esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Method
### Numerical Method and Mesh Discretization  
The static equation Equation (11) was discretized into a five-point difference scheme and solved by the iterative method. The static viscosity should be updated along with every iteration step of static pressure based on the Barus pressure-viscosity relationship [26] in this research. The local Reynolds number at each node need to be calculated for identifying the flow state. At the turbulence zone of lubricating film, the coefficients \(k_{x0}\) and \(k_{z0}\) should also be calculated during the iteration of pressure \(\overline{p}_{0}\). If the static pressure \(\overline{p}_{0}\) lower than zero occurs at a certain node during the iteration, its value would be set as zero. When the static pressure \(\overline{p}_{0}\) reaches converge judge Equation (23), the Reynolds boundary condition Equation (15) for \(\overline{p}_{0}\) is satisfied.  
\[\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left|\overline{p}_{0,\left(i \right)}^{new}-\overline{p}_{0,\left(i\right)}^{old}\right|}{\sum\limits_{i=1 }^{m}\sum\limits_{j=1}^{n}\left|\overline{p}_{0,\left(i\right)}^{old}\right|} \leq 10^{-5} \tag{23}\]The solution of the static equation is repeated with the iteration of the static tilting angle \(\delta_{0}\) of each pad.  
\[\overline{M}=\int_{-1}^{1}\int_{\theta_{L}}^{\theta_{T}}(\overline{p}_{0}-1) \sin(\beta_{l}-\theta)d\theta d\lambda \tag{24}\]  
When the dimensionless pivoted moment \(\overline{M}\) shown in Equation (24) is lower than \(10^{-5}\), the static tilting angle \(\delta_{0}\) of the \(l\)th pad at its static equilibrium position is obtained.  
All the variables solved from the static function Equation (11) are essential for the perturbed Reynolds equation Equation (12). Then the complex pressure distribution \(P_{\epsilon}\), \(P_{\varphi}\) and \(P_{\delta}\) were also solved numerically by finite difference method with over-relaxation iterative. At the node where the static pressure \(\overline{p}_{0}\) equals zero, the values of the \(P_{\epsilon}\), \(P_{\varphi}\) and \(P_{\delta}\) should be set as zero for satisfying the Reynolds boundary condition Equation (15). The converge judges for the \(P_{\epsilon}\), \(P_{\varphi}\) and \(P_{\delta}\) are given by Equation (25).  
\[\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left|P_{\epsilon_{(i)}}^{new }-P_{\epsilon_{(i)}}^{old}\right|}{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n} \left|P_{\epsilon_{(i)}}^{old}\right|}\leq 10^{-5} \tag{25}\]  
where the subscript \(\cdot\) represents \(\epsilon\), \(\varphi\), and \(\delta\).  
The \(m\) and \(n\) in Equations (23) and (25) are grid numbers in the circumferential and axial direction, respectively. When the number of grids of each pad is larger than 32 \(\times\) 26, the grid independence is satisfied. That is to say, the difference between results with grids of 32 \(\times\) 26 and 64 \(\times\) 52 is less than 2%. In the same way, the results with residuals of \(10^{-5}\) and \(10^{-6}\) have a small difference.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 2 Flow-Induced Vibrations (FIVs) and Energy Harvesting
### Energy Transduction in Flow Energy Harvesting  
The typical FIV energy harvester in accordance with the various energy transduction techniques can be classified into electromagnetic [27, 28], piezoelectric [29], triboelectric [30] and hybrid [31]. Out of these techniques, piezoelectric transduction has gained a lot of attention due to high voltage output, simplicity of device architecture and independence from an external magnetic field or voltage sources. Piezoelectric materials develop an electric polarization when these experience mechanical strain. As the applied stress causes the displacement of charge within the material, this leads to a separation of positive and negative charges, generating voltage across the material. Piezoelectric materials are well suited to MEMS fabrication techniques, which can significantly reduce the overall device size. So far, such self-powered piezoelectric devices are tiny, implanted sensors releasing wireless signals utilized to evaluate the performance of mechanical systems. Moreover, because of fewer moving components, it is less likely to need maintenance [32, 33]. Another method for harvesting energy from FIVs is to use electromagnetic induction [34, 35, 36] to transform mechanical energy from ambient vibration into electrical energy. The fundamental concept of an electromagnetic energy harvester (EMEH) is that as a magnet and coil undergo relative motion, the varying amounts of magnetic flux through the coil generate voltage. Although EMEHs have high power output levels, they generate comparatively low voltage when used on a small scale. Furthermore, triboelectric energy harvesting has received substantial interest in recent years due to their low cost, triboelectric effect diversity, high efficiency and robustness. Triboelectric flow energy harvesters utilize the triboelectric effect, i.e., contact-induced electrification in which a material electrifies itself after coming into frictional contact with another material. These can capture a wide range of mechanical energy, including vibration, impact, touching, linear sliding, rotation, wind and human motion [37, 38].  
