esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
## 3 Methodology
### Communication Protocol for the Gateway Layer and the Device Layer  
As the middle layer between the service layer and the device layer, the gateway layer should provide three functionalities: (1) Allowing devices connected to a gateway through different wireless communication protocols, (2) interpreting a device's description file and registering the device and its observations to a specified SensorThings API service, and (3) translating the extended SensorThings API tasking protocol to the device tasking protocol. To achieve these functionalities, this research defines: (1) Default configurations of a wireless communication protocol, which is ZigBee in this research, (2) communication operations on gateways and devices, and (3) an automatic procedure for  
Figure 6: Tasking capability of an example description file.  
tasking protocol translation and task forwarding. These three designs are introduced respectively in the following subsections.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 5 Results
### Lab Experiment
The main motivation for the lab experiment was the determination of an appropriate pseudorange variance model in the presence of a jammer. One can conclude that, first of all, the constant noise model as the simplest possible model with a standard deviation of 2 m and uncorrelated observations is a good candidate for application in the hybrid navigation filter. Furthermore, the CN0-based variance model would not be significantly superior to a constant noise model as it overestimates the pseudorange errors in the presence of a jammer. Finally, the elevation-based GNSS measurement model seems to be a better candidate than the CN0 model in the case of the jamming as the reported noise statistics depends only slightly on the jamming power. Still, an optimal pseudorange variance model could include both the dependence on elevation angle and the CN0. Such a model could in general be determined with an experimental setup used in this lab experiment but would require a far more detailed analysis and will be a subject of future work.
## Appendix A Derivation of Delay Difference Change Rate  
The change of observation geometry during \(\Delta t\) is shows in Figure 16 in which \(\mathbf{T}_{t}\), \(\mathbf{R}_{t}\), \(\mathbf{S}_{t}\), \(\mathbf{T}_{t+\Delta t}\), \(\mathbf{R}_{t+\Delta t}\) and \(\mathbf{S}_{t+\Delta t}\) are the position at time \(t\) and \(t+\Delta t\), respectively; \(\mathbf{m}_{t}\) and \(\mathbf{m}_{t+\Delta t}\), \(\mathbf{n}_{t}\) and \(\mathbf{n}_{t+\Delta t}\), \(\mathbf{k}_{t}\) and \(\mathbf{k}_{t+\Delta t}\) are the unit vector from GNSS satellite to specular point, from specular point to LEO satellite and from GNSS to LEO satellite at \(t\) and \(t+\Delta t\), respectively; \(\mathbf{v}_{\text{rt}}\) and \(\mathbf{v}_{\text{rt}}\) are the velocity of LEO and GNSS satellite at \(t\). When \(\Delta\) is a small value, \(\mathbf{m}_{t+\Delta t}\approx\mathbf{m}_{t}\), \(\mathbf{n}_{t+\Delta t}\approx\mathbf{n}_{t}\), \(\mathbf{k}_{t+\Delta t}\approx\mathbf{k}_{t}\).  
