esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 3 Depth Estimation Method  
### Depth Inversion  
As with previous video-based inversion methods, we invert estimated wave parameters into water depth based on the dispersion relationship from linear wave theory. Therefore, the water depth \(h\) can be expressed as:  
\[h=\frac{c}{2\pi f}\text{tanh}^{-1}\bigg{(}\frac{2\pi fc}{g}\bigg{)}, \tag{1}\]  
where \(f\) is the wave frequency, \(c\) is the wave celerity, and \(g\) is the gravitational acceleration. This relationship allows us to estimate water depth from the wave celerity and frequency.  
The dependency of wave celerity on water depth is strong when the wavelength is large relative to water depth. Therefore, it is more advantageous to use long-period waves for depth inversion [22]. The relationship among the relative errors in the estimation can be derived from Equation (1) as:  
\[\frac{dh}{h}=(G+1)\frac{dc}{c}+(G-1)\frac{df}{f}\text{ with }G=\frac{c^{2}}{gh} \frac{1}{1-\Big{(}\frac{2\pi fc}{g}\Big{)}^{2}} \tag{2}\]  
Figure 4: Original and orthogonal images of the test areas. (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, and (**d**) Case 4. Red lines indicated transects of bathymetry surveys.  
Equation (2) describes the relative-error propagation from wave celerity and frequency to water depth. The variable \(G\) converges to unity in very shallow water, whereas it diverges to infinity in deep water. Figure 5 shows \(G\) as a function of \(h\) and \(f\). In the range of \(h<8\) m, the relative error in wave celerity propagates to water depth by a factor of 2-3, whereas the propagation factor of the wave frequency is much smaller. In the deeper area of \(h>8\) m, the propagation factor rapidly increases in the range of high-frequency waves (\(f>0.15\) Hz). Hence, the depth inversion requires very accurate measurements of wave celerity and frequency. The key to successful depth inversion is to extract the properties of long-period swells in coastal water. However, it is difficult to capture long waves with areal footage. Nevertheless, this trade-off can be mitigated due to the flexibility that UAVs provide, enabling us to optimise the camera position and angle for the swell-signal intensification.
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 1 Introduction
The built-in solver mhdFoam supports simulations of incompressible magnetohydrodynamics, and Xisto later extended it to a compressible MHD solver [24]. [PERSON] developed ideal MHD Riemann solvers based on the Kurganov-Noelle-Petrova (KNP) and Kurganov-Tadmor (KT) schemes [25]. [PERSON] [26], using the multi-region heat transfer solver (chtMultiRegion-Foam) as a base, developed a multi-region MHD solver for tritium transport simulation in fusion systems. [PERSON] [27] also developed multi-region, multiphase MHD solvers for liquid metal simulations based on this framework. In this work, we first develop a compressible MHD solver (mhdF), based on the PIMPLE algorithm, and a magnetic diffusion solver capable of simulating resistive wall effects. These solvers are then integrated into a multi-region coupling framework, resulting in the mhdMRF solver. This solver can be applied to numerical simulations of compressible MHD coupled with finite-resistivity walls in two-dimensional (2D) or three-dimensional (3D) domains and can be utilized to investigate the effects of finite-resistivity walls on CT dynamics. The paper is organized as follows: Section 2 presents a detailed exposition of the physical models, numerical algorithms, coupling strategies, and the formulation of interfacial coupling boundary conditions adopted in the program development. Section 3 verifies the computational accuracy of the mhdF solver and the magnetic diffusion solver through two benchmark cases. Furthermore, the accurate implementation of interfacial coupling boundary conditions in the mhdMRF solver is validated using a comparative case against an analytical solution. Section 4 presents simulation results of the fundamental dynamic processes of the CT propagating through a drift tube based on the developed solver. It investigates, for the first time, the influence of the end geometry of the inner electrode and the finite-resistivity conducting wall on the behavior of the CT. The results reveal that the end geometry of the inner electrode plays a key role in regulating the magnetic and flow field distributions, while the wall resistivity directly affects the rate of magnetic field diffusion and the spatial distribution of plasma density. Finally, Section 5 concludes the study.