Recent research on FIV energy harvesting shows improvements in the comprehension of energy transduction mechanisms for transforming mechanical vibrations into electrical energy. Critical analysis, however, exposes difficulties, such as constrained scalability, sensitivity to flow conditions and efficiency issues. To address these gaps, advancement of efficient FIV energy harvesting systems should be encouraged, giving priority to developing scalable, sustainable solutions that are more adaptable to a wider range of flow conditions and have increased efficiency.
## 3 Results and Discussion
### Mechanical Behaviors  
Figure 4A,B show the tensile behavior and flexural strength of the experimental group and the control group. PP100 and PP90/TPU10 had a greater tensile strength than MPP100 and MPP90/TPU10. PP100 was composed of highly aligned molecular chains and thus had greater crystallinity, and its  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Codes**} & **Crystallization** & **Crystallization** & **Melting** & **Melting** \\  & **Temperature** (\({}^{\circ}\)C) & **Enthalpy (J/g)** & **Temperature** (\({}^{\circ}\)C) & **Enthalpy (J/g)** \\ \hline TPU100 & 62.41 & 5.82 & 145.69 & 8.49 \\ PP100 & 115.24 & 106.20 & 165.29 & 115.40 \\ PP90/TPU10 & 121.62 & 96.23 & 165.13 & 100.10 \\ MPP100 & 129.56 & 83.61 & 165.77 & 88.27 \\ MPP90/TPU10 & 130.20 & 83.71 & 165.64 & 87.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Melting and crystalline behavior of PP/TPU blends and MPP/TPU blends. PP: polypropylene; MPP: modified impact-resistant polypropylene; TPU: thermoplastic polyurethane.  
Figure 3: Melting and crystalline behavior of PP/TPU and MPP/TPU blends: (**A**) heating curve, (**B**) cooling curve, and (**C**) X-ray Diffraction (XRD) pattern.  
tensile strength reached 37.12 MPa. In addition, PP90/TPU10 possessed higher elongation than PP100, which was ascribed to TPU. TPU is a thermoplastic elastomer that provides PP90/TPU10 with greater elongation. To sum up, the elongation at break could be ranked from highest to lowest as MPP90/TPU10, MPP100, and then PP90/TPU10. MPP with a great toughness and thermoplastic elastomer TPU had a synergistic effect, demonstrated by the highest elongation at break of MPP90/TPU10.  
Table 2 shows that MPP100 and MPP90/TPU10 exhibited higher tensile strength, Young's modulus, and flexural strength than PP100 and PP90/TPU10. Moreover, PP100 outperformed PP90/TPU10 because PP at the continuous phase had a lower bonding level due to the presence of TPU, which compromised the mechanical properties. On the other hand, MPP100 and MPP90/TPU10 could bear more impact energy and possessed a higher impact strength than PP100 and PP90/TPU10. Due to modification, MPP exhibited a greater toughness than PP, and thus could withstand greater impact energy and showed higher impact strength. In particular, MPP90/TPU10 could absorb an impact energy of 43.3 J, and MPP100 had an impact strength of 8.48 kJ/m\({}^{2}\). Nevertheless, compared with MPP100, MPP90/TPU10 still had a lower impact strength. This was ascribed to modified impact-resistance polypropylene (i.e., MPP) that contained more ethylene groups that was incompatible with TPU. The differences in polarity between MPP and TPU is high, and the presence of PP may break the hydrogen bond of TPU, resulting in lower mechanical properties [10].
