esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contact Stress Distribution  
The reflection coefficient distribution of the component is shown in Figure 10. A strip bulge along the axial direction is observed, which indicates that there is wear on the mating interface during the pressing process. As shown in Figure 10b, the reflection coefficient of the thin-walled section is larger than that of the thick-walled section, which indicates that the contact stress of the thin-walled section is relatively small, which is consistent with the actual situation.  
The stress distribution is shown in Figure 11. The contact stress of the uniform cylinder is basically the same as that of the thin-walled section of the step cylinder. In addition, the stress of the thick-walled section is greater than that of the thin-walled section, because the increase in the wall thickness leads to an increase in structural stiffness, which leads to a larger radial strain at the mating surface. In order to analyze the measurement accuracy, the average stress distribution along the axis was calculated and compared with the analytical result of the thick-walled cylinder theory, as shown in Figure 12. As can be seen from the figure, the relative errors \(\delta\) of the measurement results of the two structures are within 20%.  
Figure 11: Distribution of contact stress: (**a**) uniform cylinder, (**b**) step cylinder.  
Figure 12: Measurement accuracy with different geometric shapes.  
Figure 10: Distribution of reflection coefficients: (**a**) uniform cylinder, (**b**) step cylinder.
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 4 Numerical Test Cases
### 1-D Problems  
The single and two-phase compressible attributes of the proposed CICSAM-HLLC solver are evaluated in this sub-section using 1-D test-cases. For all described cases, the domain is an equispaced structured mesh where outflow conditions are prescribed at each end.  
The first test-case considered is that of a 1-D two-phase Riemann problem [45]. The objective is to assess the accuracy due to CICSAM as compared to HLLC in discretising the VoF equation. The test-case consists of a gas-liquid unit domain where the following initial conditions are applied:  
\[(\rho,u,p,\gamma,p_{\infty})=\begin{cases}(1.241,0.0,2.753,1.4,0.0),&\text{if }0 \leq x<0.5\\ \left(0.991,0.0,3.059\times 10^{-4},5.5,1.505\right),&\text{if }0.5\leq x\leq 1.\end{cases}\]  
The flow is allowed to evolve for 0.1 s in time.  
Figure 3 demonstrates an excellent agreement between the numerical and analytical solutions for both HLLC and CICSAM applied to the VoF equation. In particular, CICSAM allows for a sharp capture of the liquid-gas interface, which leads to increased accuracy in computing internal energy.  
To quantify this improved accuracy, a mesh independence study was conducted using the \(L_{1}\) norm, \(\|\varepsilon\|_{1}\), of the absolute error,  
\[\|\varepsilon\|_{1}=\frac{\sum\limits_{l}^{N}\lvert\phi_{nu}(x_{l},t)-\phi_{ \mathit{an}}(x_{l},t)\rvert}{N},\]  
where \(\phi(x_{l},t)\) denotes the value for a variable at position \(x_{l}\) and time \(t\). Further, \(\phi_{nu}\) and \(\phi_{an}\) denote the numerical and analytical solutions, respectively.  
Figure 3: Gas–liquid Riemann problem: assessment of Hartens–Lax–van Leer Contact (HLLC) and HLLC-compressive interface capturing scheme for arbitrary meshes (CICSAM)—100 nodes: The first row depicts the total internal energy (**left**) and velocity (**right**). The second row shows the pressure (**left**) and volume fraction field (**right**).  
From Figure 4 right, for the volume fraction field, it is seen that CICSAM achieves an \(L_{1}\) norm of at least an order of magnitude lower than that of HLLC. Similarly, the sharper interface leads to an improvement in the accuracy of the internal energy Figure 4 left. Localised second-order spatial accuracy is also observed with the proposed CICSAM method when refining from a 400 to a 1600 node mesh.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 2 Dynamic Modeling  
### Dynamic Modeling of the Active Rear-Axle Independent Steering (ARIS) System  
The steering mechanism of the ARIS system is realized by two electric actuators with adjustable length installed on the left and right rear wheels. A linear electric drive motor with reducer (e.g., gear reducer and screw-nut transfer) is selected as the electric actuator. In order to facilitate the establishment of the model, the steering mechanism and actuator are simplified, and the simplified dynamic model is shown in Figure 1.  
Figure 1: The dynamic model of active rear axle independent steering mechanism.  
