esa-sceva/satcom-chunk-collection huggingface dataset:
# A Typical Small Watershed in Southwestern China Is Demonstrated as a Significant Carbon Sink
## 5 Conclusions  
In this study, we conducted two years of observations of eddy covariance measurements to understand the carbon fluxes from forest, cropland, and non-timber forest ecosystems in the Reshuihe River watershed in Southwestern China. The results showed that all three ecosystems were annual net carbon sinks during the study period. The forest site dominated by _Pinus junmanensis_ exhibited the strongest carbon sink capacity, followed by the non-timber forest site planted with _Zanthoxylum bungeanum Maxim_, and the smallest carbon sinks were found in the cropland site planted with potatoes and maize, with average annual NEE values of \(-\)540.45 \(\pm\) 52.35, \(-\)371.0, and \(-\)82.05 \(\pm\) 10.45 gC m\({}^{-2}\) a\({}^{-1}\), respectively.  
Path analysis results showed that PAR was the most important influencing factor controlling changes in daily NEE in all three ecosystems. SVWC and T\({}_{\text{air}}\) affected NEE by regulating GPP and Re. Higher SVWC promoted GPP and Re, favoring net carbon uptake. A significant decrease in GPP and Re was observed in the cropland when T\({}_{\text{air}}\) was greater than 21 \({}^{\circ}\)C, although this did not have a significant effect on NEE.  
By multiplying the annual carbon fluxes of the forest, cropland, and non-timber forest sites by the areas covered by the corresponding ecosystems, the annual carbon sink of the Reshuihe River watershed was found to be \(-\)52.15 Gg C a\({}^{-1}\). This indicates that the watershed acted as a net carbon sink. Although there are some uncertainties in this estimate, based on existing research, we believe that these uncertainties are unlikely to have a significant impact on the carbon sinks of the watershed. Given the current debates in estimating carbon sinks in Southwestern China using the top-down (atmospheric inversions) approach, our study of upscaled carbon sink estimation in small watersheds using multi-site eddy covariance observations provides new insights in accurately estimating carbon balance in such environment.  
Methodology, W.C., H.Y., X.Z. and Z.L.; Software, W.C. and X.Z.; Validation, Y.L. ([PERSON]) and H.Y.; Formal analysis, W.C.; Investigation, W.C., Y.L. ([PERSON]), X.Z., Z.L. and Y.L. ([PERSON]); Data curation, W.C., Z.L. and Y.L. ([PERSON]); Writing--original draft, W.C.; Writing--review & editing, W.C., Y.L. ([PERSON]) and H.Y.; Visualization, X.Z.; Supervision, Y.L.  
([PERSON]); Project administration, Y.L. ([PERSON]); Funding acquisition, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China for Distinguished Young Scholars (41925030) and National Natural Science Foundation of China for General Program (42171118).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
###### Abstract  
In this paper, a new fatigue life evaluation method, namely augmented-reverse notch equivalent stress method, is proposed for titanium alloy welded structures with stress singularities. First, a new three-parameter power function model is proposed in this paper, and the notch stress equivalent value method with correction factor is deduced. Combining the two, the theoretical framework of the augmented-reverse notch equivalent stress method is obtained. Within this framework, the fatigue test data of four titanium alloy welded joints with the same grade were used for analysis, and a new three-parameter power function model of titanium alloy welded structure was established. According to the calculation method of the notch equivalent stress correction factor, the correction factor suitable for the titanium alloy welded structure was obtained. Finally, the fatigue test data, different from the above titanium alloy welded structure, are used to verify the augmented-reverse notch equivalent stress method. The verification results show that the augmented-reverse notch equivalent stress method improves the effectiveness and accuracy of fatigue life evaluation and establishes a new life evaluation method for titanium alloy welded structures with stress singularities.  
augmented-reverse notch equivalent stress method; titanium alloy; welded structure; stress singularity; fatigue assessment Article
# Ontology-Based Production Simulation with OntologySim
## 5 Design Principles for the OntologySim
### Event  
The states of the simulation are not controlled by state entities and are based solely on events and the information stored within the ontology. The state of an entity, e.g., a machine or transport unit, is defined by the currently valid and connected event. Each event contains information about the start time, duration, and type of event and is connected to the entities, which are influenced by the event. From this information, the state of each entity can be extracted at any time. Events that lie in the future can also be generated. However, the agents and control algorithms do not have access to the future events but only see the current state to preserve comparability in respect to the real world. Creating future events is useful for processing tasks at once in the simulation. For example, if the task is to produce a product on a machine, then two events are created, one to change the machine setup to the new process and the other to feed the product into the machine for processing. This possibility facilitates the implementation of the simulation.  
