esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation
### Hardware-Software Partitioning  
The main advantages of the embedded system design are performance and flexibility. Although the software implementation has high flexibility with low design efforts, its performance gain is low. Instead, the intrinsic parallelism of the hardware attains high system performance, but its design is complex. Therefore, in the embedded system design, the Hardware-Software (HW/SW) codesign manages the trade-off between the reasonable performance and flexibility.  
The partitioning of the hardware and software components is a crucial stage in the HW/SW codesign of the SoC system. The analysis of the system states that the ILBP algorithm which has continually intensive matrix computation is a feasible candidate for the hardware implementation on the FPGA fabric, while the ECT-C unit is a control flow-intensive state machine which interfaces with the WBS and the LCD. Therefore, the ECT-C unit is suitable for the software implementation on the ARM processor of the FPGA platform, which reduces the required software drivers for these peripherals.  
The SoC design and implementation of the proposed ECT system, the IR unit, and the ECT-C have been carried out in two stages. In the first stage, the Matlab Hardware-Software Codesign Workflow is applied to generate an HDL IP for the IR algorithm and a C code for the software ECT-C unit. In the second stage, the generated HDL IP is joined the Altera Qsys with the ARM processor and the other system blocks such as SDRAM, LCD driver, and WBS to build the final ECT system. The generated C code for the ECT-C unit is integrated with the rest of the software components in the Altera SoC EDS toolset for debugging and performance measure.  
The Matlab HW/SW codesign workflow of the IR unit is implemented into two steps: First, a Matlab model for the ECT system is built, then it is converted to a fixed-point version; afterwards, its equivalent Simulink model is built. The created Simulink model of the proposed ECT system has the partitioned HW part (IR algorithm) and the SW part (ECT-C).
## 2 Model Formulation
### Logistic-\(\beta\) Construction Timing Sub-Model
#### 2.2.1 The URT Network Growth Curve  
The reasonable growth curve of URT network scale can be plotted by finding a fit to the scale of growth of URT developed cities in the world. Mileage growth data for URT networks in seven representative cities around the world were collected (Figure 1), namely Beijing, Shanghai, Guangzhou, Hong Kong, Tokyo, Seoul, and Singapore. These data were fitted with S-type curves using the Gompertz, Logistic [40], and von Bertalanffy [41] equations. Both the Gompertz and the Logistic equations have fixed inflection points, while the von Bertalanffy equation has variable inflection points, as shown in Table 2. The fits of results of the network scale growth curves for seven cities with three S-type curves are shown in Table 3.  
It can be seen from Table 3 that the best fit is found with the Logistic curve. Therefore, the Logistic curve was used to describe the reasonable growth curve of network scale. The function is shown as Equation (13):  
\[s=f(t)=A/(1+Be^{-Kt}) \tag{13}\]  
where \(s\) is the cumulative operation scale of URT, \(t\) is the cumulative construction time of URT, and \(A\), \(B\), and \(K\) are calibration parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Function** & **Inflection Point \(y\)** & **Inflection Point \(x\)** & **Extremum \(y\)** \\ \hline Gompertz & \(y=Ae^{-b\cdot x}\) & \(A/e\) & \(\ln B/K\) & \(A\) \\ Logistic & \(y=\frac{A}{1+Be^{-Kx}}\) & \(A/2\) & \(\ln B/K\) & \(A\) \\ Von Bertalanffy & \(A\big{(}1-Be^{-Kx}\big{)}^{3}\) & \(8A/27\) & \(\ln 3B/K\) & \(A\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common S-type curves.  