Through the mechanical analysis, the plant model of each wheel can be described by  
\[\left\{\begin{array}{l}J_{1}\ddot{\delta}_{r1}+c_{1}\dot{\delta}_{r1}+k_{1}\delta _{r1}=F_{1}\cdot b_{r}\cos\delta_{r1}+M_{f1}+M_{z1}+M_{w1}\\ J_{2}\ddot{\delta}_{r2}+c_{2}\dot{\delta}_{r2}+k_{2}\delta_{r2}=F_{2}\cdot b_{r} \cos\delta_{r2}+M_{f2}+M_{z2}+M_{w2}\end{array}\right. \tag{1}\]  
where \(J_{i}\) is the total moment of inertia of rotating parts; \(F_{i}\) is the driving force of electric actuator; \(M_{fi}\) is the friction moment; \(M_{zi}\) is the tire self-aligning torque around the kingpin; \(M_{wi}\) is the moment of uncertain force on the ground; \(c_{i}\) is the equivalent damping coefficient; \(k_{i}\) is the equivalent stiffness; \(b_{r}\) is the length from the connection point to the kingpin; \(\delta_{ri}\) is the rear wheel angle. In the above parameter variables, subscript \(i\)= 1,2 indicates the left and right wheels of rear axle, respectively.  
According to [PERSON]'s law, the friction moment produced by the rotation of the tire around the kingpin can be described as  
\[M_{fi}=M_{sfi}+F_{zri}\mu_{ri} \tag{2}\]  
where \(M_{sfi}\) is the initial value of friction moment; \(F_{zri}\) is the rear wheel vertical load; and \(\mu_{ri}\) is the Coulomb friction coefficient. In order to ensure the straight-line driving stability of the vehicle, the rear wheel kingpin should have a certain inclination, which will generate self-aligning torque around the kingpin. According to [30], the self-aligning torque, \(M_{zi}\), can be given by  
\[M_{zi}=(F_{zi}s_{i}\sin 2\gamma_{i}\sin\delta_{ri})/2 \tag{3}\]  
where \(\gamma_{i}\) is the kingpin inclination, and \(s_{i}\) is the horizontal distance from the tire center to the kingpin.  
The moment of uncertain force on the ground, \(M_{wi}\), includes the coupling force caused by the synchronization error between rear axle wheels. It is also related to vehicle speed \(v_{x}\) and lateral acceleration \(a_{y}\). Explicit modeling of this moment is not possible. Instead, it can be described with implicit form as  
\[M_{wi}=f_{wi}(\delta_{r1},\delta_{r2},v_{x},a_{y}) \tag{4}\]  
It can be seen from Equations (1) to (4) that the dynamic model of the ARIS system has strong nonlinearity and uncertainty. For this system, it must rely on a robust controller with strong anti-disturbance capacity to achieve high-precision tracking performance in the presence of system uncertainties.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 4 Discussion
### Land Cover Classification and Change Detection  
Random forest is a well established method for land cover classification [39; 55; 56] because of its applicability and flexibility, and ability to hand high-dimensional data [57]. Rapid-DEM used a random forest because of its existing implementation in GEE [22] that does not require the use of additional computational resources to train more advanced classification models (e.g., UNET, CNN) [8].  
Additionally, the resolution of PlanetScope data (3 m) allowed for either pixel-based or object-based thematic classification. We explored the use of object-based classification in GEE utilizing SNIC (Simple Non-Iterative Clustering) [58] for object segmentation and a random forest for classification, but the preliminary results provided less reliable thematic change and required longer computational run times compared to pixel-based approach. However, different implementations of object-based thematic change detection may out perform the pixel-based approach used in Rapid-DEM.  
To reduce sources of error that stem from inaccuracies in the thematic land cover classification improvements could be made in the post-thematic change detection method. One example would be to utilize the high temporal resolution of PlanetScope data to create a dense series of land cover maps and integrate approaches such as hidden Markov models (HMM) to stabilize classifications by limiting noise derived from pixel flipping (i.e., toggling between thematic classes) [59]. While the binary change mask does remove a large proportion of potential error propagated from errors in thematic change detection the mask can potentially exclude important changes. In the case study system several areas of new construction were excluded from the priority queue because they were not identified by the change mask. Additionally, alternative approaches or improvements to post-classification thematic change detection could be used to detect topographic change. For example, methods used to generate DEMs directly from remotely sensed imagery [60; 61].
