esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 4 ICI Aware Progressive MIMO-OFDM Iterative Receiver  
Studies have shown that there is usually more severe ICI in real time underwater acoustic OFDM systems. Specialized ICI equalization methods can be used to significantly improve system performance instead of considering ICI as an additive noise. The canonical ICI aware iterative receivers assume a constant ICI depth D and the preprocessing procedure is kept unchanged throughout the iterations. When the ICI depth is kept large, it covers the scenarios with large channel variations, however when the channel variation is not that large, the large ICI depth results in over calculations, more power consumption and increased complexity. On the other hand, if ICI depth is assumed small, the performance gets worse for UWA communication with large channel variations.  
Since the computationally efficient receiving algorithm is critical to the UWA communication networks, a progressive iterative receiver is designed here which adapts the ICI model according to the unknown channel conditions during iterations. The proposed receiver, as shown in Figure 3,is an iterative receiver which follows the Turbo principle, however, unlike the canonical iterative receivers, the system model used for channel identification and data demodulation changes during each iteration. It starts with a simple channel model i.e., ICI ignorant processing and then proceeds to ICI-aware processing where the severity of the assumed ICI increases as iteration goes on. The soft information obtained from the previous iteration contributes to the channel estimation, equalization, and data demodulation for the current iteration. Moreover, if the decoding of the current iteration is unsuccessful, the system increases the value of \(D\) in the next iteration, assuming a greater Doppler factor value and therefore more ICI. The increment in value of \(D\) enhances the equalization effect of the ICI and the value of \(D\) is gradually increased from 0 with each iteration. Therefore, the receiver can self-adapt to the unknown degree of channel variation progressively. The progressive iterative receiver has a very low complexity under better channel conditions and can even maintain good performance under poor channel conditions as proved in the simulations section.  
Figure 3: Progressive MIMO-OFDM iterative receiver block diagram.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 5 Results and Discussion
### Experimental Verification of Simulation Calculation Model for Wafer Warpage  
Figure 11 shows the surface node displacement of wafer during the sawing process obtained based on different combinations of process parameters shown in Table 1. It can be seen that in the combination of process parameters, the values of wire speed and workpiece feed rate are large, and the surface node displacement of the sawn wafer is also large. According to the calculation method of wafer warpage shown in Figure 5, the wafer warpage values calculated based on the finite element simulation model are compared with the measured experimental values, as shown in Figure 12. The values of the two are quite consistent, with simulation calculations being smaller than experimental measurements and an error within 12%. The main reason for this situation is that the finite element simulation ignored the influence of factors such as poor kerf lubrication and diamond wire vibration that may occur during the actual sawing process on wafer warpage. Therefore, the actual measured warpage value of the wafer will be greater than the simulation value. Moreover, as shown in Figure 12, when the diamond wire speed and the workpiece feed rate increase, the error between the simulation calculation values and the experimental measurement values also increases. This indicates that in this case, the influence of some actual situations on the finite element simulation model in sawing increases. Overall, the simulation results of the finite element model are reasonable.  
Figure 11: Surface node displacement of cut wafers under different sawing process parameter combinations.  
Figure 10: Nephogram of wafer thermal deformation at the sawing depth of 4 mm.
## 1 Energy and QoS
### Optimising Energy and QoS  
A simple analytic model that uses the combined energy-QoS cost function includes in its first part the well known Pollaczek-Khintchine formula [2,3] for the average response time, based on Poisson arrivals of jobs and general service time distributions, and in its second part the energy consumption per job:  
\[C_{job}=aE[S][1+\frac{\rho(1+C_{S}^{2})}{2(1-\rho)}]+b\frac{A}{\lambda}+bBE[S] \tag{2}\]  
where \(E[S]\) is the average job service time as before; \(C_{S}^{2}\) is the squared coefficient of variation of service time; \(\lambda\) is the job arrival rate; and the constants \(a\) and \(b\) describe the relative importance placed on QoS and energy consumption. This allows us to compute the value of the arrival rate that minimises \(C_{job}\). The result shows that the optimum setting of the load \(\rho^{*}=\lambda^{*}E[S]\) will depend on \(A\) (the idle power consumption) and on the ratio \(b/a\):  
\[\rho^{*}=\sqrt{\frac{2 bA}{a(1+C_{S}^{2})}}\left(1+\sqrt{\frac{2 bA}{a(1+C_{S}^ {2})}}\right)^{-1} \tag{3}\]The expression (3) gives us a simple rule of thumb for selecting system load for optimum operation, depending on how we weigh the importance of energy consumption with respect to average response time or how fast we are getting the jobs done. We also see that \(\rho^{*}\) increases with the ratio \(bA/a(1+C_{S}^{2})\). This tells us that the optimum load should increase with the system's idle power consumption, the relative importance that we place on energy, and with the squared coefficient of variation of service time.
