esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Harvesting over Rician Fading Channel:
## 5 Conclusions  
Recent development in wireless sensor networks have led to an exponential growth of the energy demand for operating the networks, which raises a question about how to efficiently use the available energy in the wireless environment. This paper rigorously analyzes the performance of a half-duplex AF bidirectional sensor network in which the relay node is equipped with time-switching-based energy harvesting protocol. The channel considered in this paper is a Rician fading channel. We also take into account the hardware impairment at source and relay nodes. We derive both exact and asymptotic forms of the outage probability, achievable throughput, as well as the symbol-error-rate at each destination node. The analysis results are validated by Monte Carlo simulation. From the results of this work, we can gain an insightful understanding of the effect of various parameters on the system performance. Furthermore, the optimal time-switching factor (i.e., the best energy harvesting strategy) is also founded by numerical algorithms.  
**Author Contributions:** [PERSON] created the main idea and developed the performance evaluation by analysis and simulation for the basic case that \(\lambda_{1}=\lambda_{2}\). [PERSON] set up the simulation configurations, performed the numerical experiments and wrote the paper. [PERSON] extended the analysis for the general case. [PERSON] and [PERSON] were working as the advisors of [PERSON] to discuss and advise the main idea and performance analysis together.  
**Funding:** This research received funding from the grant No. SP2018/59 conducted by VSB-Technical University of Ostrava, Czech Republic.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 11: Effect of \(\alpha\) and modulation scheme on SER.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 8 Verification and Validation of the Simulation Model
### Validation  
Validating this simulation tool had several challenges. Firstly, although the AIS dataset is large, organizing and processing it in a manner that is suitable for the simulation is a non-trivial problem that requires separate, dedicated attention. Secondly, most of the available data are from one common source, which has been used to build the model. Hence, there are no additional data currently available for testing purposes alone. Thirdly, conducting trials of the tools alongside existing icebreaker management methods requires extensive permissions, checks, and passing through regulations. Hence, different validation approaches were employed for this simulation tool, as recommended by [22]. Four methods of validation were used. They are described in detail in what follows.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 4 Performance Evaluation and Discussion: Example of GEO SAR Design
### Impact of Bistatic Scattering RFI
#### 4.2.2 GNSS Bistatic Scattering RFI Case Analysis
From Table 7, it can be noted that the required average transmitted power is very small in the non-specular scattering case and the GNSS bistatic scattering RFI has little impact on GEO SAR. However, because of the large number of the GNSS satellites and the high gain, much worse in the specular scattering case, the required average transmitted power is very high in the specular case, if we consider the maximum \(A_{gs}\) (about 30 km wide). In fact, due to the roughness, the specular area can be very small (e.g., 100 m \(\times\) 100 m), the required average transmitted power of GEO SAR is less than 1 kW, which can be achieved to mitigate the RFI impact.
## 3 Data and Method  
The active fault data and earthquake catalogue used in this article were provided primarily by the Sichuan Earthquake Agency. For the fault data, we used GF-7 satellite data to enable a detailed interpretation of the fault tracks and conducted field seismogeological work to identify the tracks of the newest activity along the Jinshajiang fault zone and to revise the fault data. On the basis of remote-sensing interpretation and field surveys, typical geomorphic displacement points were selected; then, high-resolution geomorphic images and digital elevation data of landforms were obtained via unmanned aerial vehicle (UAV) and structure from motion (SfM) technology [39; 40; 41]. Next, the evolution process of the geomorphic displacement was analyzed to determine the amount of displacement, and the displacement values were obtained using ESRI ArcMap. Finally, samples for carbon dating were collected from the corresponding geomorphic surfaces to estimate the slip rate of the Jinshajiang fault zone.  
We also selected two fault troughs and excavated two trenches across them to conduct paleoseismological studies. We used a scraper to smooth the trench walls and identified paleoseismic events via the deformation and faulting characteristics of the strata exposed in the trenches. Then, samples for radiocarbon dating were collected layer by layer from the strata and sent to the Beta Analytic Inc. laboratory (USA) for testing. The radiocarbon ages were determined using accelerator mass spectrometry referenced to 1950 AD. Finally, we estimated the paleoseismic events and obtained the paleoearthquake sequence to assess the potential seismic hazard.
