esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of Stress State on Hydraulic Openness and Permeability  
Hydraulic opening refers to the extent of internal joint openings within a rock subjected to pressure. In this section, percolation tests were conducted on models under confining pressures of 2 MPa, 4 MPa, and 6 MPa (As shown in Figure 13), with the average hydraulic opening recorded for deviatoric stresses ranging from 0 to 30 MPa. The test results reveal a negative correlation between the hydraulic opening and confining pressure, with the hydraulic opening decreasing as confining pressure increases. Simultaneously, the joint hydraulic opening diminishes as the deviatoric stress rises.  
Increasing the confining pressure renders the model more sensitive to the influence of deviating stress, resulting in a more pronounced decrease. Under a deviatoric stress of 0, the average hydraulic openness of the rocks at the three confining pressures remains roughly equal, indicating an undamaged state. As deviating stress increases, hydraulic opening gradually decreases, following a parabolic curve, with the most significant decrease occurring in the range of 20-25 MPa.  
Numerical models of weakly cemented sandstone were tested under various pore pressure conditions. Permeability was calculated by measuring the average flow velocity of each model once they reached pore pressure equilibrium. The relationship between the pore pressure difference and permeability is depicted in Figure 14. At pore pressure differences of 1 MPa and 6 MPa, the permeability values are 1.52 \(\times\) 10\({}^{-16}\) m\({}^{2}\) and 5.26 \(\times\) 10\({}^{-16}\) m\({}^{2}\), respectively. The results indicate that the permeability curve generally increases with an increase in the pore pressure difference. As the pore pressure difference escalates from 1 MPa to 6 MPa, the model's permeability increases by a factor of 2.46, signifying a significant impact of pore pressure on permeability.  
Figure 14: Permeability variation rule under pore-pressure difference.  
Figure 13: Variation in hydraulic opening under stress.
## 5 Discussion
### Fractional Crystallization and Crustal Contamination
The positive anomalies in U and Pb, which are fluid-mobile elements (FMEs), in the whole-rock phases of xenoliths indicate that those elements have been added by a subduction component related to mantle contamination by subduction fluids ([PERSON] and [PERSON], 1978; [PERSON] and [PERSON], 1978). A slight negative Nb anomaly, which is typical of arc magmatism, is also observed. This subduction signature was probably acquired during arc magmatism in the upper mantle during the late Cretaceous and early Tertiary ([PERSON] et al., 2012; [PERSON] et al., 2012). [PERSON] et al. (2012b) estimated the depth of the Jeju magma chamber to equal the depth of the lower continental crust ([PERSON] et al., 2014). Therefore, assimilation of the lower continental crust cannot be ruled out during the fractional crystallization of Jeju magma. By using the FC-AFC-FCA and mixing modeler [47], we estimated the relative ratio of assimilated material (lower continental crust) to crystallized material (\"r\") in the AFC process of magmatic evolution. AFC modeling was carried out with the average trace element concentration of the two xenolith samples as the source of magma and with the lower continental crust as the contaminant in the equation from [PERSON] (1981) [48]:  
\[\mathrm{C_{L}}=\mathrm{C_{0}}\cdot\mathrm{f}+\ \frac{\mathrm{r}}{(\mathrm{r}-1+ \mathrm{D})}\cdot\mathrm{C_{A}}\ (1-\mathrm{f})\]  
where \(\mathrm{C_{L}}\) is the concentration of the trace element in the resulting magma; \(\mathrm{C_{0}}\) is the concentration of the trace element in the remaining melt and the original magma, respectively; \(\mathrm{C_{A}}\) is concentration of trace element in the contaminant, \(\mathrm{r}\) is the ratio of rate of assimilation to rate of fractional crystallization; \(\mathrm{f}\) is \(\mathrm{F[-(r-1+\mathrm{D})/(r-1)]}\); \(\mathrm{F}\) is the fraction of magma remaining; and \(\mathrm{D}\) is the bulk distribution coefficient for the fractionating assemblage [48]. In Figure 8, Zr, Nb, and La values are plotted for \(\mathrm{r}=0.2\) (Zr vs. Nb) and \(\mathrm{r}=0.3\) (La vs. Nb). The partition coefficients of Zr, Nb, and La are referred from [PERSON] et al. (2004), [PERSON] (1991), [PERSON] (1988), [PERSON] et al. (1996), [PERSON] (1993), and [PERSON] (1998) [49; 50; 51; 52; 53; 54]. The \(\mathrm{C_{A}}\) values of Zr, Nb, and La of the lower continental crust are referred from [PERSON] (1995) [55]. The figure indicates that the ratio of the assimilation rate of continental crust to the fractional crystallization rate in Jeju magma is 0-0.3.