It is always possible to infer in both directions (bidirectionally) in the simulation. This increases flexibility and allows easier access to entities. The disadvantage of this is that more data is stored, which hurts performance. As shown by Reference [17], the performance for compact ontologies is significantly better. For this reason, past events are only made available in one direction, which has the great advantage that, when querying the state and the next steps of a machine, only a few future events need to be queried, and it is not necessary to iterate over all past and future events. Figure 10 illustrates the differences between past and future events and their connectivity to entities.  
Figure 9: Concept of a machine in OntologySim.
## 3 Case Study
### Compared with the FEM  
In order to verify the practicability, convenience, and accuracy of the proposed method, the hanger replacement process of half-through arches with a suspended deck by cable hangers was also simulated by FEM. The three-dimensional finite element model was formed in Midas Civil (2018) as shown in Figure 12; the whole model consisted of 1786 nodal points, 80 truss elements, 2735 beam elements, and 370 plate elements. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cases** & \(\mathbf{T_{i}}\)**[N]** & \(\mathbf{A_{i}}\)**[_m\({}^{2}\)_]** & \(\mathbf{F_{i}}\)**[N]** & \(\mathbf{L_{i}^{\prime}}\)**[m]** & \(\mathbf{x_{i}}\)**[mm]** & \(\mathbf{X_{i}}\)**[mm]** \\ \hline Basic parameter & & & \(E\) = 2.05 \(\times\) 10\({}^{11}\) Pa, \(L\) = 27.10 m, \(E^{\prime}\) = 2.05 \(\times\) 10\({}^{11}\) Pa, \(A^{\prime}\) = 0.0037 m\({}^{2}\), & & \\  & & & \(E_{\text{b}}\) = 2.06 \(\times\) 10\({}^{11}\) Pa, \(I_{\text{b}}\) = 0.035 m\({}^{4}\), \(S\) = 5.10 m & & & \\ Initial state & 0 & 0.0047 & 9.63 \(\times\) 10\({}^{5}\) & 27.13 & 0 & 0 \\
1 st pocket hanging & 1.93 \(\times\) 10\({}^{5}\) & 0.0047 & 8.96 \(\times\) 10\({}^{5}\) & 27.12 & 1.88 & 1.88 \\
1 st cutting & 2.33 \(\times\) 10\({}^{5}\) & 0.00376 & 7.58 \(\times\) 10\({}^{5}\) & 27.12 & \(-\)1.45 & 0.43 \\
2 nd pocket hanging & 3.85 \(\times\) 10\({}^{5}\) & 0.00376 & 7.13 \(\times\) 10\({}^{5}\) & 27.11 & 1.59 & 2.02 \\
2 nd cutting & 4.28 \(\times\) 10\({}^{5}\) & 0.00282 & 5.67 \(\times\) 10\({}^{5}\) & 27.11 & \(-\)1.53 & 0.48 \\
3 rd pocket hanging & 5.78 \(\times\) 10\({}^{5}\) & 0.00282 & 5.31 \(\times\) 10\({}^{5}\) & 27.10 & 1.69 & 2.18 \\
3 rd cutting & 6.23 \(\times\) 10\({}^{5}\) & 0.00188 & 3.77 \(\times\) 10\({}^{5}\) & 27.10 & \(-\)1.62 & 0.56 \\
4 th pocket hanging & 7.70 \(\times\) 10\({}^{5}\) & 0.00188 & 3.52 \(\times\) 10\({}^{5}\) & 27.10 & 1.81 & 2.37 \\
4 th cutting & 8.19 \(\times\) 10\({}^{5}\) & 0.00094 & 1.88 \(\times\) 10\({}^{5}\) & 27.10 & \(-\)1.72 & 0.65 \\
5 th pocket hanging & 9.63 \(\times\) 10\({}^{5}\) & 0.00094 & 1.74 \(\times\) 10\({}^{5}\) & 27.09 & 1.95 & 2.60 \\
5 th cutting & 1.01 \(\times\) 10\({}^{5}\) & 0 & 0 & 27.09 & \(-\)1.83 & 0.76 \\ \hline \hline \end{tabular} Note: \(T_{i}\): internal force of the pocket hanging hanger; \(\mathbf{A_{i}}\): the area of the hanger to be replaced; \(F_{i}\): the internal force of the hanger to be replaced; \(L_{i}^{\prime}\): the unstressed length of the pocket hanging hanger; \(\mathbf{x_{i}}\): the displacement in the current case; \(\mathbf{X_{i}}\): the cumulative displacement.  
