esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LB-MPC in Path Tracking of Mobile Platforms  
### Learning and Optimizing Prediction Model  
The prediction model forms the basis of path tracking control. The accuracy of model determines the control performance. The simplified physical model can only be used to simulate the real mobile platform and uncertain environment to a certain extent. The prediction results are not very accurate [71]. In order for the control performance to be improved, the usual approach is to build complex physical models or use nonlinear optimization solvers. However, in the control system of mobile platforms operating in uncertain environmental disturbances, a complex physical model does not perform well in some situations. In addition, the physical model does not truly reflect the interaction between the platform and environment in real time. The prediction model is adjusted on the basis of the latest data by using data-driven ML methods [72]. For instance, the neural networks adjust their parameters on the basis of the newly acquired data [73]. The reinforcement learning realizes real-time interaction between the system and environment. The GP regression assesses the uncertainty in the residual error model to adapt the complex and dynamic working environment. The performance comparisons of the physical model, deep learning model, and reinforcement learning is presented in Table 1.
## 3 Results and Discussion
### Effect of Window to Wall Ratio  
Windows play an effective role in the efficiency of a building. The ASHRAE guideline hugely focuses on window design. Various window-to-wall ratios (W/W) were considered for the house and office buildings. For that case, other design parameters such as window shades, window position, and materials were kept constant. Figure 17 shows the energy use for the tested house building. An around 80% increase in energy use was observed when the W/W changed from 15 to 95% for the tested house building. This increase in energy use is also dependent on the room size and sun exposure conditions. Figure 18 shows the energy use for the tested office building. Due to the bigger rooms and air volume, the effect of the W/W was less severe. A maximum increase to around 17% in energy use was observed for the office building for a W/W of 95%.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 4 Calibrated Data Quality Analysis
Figure 19: February 2018 SMAP-R samples as a function of the high gain antenna incidence angle: (**a**) uncalibrated and (**b**) calibrated. Note that calibrated values correspond to radar cross section values after applying calibration Equation (5), under coherent assumption. The dominant ice type within the study area is old ice (see Figure 13), although first year ice is present in the south-west corner of the area selected and the Alaskan coast. Dominant ice type in Figure 13 means that over 50% of the analyzed pixels were covered by the assigned class. Therefore, the presence of open water or other classes of sea ice is possible. Since our measurements are based on forward scattering, the presence of first year ice will cause an increase in the coherency of the reflection in our observations and open water will contribute to rougher observations. Figure 12 shows that the sea ice concentration was 100% for February 2018, which give us confidence that no or minimal open water was present within our calibration target area. This is supported by peak SNR indicating that there were only five samples with \(<\)10 dB SNR, which we assume were open water. Figure 20 shows that the calibration has not reduced or removed the variability within the scene (due to nondominant presence of first year ice within cells of dominant old ice). The calibration has only corrected for the differences caused by the GPS transmitter EIRP variability and the effect of the SMAP high gain antenna on the peak SNR. Overall, the calibrated data is more spatially consistent as shown in Figure 20. Figure 21 illustrates a formal spatial variability analysis. The spatial variability has been computed as the standard deviation of the measurements in boxes of 1\({}^{\circ}\) latitude \(\times\) 1\({}^{\circ}\) longitude. Figure 21 shows that the calibrated data, both at H-pol and V-pol, have a reduced spatial variability relative to the uncalibrated data. The highest spatial variability observed in the uncalibrated data is close to 11 dB, which is reduced in the calibrated version around \(\sim\)2-3 dB. The high spatial variability of the uncalibrated samples was caused by the two effects corrected in the  
Figure 20: SMAP-R measurements for February 2018, (**a**) uncalibrated V-pol and (**b**) calibrated V-pol, and (**c**) uncalibrated H-pol and (**d**) calibrated H-pol. Calibration includes both the correction of the GPS transmitter variability (collocating direct power information from CYGNSS) and the correction of the effect of the SMAP high gain antenna. Note that calibrated values correspond to radar cross section values after applying calibration Equation (5) under a coherent assumption. calibration process (differences between transmitted EIRP and filtering effect of the SMAP high-gain antenna) and, therefore, did not correspond to any given geophysical feature within the scene.