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 2 Methods
### Physical Modeling and Meshing  
Figure 1 shows a model of the cyclone separator designed in this study, which has an overall cylindrical shape and is divided into four main sections from top to bottom. In the working process, fly ash particles first enter the separator through the air inlet, where they are subjected to the combined effect of gravity and centripetal force, and the particles begin to move downward along the spiral path in the separation chamber. When the particles come into contact with the sloping wall, their trajectory is deflected, and the particles are gradually dispersed and separated in the process. The particles continue to move along the spiral path to the bottom of the separator, and before approaching the bottom, the smaller and less dense particles are driven by the airflow along the ascending spiral path and are  
eventually discharged from the upper discharge port. In contrast, the denser particles continue to move along the spiral path in the lower portion of the separator and gradually settle at the bottom.  
The structural design of the fly ash-cenospheres separating cyclone demonstrated in this study is detailed in Figure 1, and its detailed dimensional parameters are given in Table 1. For the purpose of numerical simulations, the cyclone separator has been meticulously meshed, where the cell type has been chosen as tetrahedral. This mesh type was specifically chosen because of its ability to reduce the total number of meshes while at the same time improving the quality of the mesh and its adaptability to complex geometries, thus ensuring the accuracy and computational efficiency of the simulations. The specific meshing of the cyclone separator is illustrated in Figure 2, and its grid number is 24,786. In this study, a grid independence study was carried out, as shown in Table 2. The grid numbers were set to 24,786, 29,120, 42,456, and 52,089, respectively, and then simulations were carried out. The variations in cenospheres separation efficiency, fly ash separation efficiency, and the Newton's efficiency were not more than 5%. Therefore, the grid number was chosen as 24786 in order to minimize the calculation time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Model Parameters**} \\ \hline \(l_{1}\) & Lower discharge tube height/mm & 60 \\ \(l_{2}\) & Sloping wall height/mm & 450 \\ \(l_{3}\) & Separation chamber height/mm & 600 \\ \(l_{4}\) & Length of feed tube/mm & 200 \\ \(l_{5}\) & Inlet height/mm & 90 \\ \(l_{6}\) & Height of upper discharge opening/mm & 30 \\ \(d_{1}\) & Separation chamber radius/mm & 200 \\ \(d_{2}\) & Lower discharge opening radius/mm & 60 \\ \(d_{3}\) & Upper discharge radius/mm & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cyclone separator model size table.  
Figure 1: Cyclone separator model structure schematic.
## 2 Related Work
### Research on Deep-Learning-Based Methods  
Recently, a convolutional neural network (CNN) has been successfully applied to 2D [1, 19, 20] and 3D [21, 22, 23] object detection, which has become a research hotspot. Deep-learning-based methods are data-driven by learning the characteristics of the annotation samples, which is robust for different scenarios.  
Many methods project 3D point clouds onto 2D images; then, image-based detection methods are applied to realize 3D object detection. [PERSON] et al. [24, 25] proposed an energy function to score a predefined 3D bounding box from 3D geometric information. In [26, 27], geometric constraints between 3D and 2D bounding boxes were used to recover the pose of 3D objects. However, these methods only generated coarse results due to the lack of depth information.  
Many researchers prefer voxel-based 3D object detection due to the fast speed and high accuracy. VoxelNet [28], SECOND [29], and PointPillars [21] project point clouds onto a voxel grid and learn features from each voxel. PointPillars is an extension to SECOND, encoding point clouds as pillars, and can achieve outstanding performance and have real-time efficiency. TANET [23] outperformed other methods for pedestrians, and is robust in noise point clouds by considering the channelwise, pointwise, and voxelwise attention of point clouds for each voxel.  
