esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Content Analysis
### Methods Integrated with ANN Applied in CM Research

Table 3: Comparison of BPNN, MLPNN and CNN. hybrid models to improve the performance and overcome the defects of a particular method [55; 56]. Figure 5 illustrates the distribution of methods/algorithms integrated with ANN more than twice in the reviewed studies. The results indicate that Fuzzy Logic (FL) (21) is used more often combined with ANN in the selected literature than the others, followed by Genetic Algorithms (GA) (15). Furthermore, case-based reasoning and long short-term memory have achieved ideal results in auxiliary ANN applications. Most research on the application of individual ANN models has focused on using its capabilities to deal with highly nonlinear problems. However, the accuracy of the estimation results is not very high when dealing with uncertainty and subjectivity issues. These disadvantages can be partially overcome by integrating FL, which is a tool for representing uncertain and imprecise information [57]. FL imitates the human decision-making process in a high-level way for automated systems to describe highly complex, undefined, or difficult-to-analyze topics [58]. However, FL encounters difficulties in determining parameters. ANN was introduced to resolve this problem and to infuse into the FL a capacity for self-learning. As the actual CM problems are always complex and uncertain due to the changing nature of the construction industry [59], FNN has been widely used in CM. GA is a stochastic search approach inspired by the natural evolution process, which involves crossover, mutation, and survival fitness evaluation. It is widely used to identify parameter values for ANN models to solve search and optimization problems. GA helps improve the convergence speed, and its mutation mechanism avoids confinement to locally optimal solutions [60]. In addition, the complementary combination of ANN, GA, and FL can maximize their respective merits [61]. When dealing with CM problems with complex, uncertain, and subjective characteristics such as dynamic project success prediction and subcontractor performance evaluation, FL is used to deal with uncertainty and approximate inference, while ANN is employed in fuzzy input-output mapping and GA is used for optimization.
## 3 Establishment of the Installation Error Model for the Drum-Shaped Grinding Tool  
### Analysis of Drum-Shaped Grinding Tool Installation Error  
The drum-shaped grinding tool and the internal helical gear moved according to a determined meshing relationship, allowing the whole tooth surface of the internal helical gear to be formed. In the absence of error, the groundtooth surface of the internal helical gear precisely corresponds to the theoretical tooth surface. However, in the actual grinding process, the meshing relationship between the drum-shaped grinding tool and the internal helical gear is changed due to factors such as drum-shaped grinding tool installation errors, force deformation, and thermal deformation. As a result, the actual groundtooth surface of the internal helical gear will deviate to varying degrees from its theoretical tooth surface.  
Tooth surface deviations resulting from drum-shaped grinding tool wear, force deformation, and thermal deformation are independent factors. In contrast, drum-shaped grinding tool installation errors are heavily influenced by the processing system. Therefore, analyzing the impact of the drum-shaped grinding tool installation errors on tooth surface deviations is of significant importance. The model of the drum-shaped grinding tool installation error is depicted in Figure 7.  
As shown in Figure 7, \(S_{w}(O_{w}\)-\(x_{w}y_{w}z_{w})\) represents the coordinate system of the theoretical position of the drum-shaped grinding tool, \(S^{\prime}{}_{w}(O^{\prime}{}_{w}\)-\(x^{\prime}{}_{w}y^{\prime}{}_{w}\)=\(x^{\prime}{}_{w})\) represents the coordinate system of the drum-shaped grinding tool in the presence of installation error, \(e\) is the eccentricity, \(\alpha\) is the eccentric angle, \(\varphi_{x}\) denotes the perpendicularity error angle around the \(x_{w}\) axis, and \(\varphi_{y}\) denotes the perpendicularity error angle around the \(y_{w}\) axis.  
Figure 6: Assembly model of drum-shaped grinding tool and internal helical gear: (**a**) entire view; (**b**) enlarged view.  
Figure 7: Model of the drum-shaped grinding tool installation error.
## 5 Predefined Path-Based Techniques
### Optimal Dual Paths (ODP)
#### 5.1.3 Advantages and Disadvantages  
ODP significantly reduces redundant unicast traffic compared with the standard HSR protocol by forwarding the unicast traffic through two predefined paths instead of duplicating and flooding in overall network. Additionally, the dual paths have optimal link metrics, resulting in the improvement of quality of service in the network. The main drawback of ODP is that it has to build and maintain the link information table, resulting in additional control overhead in the network.
