPP data for this analysis.
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 2 Fuzzy-Based Stanley Model
### Stanley Path Tracking Algorithm  
The schematic diagram of SM is given in Figure 2, where the control input of the expected front wheel angle of the SM consists of two parts, i.e., corresponding to the lateral deviation and the heading deviation, respectively:  
\[\delta(t)=\delta_{e}(t)+\delta_{\theta}(t) \tag{2}\]  
where \(\delta(t)\) is the expected angle; \(\delta_{e}(t)\) is the expected angle due to lateral deviation; and \(\delta_{\theta}(t)\) is the expected angle due to heading deviation.  
By only considering the influence of lateral deviation, the larger the lateral deviation, the larger the expected front wheel steering angle. Suppose the expected path and the tangent line intersects at a point, that in front of the closest point with respect to front wheel by \(d\), and according to the geometric relationship the following nonlinear scaling function can be obtained  
\[\delta_{e}(t)=arctan\frac{e(t)}{d(t)}=arctan\frac{ke(t)}{v(t)} \tag{3}\]  
where \(k\) is the gain coefficient; \(e(t)\) is the lateral deviation; and \(v(t)\) is the driving speed.  
If only the influence of heading deviation is taken into consideration, the front wheel deflection angle is consistent with the tangent direction of the given path. The expected steering angle of the front wheel is equal to the angle between the vehicle heading and the tangent direction of the nearest path point, that is  
\[\delta_{\theta}(t)=\theta_{e}(t) \tag{4}\]  
where \(\theta_{e}(t)\) is the heading deviation.  
By taking both the two aspects into consideration, the expected rotation angle function of the front wheel is obtained as follows:  
\[\delta(t)=\theta_{e}(t)+arctan\frac{ke(t)}{v(t)} \tag{5}\]  
Figure 2: Schematic diagram of Stanley model.
## 4 Array Processing
### Directivity Maximization Applied to ULA and NULA  
In Section 3.2, a comparative study of the ULAs and NULAs composed of five and eleven commercial antennas was carried out. Conventional uniform weighting was applied to analyze SLL, GLL and directivity in order to prove the benefits of designing NULAs. In this section, a parallel study is presentedapplying the maximum directivity weights defined in Equation (11). ULAs' maximum directivity weights are close to the conventional uniform weights, but in the case of NULAs, they are different. Tables 4 and 5 summarize the radiation pattern parameters for the two considered ULAs (with inter-element distances equal to 210 mm and 270 mm) and the NULA designed in Section 3.2 with beamformer weights calculated for maximizing the directivity, for \(N=5\) and \(N=11\) elements, respectively. The values obtained for the ULAs are very similar to those presented in Tables 2 and 3, as expected. Even though for \(N=5\), SLL and directivity of the ULA with \(d=270\) mm and the NULA are very similar, for \(N=11\), the SLL provided by the NULA is significantly better, maintaining a similar directivity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pointing Angle** & **Array** & **BW** & **SLL (dB)** & **GLL (dB)** & **D (dBi)** \\ \hline \multirow{3}{*}{\(-30^{\circ}\)} & ULA (\(d=210\) mm) & \(21^{\circ}\) & 9.62 & x & 12.73 \\ \cline{2-6}  & ULA (\(d=270\) mm) & \(16^{\circ}\) & 8.53 & 10.6 & 13.21 \\ \cline{2-6}  & NULA & \(19^{\circ}\) & 9.72 & x & 13.36 \\ \hline \multirow{3}{*}{\(-15^{\circ}\)} & ULA (\(d=210\) mm) & \(19^{\circ}\) & 11.95 & x & 12.86 \\ \cline{2-6}  & ULA (\(d=270\) mm) & \(15^{\circ}\) & 11.23 & x & 13.78 \\ \cline{2-6}  & NULA & \(18^{\circ}\) & 11.76 & x & 13.59 \\ \hline \multirow{3}{*}{\(0^{\circ}\)} & ULA (\(d=210\) mm) & \(18^{\circ}\) & 15.11 & x & 12.93 \\ \cline{2-6}  & ULA (\(d=270\) mm) & \(14^{\circ}\) & 15.36 & x & 13.87 \\ \cline{2-6}  & NULA & \(17^{\circ}\) & 14.73 & x & 13.71 \\ \hline \multirow{3}{*}{\(15^{\circ}\)} & ULA (\(d=210\) mm) & \(19^{\circ}\) & 12.12 & x & 12.88 \\ \cline{2-6}  & ULA (\(d=270\) mm) & \(15^{\circ}\) & 11.37 & x & 13.80 \\ \cline{2-6}  & NULA & \(18^{\circ}\) & 11.92 & x & 13.61 \\ \hline \multirow{3}{*}{\(30^{\circ}\)} & ULA (\(d=210\) mm) & \(21^{\circ}\) & 9.81 & x & 12.72 \\ \cline{2-6}  & ULA (\(d=270\) mm) & \(16^{\circ}\) & 8.68 & 11.24 & 13.21 \\ \cline{2-6}  & NULA & \(19^{\circ}\) & 9.71 & x & 13.36 \\ \hline \end{tabular}
\end{table}
Table 4: Main characteristics of the azimuth plane radiation patterns generated with maximum directivity weighting vectors of the ULAs with inter-element spacings of 210 mm and 270 mm and the NULA designed in Section 3.2, for \(N=5\) commercial antennas. The considered steering angles are \(\phi=[-30,-15,0,15,30]^{\circ}\).