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 2 Related Work
### VRPR Packet Flow from Server to Gateway  
When a packet is generated in the server, the server also generates its copies. One copy is sent out via the LAN NIC, and another copy is sent out through the WLAN NIC. The LAN packet travels on the wire until it reaches the LAN switch. The switch always checks the status of the switch-to-router link. If the link, from the switch to the router is down, then the packet is diverted towards the access point. The access point sends this packet towards the gateway router. The same process is replicated with the WLAN NIC when a packet enters a network or a packet acknowledgment comes through the Gateway (GW) router. The link breakage of the next hope can be identified using CTS, even after sending the maximum number of RTS retransmission attempts at the MAC layer. Using a congestion notification algorithm, a buffer overflow can be identified during the acknowledgment message. Another IETF standard named the Explicit Congestion Notification (ECN) [18] is mostly adopted to inform the destination TCP protocol if there is any congestion in the network. The packet loss is the main reason for congestion in the TCP network. Upon successfully negotiating the congestion notification, ECN-aware mesh routers mark the IP header as opposed to dropping packets. If the route failure is beyond the first two techniques, then it is known as a handover link failure. It is very crucial to determine the Round Trip Time (RTT) [19; 20; 21] to further improve the efficiency of routing protocols in mobile-based wired and wireless networks.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 3 Active Suspension's Model
### Define of Lateral Stability Parameters  
For the mobile robot, we developed the criterion proposed in [PERSON] and Rey [29]. As shown in Figure 6, the stability of the robot is evaluated with the gradient stability margin method [29], which can be summarized as follows. The line joining two consecutive terrain contacts, \(P_{i}\), defines a tip-over axis. The unit vector, \(l_{i}\), of the axis joining the robot COG, \(G\), to the center of each tip-over axis is computed. Then, angle \(\gamma_{i}\), between each \(l_{i}\) and the total force vector applied to the robot, gives the stability angle over the corresponding tip-over axis. The gradient, \(\beta\), is the minimum angle, \(\gamma_{i}\), necessary to tip the mobile robot over.  
\[\beta=\min(\gamma_{i}). \tag{11}\]  
Figure 5: Definition of coordinate systems.  
Therefore, as shown in Figure 7, we can evaluate the lateral stability with the lateral tip-over gradients [30].  
\[\beta_{l}=\min(\gamma_{1},\gamma_{2}) \tag{12}\]  
\(\beta_{l}\) denotes the lateral stability of the mobile robot. The larger the value of \(\left|\beta_{l}\right|\), the greater the lateral stability of the mobile robot over the terrain. Tip-over instability occurs for \(\beta_{l}\leq 0\).
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 2 AUV Model
### Actuators  
The AUV is equipped with a set of actuators including fins and thrusters mounted in different positions over the vehicle hull, as shown in Figure 1.  
The actuators include the main thruster, side thrusters (front and rear) and fins (stern planes and rudders). They are characterized in terms of the type, the position, and the orientation with respect to the vehicle-fixed reference frame and are described below. The combination of the forces and moments provided by the actuators determine the thrust to control the vehicle according to the controller command signals. A detailed description of the actuator dynamics for the AUV is presented in Appendix A and the parameter values are collected in Appendix B.