The delay of direct and reflected signals at \(t\) and \(t+\Delta t\) are  
\[D_{\text{d}}(t)=(\mathbf{R}_{t}-\mathbf{T}_{t})\cdot\mathbf{k}_{t} \tag{1}\]  
\[D_{\text{r}}(t)=(\mathbf{S}_{t}-\mathbf{T}_{t})\cdot\mathbf{m}_{t}+(\mathbf{ R}_{t}-\mathbf{S}_{t})\cdot\mathbf{n}_{t} \tag{2}\]  
\[D_{\text{d}}(t+\Delta t)=(\mathbf{R}_{t+\Delta t}-\mathbf{T}_{t+\Delta t}) \cdot\mathbf{k}_{t+\Delta t} \tag{3}\]  
\[D_{\text{r}}(t+\Delta t)=(\mathbf{S}_{t+\Delta t}-\mathbf{T}_{t+\Delta t}) \cdot\mathbf{m}_{t+\Delta t}+(\mathbf{R}_{t+\Delta t}-\mathbf{S}_{t+\Delta t })\cdot\mathbf{n}_{t+\Delta t} \tag{4}\]  
The DDCR is defined as  
\[DDCR(t)=\frac{[D_{\text{r}}(t+\Delta t)-D_{\text{d}}(t+\Delta t)]-[D_{\text{r }}(t)-D_{\text{d}}(t)]}{\Delta t}\]  
\[=\frac{[D_{\text{r}}(t+\Delta t)-D_{\text{r}}(t)]}{\Delta t}-\frac{[D_{\text {d}}(t+\Delta t)-D_{\text{d}}(t)]}{\Delta t} \tag{5}\]  
The second term of the right is the delay change rate of direct signals and could be simplified as  
\[\begin{split} DCR_{\text{d}}(t)\approx\frac{[(\mathbf{R}_{t+ \Delta t}-\mathbf{T}_{t+\Delta t})-(\mathbf{R}_{t}-\mathbf{T}_{t})]\cdot \mathbf{k}_{t}}{\Delta t}\\ \approx\frac{[(\mathbf{R}_{t+\Delta t}-\mathbf{R}_{t})-(\mathbf{ T}_{t+\Delta t}-\mathbf{T}_{t})]\cdot\mathbf{k}_{t}}{\Delta t}\\ \approx(\mathbf{v}_{\text{rt}}-\mathbf{v}_{\text{rt}})\cdot \mathbf{k}_{t}=\lambda f_{\text{d}}(t)\end{split} \tag{6}\]  
where \(f_{\text{d}}(t)\) is the Doppler frequency of direct signals at \(t\). Similarly, the first term of the right is the delay change rate of reflected signals and could be simplified as  
\[DCR_{\text{r}}(t)\approx\mathbf{v}_{\text{rt}}\cdot\mathbf{m}_{t}+\mathbf{v}_ {\text{rt}}\cdot\mathbf{n}_{t}=\lambda f_{\text{r}}(t) \tag{7}\]  
where \(f_{\text{r}}(t)\) is the Doppler frequency of reflected signals at \(t\). According to the derivations above, finally, DDCR is the function as the Doppler difference between reflected ans direct signals as  
\[DDCR(t)=DCR_{\text{r}}(t)-DCR_{\text{d}}(t)\approx\lambda\Delta f(t) \tag{8}\]
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
## 2 Materials and Methods
### Design and Parameter Selection
#### 2.2.1 Key Structure of Microporous Aeration Component: Centrifugal Fan
For incompressible fluids, the parallel piping has the following characteristics: the flow rate of the main pipe is the sum of the flow rates of the branches [16], as shown in Equation (3):  
\[Q_{0}=Q_{1}+Q_{2}+\cdots+Q_{i} \tag{3}\]  
where \(Q_{0}\) denotes the total flow in the pipe (in m\({}^{3}\)/h); and \(Q_{i}\) denotes the flow rate of each branch of a parallel pipeline (in m\({}^{3}\)/h). The size of the pipeline affects the performance of the fan, as too large a diameter increases the cost, while too small a diameter increases the pressure loss and power consumption. The inner diameter of the pipe d is calculated by the formula [17]:  
\[D=\sqrt{\frac{4Q}{\pi V}} \tag{4}\]  
where \(D\) denotes the inner diameter of the pipe (in m); and \(V\) denotes the cross-sectional flow rate of the pipe (in m/s). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Flow Rate** & **Length** & **Diameter** & **Airflow Rate** & **Pressure Loss along** & **Local Pressure Loss** \\  & \(Q\) **[m\({}^{3}\)/h]** & \(L\) **[m]** & \(D\) **[m]** & \(V\) **[m/s]** & **the Pipeline** & \(h_{2}\) **[kPa]** \\ \hline Main pipe & 240 & 2.00 & 0.08 & 13.4 & 3.37 & 0.136 \\ All branch pipes & 30 & 48.00 & 0.05 & 6.7 & 1.50 & 1.4872 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters for all pipes. Equation (5) indicates that the total pipe loss \(\left(P_{g}\right)\) and the water pressure (\(P_{h}\)) add up to the minimum lift of the fan under actual operating conditions:  
\[\Delta P_{1}=P_{h}+P_{g} \tag{5}\]  
where \(\Delta P_{1}\) denotes the minimum fan lift required for the water in the aquaculture tank (in kPa) and \(P_{h}\) denotes the pressure at the water depth where the aerators are located (in kPa). According to the calculation of the above formula, the mass flow rate and the minimum pressure increase under standard conditions are \(q_{v_{2}}\)= 197.7 m\({}^{3}\)/h and \(\Delta P_{1}\)= 27.7 kPa, respectively. It can be assumed that the quantity of dissolved oxygen produced by the microporous aeration component can meet the direct (grass carp) and nitrification oxygen demands. Therefore, this fan model meets basic oxygen demand in RAS culture, and the specific parameters are shown in Table 2.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 3 Proposed MAC Protocol: AdHoc-FDMAC
