esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
With the last spaceborne lidar sensor, ICESat GLAS, about 770,000 waveforms were available in the Amazon region [19], whereas in this study, about 110 million GEDI waveforms, all collected within 16 months, were compiled--exceeding the ICESat dataset by two orders of magnitude. The aim of the study was to estimate the productivity and carbon balance of the entire Amazon by fusing GEDI measurements with forest simulations. Through the application of a dynamic forest model, it is possible to simulate the growth of a forest [20] and to extract the current state of a tropical forest by lidar waveform matching [19; 21]. By merging the satellite data with the model simulations, the productivity and structure of the forest could be determined and analyzed. Uncertainties in productivity estimates could be reduced and studied in more detail by determining the structure, aboveground biomass, and growth of tropical forests. Therefore, the following questions are answered in this study:  
* What are the total productivity and carbon balance of the Amazon rainforest and how are they spatially distributed? * What are the relationships between forest productivity and other forest properties, such as aboveground biomass, canopy height complexity, net ecosystem exchange, and forest age in the Amazon rainforest?
## 2 Method
### Assessment of Optimization and Model Performance  
We used five metrics to assess the model performance, including coefficient of determination (\(R^{2}\)), root mean squared errors (\(RMSE\)), mean predictive error (\(BIAS\)), Kling-Gupta efficiency (\(KGE\)), and Nash-Sutcliffe efficiency coefficient (\(NSE\)). The five formulas are expressed as follows [50; 51]:  
\[R^{2}=\frac{\sum_{i=1}^{N}\left(O_{i}-\overline{O}\right)\left(P_{i}-\overline {P}\right)}{\sqrt{\sum_{i=1}^{N}\left(O_{i}-\overline{O}\right)^{2}\sum_{i=1} ^{N}\left(P_{i}-\overline{P}\right)^{2}}} \tag{7}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(O_{i}-P_{i}\right)^{2}}{N}} \tag{8}\]  
\[BIAS=\frac{\sum_{i=1}^{N}\left(P_{i}-O_{i}\right)}{N} \tag{9}\]  
\[KGE=1-\sqrt{\left(CC-1\right)^{2}+\left(\frac{P_{d}}{O_{d}}-1\right)^{2}+ \left(\frac{\overline{P}}{\overline{O}}-1\right)^{2}} \tag{10}\]  
\[NSE=1-\frac{\sum_{i=1}^{N}\left(O_{i}-P_{i}\right)^{2}}{\sum_{i=1}^{N}\left(O_ {i}-\overline{O}\right)^{2}} \tag{11}\]  
where \(O_{i}\) is the observed value, and \(P_{i}\) is the predicted value; \(\overline{O}\) and \(\overline{P}\) are the mean values of observations and predictions, respectively; \(O_{d}\) and \(P_{d}\) are standard deviation of observations and predictions, respectively. \(N\) is the number of total samples to be evaluated. \(CC\) is the Pearson coefficient value.
