esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Physicochemical Characterization of the Gob Spoil and Amendments  
Gob spoil soil and the organic amendments (biochar and compost) were analyzed for their properties. Sample pH and electrical conductivity (EC) were measured following standard protocols using a Orion Star A215 (Thermo Fisher Scientific, Waltham, MA, USA), Advanced pH/conductivity benchtop meter [14]. Water-holding capacities were quantified by the modified Bernard method [15] as described in [PERSON] (2018) [16]. Organic matter content was measured by a loss-on-ignition method [17]. Total C and total N were quantified by a CHNS elemental analyzer (Elementar Americas Inc., Ronkonkoma, NY, USA). The soil texture (i.e., silt, sand, and clay contents) was analyzed using the pipette method [18]. Plant-available N was determined by the sum of ammonium, nitrite, and nitrate that are available for plant uptake [19; 20]. Plant-available P was analyzed by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES, Agilent Technologies, model 5100; Agilent Technologies Inc., Santa Clara, CA, USA) after Mehlich III extraction [21].  
The total concentrations of metals (Ag, Al, As, Ba, Cd, Cr, Cu, Fe, Hg, Mn, Pb, and Se) and P in samples were measured using ICP-OES after acid digestion following USEPA Method 3050B [22]. For ICP-OES analysis, yttrium was applied as the internal standard and quality control check standards were also used to guarantee the accuracy of sample analysis.
## 3 Results
### Impact of Thrusters at Various Locations on Spacecraft Charging and Discharging Effect
Instead, it is necessary to individually observe the surface current changes at each node. Taking the potential differences at Nodes 4 and 9 as an example, comparing the surface current variations over time at these two nodes for the thruster in two different positions is depicted in Figure 12.  
By comparing the charging currents of the thrusters at different positions, we can observe that there are significant differences in the photoelectron and CEX_Xe+ currents collected from various surfaces at the same node, as shown in Table 3.  
Figure 12: Variations in spacecraft surface currents over time at Nodes 4 and 9 with the thruster in two positions: (**a**) Thruster at Position 1, Node 4 current. (**b**) Thruster at Position 2, Node 4 current. (**c**) Thruster at Position 1, Node 9 current. (**d**) Thruster at Position 2, Node 9 current. Xe+ generated after CEX collisions is prone to be attracted by the spacecraft surface potential. Consequently, Node 9, which is not illuminated and has a higher negative potential, is more likely to attract low-energy, low-speed CEX_Xe+ generated by the thruster at Position 2, which is closer to it. This results in more CEX_Xe+ attaching to the surface of Node 9, causing a decrease in its surface potential until it reaches equilibrium with the space plasma potential. When the thruster at Position 2 operates near Node 9, a large amount of CEX_Xe+ can attract photoelectrons near the antenna, which makes the number of photoelectrons diffused near the front dramatically smaller, and because of the absence of photoelectrons emitted and absorbed, a large amount of negative charge accumulates on the surface, and thus Node 4 generates a higher negative potential.
## 2 Experimental Setup and Methods
### Operating Points  
Measurements were conducted at a total of seven different operating conditions (Table 1), covering three velocities and three temperatures. The quantity of AdBlue injected at the respective operating points was defined by means of a feeding factor, \(\alpha\), which sets the injected amount of ammonia (\(\hat{\mathrm{n}}_{\mathrm{NH_{2}}}\)) in relation to the amount of nitrogen oxides (\(\hat{\mathrm{n}}_{\mathrm{NO_{2}}}\)) that would be contained in exhaust flow. The measurements, however, were carried out with dried air. If the feeding factor equals unity, the stoichiometric demand for a complete reduction of nitrogen oxides is supplied. The feeding factor is computed based on the volume flow (\(\hat{\mathrm{V}}_{\mathrm{Exhaust}}\)), gas temperature (T) and the pressure (p) (Equation (1)). It was assumed that there is a balanced ratio of NO and NO\({}_{2}\) and that two ammonia molecules are formed per urea molecule ((\(\mathrm{NH_{2}}\))\({}_{2}\)CO), corresponding to complete decomposition; \(\varphi\) denotes the volume fraction of NO and NO\({}_{2}\) respectively. To compute the molar exhaust gas flux (\(\hat{\mathrm{n}}_{\mathrm{Exhaust}}\)) from the measured volumetric flow rate (\(\hat{\mathrm{V}}_{\mathrm{Exhaust}}\)), the ideal gas law was used, where R stands for the universal gas constant.  