# Validation of Lumbar Compressive Force Simulation in Forward Flexion Condition
## 3 Result
### Regression Analysis  
Figure 7 shows the linear regression fit of the models obtained by comparing the CF estimated using the models with that obtained from invasive measurements under the same flexion angle. It can be seen that the intercept becomes 0, and only the slope reveals the relation of data close to the vertical axis (estimated CF from models) and the horizontal axis data (CF obtained by invasive measurements). As the slope between the CF and flexion angle (Figure 7a-c) approaches 1, the correlation between the invasive measurements and the data estimated using the corresponding model increases. As shown in Table 4, the slopes obtained using the dynamic CF simulator, [PERSON]'s model, and [PERSON]'s model are 1.012, 1.070, and 0.641 with \(R^{2}\) of 0.683, 0.658, and 0.675, respectively; their root mean square errors (RMSEs) are 269, 259, and 205 respectively. Thus, we selected [PERSON]'s model as the precision evaluating model.  
Figure 4: Comparison between different models and invasive measurement under free flexion. The error bar signifies the standard deviation (STD) (N) (* \(p\leq\) 0.05; ** \(p\leq\) 0.01).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**N = 10** & \multicolumn{3}{c}{**Increment of the CF at L5/S1 (N/deg)**} \\ \hline Angle range (\({}^{\circ}\)) & AR1 (from 0\({}^{\circ}\) to 10\({}^{\circ}\)) & AR2 (from 10\({}^{\circ}\) to 20\({}^{\circ}\)) & AR3 (from 20\({}^{\circ}\) to 30\({}^{\circ}\)) \\ \hline Invasive measurement & 49.0 & 26.6 & 11.5 \\ \hline CF simulator & 42.5 & 40.3 & 30.2 \\ \hline Merryweather [16] & 35.0 & 37.2 & 31.5 \\ \hline Potvin [15] & 34.0 & 30.3 & 27.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationship between compressive force (CF) and flexion angle estimated with different models and measurements.  
Figure 5: (**a**) Flexion angle versus flexion process for 2 and 10 s movements; “A” represents the greatest difference in angular velocity between 2 and 10 s, which is approximately 48\({}^{\circ}\), and “B” represents the greatest difference in angular acceleration between 2 and 10 s, which is approximately 90\({}^{\circ}\); the flexion cycle is period that starts in an upright posture via flexion and ends in an upright posture again; (**b**) Trunk angular velocity versus flexion process for 2 and 10 s flexions; (**c**) trunk angular acceleration versus flexion process for 2 and 10 s flexions.  
Figure 6: Compressive force (CF) estimated by CF simulator at 0\({}^{\circ}\), 48\({}^{\circ}\), and 90\({}^{\circ}\) with and without a 20 kg external mass.
## 4 Security Analysis
### Informal analysis
#### 4.2.2 Perfect Forward Secrecy  
Values \(S_{U_{i}}\) and \(S_{G}\) are randomly generated during communication, ensuring that the session key is randomly and evenly distributed in the key space. Attackers have no information advantage to calculate the session key from the intercepted messages \(M1(ID_{U_{i}},P_{U_{i}},ert_{U_{i}},t1)\) and \(M2(P_{G},ert_{G},t2)\). Even if the attackers have obtained the session key of one of the communicating parties, it is impossible to obtain any information about the previous session key through this session key. The session key is generated from the refresh key \(RK_{GU_{i}}\), the master key \(MK_{GU_{i}}\) and the counter value \(SC(G,U_{i})\). After each session key is generated, the refresh key applies a hash function to the current refresh key to generate a new refresh key that removes and replaces the old one. As a result, no attackers can obtain any information about the old refresh key and thus cannot generate a session key; thus, the protocol has perfect forward security.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 3 Results
### Wind Speed Comparisons
It can be seen that reducing the opening angle--implying a sharper cone which is narrower at a given height--leads to a worse correlation (i.e., increased offset, slope further from 1.0, or reduced \(R^{2}\); details are in the Figures). Results from the _linear_ model are presented in Figure 9e. The offset is 0.03 and the slope (1.026) implies results that are very close to the met mast measurements. The dispersion of the data is small  
Figure 9: Correlation between Galion lidar and the cup anemometer at 100 m above ground. The black dots represent the 10 min data points, the black line is the regression line, and the blue lines are the 5 th and 95 th quantiles. (\(R^{2}=0.976\)), the width of the 5-95 th quantile is significantly reduced. Increased relative errors occur in the lower wind speed range (\(\leq\)2 m\(\cdot\)s\({}^{-1}\)), likely due to the high turbulence intensity that reduces homogeneity across the measurement cone. However, this wind speed range is not relevant for the production of wind energy.