Figure 1: Network scale growth polylines for seven representative cities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & \multicolumn{6}{c}{**Cities**} \\ \cline{2-7}  & **Beijing** & **Shanghai** & **Guangzhou** & **Hongkong** & **Tokyo** & **Seoul** & **Singapore** \\ \hline Gompertz & – & – & – & – & 0.993 & – & – \\ Logistic & 0.978 & 0.995 & 0.966 & 0.979 & 0.992 & 0.974 & 0.943 \\ [PERSON] & – & 0.989 & – & 0.981 & 0.987 & 0.967 & 0.953 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting results of network scale growth curves with the three kinds of S-type curves.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 2 Calculation Theory for Fracture Diffusion of Bingham Fluid
### Bingham Fluid Fracture Diffusion Equation
The initiation fracture pressure value when the soil reaches fracture failure in the first stage is determined by considering the soil parameters. Reference [43] analyzed the mechanical mechanism of the soil in the first stage of fracture grouting based on plastic mechanics and large deformation theory. They regarded the first stage of fracture grouting as a circular hole expansion problem of an infinite soil body, where the soil damage obeys the Mohr-Coulomb yield criterion and derived a formula (Equation (15)) for calculating the initiation fracture pressure value and the plastic zone radius (considered as the slurry diffusion radius) according to the stress  
Figure 2: Slurry fracture diffusion model. balance and strain continuity boundary conditions. However, the diffusion radius value derived from the purely mechanical point of view deviates from the actual value because the influence of the grouting pressure and slurry properties on the effect of the slurry injection is not considered. However, a good exploration of the initiation pressure is still made. In this paper, the fracture pressure of soil used in the grouting test is calculated by referring to the calculation formula of the initiation fracture pressure value, as shown in Equation (15):  
\[P_{k}=\frac{B}{m}\left(\sigma_{ry}+\frac{\sigma_{c}}{M-1}\right)a^{m\left(1- \frac{1}{M}\right)}-\frac{\sigma_{c}}{M-1} \tag{15}\]  
In Equation (15), \(m\) is 2; \(M=\left(1+\sin\varphi\right)/\left(1-\sin\varphi\right)\); \(\varphi\) is the internal friction angle of the soil-rock mixtures; \(\sigma_{ry}=\left(\left(m+1\right)Mp_{t}+m\sigma_{c}\right)/\left(M+m\right)\); \(p_{t}\) is rest soil pressure; \(\sigma_{c}\) is the peak strength of soil-rock mixtures; \(B=\left(1+\mu\right)\left(\sigma_{ry}-p_{0}\right)/E_{s}\); \(\mu\) is Poisson's ratio of soil-rock mixtures; \(E_{s}\) is compression modulus of soil-rock mixtures; \(\varphi\), \(\sigma_{c}\), and \(E_{s}\) can be obtained by shear test. \(p_{t}\) was calculated according to the test conditions. The value of \(\mu\) was selected according to \"Building Foundation Engineering\" [44]: the value of crushed stone soil is 0.2, the value of clay is 0.35, the value of [PERSON]'s ratio of pure soil material of group #10 is 0.35, and the value of other groups is 0.2. At this point, the third quantity can be solved by giving any two of the grouting pressure \(\left(p_{0}\right)\), diffusion radius \(\left(R\right)\), and grouting time \(\left(t\right)\) according to Equation (14).
## 3 Multi-Domain Informative Path Planning
### Obstacle Avoidance Path Re-Planning
#### 3.2.1 Path Replanning in Current Fields  
As currents have different directions, their effects on the HAUV's path can be positive and negative. We firstly calculate the absolute value of current fields per meter of movement. Then its influence (positive or negative) is calculated in regard to the intersection angle \(\beta_{i}\) between the current vector and subpath vector. Each subpath between \(p_{i}\) and \(p_{i}+1\) will be traversed by step \(j_{x,y,z}\): \(\big{\{}p_{i}(x_{i},y_{i},z_{i}),p_{i+j}(x_{i}+j_{x},y_{i}+j_{y},z_{i}+j_{z}), \ldots,p_{i+1}(x_{i+1},y_{i+1},z_{i+1})\big{\}}\). We use indicator \(\eta\) to evaluate the current field. Once \(\xi_{c}^{ji}\) is greater than \(\eta\xi_{i}\), the weighted edge \(\xi_{c}^{ji}\) and coordinate \(p_{i+j}\) are recorded as obstacles. The whole framework can be generalized as shown in Figure 9:
# Algorithmic Design of an FPGA-Based Calculator for Fast Evaluation of Tsunami Wave Danger
## 2 Materials and Methods
### HLS-Based Algorithm Porting
(2008) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Mathematical Modeling in Application to Regional Tsunami Warning Systems Operations. In _Computational Science and High Performance Computing III: The 3 rd Russian-German Advanced Research Workshop_; [PERSON], [PERSON], [PERSON], Eds. ; Springer Science & Business Media: Berlin, Germany, 2008; pp. 52-68. * Vitis High-Level Synthesis User Guide. Available online: [[https://www.xilinx.com/support/documentation/sw_manuals/xilinx2021_1/ug1399-vitis-hls.pdf](https://www.xilinx.com/support/documentation/sw_manuals/xilinx2021_1/ug1399-vitis-hls.pdf)]([https://www.xilinx.com/support/documentation/sw_manuals/xilinx2021_1/ug1399-vitis-hls.pdf](https://www.xilinx.com/support/documentation/sw_manuals/xilinx2021_1/ug1399-vitis-hls.pdf)) (accessed on 20 November 2021). * [PERSON] (2015) [PERSON] Estimating Tsunami Wave Height over a Sloping Bottom in the Ray Approximation. _Numer. Anal. Appl._**2015**, \(8\), 304-313. [CrossRef]