## 5 Discussion
### Comparison of LEACH, I-LEACH, and MOD-LEACH  
For a network with 100 nodes and BS located centrally in a 100 \(\times\) 100 m\({}^{2}\) field, the I-LEACH protocol gives a better result than the LEACH protocol, with a 67% rise in throughput and network lifetime extended to 1750 rounds. The analysis conducted in Figure 11 shows that I-LEACH is a more scalable protocol than MOD-LEACH and LEACH.
## 3 System Model  
In this paper, the OFDMA-based CR system is considered. Each CR user of the system is able to receive data from multiple CCs, sense the PUs signal of the CCs, estimate the channel quality on all CCs and feed back the CQI to the transmitter. For each CC, its time and frequency resource is divided into many RBs.
# Strength Evaluation and Modification of a 3D Printed Anterior Ankle Foot Orthoses
## 3 Results
### FE Analysis  
The 3D printing product usually fractures in the direction of tensile force, so the study mainly took account of the maximum principal stress in the FE analysis. The stress of the 3 DP-AAFO was reduced following increased thickness on the neck, as listed in Table 1. When the thickness of the 3 DP-AAFO reached 4.7 mm, the displacement and stress reduced about 22% and 26%, respectively, compared to the original 3 DP-AAFO model (3.2 mm). The FE results showed stress concentrated on the neck of the 3 DP-AAFO, especially on the medial side (Figure 7), which showed that the region of higher stress was reduced after increasing its thickness.  
Figure 5: Comparison of stiffness in the TAAFO and 3 DP-AAFOs.  
Figure 6: Different patterns of the fractured 3 DP-AAFOs. Circle indicates the locations of the fracture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Plantarflexion**} & \multicolumn{2}{c}{**Dorsiflexion**} \\ \cline{3-6}  & & **Displacement (mm)** & **Stress** & **Displacement (mm)** & **Stress** \\ \hline
1 & 3.2 mm (0) & 29.2 & 20 & 29.5 & 32.7 \\
2 & 3.7 mm (0.5) & 26.6 & 18.6 & 28.2 & 28.2 \\
3 & 4.2 mm (1) & 25.4 & 15.9 & 25.6 & 28.1 \\
4 & 4.7 mm (1.5) & 22.4 & 14.6 & 22.9 & 24 \\
5 & 5.2 mm (2) & 22 & 14.6 & 23.2 & 19.8 \\ \hline \end{tabular}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{2}{c}{Unit of stress: MPa.} \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the maximum principal stress and the maximum displacement in different thicknesses of the 3 DP-AAFOs at the neck.  
Figure 7: Stress distribution of the 3 DP-AAFOs with different thicknesses around the neck in plantarflexion (**A**) 3.2 mm, (**B**) 3.7 mm, (**C**) 4.2 mm, (**D**) 4.7 mm, and (**E**) 5.2 mm. Note: Arrows indicate the location of the maximum stress.
## 5 Experimental Results
It can be seen that the reduction of imbalance continued even when the PV power was absent. In order to facilitate a better observation of  
Figure 12: Bus voltage control under availability/unavailability of PV power. the imbalance reduction process, the difference in SoC between PM1-PM2 and PM1-PM3 is shown in Figure 13, where a continuous decrease of the imbalance is obvious (20% to 18% and 10% to 9% respectively). The last test is shown in Figure 14, where the operation of the MMPC for a single module failure was investigated. At t = 12 s, a failure had been imposed to the MMPC by disconnecting the PV-source and the battery of M3. As can be seen, the collapse of M3 voltage caused the MMPC to immediately respond by increasing the voltage contribution of M1 and M2 to the maximum level to compensate for the loss of the M3 and to attempt to maintain the bus voltage at the nominal value. However, as the maximum voltage contribution of the power modules is limited to the battery voltage (36 V), the maximum reachable bus voltage was less than 72 V, which is less than the specified value (75 V). This means that if the system had consisted of a sufficient number of modules, or if the battery module rated voltage were slightly higher, it would have been possible to completely compensate for the loss of one module while maintaining the bus voltage at its specified value. Increased loading also had been applied (19 s < t < 23 s) to verify the effect of this failure at different loads. Figure 13: Reducing state of charge imbalance of the battery modules under various conditions (PVs power imbalances, load power surges, no sunlight).