The investigated operating points included cases with stoichiometric AdBlue injection (\(\alpha=1\)) and double- and triple-stoichiometric cases.  
\[\alpha=\frac{\hat{\mathrm{n}}_{\mathrm{NH_{2}}}}{\hat{\mathrm{n}}_{\mathrm{NO_ {2}}}}=\frac{0.65}{\mathrm{M}_{(\mathrm{NH_{2}})_{2}\mathrm{CO}}}.\frac{\hat{ \mathrm{m}}_{\mathrm{AdBlue}}\cdot\mathrm{R}\cdot\mathrm{T}}{\mathrm{p}\cdot \hat{\mathrm{V}}_{\mathrm{Exhaust}}\cdot\left(\varphi_{\mathrm{NO}}+\varphi_{ \mathrm{NO_{2}}}\right)} \tag{1}\]  
The gas temperature covers the range typical for SCR systems, with 180 \({}^{\circ}\)C representing the lower limit for the hydrolysis of isocyanic acid [38]. In addition, ammonium nitrate is deposited in the catalyst at temperatures well below 200 \({}^{\circ}\)C [2]. The velocities are chosen to be similar to gas flow rates in diesel exhaust and cover a wide range of Reynolds numbers.
###### Abstract  
In this paper, a novel and robust measurement method is proposed for obtaining the geometric errors of rotary tables by using LaserTRACER and the reflectors mounted on the reflector standard fixture. For the machining accuracy, the six-degree-of-freedom (6-DOF) geometric errors of the rotary axes interactively influence the manufacturing quality of the precise workpieces. Therefore, this paper mainly aims to develop a measurement method for identifying the 6-DOF geometric errors of rotary tables without using the external linear axis. Furthermore, the set-up errors of the reflector standard fixture are also considered and identified to reduce the influence of the 6-DOF geometric error measurements. For each rotary table geometric error measurement, the positions of the LaserTRACER as well as the relative distance between the reflectors and the LaserTRACER are measured and obtained for determining the 6-DOF geometric errors of the rotary tables. In addition, the homogeneous transformation matrix (HTM), multilateration method, and least squares method are used for building the mathematical measurement algorithm. Moreover, the experimental verifications are implemented to demonstrate the accuracy of the proposed measurement method. Conclusively, the experiment and simulation verification results clearly delineate that the maximal relative differences in the linear errors and the angular errors of the 6-DOF geometric errors are, at most, 3.25% and 2.30%, respectively.  
rotary table; geometric errors; multilateration method; auto-tracking laser interferometer +
Footnote †: journal: Applied
# 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework
## 4 Methodology  
Notwithstanding 6G still being nascent in its research and development, a quest to support 6G R&D decision-making in government and industry has emerged. Despite significant technological and economic uncertainty, exploration of how the potential 6G rollout can be pursued both spatially and temporally is in policy formulation. Therefore, the coverage, cost, and rollout consequences of 6G networks globally are explored by extrapolating 5G characteristics. In this study, we focus on an appropriate methodology that covers 6G vision connectivity and its impact of annual capital intensity, infrastructure sharing and the end-user speed. Furthermore, varying annual capital intensity or deployment of a shared tiny high density cell network that can greatly influence the span of time to reach the 90% threshold benefiting ubiquitous connectivity globally is explored. Additionally, by integrating new and existing spectra, a 6G network capable of achieving Tera Bit speed with massive network capacity and extreme low latency is considered for universal service globally.  
Based on the 6G challenges outlined in the sub-Section 3.1.2, the following are some of the research opportunities based on the sequential methodology that should be investigated: (i) cost modeling, (ii) ways to reduce the costs of 6G infrastructure rollout is via infrastructure sharing, (iii) spectrum and coverage obligations--frequency bands that are yet to be defined and allocated, therefore spectrum costs for 6G are unknown at present, (iv) sensitivity analysis to identify the cost boundaries which will have influence on 6G coverage and capacity, and (v) 6G policy implications.  