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 3 Prototype and Field Tests
### Internal Measurement Test  
The working principles of the internal ORLM were introduced in Section 1. As is shown in Figure 14, an external floating roof tank with a nominal volume of 50,000 m\({}^{3}\), located in Zhoushan City, China, was selected for the internal experiments. On-site environmental conditions were wind speed 18 km/h, temperature 28 \({}^{\circ}\)C, and relative humidity 65%, in line with the requirement of the ORLM. As a reference, the optical reference line measurement was dependent upon the reference circumference (188,459 mm in this tank) determined by manual strapping of the third course, 1/4 course height below the top of the course horizontal weld seam. The volume of the bottom, the height of the courses, and the volume of the appurtenances were identical for both volume tables. This tank is divided into 60 horizontal stations and 20 vertical stations. The offsets measured by the two methods are presented in Table 5. The radial deviation in the table is the average of the deviations of the 60 horizontal stations in the same ring (each ring has two vertical stations).  
It is noteworthy that the measurement performed by the manually-lifted trolley took 6 min per horizontal station, and the one performed by TWCR took 4 min per horizontal station. However, it was found to be problematical setting up power supply lines inside tanks.
## 3 Test Site and Experimental Setup  
Measurements were performed at the Terrestrial Environmental Observatories (TERENO) test site in Selhausen, which is located in the Rur River catchment, North Rhine-Westphalia, Germany. The underlying fluvial deposits from the Rhine/Meuse River and the Rur River system are Quaternary sediments, covered by eolian sediments (up to a depth of 1 m) from the Pleistocene and Holocene. The test site has a slope of 4\({}^{\circ}\), and the altitude ranges between 100 and 110 m above sea level. A mean annual precipitation of 690 mm and an average air temperature of 9.8 \({}^{\circ}\)C are observed at the test site. According to the USDA textural classification, the major soil type is siltyo Ioam. Further information along with a detailed description of the test site is given by [PERSON] _et al._[44].  
Figure 1: Measured Green’s function in the time domain, where \(t_{min}\) and \(t_{max}\) define the time window focused on the surface reflection in which inversion is performed. Time \(t_{i}\) corresponds approximately to the surface interface in the space domain, and the maximum amplitude of Green’s function is defined by the peak-to-peak \(PtP\).  
Figure 2 shows a small trench with Decagon EC -TM probes installed at a 1.5-m distance from the footprint of the GPR antenna and the fully-automated GPR experimental setup. Measurements were carried out on a bare agriculture field. Soil temperature and permittivity were recorded by EC-TM sensors (Decagon Devices Inc., Pullman, WA, USA), and Em50 data loggers (Decagon Devices Inc., Pullman, WA, USA) were used to record and store the measurements. Air temperature was recorded at a weather station located at a distance of 800 m from the setup. Soil and air temperature measurements were carried out with a constant time step of 15 min for nine days. The lateral and vertical dynamics of the soil temperature and dielectric permittivity were recorded at six depths (2, 3, 4, 5, 7, 8 cm). Two additional EC-TM probes were installed in the snow at 2 cm above the ground to monitor the dynamics of snow temperature and permittivity. The stepped-frequency continuous-wave radar system was set up using a vector network analyzer, with a monostatic antenna, thereby producing an air-launched radar. First, the antenna calibration experiment was performed in the laboratory, and later, the GPR system was set up in the field. The double-ridged horn antenna was fixed at approximately 110 cm above the ground, and the surface of the bare soil was fully exposed to natural processes, _i.e._, precipitation, evaporation and snowfall (see Figure 2b). Time-lapse GPR, temperature and permittivity measurements were performed during days 330.5-339.5 of 2010 to monitor the soil dynamics. Data acquisition was computer controlled and entirely automated over the nine-day measurement period [42].