# Metal-Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods
## 1 Introduction
Unique characteristics such as the high porosity of MOFs allow combustible gases to be adsorbed in the pores during combustion, preventing their release. Additionally, transition metal elements have excellent catalytic oxidation properties, which contribute to the formation of a stable flame retardant char layer [5]. The organic structures of MOFs also facilitate the transport of fire-resistant groups, such as phosphorus, nitrogen, and aromatic derivatives, through appropriate modifications, offering more design and functionalization possibilities than conventional organic-inorganic flame retardants due to their easily adjustable chemical composition and micro-nano morphology [6]. It is worth noting that the hybrid nature of MOFs (organic-inorganic) provides good compatibility with organic polymers and textile surfaces for use as coatings [7]. MOF-based flame retardants present significant advantages over conventional alternatives, including high flame retardancy efficiency, smoke suppression, carbonaceous residue formation, chemical versatility, and the reduction in toxic combustion products. Furthermore, the adhesion of these materials to the textile surface has already been reported and is generally achieved through continuous processes such as dip-pad-dry, layer-by-layer, and even direct synthesis (in situ). The study of the development of this functionality acquired through nanotechnology contributes to the creation of new products capable of retarding fire on cellulosic textile surfaces, favoring a reduction in the use of retardant compounds that have an impact on health and the environment.
## 3 Results and Discussion
Figures 16 and 17 present the boundary contact load and boundary friction variations along a cycle. The results show a significant reduction in the boundary contact load (share of load carried by asperities) in coated samples. This is also reflected in the value of boundary friction force, which accounts for more than 95% at the moderately loaded region. This can be related to the improved (increased) film thickness in Figure 15, which reduces the asperity interactions from both surfaces. Comparing two types of coating, WC-C reveals superior behaviour in terms of boundary friction with values which are ~70% lower in comparison with DLC. This comparative improvement can be attributed to minor differences in the values of pressure and film thickness. Figure 8:0 The boundary load and boundary friction. Figure 8:1 Central film thickness in the cam–tappet conjunction. Although boundary friction values are important, the absolute value of this quantity in comparison with viscous friction is lower. Hence, the value of viscous friction over a cycle is shown in Figure 18 for different surfaces. The values in this figure are governed by the level of pressure as well as shear, which are in turn affected by film thickness and contact area. The results show that unlike boundary friction (governed by film thickness), the viscous friction in coated surfaces is higher than that in an uncoated surface. WC-C again reveals better frictional behaviour compared to DLC. The increased viscous friction can be attributed to the extended contact area, which is subject to the shear stress. Comparing the performance of WC-C as the better-performing coating with the uncoated surface, it gains a maximum of 0.44 N in boundary friction, whilst losing maximum of 0.43 N of viscous friction. Consequently, compared with DLC as a commonly used coating for cam-tappet contacts leading to an overall loss in the frictional values, the gain from WC-C coatings outweighs the loss. Figure 8:2 The boundary load and boundary friction. Figure 8:3 The viscous friction for coating thickness of 1 micron.
## 3 Results  
### Simulation Results  
A simulation was applied to test the method shown above. The wave function of the reference beam was \(U_{R}=\exp\{j[k(\beta y+\gamma z)+\theta_{\mathrm{un}}]\}\). Here, \(\theta_{\mathrm{un}}\) was set randomly to simulate the unknown phase from the vibration of the environment. For simplicity, the phase was added to the reference beam only. As the reference wave was tilted to the monitor, we supposed that optical distance \(z_{R}\) varied from 14 to 16 mm along the \(x\) axis at the monitor plane. \(\beta\) was set to be 0.1 and \(\gamma=\sqrt{(1-\beta^{2})}=0.9950\). A 512 pixel \(\times\) 512 pixel matrix was used to simulate the transmitting area of SLM. Its length and width were set to 9.216 mm, and the wavelength was set to 532 nm.  