A sequential methodology [41], Ref. [42] (p. 639) process enables the analysis team to concisely define the requirements, then planning for design prior to execution and ultimately focus on network densification and spectrum. A prospective study to evaluate 6 GIIoE system is an integral part of developing and launching 6G network services and IIoE system technologies. In this study, we have proposed sequential approach to conduct analysis, design, implementation, and test to allow adaptations or modifications to aspects of a 6 GIIoE system to validate the integrity of the trial. The application of sequential methodology in 6 GIIoE system trials has the possibility of significantly improvement of the flexibility, and efficiency. To further advance the performance of 6 GIIoE system trials, the sequential method can exploit novel adaptive approaches of various applications identifying the future directions in this field of research and development. The sequential methodology for the 6 GIIoE system, as shown in Figure 1a,b, is adopted phase by phase: (i) the analysis, (ii) the design, (iii) implementation, and (iv) the testing.  
Figure 1: (**a**) Phase by phase sequential methodology process. (**b**) Sequential Methodology Process.  
Each part of the sequential methodology, step by step, is explained below to deal with the sources of uncertainty:
The SIS is a semi-enclosed coastal sea with an area of approximately 23,000 km\({}^{2}\), a length of approximately 450 km, and an average depth of approximately 38 m; the SIS is located in the western part of Japan [11]. It has only two connections to the Pacific Ocean through the Bungo and Kii channels and one connection to the East/Japan Sea through the Kanmon Strait. The SIS consists of approximately 700 islands and many narrow straits connecting basins and bays, including Hiroshima Bay and Osaka Bay [11] (Figure 1). The Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) can be set up to perform efficient and accurate simulations at varying scales, such as creek, lake, river, estuary, shelf, and ocean scales [12; 13; 14]. The seamless cross-scale modeling system was validated in a global simulation [15]. They showed that a single mesh spanning resolutions from 10 km to 8 m can depict the global ocean, covering a range of scales from the entire extent of the five oceans down to the scale of an estuary. Furthermore, this model supports fluent modules applicable to diverse areas such as tsunami hazards, storm surges, sediment transport, biogeochemistry, water quality, and oil spills. The adoption of an unstructured grid allows users to describe complex coastlines more precisely and the grid can be applied to the other modules as-is. In this study, we built an unstructured grid-based high-resolution hydrodynamic model of the SIS using SCHISM. The model was established to simulate coastal and oceanic processes, including any extreme weather and events, as robustly and accurately as possible by applying spatially varying filtering parameters depending on possible numerical instability factors and a spatially varying grid resolution depending on the bathymetric gradient to reduce pressure gradient errors. We validated the model results with observational data and visualized the results in a three-dimensional domain. Then, the calibrated model was applied to the extreme storm surge in Osaka Bay that was induced by Typhoon Jebi in 2018. In the following text, the characteristics of the model and the parameter-setting process are described in Section 2. Section 3 illustrates the validations of the numerical model for tides, temperatures, and currents in the SIS. The storm surge application of the model is described in Section 4. Section 5 shows the 3D visualization and modeling system plan for obtaining a digital twin of the SIS. Conclusions are provided in Section 6.
## 3 Theoretical Analysis and Model Construction
### Reconstruction Model of Hybrid Intelligent Optimization Algorithm
#### 3.2.2 Options of the Hybrid Intelligent Algorithms
Because of SIRT's fitness for sparse, irregular, and low signal-to-noise ratio measured data, as well as its robust operational performance, it is widely used to solve matrix equations with pathological characteristics obtained under incomplete projection conditions in underground engineering [41]. Therefore, this research firstly applied the MPGA to solve the global optimization problem of the target function when it reached the minimal value in electromagnetic wave CT reconstruction of the working face, and then used the obtained optimal solution as theinitial model for further iterations of the traditional linear reconstruction algorithm, SIRT, to form a hybrid intelligent algorithm model: MPGA-SIRT. Finally, the EM wave absorption coefficient distribution in the working face exploration area was obtained. Due to the fact that the results are closer to reality, this hybrid intelligent algorithm can provide a basis for the accurate prediction of the geological environments of coal mine working faces.
## 3 Simulation Results
### Effects of \(Q\) Limits Violation  
To investigate the impact of \(Q\) limit violations on VSM computation, we have studied many cases, including various load patterns, various load levels, and various \(Q\) limit violations. Among those investigated cases, some selected simulation scenarios are listed in Table 1.  
Figure 10: Comparison with \(\lambda^{\max}\) and CPU time between the proposed approach and conventional CPFLOW approach under multiple load change scenario on IEEE 118-bus system: proposed approach, \(\lambda^{\max}=3.1871\), CPU = 2.15 s: conventional CPFLOW approach, \(\lambda^{\max}=3.1871\), CPU = 6.58 s.  