\end{table} 
Table 1: Calculation results of the removal process in different cases during the old hanger demolition process. Figure 10: The change of internal force of the pocket hanging hanger and the hanger to be replaced in different cases. hangers are represented with truss elements, the deck is described by plate elements, while others are represented with beam elements. The materials of the modal are listed in Table 3. In addition, the boundary conditions imposed on the end of the arch rib and the bottom of the pier in the model were all fixed constraints.
## 2 Formulation of the Shape Optimization Problem
### Shape Expression  
In this study, _Bezier surfaces_ are employed to describe the structural geometry, which can adapt to diverse complex design requirements. The computational method for _Bezier surfaces_ is relatively simple and fast, offering high computational efficiency in practical applications.  
The _Bezier surface_\(\mathrm{S}(u,v)\) can be defined by the product of the control points \(\mathbf{P}_{i,j}=(x_{ij},y_{ij},z_{ij})\) in two parametric directions and the _Bernstein basis function_\(\mathrm{B}_{i,n}(u)\)[13], as expressed in Equations (2) and (3) as follows:  
\[\mathrm{S}(u,v)=(\mathbf{x}(\mathbf{u},\mathbf{v}),\mathbf{y}(\mathbf{u}, \mathbf{v}),\mathbf{z}(\mathbf{u},\mathbf{v})) \tag{2}\]  
where \(\mathbf{x}\), \(\mathbf{y}\), and \(\mathbf{z}\) are  
\[\left\{\begin{aligned} \mathbf{x}(\mathbf{u},\mathbf{v})=\sum \limits_{i=0}^{n}\sum\limits_{j=0}^{m}\mathbf{x}_{ij}\ \mathrm{B}_{i,n}(u)\mathrm{B}_{j,m}(v)\\ \mathbf{y}(\mathbf{u},\mathbf{v})=\sum\limits_{i=0}^{n}\sum\limits_{j=0}^{m }\mathbf{y}_{ij}\ \mathrm{B}_{i,n}(u)\mathrm{B}_{j,m}(v)\\ \mathbf{z}(\mathbf{u},\mathbf{v})=\sum\limits_{i=0}^{n}\sum\limits_{j=0 }^{m}\mathbf{z}_{ij}\mathrm{B}_{i,n}(u)\mathrm{B}_{j,m}(v)\end{aligned}\right. \tag{3}\]  
where \(\mathbf{x}_{ij}\), \(\mathbf{y}_{ij}\), and \(\mathbf{z}_{ij}\) represent the \(x\), \(y\), and \(z\) coordinates of the control point \(n\) in the \(i\)-th row and \(j\)-th column, respectively. The Bernstein basis function \(\mathrm{B}_{i,n}(u)\) is defined as follows:  
\[\mathrm{B}_{i,n}(u)=\frac{n!}{i!(n-i)!}u^{i}(1-u)\ ^{n-i},\mathrm{u}\in[0,1] \tag{4}\]Here, \(n\) and \(m\) denote the degrees of the _Bernstein basis functions_ in the \(u\) and \(v\) directions, respectively. In this study, both \(n\) and \(m\) are 5. The parameters \(u\) and \(v\) correspond to a specific position on the surface. Substituting them into the surface equation \(\mathbf{S}\) will give the x, y, and z coordinates of the point. For any \(n\) and \(m\), \(\mathrm{S}(0,0)=\mathbf{P}_{0,0}\), \(\mathrm{S}(0,1)=\mathbf{P}_{0,m}\), \(\mathrm{S}(1,0)=\mathbf{P}_{n,0}\), and \(\mathrm{S}(1,1)=\mathbf{P}_{n,m}\); that is, the surface is interpolated at the four corner points of the control grid. To determine the \(u\) and \(v\) values corresponding to a known (\(x\), \(y\)) coordinate on the surface, substitute \(x\) and \(y\) into the surface equations \(\mathrm{S}(u,v)\), forming a system of bivariate equations. Solving these equations numerically yields the (\(u\),\(v\)) parameters.
## 5 Conclusions  
This paper reviewed 168 published articles on LAI estimations for forests, crops and grassland. The paper reviewed articles in terms of study sites, satellite sensors and statistical methods used to determine LAI. The results of the study indicate that there is an increasing trend in the use of all model types. However, the majority of the literature still focused on conventional statistically focused empirical models. There is also an increasing trend in the fusion of data from different sources. The fusion of multi-source data includes using data with different spatial, spectral and even temporal resolution. In terms of passive and active sensors, LiDAR demonstrated a great ability to detect forest canopy structures and estimate forest LAI even in mixed species plantations.  