## 3 Results  
### Simulation Results  
In order to verify the theoretical model, the finite element software COMSOL is adopted here to perform simulations. The dimensions of the test structure used in the simulation are \(w\) = 10 \(\upmu\)m, \(l_{a}\) = 10 \(\upmu\)m, \(l_{b}\) = 20 \(\upmu\)m, \(\varphi\) = 45\({}^{\circ}\), \(\psi\) = 60\({}^{\circ}\) and \(n\) = 10, with the thickness of the polysilicon layer ranging from 1.5 \(\upmu\)m to 2.5 \(\upmu\)m, the thickness of the PSG layer ranging from 2 \(\upmu\)m to 3 \(\upmu\)m and the sidewall etch angle of the PSG layer ranging from 70 degree to 90 degree. Figures 6-8 compare the relationship between resistances of continuous step structures of the three units (\(R_{1}\), \(R_{2}\), \(R_{3}\)) and the geometrical parameters of thin films (\(t_{1}\), \(t_{2}\), \(\theta\)) obtained by theoretical model and the FEM simulation. It is shown that \(R_{1}\), \(R_{2}\) and \(R_{3}\) nonlinearly decrease as \(t_{1}\) increases, linearly increase as \(t_{2}\) increases, and slightly increase as \(\theta\) increases. The deviations between theoretical values and simulation results of \(R_{1}\), \(R_{2}\) and \(R_{3}\) are within 2%. These comparisons demonstrate that Equations (6)-(8) are valid.  
For a given geometry (\(t_{1}\) = 2 \(\upmu\)m, \(t_{2}\) = 2.8 \(\upmu\)m, \(\theta\) = 80\({}^{\circ}\)), simulation results of \(R_{1}\), \(R_{2}\) and \(R_{3}\) are 441.61 \(\Omega\), 852.72 \(\Omega\), and 1677.80 \(\Omega\), respectively. By substituting simulation results into Equations (9)-(11), thicknesses of the polysilicon layer and the PSG layer are estimated to be 2.039 \(\upmu\)m and 2.820 \(\upmu\)m, respectively, and the sidewall etch angle of the PSG layer is calculated as 80.90\({}^{\circ}\). In comparison with the given geometry, the relative errors for simulation results of \(t_{1}\), \(t_{2}\) and \(\theta\) are 1.95%, 0.71% and 1.13%, respectively.  
Figure 5: Schematic diagram of the experiment system.  
Figure 6: Theoretical and simulation results of the relationship between \(R_{1}\) and the geometrical parameters: (**a**) The thickness of the polysilicon layer (\(t_{1}\)); (**b**) The thickness of the PSG layer (\(t_{2}\)); (**c**) The sidewall etch angle of the PSG layer (\(\theta\)).  
Figure 7: Theoretical and simulation results of the relationship between \(R_{2}\) and the geometrical parameters: (**a**) The thickness of the polysilicon layer (\(t_{1}\)); (**b**) The thickness of the PSG layer (\(t_{2}\)); (**c**) The sidewall etch angle of the PSG layer (\(\theta\)).
## 5 Conclusions  
In this study, the bond behaviour of basalt-FRP-reinforced, newly developed self-compacting geopolymer concrete was investigated by conducting direct pull-out tests on 21 specimens under ambient-cured conditions. These testing conditions were used in other bond behaviour studies reported in the literature, where the samples were air cured in the same laboratory environmental conditions (23 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 50%) [75,76]. The results of the current study are consistent with other reported studies, where the air-cured specimens displayed promising bond performance for specimens reinforced with FRP bars or FRP sheets [75,76]. This study considered bar diameter and embedment length as the variable parameters, and compared them against the performance of steel rebar under similar conditions. This study also took into consideration the type of bar, compressive strength, and concrete cover in assessing the influence of various parameters affecting bond performance. From the test results it could be concluded that:  
1. The average bond strength offered by 10-mm steel-reinforced SCGC is higher than that of 6-mm and 10-mm BFRP bars; however, all of the BFRP-reinforced specimens gave similar bond performance for traditional, ordinary Portland cement concrete.
2. The 10-mm-diameter BFRP bars performed well when the embedment length was kept at 5 d\({}_{\text{b}}\), and when the embedment length was increased, the bond strength was found to decrease for 10-mm BFRP bars.
3. When comparing the performance of basalt bars with steel bars, we noted that the pull-out specimens reinforced with steel were able to take maximum stresses (around 500 MPa); however, for the basalt, since the tensile stress was almost 1100 MPa, the basalt bars pulled out before the peak was reached.