###### Abstract  
Harvesting energy from rotational motion for powering low-power electrical devices is attracting increasing research interest in recent years. In this paper, a magnetic-coupled buckled beam piezoelectric rotation energy harvester (MBBP-REH) with bistable and frequency up-conversion is presented to harvest low speed rotational energy with a broadband. A buckled beam attached with piezoelectric patches under dynamical axial load enables the harvester to achieve high output power under small excitation force. The electromechanical coupling dynamical model is developed to characterize the MBBP-REH. Both the simulations and experiments are carried out to evaluate the performance of the harvesters in various conditions under different excitations. The experimental results indicate that the proposed harvester is applicable for low speed rotation and can generate stable output power under wideband rotating excitation. For the harvester with two magnets that produce attractive forces with the center magnet of the buckled beam, the average power is 682.7 \(\mu\)W and the maximum instantaneous power is 1450 \(\mu\)W at 360 \(\mathrm{r/min}\).  
rotational energy harvesting; buckled beam; bistable; frequency up-conversion; magnetic plucking +
Footnote †: journal: Applied Sciences
## 1 Introduction
First, free vibration testing with an impact hammer was conducted to determine the dominant frequencies of the system and the corresponding mode shapes. Next, random and harmonic vibration tests were performed to determine whether any nonlinear behavior could be detected. Finite element analysis (FEA) was conducted and utilized to compare the computationally found natural frequencies with those found experimentally. Additionally, mode switching and variability can be determined [26; 27]. It should be mentioned that, in a recent study, [PERSON] et al. [28] examined the effects of the central cut on the dynamical properties of the box assembly component of the BARC system. The current investigation explores the influence of the central-cut width on the full BARC system, with a particular focus on the coupling effect between the box assembly and the removable component and how it affects the linear and nonlinear dynamical properties of the BARC system. In addition, it should be noted that some preliminary computational and impact testing investigations were conducted and presented at a conference by the authors [29]. These results were built upon in this current work. To begin this study, the system and experimental setup are described, beginning with a breakdown of the BARC system and what systems are being investigated, as indicated in Section 2. This section then explains the experimental setups, beginning with how the computational analysis was conducted and then moving on to the experimental setups of the impact and random/harmonic testing. Once that is completed, Section 3 focuses on the computational analysis, getting into the mode shapes and natural frequencies found through FEA. Section 4 describes the experimental results, starting with impact testing, then moving on to random vibration testing, and concluding with harmonic testing. Conclusions are drawn in Section 5.
## 6 Future Work, Challenges, and Limitations
### Variability  
IPSs are hardware device based. The location estimation and NLoS mitigation are based on performance with user devices, which include limited storage capacity and computational capability. It is challenging to implement the ML model due to the models requiring computational and storage overhead for extracting complex signal features automatically from large amounts of collected data [111]. Moreover, a trained robust model could require retraining again when the definition, state and situation are changed in real-time localization systems. However, with the exponential growth in wireless networks, such as upcoming 6G and cloud facilities, it is expected for this computational burden to be handled successfully in the future and achieve numerous robust ML-based location services indoors.
## 4 Results and Discussion
### Determination of the Optimum Characteristics
This smearing allows for more excellent adhesion to neighboring beads, as shown in Figure 4.  
Figure 10: 3D-printed lines in the y-direction with increasing amplitudes of induced vibration. Figure 9: Lines made with the pen-attachment in the y-direction with increasing amplitudes of induced vibration. In summary, higher motor speeds do not lead to greater extents of induced vibrations and hence may not result in the uniform distribution of material as anticipated. For the particular setup used in this study, S22 is likely to be the optimum motor speed for the best quality of the prints. The level of vibration will likely vary from printer to printer.
## 4 Signal Combining Schemes for ADR Solutions  
The benefits and capabilities of ADRs to reduce ICI in underground mining VLC systems were presented in detail in Sections 3.2 and 3.3. Therefore, as there are multiple PDs, it is necessary to obtain a single signal from the set of signals received by the ADR in \(R_{x}\). In this section, we will explain mechanisms to obtain this signal, which is called signal combining schemes in \(R_{x}\). To achieve this goal, and at the same time make a fair comparison between schemes; two scenarios will be presented, the first with a single PD in \(R_{x}\), and the second with the analyzed ADRs equipped in \(R_{x}\). In addition, being the overall coverage area of an ADR, the union of the coverage areas of each PD included in the ADR, it is necessary to adjust it to be the same area covered by an \(R_{x}\) with a single PD. Therefore, each scenario presented in this section will have an \(R_{x}\) with the same overall effective area, \(N_{PD}\)\(A_{eff}\), which makes the comparison fair. Applying these criteria, we increase the active area of \(R_{x}\) to improve the SINR with the geometry of the ADRs, increasing the reachable data rate while reducing the ICI.  