The development of high spatial resolution sensors such as WorldView-3 increased efficiency and improved results drastically. However, like other systems, remote sensing has its drawbacks. One of them is the dependency on weather conditions, as some measurements can only be taken under clear-skies conditions. Although a number of remote sensing methods of estimating LAI were developed, not a single method can be applied consistently and repeatedly for estimating LAI locally and continentally. The reason for this is variations in biophysical, environmental and topographic traits of vegetation in space and time. In the short term, empirical models that can be validated locally are recommended for forest and crop managers.  
Conceptualization, N.M. and R.L.; methodology, N.M. and R.L.; software, N.M. and R.L.; validation, N.M., R.L. and H.K.; formal analysis, N.M.; investigation, N.M.; resources, N.M., R.L. and H.K.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, N.M., R.L. and H.K.; visualization, N.M.; supervision, R.L. and H.K.; project administration, N.M., R.L. and H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors of this paper acknowledge the support of the University of KwaZulu-Natal and the Mondi Group; which allowed for the successful completion of this research.  
The authors declare no conflict of interest.
## 3 Methodology  
### Horizontal to Vertical Spectral Ratios (HVSR) Method  
The application of the HVSR technique to ambient vibration signals or seismic noise provides an estimation of the fundamental resonance frequency associated with the site under study. This relation is the fundamental basis of the HVSR analysis [6,32].  
Many experiments and theoretical 1D investigations have demonstrated that a strong impedance contrast between the upper layers and the underlying ones (stiffer layers) provides a clear HVSR peak around the fundamental resonance frequency [33-39]. The HVSR method has become accepted in microzonation studies, since its application and interpretation is straightforward and suitable [40-43].  
The practical implementation of the HVSR method requires only one three-component sensor for the recordings, which makes this technique relatively cheap and easy to implement. In this study, seismic noise measurements were recorded using a broadband seismic station (Guralp CMG-6 TD). Approximately 45 min to one hour of seismic noise was captured using a sampling frequency of 100 Hz, recorded in Guralp Compressed Format (GCF). A total of 26 measurements were taken in March 2019, in days of little or no wind in order to avoid disturbances at low frequencies (Figure 3).  
Figure 3: Location of sites of measurement in the Guévéjar landslide area. The colors and signatures are the same as in Figure 2.  
The HVSR curves have been estimated using the GEOPSY software from the Sesarray package [44]. The recorded signals have passed through an anti-triggering filter, and then they have been divided into non-overlapping windows of frequency-dependent length. Each window was tapered by a 5% cosine function and its length was 50 periods of the frequency analyzed at each moment [45]. After that, the frequency response was obtained for each window and component, and smoothed applying a Konno and Omachi window (coefficient b = 40) [46]. Subsequently, for each window, the squared average of the horizontal components was computed and then the horizontal-to-vertical spectral ratio was calculated. Finally, the HVSR curves from the selected windows have been averaged to provide the mean HVSR curve and its corresponding standard deviation.
## 4 Discussion
Since there is a delay between a shocked solar wind arrival and earthquake occurrence up to some months, this may suggest that solar wind energy does not trigger earthquakes immediately (when the media is ready for destruction, as it is believed at present). Instead, as suggested in (16), it may be put into the lithosphere dynamics, such as fluid upwelling, which are active participants in tectonic earthquakes (50). The emanation of lithospheric gases before strong earthquakes has been well documented (48). Moreover, such emanation may result in acoustic gravity wave (AGW) excitation (51), penetrating the ionosphere. Within the framework of such reasoning, first, the solar wind's energy provokes a rise in lithospheric gases (fluids), and the emanation of these gases excites AGWs, which spread into the ionosphere and foreshadow an upcoming earthquake. It is well known that the strongest earthquakes occur mainly in two longitudinal regions: on the American and Asian branches of the Pacific Ring of Fire. However, the literature does not discuss why these two branches of seismicity and, more broadly, why these two branches of the Pacific Ring of Fire are centered relative to the longitudes of the Earth's magnetic poles. Of course, the question can be put the other way around: \"How did it happen that the longitudes of the magnetic poles in the Northern and Southern Hemispheres are confined to the longitudes of the American and Asian branches of the Pacific Ring of Fire, which is formed by the subduction of various tectonic plates around the Pacific Ocean at convergent boundaries?\" If this is not an accident, then this fact may indicate a close connection between the tectonic activity of the Earth and its magnetic field. Based on our research, we can assume that the influence of disturbed solar wind flows on lithospheric processes will be most effective when the longitudes of the magnetic poles are located under the polar cusp.