### Data Transmission  
Data transmission in ad hoc networks is more complex than that in access point-based networks. In the access point-based network, all the nodes are connected through a common AP and a source node can easily transmit its message to the destination through AP. Conversely, if the destination node is out of the transmission range of the source in the ad hoc network, the source first selects an efficient route to the destination using a prominent routing algorithm and then transmits its data to the destination through this route using one or more relay nodes. The data transmission procedures using this AdHoc-FDMAC are described in the following two cases:  
* Transmitter and receiver are out of the data transmission range;
* Transmitter and receiver are within the data transmission range.
## 5 Optimization Model
On the basis of set of Pareto optimal solutions, colorful grid maps by means of SOM were generated. Figure 16 presents the maps generated for the 2 nd variant of optimization. For the 1 st variant, the maps generated are comparable, so they are not included in the paper. Each grid in the figure corresponds to a particular optimization criterion, whereas nodes correspond to particular solutions, with the value of each objective depicted as the unit's color. The general look and the brief analysis of the maps allow us to formulate conclusions:  
* Objectives f1 and f4 have quite a similar nature (actuator with a smaller volume generates much smaller amounts of heat);
* Objectives f3 and f5 have contradictory natures, except in the region in the upper right corner of the grid (geometry of the arms resistant to buckle can generate low displacement and vice versa),
* Objectives f4 and f5 have contradictory natures as well. Note that this compromise-solution is similar to the shape of the actuator proposed by [PERSON] et al., in the work dedicated to improving designs for an electrothermal in-plane microactuator [7]. The actuators with nonuniform beams, for which width increases close to the central shaft, allow for the generation higher of displacement and actuation force compared to the actuator with uniform beams. Other examples of selecting compromise-solutions with respect to the different assumption and restrictions, starting from another arbitrarily chosen criterion, can be found as well. Figure 16: Self-organizing maps (SOMs) generated for the set of Pareto optimal solutions obtained. Figure 17: Example of selecting potentially interesting, compromise-solutions. It should emphasized, that solutions for which the maximal equivalent stress exceeds strength of the material, and solutions for which buckling may occur (factor less than 1.0), are infeasible. That may lead to the conclusion that such criteria should be implemented in optimization as constraints. It is of course possible and can be easily implemented in the method of optimization proposed, and would cause a reduction of the total number of objectives. On the other hand, as previously mentioned, making no assumptions on weights of objectives before optimization, can result in obtaining useful information for the designer concerning trade-off relationships between criteria. Moreover, there are no criteria related to the dynamic characteristic of the actuator considered in this paper. The method of multicriteria optimization proposed allows for the formulation and implementation of more criteria as well.
## 3 Results and Discussion
### Biochar Characterization  
Biochar is a by-product of the gasification process at high temperatures (>700 \({}^{\circ}\)C). This by-product has a high carbon content and offers a very important and yet unknown potential for farmers in the agrifood sector. Due to its macroscopic structure and composition, it acts as an amendment to the soil, returning the carbon captured by olive trees to the soil and providing the following benefits:  
* Biochar boosts the growth of plants and roots. The use of biochar improves the rhizosphere, supporting the development and activity of a huge and diversified microbial community, which includes microorganisms capable of promoting plant growth [34; 35].
* Due to its porous structure, biochar can absorb up to five times its own weight. Therefore, it retains nutrients and water, avoiding leaching and soil erosion [36].