## 3 Measurement Uncertainty Analysis of the Linear Scan Method
### Discussion  
For the precision roundness measurement of cylindrical parts, the conventional rotary scan method cannot meet the requirements. Therefore, the stitching linear scan method has been proposed. Since the repeatability of measurement is very important, large numbers of experiments are necessary [28]. For practical applications, some cylindrical parts, such as the motor cylinders [29], can be measured by the stitching linear scan method. Meanwhile, the reliability of the proposed method can be verified in practical application. Contact measurements with a stylus or probe may not be accurate due to the measuring force and deformations, therefor it is necessary to develop non-contact measurements with optical linear encoder technology [30,31]. Furthermore, the proposed method and the verification manner can be applied for the measurement or testing of cylindrical parts or round parts made of other materials, such as functionally graded plates, which play a significant role in building materials [32,33]. Measurement uncertainty is necessary for testing, inspection and precision measurement. For the verification of the reliability of the proposed method, the measurement of uncertainty is carried out in this paper. The upper limitation of the proposed method can be investigated by the analysis and applications in a variety of fields.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 2 Materials and Methods  
### Study Area  
The study area corresponds to the municipality of Goiatuba, covering an area of approximately 247,000 ha in the southern part of Goias State, Brazil (latitude: \(-\)18.00\({}^{\circ}\); longitude: \(-\)49.60\({}^{\circ}\)) (Figure 1). Goiatuba is becoming one of the most important municipalities of the Cerrado in terms of agricultural production in the south/southwest part of Goias State. According to MapBiomas [6], 45% of this municipality was covered by annual crops in 2021, followed by sugarcane (17%) and cultivated pastures (9%). Nowadays, annual crops are replacing degraded pastures while sugarcane is advancing in croplands. For example, in this municipality, areas occupied by cultivated pastures decreased from 45,000 ha in 1985 to 23,000 ha in 2021 while harvested areas of soybean increased from 42,500 ha in 1985 to 76,000 ha in 2021, respectively [30; 31].  
The climate is tropical, with hot and humid summers and dry winters--Aw in the Koppen climate classification system [32]. According to the rain gauge station located in the neighboring municipality of Itumbiara (station code: 83523; latitude: \(-\)18.41\({}^{\circ}\); longitude: \(-\)49.19\({}^{\circ}\)) [33], average annual precipitation is 1394 mm, concentrated between November (monthly average of 199 mm) and March (monthly average of 204 mm). The topography is dominantly flat (47% of the municipality, typical slope of 0-3%), with surrounding depressed landforms (41%).  
Reddish, deep, and well-developed Ferralsols, according to the World Reference Base for Soil Resources (WRS) proposed by the Food and Agriculture Organization (FAO) [1] (Latossolo Vermelho in the Brazilian System of Soil Classification or Oxisols in the U.S. Soil Taxonomy), are the dominant type of soil in this municipality [34], which is highly weathered, deep (>2 m), well-drained, and with low levels of fertility and high contents of Si and Al toxicity [35]. This soil type is representative of the Cerrado biome where the two most common soil types are Ferralsols (44%) and Arenosols (21%). Figure 2 shows the ternary diagram of 153 soil texture analyses from this municipality. The soil samples were obtained at the 0-5 cm depth during the field campaign conducted in October 2022. The average percentages of sand, silt, and clay were 33%, 23%, and 44%, respectively, which is classified as fine texture in the FAO's classification or loam in the U.S. Soil Taxonomy, that is, soils having roughly balanced proportions of sand, silt, and clay contents.  
Figure 1: Location of the study area, municipality of Goiástuba in Goiás State, Brazil (**A**) and the RGB color composite of PlanetScope of bands 4 (near-infrared), 3 (red), and 2 (green) from October 2021 over the study area (**B**). GO = Goiás State.