# Pantograph-Catenary Interaction Prediction Model Based on SCSA-RBF Network
## 1 Introduction
One approach is to simplify the catenary to a spring-mass system with time-varying stiffness, in which the spring stiffness is expressed as a sinusoidal function with the time of the pantograph running in a span as a period [11] or is directly given by the displacement response of the catenary finite element model under a constant static force [12]. The relative error of the contact force at 350 km/h and below obtained through the two models is up to 20-80% [13]. An alternative approach is to simplify the catenary to a periodically spring-supported string model. For example, a three-span-long string model supported by four springs, in which the relative error of the first natural frequency is 28% [14], or an infinite string model with viscoelastic support, in which the relative error of the standard deviation of contact force (SDCF) at 350 km/h and below is up to 32% [15, 16]. Thirdly, the surrogate model of the pantograph-catenary interaction performance characterization parameters can be established by generating the data set containing several pantograph-catenary interaction cases through the finite element model. In the surrogate model, the computational cost drops several orders of magnitude at the expense of ignoring the dynamic process. For example, the quadratic function relationship between the SDCF at 305 km/h and the six pantograph parameters is established [17]. The surrogate model to represent the relationship between the SDCF at 350 km/h and six pantograph parameters, three dropper distribution parameters, three dropper length parameters, and three preload parameters is established, and the relative error of the model is less than 6.3% [18] The surrogate model to represent the relationship between the five contact force statistics parameters and running speed, static lifting force, and nine catenary parameters is established, and the determination coefficient of the model is 0.969 [19]; the surrogate model to represent the relationship between the time-domain signal of contact force and running speed in the range of 250-350 km/h and six catenary parameters is established, and the probability of the relative error of the SDCF surrogate model being lower than 4.6% is 99.73% [20]; the surrogate model to represent the relationship between the SDCF and running speed in the range of 250-450 km/h and six pantograph parameters is established, and the determination coefficient of the model is 0.996 [21]. Based on the verified pantograph-catenary finite element model, a dataset of 4000 pantograph-catenary interaction cases was generated in the whole speed range of 250-450 km/h. Combined with the selective crow search algorithm-radial basis function (SCSA-RBF) network, based on the time-domain signal of the panhead acceleration and the pantograph dynamic equation, and considering the pantograph-catenary interaction dynamic process, a data-physics coupling model that can accurately and quickly predict the pantograph-catenary interaction was proposed and applied to the multi-parameter joint optimization of pantograph dynamic parameters.
## 2 Background  
### Airport Pavements  
Like road and highway pavements, airport pavements are designed to protect the natural or imported subgrade from the traffic loads they are expected to support over their design life. Furthermore, airport pavements are generally either rigid or flexible in nature, although some composite pavements are also used [19]. Traditionally, flexible airport pavements were designed to comprise thick layers of well-compacted and high-quality crushed rock base over uncrushed gravel sub-base, with a thin bituminous surfacing. The pavement thickness was determined using the methods developed by the US Army Corps of Engineers between the 1940s and 1970s [20].  
Since that time, many counties have developed significantly different flexible pavement styles, with much thicker asphalt, bound or stabilized sub-base layers, and some use full-depth asphalt structures [21]. In contrast, Australia has largely retained flexible pavement comprised of relatively thin asphalt surfaces, often 60 mm or 120 mm thick, over high-quality crushed rock base and sub-base layers [7].  
Furthermore, rigid airport pavements have changed little since their original development. Rigid airport pavements in most parts of the world remain constructed of unreinforced concrete slabs on a cement-bound or crushed rock sub-base layer [22]. The joints are generally spaced to form 4 m to 7.5 m and approximately square slabs and are detailed to provide partial load transfer from one slab to the next, in order to avoid the need to thicken the slab edges [23]. There are also hybrid or composite pavements, usually where old concrete slabs are overlayed with an asphalt concrete surface [19]. However, these pavements are not common.  
Regardless of the type of pavement, most airports subscribe to a standard international aircraft pavement strength rating system. Although there are other systems available and historically used, the most common system is ACN-PCN [5]. As stated above, ACN-PCN was developed and implemented by ICAO in the 1980s and is being replaced by the ACR-PCR system in 2024 [17].
# Springback Prediction of Free Bending Based on Experimental Method
## 4 Conclusions  
In this paper, the springback prediction of free bending was studied, which provided a springback prediction method for tube bending. A compensation method for the transition section and the arc section was proposed which can ultimately achieve the purpose of springback prediction. The conclusions are as follows:  
1. The theoretical _U-R_ relationship cannot meet the actual processing requirements, and the actual processing is affected by many factors. The experimental _U-R_ data show that the larger the bending die offset U is, the smaller the bending radius R is. Different materials have different _U-R_ relationships;
2. From the cross-section, it can be seen that in the process of free bending, the material outside the arc is thinner, and the material inside the arc is thicker. This will cause the tube to be squeezed and stretched unnecessarily during the actual process. However, the length of the arc section of the actual bent pipe is shorter than the length of the theoretical arc section, which also verifies this point;
3. Compensating the transition section cannot solve the springback problem, while compensating the arc section can solve the springback problem well. The springback  
Figure 11: Springback experiment with 135\({}^{\circ}\) included angle: (**a**) C0; (**b**) C40; (**c**) C80; and (**d**) C38.515.  