## 6 Discussion and Future Directions
### Future Directions
#### 6.5.1 Incentive Mechanism Exploration  
Future research should investigate different combination of ML algorithms, and BC incentive mechanisms can be explored to achieve optimal efficiency in terms of energy cost and computational complexity. In AV applications, reinforcement learning can be integrated with heuristic search methods to enhance the routes planning for self-driving cars in the IoV system for supply-chain applications. Furthermore, a more efficient IoV system can be developed by exploring BC-enabled data collection set-ups, and such secure and reliable data can be utilized to train ML models in autonomous vehicles. This approach could greatly improve overall system performance.
## 4 Discussion  
### The Different Contributions of PFTs and Seasonal to CUE Extremes  
This study found that middle-high latitudes have higher CUE than low latitudes. This is consistent considering that in high latitude regions, the growing season length is shorter, and vegetations have lower respiratory and higher CUE. Conversely, in low latitude regions where the growing season is more extended, vegetation requires more energy to maintain living tissues, resulting in higher Ra and lower CUE [8].  
The study also indicated that grasslands contributed most to CUE extreme events in global terrestrial ecosystems, contributing approximately 32.4% to positive and 30.2% to negative extremes. This is because grasslands are more sensitive to climate change and have higher recovery potential than forest ecosystems, which could alter carbon storage frequently [27; 28]. On the other hand, all forest types only contributed 17.4% to positive CUE extremes and 22.5% to negative CUE extremes. This is mainly because forest ecosystems are less sensitive to global climate change and relatively stable in the proficiency of carbon transformation than other PFTs [29].  
The results indicated that MAM (spring in the Northern Hemisphere) contributed the largest to positive extremes of CUE, with a contribution of 30.5%. That is because in MAM, vegetation began to grow and growth, and more carbon fractions could transform to plant tissue to obtain more photosynthesis; thus, intelligent plants could increase their proficiency in converting energy into terrestrial biomass [27]. Additionally, JJA contributed most to negative CUE extremes, with the contribution being 29.7%. JJA is the summer in the Northern Hemisphere, and warming positively increased vegetation productivity in summer in northern and high-latitude regions and negatively increased vegetation productivity in summer in lower latitudes [30]. Thus, global warming, especially in JJA, is unfavorable to promoting global carbon absorption.
## 2 Data and Methodology
### Methodological Framework  
In this detailed study, a multifaceted approach was utilized, drawing from a variety of data sources and employing sophisticated analytical methods to explore Net Ecosystem Productivity (NEP) and its spatiotemporal variations within the targeted region. The methodology, depicted in Figure 2, unfolds through several critical phases:  
(i) Data Acquisition and Preparation: The study commenced with the collection of an extensive MODIS NPP dataset covering a 20-year span, essential for evaluating vegetation's net primary productivity. This dataset, including annual NPP values from 2000 to 2020, served as the foundation of the analysis. Additionally, relevant meteorological and geographical information was collected to enrich the study. A thorough data-cleaning process was undertaken to address any missing values and anomalies, followed by meticulous data formatting and geospatial alignment to ensure consistency and precision across datasets.  
(ii) NEP Calculations and Simulations: With the datasets ready, the research moved to the complex tasks of calculating and simulating NEP. This stage focused on leveraging the prepared data to develop a comprehensive understanding of the net ecosystem productivity across the study area.  
(iii) Analysis and Evaluation: This stage involved a broad spectrum of activities, such as multi-temporal NEP mapping, spatial trend analysis, and examining the connections between NEP variations and factors like temperature, precipitation, and land-use/cover change (LUCC). These efforts enabled researchers to deeply comprehend the dynamics of ecosystem productivity over time and its responsiveness to climatic shifts and human interventions. Furthermore, the insights from these analyses offer a scientific framework for predicting NEP's future trajectory, thus supporting decision-makers and managers in crafting more impactful environmental conservation and sustainability strategies.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data** & **Source** & **Access Date** & **Period** \\ \hline Net Primary Productivity & [[https://eartheengine.google.com](https://eartheengine.google.com)]([https://eartheengine.google.com](https://eartheengine.google.com)) & 24 January 2023 & 2000–2020 \\ \hline Temperature & Reference [27] & 5 April 2022 & 2000–2020 \\ \hline Precipitation & Reference [28] & 8 May 2022 & 2000–2020 \\ \hline Geospatial Vector Data & [[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)) & 4 November 2022 & - \\ \hline Land Cover Classification & [[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)) & 5 November 2022 & 2000–2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main data sources used in this study.  