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 1 Introduction  
Electrostatic spray technology for agricultural aviation is the application of traditional electrostatic spray technology in an airborne platform. It is one of the research topics that scholars in the field of agricultural aviation always pay attention to. Agricultural aircraft electrostatic spray technology is mainly based on induction, corona, and contact charging methods to charge the droplets. Under the action of high voltage static electricity, charged droplets make rapid directional deposition along the electric field line in the air and settle on the target [1,2]. For this reason, aerial electrostatic spray can effectively reduce drift loss during aerial pesticide application, improve droplet deposition, and mitigateenvironmental pollution [3; 4]. [PERSON] et al. [5] from the Agricultural Research Service of the United States Department of Agriculture (USDA-ARS) designed an electric rotating electrostatic nozzle in 1966, which was the first scientific research institution in the world to carry out research on aerial electrostatic spray technology for agriculture. [PERSON] also obtained the invention patent of the aerial electrostatic spray technology in 1999 [6]. The patent was certified by the United States Department of Agriculture as the world's first and only commercially proven airborne electrostatic spray system for agriculture [7]. SES (Spectrum Stack Sprayers, Inc., Houston, TX, USA) has been awarded an exclusive license to manufacture and market this innovative technology. Subsequently, scientific research institutions in the United States, Brazil, Canada, Switzerland, and China have conducted a large number of studies to gradually improve the research and application of electrostatic spray in agricultural aviation [8; 9; 10].  
The research of electrostatic spray technology for agricultural aviation started relatively late in China but developed rapidly. Especially in recent years, the rapid development of plant protection UAVs in China has attracted scholars to make a lot of attempts on the research and application of UAV-based electrostatic spray technology. This study introduced the early exploration of aerial electrostatic spray technology in China from the aspects of nozzle structure design optimization, theoretical simulation, and field experiment with manned aircraft platforms. Then, the research progress of UAV-based electrostatic spray technology in China was emphatically expounded upon, and the existing problems were discussed as well. It is proposed that future research should be carried out in the measurement technology for charged droplets, the influence of UAV rotor wind field on charged droplets, comparative study of various charging methods, and other aspects, so as to increase the accumulation of research on aerial electrostatic spray technology based on an agricultural UAV platform. This study can provide a reference for the development of the agricultural aviation electrostatic spray technology and the spray equipment.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 3 Methodology
### Estimation of Actual Displacement  
The orientation of horizontal stress is governed by the tectonic movements [24]. Therefore, based on the measurement of maximum horizontal stress, the direction of rock block movement can be estimated from the E-W component and a knowledge of the stress orientation. The in-situ stress measurement at borehole BS01 suggests the Jiujing granite region is in a compressive stress field with maximum horizontal stress in the direction of N35\({}^{\circ}\)E [21]. This indicates the overall maximum horizontal displacement (\(\rm D_{MAX}\)) in the Jiujing region is along N35\({}^{\circ}\)E. \(\rm D_{N}\) and \(\rm D_{E}\) are two components of \(\rm D_{MAX}\). \(\rm D_{E}\) can be calculated using the previous steps and based on this value, the estimation of \(\rm D_{MAX}\) towards N35\({}^{\circ}\)E and \(\rm D_{N}\) is shown in Figure 4b:  
Figure 4: The estimation of horizontal displacement components: (**a**) eastward (\(\rm D_{E}\)) through the decomposition of ascending and descending orbit direction data [45] and (**b**) northward (\(\rm D_{N}\)) and maximum displacement (\(\rm D_{MAX}\)) along N35\({}^{\circ}\)E based on the measured eastward displacement (\(\rm D_{E}\)).  
Using this trigonometric relationship, the northward displacement (D\({}_{\rm N}\)) and the N35\({}^{\circ}\)E displacement (D\({}_{\rm MAX}\)) can be expressed in Equations (6) and (7), respectively.  
\[\rm D_{N}=\frac{D_{E}}{\tan 35^{o}} \tag{6}\]  
\[\rm D_{MAX}=\frac{D_{E}}{\sin 35^{o}} \tag{7}\]  
This estimation is based on two assumptions, where the direction of horizontal deformation is in line with the stress field, and where the maximum horizontal displacement is almost homogeneous across the entire granite pluton. However, it is recognised that there is the exceptional case where the actual movements of blocks are not in NE-SW direction, and this will be discussed in Section 4.2.7.