The object beam passes through the SLM, where the gray scale in the variable part varied from 0 to 255, and the corresponding phase shift \(\theta_{\mathrm{set}}\) was set as a variable value from 0 to \(\frac{255}{256}\times 2\pi\) in the step of \(\frac{\pi}{128}\). The optical distance \(z_{0}\) from SLM to the monitor plane was set to be 10 mm. The wavefunction \(U_{\mathrm{O}}\) on the monitor plane was calculated using the angular spectrum theory. The processes were implemented in the MATLAB 2017a environment with an Intel Core i7-6500U CPU at 2.50 GHz and 8 Gb RAM.  
The interferograms with different added phases of \(\theta\) were then obtained. A typical one is shown in Figure 2a. \(\theta\) was calculated following the steps shown in Section 2. The areas surrounded by the red lines were selected for fast Fourier transform (FFT). Their positions were fixed for different interferograms. Figure 2b shows a typical spectra pattern for the variable part; it was like that for the reference part as well. The central point in the +1 order with the highest intensity was chosen to acquire its phase. As discussed above, its position was fixed for all spectra patterns. The calculated phase shift \(\theta_{\text{cal}}\) matches the set phase variation \(\theta_{\text{set}}\) very well, as shown in Figure 2c.: The relative error was introduced to quantitatively evaluate the characterization. It was defined as:  
\[E_{\text{r}}=\left\{\begin{array}{l}|\theta_{\text{cal}}-\theta_{\text{set }}|/\theta_{\text{set}}(\theta_{\text{set}}\
eq 0)\\ |\theta_{\text{cal}}-\theta_{\text{set}}|(\theta_{\text{set}}=0)\end{array}\right. \tag{9}\]  
From Figure 2d, for all the \(\theta_{\text{cal}}\), the relative errors are less than 0.01%. The results show that the new method is very exact.
## 3 Proposed Methodology
### Finite Impulse Response Wiener Filter Model for Discrete Series Ls Estimates  
The output of the Wiener filter \(\mathrm{g}[\mathrm{n}]\) is compared with the reference signal \(\mathrm{s}[\mathrm{n}]\) after being convolved with the input signal \(\mathrm{H}_{\mathrm{l}}\mathrm{s}[\mathrm{n}]\). The DFT-LS estimates (\(\mathrm{H}_{\mathrm{l}}\mathrm{s}[\mathrm{n}]\)) are supplied to a Wiener filter with coefficients \(\mathrm{a}_{0}\), \(\mathrm{a}_{1}\dots\,\mathrm{a}_{\mathrm{N}}\) of order (number of touches passed) N in order to produce the Wiener filter coefficients. The expression's result, \(\mathrm{x}[\mathrm{n}]\), is used to signify the filter's output. The difference between the estimated and original signals is reduced to a minimum via the Wiener filter model. It significantly reduces root mean square error and improves bit error rate performance. The block diagram of the F.I.R. Wiener filter with DFT_LS estimates as input is shown in Figure 2.  
Figure 2: Block diagram view of the F.I.R. Wiener filter with DFT_LS estimates as input.
# Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems
###### Abstract
LEDs are essential components in indoor VLC and VLP systems, but their limited modulation bandwidth and nonlinear power-current characteristics can lead to substantial performance degradation, creating bottlenecks in both the BER and positioning accuracy when using FT-OFDM schemes. The FRFT-OFDM scheme offers enhanced performance in nonlinear or frequency-selective systems, effectively mitigating the LED's nonlinear effects and limited bandwidth constraints on the BER and positioning accuracy. However, the potential of FRFT-OFDM for VLC and VLP applications remains largely unexplored. Motivated by the above, we propose an indoor VLC and VLP system based on the FRFT-OFDM scheme. The main contributions of this paper are summarized as follows:  
* To ensure the real-valued signal in VLC and VLP systems, we derive a reality-preserving FRFT (RPFRFT) method and implement it within these systems. The architecture of the proposed VLC and VLP systems based on the FRFT-OFDM scheme is described in detail, along with the derivation of relevant mathematical formulas. * We simulate and validate the proposed VLC and VLP system architectures based on the FRFT-OFDM scheme. The results demonstrate the significant performance improvement of the FRFT-OFDM scheme over the FT-OFDM scheme. For communication, the FRFT-OFDM scheme achieves over 6-dB \(E_{b}/N_{0}\) gain compared to the FT-OFDM scheme at a BER of \(3\times 10^{-4}\) when the receiver is positioned at (1, 1, 0.8). For positioning, the FRFT-OFDM scheme enhances positioning accuracy by more than 1 cm relative to the FT-OFDM scheme at most locations within the room. The remainder of this paper is organized as follows. Section 2 outlines the basic algorithm of the FRFT, details the system architecture of the FRFT-OFDM scheme, and presents the relevant mathematical formulations. Section 3 analyzes and discusses the simulation results. Finally, the conclusions are summarized in Section 4.
## 5 Experimental Verification  
### Experiment Setup  
An AVC experiment is conducted on a smart plate to further verify the validity of the theoretical analysis. The smart plate is hung using elastic ropes to ensure nearly free boundaries. The experimental setup is shown in Figure 7. The setup includes the controller  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & \(\gamma\) & \(v_{a}\) & \(v_{s}\) \\ \hline (2,3) & 2.6 & 4.8 & 3.3 \\ \hline (3,3) & 1.1 & 1.8 & 1.8 \\ \hline (3,4) & 1.4 & 3.7 & 1.5 \\ \hline (3,6) & 1.3 & 5.9 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lists of \(\gamma\) and related modal strain ratios at the positions of the actuator and the sensor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & \(\gamma\) & **Uncontrolled Vibration of the Plate** & **Controlled Vibration of the Plate** \\ \hline (6,1) & \(4.8\times 10^{-8}\) & 0.6 & 0.6 \\ \hline (6,1) & \(4.8\times 10^{-8}\) & 0.4 & 0.6 \\ \hline (6,1) & \(4.8\times 10^{-8}\) & 0.2 & 0.  
(HM-cSPACE 28335), the voltage and charge amplifier (self-made electronic board), the actuator (PZT), and the sensor (PZT). Before the AVC is started, an external disturbance is generated by the exciter (JZ-5A), which is determined by the signal generator (Tektronix 3390) through the power amplifier (Crown CT-8150). During AVC, the cSPACE system is used to implement the FxLMS algorithm. The PZT sensor is connected to the A/D ports of the cSPACE device through the charge amplifier. The sensing signal is transmitted to the input of FxLMS. The output of FxLMS is transmitted to the PZT actuator through the voltage amplifier. Consequently, AVC is achieved.  
The photographs of the experimental setup are shown in Figure 8a. To measure the overall vibration response of the plate, a lightweight accelerometer is placed at 60 observation points, shown in Figure 8b, in turn. In particular, the acceleration at the center of the PZT sensor is concerned with verifying the AVC effect.  
Based on the simulations above, two natural modes, namely the (3,3) and (1,4) modes, are selected to implement AVC. It should be noted that these modes in the experiment are slightly different from those in the simulations due to the influence of polarization conditions, stress/strain rates, temperatures, the additional stiffness of the exciter, etc. [30].  
In order to evaluate the AVC effect, the voltage level of the PZT sensor is defined as:  
\[L_{u}=20\text{lg}|e|, \tag{17}\]  
Figure 8: (**a**) Description of the experimental set-up; (**b**) description of the distribution of measuring points.  
Figure 7: Schematic block diagram for measurement and control.  
in which \(|e|\) is the amplitude of the sensing signal. Considering Equation (16), the ratio in Equation (15) is calculated by:  
\[\gamma=\frac{\sum\limits_{i=1}^{60}10^{0.1L_{a_{i};e}}}{\sum\limits_{i=1}^{60}10^{ 0.1L_{a_{i},e}}}. \tag{18}\]  
Here, \(L_{a_{i},\rho}\) and \(L_{a_{i},e}\) represent the open-loop and closed-loop vibration responses of the plate at the i-th observation point.