Figure 9: Comparison with \(\lambda^{\max}\)and CPU time between the proposed approach and conventional CPFLOW approach under single load change scenario on IEEE 118-bus system: proposed approach, \(\lambda^{\max}=0.9868\), CPU = 1.95 s; conventional CPFLOW approach, \(\lambda^{\max}=0.9868\), CPU = 11.27 s.  
The simulation results for the IEEE 30-bus and IEEE 118-bus are presented in Tables 2 and 3, respectively. The output is organized in five columns: the first column depicts the test case which scenario is listed in Table 1. The simulation results of \(\lambda^{\text{max}}\) computed by the proposed approach and CPFLOW approach are given in column 2 and column 3. The last two columns depict the results of CPU time (s) computed by the proposed approach and CPFLOW approach. From Tables 2 and 3, it is once again shown that the proposed approach is able to provide an accurate solution with a shorter computation time.
## 3 Results and Discussion
### \(\alpha\)-Amylase Activity  
The \(\alpha\)-amylase activity of the formulated quinoa-wheat composite flours, defined through the Falling number (FN) index, showed that FN values varied from 277 to 347 s. The regression model obtained for FN (s) is represented by Equation (5):  
\[\text{FN}\,=\,307.99\,-15.65\cdot\chi_{1}-4.53\cdot\chi_{2}\,-11.95\cdot\chi_ {1}\cdot\chi_{2}\,+9.25\cdot\chi_{1}^{2}\,-0.38\cdot\chi_{2}^{2} \tag{5}\]The results of the regression analysis for the FN index showed that the quadratic model was highly significant (\(p<0.0001\)) and defined well the real \(\alpha\)-amylase activity of the composite flour through FN index (\(R^{2}=0.88\)). The particle size of quinoa flour added had a significant negative effect (\(p<0.05\)) on the FN, while the level added had a nonsignificant effect (\(p>0.05\)). Probably, the phenolic acids present in the small particle size bind to \(\alpha\)-amylase changing its conformation and reducing its hydrolytic activity, thus determining an increase in FN index. FN was significantly negatively correlated (\(p<0.05\)) with the interaction effect between QF addition level in wheat flour and particle size, and also with quadratic term of QF addition level. The negative coefficient of the QF addition level indicated that the FN index of quinoa-wheat composite flour decreased with substitution of quinoa seed flour level increase. The effects of particle size and QF addition level indicated a decrease of FN index with particle size and QF addition increase (Figure 1a), suggesting an increase of the \(\alpha\)-amylase activity. It is well known that the FN index is inversely proportional with \(\alpha\)-amylase activity in flour [70]. The increase of \(\alpha\)-amylase activity of the composite mixture is related to the fact that \(\alpha\)-amylase is a metalloenzyme which depends on the presence of calcium ions in its molecule for activity [71]. It is known that quinoa seeds contain a high level of calcium [59] and, therefore, an increased level of quinoa flour in the quinoa-wheat composite flour would result in an increase of \(\alpha\)-amylase activity mixture, improving final product quality.  
Figure 1: Response surface graph for combined effects of quinoa flour particle size and levels added in refined wheat flour on: (**a**) Falling number index and Mixolab parameters during dough development stage: (**b**) water absorption (WA), (**c**) development time (DT), (**d**) dough stability (ST).
## 6 Mathematical Models for SNR and Signal Combining in ADRs
### Signal Selection and Combination Schemes in ADRs  
Among the signal selection and combination techniques used in ADRs with greater efficiency are the maximum ratio combining (MRC) and the select best combining (SBC) schemes. MRC is a signal-combining technique in which the signals from all of the photodiodes are combined in a weighted manner proportional to their respective SNRs. The combined signal power \(P_{\text{MRC}}\) of all photodiodes using MRC is the sum of the powers received at each photodiode, weighted by their SNRs.  