## 4 NMR Analysis  
### \(T_{2}\) Spectrum Distribution Analysis  
The NMR \(T_{2}\) spectrum test was carried out using the characteristics of the interaction between the H nucleus and the magnetic field. Due to the heterogeneity of rocks, they usually contain pore structures of different scales, and the distribution of pores can affect the test results. The relaxation time of the \(T_{2}\) spectrum reflects the distribution of pore diameter within the rock. The peak area of the \(T_{2}\) spectrum characterizes the volume of the rock pores. The continuity of the \(T_{2}\) spectrum signals characterizes the connectivity of the microscopic pore structure within the rock [31]. Figure 7 shows the fractured sandstone's NMR \(T_{2}\) spectrum distribution with different dip angles before and after 20 freeze-thaw cycles and after fatigue loading failure.  
As shown in Figure 7, the \(T_{2}\) spectral curves of the fractured sandstone showed mainly three and partly four peaks. Among them, the \(T_{2}\) spectral curves of fractured sandstone with different dips showed three peaks before the freeze-thaw cycle, and the signal intensities of each main peak differed significantly. The internal pore distribution of the rock samples was dominated by medium and small pores, with a relatively small percentage of tiny and large pores. After 20 freeze-thaw cycles, the \(T_{2}\) spectral curve shifted to the left as a whole, that is, towards the \(T_{2}\) spectrum curve of small pores, and the signal strength of the \(T_{2}\) spectrum of small pores increased. This indicates that the repeated freezing and thawing expansion and contraction of sandstone during the freeze-thaw cycles caused the internal pores of the rock samples to develop and generate new micropores, and the percentage of tiny pores increased. All three peaks of the \(T_{2}\) spectral curve in the figure showed variations, with the second peak floating most significantly, indicating that small pores were more susceptible to the freeze-thaw cycles compared to tiny pores and medium-large pores. The third peak had no obvious change before and after the freeze-thaw cycles, and the peak area accounted for a small proportion, that is, there was no obvious large-scale pore expansion, extension, and development within the rock samples after 20 freeze-thaw cycles, and the initial damage of the rock samples was small. This conclusion is in agreement with the findings of [PERSON] et al. [32] concerning granites from cold regions. The fourth peak appeared in the \(T_{2}\) spectral curve after the fatigue loading damage of some rock samples, the intensity signals of each main peak were strengthened, and the curve shifted to the left as a whole. This was due to the dynamic load disturbance; the rock sample internal particles between the cementation capacity were weakened, sprouting a lot of micropores and pore structure changes. Micropores continued to expand and the proportion increased, ultimately connecting pores with prefabricated cracks, forming a shear failure surface and causing the instability and failure of the rock sample.
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
## 4 Discussion
### General Pattern of Trees Growth along the Elevation Gradient  
The growth dependence of both species on the air temperature and moisture variables switches along the elevation gradient. Air temperature negatively influenced Siberian pine growth at low elevations (until c. 2000 m) and stimulated growth at higher elevations. Similarly, growth dependence on VPD switched from negative to insignificant at elevations above >2000 m. A similar switch was reported for this species in the northern part of the Kuznetsk Ala-Tau Mountains [31]. Similarly, an observed decline of fir, a precipitation-sensitive species, in US forests was mediated by moisture availability [42]. The positive influence of precipitation and soil moisture at low elevations changed to insignificant at high (>2000 m) elevations (Figure 4).  