## Materials and Methods
### Evaluation of In Vivo Efficacy  
#### 2.6.1 Animal In Vivo  
The present study followed the ethics criteria of the Institutional Animal Care and Use Committee at Yeungnam University Medical Center (Approval No.: YUMC-AEC2012-023). Male SD-rats (weight 250-300 g, 8 weeks of age) were given an intramuscular injection of ketamine hydrochloride (Ketara, Yuhan Corporation, Seoul, Korea) and xylazine (Rumpum, Bayer Korea, Seoul, Korea) for anesthesia, and the surgical area was shaved. The full thickness of the dermis, with a diameter of 1.0 cm \(\times\) 1.0 cm, was removed at four places on each side of the back using Metzenbaum tissue scissors. There were 4 groups of samples: the group 1 was transplanted only the thick dermis was removed, group 2 was transplanted with gauze, and a group 3 was transplanted the non-oxygen-releasing hydrogel sponge ('with alginate'). The group 4 was transplanted the oxygen-releasing hydrogel sponge ('with ORHS') (Figure 3). The dressing was soaked in a saline solution and used to cover just the area of the skin defect. The top part of the dressing was covered with a polyurethane film (Tegaderm, 3M, St. Paul, MN, USA) to protect the material within Following this, on days 1, 3, 5 and 7, tissue samples were collected for visual assessment. Visual analysis was calculated by sketching the SD-rat's scratch size over time with an Overhead Projector (OHP) film (3M\({}^{\text{TM}}\) CG6000, 3M Science. Applied to Life\({}^{\text{TM}}\), 3M Korea, Seoul, Korea) and digitizing the area using isolation (image and microscopy techniques).  
Figure 3: Wound generation on the back of rat. Wound of full thickness skin of 0.8 cm diameter was generated by punching. Four wounds were made on one rat; injury only, gauze, alginate, and oxygen-releasing hydrogel sponge (ORHS).
## 3 Results
### Proposed Calibration Models
#### 3.2.1 Simple Calibration  
Among the possible ways to translate and rotate the points for the alignment of the virtual coordinates with the real coordinates, a typical method uses calibration, herein called simple calibration. In this calibration, the Vutoria framework is used for marker recognition [19].  
Vutoria is used in this context to recognize the fiducial marker through computer vision and then instantiate a hologram that respects the orientation and proportions of this marker. From there, the position and orientation of this hologram are treated as the origin of the virtual coordinate system. Next, previously knowing the coordinates of any fixed  
Figure 4: Example of a fiducial marker used in calibration.  
point within the physical scenario which will be the origin of the real coordinate system, as well as the orientation and the distance in meters between these two origins, it is possible to determine the orientation and position of the axes of both coordinate systems and thus calibrate the system.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
###### Abstract  
This paper explores physical layer group key generation in wireless relay networks with a star topology. In this setup, the relay node plays the role of either a trusted or untrusted central node, while one legitimate node ([PERSON]) acts as the reference node. The channel between the relay and Alice serves as the reference channel. To enhance security during the channel measurement stage, a cooperative jamming-based scheme is proposed in this paper. This scheme allows the relay to obtain superimposed channel observations from both the reference channel and other relay channels. Then, a public discussion is utilized to enable all nodes to obtain estimates of the reference channel. Subsequently, the legitimate nodes can agree on a secret key (SK) that remains secret from the eavesdropper ([PERSON]), or a private key (PK) that needs to be secret from both the relay and Eve. This paper also derives the lower and upper bounds of the SK/PK capacity. Notably, it demonstrates that there exists only a small constant difference between the SK/PK upper and lower bounds in the high signal-to-noise ratio (SNR) regime. Simulation results confirm the effectiveness of the proposed scheme for ensuring security and efficiency of group key generation.  
physical layer security; group key generation; secret key; private key; key capacity Article