Other methods are directly based on point clouds. [PERSON] et al. [30] presented the PointNet architecture to directly learn point features from raw point clouds, and its applications include object classification and segmentation, and scene semantic segmentation. The follow-up works [31] further improved the extracted feature quality by a hierarchical neural network. PointRCNN [22] is a two-stage 3D detector that extracts features through PointNets and then estimates the final results with two-stage detection networks.  
Since the above deep-learning-based methods have achieved high detection accuracy, only trained objects could be detected. Detection performance is poor for untrained objects, especially in driving scenarios that are complicated and volatile.  
A common limitation of the above methods is that they all require complex parameters or can only detect trained object categories. This paper proposes 3 DSG, fusing semantic and geometric features for autonomous trucks in mining environments, and has wide parameter thresholds and does not depend on trained categories.
## 2 Materials and Methods
### Materials
#### 2.1.1 Synthesis Procedure  
Iron (II, III) oxide nanoparticles were synthesized using standard co-precipitation of ferric and ferrous ions under basic conditions [61; 62]. The Fe\({}_{3}\)O\({}_{4}\) nanopowder was subsequently used for functionalization with (3-glycidoxypropyl)trimethoxysilane (GPTMS). For this purpose, 20 mL of water was added to 100 mg of Fe\({}_{3}\)O\({}_{4}\). Subsequently, 1 g of citric acid was dissolved in 10 mL of water and introduced into the aforementioned solution, where it was stirred for 2 h. Finally, the solution underwent sonication for 5 min to achieve a stable suspension. Simultaneously, a pre-hydrolyzed GPTMS solution was created by combining 10 mL of water and tetrahydrofuran (THF) mixture with 107 mg of GPTMS, followed by stirring for 1 h [63]. Then, 1 mL of the prepared solution was added to 20 mL of the previously prepared suspension and stirred constantly for 6 h. The nanoparticles functionalized with GPTMS underwent magnetic separation and were subsequently rinsed three times with distilled water. Finally, the glycidoxypropyl-functionalized magnetite nanoparticles (Fe\({}_{3}\)O\({}_{4}\)@GLY) were dried under vacuum for 16 h at 50 \({}^{\circ}\)C.  
A portion of Fe\({}_{3}\)O\({}_{4}\)@GLY nanoparticles was used for the synthesis of TEMPOL-functionalized magnetite. For this purpose, 20 mL of THF was added to 100 mg of Fe\({}_{3}\)O\({}_{4}\)@GLY. Then, 30 mg of TEMPOL and 5 mg of the strong base, 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), were added. The suspension was stirred for 5 h. The final product, Fe\({}_{3}\)O\({}_{4}\)@TEMPOL nanoparticles, underwent magnetic separation and were rinsed with THF three times. Subsequently, the magnetic nanoparticles were dried under vacuum conditions.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 3 Supervised ML Solution
### Regional Applicability Assessment  
The structural types of buildings may differ from different regions. Therefore, if the prediction model is trained with the data of a city, the prediction accuracy may decrease when it is used in other cities.  
To assess the effects of different regions on the prediction accuracy, the model trained by Tangshan city was used to predict the structural types of 31,154 buildings in the downtown of Taiyuan city, China. Note that the building data of Taiyuan were provided by the department of urban planning in the local government. As demonstrated in Figure 6, the prediction accuracy of Taiyuan is 90.6% using the sample data in Tangshan, while that of Tangshan is 98.3% using the same data.  
It is noted that Taiyuan and Tangshan are typical northern cities in China; therefore, the two cities are similar. However, if a southern city is used, the prediction accuracy may be not very high. Therefore, the designed supervised ML solution is recommended to be applied for cities similar to the sample city. Furthermore, a semi-supervised prediction model is designed for better regional applicability.
## 4 Main Issues and Challenges
### Open Issues
#### 4.2.2 Lack of a Common Specification for Mission Plans  
Continuing with planning, most architectures use an ad-hoc mission plan specification, preventing its rescue in other projects. It is true that there are formal description languages from the automatic planning domain, like PDDL and RDDL that could be used. But it is also true that those languages are hard to understand for non-experts in automated planning. In addition, an interesting thought about the specification of the mission plan, including the mission domain and mission problem, is that nowadays systems also use context awareness descriptions that should be synchronized to the domain description of the problem. That being said, it could be worth to consider not only the specification of a common description language for mission plans, but also a mediator component integrated with the mission manager and the context manager that could adapt the descriptions in different formats to the needs of the components that will use them (like mission planners and reasoners), keeping the information synchronized for every participating agent.