* [PERSON] (2017) [PERSON] Benchmark solutions for tsunami wave fronts and rays. Part 2: Parabolic bottom topography. _Sci. Tsunami Hazands_**2017**, _36_, 70-85. * [PERSON] (1997) [PERSON]; [PERSON] _Implementation and Testing of the Method of Splitting Tsunami (MOST) Model_; NOAA Technical Memorandum ERL. PMEL-112; Pacific Marine Environmental Laboratory: Seattle, WA, USA, 1997. * [PERSON] (1957) [PERSON] _Water Waves. The Mathematical Theory with Applications_; Interscience publishers: New York, NY, USA, 1957. * JODC (2021) JODC 500 m Gridded Bathymetry Data. Available online: [[https://jdoss1.jodc.go.jp/vpage/depth500_file.html](https://jdoss1.jodc.go.jp/vpage/depth500_file.html)]([https://jdoss1.jodc.go.jp/vpage/depth500_file.html](https://jdoss1.jodc.go.jp/vpage/depth500_file.html)) (accessed on 20 November 2021). * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling of the 2011 Japan Tsunami: Lessons for Near-Field Forecast. _Pure Appl. Geophys._**2013**, _170_, 1309-1331. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Numerical simulation of the 2011 Tohoku tsunami: Comparison with field observations and sensitivity to model parameters. In Proceedings of the Twenty-second (2012) International Offshore and Polar Engineering Conference, Rhodes, Greece, 17-22 June 2012.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Impact of Network Size  
In this section, we evaluate ACE performance with varying size of network. We simulated the impact of network size with different dense of nodes in a 20 \(\times\) 25 = 500 m\({}^{2}\) grid topology shown in Figure 9. The source node sends on/off messages 1000 times to the destination node at rate of 10 pks/s.  
The computational overhead will increase with the network size. we evaluate the time for nRF52840 to search routing tables with different size. The result is shown in Table 1. For the small scale network, the table searching time is negligible. However, in large scale  
Figure 8: Comparison for the latency and PDR using a varying of external BLE interference. The performance of ACE is more stable since ACE can avoid most of the three advertising channels. (**a**) Latency. (**b**) PDR.  
networks, the routing table searching will have a greater impact on the transmission latency. Besides, for each hop of packet forwarding, all nodes need to search the routing table once, which greatly increase the multi-hop data transmission.  
The results in Figure 10 show that Bluetooth Mesh suffers a severe loss of reliability when the network size becomes larger and denser. In contrast, ACE maintains a high PDR despite the scale of the network, verifying better scalability of ACE than Bluetooth Mesh. The performance improvement in scalability benefits from efficient traffic flows of ACE, which can reduce the possibility of packets collisions and further improve network reliability.
## 4 Results and Analysis
### Debris Flow Characteristics
#### 4.1.2 Flow Depth  
Figure 7 shows the flow depths observed through the flume experiments and their changes due to the installation of the berm. Figure 7a,b show the debris flow depth according to \(\alpha\) and \(C_{V}\), respectively. Figure 7c,d show the percent change in debris flow depth according to \(\alpha\) and \(C_{V}\), respectively, after the berm was installed. In the case of the straight channel test, the debris flow depth decreased as \(\alpha\) increased or \(C_{V}\) increased (Figure 7a,b). The decrease in flow depth with increasing \(\alpha\) slowed when \(C_{V}\) was 0.50 or greater, and the decrease in flow depth with increasing \(C_{V}\) slowed when \(\alpha\) was 15\({}^{\circ}\) or greater. In the case of the single-berm channel test, the debris flow depth decreased as \(C_{V}\) increased (Figure 7b). The decrease in flow depth with increasing \(\alpha\) changed when \(C_{V}\) was 0.50, and the flow depth suddenly increased when \(\alpha\) was 25\({}^{\circ}\) and \(C_{V}\) was 0.45 or less (Figure 7a). The installation of the berm was found to decrease the debris flow depth by an average of 3.9-71.2%, even though the flow depth increased by 15.6% when \(\alpha\) was 25\({}^{\circ}\) and \(C_{V}\) was 0.40 (Figure 7c,d). The average decreases in debris flow depth were 43.7%, 53.8%, 44.4%, and 1.1% for a \(C_{V}\) of 0.40-0.50 and \(\alpha\) values of 10\({}^{\circ}\), 15\({}^{\circ}\), 20\({}^{\circ}\), and 25\({}^{\circ}\), respectively, and 25.4%, 37.6%, and 44.4% for an \(\alpha\) of 10\({}^{\circ}\)-25\({}^{\circ}\) and \(C_{V}\) values of 0.40, 0.45, and 0.50, respectively. This confirmed that the installation of the berm was more effective in reducing the debris flow depth at larger \(C_{V}\) values.  