Figure 2: Illustrate the methodology utilized in this research with a diagram.
## 3 Results
### Pain and Pressure Pain Thresholds  
The pain intensity measured by VAS did not change immediately after the intervention (\(p=0.177\)). Nevertheless, PPTs of masseter, temporalis, upper trapezius, suboccipital, splenius capitis (left side) muscles, TMJ, and then eminence were significantly reduced immediately after the intervention (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Baseline** & \
\begin{tabular}{c} **Post-Endodontic** \\ **Intervention** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Effect Size** \\ **(Cohen’s d)** \\ \end{tabular} \\ \hline Mouth opening (mm) & 46.28 \(\pm\) 6.17 & 41.90 \(\pm\) 6.21 & \textless{}0.001 * & 0.9021 \\ \hline Flexion (\({}^{\circ}\)) & 57.25 \(\pm\) 9.12 & 51.85 \(\pm\) 10.60 & \textless{}0.001 * & 0.7674 \\ \hline Extension (\({}^{\circ}\)) & 51.18 \(\pm\) 10.72 & 47.97 \(\pm\) 9.35 & 0.009 * & 0.5206 \\ \hline Right side-bending (\({}^{\circ}\)) & 35.68 \(\pm\) 10.12 & 32.39 \(\pm\) 8.94 & 0.006 * & 0.5512 \\ \hline Left side-bending (\({}^{\circ}\)) & 36.40 \(\pm\) 9.50 & 35.5 (8–47) & 0.005 * & 0.6005 \\ \hline Right rotation (\({}^{\circ}\)) & 68.61 \(\pm\) 13.04 & 67.27 \(\pm\) 11.86 & 0.415 & 0.1536 \\ \hline Left rotation (\({}^{\circ}\)) & 71.87 \(\pm\) 11.54 & 66.46 \(\pm\) 12.27 & \textless{}0.001 * & 0.8767 \\ \hline Upper flexion (\({}^{\circ}\)) & 12.28 \(\pm\) 3.73 & 11 (6–29) & 0.210 & 0.2109 \\ \hline Upper extension (\({}^{\circ}\)) & 18.25 \(\pm\) 6.50 & 18.97 \(\pm\) 8.05 & 0.421 & \textless{}0.1515 \\ \hline \end{tabular}
* = Statistically significant (\(p\) value \(<0.05\)).  
\end{table} 
Table 1: Range of motion of the TMJ and cervical spine at baseline and after the intervention. Values are expressed as mean \(\pm\) standard deviation or mean (min–max).
## 3 Results and Discussion
### Effect of H\({}_{2}\)/CO\({}_{x}\) Molar Ratio in the Feed  
The influence of H\({}_{2}\)/CO\({}_{x}\) molar ratio over the reaction indices has been studied, maintaining constant the remaining operating conditions in the following values: Feed, ternary mixtures of H\({}_{2}\) + CO + CO\({}_{2}\) with CO\({}_{2}\)/CO molar ratios between 1/3 and 1; temperature, 275 \({}^{\circ}\)C; pressure, 30 bar; space time, 10 g\({}_{\rm cat}\) h (mol\({}_{\rm C}\))\({}^{-1}\). The main target is to assess the possible improvement in the reaction indices with a higher H\({}_{2}\)/CO\({}_{x}\) molar ratio than that used in the preceding sections (3).  
The results of CO\({}_{x}\) conversion, DME yield and selectivity in Figure 5a (corresponding to feeds with a CO\({}_{2}\)/CO molar ratio of 1, taken as an example) point out that the improvement in these reaction indices when increasing H\({}_{2}\) concentration in the feed from H\({}_{2}\)/CO\({}_{x}\) = 3 to 4 is negligible. The same behavior was observed for the rest of the feeds studied (CO\({}_{2}\)/CO molar ratios of 1/3 and 2/3). However, the increase of H\({}_{2}\)/CO\({}_{x}\) ratio above 3 is effective for increasing CO\({}_{2}\) conversion (Figure 5b), since the transformation of CO\({}_{2}\) to CO through the reverse WGS reaction (reverse Equation (3)), is favored.  