An analogous relationship was observed between larch growth and climate variables along the elevation gradient (Figure 5). Within low elevations, larch growth was negatively influenced by VPD, although less so in comparison with Siberian pine. A water stress influence on larch growth was also found in the foothills (within the transition to forest-steppe) in the West Sayan Mountains to the north [22]. Meanwhile, within the timberline and treeline, excessive moisture suppressed larch growth, while Siberian pine growth was insensitive. Generally, the warming restart significantly changed both species' growth dependence on the hydrothermal variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{6}{c}{**Land Cover Area (sq. km)**} \\ \cline{2-7}
**2001–2003** & & \multicolumn{6}{c}{**2020–2022**} \\ \cline{2-7}  & \begin{tabular}{c} **Siberian Pine** \\ **Stands** \\ \end{tabular} & **Larch Stands** & **Birch Stands** & **Mixed Stands** & \begin{tabular}{c} **Shrubs and** \\ **Grasses** \\ \end{tabular} & **Background** \\ \hline \begin{tabular}{c} Siberian pine \\ stands \\ \end{tabular} & **713.6** & 60.2 & 0.0 & 428.5 & 33.4 & 0.0 \\ \hline \begin{tabular}{c} Larch stands \\ \end{tabular} & 14.4 & **316.2** & 10.9 & 195.3 & 10.5 & 0.0 \\ \hline \begin{tabular}{c} Birch stands \\ \end{tabular} & 0.0 & 1.5 & **9.9** & 9.2 & 1.1 & 0.0 \\ \hline \begin{tabular}{c} Mixed stands \\ \end{tabular} & 359.7 & 940.7 & 21.4 & **3730.2** & 159.7 & 0.0 \\ \hline \
\begin{tabular}{c} Shrubs and grasses \\ \end{tabular} & 6.0 & 34.3 & 1.3 & 1648.9 & **9441.5** & 3.4 \\ \hline
\begin{tabular}{c} Background \\ \end{tabular} & 0.0 & 0.0 & 0.0 & 0.0 & 0.4 & **3.0** \\ \hline \end{tabular}
\end{table}
Table 5: The transformation matrix of the land cover classes during 2001–2022 within the Tannu-Ola Ridge based on the MCD12Q1 IGBP maps. The bold text indicates unchanged areas.
## 4 Discussion  
As described in the last paragraph in Section 3.2, the optimization of query routing is effective in the proposed scheme because the number of messages among physical nodes can be reduced. In overlay networks, virtual node techniques have been researched in order to avoid load concentration into the specific physical node [25; 26]. In [25; 26], [PERSON] et al. also assume \"flash crowd\" where many requests are concentrated to the specific service in a short time [27]. The load-concentrated node asks low-load nodes for help. The asked nodes generate virtual nodes with the same key to the load-concentrated node and add the virtual nodes into the overlay network. By this process, not only the loads to the specific node but also the loads for the keys around the specific node can be distributed. Although this paper assigns keys to virtual nodes at random in Sections 2 and 3, the proposed method can employ the same approach.  
As another limitation of the proposed scheme, a specific physical node failure immediately affects the multiple virtual nodes assigned to the failure node. To enhance the reliability and feasibility of overlay networks, the related techniques have also been researched [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. First, a variety of replication schemes, such as path replication, have been proposed for unstructured P2P networks [28], where nodes search for content by forwarding queries to publishing nodes via neighboring links; and the path replication schemes replicate the replied content on the nodes between the publishing and requesting nodes. Related to the path replication schemes, a number of methods have been proposed based on specific factors such as the number of queries, the probability to put replicas, churn situations, and so on [29; 30; 31]. In addition, replication schemes have also been proposed for structured P2P networks, in order to increase the efficiency of replica maintenance and searches. Scalaris, for example, an Erlang implementation of a distributed key/value store [32], uses replication for data availability and majority-based distributed transactions for data consistency. Plover is a proactive low-overhead file replication scheme with replication among physically proximate nodes based on their available capacities [33]. Here, the physically proximate nodes are grouped in clusters,  
Figure 12: The number of messages among virtual nodes for one query.  
each of which has a supernode with high capacity and rapid connections. In RelaxDHT, nodes are divided into data blocks [34], with each block having a root node that manages the metadata of replicas on other nodes in their own different data blocks. Although these techniques do not assume interval queries, the proposed scheme can employ the same or similar ideas such as node/data replication to enhance its reliability.
## 3 Results and Discussion
### Total Phenolics Content  
It has been proven that tremendous changes in functional substances are closely linked to germination of soybeans, such as high phenolics content [4]. Additionally, chemical elicitors can significantly increase the phenolics content in bean sprouts [24]. In this study, total phenolics content of soybean sprouts in all treatments with chitooligosaccharide was shown in Figure 2. Overall, the phenolics content of soybean sprouts was significantly increased by the chitooligosaccharide in three molecular weights (1 kDa, 2 kDa, and 3 kDa). The smallest molecular weight led to the highest increase in phenolics under the same treatment concentration. Total phenolics content increased gradually along with the increase in chitooligosaccharide until 0.01%, and after that it gradually reduced. Soybean sprouts treated with 0.01% of 1 kDa chitooligosaccharide exhibited the highest phenolics content of 25.60 mg/g dry weight, which was approximately 28% higher than that of the control. The total phenolics content of soybean sprouts treated with 0.01% of 2 kDa and 3 kDa chitooligosaccharide increased by 23% and 20%, respectively. These results were similar to the increase in phenolics as a result of ethephon treatment [25]. Compared with ethephon, chitooligosaccharide is safe and environmentally friendly, so it can be a useful alternative in increasing phenolic secondary metabolites in soybean sprouts.  