Figure 6: (**a**) Flow velocity according to \(\alpha\) (at \(C_{V}\) = 0.40–0.55); (**b**) flow velocity according to \(C_{V}\) (at \(\alpha\) = 10\({}^{\circ}\)–25\({}^{\circ}\)); (**c**) percent decrease in flow velocity due to berm according to \(\alpha\) (at \(C_{V}\) = 0.40–0.55); (**d**) percent decrease in flow velocity due to berm according to \(C_{V}\) (at \(\alpha\) = 10\({}^{\circ}\)–25\({}^{\circ}\)).
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 3 System Performance Analysis
### EE Analysis  
The EE of a communication system is defined as the number of transmitted information bits per unit of energy. The number of transmitted information bits of each subblock in the MM-OTFS-IM signal frame is \(p_{1}+p_{2}=\lfloor\log_{2}(s!)\rfloor+s(\log_{2}(M_{mod}))\), where \(p_{1}=\lfloor\log_{2}(s!)\rfloor\) are the number of index bits, which do not need to consume energy resources, and \(p_{2}=s(\log_{2}(M_{mod}))\) are the modulation bits, which consumes \(s\) units of energy. Therefore, the energy efficiency of the MM-OTFS-IM system can be obtained  
\[EE_{MM-OTFS-IM}=\frac{\lfloor\log_{2}(s!)\rfloor+s(\log_{2}(M_{mod}))}{s} \tag{15}\]  
Under the same parameter conditions, the EE of the OTFS, OTFS-IM, and OTFS-DM-IM systems can be expressed as  
\[EE_{OTFS}=\log_{2}(M_{mod}) \tag{16}\]  
\[EE_{OTFS-IM}=\frac{\lfloor\log_{2}(C(s,k))\rfloor+k(\log_{2}(M_{mod}))}{k} \tag{17}\]  
\[EE_{OTFS-DM-IM}=\frac{\lfloor\log_{2}(C(s,k))\rfloor+k(\log_{2}(M_{modA}))+(s -k)(\log_{2}(M_{modB}))}{s} \tag{18}\]  
Due to the application of the index modulation, compared with the OTFS system, the EE of the OTFS-IM, OTFS-DM-IM, and MM-OTFS-IM systems improved to some extent. At the same time, the EE of the OTFS-IM, OTFS-DM-IM, and MM-OTFS-IM systems is not only related to the modulation order of the constellation signal but also the subblock structure of the signal frame. When the constellation signal modulation order is the same, the MM-OTFS-IM system can transmit the most index bits, so the MM-OTFS-IM system can achieve a higher EE.
###### Abstract
The advantage of TcPO2 is that it is a direct indicator of microvascular function, as it measures the oxygen supply available to skin cells without the need to draw arterial blood [13]. The normal lower limb TcPO2 values are approximately 60 mmHg in healthy subjects and remain constant along the limb: 73.8 \(\pm\) 10.9 mmHg at the thigh, 67 \(\pm\) 10 mmHg at the dorsal skin of the leg, 70 \(\pm\) 9 mmHg at the pretibial level, 70.0 \(\pm\) 12.1 mmHg at the calf, and 69.8 \(\pm\) 5.3 mmHg at the foot. However, in diseased individuals, a progressive decrease in TcPO2 is observed. The influence of TcPO2 is multifaceted, being subject to factors such as tissue temperature, alterations in the hemoglobin dissociation curve, the degree of tissue oxygen metabolism, the circulatory status, peripheral blood perfusion, local skin conditions, and anatomical factors [11; 14]. Electrochemical sensors have been utilized for the measurement of TcPO2 for several decades; however, recent advancements have led to the development of a fluorescence-sensitive photo-optical sensor, which capitalizes on the interactions between light and matter. In comparison with electrochemical TcPO2, this method has the advantages of not requiring oxygen consumption or extraction, and of being less time-consuming for probe preparation, with no need for calibration prior to each measurement [15; 16]. While the advantages of TcPO2 measurement in diabetic foot syndrome are well-documented [17], it is noteworthy that the application of the same technique for the study of pressure injuries is typically limited to the sacrum, trochanter, and the lateral side of the calcaneus [10,11]. Conversely, in the context of TcPO2 measurements in the context of diabetic foot syndrome, the sensor is specifically positioned on the dorsal aspect of the foot, between the navicular bone and the medial malleolus [17]. While the lateral aspect of calcaneus has been shown to coincide with regions of pressure injury occurrence, no comparable data are available from other areas of comparable frequency: styloid process and the fifth metatarsal head and the lateral malleolus [18,19]. It is not certain whether data are lacking in these areas because the application of fluorescence-sensitive photo-optical sensors in the described anatomical locations of the foot does not provide reliable and repeatable data. In view of the aforementioned points, the primary objective of this study was to assess the reliability and repeatability of TcPO2 measurements using a fluorescence-sensitive photo-optical sensor at three sites that are susceptible to foot PIs for which no data were available: the styloid process and head of the fifth metatarsal and the lateral malleolus.