## 3 Measurement Setup
### Hardware Description  
The radio frequency signal was transmitted by using an Anristu MG369 xC signal generator (with Pt = 9 dBm) through a wireless channel and was captured by a narrow-band horn directional antenna at the Rx side, in order to present the directional propagation path loss model. At the Tx side, the continuous radio wave was generated by using an Anritsu MG369 xC Series synthesized signal generator device (see Figure 1d), which was connected to a highly directional horn antenna.  
Figure 1: (**a**) line-of-sight (LOS) environment, (**b**) non-line-of-sight (NLOS) environment, (**c**) measurement scenario, (**d**) signal generator, (**e**) signal analyzer.  
On the other side (i.e., Rx), the received power level was captured using an Anritsu MS2720T handheld spectrum analyzer (see Figure 1e), which was operated at zero spans with 50 ms sweep time connected to the horn antenna. The noise contribution was -85 dBm/Hz when the resolution channel bandwidth (RBW) of the spectrum analyzer was set to 100 kHz at a 38 GHz frequency setting.
## 3 Uncertainty Analysis Model
### Uncertainty Propagation  
The conceptual representation of the uncertainty propagation is demonstrated in Figure 3. It demonstrates that the dispersions of the model outputs of the maximum temperature and thermal stress inside the receiver tube wall can be attributed to the input uncertainties. Due to high accuracy, the MC simulation method [33] is applied to conduct uncertainty propagation and determine the dispersion of the model output. The calculation steps of the MC simulation method are given below:  
1. The probability distributions of the uncertainties are given;
2. The sample size \(N\) is specified for the MC simulation;
3. An appropriate sampling method is chosen. There are several methods for sampling probability distributions, such as random sampling, Latin Hypercube sampling, and Sobol sampling [34]. Rather than generating random numbers, the Sobol sampling method generates a uniform distribution in probability space. As a result, the Sobol sampling method is more accurate than other sampling methods and, therefore, was selected in this paper. The result of this step is to construct a matrix that contains the sampled values of the uncertain input parameters, which can be expressed as follows: \[\mathbf{p}=[\mathbf{p}_{1}\mathbf{p}_{2}\cdots\mathbf{p}_{k}]=\left[\begin{array}{cccc}p_{ 11}&p_{12}&\cdots&p_{1k}\\ p_{21}&p_{22}&\cdots&p_{2k}\\ \vdots&\vdots&\ddots&\vdots\\ p_{N1}&p_{N2}&\cdots&p_{Nk}\end{array}\right]\] (5)
4. Based on the sampled values of the uncertain input parameters, the temperature and thermal stress simulation procedures are repeated \(N\) times. The dispersion of the model output can be obtained and expressed as follows: \[\mathbf{y}=\left[\begin{array}{c}y_{1}\\ y_{2}\\ \vdots\\ y_{N}\end{array}\right]=f(\mathbf{p})=\left[\begin{array}{c}f(p_{11},p_{12}, \ldots,p_{1k})\\ f(p_{21},p_{22},\ldots,p_{2k})\\ \vdots\\ f(p_{N1},p_{N2},\ldots,p_{Nk})\end{array}\right]\] (6)  
\begin{table}
\begin{tabular}{c c c} \hline