* Biochar from gasification at temperatures above 700 \({}^{\circ}\)C leads to the formation of macroscopic structures with high conductivity and cation exchange capacity (graphene). This type of biochar facilitates and promotes ion exchange, benefiting the microbial life of the soil and increasing the potential for microorganisms and roots [37].
* Finally, from an environmental sustainability point of view, biochar captures carbon in agricultural soil. Therefore, the carbon that was earlier retained by the olive tree is later returned to the soil, avoiding its emission into the atmosphere in the form of carbon dioxide [24].  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Theoretical Composition of Wet Exhaust Gas at 100\% Fuel Gas Combustion (mg/Nm\({}^{3}\))** \\ \hline O\({}_{2}\) & 21,428 \\ CO\({}_{2}\) & 179,143 \\ CO & 1500 \\ NO\({}_{\text{X}}\) & 150 \\ \hline
**Emissions (vol. \%)** & \\ \hline O\({}_{2}\) & 1.50 \\ CO\({}_{2}\) & 9.12 \\ CO & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Exhaust gas composition from the spark–ignition engine running on the producer gas from gasification of exhausted olive pomace pellets.  
The biochar produced during the gasification process of exhausted olive pomace pellets was physicochemically characterized. Table 9 shows the main physicochemical properties of this by-product, such as proximate analysis, ash elemental analysis, particle size or heating value.  
Another essential aspect of biochar is its current market price. The sale price of biochar in England ranges between EUR162-480/t [38]; while other publications estimate its price at around EUR150/t [39]. These values are very attractive to boost the economy of the agrifood sector with new business models.
## 2 Simulation Scenario and Models
### GNSS-R Scattering Models  
The GNSS-R scattering model was derived by [PERSON] et al. (2014) using bistatic equation model and Kirchhoff Approximation as  
\[|Y_{0}(\tau)|^{2}=\frac{T_{\mathrm{coh}}^{2}P_{t}G_{t}\lambda^{2}}{(4\pi)^{3} }\int_{\Omega_{5}}\frac{G_{r}(\mathbf{r})\Lambda^{2}(\tau-\tau_{r}(\mathbf{r}) )S^{2}(T_{\mathrm{coh}}(f_{c}-f_{r}(\mathbf{r})))}{R_{r}^{2}(\mathbf{r})R_{t}^ {2}(\mathbf{r})}\pi|\mathcal{R}|^{2}\frac{q^{4}(\mathbf{r})}{q_{z}^{4}(\mathbf{ r})}P\left(-\frac{\mathbf{q}_{\perp}}{q_{z}}\right)d\mathbf{r} \tag{2}\]  
where \(P_{t}\) and \(G_{t}\) are the transmitted power and transmitter gain, which are 26.8 W and 12.1 dB for GPS L1 signal; \(\lambda\) is the wavelength of the signal, which is 0.19 m for GPS L1 signal; \(\tau(\mathbf{r})\) and \(f(\mathbf{r})\) are the delay and Doppler frequency for signals reflected by scattering unit \(\mathbf{r}\); \(f_{c}\) is the local carrier  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Units** & **Value** \\ \hline GNSS Satellite Height \(h_{\mathrm{t}}\) & km & 20,200 \\ LEO Satellite Height \(h_{\mathrm{r}}\) & km & 659 \\ GNSS Satellite speed \(v_{\mathrm{t}}\) & km & 3.07 \\ LEO Satellite speed \(v_{\mathrm{r}}\) & km & 7.60 \\ Earth Radius \(R_{\mathrm{e}}\) & km & 6371 \\ Maximum Gain \(G_{0}\) & dB & 12 \\
3 dB Beam Width \(\varphi\) & deg & 25 \\ incident angle \(\theta\) & deg & [0:50] \\ Moving direction of GNSS Satellite \(\alpha_{\mathrm{t}}\) & deg & [0:360] \\ Moving direction of LEO Satellite \(\alpha_{\mathrm{r}}\) & deg & [0:360] \\ Coherent integration time \(T_{\mathrm{coh}}\) & ms & 1 \\ Incoherent Integration time \(T_{\mathrm{incoh}}\) & s & 16 \\ Tracking Refresh Period \(T_{\mathrm{TR}}\) & ms & [1, 1000, 3000] \\ Wind Speed \(W\) & m/s & [1:20] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of Simulation Scenario.  