Figure 2: Total phenolics content of soybean sprouts under different chitooligosaccharide treatments. The treatments were conducted with chitooligosaccharides of three molecular weights (1, 2, and 3 kDa) at four different concentrations (0.001 to 1%). The control treatment (0%) included the same solutions without chitooligosaccharides. Vertical bars represent the standard error of three replicates. Different letters indicate statistically significant difference (\(p<0.05\)).
## Appendix B Calculating Translational Velocity, Momentum, and Rotational Energy
### Local  
The local angular velocity vector (\(\overrightarrow{\omega}_{loc}\)) of each segment (i.e., about its own CoM) was calculated frame-by-frame using the formula of [PERSON] [14], as cited in [PERSON], [PERSON], [PERSON], and [PERSON] [15], as  
\[\overrightarrow{\omega}_{loc}=2.\cdot\frac{\left(\overrightarrow{v}_{1} \times\overrightarrow{v}_{2}+\overrightarrow{v}_{2}\times\overrightarrow{v }_{3}+\overrightarrow{v}_{3}\times\overrightarrow{v}_{1}\right)}{\left( \overrightarrow{v}_{1}.\left(\overrightarrow{r}_{2}-\overrightarrow{r}_{3} \right)+\overrightarrow{v}_{2}.\left(\overrightarrow{r}_{3}-\overrightarrow {r}_{1}\right)+\overrightarrow{v}_{3}.\left(\overrightarrow{r}_{1}- \overrightarrow{r}_{2}\right)\right)}\]where \(\overset{\rightarrow}{r}_{1}\), \(\overset{\rightarrow}{r}_{2}\), and \(\overset{\rightarrow}{r}_{3}\) are the orthogonal unit vector endpoints corresponding to the segment's local axes, expressed in global coordinates, and \(\overset{\rightarrow}{v}_{1}\), \(\overset{\rightarrow}{v}_{2}\), and \(\overset{\rightarrow}{v}_{3}\) are the three-dimensional velocity vectors (first derivatives) of \(\overset{\rightarrow}{r}_{1}\), \(\overset{\rightarrow}{r}_{2}\), and \(\overset{\rightarrow}{r}_{3}\), respectively.  
The corresponding inertia tensor (\([J^{\prime}]\)) for each segment was obtained frame-by-frame by taking the segment's diagonalized inertia tensor (\([J]\))  
\[J=\left[\begin{array}{ccc}J_{xx}&0&0\\ 0&J_{yy}&0\\ 0&0&J_{zz}\end{array}\right]\]  
wherein \(J_{xx}\), \(J_{yy}\), and \(J_{zz}\) are the segment's moments of inertia about its own three principal axes, and transforming it to the global frame using a transformation matrix (\(T\)) and the transposition thereof (\(T^{\prime}\)) with the following equation according to [PERSON] [16]:  
\[[J^{\prime}]=[T]\ [J]\ [T]^{t}\]  
within \([J]\), \(J_{xx}=J_{yy}=m_{seg}\cdot{r_{G}}^{2}\) and \(J_{zz}=\frac{1}{2}\cdot m_{seg}\cdot{r^{2}}\), with \(r\) and \({r_{G}}\) being the segment's radius about the longitudinal axis (cylindrical radius) and its radius of gyration about the other two principal axes, respectively.  
Local rotational kinetic energy (\(E_{loc}\)) was calculated as  
\[E_{loc}=\frac{1}{2}\cdot[J^{\prime}]\ [\overset{\rightarrow}{\omega}_{ loc}]\ [\overset{\rightarrow}{\omega}_{loc}]\]