###### Abstract  
The objective of this article is to establish a fatigue equation based on the true stress ratio for cement-treated aggregate base materials. The true stress ratio herein means the ratio of the stress and the true strength of the cement-treated aggregate base materials related to loading rates and curing times. The unconfined compressive strength tests and compressive resilience modulus tests were carried out under various loading rates and curing times of 3, 7, 14, 28, 60, 90 days, respectively. According to the test results, the relationship between the unconfined compressive strength (a mix design parameter in China) and the compressive resilience modulus (a structural design parameter and the construction quality control parameter in China) of the cement-treated aggregate base material with different curing times was established. However, it was found that the strengths varied with the loading rates, which is not reflected in the existing fatigue equations. Therefore, it is questionable to obtain the stress ratio of fatigue tests with a fixed strength value obtained from the standard strength test where the loading rate is fixed (in China, the fixed loading rate is \(1\) mm/min for cement-treated aggregate base materials). Thus, in this paper, the four-point bending strength (i.e., flexural strength) test was carried out at different loading rates to resolve such deficiencies. Based on the strength test results at different loading rates, the true stress ratio of the fatigue test corresponding to the fatigue loading rate can be calculated. Then the four-point bending fatigue test was conducted to establish an improved fatigue equation characterized by the true stress ratio. The results show that the patterns of variation for unconfined compressive strength increasing with the curing time were similar to that of the compressive resilience modulus. The fatigue equation curve based on the true stress ratio can be extended to the strength failure point of (1, 1), where both the true stress ratio and the fatigue life value are one. The internal relationship between the strength failure and the fatigue failure was unified. This article provides a theoretical method and basis for unifying the mix design parameters and the construction quality control parameters.  
Keywords:cement-treated aggregate base; fatigue equation; loading rates; true stress ratio; flexural strength +
Footnote †: journal: Applied Soft. 2018
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Terrestrial Networks (TN) and Non Terrestrial Networks (NTN) Integration  
Traditionally, up to 5-10 years ago, the research focusing on TN was separated from the research focusing on NTN (e.g., satellite signals and systems). However, similarly to the convergence of the communication, sensing, and positioning research areas, a convergence betweenTN and NTN research has happened in the past few years, which has also been reflected in the standardization committees and published documents (e.g., the 3 GPP TR 38.821 document details this integration).  
This integration of TN and NTN in the context of ISAC is discussed, for example, in [66]. The seamless localization and communication achievable based on a TN-NTN integration is discussed in [67]. Jammer detection in JSAC with integrated TN-NTN networks is discussed in [68]. Nevertheless, many research questions still remain unanswered in the area of integrated TN-NTN networks, such as: how to ensure seamless interoperability between the TN and NTN components, how to perform optimal data fusion with heterogeneous TN and NTN signals, how to overcome the long propagation delays in NTN signals in order to achieve low-latency JPSAC solutions, how to allocate the TN and NTN resources in a dynamic way in order to adapt to varying network conditions and user demands, etc.
## 2 Test Setup and Simulation Environment  
The C-V2X communication technologies performance evaluation was performed using a federated telco-traffic simulation [22; 23; 24; 25]. We have used Objective Modular Network Testbed in C++ (OMNeT++) [26] as a communication network simulator. Vehicle flows have been simulated using Simulation of Urban Mobility (SUMO) [27] microscopic traffic simulation package. To interconnect the two simulators in real time, we have adopted the commonly used Veins [22] simulation framework. Technology-specific communication stacks have been modeled using the following simulation frameworks, i.e., SimuLTE [28] for LTE-based C-V2X and Simu5G [29] for 5G-based C-V2X communications.  
It is worth noting here that this study represents an extension of our previous study published in [16] towards 5G-based C-V2X communication, while it deploys the same methodology in order to maintain the backward comparability of the results. Preliminary simulation runs have suggested that the infrastructure density might severely impact the performance of the cellular technologies, especially the 4G-based one, in the investigated context, i.e., urban scenarios and V2I communication. Therefore, we have extended our investigation to two different simulation scenarios, i.e., one involving a fixed infrastructure density and the other one focusing on a varying infrastructure density to study the impact of infrastructure density on the V2I C-V2X communication performance. In comparison to the previous study published in [16], the default number of cells has been increased from two to four. This change was necessary due to an inability of 5G-enabled devices to maintain connection reliably throughout the simulated area with just two cells.
# Study of the Ciprofloxacinacin Adsorption of Activated Carbon Prepared from Mangosteen Peel
## 3 Results and Discussion
### Isotherm Adsorption Model  
#### 3.5.1 Langmuir Isotherm Adsorption Model  
To study isotherm adsorption models, experiments on the effect of the initial CIP concentration to the adsorption capacity of the ACMP were performed in the concentration range of 50 to 400 ppm (Table 1).  