# Current Research in Future Information and Communication Engineering 2022
## 1 Introduction  
The digital revolution has transformed the way we communicate, access information, and interact with technology [1]. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. This Special Issue contains a series of excellent research works on ICE selected from the International Conference of Future Information and Communication Engineering (ICFICE 2022), which was held at the Jeju Island in the Republic of Korea on 12-14 January 2022, and also those related to the future of ICE. This Special Issue consists of 18 regular and 2 review papers focusing on the following areas: communication systems and applications, networking and services, intelligent information systems, multimedia and digital convergence, semiconductor and communication services, biomedical imaging and engineering, ubiquitous sensor networks, databases and internet applications, IOT and big data, and information technology (IT) and convergence technology. There were 28 papers submitted to this Special Issue, and 20 papers were accepted (i.e., a 71% acceptance rate). Looking back on the Special Issue, various topics were addressed, mainly on intelligent information systems communication systems and application, biomedical imaging, and engineering, IOT, and multimedia and digital convergence.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
###### Abstract  
The currently available triple-frequency signals give rise to new prospects for precise point positioning (PPP). However, they also bring new bias, such as time-varying parts of the phase bias in the hardware of receivers and satellites due to the fact that dual-frequency precise clock products cannot be directly applied to triple-frequency observation. These parameters generate phase-based inter-frequency clock bias (PIFCB), which impacts the PPP. However, the PIFCBs of satellites are not present in all GNSSs. In this paper, various IF1213 PPP models are constructed for these parts, namely, the triple-frequency PIFCB (TF-C) model with PIFCB estimation, the TF inter-frequency bias (IFB) (TF-F) model ignoring the PIFCB, and the TF-PIFCB-IFB (TF-CF) model with one system PIFCB estimation. Additionally, this study compares these IF1213 PPP models with the dual-frequency ionosphere-free (DF) model. We conducted single system static PPP, dual-system static and kinematic PPP experiments based on BDS/GPS observation data. The GPS static PPP experiment demonstrates the reliability of the TF-C model, as well as the non-negligibility of the GPS PIFCB. The BDS static PPP experiment demonstrates the reliability of the TF-F and TF-CF models, and that the influence of the BDS-2 PIFCB can be neglected in BDS. The BDS/GPS PPP experimental results show that the third frequency does not significantly improve the positioning accuracy but shortens the convergence time. The positioning accuracy of TF-C and TF-CF for static PPP is better than 1.0 cm, while that for kinematic PPP is better than 2.0 cm and 4.0 cm in the horizontal and vertical components, respectively. Compared with the DF model, the convergence time of the TF-C and TF-CF models for static PPP is improved by approximately 23.5%/18.1%/13.6%/9.7%, and 19.8%/12.1%, while that for kinematic PPP is improved by approximately 46.2%/49.6%, 33.5%/32.4%, and 35.1%/36.1% in the E, N and U directions, respectively. For dual-system PPP based on BDS/GPS observations, the TF-C model is recommended.  
GPS; BDS; triple-frequency signals; IFCB; PPP 2022
## 3 Results
### Combustion Characteristics
#### 3.1.4 Maximum Heat Release Rate  
Figure 5 shows the maximum value of HRR for all test fuels according to various engine loads. The HRR depends on several factors, such as the calorific value of fuel, combustion quality, equivalence ratio, etc. The increase of HRR indicates that the combustion rate of the charge in the combustion chamber increases [30]. HRR is the main factor affecting the change of cylinder pressure and functional engine parameters, which is regarded as the amount of fuel burned at each step of crankshaft rotation during combustion [31]. As shown in Figure 5, as the engine load increases from 30 Nm to 90 Nm, the maximum HRR of all test fuels shows a gradual increase trend. The average maximum HRR of all test fuels at 60 Nm and 90 Nm is respectively increased by 26.28% and 56.88% compared with that at 30 Nm. This is because more fuel is injected into the combustion chamber, and more fuel combustion produces more heat. On the other hand, on the whole, adding a small amount (e.g., 5 vol.% and 10 vol.%) of ethanol to the diesel leads to a slight decline in peak HRR. For example, the maximum HRR of D95E5 is respectively reduced by 1.55%, 4.53%, and 4.05% at 30 Nm, 60 Nm, and 90 Nm, compared with D100. However, when a large amount of ethanol such as 15 vol.% is added to diesel, the maximum HRR has a different upward trend. For example, the maximum HRR of D85E15 is respectively increased by 6.07%, 1.62%, and 3.14% at 30 Nm, 60 Nm, and 90 Nm, compared with D100. This may be because only when a large amount of ethanol is mixed with diesel for combustion, the high oxygen content in ethanol and evaporative cooling effect can play a major role [28]. Overall, the addition of bioethanol did not have a significant impact on the maximum HRR, which was caused by the same reason as the above in-cylinder pressure.