Figure 1: Scattering scenario of GNSS-R.  
frequency; \(\Lambda(\tau)\) is the autocorrelation function of the code; \(S(f)=\sin(\pi f)/\pi f\); \(R_{\tau}(\mathbf{r})\) and \(R_{t}(\mathbf{r})\) are the distance from LEO and GNSS satellite to the scattering unit \(\mathbf{r}\); \(\mathcal{R}\) is the Fresnel reflection coefficient; \(\mathbf{q}\) is the scattering vector \((q_{\perp},q_{z})\); \(P(\cdot)\) is the probability density function of ocean surface slopes, and is assumed as two-dimensional Gaussian function. The variances of \(P(\cdot)\) are obtained by Elfouhaily spectrum Elfouhaily (2016) which is relative to wind speed.
## 4 Numerical Simulations  
### Simulation Descriptions  
In this section, two simulations on a 3-DOF manipulator are implemented to show the efficiencies of the proposed method. In the first simulation, the controllers are applied on the manipulator under the presence of the friction model and known control direction. In the second simulation, besides the friction model, the unknown control direction is taken into account in the manipulator dynamics. The simulation environment is MATLAB 2020a with a sampling time of \(10^{-3}\) s. The 3-DOF manipulator dynamics are mentioned in [5]. Their parameters are presented in Table 1.  
The friction model in the manipulator dynamics consists of the viscous and Coulomb frictions, presented as follows:  
\[\textbf{d}(t)=\textbf{bx}_{2}(t)+\textbf{cs}ign(\textbf{x}_{2}(t)) \tag{27}\]  
where \(b=0.05I_{3\times 3}(\text{Nms}/\text{rad})\) and \(c=0.05I_{3\times 3}\).  
The trajectory reference in the Cartesian coordination is presented as \(x_{d}=0.4+0.15\cos(0.2\pi)(\text{m})\), \(y_{d}=0(\text{m})\), \(z_{d}=-0.15\cos(0.2\pi+\pi/2)(\text{m})\), and the rotary angle around the z-axis is zero.
## 5 RF Feature Extractors
### Wavelet Transforms  
A wavelet transform decomposes an incoming signal into some 'coarse' and 'fine' coefficients, based on shifted and scaled versions of a so-called'mother wavelet' function. Unlike the Fourier transform that cannot offer compact support in both the time and frequency domains, a wavelet transform can offer a compact/bounded support in both tone- and wavelet-domains. Wavelet transforms have been extensively used in watermarking and image-processing applications, and have been reported to be able to identify 'hidden' features; thus, they look like relevant feature extractors for RF fingerprints. Wavelet transforms, in the context of RF fingerprinting, have been previously used, for example in [25; 60; 61]. The work in [25; 60] was only focusing on narrowband signals, in contrast to GNSS. The work in [61] used GNSS simulation-based signals, but only focused on a few simplified transmitter HW impairments. While the work in [61] showed some limited promising results with the discrete wavelet transforms in the context of RFF, our further investigations with more realistic transmitter models as described in Sections 3 and 4 did not show any improvement by using a wavelet transform instead of a spectrogram. Wavelet transforms have an increased complexity compared to other feature-extraction transforms because they output two pairs of complex coefficients (the coarse and fine-approximation coefficients); by distinction, for example, the spectrogram only has one complex output sequence.