\[P_{\text{MRC}}=\sum_{k=1}^{N_{\text{PD}}}\text{SNR}_{k}P_{r,\text{PD}_{k}}. \tag{38}\]  
The combined SNR for the MRC scheme, \(\text{SNR}_{\text{MRC}}\), is the sum of the SNRs at all photodiodes:  
\[\text{SNR}_{\text{MRC}}=\sum_{k=1}^{N_{\text{PD}}}\text{SNR}_{k}=\sum_{k=1}^{ N_{\text{PD}}}\frac{P_{\text{MRC}}}{N_{0}B}. \tag{39}\]  
On the other hand, in SBC, only the photodiode with the highest SNR is selected for signal reception. The resulting SNR for the SBC scheme is the maximum SNR among all the photodiodes:\[\text{SNR}_{\text{SBC}}=\max_{k=1}^{N_{\text{PD}}}\text{SNR}_{k}=\max_{k=1}^{N_{ \text{PD}}}\frac{P_{\text{SBC}}}{N_{0}B}, \tag{40}\]  
where \(P_{\text{SBC}}\) is the best power selected by the method of the powers received by each photodiode in the ADR.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 4 Conclusions  
From the results of the sand and sand mixtures, a clear trend can be observed: increased hydrophobicity (or contact angle) from the presence of more hydrophobic soil particles caused a reduction in the saturation degree at every value of suction. Thus, increased hydrophobicity caused air entry at lower suctions and less water to be retained at any value of suction. These trends in the WRC could be inferred from the existing literature; however, this study allows the trends to be more confidently stated due to all other influencing parameters being kept similar between the hydrophobic and hydrophilic sand and the WRCs measured and expressed in volumetric terms.  
The coal used in this study had its receding contact angle measured to be 90\({}^{\circ}\), which indicated it was hydrophobic. However, when comparing the WRCs of coal to hydrophilic rock with a similar particle size, shape and distribution, no reduction in the saturation degree at every suction was observed. A reasonable explanation for this discrepancy cannot be provided, which indicates other unknown phenomena relating to the interaction of coal and water are affecting the measured WRCs, requiring further research to understand the discrepancy.  
Using an existing model with the measured receding contact angles of the material to predict the drying paths of different WRCs showed its limitations when applied to crushed coal. Hence, more research is needed to properly predict the retention properties of crushed coal particles and tailings for engineering purposes.  
Figure 12: Predicted drying paths of the WRCs for crushed coal at different contact angles: (**a**) 212–300 \(\upmu\)m size fraction; (**b**) 150–212 \(\upmu\)m size fraction; (**c**) 75–150 \(\upmu\)m size fraction and (**d**) 50–75 \(\upmu\)m size fraction. For each prediction, CA values of 0 (black lines) and \(\text{CA}_{\text{mean}}-\text{CA}_{\text{SD}}\) (blue lines) were used.  
**Author Contributions:** Conceptualization, A.V., O.B. and S.F.; methodology, A.V., O.B. and S.F.; validation, A.V., O.B. and S.F.; writing--original draft preparation, A.V.; writing--review and editing, A.V., O.B. and S.F.; supervision, O.B. and S.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge the use of the instruments and scientific and technical assistance of [PERSON] at the Monash Centre for Electron Microscopy, a node of Microscopy Australia. This research used equipment funded by Australian Research Council grant ARC Funding (LE0882821).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Performance of Studied Asphal Mixtures
#### 3.4.2 Permanent Deformation Resistance of Studied Asphalt Mixtures  
The rut resistance test evaluates the susceptibility of asphalt materials to permanent deformation. It consists of measuring the depth of a rut formed after a number of load applications of a wheel over an asphalt slab at high temperature conditions. Figure 10 represents the rut depth measured, for each mixture, up to the 10,000 th cycle, and the wheel-tracking slope (WTS\({}_{\text{AIR}}\)) calculated as the average rate at which the rut depth increases with repeated applications of a wheel load (mm/10\({}^{3}\) cycles), between the 5000 th and the 10,000 th cycles.  
By analysing the results obtained for the rut resistance, it can be concluded that the mixtures with PEX deformed much less than the mixtures with mineral aggregates. Looking at the performance results of M1 and M3 (same binder and binder content but with and without PEX as aggregate) the reduction in terms of deformation is very significant, which is also visible in the reduction of the WTS\({}_{\text{AIR}}\) value. When increasing its binder content by 0.5% (M4), the deformation increases, as expected when compared with M3, but the performance is still very good, being close to the performance to the PMB mixture without PEX (M2). Between the mixtures with PMB binder, the use of PEX also decreased the deformation, but in this case, its effect is not that significant.  
Regarding the WTS\({}_{\text{AIR}}\), the conclusions are similar. Thus, the asphalt mixtures with PEX have much better permanent deformation performance than the conventional mixture (M1).