## 2 Materials and Methods
### Mass Transport
#### 2.3.3 The Adsorptive Layer (DOM2)  
Isothermal mass transport of the toxins within the adsorptive layer is considered, including diffusion into the membrane pore structure, and physical adsorption on the activated carbon particles. In this work, we adopted the modeling procedure followed in [20; 21], which involves macroscopic mass transport of the dissolved toxins that is accompanied by the bulk fluid motion and adsorption and described by the transient diffusion-reaction equation. The activated carbon sorbent is assumed to be a homogeneous medium during adsorption modeling, uniformly packed in the interior of the adsorptive membrane layer. Moreover, instantaneous equilibrium between the toxin and the sorbent is assumed, and any viscous force that acts on the sorbent is neglected.  
More analytically, the following assumptions, are considered:  
* Film diffusion effects are negligible.
* Diffusion is fast compared to intra-particle adsorption; hence, local equilibrium conditions apply.  
Adsorption is modeled using Langmuir isotherms. The isotherms for the creatinine and the _toxin_ adsorption in the dual-layer mixed-matrix membranes (MMMs) appear to be of Langmuir type, according to literature [22]. Thus, the differential mass balance of the _toxin_ is given by:  
\[\frac{\partial C_{toxin,i}}{\partial t}-\
abla\cdot\left(D_{eff2,toxin,i} \
abla C_{toxin,i}\right)=-\frac{\rho_{AC}}{\varepsilon}\frac{\partial q_{ toxin,i}}{\partial t}\text{,} \tag{10}\]  
where \(C_{toxin,i}\), \(q_{toxin,i}\) are the (pore) concentration of _toxin_\(i\) in the fluid and the adsorbed mass of the _toxin_ on the sorbent, respectively. \(\rho_{AC}\) is the density of the carbon in the membrane volume, and \(\varepsilon\) is the porosity of the adsorptive membrane layer. The accumulation rate is given by the chain rule with respect to the concentration in the fluid:  
\[\frac{\partial q_{toxin,i}}{\partial t}=\left(\frac{dq_{toxin,i}}{dC_{toxin,i }}\right)\frac{\partial C_{toxin,i}}{\partial t}\text{.} \tag{11}\]The correlation of the _toxin_ concentration in the plasma to that of the monolayer capacity on the activated carbon is given by the Langmuir formalism as follows:  
\[q_{toxin,i}=\frac{q_{toxin,0i}K_{toxin,i}C_{toxin,i}}{1+K_{toxin,i}C_{ toxin,i}}, \tag{12}\]  
\[\frac{\partial q_{toxin,i}}{\partial C_{toxin,i}}=\frac{q_{toxin,0i}K_{ toxin,i}}{(1+K_{toxin,i}C_{toxin,i})^{2}}, \tag{13}\]  
\(q_{toxin,0i}\) is the maximum adsorption constant, and \(K_{toxin,i}\) is the Langmuir kinetics constant. In this work, in the case of creatinine, the isotherm of a similar (previous generation) membrane absorber [22] under batch adsorption condition was used, while for PCS and IS the adsorption isotherms were unknown. The Langmuir parameters of the equilibrium for the _toxin_ data were extracted by a fitting procedure.
## 3 Results and Discussion
### Bending Strength of Double Raschel GCCM with Curing Time  
Typically, the curing period required to obtain the desired physical properties of concrete is around 28 days. However, in the scope of this study, the bending strength was assessed at two different curing periods: 10 days and 30 days, using the fast-setting cement. Table 5 shows the bending strength of double raschel GCCM with curing period. From this, it is seen that fast-setting cement can be effectively applied in double raschel GCCM applications, with a curing period of 10 days. Furthermore, this suggests the potential application of this material for emergency restoration purposes.  
The double raschel GCCM, when subjected to a 30-day curing period, showed excellent bending strength, reaching a value of 9.29 MPa. This improvement is attributed to the reinforcing influence of basalt fiber within the upper and lower textiles, thus contributing to the overall performance enhancement. From this, it is suggested that the integration of fast-curing cement with double raschel GCCM could potentially lead to a reduction in construction period while simultaneously achieving enhanced physical properties during field application.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Insights on Strategies Used to Synchronize LoRa Signals  
In order to properly measure the contribution we make in this paper, we recall, in this section, the main principles of the synchronization methods commonly used in LoRa [14; 19]. It must be recognized that the synchronization method developed in [14] is very clever and offers an excellent compromise between the performance and the implementation complexity. However, the low computational complexity of the synchronization proposed by the latter work leads to the constraint of the maximum CFO estimable of \(\frac{B}{4}\)[20; 26].  