## 4 Vibration Characteristics for Bearing Clearance Monitoring  
According to the International Organization for Standardization (ISO), the clearance of deep groove ball bearing is divided into five classes, namely C2, CN (Normal), C3, C4 and C5 [19]. Table 4 depicts the maximum and minimum of these five classes of clearance under different nominal bore diameters. Note that these clearances merely showed the rated clearance of bearings after manufacturing. With bearings being put into operation, they suffer from various levels of wear and tear, which will lead to the increase in the clearance. Investigations have shown that the clearance has a vital effect on bearing useful life, and the increase in clearance will shorten the bearing service life by approximately 30% [16].  
To gain a comprehensive understanding of the vibration characteristics caused by bearing clearance and load conditions, a group of simulation studies were carried out. In the simulation study, the clearances ranged from 0 to 100 \(\upmu\)m with the interval as 10 \(\upmu\)m, while the load ranged from 800 to 4800 N with the interval as 800 N. Note that the load was set according to the bearing rotating life \(L_{100}\). When the basic rated dynamic load was 20.3 kN (bearing 6206), the rotating life under each load was  
Figure 10: Envelope spectrum of CN and C4 bearings under (**a**) 0 N, (**b**) 800 N, (**c**) 1600 N, (**d**) 2400 N.  
about 181,541, 22,693, 6724, 2837, 1452 and 840 h, respectively. Thus, these loads could be divided into light load (800 N, 1600 N), medium load (2400 N, 3200 N) and heavy load (4000 N, 4800 N).
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Mobility Models  
UAV networks are vulnerable to high mobility. Frequent topology changes can damage both cooperation and communication among UAVs [122]. Several mobility models have been proposed to overcome mobility challenges but they are insufficient to deal with communication issues. These models must be designed considering network requirements. Network coverage is significantly reduced in case of UAV mobility at low speed, results in higher network delays. However, mobility models with minimal latency are required in emergency scenarios. Furthermore, continuous network connectivity leads to degradednetwork performance. Thus, novel mobility models should be developed for certain applications and environments to tackle mobility challenges.
## 3 Results
### Relationship between Community Composition and Spectral Response  
The results of the principal component analyses (PCAs) of our plot-level spectral datasets indicated that variation in plot-level vegetation characteristics was related to differentiation in spectral reflectance at both sites (Figure 8). At APEX, results from our permutation tests indicated that spectral reflectance varied by treatment (r\({}^{2}\) = 0.45, p = 0.005), with the most variation in reflectance explained by plot-level species diversity (Table 5). Variation in the spectra at APEX was closely associated with the percent cover of mosses and forbs. Mosses and forbs together accounted for less than 10% on average of the cover at APEX, but had a strong effect on spectral reflectance, particularly in the control and raised water table treatment plots. The lowered water table treatment was associated with a greater litter cover, coinciding with lower plot-level species diversity (Figure 8, left panel). Overall, the first principal component explained 79% of the variation in the dataset, driven primarily by greater diversity in the control and raised water table treatments, and higher litter cover in the lowered water table treatment (Figure 8, left panel, Table 5).  
PCA results from the reflectance data at SPRUCE showed significant spectral differences across temperature (r\({}^{2}\) = 0.28, p = 0.01) and CO\({}_{2}\) treatments (r\({}^{2}\) = 0.19, p = 0.01) (Figure 8, right panel). Like APEX, reflectance spectra responded to differences in forb cover (Table 6). Unlike APEX, shrub cover also emerged as a significant vector in the SPRUCE data. Overall, the first principal component explained 96% of total variance among spectral scans. Since we did not find any strong differences in diversity among treatments, diversity did not emerge as a significant vector in the analysis. These results suggest that the relative cover of shrubs versus forbs associated with temperature and CO\({}_{2}\) level is the main driver of spectral differences among treatments at SPRUCE.  
Figure 8: (**Left**) Principal component analysis for APEX spectral data demonstrating distribution of treatment plots with community variables mapped. Arrow length represents the strength of the gradient. (**Right**) Principal component analysis of spectral data at SPRUCE, demonstrating the distribution of temperature treatments (shown in \({}^{\circ}\)C) with community variables mapped on. The length of the arrow represents the strength of the gradient.