Figure 4: Effect of space time on the conversion of CO\({}_{x}\); (**a**) Product yield; (**b**) CO\({}_{2}\) conversion (or CO\({}_{2}\) yield, for negative values); (**c**) Reaction conditions: Feed, H\({}_{2}\) + CO + CO\({}_{2}\) with H\({}_{2}\)/CO\({}_{x}\) molar ratio of 3, and CO\({}_{2}\)/CO molar ratio of 2/3; 275 \({}^{\circ}\)C; 30 bar.  
Figure 5: _Cont._
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 6 Fault Detection System
### Fault and Normal Classification with One-Class SVM and ChangeFinder  
In this section, we show how to perform anomaly detection using the discriminant model constructed in Section 6.1. Fault detection is achieved in real time by performing fault detection for each control step. The process for each control step is shown below.  
1. The temperature is measured by sensors.
2. Control inputs and change-point scores are calculated from temperature measurements.
3. Similar to Equation (43), a variable \(X\) is created for anomaly determination, to be input to OCSVM.
4. If classified as faulty in 3, the fault tolerance control system is activated.  
Figure 10: Hyperparameter optimization process using the real-coded genetic algorithm.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p  
**Definition 1**.: _LS Estimation: The commonly used channel estimation scheme for the IEEE 802.11p is LS estimation [1]. It combines the two received LTSs, which are represented by \(R_{T_{1}}(k)\) and \(R_{T_{2}}(k)\). Let X(k) represents the transmitted LTS. The channel response can be derived by Equation (1)._  
\[H(k)=\frac{R_{T_{1}}(k)+R_{T_{2}}(k)}{2X(k)} \tag{1}\]  
This channel response \(H(k)\) can be used for the equalization of the subsequent symbols, with the assumption that the channel will remain constant during the symbol duration. However, the channel response for the IEEE 802.11p does not remain constant for the symbol duration, and LS estimation cannot provide good results in V2V channels.
## 3 Simulation and Analyses
### Micro-Crack Signal Assessment under the Effect of Front-Surface \(R_{a}\)  
To further validate the effect of reflected and transmitted attenuations caused by rough surface on micro-crack ultrasonic detection, micro-cracks 100 \(\upmu\)m to 400 \(\upmu\)m deep were first selected for ultrasonic simulation. For better contrast, the front surface was rough while the back surface was smooth. The horizontal direction parameter \(R_{sm}\) of the rough surface was set to 300 \(\upmu\)m, width of the crack was 250 \(\upmu\)m, and \(R_{a}\) changed from 0 to 25 \(\upmu\)m. For the surface with determined \(R_{a}\) and \(R_{sm}\), multiple simulations were performed and amplitudes of micro-cracks were averaged. Figure 7a shows that the mean amplitudes of crack signals decrease with an increase in \(R_{a}\) for micro-cracks with different depths. It can be seen that the mean amplitudes increase obviously when the crack depth changes from 100 \(\upmu\)m to 200 \(\upmu\)m. The trend remains almost unchanged as the depth increases from 200 \(\upmu\)m to 400 \(\upmu\)m. Meanwhile, the mean amplitudes of crack signals tend to be the same when \(R_{a}\) is larger than 15 \(\upmu\)m.  
The SARF critical solid line that represents the critical value 3.16 is displayed in Figure 7b. It can be seen that the signal-to-noise ratio of the crack increases with depth and SARF gets smaller as \(R_{a}\) increases. The significant decrease of SARF from \(R_{a}\) 12.5 \(\upmu\)m is mainly caused by two aspects. On one hand, the amplitude of the echo signal drops significantly for cracks with different depths, as shown in Figure 7a. On the other hand, the mean amplitude of noise signal is nearly doubled compared to that of \(R_{a}\) 6.3 \(\upmu\)m. The SARF tends to be less than the critical value when \(R_{a}\) is larger than 15 \(\upmu\)m, which means that it is difficult to distinguish useful crack signals. Additionally, the smaller depth of micro-crack corresponds to a smaller SARF critical value because the depth of the micro-crack is close to the order of magnitude of the height of the rough surface and the scattering attenuation becomes more serious, as described in the previous section.  
Figure 6: Ultrasonic time-domain signal when front-surface roughness \(\mathbf{R_{a}}\) is (a) 3.2 \(\upmu\)m, (b) 12.5 \(\upmu\)m, and (c) 25 \(\upmu\)m and there is a micro-crack on the smooth back surface. Reference signal in (d) while front-surface roughness \(R_{a}\) is 25 \(\upmu\)m and there is no micro-crack on the smooth back surface.