4. Even though the ANN model is a non-traditional approach, it proved to provide realistic prediction in the case of the newly developed composite (SCGC-BFRP) com  
Figure 28: ANN model comparison against other prediction models for all specimens.  
pared to the existing theoretical and analytical models, such as ACI and CSA, which overpredicted the bond strength.
* The ANN model gave good prediction of bond strength, with the lowest MSE values, and R\({}^{2}\) values of 0.99 for test, 0.97 for validation and 0.96 for training for all types of basalt FRP specimens. Future works on this topic would enhance the database to give more accurate predictions.  
S.K.R.: methodology, investigation, writing--original draft; R.A.-A.: supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available upon request from the corresponding author.  
The authors gratefully acknowledge the technical staff--[PERSON] and [PERSON]--for their support and assistance in completing the experimental investigations at the Structures Laboratory, Deakin University.  
The authors declare no conflict of interest.
## 3 Results
### Mangrove Sediment-Water Fluxes
#### 3.3.2 Dissolved Nutrient and Organic Carbon Fluxes  
Fluxes of dissolved inorganic nitrogen (DIN) were always directed into the sediment (Figure 6). Uptake was stronger during dark conditions, while diel fluxes were comparable in March and October. Flux directions of individual DIN species were equal during light and dark incubations. In March, NO\({}_{3}\) showed influx, while NO\({}_{2}\)- and NH\({}_{4}\)+ were released from the sediment. NO\({}_{3}\)- influx was lower in October and influx of DIN was maintained by uptake of NH\(4\)+ and NO\(2\)-. Uptake of dissolved Si occurred during light conditions and was stronger in October. No net flux was detected in the dark. In March, PO\({}_{4}\)- fluxes were directed into the sediment. Higher light influx in October was balanced by dark efflux to result in similar diel PO\(4\)- uptake as in March. Dissolved organic carbon (DOC) was generally taken up by the sediment, except for a strong release during the dark in October, which resulted in a net diel efflux.  
Figure 5: Hourly (**a**) and diel (**b**) total fluxes of dissolved oxygen. Respiration (Resp) equals measured dark fluxes, net primary production (NPP) equals measured light fluxes, and gross primary production (GPP) is calculated as their difference. Panel (**b**) also shows the ratio of GPP to respiration (P:R).
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
The mobility of the network topology may decrease the time for communication and gathering the data; therefore, it is important to maximize the quality of the communication. This can be achieved by finding and using the links and the communication routes with the best quality, thus improving the performance in terms of latency, delivery ratio, and bandwidth, among others. For the success of this type of routing, accurate link quality estimation is essential. Although several Link Quality Estimators (LQE) have been proposed over the past years [3], many approaches rely on active probing, causing more overhead in the network, or overhearing all the packets (even if the node is not the destination) to infer the Received Signal Strength (RSSI), which is also expensive. In this paper, we propose a platform for data gathering in aquatic monitoring with a swarm of USVs communicating through DTN routing using a passive link quality estimation approach, i.e., a link quality estimation based on a monitoring process that does not disturb the network (in terms of collision and energy consumption). Moreover, since in aquaculture the water tanks can be off the coast with no Wi-Fi or other short range communication, this platform integrates multiple technologies (both long and short range communications) for a better real-time data gathering. The proposed approach is both tested in a real environment to assess the real link quality of the network, and in a simulated environment to assess the performance of the approach with a swarm of USVs. The main contributions of this work can be summarized as follows:  
1. Aquatic monitoring platform through a swarm of USVs;
2. Data collection units with aquatic environment monitoring;
3. Passive link quality estimation;
4. DTN routing strategies through the best quality path in a dynamic approach;
5. DTN supporting a mobile sensing network and multi-technology communication (both long and short range communications); and
6. Network and path simulation in Robot Simulator (ROS) which can be integrated with real USVs in a real environment. The remainder of this article is organized as follows. Section 2 discusses the related work and positions our own work. Section 3 describes the platform architecture, the network elements, and the network functionalities, while Section 4 presents the passive link quality estimation DTN routing strategies. Section 5 presents the results of link quality estimation in real environments, and Section 6 discusses the performance results in the simulated swarm of USVs. Finally, Section 7 enumerates the conclusions and future work.