**Uncertain Parameter** & **Central Value** & **Distribution** \\ \hline \(p_{1}\) & Peak incident solar flux (kW/m\({}^{2}\)) & 900 & Normal \(\pm 5.4\%\)[20] \\ \(p_{2}\) & Tube coating absorptivity (-) & 0.93 [22] & Uniform 0.91-0.95 [22] \\ \(p_{3}\) & Specific heat of molten salt (J/(kg\cdot\)K) & \(1443+0.172\times(T-273.15)\)[32] & Uniform \(\pm 5\%\)[24] \\ \(p_{4}\) & Viscosity of molten salt (Pa\(\cdot\)s) & \((22.714-0.12\times(T-273.15)+\) \\  & \(2.281\times 10^{-4}\times(T-273.15)^{2}-\) & Uniform \(\pm 5\%\)[24] \\  & & \(1.474\times 10^{-7}\times(T-273.15)^{3})\times 10^{-3}\) & Uniform \(\pm 10\%\)[24] \\ \(p_{5}\) & Thermal conductivity of molten salt & \(0.443+1.9\times 10^{-4}\times(T-273.15)\)[32] & Uniform \(\pm 10\%\)[24] \\ \(p_{6}\) & Specific heat of tube material (J/(kg\cdot\)K) & 500 [2] & Uniform \(\pm 5\%\) \\  & Thermal conductivity of tube material & 21.5 [2] & Uniform \(\pm 5\%\) \\ \(p_{7}\) & (W/m\(\cdot\)K) & \(1.43\times 10^{-5}+7.34\times 10^{-9}T-2.65\times 10^{-12}T^{2}\)[2] & Uniform \(\pm 5\%\) \\ \(p_{8}\) & Thermal expansion coefficient of tube material (1/K) & \(1.43\times 10^{-5}+7.34\times 10^{-9}T-2.65\times 10^{-1
## 3 Capacity Analysis of the IWMSNs  
The multibeam satellite has previously been stated as operated at the S/L band, and the Rician fading channel is used as the LOS return link of IWMSNs. The optimal joint decoding capacity and linear MMSE capacity are analyzed under the Rician fading channel. The Haar measure approximations are utilized to derive the closed form approximations of the optimal capacity and MMSE capacity; [48]has proven that the random Haar matrices could get the asymptotic performance as most deterministic precoding matrices.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Proposed Method
### R-\(\lambda\) Model
#### 3.4.2 Coding Parameters Update  
According to the relationship of \(\lambda\) and \(R\)[25]:  
\[D(R)=CR^{-K} \tag{15}\]  
where \(R\) denotes the bit rate; \(D\) is the MSE (mean squared error) distortion; and are model parameters related to the characteristics of the video source [25]; \(\lambda\) denotes the slope of the R-D curve, and \(\alpha\), \(\beta\) signifies the parameters related to the video content. That is, \(\lambda\) can be directly calculated by \(R\) through \(\alpha\) and \(\beta\) However, \(\alpha\) and \(\beta\) are parameters related to the content characteristics of the video, and the values of different contents are significantly different. The \(R\)-\(\lambda\) model solves this problem by introducing the following algorithm. Firstly, the following formula is used to calculate the \(\lambda\) of the frame and LCU [25]:  
\[\lambda=abpp^{\beta} \tag{16}\]where \(bpp\). bit rate, \(\alpha\) and \(\beta\) are different for each frame and each LCU. In order to realize content adaptation, \(\alpha\) and \(\beta\) will be updated continuously in the encoding process. After completing the encoding of an LCU or a frame, \(\alpha\) and \(\beta\) are updated as follows:  
\[\lambda_{comp}=\alpha_{old}bpp^{gold}_{real} \tag{17}\]  
\[\alpha_{new}=\alpha_{old}+\delta_{\alpha}.(\ln\lambda_{real}-\ln\lambda_{comp }).\alpha_{old} \tag{18}\]  
\[\beta_{new}=\beta_{old}+\delta_{\beta}.(\ln\lambda_{real}-\ln\lambda_{comp}). \ln bpp_{real} \tag{19}\]  
where is the actual \(bpp_{real}\)is the actual \(bpp\) in the coding process; \(\lambda_{real}\) is the actual value of \(\lambda\) in the coding process; \(\delta_{\alpha}\) = 0.1 and \(\beta\) = 0.05 [25].  
Note that \(bpp\) is defined as:  
\[bpp=\frac{R}{f.w.h} \tag{20}\]  
where \(f\) is the frame rate, \(w\) and \(h\) are the width and height of the picture, respectively [25].  
When \(\lambda\) is determined, \(QP\) is calculated according to [26].  