Figure 12: Comparison before and after forming of double-bending 90\({}^{\circ}\) tube: (**a**) before treatment; and (**b**) after treatment.  
Figure 10: Springback experiment with 90\({}^{\circ}\) included angle: (**a**) C0; (**b**) C40; (**c**) C80; and (**d**) C47.29.  
angle, with a small error, can be obtained using the experimental method. The general rule of the springback of the tube can be obtained by the springback compensation experiment: with the increase of the compensation value, the included angle is smaller. An ideal tube can be obtained according to that rule.  
Conceptualization, M.H. and W.Z.; methodology, M.H. and W.Z.; validation, M.H. and W.Z.; formal analysis, M.H. and W.Z.; investigation, M.H. and W.Z.; resources, M.H. and W.Z.; data curation, M.H. and W.Z.; writing--original draft preparation, M.H. and W.Z.; writing--review and editing, M.H. and W.Z.; visualization, M.H. and W.Z.; supervision, M.H. and W.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 52179129.  
Not applicable.  
The authors declare no conflict of interest.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations
### The Rule Preference Formalization  
The rule preference indicates the user's explicit and implicit preferences regarding the rule content. It is defined by the pair \(\langle term,\;weight\rangle\). The _weight_ is between \(-1\) and \(1\) reflects the level of the user's interests in such terms. For instance: \(\langle Home;\;0.75\rangle\) ; \(\langle Security;\;1\rangle\) ; \(\langle Health;-0.75\rangle\).
## 2 Materials and Methods
### RNA extraction  
Samples (300 \(\mu\)L) were taken of mono- and co-culture fermentations of the three strains of _L. rhamnus_ and immediately treated with RNAprotect Bacteria Reagent (Qiagen) according to the manufacturer's instructions. The cell pellets were stored at -80 \({}^{\circ}\)C until RNA extraction. Total RNAwas extracted using the RNeasy Mini kit (Qiagen), with minor modifications of the manufacturer's RNA protect Bacteria Reagent protocol (Qiagen). The cell pellets were suspended in 100 \(\upmu\)L (RNeasy Mini kit) TE buffer (30 mM Tris base, 1 mM EDTA, pH 8.0) containing 20 mg/mL lysozyme (Sigma-Aldrich), 10 \(\upmu\)L proteinase K (20 mg/mL, Sigma-Aldrich), and 10 \(\upmu\)L mutanolysin (20 U/mL; Sigma-Aldrich) and were incubated at 37 \({}^{\circ}\)C for 60 min. After the addition of 700 \(\upmu\)L (RNeasy Mini kit) of RLT buffer, cell suspensions were transferred to sterile tubes containing 50 mg of zirconium beads (0.5 mm; Biospec Products, Bartlesville, OK, USA), shaken three times at high intensity for 60 s in a Bead Ruptor (Omni International), and centrifuged at maximum speed for 20 s to remove cell debris. DNA was removed during RNA isolation by performing an on-column RNase-free DNase I (Qiagen) treatment according to the manufacturer's instructions. After elution of RNA with 50 \(\upmu\)L (RNeasy Mini kit) of RNase-free water, 2 \(\upmu\)L (RNeasy Mini kit) of Protector RNase inhibitor (40 U/\(\upmu\)L; Roche) was added and a second RNase-free DNase I treatment (3 \(\upmu\)L Dase I [10 U/\(\upmu\)L]; Roche) was performed for 30 min at 37 \({}^{\circ}\)C. DNase I was inactivated by adding 2 \(\upmu\)L of EDTA (25 mM) to the reaction mixture and heating at 65 \({}^{\circ}\)C for 15 min. The absence of DNA in the RNA samples was verified by qPCR using the _tuf_ primers designed for the strains of _L. rhamns_ (Table S1). RNA quality (RNA quality control RNA integrity number [RIN]) and quantity were determined using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). All extracted RNA was of good quality and RIN varied between 8.3 and 10.0 (with an average RIN of \(9.5\pm 0.4\)).