# A Numerical Simulation of the Underwater Supersonic Gas Jet Evolution and Its Induced Noise
## 2 Numerical Methods
### Initial and Boundary Conditions  
The expansion ratio of the nozzle model in the present study was 4, the throat diameter of the nozzle was \(D_{t}\) = 24 mm, and the exit diameter was \(2D_{t}\). Assuming a specific heat ratio \(\gamma=1.4\), according to Equations (6) and (7), the design Mach number of the nozzle can be computed to be \(M_{d}\) = 2.94, corresponding to a design nozzle pressure ratio (NPR) of 33.56. Two working conditions with the same total temperature and water depth but different total pressures were set, corresponding to the under-expanded and fully expanded conditions. The detailed parameters are listed in Table 1.  
\[\frac{A_{e}}{A_{t}}=\frac{1}{M_{d}}\bigg{[}\frac{2}{\gamma+1}\bigg{(}1+\frac{ \gamma-1}{2}M_{d}^{2}\bigg{)}\bigg{]}^{\frac{\gamma+1}{2(\gamma-1)}}, \tag{6}\]  
\[\bigg{(}\frac{p_{0}}{p_{b}}\bigg{)}_{d}=\bigg{(}1+\frac{\gamma-1}{2}M_{d}^{2} \bigg{)}^{\frac{\gamma}{\gamma-1}} \tag{7}\]  
A schematic of the simulation domain of the jet flow field is shown in Figure 1. The simulation domain is divided into three regions: the nozzle area, the jet area, and the buffer zone. A damping region, also referred to as a buffer zone, is established near the outer boundary to minimize the impact of the finite boundaries on the acoustic field calculation results. The entire simulation domain is cylindrical with an axial length of \(120D_{t}\), an upper diameter of \(50D_{t}\), and a lower diameter of \(100D_{t}\). Between the jet area and buffer zone is a cylindrical sound source integration surface with an axial length of \(86D_{t}\) and a radial length from \(16D_{t}\) to \(33D_{t}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Total Pressure at** & **Total Temperature at** & **Water Depth \(H\) (m)** & **NPR \(p_{0}\)/\(p_{b}\)** \\  & **Nozzle Inlet \(p_{0}\) (MPa)** & **Nozzle Inlet \(T_{0}\) (K)** & **Water Depth \(H\) (m)** & **NPR \(p_{0}\)/\(p_{b}\)** \\ \hline Under-expansion condition & 15 & 2300 & 20 & 50.45 \\ Full expansion condition & 10 & 2300 & 20 & 33.63 \\ \hline \end{tabular}
\end{table}
Table 1: Nozzle calculation conditions.  
The outer boundary of the flow field is set as the pressure outlet and outlet pressure (\(p_{b}\)) corresponding to the water depth, and the temperature is 300 K. The total pressure and temperature at the pressure inlet are consistent with the total pressure (\(p_{0}\)) and temperature (\(T_{0}\)) at the nozzle inlet. The remaining flow field boundaries are set as non-slip walls. The minimum grid size of the jet core area is 8 \(\times\) 10\({}^{-4}\) m, and the maximum grid size downstream from the jet area is 1 \(\times\) 10\({}^{-3}\) m. The grid size of the first layer of the nozzle boundary layer is 3.3 \(\times\) 10\({}^{-6}\) m, the calculated \(y+\) is less than 5, and the total number of grids is 2.2 million. In the initial state, the gas inside the nozzle is still and the pressure is consistent with the outlet pressure (\(p_{b}\)). The unsteady time step is 1 \(\times\) 10\({}^{-5}\) s, approximately 0.21 s in total.