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 2 Materials and Methods
### Fibres' Physical, Chemical, Thermal, and Mechanical Characterization
#### 2.4.2 Scanning Electron Microscopy (SEM)  
To examine the morphology and differentiation between the fibre structure (uniaxial, hollow or coaxial), the CA and CAt sheath, and the corresponding cores within the fibres, fibres were characterised by SEM micrographs obtained using a dual-column ultra-high-resolution field emission SEM (NOVA 200 Nano SEM, FEI Company, Houston, TX, USA) with an accelerating voltage of 10 kV. The samples were initially coated with a 10 nm gold-palladium film (Au-Pd, 80-20% \(w/v\)) using a 208 High-Resolution Sputter-Coater (Cressington Company, Watford WD19 4 BX, UK) coupled to an MTM-20 High-Resolution Thickness Controller (Cressington Company). Images were taken at 500\(\times\) magnification.
## Appendix B Ms-Kfpe  
In Figure 2, the equations associated with (1) indicate using MS-KFPE. The SF in MS-KFPE uses the current time step's filtering information provided from the master filter to correct the covariance \(\mathbf{Q}_{k}\) for the improvement of the overall filtering performance [13]. The following equations are used in the state-space model for SF of MS-KFPE:  
\[\mathbf{z}_{k+1}^{s}=diag\bigg{(}\frac{1}{N_{k}}\sum\
olimits_{i=k-N_{k}}^{k+ 1}[\mathbf{K}_{k+1}\mathbf{v}_{k+1}][\mathbf{K}_{k+1}\mathbf{v}_{k+1}]^{\text {T}}\bigg{)}\] (A5)  
\[\mathbf{d}_{k+1}=diag\Big{(}\mathbf{P}_{k+1|k}^{\mathbf{xx}}-\mathbf{P}_{k+1} ^{\mathbf{xx}}-\mathbf{Q}_{k}\Big{)}\] (A6)  
\[z_{k+1}^{s}=\mathbf{q}_{k+1}+\mathbf{d}_{k+1}+\mathbf{v}_{k}\] (A7)  
where \(N_{k}\) is the time-window, which controls how fast the adaptation of \(\mathbf{Q}_{k}\) is achieved. Equation (A5) describes empirical covariance based on residuals as a measurement for slave filter. The first two terms in the right-hand side of Equation (A6) describe the prediction of residual's covariance from the master filter. Then, through Equation (A7), the empirical covariance of the residual is matched to the prediction value. The gap between those two covariance matrices is utilized to compensate wrong assumption of covariance of artificial process noise in MF. This correction procedure of SF is conducted in the part of measurement-update of UKF to use the corrected covariance in the next time step. MS-KFPE is required to select additional parameter, i.e., time-window unlike MS-KFME. Although the time-window can be determined, there is still a possibility of biased estimation due to inaccurate increases of covariance of artificial process noise. A similar situation occurs when the technique described in Appendix A is used.
## 5 Experimental Evaluation
### Discussion
#### 5.3.1 Does Our Method Improve Project Comprehension?  
Since only experimental group subjects utilized the heatmaps generated by our method, we believe that our method supports subjects' comprehension by reviewing tickets and features.  
* Does our method improve project comprehension? **Subjects of the experimental group outperformed those of the control group for ticket activeness comprehension and feature comprehension tasks. Our method successfully supports subjects to comprehend the project by reviewing ticket categories' lifetime and keywords.**  
* For Which Tasks Is Our Method Effective?  
Although there are significant differences in the representative values of the two groups for all tasks, the statistical test indicates that the feature activeness comprehension tasks (\(A_{F1}\) and \(A_{F2}\)) have the most significant differences between the two groups. Since the comprehension of feature activeness requires understanding of multiple aspects of tickets such as their lifetimes as well as contained keywords, the non-tool-assisted exploration of tickets does not work well. On the other hand, our method supports subjects' comprehension by visualizing lifetimes and keywords.  
* For which tasks is our method effective? **Our method is effective for all tasks, including ticket activeness comprehension tasks and feature comprehension ones. A statistical test confirms that our method is most effective for feature activeness comprehension tasks.**