###### Abstract  
Image quality metrics are a critical element in the iterative Fourier transform algorithms (IFFAs) for the computer generation of phase-only holograms. Conventional image quality metrics such as root-mean-squared error (RMSE) are sensitive to image content and unable to reflect the perceived image quality accurately. This would have a significant impact on the calculation speed and the quality of the generated hologram. In this work, the structure similarity (SSIM) was proposed as an image quality metric in IFTAs due to its ability to predict the perceived image quality in the presence of the white Gaussian noise and its independence on the image content. This would enable IFTAs to terminate when further iterations would no longer lead to improvement in the perceived image quality. As a result, up to 75% of unnecessary iterations were eliminated by the use of SSIM as the image quality metric.  
holography; structural similarity; perceived image quality Article
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 2 System Model
### Remaining Caching Capacity  
During the handover period, the user's unsent data will be cached to the serving satellite, and they will not be sent to the target satellite until the handover is completed. The data will be cached and utilized to ensure the integrity of user data and avoid packet loss caused by handover. Apart from the occupied caching resources, the remaining caching resources that can be used for caching the data during the handover process are called the remaining caching capacity. However, the limited satellite on-board caching resource is difficult to meet the requirement of a large amount of data caching caused by multi-user simultaneous handover processes, and it will lead to handover failure and data packet loss. Therefore, the on-board caching resource is regarded as one of the decision factors of handover, and the information can also be obtained from the information interaction between adjacent satellites.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 3 Results and Analysis
### Durability Assessment of Soil Samples by Sintered Gypsum and Glass Powder  
As shown in Figure 8a-d, the presence of GP aggravates the durability factor of mixed samples. However, by increasing the GP content from 4% to 12%, the porosity decreases lightly, and it is declared that the worst GP content for durability is 4%. Regarding the charts, samples with more curing days express less ALM (%), which means that pozzolanic reactions between additives and the clay soil continue over 56 days. It is discussed in the microstructural analysis. As seen from the high regression values of the ALM graphs, the relation between the ALM and porosity in this study is meaningful. After normalization of the ALM graph for all durability tests and comparing Figure 8e,f, an increase of 1% is apparent, strengthening the link between the ALM and the porosity index.  
Figure 8: _Cont._  
Figure 7: Correlation between (**a**) UCS and \(G_{0}\), (**b**) UCS and \(E\), and (**c**) \(E\) and \(G_{0}\).  
Conversely, similar to the formulas in the physical properties part, a formula has been generated to predict the ALM by various combinations of additives. Equation (7), with a strong regression coefficient (R\({}^{2}\) = 0.867), expresses an accurate prediction tool for the ALM assessment in further investigations.  
\[ALM=\text{ALM}_{(\eta/X_{\text{ir}},0.15-29)}\times(0.0000069)\left(\eta/X_{ \text{ir}}^{0.15}\right)x^{(4.21)} \tag{7}\]
## 3 Results and Discussions
### Classification Result of Decision Tree
#### 3.3.3 Land Cover Mapping of Dunkuotan Village  
The automatic classification result is displayed in Figure 9 and is based on the decision tree algorithm. We can see that there are many areas of land with plastic film cover detected. The vegetation and bare soil were discriminated in accordance with the NDVI/EVI features. Moreover, the buildings with a metal rooftop were correctly identified, which are at an intersection or beside the main street of the village (at the center of Figure 9). Some of the buildings with a cement rooftop, however, were wrongly classified as the plastic film on the ground (at the bottom left of Figure 9) because of its extremely high spectral reflectance, which is close to that of the rooftop of cement.  
Figure 9: Land cover classification map of the center of Dunkuotan Village by decision tree.