Langmuir adsorption, which was primarily designed to describe gas-solid phase adsorption, is also used to quantify and contrast the adsorptive capacity of various adsorbents. The Langmuir isotherm accounts for the surface coverage by balancing the relative rates of adsorption and desorption (dynamic equilibrium). Adsorption is proportional to the fraction of the surface of the adsorbent that is open, while desorption is proportional to the fraction of the adsorbent surface that is covered [39]. The Langmuir equation can be written in the following linear form:  
\[\frac{C_{0}}{q_{e}}=\frac{1}{q_{max}b}+\frac{C_{e}}{q_{max}} \tag{6}\]  
where \(q_{e}\) and \(q_{max}\) are the equilibrium adsorption capacity and the maximum adsorption capacity (mg/g), respectively, and b is the Langmuir constant. A Langmuir equation is characterized by the parameter \(R_{L}\):\(R_{L}=1/(1+b\cdot C_{0})\). If \(0<R_{L}<1\), then the adsorption is favorable, but if \(R_{L}>1\), then the adsorption is not favorable. If \(R_{L}=1\), then the adsorption is linear.  
As seen in Figure 12A, the maximum adsorption capacity was determined as \(q_{max}=29.76\) mg/g, with a constant b = 0.173 (L/g). The CIP adsorption of the ACMP is  
Figure 11: Effect of the adsorbent dosages (**A**) and temperatures (**B**) on the CIP adsorption efficiency.  
well described according to the Langmuir isotherm adsorption model. This is expressed through the regression coefficient of the equation (R\({}^{2}\) = 0.9886).  
The results of comparing the CIP adsorption capacity on different adsorbent materials (Table 5) show that the maximum adsorption capacity (q\({}_{\rm max}\)) of the ACMP for the CIP is relatively high. From the calculated parameter R\({}_{\rm L}\) value presented in the graph of Figure 12B, we can see that the values in the range of 0.154 to 0.014 are all less than 1, validating that the Langmuir isotherm adsorption model is appropriate with the CIP adsorption of the ACMP.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 3 Experimental Testing and Analysis of the Developed System
### Experimental Tests and Results
#### 3.2.1 Adjustment Test of Working Parts  
The adjustment of the working parts included the rotational speed control of the pulling rollers, operating speed control, and header height control. The rotational speed of the pulling rollers was regulated most strictly. When there was no load (i.e., no maize was harvested), the control strategy was tested to verify that the rotational speed of the pulling rollers could be automatically adjusted. First, the rotational speed of the pulling rollers was adjusted to a certain value (slightly higher than the usual working range) through manual button control before a harvest. Then, a maize picking loss rate which is higher or lower than the standard value (the standard loss rate in China is \(<\)4%) was input by manual to the controller, and it is observed whether the rotational speed would change according to the designed strategy. Figure 9 shows the corresponding changes in the control signal and rotational speed of the pulling rollers. The rotational speed of the pulling rollers was manually adjusted to approximately 1260 r/min. After approximately 35 s, the input of a high maize picking loss rate caused the controller to send a flow reduction control command to the proportional valves, and the rotational speed of the pulling rollers began to decrease by approximately 50 r/min every 15 s until the lower limit for the optimal rotational speed was reached. At approximately 80 s, a low maize picking loss rate was input, and the controller issued a flow increase control command to the proportional valves while ensuring that the loss rate did not exceed the allowable range. The rotational speed of the pulling rollers was gradually increased to improve the harvesting efficiency. The header took 12 s to rise from 0 to 1100 mm, and it took 11 s to descend from the maximum height to the minimum. The operating speed could be adjusted from 0 to 25 km/h according to the position of the analog handle from 0 to 1000.
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Differential scanning calorimetry (DSC) is one of the most convenient approaches to shed light on the degree of crystallinity along with the thermal behavior of polymeric samples, and was used to characterize our electrolyte samples [26]. The Tg of a polymer indicates the transition from a rubbery to a glassy state and vice versa. Therefore, the polymer is flexible above the Tg, and hard and brittle below the Tg[13]. This is an endothermic transition while the crystalline melting point (Tm) is an exothermic transition. In our study, DSC analysis was carried out with a Mettler Toledo differential scanning calorimeter instrument under an argon atmosphere with a flow rate at 70.0 mL min\({}^{-1}\)[4] between \(-\)100 \({}^{\circ}\)C and 150 \({}^{\circ}\)C [27]. Samples of around 5-7 mg were sealed in a 40 \(\upmu\)L aluminum pan with a perforated lid to allow the release and removal of the decomposition products [28]. Two stages of DSC scanning were set: (1) \(-\)100 \({}^{\circ}\)C isothermal for 10.0 min, and (2) heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and temperature range from \(-\)100 to 150 \({}^{\circ}\)C. An empty aluminum pan served as the reference. The DSC results are presented in Figure 9 and the essential glass transition and melting points for various electrolyte samples are listed in Table 6.  