## 4 Engineering Application and Analysis
### Analysis of Calculated Results for Rock-Lining Synergy
#### 4.3.4 Analysis and Refinement of the Tunnel Wall Displacement Fitting Approach  
It is noteworthy that when utilizing the displacement fitting curve of the surrounding rock wall from Figure 10 to calculate the coordinated deformation between the rock and lining, an unexpected trend is observed: the hoop stress at the rock wall increases after reaching its minimum, and the plastic zone thickness begins to diminish after peaking. This behavior suggests that once the relaxation of the surrounding rock surpasses a threshold, the loosening pressure increases, leading to higher hoop stress at the tunnel wall and a reduction in the plastic zone thickness, which contradicts typical engineering observations. Preliminary analysis suggests that this discrepancy may stem from the upward trend in the displacement fitting curve beyond 24.17 mm. To address this, the fitting parameters in Figure 10 were adjusted, setting parameter \(n\) to the negative of the maximum wall displacement \(u_{\text{wmax}}\). This modification enhanced the fitting accuracy, as demonstrated in Figure 16.  
Upon fixing parameter \(n\), it is observed that parameter \(m\) is correspondingly adjusted, leading to a slight reduction in the fitting curve's coefficient of determination (\(R^{2}\)) relative to the curve in Figure 10. Nevertheless, as depicted in Figure 16, the fitting curve's variation remains minimal in the two extreme cases of the initial deformation of the surrounding rock stress release rate. Consequently, the deformation curve (green curve) at \(\Delta u=0\) (i.e., \(R_{0}=7.18\) m) is adopted as the representative displacement law for calculating the stress at the tunnel wall and the plastic zone radius corresponding to various reserved deformations, with the results presented in Figure 17.  
Figure 15: Stress and plastic zone thickness variations at tunnel walls with different lining outer radii.  
Figure 17a demonstrates that the hoop stress at the tunnel wall decreases linearly with increasing reserved deformation, without the anomalous upward trend observed in Figure 14a. This confirms the validity of the adjusted fitting curve in depicting the changes in hoop stress and plastic zone thickness. Consequently, the fitting approach with a fixed parameter \(n\) more reasonably represents the progression of the plastic zone in the surrounding rock and the stress conditions at the tunnel wall.  
Accordingly, for scenarios with significant reserved deformation, employing the fitting method with a fixed parameter \(n\) is recommended to ensure an accurate representation of the plastic zone extent and the stress in the surrounding rock. Conversely, for cases with minimal reserved deformation, utilizing the fitting method with an unfixed parameter \(n\) is preferable to minimize computational errors and enhance the precision of the outcomes.
# Numerical Evaluation of Residual Stress Influence on SIF in CT Specimen
## 4 Stress Intensity Factor Calculation
### Results of Residual Stress and Stress Intensity Factor Calculation
Negative SIF values do not have much of a meaning, but, as these are only intermediate results,  
Figure 12: Comparison of analytical and numerical calculation of SIF. they can be ignored. The crack closure effect will be much more important when the CT specimen under tension is analyzed. Finally, the last two cases were analyzed: SIF calculation with and without residual stress. The distributions of crack opening stress \(\sigma_{y}\) for the case with residual stress is shown in Figure 14. The highest stresses were obtained at the crack tip. FEA results of SIF calculation through the thickness of the specimen with and without residual stress load cases are shown in Figure 11.0  
Figure 11.1 Distribution of crack opening stress \(\sigma_{y}\) (direction TRD) in loading case with residual stress (MPa). Figure 11.2 Stress intensity factor values through the thickness of the specimen due to residual stress. As it is shown in Figure 15, the SIF values for the load case without residual stresses are 14.9 MPa\(\cdot\)m\({}^{0.5}\) at both sides of the CT specimen and 20.3 MPa\(\cdot\)m\({}^{0.5}\) in the middle of the specimen, i.e., lower values are at the sides and higher in the middle. However, the results are opposite in the loading case with the residual stresses, where SIF values at the side surfaces are 16.7 MPa\(\cdot\)m\({}^{0.5}\) and in the middle is 16.2 MPa\(\cdot\)m\({}^{0.5}\). The reason for the lower values of K\({}_{\rm I}\) in the case with the residual stresses is the dominant compression stress in the residual stress profile which compensates for the stress occurring due to tension load. As a consequence, the values of K\({}_{\rm I}\) decreased and matched the shape of the residual stress profile. Such an unusual increase in K\({}_{\rm I}\) values is dangerous from the crack propagation perspective, especially for stress corrosion cracking mechanism as, due to a corrosive environment, such cracks tend to form and propagate from the free surface of material [17].