## 4 Conclusions  
In this study, a combination of experiments and numerical simulations were conducted to investigate the fatigue strength of butt-brazed joints in oscillating tubes. The main conclusions are as follows:  
1. Through tensile tests on the butt-brazed joints, the average value of the fracture load was 10,598 N. Based on the CZM, the simulated value of the tensile fracture load of the butt-brazed joint was 10,460 N, with an error of 1.3%. Therefore, the critical damage stress of the CZM was determined as 525 MPa, which verified the accuracy of the VUMAT subroutine of the cyclic CZM;  
2. The fatigue crack propagation process of the butt-brazed joints was analyzed using a bilinear CZM. Through cyclic loading, it was observed that the damage at the edge of the brazed interface layer first reached its maximum value, which was the crack initiation point. As the number of cycles increased, cracks propagated along the weld seam from the edge layers on both sides to the middle of the weld seam until fracture. This discovery provides a reliable numerical simulation method for predicting the fatigue life of brazed structures in gas-wave machines;  
3. The effects of cyclic displacement and butt-joint angle on the fatigue life of the brazed joints were simulated and analyzed. A fatigue life-fitting equation was obtained as \(y=5458.97x^{-3.545}\). It was observed that as the butt joint angle increased, the area of the scarf surface of the brazed joint enlarged, and the secondary crack width and fatigue striations at the fracture surface gradually diminished, resulting in enhanced connection strength. It was found that the fatigue life of butt-brazed joints with \(\alpha=45^{\circ}\) increased by 334.5% than \(\alpha=0^{\circ}\). A fatigue life analysis of the DN285 \(\times\) 450 mm oscillating tube model was also conducted, revealing that the fatigue initiation life of butt-braised joints with \(\alpha=45^{\circ}\) increased by over 102.92% than \(\alpha=0^{\circ}\). This indicates that during the design of the brazed structure of oscillating tube blades, brazed joints with a butt-joint angle of \(\alpha=45^{\circ}\) are preferred. This conclusion provides a theoretical basis for the design and safe operation of the brazed structure of the oscillating tube blades in gas-wave machines.  
Conceptualization, Y.Z. and A.X.; Software, Y.Z.; Formal analysis, C.S.; Investigation, Q.L.; Resources, Q.L.; Data curation, A.X. and C.S.; Writing--original draft, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key Research and Development Program of China with the grant number 2018 YFA0704604.  
**Institutional Review Board Statement:** Not applicable.  
Figure 22: Crack initiation life of oscillating tubes with different butt-joint angles.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Grid Monitoring and Control in Operation  
Effective grid monitoring and control are crucial for the operational efficiency of modern distribution grids. Utilizing advanced digital tools like smart sensors, the IoT, and real-time data analytics enables grid operators to achieve precise control and real-time oversight. This integration ensures a reliable electricity supply, an optimal response to fluctuating demand, efficient resource management, the maintaining of grid stability, and addressing challenges in the evolving energy landscape.
## 2 Experimental Setup  
### Storage Material and Magnets  
Lanthanum Nickel (LaNi\({}_{5}\)) was selected for the experiments since it has fast and reversible sorption, a rather low operating pressure compared to pressurized gas at near ambient temperatures, and a good cycling life [49]. This metal hydride also has a low activation energy, good reaction kinetics, and the capability to store hydrogen at temperatures and pressures near normal conditions (1.7 to 20 bars for temperatures between 293 and 373 K, respectively) [1]. LaNi\({}_{5}\) also demonstrates properties of paramagnetic materials and shows segregation of lanthanum in a surface layer and the formation of ferromagnetic particles of nickel when exposed to a magnetic field [44]. Also, its hydrogen absorption capacity is 1.5 wt %. Particles of the powder employed in this work are flake formed with a density of 7.950 g/cm\({}^{3}\)[49].  
Magnets utilized in this work are of N42 grade which is made of a composition of neodymium, iron, and boron, and is coated to avoid rusting. The magnets are axially magnetized disc magnets with a diameter of 5 cm. The thickness of magnets 1 and 2 is 1.27 and 2.54 cm, respectively. Nominal and measured magnetic intensities of the magnets are given in Table 1.