\[QP=C_{1}*\ln(\lambda)+C_{2} \tag{21}\]  
where \(C_{1}\) = 4.2005 and \(C_{2}\) = 13.7122.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 1 Introduction
It also requires specifying an energy storage capacity two to five times the required capacity, to reduce the depth of discharge, thus leading to a higher cost. Supercapacitors have been tested for these types of applications; however, with more or less the same capital cost as flywheels [1], their operational lifetime is relatively low (reaching up to 12 years) [3]. To make more use of such a system and minimise its capacity in order to reduce the cost, it is more useful for the storage system to be used many times a day, to allow for the time shifting of demand and to feed into the grid at times of high demand. Interest in this new paradigm of how energy is used will be greatly enhanced once Time of Use (ToU) tariffs are in place. A number of reviews of flywheel storage systems have been presented by several papers in the literature. A comparison of energy storage technologies is made in [14], where a numerical and graphical review demonstrates the improvements and problems associated with FESS. A comparative analysis of energy storage technologies for high power applications is carried out in [15] and a survey of FESS for power system applications is provided in [16]. The control of high speed FESS in space applications is discussed in [17]. FESS is briefly reviewed in [18] and an overview of some previous projects is presented in [9]; however, such sources offer a scarcity of information. The authors of [19] focus on the developments of motor-generator (MG) for FESS, where the common electrical machines used with flywheels, along with their control, is reported in [20]. A review and simulation of FESS for an isolated wind power system is presented in [10]. This review takes a different approach from earlier work and particularly picks up on very recent literature in what is a rapidly developing subject. This paper focuses on the description and applications of FESS, providing an overview of some commercial projects for each application. Many of the above papers have provided reviews of FESS, but what is missing in the literature is a comprehensive review of FESS with a description of the applications which are commercially available. Following the introduction, a description of FESS is presented. The main components of FESS, including the rotor, electrical machine, bearings, and flywheel containment are discussed in detail in Section 2. A flywheel's main characteristicsare stated in Section 3 and its applications are described in Section 4. The paper concludes with recommendations for future research in Section 5.
## 4 Numerical Method
### Equations  
As previously mentioned, \(\dot{W}\) and \(\dot{Q}\) are expressed by \(P\) and \(U\), and the efficiency of the energy conversion that occurs in the stacks depends on the acoustic impedance \(Z=P/U\). Hence, COP\({}_{total}\), \(\eta_{e}\), COP\({}_{c}\), and \(\eta_{tube}\) depend on the distributions of \(P\) and \(U\). Therefore, \(P\) and \(U\) are calculated at a given point along the looped tube.  
The following two equations, derived in a study by [PERSON] [9], are used to calculate \(P\) and \(U\):  
\[\frac{dP}{dx}=-\frac{i\omega p_{m}}{1-\chi_{v}}U, \tag{9}\]  
\[\begin{array}{ll}\frac{dU}{dx}&=-\frac{i\omega[1+(\gamma-1)\chi_{c}]}{\gamma p _{m}}P\\ &+\frac{\chi_{a}-\chi_{v}}{(1-\chi_{v})-1-\gamma}\frac{1}{1-\chi_{v}}\frac{dT_ {m}}{dx}U.\end{array} \tag{10}\]  
The cooler is divided into the ten components and the two equations are computationally integrated [14] along each component. The value of \(dT_{m}/dx\) is important in performing the integration. Two temperature conditions are used, namely that \(dT_{m}/dx\) is assumed to be constant and that \(dT_{m}/dx\) is calculated based on the assumption that the tube is insulated from its surroundings. In order to satisfy this insulation assumption in the calculation, it is necessary for the value of the enthalpy flow \(\dot{H}\) along an insulated tube to be constant [2]. According to [PERSON] [10], \(\dot{H}\) is expressed as  
\[H = \dot{W}-\dot{Q}. \tag{11}\]  
Equations (1) and (2) are substituted in Equation (11) to obtain the following expression:  
\[\frac{dT_{m}}{dx} = \frac{\dot{H}-\frac{4}{2}\Re\left[P\tilde{U}\left(1-\frac{\hat{ \chi}_{v}-\chi_{a}}{(1+\sigma)(1-\hat{\chi}_{v})}\right)\right]}{\frac{A\rho_ {m}c_{p}|U|^{2}}{2\omega(1-\sigma^{2})|-\hat{\chi}_{v}|^{2}}\Im\left[\chi_{a} +\sigma\hat{\chi}_{v}\right]}. \tag{12}\]  
If the boundary conditions about \(P\) and \(U\) are given, then the temperature gradient along the tube with \(dT_{m}/dx\
eq 0\) is calculated by coupling Equations (9)-(12). It should be noted that the thermal conduction along the \(x\)-axis and the acoustic streaming [2; 15; 16] were neglected for purposes of simplicity.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 4 CFD Analysis of Ramjet Nozzles
### CFD Setup
#### 4.1.1 Geometry  
The geometry was constructed utilising Ansys Workbench 17. For the purpose of computational efficiency, only half of the geometry was constructed as the ramjet C-D nozzle is symmetrical. The geometry created is presented in Figure 11. Additionally, a \"face split\" function was featured in the geometry for further improvement of the meshing.