To understand the synchronization process of LoRa, which leads to the latter constraint of maximum CFO estimable, it is mandatory to give a brief overview on the structure of the specific LoRa preamble used in this purpose.
## 3 Results
### Extent of Irrigated Area
#### 3.1.2 USA  
In the USA, the irrigated area decreases by 27% at the resolution of 300 m and by 48% at the coarsest resolution of 1000 m (Figure 3 and Table 1). At the finest resolution, the irrigated area around the Arkansas River in the south of the scene is dense and, therefore, not affected by the coarser resolutions. In the northwestern part of the scene, some single fields and fields in small irrigation clusters exist. Small single irrigated fields or smaller irrigated clusters are scattered over the whole tile. By decreasing the spatial resolution, the small, irrigated fields disappear and the fields in the larger agglomerations prevail.
## 3 Binary Mn-Based Metal Oxide Catalysts
### 3.1. \(MnO_{x}\)-CeO\({}_{2}\) Catalysts
The catalytic performance of MnO\({}_{\rm x}\)-CeO\({}_{\rm 2}\) below 200 \({}^{\circ}\)C is in the order of MnO\({}_{\rm 2}\)-CeO\({}_{\rm 2}\) > Mn\({}_{\rm 2}\)O\({}_{\rm 3}\)-CeO\({}_{\rm 2}\) > Mn\({}_{\rm 3}\)O\({}_{\rm 4}\)-CeO\({}_{\rm 2}\). Between 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, the performance order is MnO\({}_{\rm 2}\)-CeO\({}_{\rm 2}\) > Mn\({}_{\rm 2}\)O\({}_{\rm 3}\)-CeO\({}_{\rm 2}\)\(\approx\) Mn\({}_{\rm 3}\)O\({}_{\rm 4}\)-CeO\({}_{\rm 2}\). Characterization results suggested that MnO\({}_{\rm 2}\)-CeO\({}_{\rm 2}\)'s superior performance is due to its large specific surface area, strong redox performance, and greater number of surface acid sites. Additionally, the preparation method significantly affects the low-temperature catalytic performance of MnO\({}_{\rm x}\)-CeO\({}_{\rm 2}\) catalysts. Liu et al. [50] prepared a series of Mn-Ce mixed oxide catalysts with different Mn/Ce molar ratios using the surfactant-template method (ST) and the conventional co-precipitation method (CP). The low-temperature denitration test results showed that the Mn-Ce mixed oxide catalyst prepared by the ST method exhibited stronger denitration activity compared to that prepared by the CP method. The Mn-Ce mixed oxide catalyst prepared by the ST method achieved nearly 100% NOx conversion in the 100-200 \({}^{\circ}\)C range and exhibits higher resistance to H\({}_{2}\)O and SO\({}_{2}\). This is attributed to its larger specific surface area, which enhances the adsorption and activation of NH\({}_{3}\) and NO\({}_{\rm x}\), thereby facilitating NOx reduction. Qi et al. [51] developed a series of highly active manganese-cerium oxide catalysts using the citric acid (CA), co-precipitation (CP), and impregnation (IM) methods. A low-temperature denitrification test (373-453 K) revealed that the catalyst synthesized via the CA method had the smallest particle size and the weakest XRD peak intensity. The MnO\({}_{\rm x}\)-CeO\({}_{\rm 2}\) catalyst prepared through co-precipitation and the CA method generated oxygen vacancies in the CeO\({}_{\rm 2}\) lattice due to the addition of Mn atoms, which can adsorb and activate molecular oxygen to form reactive oxygen species. tion results attribute this to the MnCeO\({}_{\rm x}\) catalyst's increased acid sites and improved redox performance. In addition, Ce\({}^{4+}\) doping inhibits the crystallization of the MnO\({}_{\rm x}\) catalyst, resulting in a more dispersed active component and a larger specific surface area.