By adjusting those physical parameters in both ADRs so that they can be comparable, the \(R_{x}\) with more PDs can achieve a better SINR performance despite the fact that it receives a smaller portion of the desired signal. As the equivalent \(\Theta\) of each ADR is also the same, when more PDs are on the same ADR, each PD will has a smaller \(\Theta\), which results in a smaller coverage area. Due to the diffusive nature of the reflected light, a smaller area means less reflected interference signal is picked up by each PD. Moreover, a larger number of PDs enables the system to benefit from better receiver diversity by means of the signal combining schemes, as long as we consider a limit on the number of PDs that we place. As a result, the pyramidal ADR and hemi-dodecahedron can be compared.  
Figure 6: General structure of the hemi-dodecahedron ADR. (**a**) Side view, (**b**) top view, and (**c**) coordinate system of the PDs.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 3 SD6 WSN Architecture
### SD6 WSN Controller
#### 3.1.1 Topology Discovery and Management  
To maintain the unified view of the network, the controller needs to identify the active nodes, but also the neighbors that each node sees, including the quality of the wireless links that connect them, which is indicated by ETX. The SD6 WSNP protocol uses the information sent in flow-mod notifications to include in and exclude nodes from the SD6 WSN topology. In the case of inclusion, the controller checks if the registered node is part of a preloaded table of authorized IPv6 addresses, sending to the node the info-get/nbr-etc message to obtain its neighbors and the respective quality of links (ETX). This message is sent with the observe option enabled, so that whenever a significant change in the quality of the wireless communication channels occurs, this can be notified to the controller. Upon receiving the information, the controller informs the changes to networking applications, which perform the recalculation of the existing flows and inform the controller of the necessary modifications. When a node is excluded, 6 LBR notifies the controller, which warns the applications about the topology change, allowing them to decide if there is a need to recalculate the paths of existing flows. In some situations, such as a reboot of a node, it is possible for a CoAP observe record to be lost. To work around, the controller starts a timer when an info-get/nbr-etc message is sent to a node, which is restarted whenever a response arrives at the controller. If the timer exceeds the defined limit, a new info-get message is sent to the node in question. The controller can use the info-get message to  
Figure 4: Diagram of the interaction of the controller modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Message** & **Method** & **Observe** & **Process** \\ \hline node-mod & GET & Required & Topology discovery and management \\ info-get & GET & Required in 1 st message, optional in others & Topology discovery and management \\ flow-mod & PUT & Required & Flow control \\ packet-in & GET & Required & Flow control \\ \hline \hline \end{tabular}
\end{table}
Table 3: SD6 WSNP messages.  
request other information from nodes (e.g., battery status), which may be sent with the observe option if it is necessary to follow the variation of these parameters.
## 2 Numerical Analysis
### Computational Mesh, Boundary, and Initial Conditions
For the initial conditions of airflow inside the engine cylinder, as has been known, the flow inside the cylinder of a DI diesel engine is characterized by squish, tumble, and swirl intensities. They influence the fuel/air mixing quality between the supplied fuel and charge air. Their turbulent intensities are primarily affected by the inlet valve shape, inlet port design, engine (piston) speed, and inflow velocity of charge air (for DI engines) or fuel-air mixture (for port-injection engines). Because the squish flows inside the cylinders are primarily formed when the piston of the engine reaches the TDC, the characteristics of initial flows inside the engine cylinder are typically characterized solely by tumble and swirl. Based on the engine speed (900 rpm), mean piston speed (9.3 m/s), and charge air pressure (2.5 bar), which causes charge air velocities, and on the guidance suggested in the literature [41], a level of 2880 (1/min) for tumble and swirl intensities has been set as the in-cylinder flow initial condition for the simulations in this research. The engine was supplied with a mass flow rate of 70.2 kg diesel oil per hour, corresponding with 0.0195 kg/s. With an engine speed of 900 rpm, the amount of diesel fuel supplied per engine working cycle was 1.3 g. This fuel amount was injected into the engine cylinder in a 32 CADs duration. Therefore, the injection rate of the injector was 0.219375 kg/s. The single injection strategy for fuel injection was applied to this engine. The injection configuration is presented in Figure 3. This injection configuration was applied to all three piston bowl cases of the engine. The boundary and initial conditions for simulations were kept the same as in the experiments reported in the technical measurement profile of the researched engine. In the present research, C\({}_{13}\)H\({}_{23}\) was employed to represent diesel oil. The main physicochemical properties of diesel oil in the present study had been presented and can be referred to in our previous works [8,9,43], in which all physical properties of diesel oil were set as temperature-dependent to ensure the right response of fuel to any change in temperature. Table 3 lists the boundary and initial conditions for numerical simulations in this study.