Figure 8: Thermogravimetric analysis (TGA) curves under air flow for PSTFSILI, PSPhSILi, and PSDTTOLi.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Decomposition**} & \multicolumn{2}{c}{**Percentage Weight Loss [\%] at Various Temperature**} \\ \cline{3-6}  & **Temperature**, **T\({}_{\mathbf{d}}\)** [\({}^{\circ}\)C] & **200 \({}^{\circ}\)C** & **300 \({}^{\circ}\)C** & **400 \({}^{\circ}\)C** & **500 \({}^{\circ}\)C** \\ \hline PSTFSILI & 376 & 4.5 & 6.6 & 11.1 & 29.9 \\ PSPhSILi & 450 & 0.7 & 2.1 & 2.7 & 22.5 \\ PSDTTOLi & 449 & 0.5 & 1.5 & 4.8 & 23.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermal degradation of the PSLSs at different temperatures.  
The T\({}_{\text{g}}\) of all electrolytes was found to be concentrated around \(-50\)\({}^{\circ}\)C. In contrast, T\({}_{\text{m}}\) showed some separations. We presume that this was due to the addition of PSLSs, which caused a change in the shape of the endothermic peak, and the peaks were slightly shifted toward lower temperatures compared to the control (Figure 9h,i). These observations clearly suggest that a significant contribution to conductivity enhancement comes from structural modifications associated with the polymer host caused by the salts. The reorganization of the polymer chain may be hindered by the crosslinking centers formed by the interaction of the functional groups.
## 8 Policy-Compliant Maximum Network Flows  
Computer network administrators are often interested in the maximal bandwidth that can be achieved between two nodes in the network, or how many edges can fail before the network gets disconnected. Classic maximum flow algorithms that solve these problems are well-known. However, in practice, network policies are in effect, severely restricting the flow that can actually be set up. These policies are put into place to conform to service level agreements and optimize network throughput, and can have a large impact on the actual routing of the flows.  
In [7], the authors model the problem and define a series of progressively more complex conditions and algorithms that calculate increasingly tighter bounds on the policy-compliant maximum flow using regular expressions and finite-state automata. This is the first time that specific conditions are deduced, which characterize how to calculate policy-compliant maximum flows using classic algorithms on an unmodified network.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion
### Experimental Results  
Table 2 shows the experimental results in terms of the number of messages, water consumption, and irrigation error.  
Both self-triggered approaches \(\gamma_{1}\) and \(\gamma_{2}\) significantly reduced the number of communication messages compared with the periodical strategy \(\gamma_{0}\), and still achieved practically the same control performance in terms of water consumption and irrigation error. Periodical strategy \(\gamma_{3}\) had a similar number of communication events compared with the self-triggered strategies; however, the water consumption was considerably larger, and it also had a higher irrigation error.  
Notice that self-triggered algorithms effectively reduced the communication messages while achieving the control objectives. When comparing both self-triggered implementations, the Lyapunov-based algorithm better reduced the water consumption, while the error-based algorithm obtained slightly better results in terms of communication messages and irrigation error. However, these differences are marginal, and the minimum adjustment in the configuration parameters (\(\sigma\),\(P\)) would impact these results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sampling** & **Description** & **Communication Messages \(J_{1}\)** & **Water Consumption \(J_{2}\)** & **Irigation Error \(J_{3}\)** \\ \hline \(\gamma_{0}\) & Periodical 10 min & 4032 & 108.8200 & 7.4783 \\ \(\gamma_{1}\) & Error-based & 480 & 108.6529 & 7.3970 \\ \(\gamma_{2}\) & Lyapunov-based & 492 & 107.8835 & 7.4641 \\ \(\gamma_{3}\) & Periodical 80 min & 504 & 134.3956 & 8.4579 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation experimental results.
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 5 Results and Discussion
* The smaller warehouse capacity (\(W=900\) totes) provides more throughput per month and less operating time of an AGV per transaction compared to the larger one (\(W=1800\) totes). This is mainly because of the decreased travel time of AGVs and MLs. * For both warehouse capacities, the system design with two I/O points works better in terms of \(t_{o}\) and \(\lambda\). The two I/O points design scenarios provide roughly 62% better performance in terms of throughput rate performance metric than the single I/O point scenarios. * Increased number of AGVs provides better results (i.e., at least 25% improvement in terms of \(\lambda\) and 20% in terms of \(t_{f}\)) in the single I/O point design. * In all scenarios, the utilization levels are 95% for AGVs, and significantly less for lifts (48% to 76%). * The conclusions above confirm our opinion that when more efficient routing congestion algorithms and a higher number of AGVs will be included in the system, a much better performance from the novel AVS/RS/ML system can be demonstrated.